Frontiers in Drug Design and Discovery, Volume (4)

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Frontiers in Drug Design & Discovery Bentham Science Publishers Ltd. http://www.bentham.org/fddd

Volume 4, 2009

Contents In Vivo PK/PD and In Vitro ADME Techniques Editorial: Biopharmaceutical & Pharmacokinetic Considerations in Drug Design & Discovery G.W. Caldwell, Atta-ur-Rahman, Zhengyin Yan and M.I. Choudhary

i

Present and Future Mass Spectrometry-Based Approaches for Exploratory Drug Metabolism and Pharmacokinetic Studies Y. Hsieh

1

Glutathione Transferases in Drug Discovery and Development: Towards Safer and Efficacious Drugs K. Skopelitou, D. Platis, I. Axarli and N.E. Labrou

23

New Sampling Techniques for PharmacokineticPharmacodynamic Modeling C. Höcht, M. Mayer, J.A.W. Opezzo, G.F. Bramuglia and C.A. Taira

43

Role of Inflammatory Biomarkers in Establishing PK/PD Relationships and Target Organ Toxicity S. Pillarisetti and I. Khanna

81

Important Drug Interactions for Clinical Oncologists H. Ishiguro, I. Yano and M. Toi

97

Pharmacogenomic Considerations in Breast Cancer Management H. Ishiguro, I. Yano and M. Toi

122

More Than Skin Deep: The Human Skin Tissue Equivalent as an Advanced Drug Discovery Tool A.S. Bause, S.D. Lamore and G.T. Wondrak

135

Across Skin Barrier: Known Methods, New Performances K. Cal

162

The Tape Stripping Method as a Valuable Tool for Evaluating Topical Applied Compounds J.J. Escobar-Chávez, L.M. Melgoza-Contreras, M. López-Cervantes, D. Quintanar-Guerrero and A. Ganem-Quintanar

189

Methylphenidate Extended-Release Capsules: A New Formulation for Attention-Deficit Hyperactivity Disorder P. García-García, F. López-Muñoz, J.D. Molina, R. Fischer and C. Alamo

228

Osmotic-Controlled Release Oral Delivery System (OROS Technology) in Chronic Pain Management F. Coluzzi and C. Mattia

247

Drug Delivery Systems Prepared by Membrane Emulsification C. Charcosset and H. Fessi

273

In Silico ADME Approaches N.E. Campillo and J.A. Páez

291

Advances in ADMET Predictions and Modeling: Rapid Drug Discovery Efforts in 21st Centuries M.T.H. Khan

333

Computational Intelligence Methods for ADMET Prediction D. Hecht and G.B. Fogel

351

Data Modeling and Chemical Interpretation of ADME Properties Using Regression and Rule Mining Techniques K. Hasegawa and K. Funatsu

378

A Review on Virtual Reality and Haptics Approaches in Drug Design and Discovery S.K. Lai-Yuen

429

Contributors

454

Editorial

Frontiers in Drug Design & Discovery, 2009 Vol. 4 i

Editorial: Biopharmaceutical & Pharmacokinetic Considerations in Drug Design & Discovery The goal of all pharmaceutical scientists is to bring affordable, safe and effective drugs to patients globally. The Frontier in Drug Design and Discovery series is dedicated to contributed by leading researchers present comprehensive reviews with fresh new ideas on drug design and drug discovery. The first volume (2005) brought together experts to review and discuss the advantages and limitations of modern screening techniques used in the drug discovery process to identify potential drug candidates. The second volume (2006) discussed new technological and conceptual approaches to accelerate and to improve the predictability of the discoveries made in the laboratory into clinical testing. The third volume (2007) reviewed ways of applying structure-based design to identify potent lead drug candidates for a variety of diseases using techniques such as in-silico screening, peptidomimetics, fragment-based approaches, protein crystallography, and NMR spectroscopy. In the fourth volume of this series (2009), reviews and discussions are presented regarding biopharmaceutical and pharmacokinetic techniques to identify potent lead drug candidates. Pharmaceutical companies have embraced new technologies, such as analytical instrumentation, chemistry and biological screening robotic systems, computerized data handling systems, and computational and simulation software. While many successes have arisen from these changes, the cost to discover and market new drugs remains staggeringly high. There are many reasons why pharmaceutical drug development costs remain high. One reason is the continuing high attrition rates of drugs during costly phase II and III human clinical trials. One decision-making strategy used by pharmaceutical companies is to eliminate high-risk drug candidates as early as possible in the drug development process. By shifting the attrition of drug candidates to an earlier stage in development, pharmaceutical companies are aiming to cut the costs associated with developing and regulatory approval of a drug. The strategy used by most companies to identify unsuccessful likely drugs involves using biopharmaceutical and pharmacokinetic principles early in the discovery process to improve decision-making in drug selection. While biopharmaceutical principles are used to understand the physical properties of drug candidates, pharmacokinetic principles are used to quantitatively model the time course for drug concentration in the body and predict various parameters such as absorption, distribution, metabolism, and excretion (ADME) of the drug. Physical and ADME properties can be used to optimize drug design in relationship to pharmacological effects, such as efficacy and toxicity. Since efficacy and toxicity deficiencies are related in part to biopharmaceutical and pharmacokinetic properties, detecting problems in drug candidates as early as possible would be highly valuable in making go/no-go decisions. We have carefully selected authors to review biopharmaceutical, in-vitro ADME and pharmacokinetic applications in drug design. Hsieh has reviewed the current mass

ii Frontiers in Drug Design & Discovery, 2009, Vol. 4

Editorial

spectrometry-based approaches and their future potential in supporting exploratory invitro and in-vivo drug metabolism/pharmacokinetic studies, that include pharmacokinetic profiling, physical property, metabolite identification, and molecular imaging tests. The chapter by Labrou and colleagues gives a detailed description of several biotransformation reactions catalyzed by glutathione transferases (GSTs). This information is useful in the early phases of drug development to eliminate unsuitable drug candidates in the drug discovery process. Chapter by Höcht and colleagues gives an excellent review of sampling techniques, such as microdialysis and ultrafiltration. In addition, a review is presented on imaging techniques, such as positron emission topography and magnetic resonance spectroscopy. These techniques allow the measurement of drug concentration at the target site. The chapter by Pillarisetti and Khanna gives an overview of biomarkers, such as the tumor necrosis factor (TNF), interleukins (IL-1, IL-6, IL-8 and IL-18), vascular cell adhesion molecules, and markers of macrophage inflammation (e.g. MMPs). Their potential applications in monitoring inflammatory responses in normal animals and case histories linking these biomarkers to PK/PD correlation from preclinical and clinical studies are discussed. Ishiguro and colleagues give an overview of the importance of ADME properties in the understanding of antineoplastics drugs. Their second paper describes the important of pharmacogenomic factors associated with drugs for the treatment of breast cancer. Wondrak and colleagues wrote an excellent review of the development of in-vitro intact stratum corneum human skin reconstruct from matrixembedded dermal fibroblasts and epidermal primary keratinocytes. These human skin reconstructs, which closely resemble the complex architecture and functional complexity of skin, can be used to screen skin care products in a 96-well format. Cal contributed a well-balanced review of transdermal drug delivery principles. Escobar-Chávez and colleagues describe the applications of the tape stripping technique to evaluate drug penetration through the skin, as well as stratum corneum composition and physiology, underlining its versatile application in the area of topical and transdermal drugs. The chapter by López-Muñoz and colleagues describe the pharmacokinetics of new extended-release formulation of methylphenidate (Medikinet®) for the treatment of Attention Deficit Hyperactivity Disorder (ADHD). They compare of the characteristics of Medikinet® with those of other formulations used for treating. Coluzzi and Mattia review the osmoticcontrolled release oral delivery system (OROS®). This drug delivery technology, which employ osmotic pressure as the driving force to deliver pharmacotherapy, is demonstrated for hydromorphone. Chapter by Charcosset summarizes the progress made in drug delivery systems based on membrane emulsification. Campillo and Páez review the progress in ADME in-silico methods, focusing on oral absorption, blood-brain barrier, metabolism, and some aspects on excretion. Khan reviews in-silico ADMET predictions of the potential drug candidates based on Quantitative Structure-Activity Relationship (QSAR) modeling approaches. The chapter by Hecht and Fogel reviews the utility of some of the more popular applications of computational intelligence to Quantitative Structure-Property Relationship (QSPR) modeling approaches including: artificial neural

Editorial

Frontiers in Drug Design & Discovery, 2009 Vol. 4 iii

networks, fuzzy logic, and evolutionary computing. Hasegawa and Funatsu review ADME modeling techniques including regression, multiple linear regressions (MLR), partial least squares (PLS), artificial neural networks (ANN), and support vector machines (SVM). Finally, S.K. Lai-Yuen introduces the reader to virtual reality and haptics techniques for in-silico drug design. We are grateful to all contributors and also wish to express our gratitude of staff and editorial assistants for their untiring efforts in beinging this volume in good shape and in timely manner.

Atta-ur-Rahman M. Iqbal Choudhary

Gary W. Caldwell Zhengyin Yan

Editorial

Frontiers in Drug Design & Discovery, 2009 Vol. 4 i

Editorial: Biopharmaceutical & Pharmacokinetic Considerations in Drug Design & Discovery The goal of all pharmaceutical scientists is to bring affordable, safe and effective drugs to patients globally. The Frontier in Drug Design and Discovery series is dedicated to contributed by leading researchers present comprehensive reviews with fresh new ideas on drug design and drug discovery. The first volume (2005) brought together experts to review and discuss the advantages and limitations of modern screening techniques used in the drug discovery process to identify potential drug candidates. The second volume (2006) discussed new technological and conceptual approaches to accelerate and to improve the predictability of the discoveries made in the laboratory into clinical testing. The third volume (2007) reviewed ways of applying structure-based design to identify potent lead drug candidates for a variety of diseases using techniques such as in-silico screening, peptidomimetics, fragment-based approaches, protein crystallography, and NMR spectroscopy. In the fourth volume of this series (2009), reviews and discussions are presented regarding biopharmaceutical and pharmacokinetic techniques to identify potent lead drug candidates. Pharmaceutical companies have embraced new technologies, such as analytical instrumentation, chemistry and biological screening robotic systems, computerized data handling systems, and computational and simulation software. While many successes have arisen from these changes, the cost to discover and market new drugs remains staggeringly high. There are many reasons why pharmaceutical drug development costs remain high. One reason is the continuing high attrition rates of drugs during costly phase II and III human clinical trials. One decision-making strategy used by pharmaceutical companies is to eliminate high-risk drug candidates as early as possible in the drug development process. By shifting the attrition of drug candidates to an earlier stage in development, pharmaceutical companies are aiming to cut the costs associated with developing and regulatory approval of a drug. The strategy used by most companies to identify unsuccessful likely drugs involves using biopharmaceutical and pharmacokinetic principles early in the discovery process to improve decision-making in drug selection. While biopharmaceutical principles are used to understand the physical properties of drug candidates, pharmacokinetic principles are used to quantitatively model the time course for drug concentration in the body and predict various parameters such as absorption, distribution, metabolism, and excretion (ADME) of the drug. Physical and ADME properties can be used to optimize drug design in relationship to pharmacological effects, such as efficacy and toxicity. Since efficacy and toxicity deficiencies are related in part to biopharmaceutical and pharmacokinetic properties, detecting problems in drug candidates as early as possible would be highly valuable in making go/no-go decisions. We have carefully selected authors to review biopharmaceutical, in-vitro ADME and pharmacokinetic applications in drug design. Hsieh has reviewed the current mass

ii Frontiers in Drug Design & Discovery, 2009, Vol. 4

Editorial

spectrometry-based approaches and their future potential in supporting exploratory invitro and in-vivo drug metabolism/pharmacokinetic studies, that include pharmacokinetic profiling, physical property, metabolite identification, and molecular imaging tests. The chapter by Labrou and colleagues gives a detailed description of several biotransformation reactions catalyzed by glutathione transferases (GSTs). This information is useful in the early phases of drug development to eliminate unsuitable drug candidates in the drug discovery process. Chapter by Höcht and colleagues gives an excellent review of sampling techniques, such as microdialysis and ultrafiltration. In addition, a review is presented on imaging techniques, such as positron emission topography and magnetic resonance spectroscopy. These techniques allow the measurement of drug concentration at the target site. The chapter by Pillarisetti and Khanna gives an overview of biomarkers, such as the tumor necrosis factor (TNF), interleukins (IL-1, IL-6, IL-8 and IL-18), vascular cell adhesion molecules, and markers of macrophage inflammation (e.g. MMPs). Their potential applications in monitoring inflammatory responses in normal animals and case histories linking these biomarkers to PK/PD correlation from preclinical and clinical studies are discussed. Ishiguro and colleagues give an overview of the importance of ADME properties in the understanding of antineoplastics drugs. Their second paper describes the important of pharmacogenomic factors associated with drugs for the treatment of breast cancer. Wondrak and colleagues wrote an excellent review of the development of in-vitro intact stratum corneum human skin reconstruct from matrixembedded dermal fibroblasts and epidermal primary keratinocytes. These human skin reconstructs, which closely resemble the complex architecture and functional complexity of skin, can be used to screen skin care products in a 96-well format. Cal contributed a well-balanced review of transdermal drug delivery principles. Escobar-Chávez and colleagues describe the applications of the tape stripping technique to evaluate drug penetration through the skin, as well as stratum corneum composition and physiology, underlining its versatile application in the area of topical and transdermal drugs. The chapter by López-Muñoz and colleagues describe the pharmacokinetics of new extended-release formulation of methylphenidate (Medikinet®) for the treatment of Attention Deficit Hyperactivity Disorder (ADHD). They compare of the characteristics of Medikinet® with those of other formulations used for treating. Coluzzi and Mattia review the osmoticcontrolled release oral delivery system (OROS®). This drug delivery technology, which employ osmotic pressure as the driving force to deliver pharmacotherapy, is demonstrated for hydromorphone. Chapter by Charcosset summarizes the progress made in drug delivery systems based on membrane emulsification. Campillo and Páez review the progress in ADME in-silico methods, focusing on oral absorption, blood-brain barrier, metabolism, and some aspects on excretion. Khan reviews in-silico ADMET predictions of the potential drug candidates based on Quantitative Structure-Activity Relationship (QSAR) modeling approaches. The chapter by Hecht and Fogel reviews the utility of some of the more popular applications of computational intelligence to Quantitative Structure-Property Relationship (QSPR) modeling approaches including: artificial neural

Editorial

Frontiers in Drug Design & Discovery, 2009 Vol. 4 iii

networks, fuzzy logic, and evolutionary computing. Hasegawa and Funatsu review ADME modeling techniques including regression, multiple linear regressions (MLR), partial least squares (PLS), artificial neural networks (ANN), and support vector machines (SVM). Finally, S.K. Lai-Yuen introduces the reader to virtual reality and haptics techniques for in-silico drug design. We are grateful to all contributors and also wish to express our gratitude of staff and editorial assistants for their untiring efforts in beinging this volume in good shape and in timely manner.

Atta-ur-Rahman M. Iqbal Choudhary

Gary W. Caldwell Zhengyin Yan

Frontiers in Drug Design & Discovery, 2009, 4, 1-22

1

Present and Future Mass Spectrometry-Based Approaches for Exploratory Drug Metabolism and Pharmacokinetic Studies Yunsheng Hsieh* Department of Drug Metabolism and Pharmacokinetics, Schering-Plough Research Institute, 2015 Galloping Hill Road, K-15-3700, Kenilworth, NJ 07033, USA Abstract: For more than a decade, mass spectrometry (MS) has played an important role in absorption, distribution, metabolism, excretion and toxicology (ADMET) studies for drug discovery to help convert lead compounds into drug candidates. Drug discovery efforts have been focused on identifying drug metabolism and pharmacokinetic (DMPK) issues at the earliest possible stage to reduce attrition rate of drug candidates throughout the drug development process by applying cutting edge MS-based techniques. These emerging techniques have proven to be extremely valuable to accelerate the lead optimization and characterization processes by eliminating potentially unpromising candidates. In this article, the current MS-based approaches and their future perspectives in supporting exploratory DMPK studies including in vitro and in vivo pharmacokinetic profiling, physical property, metabolite identification and molecular imaging tests are reviewed.

INTRODUCTION Combinatorial chemistry is an efficient way of synthesizing and testing very large libraries for bioactivity to discover leads more quickly than was formerly possible. The selected leads with an appropriate in vitro potency are further screened to provide medicinal chemists relevant pharmacokinetic parameters such as tissue penetration, protein binding, intestinal absorption, drug-drug interaction potential, metabolic stability and pathway to embark on a lead optimization campaign. Drug design is a fast moving and iterative process for creating a chemical compound with drug-like properties suitable for development as a therapeutic agent (Fig. 1). The early evaluation of pharmacokinetics, pharmacodynamics and toxicological effects should lead drug candidates with optimum performance characteristics to proceed further into development. The major purposes of obtaining DMPK knowledge during the early drug discovery stage are to identify DMPK issues earlier to resolve problems arising from lead optimization, to ensure sufficient exposure multiples achievable in all toxicology test species and to reduce the attrition rate for drug development [1-10].

*Corresponding Author: E-mail: [email protected]

Gary W. Caldwell / Atta-ur-Rahman / Z. Yan / M. Iqbal Choudhary (Eds.) All rights reserved – © 2009 Bentham Science Publishers.

2 Frontiers in Drug Design & Discovery, 2009, Vol. 4

Yunsheng Hsieh

Modified drug discovery compounds Biological activity Protein binding Drug-drug interaction

Physical properties

Metabolite identification

In vitro screening

Permeability

Metabolic stability

In vivo pharmacokinetics / pharmacodynamics In vivo toxicology Nomination for preclinical development Fig. (1). Strategy for use of early ADME screens in the lead optimization and characterization processes.

Drug-drug interactions can occur when one drug inhibits the biotransformation of another if patients take more than one drug. Cytochrome (CYP) P450s are a family of enzymes primarily responsible for metabolizing xenobiotics [11-15]. For CYP enzyme inhibition screening, direct and metabolism/ mechanism-based inhibition potentials of CYP of new leads are frequently quantified in terms of 50% inhibitory concentration (IC50) values for prioritizing [11]. For direct CYP inhibition measurement, the individual compounds are incubated with human liver microsomes, probe substrate and NADPH. The IC50 is calculated using certain substrate assays by fitting the inhibition data to the following equation; y = 100/ [1+(x / IC50)S] where y is % of control, x is concentration of the inhibitor and S is a slope factor [12]. Metabolite production from each enzyme inhibition assay can also be quantified to reflect the ability of the probe compounds to

Present and Future Mass Spectrometry-Based

Frontiers in Drug Design & Discovery, 2009, Vol. 4 3

directly inhibit the activity of P450s. For metabolism/mechanism-based inhibition, the test compounds are generally pre-incubated first with liver microsomes in the presence of NADPH so that metabolites might be produced before the addition of the test compounds. Compounds with low IC50 values under the pre-incubation conditions compared with co-incubation conditions are suspected to be metabolism/mechanism-based inhibitors. Oral administration is the preferred drug-dosing route for the drug development in the pharmaceutical industry. Various in vitro assays such as Caco-2, a human intestinal epithelia cell line, as a rapid permeability screening tool have been developed to predict the extent of absorption of drug candidates after oral administration in early discovery [16-20]. As an example, the Caco-2 immortal cell line forming a monolayer presents a barrier to ion flow due to the tight junctions between cells. Compounds with sufficient rates of passive diffusion across membranes will demonstrate good permeability in the Caco-2 experiments. The analyte concentrations from both sides of the cell were used to calculate the permeability and recovery of the drug candidates as follows [16]: Permeability = (VR/(S*7200)) (CR,2 hr / CD,0 hr) % Recovery = [(CD,2 hr *VD *CR,2 hr *VR) / ( CD,0 hr *VD)]*100 where S is the membrane surface area; CD, 0hr and CD, 2hr are the donor concentration immediately after dosing and after two hours incubation, respectively; CR, 2hr is the receiver concentration after two hours incubation; VD and VR are the volume of donor and receiver compartments, respectively. In addition to oral absorption, first-pass metabolism, a process of converting a dosed compound to more water soluble metabolites to be easily eliminated from the human and animal body, is the other factor to contribute the bioavailability of a drug after oral administration. In general, drug metabolism is divided into phase I and phase II reactions. Phase I reactions involve oxidation reduction and hydrolysis reactions primarily catalyzed by CYP and flavin-containing monooxygenases. Phase II reactions known as conjugation reactions involve the interactions of the polar functional groups of phase I metabolites [21-27]. Metabolic stability refers to the susceptibility of compounds to biotransformation. In the context of drug discovery screens, intrinsic metabolic clearance (CLint), the proportionality constant between drug concentrations at the enzyme site (Ce) and rate of metabolism (vo), is often referred to as metabolic stability: CLint = vo / Ce [28]. The intrinsic half-life is determined by incubating a test compound with liver subcellular preparations for a certain period of time and plotting ln % remaining versus times as t1/2 = 0.693 / m where m is the slope of the plot. Integration of metabolic stability assessment with identification of the site (soft spot) of metabolism is valuable in leading toward a modified compound with an appropriate rate of metabolism to achieve an ideal pharmacokinetic profile. Meanwhile, characterization of reactive and pharmacologically active metabolites following incubation in microsomal systems for a predetermined period is also critical in designing new drug candidates with an improved toxicological profile and efficacy [29-37]. Detection of glutathione conjugates in both in vitro and in vivo suggests the production of potentially reactive intermediates. Acyl-glucuronides metabolites are susceptible to both hydrolysis and intramolecular acyl migration to form reactive intermediates for covalently binding to proteins which may implicate in various drug-induced toxicities [29, 38]. The other objectives of metabolite profiling in drug research settings are to characterize major me-


Yunsheng Hsieh

tabolites across species and to identify human-specific metabolites to allow for assessment of potential human safety issue. Animal PK results are considered the most predictive of human PK parameters and used to select compounds with no potential PK deficiency such as poor bioavailability, short half-life, circulating reactive metabolites and so on for drug development [39-47]. A variety of in vivo PK parameters such as oral bioavailability, volume of distribution, clearance, and mean resident time calculated from the drug concentrations in animal plasma samples can sometimes be employed to predict human PK via allometric scaling. Animal studies can also be used to assess the single-dose and multiple-dose non-clinical toxicity liability in the final lead characterization of drug candidates. Also, to conduct an Animal Safety program to meet IND requirements for enabling Phases I clinical trials, it is important to achieve sufficient exposure multiples following drug administration to the rodent and non-rodent toxicological species. In all aforementioned cases, what have enabled these in vitro and in vivo experiments to drug discovery to be efficient are the development of compound-specific analytical methods. Among these methods, MS-based approaches as described in the following sections have evolved to become irreplaceable techniques in the analysis of drug- related molecules in biological samples (Table 1) [48-55]. HIGH PERFORMANCE LIQUID CHROMATOGRAPHY-MS/MS The combination of high performance liquid chromatography and tandem mass spectrometry (HPLC-MS/MS) has rapidly matured to become a very powerful analytical tool applied in many DMPK areas for qualitative and quantitative determination of new chemical entities (NCEs) and the marketed pharmaceuticals (Fig. 2) [56-66]. Due to its inherent selectivity, the HPLC-MS/MS system normally requires neither labor-intensive sample preparation procedures nor extensive chromatographic run times to avoid matrix interference. This allows researcher to shorten chromatographic times and in turn to increase sample throughput. Micro-column [67-69], monolithic column [70-76], high temperature [77] and ultra high pressure [78-85] HPLC-MS/MS methods (Fig. 3) are four popular approaches to achieve faster HPLC cycle times for small molecule assays prior to tandem mass spectrometric detection. 200

100

0 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Fig. (2). Swift growth of HPLC-MS/MS applications for drug assays in the PubMed database.


Table 1.


Representative MS-Based Methods Used for DMPK Studies

Study

Rationale

MS-Based Method

Reference

P450 enzyme inhibition

To evaluate direct and mechanism-based inhibition of CYP enzymes that are responsible for metabolism of most drugs to assess the potential for drug-drug interactions.

HPLC-MS/MS Direct-injection HPLC-MS/MS LDTD-MS/MS

[11, 188] [189]

P450 enzyme profiling

To determine which P450 is primarily responsible for metabolizing the compound to anticipate drug-drug interactions and polymorphisms.

HPLC-MS/MS

[190]

Caco-2 permeability and efflux substrate screening

To evaluate permeability in human intestinal cell line to project in vivo absorption potential.

HPLC-MS/MS Nano-ESI-MS/MS

[19] [16]

Plasma protein binding

To determine the magnitude of plasma protein binding in different species. The free drug concentration can be used to interpret PK/PD relationships.

HPLC-MS Affinity chromatography-MS AMS

[191] [144, 192]

To evaluate compound stability following incubation in plasma. Results can assist in understanding an in vivo clearance value that exceeds hepatic blood flow.

Direct-injection HPLC-MS/MS HPLC-MS/MS

[112]

Intrinsic clearance (hepatocyte stability)

To evaluate the magnitude of hepatic clearance to predict in vivo clearance and assess species differences.

HPLC-MS/MS SFC-MS/MS Pulsed ultrafiltration-MS

[195] [126] [151]

In vitro and in vivo metabolite identification

To examine the metabolic profile of a compound across species with particular emphasis on the identification of any human specific metabolites.

HPLC-MS/MS nano-ESI-MS

[119, 196, 197] [156]

Animal PK study

To access in vivo PK parameters and to support the safety pharmacology cardiovascular study.

HPLC-MS/MS SFC-MS/MS nano-ESI-MS

[198-200] [140] [157, 201]

Blood-Brain barrier

To access the potential of compounds to penetrate the blood-brain barrier.

HPLC-MS/MS

[202, 203]

Mass balance

To evaluate the amount of drug that is recovered over time via the different elimination routes of the body.

AMS

[179]

Tissue Distribution

To access the distribution of a drug and its metabolites in various tissues from laboratory animal experiments.

MALDI-IMS

[185, 187]

Plasma stability

[163]

[193]

[194]


Yunsheng Hsieh

Higher-Throughput LC-MS Strategies in Drug Discovery Fast Chromatography Micro-column HPLC Monolithic column HPLC High-temperature HPLC Ultra performance liquid chromatography (UPLC) Parallel Instrumentation micro parallel liquid chromatography (uPLC) Staggered parallel liquid chromatography Multiple Ionization Sources APPI, ESI, APCI Ion-pairing HPLC Porous graphite carbon HPLC Mixed-mode HPLC Hydrophilic interaction chromatography (HILIC) Direct injection HPLC

Fig. (3). Strategies of higher throughput HPLC-MS approaches in drug discovery.

According to chromatographic concepts, one effective way to improve the HPLC column efficiency is to reduce the particle size of the packing materials that indicate better diffusion and mass transfer in the mobile and the stationary phases. Micro-column technique adopts a shorter (less than 5 cm length) narrow-bore (~ 2 mm i.d.) column packed with small particles and operated at higher than optimal flow rates to provide for fast chromatographic separations while still maintaining satisfactory chromatographic resolution. Monolithic silica columns made from a single piece of porous silica gel can be operated at higher flow-rates than conventional HPLC columns due to their lower back pressure. The low back pressure despite higher mobile phase flow rates is due to the higher permeability of monolithic silica versus particulate silica columns to make higher speed separation possible without a noticeable effect on chromatographic resolution. The viscosity of the mobile phases used in reverse-phase chromatography decreases as the temperature increases. Consequently, the column pressure drop, P, decreases significantly to allow higher flow rates within the normal HPLC pressure limits when elevated column temperature chromatography is employed. Ultra high-pressure liquid



chromatography (UHPLC) is another technical advance that allows the liquid handling system to handle the high backpressure resulting from the stationary phase with sub-2 m particles. UHPLC offers theoretical advantages in chromatographic resolution, speed and sensitivity over conventional HPLC systems. The concept of using supercritical porous silica packing through fused-core technology was further extended for the design of a novel stationary phase for separation of small molecules [79]. The columns packed with sub-2 m stationary phases require expensive ultra-pressure equipment to achieve optimum performance. The fused-core silica columns generate backpressure slightly higher than the columns normally packed with 3- and 5-m particles which are still compliable with most existing HPLC pumps (Fig. 4). 6 0.74

4.2e5

Intensity, cps

A 1

4 2

3

5 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

6

Time, min

B

Intensity, cps

4.4e5

4 2

3

1 5 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Time, min

Fig. (4). Fast separation of six pharmaceutical compounds using (A) a 2.7-m fused-core silica column (the first injection- (solid line) and the 250th injection- (dotted line)) and (B) 1.7-m porous silica column at 40 oC. Compounds 1 through 6 are ketoconazole, thioridazine, clofazimine, amiodarone, felodipine and rimonabant, respectively [79].

Due to its inherent selectivity and sensitivity, HPLC-MS/MS method allows the simultaneous determination of multiple components sharing the same retention times. An alternative approach for higher-throughput assay without sacrificing chromatographic integrity is the use of parallel HPLC where samples were injected alternately onto each of two analytical columns in parallel at specified intervals within a single chromatographic run time [86]. The parallel system makes use of unused chromatographic time windows and can be controlled by the computer where the analyst defines the acquisition


Yunsheng Hsieh

windows placed into HPLC timetable to provide a boost in throughput. To further speed up assays, the concept of using a staggered sample injection technique has been extended to multiple parallel HPLC-MS/MS methods for quantitative applications [87, 88]. The exponential growth in HPLC-MS applications is mainly due to the introduction of the atmospheric pressure ionization (API) interfaces between HPLC and a mass spectrometer. Electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) sources are the major ionization interface designs for the HPLC-MS/MS systems for qualitative and quantitative analysis of small molecules over the last decade. However, there is no single ionization source for HPLC-MS/MS system to cover all target compounds. New instrumental developments have resulted in combined sources such as APCI/ESI and APPI/APCI sources for on-line HPLC-MS/MS [89-91]. For example, the ESI/APCI source allows alternate on-line ESI and APCI scans with polarity switching within a single analysis. The new combined sources have reduced the analysis time of sample plates by eliminating the need for a source hardware change and repeat analyses. One of the common goals in the exploratory PK area is to develop a generic HPLCMS/MS method to simultaneously monitor a wide range of drug candidates and their metabolites in biological fluids. Reversed-phase HPLC is the most widely employed technique in pharmaceutical analysis due to its extensive application to most small drug molecules which are separated by their degree of hydrophobic interaction with the stationary phase. However, it is very challenging to establish a reliable bioanalytical method for the determination of NCEs with lower octanol-water partition coefficient, log P, because these polar compounds show little or no retention on traditional reversed-phase columns prior to mass spectrometric detection. Ion-pairing chromatography was reported as an effective way for obtaining satisfactory retention of polar analytes under reversedphase conditions [92-95]. The ion-pairing reagents added into mobile phase are used to improve chromatographic retention on the lipophilic stationary phase through the formation of neutral ion pairs. It was proven that the porous graphic carbon (PGC) column was able to provide more efficient retention than other kinds of the reversed-phase columns designed to trap and separate very polar compounds [96, 97]. The overall retention on PGC columns involves two major mechanisms: (1) dispersive interaction between analyte-mobile phase and analyte-graphite surface and (2) dipolar and ionic interaction of a polar analyte with the polarizable graphite. The mixed-mode column with an embedded ion-pairing group in the reversed-phase stationary providing the capability for ionexchange and hydrophobic interactions requires no ion-pairing reagent in the mobile phase to retain and to separate ionizable polar compounds. The mixed-mode HPLC allows for retaining hydrophobic compounds by the reversed-phase mechanism and hydrophilic compounds by the ion exchange mechanism at higher organic contents in the mobile phase [98]. Hydrophilic interaction chromatography (HILIC) using low aqueous/high organic mobile phase is emerging as another valuable supplement to the reversed-phase HPLC–MS/MS for the retention of polar pharmaceuticals [99-105]. The solvents used for HILIC-MS/MS systems, such as methanol, provide low column backpressure but also are MS-favorable for greater ionization efficiency. A simplified sample preparation step using protein precipitation technique to remove proteins from biological samples is normally sufficient for small molecule determination when HPLC-MS/MS methods are employed. This single clean-up step is required to prevent the HPLC column from clogging in reversed-phase chromatography but also to



avoid ion source contamination to reduce matrix ionization suppression in the mass spectrometer. The working principle of on-line extraction phases is to isolate macromolecules from the targeted small molecules in biological fluids based upon their sizes. The large macromolecules such as proteins, which are unable to penetrate into the hydrophobic pores, are first eluted to waste. The small molecules such as drug compounds that penetrate the pores are retained through the hydrophobic forces [106-111]. The directinjection HPLC-MS/MS method had been applied for the automatic measurement of plasma stability of drug candidates to eliminate the traditional labor-intensive sample preparation procedures [112]. The procedure makes use of a thermostatic autosampler as an incubator combined with the direct-injection HPLC-MS/MS method (Fig. 5). The untreated plasma samples from several species containing the test compound was directly and sequentially injected into a mixed-function column for on-line protein removal and chromatography. The injection-to-injection time was set at a certain time period. The peak responses of the test article were repeatedly monitored after each injection cycle which can be related to a constant incubation time period for individual plasma samples. The stability results of the test compound in all plasma samples obtained by the direct-injection HPLC-MS/MS method was found to be consistent with those obtained by the traditional HPLC-MS/MS approach (Fig. 4). Mouse Rat

Human Monkey Dog

120

% Remaining

100 80 60 40 20 0

Direct-injection HPLC-MS/MS

0

30

60

90

120 150 180 210 240 270 300

Time (min)

Result Spiking + Autosampler Fig. (5). Schematic of a direct-injection HPLC-MS/MS system for semi-automatic plasma stability measurement.

Tandem mass spectrometry covering a variety of scanning techniques such as precursor ion, neutral-loss and product ion scanning provides complementary information on small molecular structures [113, 114]. Triple-quadrupole (QqQ) is a tandem mass spectrometer commonly used for conducting product ion scan (MS2), precursor ion scan, neutral loss scan modes for structure elucidation and selected reaction monitoring (SRM) for the quantitation of trace components. The QqQ mass spectrometer is the most popular instrument for both quantitative pharmacokinetic (PK) assays and qualitative DMPK studies [50-52]. Linear ion trap (LIT) mass spectrometers perform multiple MS/MS (MSn) with a much faster trap scan speed than the QqQ mass spectrometer to generate more detailed structure information. A hybrid quadrupole linear ion trap (QqLIT) combines the features of both QqQ and LIT mass spectrometers with improved performances for supporting DMPK studies under one system [115-125]. With the QqLIT technology, collision-induced dissociation (CID) occurs in a quadrupole collision


Yunsheng Hsieh

cell and fragment ions are trapped and analyzed in the LIT mode to simultaneously perform multiple experiments without the need to repeat the runs on different MS platforms and substantial sensitivity loss. The hyphenation of HPLC and tandem mass spectrometry has widely become a powerful analytical tool for the characterization of metabolites of pharmaceuticals. SUPERCRITICAL FLUID CHROMATOGRAPHY-MS Supercritical fluid chromatography (SFC) is a hybrid of gas and liquid chromatography that eases the resolution of a mixture of compounds not conveniently resolved by either gas or liquid chromatography. The mobile phases for SFC have low viscosities and high diffusion coefficients properties compared to those for HPLC to allow for high efficiency separations. The commercial API sources used for HPLC-MS/MS system were proven to be applicable to the SFC-MS/MS system with no modification [126,127] (Fig. 6). According to the US Food and Drug Administration’s (FDA) policy statement for the development of new stereoisomeric drugs, in order to evaluate the pharmacokinetics of a single enantiomer or mixture of enantiomers, manufacturers should develop quantitative assays for individual enantiomers in in vivo samples early in drug development. SFC, offering higher resolution per unit of time and faster column re-equilibration, has been becoming the top choice among enantioselective chromatographic techniques such as capillary electrophoresis (CE) [128-130] and HPLC [131-135]. Although the MS/MS detection provides little selectivity for stereoisomeric drugs, it can provide complete resolution of the administered drugs from endogenous materials and their metabolites. By taking advantage of the inherent selectivity and sensitivity of MS/MS detection, implementing chiral SFC-MS/MS method together with sample pooling technology allows us to simultaneously monitor samples containing multiple pairs of enantiomers with different masses within the same chromatographic run to further increase assay productivity [136]. The coupling of chiral SFC to MS/MS detection was shown to be comparable for most bioanalytical attributes such as specificity, linearity, accuracy and ruggedness but advantageous for higher sample throughput and peak resolution power with respect to the HPLC-MS/MS approach [137-139]. Oven & analytical column Autosampler SFC

Tandem mass spectrometer

Fig. (6). Schematic of SFC-MS/MS system.

SFC best emulates normal-phase chromatography, but has the capability to separate a much broader range of analyte polarities amenable to SFC-MS if appropriate mobilephase modifiers, additives and columns are utilized [140-142]. As a general strategy,



CO2 combined with more polar solvents containing either acidic or basic additives as modifier to enhance the solvent strength of the mobile phase will allow for the elution of the analytes on packed column with high efficiency. SFC is not limited to relatively nonpolar compounds but could be extended to allow for the elution of polar and ionic compounds. In HPLC, the direct serial coupling of achiral and chiral columns for the simultaneous achiral/chiral separation of a range of components in a complex mixture was not feasible due to either the increased backpressure or the different mobile-phase requirement. However, this concept is not restricted for SFC because the supercritical fluid has a significantly lower viscosity than a liquid, but also is an effective eluent for both chiral and achiral stationary phases [140]. AFFINITY CHROMATOGRAPHY-MS Affinity chromatography makes use of affinity-based interactions for the analysis of specific analytes within a sample. One of the examples of these interactions includes the binding of a serum albumin with drugs where albumin is the major contribution to drugprotein binding in the plasma [143-147]. Such binding processes are performed by placing a serum albumin as a known ligand onto a solid support within a column to interact with the desired analyte. Immobilized serum albumin affinity chromatography coupled to tandem mass spectrometry has been applied for chiral separation and drug-protein binding determinations [146, 147]. In this approach, the test compounds are injected to immobilized serum albumin column and the expression of 100 [k’ / (k’+ 1)] is used to estimate the % protein binding of the analytes where k’ is the capacity factor. Good correlation between the protein binding results of the analytes obtained from the affinity chromatography-MS methodology and those obtained from ultrafiltration or dialysis methods were reported [144]. The compounds with the high non-specific binding to artificial membrane which are not applicable to ultrafiltration assay can be analyzed by this affinity chromatographic method. The immobilized serum albumin column-MS method enabled the simultaneous estimation of the individual protein-binding affinities of a mixture of compounds but also was proved to be reproducible to rank order a series of molecules according to their protein-binding affinities in less than an hour [147]. PULSED ULTRAFILTRATION-MS Ultrafiltration is a separation process using membranes with a variety of pore sizes to remove high molecular-weight substances. Pulsed ultrafiltration interfaced with MS was proved to be a powerful method with multiple uses for combinatorial libraries, metabolic stability and metabolites screening [148-151]. As an example, in the pulsed ultrafiltration (PU) experiment for metabolic stability screening, a pulse containing a library of compounds is loaded and pumped through a stirred ultrafiltration chamber fitted with an appropriate molecular weight cut-off ultrafiltration membrane (Fig. 7). Liver microsomes from various species are trapped in an ultrafiltration chamber. A continuous flow of a buffer is pumped through the chamber and into an API interface and a mass spectrometer. Substrates for cytochrome P450 together with cofactors are flow injected through the chamber and the targeted metabolites are then monitored on-line by using mass spectrometry [149]. Pulsed ultrafiltration in conjunction with LC-MS-MS can be extended to screen mixtures for compounds that might be activated metabolically by cytochrome P450. The pulsed ultrafiltration-MS method provides comparable qualitative results of a metabolic stability test with those obtained via a microsomer off-line incubation but has


Yunsheng Hsieh

the potential for increasing the throughput of such an analysis by carrying out multiple ultrafiltration reaction chambers in parallel with a HPLC injector/autosample and a single mass spectrometer [149].

Fig. (7). Scheme for a parallel pulsed ultrafiltration mass spectrometry system to screen drug candidates for drug metabolism [149].

COLUMNLESS MS MS-based methods frequently demonstrate no chromatographic interference from endogenous components. The attempt to further increase sample throughput has led some bioanalytical laboratories to replace chromatographic separation with flow injection analysis (FIA) [152-154], nanoelectrospray infusion [155-162], laser diode thermal desorption (LDTD) techniques [163], or matrix-assisted laser desorption/ionization (MALDI) [164-166] that eliminate the HPLC component prior to mass spectrometric detection. Nano-electrospray (nanoESI) ionization refers to an electrospray emitter consists of a glass capillary with a tip pulled to a narrow internal diameter operated at a flowrate between 10 to 100 nL/min. NanoESI technology initiated by microfluidic devices offers the possibility of increased sensitivity over conventional ESI. NanoESI system using a robotic platform for sample handling can automatically infuse 96 samples in approximately an hour to microfabricated nanoESI nozzles for unattended quantitative and qualitative analysis [117, 167]. For each analytical run, a new tip and a new nozzle is used to eliminate the potential carry-over issue associated with HPLC autosamplers. LDTD-MS is a direct sample introduction technique combining thermal desorption and mass spectrometry. The sample plate is held in an X-Y stage that positions it in alignment with the laser. The flare-shaped portion of the nozzle abuts on the 96-well plate. Thermal desorption by an infrared laser diode at the back of each well without addition of any matrix generates the gaseous molecules to be introduced directly into the mass spectrometer.



Laser power and duration and the carrier gas flowrate are the major parameters for optimization of the vaporization efficiency of analytes from the target samples [163]. Matrix-assisted laser desorption/ionization (MALDI) involves mixing the analyte with a large molar excess of a matrix solution. Once the mixture is applied to a stainless steel target, the sample is allowed to air evaporate for co-crystallization. The sample plate is then inserted into the mass spectrometer for laser bombardment. These crystals containing matrix materials are subject to absorb energy of the laser resulting in desorption and ionization of the analytes. The coupling of MALDI to a mass spectrometer has extended its application for the direct analysis of pharmaceuticals [165, 166]. The major concerns for direct detection of low molecular weight compounds extracted from biological fluids using MALDI-MS are poor shot-to-shot reproducibility due to the inhomogeneous distribution of analytes within the sample spots and ionization suppression [165]. In addition to a higher-throughput potential, these “columnless” direct MS-based methods offer the additional advantages over the hyphenated-MS methods which are no carry-over between samples, low sample consumption and no chromatographic method development time. However, extensive optimization of the sample clean up process would be normally required to eliminate matrix ion suppression and interferences from endogenous interferences or the metabolites of the dosed compound when attempting to perform these “columnless” MS-based assays. ACCELERATOR MS Accelerator mass spectrometry (AMS) is a nuclear physic technique to allow the remarkably sensitive means for counting elemental isotopes at the individual atom level in less than 1 milligram samples [168-171]. AMS uses a particle accelerator in conjunction with ion sources, magnets, filters and mass spectrometers to separate out interferences and detect single atoms in the presence of stable atoms. Briefly, various negative elemental or molecular ions are first created through bombardment by a beam of Cs+ ions to sputter material from the sample. The ion source is then introduced to the gas phase and enters an electrostatic accelerator. In the middle of the accelerator, all molecular species are destroyed and the outer valency electrons are stripped. The resulting positively charged species continue their acceleration toward a magnetic quadrupole lens. The desired isotope ions are separated and counted by a mass spectrometer [172, 173]. For the direct measurement of 14C atoms, AMS is at least 1000 times more sensitive than radioactivity decay counting and any other methods. This makes it possible to use for a wide variety of tracing applications for drug compounds enriched with 14C in the DMPK, pharmacology and toxicology areas [174-176]. For microdosing and mass balance studies, animals and human are administrated with 14C-labelled drug components [173, 177, 178]. At certain post-dosing time intervals, blood, urine and other samples are collected for AMS analysis. The specimen for AMS experiment must be converted to a thermally and electrically conductive solid form. As an example, carbon samples are reduced to graphite prior to each AMS experiment. Ultilization of ultrasensitive AMS technique permits drug administration with dose at least 1000 times lower than that used in conventional radioactive studies to reduce radiation exposure. Although AMS is not able to provide chemical identity information, it can be used to access the extent of metabolism through HPLC-MS metabolite profiling [179].


Yunsheng Hsieh

MALDI-IMAGING MASS SPECTROMETRY During the drug discovery and development stages, often the question is raised as to whether the drug can reach the site of action which helps researchers better assess the potential value of that compound as a pharmaceutical product and the potential for toxicological outcomes. Most MS-based approaches are not able to provide the answer to certain questions regarding the distribution of a drug in various organs or tissues from in vivo animal experiments. Whole body autoradiography (WBA) normally provides a standard way to answer this question on the time course of the drug candidates [180]. However, the major disadvantage in the autoradiographic technique is that it provides for visualization of total drug-related materials, and provides no discrimination of the administrated drugs vs. metabolites. In addition, the availability of radiolabeled compounds is another concern at the drug discovery stage. To overcome these issues, matrix-assisted laser desorption/ionization-mass spectrometric method (MALDI-MS) has been developed to directly and simultaneously determine the distribution of pharmaceuticals and their metabolites in tissue sections which might unravel their disposition or biotransformation pathway for new drug development [181-186]. The concept of MALDI-IMS was introduced to direct profiling of the analytes within a tissue section or an organ. In this process, matrix materials are uniformly deposited over various tissue sections to extract analytes into the surface of the tissue and to produce crystals. A raster of organ sections containing the compounds of interest from a small animal under a stationary laser beam is then performed over a predetermined twodimensional array to generate ion plumes directly from the tissue sections in a large (5 cm x 5 cm) sample plate sufficient to contain whole tissue slices. The movement of the sample stage is automatically accomplished in the x and y directions to locate the edges of the tissue sample and to define the exact region of interest. During an imaging mass spectrometric experiment, MALDI-MS signals of small molecules from tissue sections within a user-defined area are first obtained as a function of acquisition times which are associated with the location of an array of pixels. Thus, two-dimensional ion maps of biological tissues are reconstructed with drug signals of a given m/z value monitored in each spectrum from each pixel to provide specific molecular images. The reliability of using MALDI-IMS for direct intact analysis of pharmaceuticals in tissues had been cross-validated by autoradiographic techniques [187]. For a fair parallel comparison between these molecular imaging technologies to visualize the accumulation of a well-established drug in rat brains, the animals had been administrated by direct infusion with 3H-clozapine into rat brain to avoid any possible biotransformation. Rat brain was chosen as a tissue model due to its unique symmetry and well-defined anatomy. The optical image of a sagittal section of the brain defined the presence of the cortex, limbic system, cerebellum, brain stem, and ventricles (Fig. 8). Results from the auto-radiography suggest that clozapine is distributed throughout the brain, with the highest concentration found in the lateral ventricle. The result from MALDI-IMS was in a good agreement with that obtained from autoradiography with regard to the distribution of clozapine in rat brain after intracerebral ventricular injection. MALDI-IMS could also be beneficial as a diagnosis, screening or discovery tool where tissue sections can be explored without knowing in advance what specific molecules have changed in a comparative study. Image comparison between control tissues and study tissues from small molecules will allow researchers to identify differences in resulting changes induced by drug candidates or their metabolites that could yield important toxicological information.



(a)

(b)

(c)

Fig. (8). (A) Optical images, (B) radioautographic images and (C) MALDI-MS/MS images from the study rat brain tissue section.

FINAL REMARKS The role of the DMPK screens is to provide an efficient paradigm for improving the drug likeness of the NCEs by using various in-vitro and in-vivo experiments for lead optimization and characterization. Hyphenation of the high-resolving HPLC to a tandem mass spectrometer (MS/MS) provides straightforward method development capabilities with excellent analytical linearity, sensitivity and selectivity for monitoring drug-related


Yunsheng Hsieh

compounds. HPLC-MS/MS techniques will continue to be the mainstream as a bioanalytical tool in DMPK screens for the next decade. The search for universal API interfaces to cover more NCEs and to look for strategies to ease the ever-increasing demand for faster sample turnaround time using HPLC-MS/MS are currently in progress. Any improvement in HPLC–MS/MS instrumentation should enhance not only throughput but also the chance of success in attempting the structural identification of metabolites in biological matrices. ABBREVIATIONS AMS

=

Accelerator Mass Spectrometry

APCI

=

Atmospheric Pressure Chemical Ionization

API

=

Atmospheric Pressure Ionization

CE

=

Capillary Electrophoresis

CLint

=

Intrinsic Metabolic Clearance

DMPK

=

Drug Metabolism and Pharmacokinetics

ESI

=

Electrospray Ionization

FDA

=

United State Food and Drug Administration

FIA

=

Flow Injection Analysis

HILIC

=

Hydrophilic Interaction Chromatography

HPLC

=

High Performance Liquid Chromatography

IMS

=

Imaging Mass Spectrometry

IND

=

Investigational New Drug

LDTD

=

Laser Diode Thermal Desorption

LIT

=

Linear Ion Trap

MALDI

=

Matrix-Assisted Laser Desorption/Ionization

MS

=

Mass Spectrometry

MS/MS

=

Tandem Mass Spectrometry

NCEs

=

New Chemical Entities

P450

=

Cytochrome P450

PGC

=

Porous Graphic Carbon

PU

=

Pulsed Ultrafiltration

QqLIT

=

Hybrid Quadrupole Linear Ion Trap

SFC

=

Supercritical Fluid Chromatography

UHPLC

=

Ultrahigh-Pressure Liquid Chromatography

WBA

=

Whole body autoradiography



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23

Glutathione Transferases in Drug Discovery and Development: Towards Safer and Efficacious Drugs Katholiki Skopelitou, Dimitris Platis, Irene Axarli and Nikolaos E. Labrou* Laboratory of Enzyme Technology, Department of Agricultural Biotechnology, Agricultural University of Athens, 75 Iera Odos Street, GR-11855-Athens, Greece Abstract: Glutathione transferases (GSTs) are enzymes involved in cellular detoxification by catalysing the nucleophilic attack of glutathione (GSH) on the electrophilic centre of a number of electrophilic compounds of both endogenous and exogenous origins. This conjugation reaction usually makes the electrophilic substrates more water soluble and, thereby, facilitates their excretion from the body. Determination of metabolic properties of a new chemical entity (NCE) is one of the most important steps during the drug discovery and development process. Nowadays, in vitro methods are used for early estimation and prediction of in vivo metabolism of NCEs. In this review detailed descriptions are given of several biotransformation reactions catalyzed by GSTs that can be used at very early phases of drug development, thereby enabling unsuitable candidates to be eliminated from consideration much earlier in the drug discovery process. Knowledge of the structure-function relationships in classes of compounds that are substrates for GSTs enables the design of molecules that can be stable, or labile which has potential applications in drug and prodrug design.

Key Words: Detoxification, enzyme-activated prodrug, glutathione transferase. 1. INTRODUCTION GSTs are a family of detoxification enzymes that catalyse the conjugation of GSH to a wide variety of endogenous and exogenous electrophilic compounds. The GST superfamily can be subdivided into a number of classes on the basis of their amino acid sequence [1]. Within mammals, the following classes have been defined: alpha, mu, pi, sigma, theta, zeta, kappa and omega [2]. Analysis of the GST gene family in the Human Genome Organization database showed 21 putatively functional genes [3]. Upon closer examination, however, GST-kappa 1 (GSTK1), prostaglandin E synthase (PTGES) and three microsomal GSTs (MGST1, MGST2, MGST3) were determined as encoding membrane-bound enzymes having GST-like activity, but these genes are not evolutionarily related to the GST gene family [3]. Therefore, the complete GST gene family comprises 16 genes in six subfamilies: alpha (GSTA), mu (GSTM), omega (GSTO), pi *Corresponding Author: Tel: +30 (210) 5294308; Fax: +30 (210) 5294308; E-mail: [email protected] Gary W. Caldwell / Atta-ur-Rahman / Z. Yan / M. Iqbal Choudhary (Eds.) All rights reserved – © 2009 Bentham Science Publishers.


Skopelitou et al.

(GSTP), theta (GSTT) and zeta (GSTZ). In other organisms additional soluble GST classes have been reported. For example, in insects: delta and epsilon [4]; in plants: phi, tau, lambda, dehydroascorbate reductase [5]; and in bacteria: beta [6] and chi [7]. In general the catalytic efficiency of GSTs towards xenobiotics is relatively low [17]. As in the case with other xenobiotic metabolizing enzymes (e.g., cytochrome P450, glucuronosyl transferases, etc.) low catalytic efficiency appears to be a trade off with regard to broad substrate specificity [8,9]. On the other hand, constitutive levels of GSTs are high (3-10% of total cytosolic protein) and therefore, although catalytic efficiency is relatively low, the overall capacity due to the high constitutive expression is important. The biological function of the GSTs is far from established [8,9]. Various electrophilic xenobiotics can be substrates for GSTs. Electrophilic centres for GSH conjugation are found in areneoxides, aliphatic and arylic halides, in carbonyls, organonitro-esters and organic thiocyanates. Industrial substrates for GST are haloalkanes, chlorobenzenes, thiocarbamates, diphenylethers, triazines, chloracetanilide. Acrolein, propenals, lipid hydroperoxides, chlorambucil and fosfomycin are additional substrates. Identification of the GST-mediated pathway for drug cleavage has been useful for elucidating the mechanism of metabolic biotransformation of compounds that have been brought forward for clinical studies [10,11]. Knowledge of the structure-function relationships in classes of compounds that are cleavage by GSTs enables design of molecules an be stable, or labile which has potential applications in drug and prodrug design. In the next sections of this chapter the different reactive groups and reaction types operating under catalysis by GSTs will be discussed. 2. STRUCTURE AND CATALYTIC MECHANISM OF CYTOSOLIC GSTs 2.1. Structure of Cytosolic GSTs Cytosolic GSTs are dimeric proteins as shown in Fig. (1). Each subunit is composed 200-250 amino acid residues with typical molecular masses ranging from 20–28 kDa. Each GST subunit adopts a canonical GST fold of seven to nine -helices and four sheets to produce two distinct domains, the N- and C-terminal domains [1,2,8,12]. The interface between the two subunits can be hydrophobic or hydrophilic, and interactions between residues in both subunits are essential for dimer stability. For example, the subunit interface for the alpha class GSTs is that of a ‘lock-and-key’ type joint with the Phe52 serving as the ‘key’ and the hydrophobic ‘lock’ residues residing between 4 and 5 helices of C-terminal [8]. All alpha, mu, phi, zeta, tau and pi class GSTs have such a ‘lock-and-key’ style interface. On the other hand, in theta class GSTs the main subunit interface lacks a ‘lock-and-key’ motif. Structurally it seems that the theta class GSTs are missing the loop feature that holds the Phe residue for interaction with the 4 and 5 helices and the hydrophobic pockets within the helices themselves [8]. In this case subunit interface rely mainly on electrostatic and hydrogen bond interactions. Incompatibility in interfacial residues prevents the formation of heterodimers between two GST subunits from different classes. However, within a class, the formation of heterodimers can expand the range of functional proteins formed. Each GST is known to contain a G-site capable of binding the GSH substrate and an H-site that has xenobiotic compound binding capabilities, (Fig. 1A). he G-site share a highly conserved amino acid sequence. The G-site is mainly composed of amino acids in

GSTs in Drug Discovery and Development


A

B

Fig. (1). A: Subunit structure of human GSTA1-1 in complex with an ethacrynic acid-GSH conjugate (PDB code 1GSE). The G- and H-site and the N- and C-terminal are labeled. B: Ribbon diagram of the dimeric GSTA1-1 structure.

the N-terminal, including the active site residue that interacts with and activates the sulfhydryl group of GSH to generate the catalytically active thiolate anion [2,8,12,13]. In the alpha, mu, pi, sigma the active site residue is a Tyr. In the delta, epsilon, theta, and zeta GSTs, the active site residue is a Ser, and in omega class GSTs, it is a Cys [14]. Several


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crystal structures have shown that this active site residue is in hydrogen bond with the sulfur atom of GSH and is located at a position that would allow it to stabilize the thiolate anion of GSH and enhance its nucleophilicity. In addition, this residue may also contribute to the correct orientation of the sulfhydryl group of GSH in catalysis. Ser 11 and Tyr 9, the active site residues of rat GSTT2-2 [15] and rat GSTA1-1 [16] respectively, have been reported to play important roles in efficient product release and in controlling the C-terminal dynamics. The xenobiotic hydrophobic H-site is mainly found in the C-terminal. The H-site is hydrophobic but much less specific for substrate types allowing numerous substrates to bind [8]. This is the reason why initial attempts to classify GSTs according to substrate types (e.g. aryl-, alkyl-transferases) failed. Interclass amino acid sequence identity is rarely greater than 35% in the H-site region. Besides catalytic binding sites, GSTs have frequently been shown to possess noncatalytic binding sites. Numerous hydrophobic compounds have been identified to bind to these pockets such as bilirubin, heme, steroids, hormones and bile salts. This function has led to the name ‘ligandins’ for several of the enzymes [17]. The precise functions of ligandin GST binding to non-substrate ligands remain unclear. One possibility is that binding of non-substrate ligands to GSTs prevents oxidation of the molecules in vivo [17]. Another possibility is that GSTs prevent cellular damage from cytotoxic and genotoxic compounds that can oxidize protein and intercalate into DNA [18]. The third possibility is that GSTs facilitate delivery of the ligands to specific receptors or cellular compartments [17,18]. Some general structural features, (Fig. 2), of the main mammalian GST classes (alpha, mu, pi, theta, zeta and omega) will be discussed in the next paragraphs. Compared to the pi and mu GSTs the C-terminal of the alpha class GSTs is longer by some 4 to 8 amino acid residues [19]. The longer alpha C-terminal also forms an -helix (9), which comprises a portion of the smaller H-site [20]. This helix is thought to be important to dimer stabilization and affects both the GSH-binding rate and ionization state of the catalytically essential residue Tyr 9 [21]. Thus the alpha class C-terminal has a positive effect on catalytic activity. A special recognizable feature of the mu class GSTs is the socalled mu loop, which is the result of an insertion in N-terminal domain. Another structural attribute, which is recognizable in both mu and pi classes of GST, is the C-terminal wall [21]. On average, the C-terminal end is 4 to 8 amino acid residues shorter than their alpha counterpart and forms a wall that results in a partially blocked access to the xenobiotic binding site [22]. In addition, the pi and mu classes share a larger H-site that is more accessible to solvent entry than the alpha class GSTs. The theta class enzymes are quite distinct from alpha, mu, or pi – GST classes. The G-site of the theta class GSTs is, in general, much deeper than that of alpha, mu, and pi GST classes [8,12]. Several features set omega GSTO1-1 apart from the other members of the GST superfamily. Its N-terminal region has a unique extension of approximately 19 residues when compared with other cytosolic GSTs. This contains a proline-rich segment that in conjunction with the C-terminus forms a distinct structural unit with an unknown function. Unlike other mammalian GSTs, GSTO1-1 appears to have an active site cysteine that can form a disulfide bond with GSH. There are many structural features of the zeta class enzymes found in other GST classes, although it does not appear that GSTZ resembles more closely any one class in



particular. GSTZ has a truncated C-terminus and lacks the typical V-shaped dimer interface and hence resembles the theta class in this regard. The large loop preceding helix 2 is reminiscent of the mu loop previously seen in the mu class structure.

Fig. (2). The common chain fold of the GST superfamily. Protein data bank codes for the structures used are: alpha, 1gse; theta, 1ljr; mu, 1hna; pi, 1glp; omega, 1eem and zeta, 1fw1. Images in this figure were drawn using the program PyMol.

2.2. Kinetic Mechanism Conjugation reactions with GSH have been reported for a vast number of compounds and the kinetic mechanism has been clarified [22-27]. In general, kinetic mechanism of the GST-catalyzed conjugation reaction is very complex and class dependent. For example, several catalytic mechanisms, including random, ping-pong, and sequential, have been proposed [13,22-25], but random binding order of substrates seems to prevail. For instance, the maize GST I-catalyzed conjugation reaction between GSH and CDNB follows a rapid equilibrium random sequential Bi Bi kinetic mechanism [13], whereas a steady state sequential rapid Bi Bi mechanism was proposed for octopus GST, rat GSTs M1-1, M1-2, and A3-3 [26,27]. From a physiological point of view GSH binding should occur first because of the availability of GSH in millimolar concentrations in the cells. This value is about three magnitudes larger than the dissociation constant between GSH and GSTs [28]. 3. CATALYTIC FUNCTION The different reaction types operating under catalysis by GSTs are:


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3.1. Nucleophilic Displacement of an Alkyl or Aryl Halogen or a Nitro-Group Nucleophilic displacement of an alkyl or aryl halogen or a nitro-group, (Fig. 3), seems to be the most frequently observed reactions. Halogens or nitrogroups of these molecules are soft electrophiles and react readily with the GSH. In fact, the standard enzyme assays for GST activity use 1-chloro-2,4-dinitrobenzene or l,2-dinitro-4chlorobenzene as substrates. These are nucleophilic aromatic substitution reactions that occurs via an addition-elimination sequence involving a short-lived -complex intermediate [8,9]. The reaction of GSH with nitroalkanes involves attack of an electrophilic carbon atom leading to the formation of a thioester and to the release of nitrite as shown in Fig. (3). A Cl

SG +

+ GSH O2N

NO2

O2N

HCl

NO2

B Cl

SG

Cl

NO2

GS NO2

NO-2

NO2 CDNB

NO2

-Cl -

GS -

NO2

-complex

C RCH2NO2 + GSH

RCH2SG + HNO2

Fig. (3). A: The reaction of 1-chloro-2,4-dinitrobenzene with GSH catalyzed by GSTs. B: The nucleophilic aromatic substitution reaction occurs via an addition-elimination sequence involving a short-lived -complex intermediate. C: The reaction of GSH with nitroalkanes catalyzed by GSTs.

Several GSTs catalyze the conjugation of the GSH with alkyl halides and related compounds [29]. For example, conjugation of the drug chlorobucil is an example for this type of reaction. Examples of the GSH conjugations catalyzed by GSTs are presented in Fig. (4). GSH conjugations of alkyl halides can be detoxication or bioactivation reactions. In the later case the substrate is activated leading to reactions with other nucleophiles, particularly DNA and proteins. The theta class GST enzymes are the most active [30], towards alkyl halides although other mammalian GSTs also have some activities. 3.2. Addition of GSH to Epoxides and Arene Oxides GSH transferases catalyze the addition of GSH to epoxide and arene oxide substrates, as illustrated (Fig. 5) [8]. Carcinogenic intermediates have been identified as both



bay- and fjord-region diol epoxides (DEs) [31,32]. GSTA1-1, an alpha class enzyme, has efficient catalytic activity towards stereoisomers of a series of bay region diol epoxides fjord region diol epoxides such as (±)-anti-B[]PDE, (±)-syn-B[]PDE and (±)-antiB[c]PhDE [33,34]. It is important to mention that the chemically more reactive syn diastereomers are better substrates for GSTA1-1 and this is propably due to differences in the lipophilicity of the compound. The activity and enantioselectivity of human GSTs towards diol epoxides depend on the absolute configuration and the geometry of the aromatic structure. Human mu class enzymes catalyse, in most cases, the conjugation of the stereoisomeric bay-region DEs, in particular, the syn-diastereomers [35]. OH GSH + RX

GS-R (+HX)

SG H2O

X GSH +

X

_

HX

X SG

_ -

X

+ SG

SG

GSH

SG Nucl

Nucl SG

Fig. (4). The conjugation of GSH with alkyl halides catalyzed by GSTs.

In contrast, human pi class enzyme, showed, in most cases, a noticeable activity towards bay-region anti-DEs and a high preference for conjugation of enantiomers with (R,S,S,R)-configuration [35-37]. Whereas the alpha class has no catalytic activity towards the optical isomers of benzo[c]phenanthrene DE B[c]PhDE, both the mu and the pi class, have a lower catalytic activity using as substrate these metabolites than the other bay-region DEs [35]. One relatively large, bulky substrate for certain GSTs is the epoxide of aflatoxin B1 (AFB), (Fig. 5C) [38]. AFB, one of a group of related mycotoxins produced by the common fungal mold Aspergillus flavus, is a well documented rat and human carcinogen [15,16]. AFB is activated to the highly reactive aflatoxin 8,9-exo-epoxide by certain cytochromes P450. AFBO then serves as a substrate for some, but not all, GST isoenzymes [39]. 3.3. Addition of GSH to Carbon-Carbon-Double Bonds Addition of the thiolate to carbon-carbon-double bonds is a special type of reactions on compounds with reactive carbon-carbon double bonds neighboured by an electronwithdrawing group [40]. The conjugation on these bonds is a so-called Michael addition reaction and leads to a labile conjugate that may be sensitive to pH changes. Other similar substrates include alkenals [4] and particularly the (E)-4-hydroxyalk-2-enals (4hydroxy-alkenals), illustrated in Fig. (6). 4-Hydroxy-2,3-trans-nonenal (4-HNE), a racemic lipid peroxidation product, is the most abundant hydroxyl-alkenal formed during the peroxidative breakdown of n-6 polyunsaturated fatty acid residues of membrane lipids, such as linoleate and arachidonate [41,42] and also the most cytotoxic and genotoxic product, involved in a various cellular pathologies [42,43]. GSTs are involved in the predominant mechanism for protection against 4-HNE toxicity in mammalian liver [44,45], however, despite the fact that hGSTA1-1 is the major human liver GST isoform


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A GSH + SG

O

OH

OH phenanthrene 9,10 - oxide

SG

(9S, 10S) - GSPhen

(9R, 10R) - GSPhen

B Bay-region PAH Diol Epoxides

Diol Epoxides O S S HO

OH

CH3

R

HO R

S R S R

CH3 H3C

OH chrysene O

HO

RS R S

O

CH3 O

CH3

S R CH3

HO

OH

CH3

R S

CH3

OH benzo[a]pyrene

O

C O

dibenzo[a,h]anthracene

O O

HO O Glutathione

S

Fig. (5). A: GSTs catalyze the addition of GSH to epoxide and arene oxide substrates, such as phenanthrene 9,10-oxide. B: Structures of bay-region polycyclic aromatic hydrocarbons (PAHs). The stereochemistry of the syn- and anti-diol epoxides are shown to the right and left, respectively. The prefix syn indicates that the oxirane ring and the benzylic hydroxyl group are located on the same face of the molecule, while anti indicates that these groups are located on opposite faces. C: The structure of aflatoxin B1-GSH conjugate.



O H2C G-SH +

HC

O

CH H2C

CH

OH H2C

CH G

OH

S

O

CH CH

CH R

4-hydroxy-alkenal

G

CH

S

CH R

CH

R

OH

cyclic hemimercaptal

glutahione conjugate

Fig. (6). Reaction between GSH and 4-hydroxy-alkenals catalyzed by GSTs. The primary product is further transformed to a cyclic derivative.

it is relatively inefficient at conjugating 4-HNE [46,47] as well as other alkenal substrates. GSTA4-4 comprises the basic detoxification pathway for the removal of 4-HNE in the human liver mitochondria [48-50], but its contribution in other tissues is unknown due to its relatively low expression in human tissues [51]. 3.4. Addition of GSH to Organic Isothiocyanates and Thiocyanates Organic isothiocyanates, which are abundant in edible plants, undergo conjugation with GSH enzymatic as well as non-enzymatic to form dithiocarbamates [52,53], as presented in Fig. (7A). These dithiocarbamates are degraded in vivo to the corresponding S(N-acetyl)cysteinyl conjugates (mercapturates), which undergo rapid renal excretion. A number of isothiocyanates have also been shown to be inducers of GSTs in animal tissues and to display pronounced anticarcinogenic activity. In general, GSTM1-1 appears to be the most efficient catalyst towards isothiocyanates, closely followed by GST P1-1, whereas GSTAl-1 is less active by one and GSTM4-4 by even two orders of magnitude [53]. All four enzymes exhibit higher catalytic rate with increasing aliphatic chain length, and, of all the aliphatic substrates with linear carbon chains tested, hexyl-NCS gives the highest rates for all of them. The aromatic substrates, benzyl-NCS and phenethyl-NCS, tend to be the best ones. Moreover, GSTM4-4 has a preference for allyl-NCS among the aliphatic compounds. GSTs also catalyze the attack of the GSH thiolate ion on the electrophilic sulfur atom of several organic thiocyanates, resulting in the formation of an asymmetric glutathionyl disulfide and cyanide as shown in Fig. (7B). A R

C N

B

S

S

RSCN + GSH

+ GSH

HN R

C S

G

RSSG + HCN

Fig. (7). A: Organic isothiocyanates undergo enymatic conjugation with GSH to form dithiocarbamates. B: GSTs catalyze the attack of the GSH on the electrophilic sulfur atom of organic thiocyanates.


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3.5. Sulfonamidase Activity The mechanism of sulfonamide cleavage by GST has been investigated using as a model substrate the PNU-109112, a potent HIV-1 protease inhibitor [10,11]. The mechanism of reaction, shown in Fig. (8), could involve initial GS- attack on either the sulfonyl S atom or the adjacent carbon atom in the cyanopyridine ring. The net result is either direct or indirect sulfonamide N-S bond cleavage. C

N

O RNH2

N

+

C

S

S O

G

GSH O S

H N

N C

O

R RNH2

+

SO2

+

G

S

Fig. (8). GST/GSH-mediated sulfonamide leavage of PNU-109112.

The GST activity toward sulfonamide bond cleavage has been characterized with respect to the molecular mechanism of the enzymatic reaction and substrate structureactivity relationships [10,11]. For example, studies of GST-mediated cleavage of PNU109112 and a wide variety of other sulfonamides reveal two striking structural features common to sulfonamide substrates [10,11]. Groups capable of withdrawing electron density from the carbon atom to the sulfonyl group are an absolute requirement. Such groups positioned ortho and/or para on aromatic or heteroaromatic rings were shown to activate the corresponding sulfonamides. The electrophilic substructure of the sulfonyl group is solely responsible for activation of the sulfonamide bond toward cleavage. On the other hand, the amine portion has little or no impact on the cleavability of sulfonamide substrates [10,11]. Recombinant forms of GSTA1–1, GSTM1–1, and GSTP1–1 were tested for sulfonamidase activity. Activity was observed for all three isoenzymes; the M1-1 isoenzyme was 5.4 and 16 times more active than the A1-1 and the P1-1 forms, respectively [10,11]. 3.6. Peroxidase Activity The release of oxygen radicals, such as O2-, H2O2, singlet oxygen (1O2) and hydroxyl radical (.OH) during oxidative stress conditions can initiate an autocatalytic chain of lipid peroxidation which can breach membrane integrity [54,55] or even cause DNA damage [56]. In fact, it has been reported that peroxidation of membrane lipids in cells and tissues is known to produce various aldehydic compounds [41,42], seriously suspected to cause a variety of chronic diseases, including atherosclerosis [57], Alzheimer's disease [58], cataractogenesis [59], Parkinson's disease [60] and cancer [42]. Glutathione peroxidase (GPx, EC.1.11.1.9) is a selenoenzyme that functions as an antioxidant by catalyzing the reduction of H2O2, lipid hydroperoxides and other organic



peroxides with GSH. The selenium atom in the enzyme catalytic site undergoes a redox cycle involving the selenolate anion as the active form which reduces hydrogen peroxides and organic peroxides. The selenolate, which is oxidized to selenenic acid (EnzSeOH), reacts with reduced GSH to form a selenosulfide adduct (Enz-SeSG). Finally, the nucleophilic attack of a second GSH to Enz-SeSG regenerates the active form of the enzyme by attacking the selenosulfide to form oxidized GSH (GSSG), as illustrated in Fig. (9) [61,62]. Regeneration of the reduced enzyme is accomplished via a two-step process involving reduced GSH as reductant. In the present of high concentrations of hydroperoxide, selenenic acid (Enz-SeOH) can be futher oxidized to a seleninic acid (Enz-SeO2H) [63].

Fig. (9). Scheme of the mechanism of the peroxidase activity.

Several GSTs were reported to display GPx activity towards organic hydroperoxide. The peroxidase activity associated with the GSTs is referred to as NonSe-GPx activity, which represents one of the important antioxidant mechanisms that exist in cells for protection against hydroperoxides [64,65]. In humans, the NonSe-GPx activity of GSTs towards lipid hydroperoxides is predominantly associated with alpha class [66, 67]. In addition, NonSe-GPx activity is a common characteristic of mammalian theta class and zeta class GSTs. GSTs belonging to the alpha class have been implicated in the detoxification of the produced peroxides and shown to exhibit high peroxidase activity towards these compounds [46,68]. In this case there is a nucleophilic attack of the GSH thiolate on an oxygen atom of the peroxy group and the unstable sulfenic acid derivative is subsequently reduced by a second GSH molecule as presented in Fig. (9). In the human liver, this Se-independent glutathione peroxidase activity is expressed primarily by isoenzymes hGSTA1-1 and hGSTA2-2 [56,69,70], and although their activity is much lower that the Se-dependent human GPx-I [71], the high amount of GSTs in the liver compared to other GPx activities [72] compensates for the low activity. Both hGSTA1-1 and hGSTA2-2 exhibit high peroxidase activity towards fatty acid hydroperoxides, phospholipid hydroperoxides, and cumene hydroperoxide [73]. Similar phospholipid hydroperoxide activities have been detected in


Skopelitou et al.

the human lung [74]. In mammalian cells, besides cytosolic GSTs, microsomal GSTs also express peroxidase activity [75,76]. 3.7. Transthiolation GSTs are induced in conditions of oxidative stress where it is known that sulfhydryl groups of cellular proteins can form S-thiol adducts (protein-thiol mixed disulfides, protein-S-S-glutathione (PSSG) and protein-S-S-cysteine (PSSC)) [77,78] with cysteine and GSH, through a process called S-thiolation. This result to deactivation, changes in conformation of even aggregation of the proteins involved. S-glutathionylation is a reversible post-translational modification with critical roles in sulfhydryl homeostasis acting as a protection mechanism of the cysteine residues from irreversible oxidation during periods of oxidative stress or a transport/storage form of cysteines [79]. Transthiolation reactions are not uncommon to proceed in cells without the aid of enzymes, however, cells do possess specific enzymes that catalyse the reduction of protein disulphides. In many cases this role has been attributed and accomplished by glutaredoxin (Grx). Modification of cysteine residues with GSH has also been reported for many enzymes, such as carbonic anhydrase [80] and fatty acid synthetase [81]. The transthiolation reaction consists of two steps. The first step involves the formation of a mixed disulfide with GSH which is subsequently reduced further by GSH with the production of GSH disulfide followed by complete reduction of the previously conjugated thiol residues. It is interesting that this reaction cannot distinguish between catalysis of the glutathionylation step (R-SSG formation, characteristic of a GST-mediated conjugation) and the deglutathionylation step (R-SSG reduction, characteristic of a deglutathionylase-like GRx). GST-dependent thioltransferase activity is generally very uncommon among GSTs but has been reported in mu, omega and pi classes. For example, thioltransferase activity has been observed in mu class GST from bovine lens, in three omega class GSTs in Saccharomyces cerevisae [82,83], and in human omega class GST (GSTO1-1) [14]. In addition, a pi class member has been shown to mediate the reactivation 1CysPRx, a protein that protects cells against membrane oxidation through GSH-dependent reduction of phospholipid hydroperoxides to corresponding alcohols. 3.8. Isomerase Activity It is well known that GSTs catalyze several types of isomerization reactions, with the conversion of cis double bonds to the trans configuration being the most well-studied [84-86], as in the case of isomerization of diethyl maleate to diethyl fumarate. Interestingly, GST-dependent isomerization reaction can proceed in a GSH-independent manner, as in the case of the cis-trans conversion of retinoic acid catalyzed by hGSTP1-1 [87]. The available information suggests that the prementioned cis-trans isomerization reactions are thermodynamic driven by the stability of the trans isomer, proceeding via the addition of GSH to the double bond, rotation around the resulting single bond, and elimination of GSH [86]. Positional isomerization of double bonds by GST-dependent reactions has also been been reported. The most well-characterized example of positional isomerization is the reaction in which endogenous 5-3-ketosteroids are converted to 4-3-ketosteroids [88,85,86]. For these reactions, the driving force is the stability of the conjugated double bonds in the product. In fact, GSTs appear to play an important role in the biosynthesis



of steroid hormones. All steroid hormones in humans are derived from cholesterol and in recent years it has been shown that GSTs are involved in the biosynthetic path of testosterone and progesterone [89,90]. Benson and Talalay [91] have initially discovered a GSH-dependent ketosteroid activity in rat liver, which was later identified as a GST [88]. The spontaneous conversion of 5-3-ketosteroid androstene-3,17-dione to the 4isomer product proceeds at a slow rate, which is markedly raised by the addition of GSH along with GSTA1-1, the first human GST discovered that exhibited double bind isomerase activity [92]. Another member of the alpha class, GSTA3-3 was found to exhibit approximately 20 fold higher catalytic isomerase activity towards 5-ketosteroids in human tissues [89,90] and is selectively expressed in gonads, placenta and the adrenal gland. GSTA3-3 efficiently catalyzes double-bond isomerizations of 5-androstene3,17-dione (5-AD) and of 5-pregnene-3,20-dione, intermediates in the biosynthesis of the steroid hormones progesterone and testosterone [8]. GST-catalyzed isomerization has also been observed during the biosynthesis of prostaglandins. Prostaglandin (PG) D synthase (PGDS) is the enzyme responsible for the formation of PGD2 and the J series of PGs [93]. In addition, prostaglandin H2 (PGH2) is obtained from prostaglandin D2 (PGD2) in a reaction catalyzed by a sigma class GST in vivo and alpha class GSTs in vitro [94]. The sigma class GST, isomerizes PGH2 to PGD2 selectively, whereas other GST isoenzymes catalyze the conversion of PGH2 nonselectively to produce PGD2, PGE2, and PGF2 [95]. A zeta class GST has also been identified as a maleylacetate isomerase [96], catalyzing the penultimate step in tyrosine degradation pathway, a pathway that has been associated with many disorders. [97,98]. This enzyme converts maleylacetoacetate into fumarylacetacetate, and maleylacetone into fumarylacetone. 3.9. Ester and Ether Hydrolysis Aromatic carboxylic acid esters and nitrate esters serve as substrates for GSTs as shown in Fig. (10). In the case of nitrate esters, the attack is on the electrophilic nitrogen. Experimental evidence supports the GSH sulfenyl nitrite intermediate, which is attacked nonenzymatically by a second molecule of thiol. GSTs also catalyze ‘reverse’ reactions, such as the hydrolysis of GSH thiol esters [99-101]. Reverse reactions are of interest as potential tumor-directed pro-drug activation strategies and as mechanisms for tissue redistribution of carboxylate containing drugs. Examples of ‘reverse’ GST reactions have been limited to retro-Michael additions [101], and hydrolysis of activated carbamate thiol esters and thiocarbamate thiol esters formed from GSH conjugation with isocyanates and isothiocyanates, respectively [53]. The reverse reactions are of therapeutic and toxicological importance, because the generation of GSH and an electrophile from GSH conjugates represents a potential pathway for delivery of ‘latent’ toxins or drugs to tissues remote from the initial conjugation reaction, whether release of the conjugate is mediated by GSTs or by nonenzymatic processes. The GSH thiol esters of ethacrynic acid (E-SG) (Fig. 11) and several nonsteroidal anti-inflammatory agents such as, sulindac, flurbiprofen, and diclofenac, have been tested as substrates with human GSTs [99]. Because ethacrynic acid produced by hydrolysis of E-SG contains a reactive --unsaturated ketone, it could form adducts with


Skopelitou et al.

protein nucleophiles, as is well established for Cys-47 of GSTP1-1. Adduction of Cys-47 by ethacrynic acid results in a slowly reversible inhibited complex. O

A

OH O

O +

+ GSH

C

GS

CH3 NO2

NO2

RCH2ONO2 + GSH

B C

RCH2OH + HNO2 + GSSG

RCH2OH + [GSNO2]

RCH2ONO2 + GSH [GSNO2] + GSH

GSSG + HNO2

Fig. (10). GSTs catalyze the attack of the GSH thiolate ion on the electrophilic carbon of carboxylic (A) and nitrate (B) esters. The later reaction takes place through a GSH sulfenyl nitrite intermediate (C), which is attacked nonenzymatically by a second molecule of thiol.

E-SG

EA

GS-EA + GSH

Cl Cl

O

Cl

O

Cl

SG O

O

O O

Cl Cl

OH

O O

OH

O

SG

Fig. (11). Reaction pathways of ethacrynic acid (EA) following its release from thiol ester (E-SG). The released EA may further react with GSH to form GS-EA.

Other example of S-ether hydrolysis involves the biotransformation of the prodrug azathioprine, to generate its active form 6-mercaptopurine as illustrated in Fig. (12) [102]. The mechanism of action of GSTs with azathioprine involves a nucleophilic attack of the deprotonated form of GSH on the electrophilic 50 carbon in the imidazole moiety of azathioprine. Thus, 5-mercaptopurine and a GSH–imidazole conjugate are released [103]. Among several human GSTs tested, the alpha class GSTA2-2 had the



highest specific activity, whereas a second alpha class enzyme GSTA1-1, as well as the mu class GSTM1-1, also demonstrated prominent catalytic activity. NO2

N

Electrophilic center N

S

H3C N

S

N

N

N H

Fig. (12). GST catalysis of S-ether hydrolysis of azathioprine, to generate its active form 6mercaptopurine.

3.10. GST-Activated Pro-Drugs Pro-drug development using the platform of GST has recently produced a number of lead compounds [104-106]. GSTs are frequently overexpressed in neoplastic tissues, with the GSTP1-1 isoform most commonly overexpressed in cancers resistant to drugs. Therefore GSTP1-1 proved to be an attractive molecular target for pro-drug activation. Two such agents that are in preclinical development will be discussed in the next section. High GST-expression levels in human cancers together with the knowledge that nitric oxide (NO) has therapeutic potential, provided the rationale for the design of the NO-releasing GST-activated pro-drug such as O2-arylated diazeniumdiolates derivatives (Fig. 13) [104,106]. The general mechanism of action of such prodrugs involves nucleophilic aromatic substitution by GSH, generating the nitric oxide-releasing diazeniumdiolate ion. Nitric oxide prodrugs of the ionic diazeniumdiolate class that spontaneously dissociate at definitive rates to form NO are useful as possible therapeutic agents in the treatment of several disease states, such as cancer. Due to the involvement of NO in diverse physiological processes, site-directed delivery of therapeutic nitric oxide is essential to avoid any undesirable side-effects. SG NR2

NR2 O

N+

O2N

NO2

N+ N

O GSH

O

NR2

N SG

N

O

O O2N

NO2

O2N

N+

H+ 2 NO

O

R2NH

NO2

Fig. (13). Structure of an NO-releasing GST-activated pro-drug, and its reaction with GSH.


Skopelitou et al.

Another prodrug, illustrated in Fig. (14), activated by GSTP1-1 is TLK286 [105]. This drug is activated through a -elimination reaction that cleaves the drug into a phosphorodiamidate (the eventual alkylating moiety) and a GSH analogue. Drug design ensured that this reaction was preferentially catalyzed by the Tyr 7 in the active site of GSTP 1-1.

Tyr 7 Cl Cl O

O~

O

P

Cl

N

O

H

Cl

S O H N

O H2N

N

COOH

N H

COOH

O Cl Cl CH2

O

H N

O H2N COOH

N

O

S O

P

+ COOH

Cl

N

O

Cl

N H O

Cl N

O

P O

Cl

N Cl

Fig. (14). Structure of TLK286 and its activation by human GSTP1-1. Tyr 7 is the catalytic residue.

3.11. The Negative Side of the GST-Detoxification System Despite the fact that detoxification is generally considered a significant positive aspect of GST function, in certain cases, chemically reactive metabolites produced by GST can have potentially harmful effects. A characteristic example is the case of the drug



acetaminophen (paracetamol) where the reactive metabolite N-acetyl-p-benzoquinoneimine is formed when the drug is ingested in high doses. In this case, the action of GST results in hepatic failure [107]. In fact, Henderson et al. were able to show that complete absence of pi class GSTs in mice was sufficient to alleviate any signs of hepatotoxicity and significantly improve GSH recovery rates [108]. 4. CONCLUSIONS The development process of new drugs is constantly being improved by defining new strategies and implementing new techniques. The human body contains a variety of enzymes that are involved in the metabolism of several chemicals that comprise today’s pharmaceuticals. One of the most important classes of metabolic enzymes is GSTs which are directly involved in the clearance of drugs from the body. During this process, drug metabolites are generated, some of which are biologically inactive or active. GSTs catalyze a wide range of reactions that can be described as accompanying the many varieties of catalysis in which the GSH thiolate anion participates as a nucleophile. The substrates are rather diverse and include important endogenous modulators, as well as xenobiotic chemicals and the oxidation products of the transformation of xenobiotics by other enzymes. In addition, GST-activated pro-drugs provide viable platform for the development of novel cancer drugs. Investigation of these biotransformation reactions in vitro can be very useful at very early phases of drug development, thereby enabling unsuitable candidates to be eliminated from consideration much earlier in the drug discovery process. ABBREVIATIONS AFB

=

Aflatoxin B1

CDNB

=

1-Chloro-2,4-dinitrobenzene

DE

=

Diol epoxide

EA

=

Ethacrynic acid

GPx

=

Glutathione peroxidase

Grx

=

Glutaredoxin

GSH

=

Glutathione

G-site

=

Glutathione binding site

GSSG

=

Oxidised GSH

GST

=

Glutathione transferase

hGST

=

Human glutathione transferase

4-HNE

=

4-Hydroxy-2,3-trans-nonenal

H-site

=

Hydrophobic binding site

NCE

=

New chemical entity

PG

=

Prostaglandin

PGDS

=

Prostaglandin D synthase

ROS

=

Reactive oxygen species


Skopelitou et al.

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43

New Sampling Techniques for PharmacokineticPharmacodynamic Modeling Christian Höcht1,2,*, Marcos Mayer1,2, Javier A.W. Opezzo1,2, Guillermo F. Bramuglia1 and Carlos A. Taira1,2 1

Cátedra de Farmacología, 2Instituto de Fisiopatología y Bioquímica Clínica, Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires, Junín 956, (C1113AAD) Buenos Aires, Argentina Abstract: Considering that pharmacokinetic–pharmacodynamic (PK–PD) modeling describes the relationship between tissue concentrations of drugs and their corresponding pharmacological response, an important issue of PK-PD studies is the availability of powerful sampling techniques that allow measurement of tissue concentrations of drugs at multiple time points. Traditional sampling techniques, including biopsy, blood and saliva sampling, and skin blister sampling, have several limitations for drug monitoring during PK-PD studies, considering that these techniques did not allow the measurement of drug concentrations at the site of action. In the last decades, new sampling techniques, including membrane based techniques (microdialysis and ultrafiltration) and imaging techniques (positron emission tomography and magnetic resonance spectroscopy), have been available for measurement of drug concentration at the target site. The possibility of simultaneous monitoring of target site concentrations of drugs and their pharmacological effect with these new sampling techniques have significantly improves current knowledge of PK-PD modeling. In addition, membrane based techniques also allow simultaneous monitoring of endogenous compounds and therefore permit the study of the relationship between drug target site concentrations and their effect on biochemical markers, making these techniques highly useful for PK-PD modeling studies. The aim of this chapter is to describe the principles of membrane based techniques and imaging techniques, and their applicability for drug monitoring in PK-PD modeling.

INTRODUCTION The demand for efficacious pharmacological agents is increasing due to higher lifestyle expectations and changes in demographic profiles. The discovery of the existence of a large number of orphan receptors will increase the number of new therapeutic agents to be tested. The availability of new techniques, such as the Human –Genome Project and the High throughput screening and combinatorial chemistry, will also *Corresponding Author: Tel: +(54-11)-4964-8265; Fax: +(54-11)-4508-3645; E-mail: [email protected]



Höcht et al.

identify an exploding number of new molecular targets as well as potential new therapeutic agents [1]. Therefore, a more rigorous selection process will be needed at early stages of drug development. Integration of pharmacokinetic-pharmacodynamic (PK-PD) concepts through PK-PD modeling is a potential tool to enhance the information gain and the efficiency of drug selection during drug development [2-4]. PK-PD modeling became as an important tool in clinical practice due to the possibility of an adequate dose regimen selection and the early detection of non-responders to the pharmacological therapy [2, 4]. Another important trend of drug properties to evaluate at early stages of drug development is its capability to reach the target site at sufficient amount to exert its therapeutic effect. It has been estimated that up to 40% of new chemical entities fail in the early phases of drug development because of inappropriate pharmacokinetic properties, such as inadequate drug distribution [5]. Target site distribution is also an important factor in the clinical use of pharmacological agents, especially antiinfective, antineoplastic and central acting drugs, to ensure success of pharmacological interventions. Although pharmacokinetic properties of drugs can be studied by means of traditional blood sampling, this technique is limited by the impossibility to evaluate variability in drug distribution at the target site. Therefore, in the last years, regulatory authorities have been emphasizing the evaluation of drug distribution at the target site during drug development (http://www.fda.gov/cder/guidance/2580dft.pdf). Although other traditional sampling techniques, such as biopsy and skin blister, allow measurement of tissue concentrations of drugs, the utility of these methodologies is strongly limited by the existence of serious limitations, including the inability of serial sampling for the study of the complete tissue pharmacokinetic profile of the drug [6]. In the last decades, new sampling techniques, including microdialysis and imaging techniques, have been available and validated for the study of drug distribution at the target site and PK-PD modeling [6]. Use of these techniques in preclinical and early clinical drug development might reduce failing rates of new chemical entity, also improving cost and the time required until a new chemical entities reaches the market. In addition, these new sampling techniques have also been used in clinical practice to ensure adequate tissue distribution of drugs and for the study of PK-PD models of drugs. Taking into account these antecedents, the aim of this chapter is to: i) describe the concepts of drug distribution at the target site and PK-PD modeling; ii) compare the available techniques for the study of tissue distribution of drugs and PK-PD modeling and iii) to comment the methodological and practical aspects of microdialysis and imaging techniques, and their application for the study of drug distribution and PK-PD modeling of drugs and new chemical entities during drug development and clinical practice. THE IMPORTANCE OF MEASURING TARGET SITE CONCENTRATIONS OF DRUGS The selection of the adequate dose and dosing timing is a fundamental step to achieve therapeutic success with any pharmacological treatment. Selection of the best drug and dosing regimen does not only increase the likelihood of therapeutic success but also reduces the appearance of adverse drug reactions. However, the selection of the most adequate drug and dosing interval of the same is not an easy issue. For most thera-

New Sampling Techniques


peutic agents, drug and dose regimen are empirically selected based on trial and error and by adjusting drug dosing taking into account patient weight. However, this strategy is subject to high clinical failure because of large interindividual variability in pharmacokinetic and pharmacodynamic properties of therapeutic agents in patient population [7, 8] (Fig. 1). Currently, therapeutic drug monitoring (TDM) (Fig. 1) is applicated for drugs with small therapeutic window, including antiepileptic drugs, antimicrobials, inmunesupressors, antiviral agents, teophylline and others, to individualize pharmacological interventions [9]. TDM guides patient dosing according to plasma concentrations of therapeutic agents. In other words, measurement of plasma concentrations has the objective to consider the relationship between dose and attained plasma concentrations in the large interindividual variability of drug response in a treated population [9]. Nevertheless, TDM has several limitations and its cost-benefit relationship is controversial [10]. It must be taken into account that most therapeutic agents exert their pharmacological effect in peripheral tissues rather than the blood compartment, and tissue and plasma pharmacokinetics may differ substantially in patients and healthy volunteers [11-13]. Large interindividual variability also exists between tissue concentrations and therapeutic response because of different receptor sensitivity along the patient population [7]. In conclusion, although TDM has been established as a valuable therapeutic tool for some drugs, measurement of plasma concentrations of drugs is a surrogate parameter with several drawbacks, since it does not exclude interindividual variability in drug distribution at the target site and pharmacodynamic variability.

Fig. (1). Sources of interindividual variability in drug response and methods available for their assessment.

Based on the assumption that unbound plasma concentrations and free tissue levels are equal at equilibrium, it is frequently considered that total plasma concentrations and plasma protein binding can be used to predict free tissue levels of therapeutic agents. However, many studies have shown lower tissue unbound levels than plasma concentrations [14-17]. Tissue distribution is also affected by anatomic barriers, such as blood-brain barrier (BBB), the presence of active transport systems, like P-glycoprotein, and tissue metabolism. For instance, BBB prevents water soluble xenobiotics from entering the brain [18]. BBB is mainly formed by brain capillary endothelial cells, pericytes, astrocytes, and


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neuronal cells. Endothetial cells are different from peripheral cells because of the presence of tight junctions, which prevent paracellular transport of water-soluble compounds from blood to the brain [19]. It has also been discovered that many transport systems play an important role in determining tissue concentrations of drugs. These comprise carrier- and receptormediated transport systems, including cationic and anionic influx and efflux systems such as P-glycoprotein [20], multidrug-resistance (MDR) proteins [21], nucleoside transporters, organic anion transporters, organic cation transporters, large amino acid transporter and the scavenger receptors SB-AI and SB-BI [22]. These transporters modulate drug disposition process, with particular importance in terms of the clinical implications of transporters for drug-drug interactions, drug toxicity, interindividual variability in drug response, and disease [8]. Efflux transporters, such as P-glycoprotein, are expressed in several tissues, especially in the central nervous system, hepatocytes, kidney and the small intestine. At these sites, efflux transporters reduce drug bioavailability, tissue distribution and enhance drug elimination in order to reduce drug exposure in a given patient [23]. Moreover, an overexpression of efflux transporters exists in several pathologies affecting therapeutic success because of limited distribution of the drug at the target site [24]. For instance, reduction of central bioavailability of antiepileptic drugs by overexpression of efflux transporters has been established for several drugs in different models of experimental epilepsy as a mechanism of pharmacoresistance (for review see [25, 26]). The need to study antiepileptic drug distribution at the target site is emphasized by the fact that overexpression of efflux transporters seems only to affect drug distribution in the biophase and not in other central nuclei. The finding that resistant epileptic patients have similar central adverse effect to drug treatment than responders supports this conclusion [22]. Success of cancer treatment is also affected by the existence of an enhanced efflux transporter activity. In this case, efflux transporters reduce intracellular concentrations of cytotoxic drugs compromising the activity of chemotherapeutic agents [27]. Tissue metabolism also affects the relationship between plasma unbound concentrations of therapeutic agents and their levels at the biophase. Although most part of drug metabolism takes place in hepatocytes, other tissues present relative high metabolic activity, including small intestine, central nervous system and kidney [28]. Therefore, tissue biotransformation could also influence target site concentrations and pharmacological effects. Taking into account these antecedents, the Food and Drug Administration (FDA) encourages the study of tissue distribution of antimicrobial agents in unaffected and infected target sites and the relationship of unbound drug concentrations at the site of action to the in vitro susceptibility of the infecting microorganism (http://www.fda.gov/ cder/guidance/2580dft.pdf). In conclusion, the utility of therapeutic tools actually used for rational selection of the best drug and dosing regimen, such as TDM, is limited by the fact that this approach allows the determination of plasma drug concentrations rather than their levels at the target site. Several pharmacokinetic factors affect tissue distribution of drugs and their pharmacological effect. Therefore, an important fact in drug design and clinical practice



is the evaluation of the in vivo pharmacokinetics of new chemical entities and drugs at the biophase. In the following sections of the present chapter, available sampling techniques for the study of drug distribution at the target site, including imaging techniques and microdialysis, are discussed. Variability in drug response is also due to pharmacodynamic factors [7] (Fig. 1). Transporters activity, concentration of endogenous agonist and expression of receptors and their sensitivity to drug action vary among different patients and during the course of drug treatment on a single patient. Therefore, selection of the adequate dose of a therapeutic agent and its dose regimen also depends on pharmacodymanics of the drug at the target site. In the last years, PK-PD modeling have become an interestingly tool of pharmacological sciences for the study of factors influencing drug response [3] (Table 1). Table 1.

Information Obtained from Pharmacokinetic-Pharmacodynamic Modeling During Preclinical and Clinical Phases of Drug Development and Clinical Practice

Preclinical phase of drug development Precise definition of the dose–concentration–pharmacological effects and dose–concentration–toxicity relationship Determination of the appropriate dosing regimen for Phase I studies Identification of biomarkers and animal models for efficacy and toxicity Explore any dissociation between plasma concentration and duration and onset of pharmacological effect Provide information on drug effects that would be difficult to obtain in human subjects Provide insights regarding the mechanism of action of the new chemical entity Allows the study of the influence of the physiopathological state on PK-PD parameters Clinical phase of drug development Understanding the dose-concentration–pharmacological effects and dose–concentration–toxicity relationship in healthy volunteers. Characterization of pharmacokinetics and pharmacodynamics in special population Study of tolerance development Determination of the dosing regimens for Phase II studies. Confirms and explores the relationship between dose–concentration–effect in patients. Examination of a variety of therapeutic endpoints to understand the most adequate for further modeling. Determination of the dosing regimens for Phase III studies Prediction of the probability distribution of further clinical trial outcomes Clinical practice Early detection of poor responders or non-responders. Optimization of antihypertensive drug regimen, in terms of dose, sampling interval and time of dosing. Evaluation of the clinical impact of drug interactions.

UTILITY OF PHARMACOKINETIC-PHARMACODYNAMIC MODELING IN DRUG DISCOVERY AND CLINICAL PRACTICE Two different approaches exist for characterizing the pharmacodynamics of a drug, namely the classical dose response trial and PK–PD modeling [4]. PK–PD modeling has


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several advantages over classical dose–response studies. PK–PD modeling allows not only better pharmacodynamic characterization of drugs, but also permits screening and dosage–regimen selection [4]. More recently, the introduction of mechanism-based PKPD models have allowed the study of the influence of physiological parameters on PKPD properties of the drug. Information obtained from PK-PD modeling during drug development and clinical practice is resumed in Table 1. PK-PD relationships build a bridge between the time course of drug concentrations in the organism, as assessed by pharmacokinetics (PK), and the intensity of the observed pharmacological response, as quantified by pharmacodynamics (PD). The link between PK and PD of a drug is established by the use of mathematical models, allowing the estimation of parameters such as effective concentration to yield half-maximal response (EC50) and maximal efficacy (Emax). PK-PD modeling also provides information about the onset, magnitude and duration of the pharmacological effect [4]. A drawback of PK–PD modeling is the need of simultaneous measurement of drug tissue levels and their corresponding pharmacological effect at multiple time points [4]. Blood sampling, which has traditionally been used for this purpose, has the disadvantage that the removal of samples by themselves may interfere with PK and PD drug behavior, especially in preclinical studies with small animals [29]. Furthermore, traditional sampling techniques allow the measurement of plasma concentrations of pharmacological agents rather than their levels at the target tissue. These limitations could be resolved by the application of new sampling techniques, including in vivo microdialysis and imaging techniques. Accurate measurement of the intensity of the pharmacological effect of the active compounds is also necessary for a PK-PD modeling design. A drug effect could be considered as any change in physiological parameters induced by the administration of a drug, compared to respective baseline values. Measurement of the effect should meet validation parameters, such as continuity, sensitivity, objectivity and repeatability [30]. To obtain the greatest precision in estimating PK-PD relationships, the number of measurements of drug tissue levels and its corresponding effect must be as large as possible [30]. However, multiple time point sampling is not always possible in the clinical setting. To overcome this limitation, population PK and PK-PD modeling is increasingly being introduced [31, 32]. PK-PD relationships have been described using diverse mathematical models depending on the nature of drug administration, the magnitude of the pharmacological effect and the time dependency of pharmacodynamics of the tested drug [3, 31]. Relative simple PK-PD models are needed for describing PK-PD relationship after multiple doses or long-term infusion, because the system is kinetically at steady state [31]. The most common mathematical equations employed at steady state condition are the linear, log linear and the Emax model. Although the linear and log linear model allow easy parameter estimation, these models erroneously assume that the effect can increase with concentrations without limits [30]. Therefore, the Emax model is the most broadly applied to characterize a myriad of pharmacological effects. This model derives from the classical theory of drug-receptor interaction, relating the effect to drug concentrations as in the following equation:



E = E0 +

E max C EC 50 + C

where E0 is the baseline effect, Emax the maximal effect and EC50 the effective concentration yielding half maximal response. Conversely, more complex PK-PD models are needed for describing PK-PD relationship after single dose administration or when time dependency in the pharmacodynamics of the drug is present [31]. Plotting drug effects as a function of drug concentrations and connecting data in a chronological order allows the determination of possible delays between drug response and its tissue levels [4]. A hysteresis loop appears in the plotting when the magnitude of an effect corresponds to more than one drug concentration. Anticlockwise hysteresis loop could be explained by the disequilibrium between biophase and plasma compartment [33], appearance of active metabolites [34] or indirect mechanism of action [35]. On the other hand, tolerance to the pharmacological effect is suggested when a clockwise hysteresis loop is observed [36]. In theses cases, plasma concentrations can not be directly linked to drug effect, and more complex PK-PD models, such as an effect compartment model and a physiological indirect response model, are needed [31]. The effect compartment model considers a hypothetical effect compartment as an additional compartment of a pharmacokinetic compartment model, representing the drug concentration at the effect site. The time-dependent aspects of the equilibrium between plasma concentration and effect are characterized by the first-order rate constant ke0 that represents the irreversible disappearance of the drug from the effect compartment and is in equilibrium with the rate constant of entrance of the drug to the effect compartment. The time course of drug concentration in the effect compartment is described by the following equation:

dC e = k e 0 (C p C e ) dt where Ce and Cp represent the concentration in the effect compartment and plasma, respectively and ke0 the equilibration rate constant. This approach has been successfully applied to predict the PK-PD relationship of diverse drugs [37]. Physiologic indirect response models have been employed in a variety of studies regarding biological responses such as muscle relaxation, synthesis and secretion of endogenous compounds, mediator flux, cell trafficking, enzyme induction, or inactivation, among others [35]. The physiological indirect response can be described as an inhibitory or stimulatory function applying the following equations:

I (t ) = 1

C (t ) IC50 + C (t )

S (t ) = 1 +

E max C (t ) EC 50 + C (t )

where IC50 and EC50 are the concentrations eliciting 50% of inhibition or the maximal effect (Emax), respectively.


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PK–PD modeling should cover the complete pharmacodynamic range of a drug after a single administration [4]. Often, in clinical pharmacology, it is not possible to determine the maximal effect of a drug because of the appearance of adverse drug reactions, in which case an alternative PK–PD model must be applied [38]. In this model, the authors replaced the parameter EC50 with S0 in the Emax equation, which represents the initial sensitivity to the drug. The following equation describes the relationship between drug concentration and its pharmacological effect for this PK-PD model:

E = E0 +

S 0 E max C E max + S 0 C

Considering that the selection of an inadequate PK–PD model according to the PK– PD study characteristics might lead to an erroneous interpretation, it is extremely important to determine which PK–PD model is going to be applied for the analysis of the data. When selecting the PK–PD model, the investigator must keep in mind experimental variables, including type of drug administration, type of pharmacological effect measured, the existence of time dependency in the pharmacological effect of a drug and the possibility to reach a maximal response in their experimental design. More recently, development of mechanism-based models has improved knowledge of the interaction between PK-PD properties of drugs and clinical response (for review see [39, 40]). Mechanism-based PK-PD modeling integrates parameters for describing drug-specific properties with biological system-specific properties, and therefore characterizes the causal path between drug exposure and drug response. By estimating drug target-site distribution, target binding and activation and transduction process, mechanism-based PK-PD models will also characterize the interaction of the drug effect with disease processes and disease progression [39, 40]. OVERVIEW OF AVAILABLE TECHNIQUES FOR THE STUDY OF DRUG DISTRIBUTION AND PK-PD MODELING Target site concentrations of drugs and new chemical entities can be evaluated by means of traditional techniques, including biopsies, skin blisters, or using more recently available methodologies, such as imaging techniques, ultrafiltration and microdialysis [6]. Biopsy consists of homogenized tissue samples those results in cellular lysis. Therefore, biopsy gives information about analyte concentrations in the homogenate without distinguishing between blood, intra- or extracellular drug levels. Moreover, it is an invasive method associated with the risk of infection due to cross-contamination and scarring. As tissue has to be removed, biopsy is not suitable for the study of the concentration–time course of a drug and, therefore, PK-PD modeling of drugs [41]. Advantages of biopsy are that no special equipment is needed, there is no limitation with the size of the drug and no calibration is necessary [41]. Suction blisters have been an established method for more than 30 years for the study of pharmacokinetics in the dermis [42]. The method principle relies on the separation of the epidermis from the dermis along the lamina lucida due to the application of prolonged suction to the skin surface (suction blister technique) or due to the adverse reaction effect of cantharidin (cantharides blister technique). The fluid drawn into the sepa-



rated epidermis can be sampled and analyzed for drug content [41]. Skin blister technique has been compared to other sampling techniques, such as microdialysis, concluding that skin blister seems to be only valid for the dermal study of low protein bound drugs. Other limitations of skin blister sampling is the production of a great discomfort in the patients and is only applicable for studies at a certain time point not allowing continuous monitoring of drug skin penetration to the same test area. Traditional techniques have several drawbacks that limit their applicability for the evaluation of tissue distribution and PK-PD modeling during drug development. These methodologies have largely been replaced by modern analytical techniques, including ultrafiltration, imaging techniques and microdialysis sampling (Table 2). Table 2.

New Sampling Techniques for Pharmacokinetic and PK-PD Studies

Technique

Advantages

Microdialysis Simultaneous determination of the bioactive concentration of drugs, metabolites and endogenous compounds in the biophase. Good temporal and spatial resolution. Online coupling of analytical determination Monitoring of drug concentrations in different tissues by multiprobe microdialysis. Monitoring of drug effect on endogenous compounds Possibility of local drug administration. Cheap technique.

Drawbacks Semi-invasiveness technique Diluting effect of the microdialysis procedure Need of highly sensitive analytical methods In vivo calibration of the microdialysis probe during the experiment Sticking of lipophilic drugs to tubing and probe components Low recovery of large molecules

PET

Non-invasive technique High spatial and time resolution No time consuming Allows estimation of in vivo drug-receptor interactions

Short physical half-life of the most used radioisotope. Did not discriminate between drugs and its metabolites. Determinates total tissue concentrations of drugs. Expensive technique.

MRS

Allows discrimination between drugs and its metabolites. Non-invasive technique No time consuming

Poor spatial resolution. Low sensitivity Expensive technique

Ultrafiltration is an alternative membrane sampling technique to microdialysis. This technique collects a sample by the application of negative pressure as driving force. The rate of fluid collection is determined by the amount of negative pressure applied, the membrane surface area and the hydraulic resistance [43]. The mayor advantage of ultrafiltration with regards to other membrane sampling techniques, such as microdialysis, is that in vivo calibration of the technique is not necessary because the in vivo recovery is greater than 95% for small molecules [43]. However, continuous tissue sampling with


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ultrafiltration depends on rapid replacement of interstitial fluid by blood vessels. In tissues with limited flow rate and low replenishment of interstitial fluid, such as subcutaneous tissue, only low sampling rates are possible [43]. Brain is also unsuitable for ultrafiltration sampling because of the limited extracellular space. In addition, ultrafiltration does not allow the administration of a substance into the extracellular space through the probe. Although the applicability of ultrafiltration has been demonstrated for drug kinetic studies and glucose and lactate monitoring [43], the scope of application of ultrafiltration technique is limited in clinical pharmacology. Microdialysis is a membrane sampling technique that allows continuous monitoring of extracellular concentrations of new chemical entities and drugs withouth fluid loss [29]. As discussed later, microdialysis has several advantages compared to other sampling techniques, including the capability to monitor unbound tissue concentrations of drugs and their metabolites, the simultaneous study of drug concentrations in several tissues and the possibility of concomitant sampling of endogenous compounds or biochemical markers [37]. However, it must be pointed out that microdialysis is a semiinvasive methodology that often requires calibration during the experiment [29]. Alternatively, several imaging techniques such as planar -scintigraphy, single photon emission computed tomography (SPET), positron emission tomography (PET), and magnetic resonance spectroscopy (MRS) have been developed for the study of drug distribution in basic and clinical settings [13]. PET is a new nuclear imaging technique that employs molecules labeled with positron-emiting radioisotopes [44]. Advantages of this imaging technique reside in its non-invasive nature, high spatial resolution (1-5 mm) and time resolution (30 seg.). However, the physical half-life of the most used radioisotope 11 C (20.4 min) does not allow monitoring of tissue levels of radiolabeled drugs over several elimination half-lifes as desired in pharmacokinetic studies [13]. Another drawback of PET is that this methodology samples total tissue concentrations of drugs and their metabolites and, consequently, does not allow to differenciate them [13]. MRS utilizes nuclear magnetic resonance phenomenon, which consists in the emission of a radiosignal equal to the respective resonance frequency with amplitude proportionally to the number of nuclei present in the examinant objective [13]. MRS can be performed serially with a temporal resolution of minutes. Contrary to other imaging techniques, MRS is capable of identifying drug metabolites in tissues [13]. However, an important limitation of MRS is its low sensitivity, allowing only the evaluation of drug distribution of xenobiotics that are present in large concentrations. In addition, spatial resolution of this imaging methodology is low [13]. Although imaging techniques are non-invasive, they are only applicable for a small group of compounds with special functional groups. Moreover, imaging techniques are very expensive and labor-intensive and, therefore, not suitable for clinical routine settings [13]. In conclusion, to date, modern techniques exist for the evaluation of tissue distribution of drugs at the target site and simultaneous PK-PD modeling, providing information that has been inaccessible with previously available methods. In the next sections, the principles and applicability of microdialysis, PET and MRS for evaluation of pharmacokinetic and pharmacodynamic properties of drugs will be discussed.



PRINCIPLES OF MICRODIALYSIS SAMPLING In the past twenty years, the microdialysis technique has become a method of choice for the study of tissue concentrations of both endogenous and exogenous substances. Microdialysis is a powerful and versatile sampling technique based on the dialysis principle [45]. In this technique, a probe that is inserted into a tissue mimics the function of a capillary blood vessel (for review see [29]). The probe has a hollow fiber that is permeable to water and small molecules, and when the perfusate solution passes through the dialysis membrane, molecules diffuse into (recovery) or out of (delivery) the perfusion fluid depending on the concentration gradient. Thus, microdialysis can be used for both collecting a substance in the dialysate as well as delivering it into the periprobe fluid. The latter is referred to as reverse microdialysis [46]. Then, dialysate samples are analyzed using quantitative techniques like liquid chromatography (LC) or capillary electrophoresis (CE). The basic setup for a microdialysis experiment consists of a microdialysis probe, a perfusion pump, and an analytical method with the required sensitivity to quantify small concentrations of substances [29]. As microdialysis sampling is not performed under equilibrium conditions because the perfusate is constantly being pumped through the probe, concentrations of the drug in the sample are some fraction of that in the surrounding tissue. Therefore, limit of quantification of analytical methods should be extremely low [47]. On the other hand, to obtain true tissue concentrations the factor by which these concentrations are interrelated needs to be determined. This factor, which is obtained during an in vivo or an in vitro calibration procedure, is called relative recovery [29]. Microdialysis sampling offers several advantages over conventional methods and imaging methods of studying the pharmacokinetics and pharmacodynamics of drugs. Microdialysis technique allows continuous tissue sampling without removing liquid, achieving higher temporal resolution than traditional techniques, without interfering with the PK and PD behavior of the drug. An economical and ethical advantage is that 5-10 times fewer animal experiments have to be performed to determine the time profile of a drug [29]. Placement of multiple microdialysis probes in different tissues allows monitoring of the drug time course in different organs in the same animal, supplying information about the distribution process of xenobiotics. The fact that the microdialysis technique provides protein free samples permits the analysis of the sample without pretreatment and LC or CE on-line coupling. Conversely, traditional blood sampling requires clean up procedures prior to analysis with the possibility of analyte loss during protein precipitation and the need of an internal standard for an accurate determination of the drug. On the other hand, tissue enzymes are also excluded from the dialysate sample without further enzymatic degradation of the drug [29]. The microdialysis technique not only allows sampling of extracellular levels of drugs but also endogenous compounds such as neurotransmitters, metabolites, glucose, lactate and low molecular wheighing peptides [29]. Therefore, this technique is highly useful for the simultaneous study of the pharmacokinetic behavior of drugs and their effects on extracellular levels of endogenous compounds [37].


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However, the microdialysis sampling technique has some disadvantages compared to traditional sampling methods. Drug tissue concentrations often are in the picomolar to micromolar range and the limit of detection of the analytical methods should be for some drugs extremely low. On the other hand, in vivo recovery of the microdialysis probe must be determined during the experiment to permit conversion of microdialysis concentration into extracellular levels if an absolute concentration is needed [29]. The calibration aspects of the microdialysis probe will be discussed in following sections. In addition, a minimal lesion of the tissue surrounding the probe is produced by implantation of the microdialysis probe, causing tissue responses such as a compromise of BBB [48, 49] and acute inflammation in different tissues [47, 50, 51]. Nevertheless, several studies have demonstrated that baseline conditions are reached following periods of 60 min after probe implantation [29]. Methodological Aspects of Microdialysis Sampling The microdialysis probe is perhaps the nucleus of the microdialysis experiment. Many types are described and they are used in different experiments. The different geometry of microdialysis probes enables their use in virtually any tissue and any fluid of the body [52]. Many laboratories have designed their own probe considering that its construction takes no longer than several minutes. To date, there are also many commercial approved probes for studies in soft tissues and brain of humans and animals. Probes will have a longitudinal, a semicircular or an I-shape design. Various designs have been described, including concentric cannula probe, linear probe, and shunt probe. In addition, several modified probe designs have been reported, such as spinal loop dialysis catheter, flexible intravenous probe, and shunt intraarterial microdialysis probe [29]. For soft peripheral tissues like muscle, skin, liver, tumor, and fluids like blood and bile, flexible probes can be used. Linear probes are useful for monitoring transdermal drug delivery. However, an increase in skin blood flow, erythema and skin thickness was demonstrated after insertion of a microdialysis probe [53]. Rigid microdialysis probes, such as the concentric cannula, are placed in hard tissues, including the brain. In this case, guide cannulas can also be implanted, opening up the possibility to insert the probe after surgical recovery, thereby decreasing negative effects of anesthesia [29]. The choice of the membrane type is an essential element to optimize the probe for a particular microdialysis protocol. Conventional microdialysis probes are constructed with 20 kDa molecular weight cut-off membranes enabling measurement of small molecules [29]. Recently, a 100 kDa molecular weight cut-off microdialysis catheter have been used to sample larger molecules such as cytokines [54]. These probes can be constructed with polyethylenesulphone [55]. A technical problems encountered with the 100 kDa microdialysis membrane is the issue of poor sample volume retrieval, considering that it enables passage of high molar mass molecules. One way of addressing this problem is to equalize the pressure differences between in-flowing and out-flowing perfusate at the membrane by increasing the colloidal pressure of the perfusate using dextran [56]. Molecular weight of the substance of interest must be taken into consideration for selection of the most adequate dialysis membrane. Only substances with a molecular weight lower than the membrane cut-off are able of passing the membrane. However, even if the molar mass falls below the molecular weight cutoff, an acceptable relative



recovery will only be attained with substances having a molar mass lower than approximately one-fourth of the membrane cutoff [41]. The size of the microdialysis membrane influences the relative recovery. According to Fick’s law, perfusion across a membrane is proportional to its area. Increasing the length of the microdialysis membrane will lead to an increase in relative recovery [41]. Increasing the outer diameter of the inner cannula may also enhance relative recovery of the probe [57, 58]. An important key is that the membrane does not interact with the surrounding tissue or with the perfusate. Low recoveries of acid aminoacids have been described due to the presence of surface charge [59]. Recovery of neuropeptides can vary as much as 20% with different dialysis membranes [60]. Different perfusion media have been used in microdialysis experiments and they vary widely in their composition and pH. Composition, ion strength, osmotic value and pH of the perfusion solution should be as close as possible to those of the extracellular fluid of the dialyzed tissue. In most experiments, the perfusate is an aqueous solution of sodium and potassium salts and other ions in a minor proportion, without proteins or a very small concentration of them. Addition of dextran to the perfusion solution prevents fluid loss. Rosdahl et al. [61] have demonstrated that the estimated concentrations of dextran in the perfusion solution at which no net loss of perfusion fluid occurred produces a colloid osmotic pressure similar to the reported values for plasma. This implies that the plasma colloid osmotic pressure contributes to a mass transfer of fluid from microdialysis catheter to capillaries. In some cases, proteins should be added to the perfusion medium to prevent sticking of drugs to the microdialysis probe and tubing connection [62]. When microdialysis probes are implanted into a tissue, it is necessary to keep asepsis in the area immediately around the site of the insertion. Huff et al. [63] reported a preliminary evaluation of several disinfection/sterilization techniques used with experimental microdialysis probes. These authors found that two disinfection methods, 70% ethanol and a commercial contact lens solution, and two sterilization methods, hydrogen peroxide plasma, and e-beam radiation, did not affect the functionallity of the probes. However, hydrogen peroxide plasma and contact lens solution groups reduced extraction efficiencies of microdialysis probes. Gamma irradiation has also been used for sterilization of microdialysis probes [64]. Probes were sterilized by gamma irradiation (32 kGy) prior to use. Subsequent in vitro experiments confirmed that the irradiated probes behaved identically to native probes from the supplier. In this work, no sign of infection at the implantation sites was found, probably due to the strict attention to sterility during the probe implantation as well as to prophylactic treatment with a broad-spectrum antibiotic [64]. However, one of the most popular methods to sterilize microdialysis probes is the gas sterilization method with ethylene oxide [65, 66]. An important aspect of microdialysis in pharmacokinetic studies is the selection of an adequate analytical method for drug concentration determination. Microdialysis generates small volume samples (1-10 l), because of the need of slow perfusion rates (0.12 l/min) to obtain high recoveries of the drug maintaining an adequate temporal resolution. In addition, microdialysis samples often contain the analyte at low concentration (pM-M range) needing of a high sensitive analytical method [29]. A wide range of analytical methods can be used for the analysis of microdialysis samples. Non-separation-based methods allow the detection of one analyte at a time, in


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contrast to separation-based methods that can be used for the detection of multiple analytes (original drug and metabolites) in each sample. Therefore, its applicability for microdialysis studies is scarce [29]. Separation-based methods available for analysis of microdialysis samples include LC, microbore LC, capillary LC and CE. A wide range of different detectors can be coupled to the separation method. Immunoassay, ultraviolet absorbance, electrochemical detection, fluorescence and mass spectrometry (MS) are the most common detectors for LC analysis [29]. For CE, electrochemical and laser induced fluorescence detection have been most commonly employed [29]. The drawback of the separation-based methods is the dilution of the microdialysis sample. Nevertheless, LC with tandem MS/MS detection, due to its low limit of quantification, is a powerful analytical technique for quantification of drug concentrations in microdialysis samples. In comparison to the non-separation-based methods, these methods allow the detection of many compounds in each sample allowing simultaneous quantification of drug metabolites. The great diversity of detection methods provides to the researcher the chance to select the more adequate analytical system for the determination of the compound of interest [67]. The most important aspect of the analytical system is its sensitivity in order to improve the temporal resolution of a microdialysis protocol. Importance of Microdialysis Probe Calibration Use of microdialysis sampling for determination of target site concentrations of drugs needs accurate calibration of the microdialysis probe, considering that the desired information is the absolute drug concentration in the tissue. The relationship between concentration of the drug in the dialysate and its concentration in the sample matrix may be thought of as the recovery of the probe. Several factors influence recovery of the microdialysis probe, including temperature, perfusate flow rate, chemical and physical properties of the dialysis membrane, probe geometry, membrane surface area, chemical properties of the drug and the diffusion rate of the drug within the matrix [68]. All of these parameters remain constant during the experiment under normal conditions. Few parameters, such as membrane surface area and perfusate flow rate, can be modified by the researcher to improve recovery of the microdialysis probe. If high microdialysis probe recovery is needed to obtain concentrated samples, the researcher can opt among increasing the size of the surface area or decreasing the perfusate rate. However, increase of membrane surface area implies loss of spatial resolution and decrease of the perfusate rate implies loss of temporal resolution, because of prolongation of the sampling interval [29]. Recovery of the microdialysis probe can be determined by both in vitro and in vivo assays. The in vitro estimation of the probe recovery, serves only to prove if the microdialysis probe works, since in vitro recovery values are often an overestimation of the in vivo recovery [29]. In vivo recovery of the microdialysis probe can be determined through different methods, including flow-rate or stop- flow method [69], zero-net-flux method [45], retrodialysis method [70] and delivery method [71]. Because the zero-net-flux method and the flow rate method require that examination of the study subject under steady state conditions prior to the experiment, the total study time is extended. This limits the application of these methods for pharmacokinetics purposes [29].



Determination of the in vivo recovery with the retrodialysis or delivery method is more appropriately. Both methods are based on the principle that recovery is independent of the direction of analyte diffusion. Therefore, loss of the analyte from the perfusion media to the extracellular matrix should be equal to gain of the analyte from the tissue to the perfusate. This equality must be determined by the determination of in vitro recovery of the analyte both by loss and gain methods [29]. The retrodialysis method consists in the diffusive loss of molecules of a calibrator from the perfusate into the environment surrounding the probe. The dialysance or permeability-area product (PeA) of the calibrator must be similar to that of the compound of interest [29]. The in vivo recovery of the calibrator is calculated with the following equation: R= (Cin - Cdial)/ Cin where R is the calibrator in vivo recovery, Cin is the concentration of the calibrator in the perfusate and Cout is the concentration of the calibrator in the dialysate. Unbound concentrations of the compound of interest in the surrounding environment (C) are calculated using the following equation: C = Cout / R where Cout is the compound of interest concentration in the dialysate and R is the in vivo recovery of the microdialysis probe. The delivery method consists in the determination of the in vivo recovery of the compound of interest by retrodialysis before the actual experiment. In vivo recovery of the compound of interest is determined before drug administration by perfusing microdialysis probe with a solution of the compound of interest, taking the proportion of loss across the dialysis membrane as an estimate of the recovery [29]. Comparing the last two methods, a limitation of the retrodialysis is the necessity of an analyte with similar physico-chemical characteristic as the compound of interest. On the other hand, if the analyte is added to the perfusate at high concentrations, it could introduce unwanted perturbations in the surrounding tissue area [29]. The delivery method overcomes the uncertainty introduced by using a standard to mimic the compound of interest. The shortcoming of this approach is that recovery changes resulting from the experiment are not detected. However, recovery of the microdialysis probe generally remains constant in pharmacokinetic studies [29]. Another issue of microdialysis sampling in pharmacokinetic studies is the fact that microdialysis generates data that are the integral of the concentration surrounding the probe during the sampling interval, conversely to tissue samples, which provides a point measurement during the same interval. To estimate pharmacokinetic parameters, microdialysis data must to be transformed from a series of integrals to a series of points corresponding to the same time of tissue samples collection [72]. CURRENT APPLICATIONS OF MICRODIALYSIS SAMPLING FOR EVALUATION OF TISSUE DISTRIBUTION AND PK-PD MODELING OF DRUGS Microdialysis sampling has been extensively used for the study of target site distribution and PK-PD modeling of established drugs and new chemical entities in both pre-


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clinical and clinical protocols (for review see [12, 29, 37, 41]). Although pharmacokinetic properties and PK-PD relationship of different therapeutic groups have been evaluated by means of microdialysis sampling, most of microdialysis studies addressed drug distribution and PK-PD modeling of antiinfective, antineoplastic and central acting drugs [12, 37]. Microdialysis sampling is also useful for PK-PD modeling of cardiovascular drugs in laboratory animals and for the evaluation of dermal pharmacokinetics of drugs after its topical application [12, 36]. In this section, most important microdialysis studies in this area are discussed. Antiinfective Drugs Traditionally, pharmacokinetic assessment of antimicrobial agents was largely based on the measurement of total plasma concentrations. Nevertheless, the use of plasma antibiotic levels is not ideal, because most infections occur in tissue sites, and therefore the ability of antibiotics to reach the target site is a key determinant of clinical outcome. It is considered that total plasma concentrations and plasma protein binding can be used to predict free tissue levels of antibiotics, based on the assumption that unbound plasma concentrations and free tissue levels are equal at equilibrium, considering that tissue distribution is generally mediated only by passive diffusion. However, many studies have shown lower tissue unbound levels than plasma concentrations [14-17]. As discussed above, tissue distribution is also affected by anatomic barriers, presence of active transport systems and tissue metabolism. On the other hand, time to reach equilibrium between plasma and tissue concentrations of antibiotics may range from minutes to days [73]. Therefore, measurement of unbound drug concentrations in the interstitial fluid of the target tissue should be considered a gold standard for improvement of antimicrobial therapy and dose adjustment. Microdialysis has been used to measure various antimicrobials agents in human and laboratory animal tissues, including aminoglucosides, penicillins, cephalosporines, fosfomycin, fluoroquinolones and antiviral agents (for review see [74-76]). These studies have served to evaluate drug distribution in several organs, including infective tissues, and to develop in vivo PK- in vitro PD models at the target site using the same parameters calculated in plasma: time (T) above the minimum inhibitory concentration (MIC) (T>MIC), the ratio of the maximum concentration of drug in serum (Cmax) to the MIC (Cmax/MIC), the area under the inhibitory curve or the area under the curve (AUC)/MIC ratio [76]. Using microdialysis sampling, the relevance of protein binding in therapeutic failure was demonstrated. A relationship was found between the failure in cefoperazone treatment in serious illness and the degree of drug bounded to proteins [77]. In addition, cefpodoxime, with lower protein binding than cefixime (25 vs 65%) showed higher peak concentration and tissue penetration than cefixime [78], suggesting a favorable efficacy of cefpodoxime, and this is supported by clinical trial data provided by a study in paediatric acute otitis media [79]. Microdialysis sampling was also used for the determination of antimicrobial agents in the interstitial fluid of non-infected soft tissues of healthy volunteers and rats. Several studies have demonstrated that tissue concentrations of different antibiotics, including ertapenem [80], gemifloxacin [81], telithromycin [82], and imipenem [83] among others, are similar in soft tissues with regards to unbound plasma concentrations, indicating a good distribution of antimicrobial in non-infected tissues.



In addition, drug distribution of antimicrobial drugs in infected tissues was also evaluated by means of microdialysis sampling both in basic research and clinical setting. Experiments conducted in rats showed that lung and peritoneal penetration of imipenem is not reduced in rats with pneumonia and peritonitis, respectively [84, 85]. In another study, lung penetration of meropenem was determined in patients with pneumonia and metapneumonic pleural emphysema by means of microdialysis [86]. Although meropenem rapidly penetrated infected lung tissue, interstitial lung fluid levels were lower than serum concentrations with target site drug concentrations above the MIC90 threshold for many clinically relevant pathogens for up to 6 hours [86]. In another clinical protocol, moxifloxacin exhibited similar penetration into the interstitial space fluid in normal subcutaneous tissue and infected decubitus ulcers in patients with spinal cord injury [87]. Conversely, penetration of the fluorquinolone levofloxacin into tissues appears to be unaffected by local inflammation. Bellman et al. [88] observed that administration of a standard dose of levofloxacin reached adequate levels at target site, although the extent of tissue penetration showed a high interindividual variability. Zeitlinger et al. [89] have also demonstrated a reduced distribution of levofloxacin in infected lung with regards to interstitial fluid of non-infected soft tissues obtained from healthy volunteers. Microdialysis sampling was also extensively employed for PK-PD modeling of antiinfective agents (for review see [75, 76]). A three step approach has been used for the in vivo PK- in vitro PD modeling by means of microdialysis. Firstly, interstitial fluid concentrations of the antibacterial drug at the target site are measured by means of microdialysis. Secondly, time versus drug concentration profile measured in vivo is simulated in an in vitro setting on bacterial cultures. Thirdly, unbound antibiotic concentrations are linked to bacterial kill rates by means of a PK-PD model [90]. Delacher et al. [90] have demonstrated a significant correlation between the maximal bactericidal effect and several pharmacokinetic surrogate parameters, such as AUC/MIC, Cmax/MIC and T>MIC. The authors concluded that the therapeutic success or failure in antibacterial therapy depends on the target site concentrations of the antimicrobial agent. Moreover, in vivo PK- in vitro PD modeling provides valuable guidance for drug and dose selection of antibacterial drugs [90]. In vivo PK – in vitro PD modeling of antimicrobial drugs was also studied in critical ill patients by means of microdialysis. Zeitlinger et al. [91] have applied an in-vivo pharmacokinetic/in-vitro pharmacodynamic method to simulate bacterial killing in plasma and the interstitium of skeletal muscle tissue after intravenous administration of cefpirome and fosfomycin alone and in combination to patients with sepsis. The in vitro simulation of in vivo plasma and tissue pharmacokinetics of cefpirome and fosfomycin has shown that both antimicrobial agents kill Staphylococcus aureus and Pseudomonas aeruginosa strains after single dose administration, observing a synergic antimicrobial effect by the combined use. Therefore, this data confirms antimicrobial strategies of simultaneous administration of cefpirome and fosfomycin in patients with severe soft tissue infection [91]. It must be pointed out that all PK–PD studies of antimicrobial drugs by means of microdialysis have used a combined in vivo PK–in vitro PD simulation without applying mathematical PK–PD models in their analysis. Recently, (Liu et al.) [92] demonstrated that a PK–PD model based on unbound antibiotic concentrations at the site of infection,


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and a sigmoid Emax relationship, effectively described the antimicrobial efficacy of both cefpodoxime and cefixime. This approach offers a more detailed information than the MIC does about the time course of antibacterial efficacy of antibiotics [92]. In this way, (Huang et al.) [93] have evaluated the simultaneous estimation of amoxicillin influx/efflux in chinchilla middle ear fluid and its antibacterial effect. The authors found that the microdialysis procedure did not interfere with the bacterial growth-kill profile, thereby enabling pharmacokinetic and pharmacodynamic evaluation concomitantly. In summary, several microdialysis studies have demonstrated that anti-infective levels at the target site are sub-inhibitory, although effective concentrations are attained in plasma. Therefore, study of target site concentrations and PK-PD modeling of antiinfective agents in clinical drug development and in critically ill patients by means of microdialysis should enhance knowledge on adequate antibiotic drug dosing and improve patient's outcome. Preliminary studies demonstrated that microdialysis sampling did not interfere and therefore might be an interesting approach for the simultaneous estimation of target site concentrations of the antimicrobial agent and their in vivo antibacterial effect. Antineoplastic Drugs Tumor drug exposure, a marker linked to clinical outcome, may be dramatically reduced due to diffusion barriers in solid tumors [27]. Differences in tumor drug distribution do not allow to predict the antineoplastic response from plasma profiles [94], thus measurements of drug exposure into tumor interstitium by microdialysis may help to develop clinical PK-PD models with the aim of individualize drug therapy [95]. Microdialysis has been employed for the characterization of different antineoplastic drugs. Methotrexate, cisplatin, capecitabine, 5-fluorouracil, dacarbacine and melphalan have been measured into the tumor using clinical microdialysis in several types of malignancies such as breast cancer, melanoma, osteosarcoma and malignant fibrous histiocitoma (for review see [95, 96]). Measurements of plasma drug concentration have been employed during high-dose methotrexate treatment [97]. Although this practice has served to identify individuals with impaired renal function in order to avoid serious side effects, it assumed a relationship between pharmacological response and circulating levels of drug. Müller et al. [98] have studied interstitial tumor pharmacokinetics and plasma-to-tumor transfer rates of methotrexate in breast cancer patients by insertion of microdialysis probes into the primary tumor and the periumbilical subcutaneous adipose layer of previously chemotherapy-naive breast cancer patients. Absence of correlation between plasma AUC and the AUC in the interstitial space of tumor tissue was found, together with a high interindividual variability in transendothelial methotrexate transfer. Therefore, plasma levels of methotrexate were not predictive of intratumor levels. Similar results were obtained for 5-fluorouracil (5-FU), showing that plasma or subcutaneous levels of 5-FU failed to predict tumor response. Conversely, high interstitial tumor concentrations of 5-FU were associated with increased tumor response. This information may explain drug resistance in some patients and help to optimize dosing and administration schedules [99].



Microdialysis has also been used to assess the pharmacodynamics of chemotherapeutic agents [96]. Castejon et al. [66] have determined plasma concentrations of serotonin and 5-hydroxyindoleacetic acid during cisplatin treatment by means of microdialysis. Serotonin is involved in the production of emesis associated to antineoplastic treatment. Microdialysis has also been used for the monitoring of extracellular levels of growth factors, such as the vascular endothelial growth factor (VEGF), during treatment with tamoxifen in a mouse model of human breast cancer [100]. Although to date scarce PK-PD modeling studies applying microdialysis were made, integration of the pharmacological response with tumor PK profiles of the corresponding drug would help to define PK-PD relationship, which is essential for the rational design of drug administration regimens in cancer patients. Thompson et al. [101] studied the clinical and biochemical responses to the time course of melphalan in the subcutaneous interstitial space and in tumor tissue from patients with various limb malignancies. The authors showed a significant correlation between the melphalan mean concentration in subcutaneous microdialysate and tumour response [101]. In another PK-PD protocol, (Müller et al.) [102] have determined the unbound interstitial drug pharmacokinetics of 5-fluorouracil and methotrexate in solid tumour lesions of patients by means of in vivo microdialysis. The authors then made a pharmacodynamic simulation of the time versus drug concentration profile in an in vitro setting by exposing breast cancer cells to interstitial tumour concentration of the antineoplastic drugs. The authors concluded that in vivo PK–in vitro PD models might provide a rational approach for describing and predicting pharmacodynamics of cytotoxic drugs at the target site [102]. In summary, high interindividual variability in intratumoral drug distribution exists, indicating lack of correlation between plasma concentration of antineoplastic drugs and their interstitial tumoral levels. Plasma measurements do not serve as surrogates for intratumoral concentration and microdialysis may help to design optimal treatment schedules and to select appropriate drug, doses and dosing intervals for antineoplastic agents. Surprisingly, in the last three years, only few clinical microdialysis protocols were conducted for the study of intratumoral distribution of anticancer drugs. A possible explanation is that microdialysis experiments in cancer patients must be conducted in strict compliance with regulatory demands and need to be based on appropriate ethical conditions. In addition, there are other factors that limit the use of microdialysis in cancer research. Puncture of solid tumors by microdialysis catheter implantation may induce metastasis. However, the estimated incidence of metastasis by puncture ranged from 0.003% to 0.005 % and there is no evidence that puncture of tumor lesions affected the course or prognosis of the underlying disease [103]. Another limitation of the applicability of microdialysis sampling in oncology is the fact that the majority of the antineoplastic drugs act within cells. The relationship between extracellular drug concentrations and intracellular drug levels remains unknown. This drawback could be overcome by applying an attractive approach consisting in the simultaneous study of drug distribution with microdialysis and PET. PET measures total (intracellular, extracellular, and intravascular) concentrations of radiolabeled drugs in tissue, and microdialysis determines unbound drug concentrations in the extracellular space fluid of tissue. Therefore, combination of both techniques allows the description of the intracellular drug pharmacokinetics. Langer et al. [104], using 18F-labeled-cipro-


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floxacin as a model drug, have found that in vivo intracellular ciprofloxacin pharmacokinetics was in accordance with previous in vitro data describing cellular ciprofloxacin uptake and retention. Therefore, PET/microdialysis combination might be useful during research and development of new drugs, for which knowledge of intracellular concentrations is of interest. Finally, some antineoplastic drugs, such as 5-fluorouracil, requires intracellular enzymatic conversion in order to exert their cytotoxic activity. In addition, other aspects like tumor location and accessibility for microdialysis probe implantation, and the possibility of variation in interstitial concentrations of cytotoxic drugs in different metastases in a patient restrict the utility of microdialysis for studies of antineoplastic drug distribution [12]. Central Acting Drugs Knowledge of brain uptake is an important issue for drugs acting in the central nervous system, such as anticonvulsivants, antidepressants, anaesthetics and anticancer drugs. Concentration-time profile of the drug in the central nervous system determines the intensity and duration of the effects of a central acting drug. As in the clinical setting, the measurement of brain concentrations of the drug is highly restricted; alternative determination of lumbar or ventricular cerebrospinal fluid is sometimes used. Nevertheless, drug concentrations in the cerebrospinal fluid provides only limited information with respect to drug distribution into the brain, because brain parenchyma distribution of a central acting agent is determined by multiple factors, including active biotransformation, active transport at the BBB and intracellular-extracellular exchange [105]. Intracerebral microdialysis would be a significant improvement, considering that it allows the study of the pharmacokinetic profile of the unbound drug fraction at a specific region within the brain. However, as discussed previously, microdialysis is a semi-invasive technique and may bear risks. Therefore, microdialysis sampling is restricted in the clinical setting for the study of brain drug distribution used in critical care patients subjected to surgery. Another drawback of microdialysis for the study of central acting drugs is the fact that this technique records the average drug concentration over a sampling interval of at least 5 min, being this sampling frequency not suitable to assess central pharmacokinetics of drugs that rapidly penetrate the brain (e.g. anesthetic induction agents) [106]. Nevertheless, microdialysis can be used in the early phase of clinical trials to establish brain penetration of drugs. A phase II trial of topiramate in severe traumatic brain injury demonstrated different free-drug concentrations in the extracellular space from that measured in cerebrospinal fluid (CSF) [107]. An interesting field of intracerebral microdialysis in the clinical setting is the study of brain distribution of anticonvulsivant agents. Despite the existence of a large variety of antiepileptic drugs, almost 30% of epileptic patients are resistant to treatment [108]. There is increasing evidence that over-expression of multidrug transporters such as Pglycoprotein is involved in the generation of pharmacoresistance to antiepileptic drugs because of a greater efflux from the brain of the central acting agent [24]. Therefore, monitoring of target site concentration of antiepileptic drugs by means of intracerebral microdialysis could elucidate the involvement of the efflux transport in the generation of the pharmacoresistance to antiepileptic drugs.



In the clinical setting, microdialysis protocols have demonstrated that intracerebral concentrations of valproic acid [109] and phenytoin [110] were slightly lower than plasma levels, suggesting that both anticonvulsivant drugs may be substrate for an energy-dependent carrier transport out of the central nervous system. Conversely, carbamazepine and carbamazepine-10,11-epoxide concentrations in the extracellular brain fluid closely mirror their unbound serum concentrations [111]. Intracerebral microdialysis was also used for measurement of brain concentration of other central acting drugs. Ederoth et al. [112] studied the BBB transport of morphine in patients with severe brain trauma by simultaneous microdialysis of morphine levels in "better" and "worse" brain tissue. The authors found that unbound brain morphine levels were lower than plasma concentrations suggesting the existence of an efflux transport system for morphine across the human BBB. In addition, the results suggested an increase of BBB permeability to morphine in the "worse" brain tissue. Brain penetration of anticancer drugs was also evaluated using microdialysis sampling in basic research. Zhuang et al. [113] have shown that P-glycoprotein and breast cancer resistance protein 1 (BCRP-1) limit topotecan distribution in brain parenchyma. Microdialysis sampling also allows the study of drug distribution to brain tumors. A significant increase in the penetration of methotrexate into the tumor tissue was found [114]. A hybrid physiologically-based pharmacokinetic model was used by the authors to characterize the mechanisms that may be responsible for the high methotrexate tumor concentration. Although microdialysis sampling is restricted in the clinical setting, this membranebased sampling technique is highly attractive for the preclinical evaluation of central acting drugs, considering the possibility of the simultaneously monitoring of brain drug concentrations and their effect on neurotransmission. Both traditional neurotransmitters and neuropeptides have a low molecular weight and diffused through the membrane of the probe, and therefore microdialysis allows the evaluation of drugs actions on biophase concentration of virtually all neurochemical substance [29]. Intracerebral microdialysis sampling has been extensively used for the study of antiepileptic drug concentrations at the target site in non-epileptic animals and experimental models of refractory epilepsy. Involvement of drug efflux transporters in brain parenchyma distribution of antiepileptic drugs was demonstrated in non-epileptic animals by enhancement of drug central levels by the application of drug efflux transporters inhibitors [24, 25]. Only few studies evaluated central pharmacokinetics of antiepileptic drugs in refractory epilepsy models with conflicting results. Whilst Rizzi et al. [115] using the Kainate model of epilepsy showed that overexpression of P-glycoprotein in the hippocampus is associated with reduced brain concentrations of phenytoin compared with control animals, Potschka and Löscher [116] did not find altered levels of phenytoin in hippocampus and amygdala of kindled when compared to non-kindled rats. Recently, we have demonstrated a critical role of P-gp overexpression in the development of pharmacoresistance to phenytoin in a model of epilepsy induced by 3-mercaptopropionic acid chronic administration, suggesting that administration of efflux transporters inhibitors could be an effective strategy to decrease pharmacoresistance to phenytoin antiepileptic treatment [117]. Comparing pharmacokinetics of anticancer agents in tumor and non-tumor extracellular fluid of brain might be an attractive approach to establish the therapeutic potential


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of antineoplastic therapies. Apparaju et al. [118] have found, by means of intracerebral microdialysis, that gemcitabine levels in the tumor extracellular fluid was 2.2-fold greater than the corresponding value in the tumor-free extracellular fluid of the brain, suggesting that the greater tumoral distribution of gemcitabine region may facilitate selectively higher cytotoxicity against brain tumor cells. Pharmacodynamics and PK-PD modeling of antidepressant drugs have also been evaluated using intracerebral microdialysis. In an elegant study, (Bundgaard et al.) [119] validated an integrated microdialysis rat model for multiple pharmacokinetic/pharmacodynamic investigations of serotonergic agents, such as antidepressant drugs. Concomitant with brain microdialysis, serial blood sampling was conducted by means of an automated blood sampling device. Pharmacokinetics of escitalopram have been characterized simultaneously in plasma and the hippocampus of conscious, freely moving rats. Concomitantly, the modulatory and functional effects of escitalopram could be monitored as increases in brain 5-HT and plasma corticosterone levels following drug administration. Therefore, microdialysis sampling allows simultaneous monitoring of hippocampal escitalopram levels and their effect on serotonin extracellular concentrations and therefore the assessment of PK-PD modeling of neurochemical actions of antidepressant drugs [119]. Neurochemical effects of desvenlafaxine, a novel antidepressant drug, on cathecholamine hypothalamic levels were also studied in freely moving rats by microdialysis. Administration of desvenlafaxine induced an increase in noradrenaline levels in hypothalamic dialysate without any effect on dopamine and serotonin concentrations, suggesting its utility in a variety of central nervous system-related disorders [120]. In another report, using in vivo microdialysis, the protective effect of NGP1-01, a dual blocker of neuronal voltage- and ligand-operated calcium channels, was evaluated by monitoring choline release during N-methyl-D-aspartic acid (NMDA) infusion as a measure of excitotoxic membrane breakdown [121]. Intraperitoneal administration of NGP1-01 reduced NMDA-induced membrane breakdown demonstrating that NGP1-01 simultaneously blocks both major neuronal calcium channels and is sufficiently brainpermeable. Therefore, NGP1-01 is a promising lead structure for a new class of dualmechanism neuroprotective agents. Microdialysis was also used to describe, by means of PK–PD modeling, the effect of drug candidates on dopaminergic activity at different nuclei of the central nervous system (CNS) [122, 123]. The effect of benzatropine analogues on dopamine concentration in the nucleus accumbens after its intravenous administration was evaluated [123]. The authors fitted plasma concentration of the analogues and their effects on extracellular dopamine levels to two different PK–PD models, such as an effect compartment model and a model with indirect physiological response. The authors demonstrated that the indirect model is more suitable for PK–PD modeling of benzatropine analogues than the linked PK–PD model. These results are in accordance with the mechanism of action of the analogues because these drugs bind to the dopamine transporter inhibiting the dopamine re-uptake and consequently elevate dopamine extracellular levels. Brain microdialysis has also been used for PK-PD modeling of therapeutic agents at the central nervous system. PK–PD modeling allows the study of the mechanism responsible for the time delay of central actions of drugs. In an elegant study, (Bouw et al.) [124] have simultaneously determined blood and brain concentrations of morphine-6-



glucuronide and its antinociceptive effect by means of microdialysis sampling. By applying a PK–PD model with an effect compartment, the authors found a greater delay in the onset of the effect when antinociceptation was related to plasma morphine-6glucuronide concentrations with regard to brain levels. Therefore, it was concluded that half of the effect delay could be explained by transport across the blood–brain barrier, suggesting that the remaining delay is a result of drug distribution in the brain parenchyma [124]. Microdialysis was used to describe the relationship between norfloxacin concentrations in the CNS and its adverse reactions, such as convulsive effect [125]. Brain extracellular concentrations of norfloxacin by means of microdialysis and a quantitative electroencephalogram (EEG) were simultaneously determined. Blood samples were also collected to determine norfloxacin plasma levels. Although norfloxacin brain concentrations peaked early after its intravenous administration, the effect on the EEG measurement was delayed. By applying a PK–PD model with an effect compartment, the authors demonstrated that the delayed EEG effect of norfloxacin is not due to BBB transport [125]. In conclusion, microdialysis sampling is a promising technique for drug development of central acting drugs, especially in the preclinical phase. The most attractive application of brain microdialysis seems to be the evaluation of brain parenchyma distribution of developing antiepileptic drugs and its ability to overcome pharmacoresistance in chronic epileptic animal models. In addition, impact of therapeutic strategies to enhance brain penetration of antiepileptic, such as efflux transporters inhibitors, could also be evaluated during preclinical phase of drug development by means of microdialysis sampling. Antihypertensive Drugs A poor concentration-response relationship with regard to the blood pressure effect of antihypertensive drugs has been found. Taking into account the pharmacodynamic properties of antihypertensive drugs, the suggestion of absence of relationship between plasma levels of antihypertensive drugs and its blood pressure lowering effect could reflect an inadequacy or failure in the approaches designed to detect such correlation. A number of factors have hampered the possible identification of a correlation, including failure to study individual patients, inability to collect sufficient pharmacodynamic data, failure to identify and account for temporal delay in the onset of the pharmacological effect, the use of restricted concentration ranges and the use of dose rather than concentration [126, 127]. Using a ‘shunt’ intra-arterial microdialysis probe, a good relationship was found between metoprolol concentration in the effect compartment and its hypotensive and chronotropic effect [128-130]. Moreover, the maximal response was significantly greater in hypertensive animals, such as spontaneously hypertensive rats and animals with aortic coarctation, with regards to their respective control animals. Therefore, this data suggests that the proposed lack of relationship between plasma levels of -blockers and its antihypertensive effect is probably a consequence of an inadequate experimental design and data analysis. More recently, by using in vivo microdialysis, we have demonstrated that the modified Emax designed by Schoemaker et al. [38] allows an accurate and precise estimation


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of diltiazem and verapamil sensitivity to the hypotensive effect in conditions when maximal pharmacological response can not be attained [131, 132]. In conclusion, microdialysis sampling is a powerful tool for PK–PD modeling of cardiovascular drugs in basic research, taking into account that it allows continuous and simultaneous determination of antihypertensive drugs plasma levels and their corresponding effect on blood pressure and heart rate in the same animal. Moreover, PK–PD modeling not only allows a better pharmacodynamic characterization of blood pressure lowering agents, but also permits the study of the physiopathological mechanisms of the hypertensive stage in different experimental models. APPLICABILITY OF PET FOR TISSUE DRUG DISTRIBUTION STUDIES AND PK-PD MODELING Principles of PET Imaging In the last years, several imaging techniques have been developed for the evaluation of the pharmacokinetic and pharmacodynamic properties of therapeutic agents. PET, SPECT and MRS are the most popular imaging techniques used for this purpose [6]. However, SPECT and MRS have limited applicability in drug distribution studies because of the existence of technical drawbacks, including low sensitivity, impressive quantification and low spatial resolution [133]. Conversely, the main problems inherent to SPECT and MRS are not important issues with PET, and therefore this imaging technique became the most applied method for non-invasive pharmacokinetic studies of drugs. Principles of PET have been recently reviewed in several works and therefore only a brief description will be included in the present chapter (for review see [133-135]). PET is based on the emission of positrons (positive electrons, b+) of radionuclides with low atomic number and relative proton excess. After positron emission, it rapidly loses kinetic energy and sometimes interacts with an electron, generating an annihilation event. In this event, a positron-electron pair is converted to energy, resulting in the generation of two photons that emitted in opposite directions. These photons are then sensed by detector pairs positioned at 180° to each other [133]. Measurement of tissue drug levels of drugs by means of PET imaging requires radiolabelling of the compound of interest with an isotope that emits radiation that can be detected by imaging. It is important to mention, that a strict condition of PET imaging is that radiolabelling of the drug does not modify the pharmacokinetic and pharmacodynamic properties of the pharmacological agent [133]. One strategy is the radiolabelling of atoms that form part of the native structure of the drug, such as carbon, hydrogen, nitrogen or oxygen. However, there are great limitations in the use of isotopes of these atoms for PET imaging of drugs. Firstly, drugs labeled with 14C or 3H generates negative electrons that only diffused few millimeters from their site of generation. Therefore, although these isotopes could be used for the evaluation of tissue distribution of drugs in small laboratory animals, their applicability in human studies is neglected [133]. Secondly, the half-life of the decay of 15O, 13N, 11C, is extremely short to perform pharmacokinetic studies of drugs using PET imaging. Conversely, 18F, 76Br and 124I have long half-life that allows their used for PET imaging of pharmacological agents. However, only few therapeutic agents contain one or more atoms of F, Br or I that could be subjected for radiolabelling [133].



Alternatively, positron emitting halogen isotopes could also be substituted for hydrogen of hydroxyl groups. Although these analogues are not guaranteed to retain pharmacological properties of the original drug, fluoride substitution are generally hlghly conservative and have shown to be useful probes in many situations [133]. It is important to take into account that radiolabelled compounds need to be synthesized, isolated, purified and formulated as a sterile, pyrogen-free solution within 2 halflife of the radionuclide. Therefore, drug precursors that can be labelled in a single step are needed for most therapeutic agents. In addition, PET imaging studies requires both a cyclotron and a PET scanner in close proximity [106]. Compared with traditional methods for tissue sampling of drug concentrations, PET imaging has several advantages, including its non-invasive nature, high sensitivity and its universal nature, considering that theoretically the availability of PET radionuclides of nitrogen, carbon and fluorides makes possible to prepare for almost any drug [133]. In addition, PET has a high spatial resolution, allowing determination of drug tissue distribution in small volumes of tissue and also permits differentiation of physiological alterations that occurs nanometers apart [106]. Nevertheless, PET imaging studies have some drawbacks that could limit its applicability for pharmacokinetic studies. As comment above, most radiolabelled drugs need to be prepared in close proximity to the imaging laboratory [106]. In addition, due to rapid decay of most radionuclides, the time interval of the pharmacokinetic study is limited. For instance, pharmacokinetic study needs to be completed in 40 min with tracers containing 13N, 80 min with 11C and 8 hours with 18F [133]. Another drawback of PET imaging is that this methodology did not discriminate within positron emission from the parent drug and their metabolites, and therefore quantification is only accurate if drug biotransformation is negligible [133]. Additionally, PET imaging monitors total drug tissue concentrations rather than extracellular levels. Although anatomical resolution of PET imaging is high, it is much lower than other imaging techniques, including computed tomography, MRI and ultrasound [133]. Applicability of PET Imaging for Pharmacokinetic Studies In the field of pharmacology, the most attractive application of PET is the noninvasive evaluation of tissue distribution of drugs, especially in the treatment of infection and cancer. For these studies, the drug is radiolabelled with a positron emitter, preferentially with 18F, and injected in the experimental subject. Thereafter, PET images are acquired at different time points and quantified by means of maps of radioactivity concentration in the tissue [133]. When negligible biotransformation of the drug occurs, regional levels of the parent drug could be obtained by simply dividing each pixel in the images by the specific activity of the radiotracer. Therefore, repeating this procedure at different time points after drug administration allows the estimation of classical pharmacokinetic parameters, including peak concentration, plateau concentration and area under the curve of the drug. In the following section, we reviewed the most relevant PET based pharmacokinetic studies of different therapeutic agents [133]. Antiinfective Agents PET imaging was firstly used to describe tissue distribution of erythromycin in infected tissues [136]. Radiolabelled erythromycin is obtained by reductive methylation of


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N-demethyl-erythromycin with [11C]formaldehyde yielding a product with similar chromatographic properties. By using this radiotracer, (Wollmer et al.) [136] showed that drug distribution in infected lung tissue is similar to normal pulmonary tissue. Although radiolabelling of erythromycin with carbon isotopes allows a successfull estimation of eryhtromycin tissue distribution, it must be taken into account that most antimicrobial agents have a much slower tissue equilibration, requiring the design of radiolabelled agents with longer lived radionuclides than 11C [136]. For instance, the presence of fluorine atoms in the molecular structure of fluconazole made this drug an ideal candidate for PET imaging. PET imaging of fluconazole is also justified considering that the drug undergoes minimal hepatic biotransformation and therefore measurement of tissue radioactivity reflects levels of the parent drug [133]. In an interestingly preliminar study [137], 18F-labelled fluconazole was used to compare pharmacokinetic properties of a microdose of the agent with regard to a pharmacological dose. Coadministration of 18F-fluconazole with a pharmacological dose of the unlabelled parent drug shows a rapid equilibration and an uniform distribution in most organs of the rabbit [137]. Conversely, application of the labelled drug in a “carrier-free” solvent accumulation of fluconazole was decreased in the heart, spleen and muscle and increased in the liver. This study clearly demonstrates the limitation of microdosing in the human phase 0 of drug development [137]. Pharmacokinetic properties of fluconazole were also evaluated in healthy volunteers by means of PET imaging [138]. The administration of 18F-fluconazole with unlabelled parent drug showed a non-uniform tissue drug distribution with accumulation of the drug in the spleen and lower levels in the bone [138]. Therefore, PET imaging demonstrated that fluconazole, at a dose of 5 mg/kg, is effective in the treatment of candidiasis at the hepatoesplacnic and urinary tract, but required higher doses for treatment of infections of the bone and central nervous system [138]. Fluoroquinolones also contain fluorine atoms in their chemical structure, and therefore are candidates for radiolabelling and PET imaging for in vivo pharmacokinetic studies. Fischman et al. [139] studied fleroxacin pharmacokinetics after administration of [18F]fleroxacin in 12 healthy volunteers. The authors demonstrated that fleroxacin rapidly distributed in most tissues, including heart, liver, lung myocardium and spleen, achieving concentrations two-fold above the MIC for 90% of Enterobacteriaceae strains tested. Conversely, a limited distribution of distribution of [18F]fleroxacin into the brain was observed. Additionally, concentrations of fleroxacin in tissue were similar in males and females [139]. Langer et al. [140] also studied the pharmacokinetic properties of radiolabelled [18F]ciprofloxacin in patients with soft tissue infections. Distribution of [18F]ciprofloxacin increased in infected tissues with peak ratios between infected and uninfected tissue ranging from 1.8 to 5.5. Nevertheless, radioactivity was not retained in infected tissue withouth differences in the elimination half-life of [18F]ciprofloxacin when comparing infected and uninfected tissues. The authors concluded that pharmacokinetics of [18F]ciprofloxacin in infected tissue are governed by increased blood flow and vascular permeability due to local infection [140]. Previously, the same authors have shown, by using [18F]ciprofloxacin, high tissue levels of this fluoroquinolone in several tissues, including kidney, heart, spleen, liver, musclea and lung [141]. Whilst [18F]ciprofloxacin rapidly wash out from the kidney, heart and spleen, prolonged readiotracer retention was



observed in liver, muscle and lung. Brain radioactivity concentrations of [18F]ciprofloxacin were under the limit of quantification suggesting that ciprofloxacin is not suitable for the treatment of infection at the central nervous system [141]. Briefly, PET imaging was also used for the study of trovafloxacin pharmacokinetics, showing that this fluoroquinolone will be useful in the treatment of a broad range of infections at diverse anatomic sites [142]. Drugs Acting at the Central Nervous System PET imaging have also been used for the study of different pharmacokinetic and pharmacodynamic properties of central nervous system acting drugs. Applicability of PET can be divided in direct and indirect studies. In direct studies, dynamic imaging is performed after administration of the radiolabelled drug, obtaining as information the distribution of the drug into different nuclei of the central nervous system [133]. However, PET imaging is also a valuable tool for the estimation of parameters describing in vivo drug-receptor interactions. For these studies, PET protocols are much more complex than for the study of drug distribution. Experimental design depends on the pharmacodynamic properties of the drug under study. For instance, for drugs that interact with a single subtype of receptors, time-activity curves from a single or multiple administration of the drug can be analysed by compartmental modeling to estimate receptor density (Bmax), binding affinity (KD), or binding potential (Bmax/K D). Conversely, in the case of drugs that bind to multiple receptor subtypes, kinetic parameters for a specific receptor can be estimated by blocking other receptors with unlabelled ligands [133]. Central distribution of zolmitriptan has been studied by means of PET imaging, considering the lack of studies regarding central nervous system access of this drug [143]. The authors found that [11C]zolmitriptan rapidly reached therapeutic concentrations at the central nervous system after its intravenous application of the triptan. Therefore it could be concluded that zolmitriptan enters the brain parenchyma in humans, achieving an uptake rate and concentration compatible with a central mechanism of action [143]. Brain distribution of vinpocetine, a compound used in the prevention of cerebrovascular disease, was evaluated by means of PET imaging after intravenous administration of [11C]vinpocetine [144]. The drug rapidly reached the central nervous system and distributed heterogeneously in different brain nuclei, suggesting the presence of specific binding site. Additionally, the authors found that the brain regions which showed increased uptake in the human brain correspond to those in which vinpocetine has been shown to induce elevated metabolism and blood flow [144]. Nevertheless, the most attractive application of PET for drugs with an action on the central nervous system is the in vivo study of receptor binding interaction. In this regard, a large number of radiotracer with specific affinity for different subtypes of receptors have been developed and validated, allowing the evaluation of in vivo binding properties of different pharmacological agents, including antipsychotics, antidepressants, anxiolytics, antiepileptics, among others (for review see [145]). On the other hand, these radiotracers also allow the study of brain receptor distribution. Table 3 summarizes actually available radiotracers for the evaluation of in vivo binding of different therapeutic drugs.


Table 3.

Höcht et al.

Radiotracers Designed for Study of Drug-Receptor and Drug-Transporters Binding Studies Radiotracer

[11C]NNC 112, [11C]SCH 23390

Target D1 dopamine receptor

11

D2 dopamine receptor

18

[ F]fallypride

D2/D3 dopamine receptor

[11C]CFT

Dopamine uptake trabsporter

[11C]carfentanil, [11 C]diprenorphine

Opiate receptor

[ C]racloporide

18

Opiate sigma-1 receptor

11

5-HT1A serotonin receptor

11

5-HT2A serotonin receptor

[ F]FPS [ C]WAY 100635, [18F]MPPF [ C]MDL 100907 11

[ C]DASB 11

Serotonin transporter 11

[ C]flumazenil, [ C]Ro15-4513 11

Benzodiazepine receptor

[ C]TMSX

A2A adenosine receptor

NCFHEB

Nicotinic acetylcholine receptors

18

[ F]MK-9470

CB1 cannabinoid receptor

11

Abreviattions: [ C]TMSX: [7-methyl-(11)C]-(E)-8-(3,4,5-trimethoxystyryl)-1,3,7-trimethylxanthine ([(11)C]TMSX); NCFHEB: Norchloro-fluoro-homoepibatidine; [11C]NNC 112: (+)-8-Chloro-5-(7-benzofuranyl)-7-hydroxy-3-[11C]methyl-2,3,4,5tetrahydro-1H-3-benzazepine; [11C]SCH 23390: ((R)-(+)-8-chloro-2,3,4,5-tetrahydro-3-methyl-5-phenyl-1H-3-benzazepin-7ol); [11C]CFT: 2ß-carbomethoxy-3ß-(4-fluorophenyl)tropane; [18F]FPS: 3-fluoropropyl-4-((4-cyanophenoxy)methyl)-. Piperidine; [11C]WAY 100635: N-[2-[4-(2-methoxyphenyl)-1-piperazinyl]ethyl]- N-(2-pyridyl)cyclohexanecarboxamide trihydrochloride; [18F]MPPF: 2'-Methoxyphenyl-(N-2'-pyridinyl)-p-18F-fluoro-benzamidoethylpiperazine; [11C]MDL 100907: ((R)-(+)-4 -(l-hydroxy-1-(2,3-dimethoxyphenyl)methyl)-N-2-(4-fluorophenylethyl)piperidine; [18F]MPPF: 2'-methoxyphenyl-(N-2'-pyridinyl)-p-18F-fluoro-benzamidoethylpiperazine; [11C]MDL 100907: (R)-(+)-4 -(l-hydroxy-1-(2,3-dimethoxyphenyl)methyl)-N -2-(4-fluorophenylethyl)piperidine; [11C]DASB: [(11)C]-3-amino-4-(2-dimethylaminomethyl-phenylsulfanyl)-benzonitrile.

Antineoplastic Agents PET imaging of antineoplastic drugs provides information regarding tumor pharmacokinetics and treatment response, making this sampling technique highly attractive both in drug development and clinical use of anticancer drugs (for review see 146-150]). PET can provide information that is often difficult to measure in the intact animal or patient. Several antineoplastic drugs were radiolabelled with the intention to study their tissue distribution and pharmacological response, including 5-fluorouracil (5-FU), N-[2(dimethylamino)ethyl]acridine-4-carboxamide (DACA), temozolomide, docetaxel, ifosfamide, gemcitabine, carboplatin, among others [133]. An example of the applicability of PET in drug development is the evaluation of tissue distribution properties of DACA in a pre-phase I study, using a radiotracer dose equivalent to 1/1000 of the phase-I starting dose [151, 152]. Radioactivity was shown to accumulate in vertebra < brain < tumour < kidney < lung < myocardium < spleen < liver. Tissue accumulation profile of DACA suggested that myelotoxicity and neurotox-



icity were less likely to be dose-limiting, and probably cardiotoxicity limited dose escalation in the clinical practice. PET study also showed a variable tumor uptake of [11C]DACA that was moderately correlated with blood flow [151-153]. Saleem et al. [154] evaluated the tumor, normal tissue, and plasma pharmacokinetics of temozolomide in vivo, and also determined whether such pharmacokinetics resulted in tumor targeting. Considering the postulation that temozolomide undergoes decarboxylation and ring opening in the 3-4 position to produce the highly reactive methyldiazonium ion that alkylates DNA, the authors used a dual radiolabelling strategy, with [11C]temozolomide separately radiolabelled in the 3-N-methyl and 4-carbonyl positions, to establish the mechanism of action. Both radiolabelled forms of [11C]temozolomide undergo rapid systemic clearance. A decrease in tissue exposure to [11C]temozolomide was also observed with [4-11C-carbonyl] temozolomide compared with [3-N-11Cmethyl]temozolomide [154]. Another interesting result of this study was the higher [11C]radiotracer and [11C]temozolomide exposure (AUC(0-90 min)) in tumors compared with normal tissue. The significantly higher amounts of plasma and exhaled [11C]CO(2), in addition to the lower normal tissue and tumor [11C]temozolomide AUC(0-90 min) observed with [4-11C-carbonyl]temozolomide, confirmed the mechanism of metabolic activation of temozolomide postulated by the authors. Considering these results, the authors concluded that the higher tumor [11C]temozolomide exposure compared with normal tissue and the tissue-directed metabolic activation of temozolomide may confer potential therapeutic advantage in the activity of this agent [154]. The work of (Saleem et al.) clearly demonstrates that PET imaging not only allows the noninvasive study of tumor durg distribution, but also permits the elucidation of the mechanism of action of anticancer agents. PET imaging also allows comparing drug uptake of primary tumors with regard to metastatic lesions. Jayson et al. [155], using iodinated radiotracer of HuMV833, a humanized anti-VEGF antibody, showed that primary tumors exhibited clearance rates that were three 3 times faster compared to metastatic tumors. The authors suggested that the heterogeneous tumor accumulation might explain the lack of response to antitumoral therapy in some patients. In addition, the study also found discrepancies between plasma and tumoral pharmacokinetics, indicating that traditional plasma sampling cannot be used to predict intratumoral drug concentrations [155]. PET imaging is also useful for monitoring antitumoral response to antineoplastic drugs. Tumor response to therapy has been assessed by determining the glycolytic response of tumors to chemotherapy with 18F-fluorodeoxyglucose (FDG) [147]. This tracer allows obtaining a mapping of tumor glucose use, and therefore establishing activity of malignant tumors. It was found that changes in FDG uptake by tumors after the first chemotherapy cycle are highly correlated with patient survival, suggesting that FDGPET might become an early indicator for treatment efficacy in drug development and clinical studies [156]. More recently, 18F-3-deoxy-3-fluorothymidine (FLT), a marker of DNA replication and cell proliferation, was used as an early imaging biomarker to assess antitumoral response in patients with glioblastoma [157, 158]. It was found that patients with an adequate metabolic response to bevacizumab and irinotecan regimen, defined as a decrease of at least 25% in FLT uptake by the tumor, showed a significantly longer survival than patient without response. Moreover, responders were identified only 2 weeks after the start of the therapy [157, 158].


Höcht et al.

Utility of PET in PK-PD Modeling Although PET imaging has been extensively used for the study of tissue pharmacokinetics (eg antiinfective, antineoplastic and central acting drugs) and for monitoring drug-receptor interactions and pharmacological response, to the best our knowledge, this sampling technique has not been applied with the intention to integrate pharmacokinetic and pharmacodynamic concepts. A possible explanation for the lack of PK-PD modeling studies with PET imaging is the uncertainty of tissue concentration of the parent drug, considering that PET imaging did not allow discriminating between the original drug and its metabolites. Another limitation of PET for PK-PD studies is the impossibility to study multiple PET tracers in a single experiment. Therefore, this technique only allows the estimation of the pharmacokinetic or the pharmacodynamic component of a PK-PD modeling study. PRINCIPLES AND APPLICABILITY OF MRS FOR PHARMACOKINETIC STUDIES MRS is an alternative imaging technique to PET that allows discrimination between parent drug and metabolites. However, a strong limitation of MRS is its low sensitivity [133]. MRS employs magnetic field gradients to produce images generated by the relaxation of nuclei with magnetic spins. Considering that individual atoms of all compounds have specific resonance frequencies, it is theoretically possible to obtain specific MR images of any drug and drug metabolites [133]. However, considering its low sensitivity, in most applications of MRS images cannot be recorded, and therefore field gradients are used to select a specific tissue site for a spectrum record. Nevertheless, with this approach, sensitivity of MRS is much lower than PET imaging. MRS measurements can be performed serially, giving the possibility of pharmacokinetic analysis with a high temporal resolution [133]. Another advantage of MRS over PET imaging is that the number of tracers available for MRS studies is potentially unlimited. Isotopes with non-zero are available for all atoms in drugs, including 1H, 13C, 15N, 17O, 19F and 31P. Majority of MRS studies have been performed with 19F isotope, considering its high intrinsic sensitivity and zero natural background [133]. The existence of high background represents a limitation for the use of 1H.In addition, 13C and 15N show lower intrinsic sensitivity. MRS tracers are stable compounds and therefore the time interval of pharmacokinetic studies is unlimited with MRS. In other words, matching half-life of the tracer to the time scale of the pharmacokinetic study is not an issue with MRS [133]. In the next section, examples of MRS applications for pharmacokinetic studies of drugs will be described. Tumor distribution of several antineoplastic drugs has been evaluated using MRS imaging. One of the most anticancer drugs studied with this methodology is 5-FU. Estimation of tumoral half-life of 5-FU is highly important in anticancer therapy, considering that trapping of 5-FU is a necessary condition for pharmacological response [159]. It was found that patients with larger tumoral half-life of 5-FU assessed by MRS showed a greater pharmacological response. Therefore, 19F-MRS might become a useful technique for early detection of non-responders to 5-FU therapy [159]. 19 F-MRS ahs also been used for the study of liver and muscle distribution of fleroxacin in healthy volunteers [160]. Compared with PET imaging, this study has demonstrated that spatial resolution of MRS was low requiring larger volumes of tissue for



quantification of fleroxacin tissue levels [160]. Nevertheless, combining PET and MRS imaging could be an attractive approach, considering that PET imaging provides precise measurement of total drug levels in small volumes of tissue, and MRS can additionally discriminates the contributions of different molecular species (parent drug and metabolites) and differentiates between intra and extracellular drug concentrations. Whole brain concentrations of psychotropic drugs have been also monitored by MRS imaging. In a pioneer study, by using 7Li-MRS, it was found that a relative slow lithium accumulation may explain the delay in therapeutic response to antimaniac therapy in some patients [161]. In another work, (Renshaw et al.) [162] demonstrated that brain concentrations of fluoxetine and its active metabolite, norfluoxetine, were 2.6-fold higher with regards to plasma levels, which may have implications for understanding therapeutic and toxic effects of fluoxetine. CONCLUSIONS The development of new sampling techniques greatly enhances our current knowledge regarding tissue drug distribution and PK-PD models. Membrane-based sampling and imaging sampling techniques monitor with high spatial and temporal resolution drug levels at the target site. Also, these methodologies allow the simultaneous assessment of drug-receptor interactions and the effect of drugs on endogenous compounds. Therefore, considering that microdialysis and imaging techniques, especially PET, provide information about tissue pharmacokinetics and pharmacodynamics, they could be considered as the reference techniques in PK-PD modeling studies. Table 4 shows the most attractive application of microdialysis and imaging techniques for pharmacokinetic, pharmacodynamic and PK-PD modeling studies. However, as pointed out in the above mentioning sections, these techniques have some drawbacks that could limit their applicability. Whilst intracerebral microdialysis is restricted to use in critical care patients in the clinical setting, PET imaging does not allow discriminating between concentration of the parent drug and its metabolites. Nevertheless, it is important to take into account that microdialysis sampling and PET imaging are complementary techniques and the combination of the same could greatly improve the information gained in PK-PD studies. For instance, as comment above, whilst microdialysis sampling assesses extracellular tissue drug concentrations, PET imaging gives information regarding total tissue levels. Therefore, simultaneous microdialysis/PET studies allow a precise estimation of intracellular drug levels that may be highly relevant for drugs acting within the cellule. Another example of the benefits of microdialysis/PET combination is the assessment of PK-PD properties of drugs that affect central neurotransmission. As shown by Schiffer and collaborators [163], whilst PET imaging allows the study of the dopamine receptorsbinding properties of 11C-raclopride, microdialysis assesses the effect of the drug on dopamine extracellular levels. In addition, combining microdialysis sampling with PET imaging could be attractive for the evaluation of the impact of metabolite generation in PET. As comment above, PET imaging does not discriminate between parent drug and metabolites. Simultaneous intravenous microdialysis may give insights regarding drug biotransformation by assessing circulating levels of the drug and its degradation products.


Table 4.

Applications of New Sampling Techniques for Pharmacokinetic and PK-PD Studies

Technique

Therapeutic Group

Antimicrobial agents

Applications

Microdialysis

Central acting drugs

Cardiovascular drugs Antimicrobial agents

PET imaging

Comments

Antibiotic penetration in non-infected soft Highly attractive for PKtissues PD modeling of antimicroDistribution of antimicrobial agents in infected bial agents tissues In vivo PK- in vitro PD modeling of antibiotics In vivo evaluation of prodrugs

Anticancer drug distribution in solid tumors Compartmental distribution within a tumor Antineoplastic Pharmacodynamics of antineoplastic drugs on drugs biochemical markers In vivo PK-PD modeling of anticancer agents

MRS imaging

Höcht et al.

Limited by the fact that most antineoplastic drugs act within cells. Tumor distribution is not homogenous Ethical considerations

Brain penetration of anticonvulsant agents Distribution of anticonvulsivants at the epileptic focus Evaluation of central pharmacokinetics of developmental anticonvulsivant drugs Evaluation of efflux transporters inhibitors as a therapeutic strategy to overcome antiepileptic drug pharmacoresistance In vivo PK-PD modeling of antiepileptic drugs In vivo evaluation of neurochemical effects of antidepressants and antipsychotic agents

Use of clinical microdialysis limited to critical care patients. Highly attractive for preclinical drug evaluation

In vivo PK-PD modeling of antihypertensive agents

Limited to preclinical drug evaluation

Antibiotic penetration in non-infected soft Limited to specific antimitissues crobial agents Distribution of antimicrobial agents in infected tissues

Anticancer drug distribution in solid tumors Antineoplastic Pharmacodynamics of antineoplastic drugs on drugs biochemical markers

Allows evaluation of heterogeneous drug distribution in different tumors

Central acting drugs

Estimation of in vivo drug receptor interaction Allows simultaneous monitoring of drug accuCentral distribution of drugs mulation in different nuclei

Antimicrobial agents

Antibiotic penetration in non-infected soft Limited to specific antimitissues crobial agents Distribution of antimicrobial agents in infected tissues


Frontiers in Drug Design & Discovery, 2009, Vol. 4 75 (Table 4) contd....

Therapeutic Group

Technique

Applications

Comments

Anticancer drug distribution in solid tumors Antineoplastic Pharmacodynamics of antineoplastic drugs on drugs biochemical marker

Only allows estimation of tumor half-life

Central distribution of drugs

Only allows estimation of whole brain distribution Differentiates between parent drug and metabolites

MRS imaging Central acting drugs

These few examples strength out the potential of combined microdialysis/PET for PK-PD studies. To the best our knowledge, no studies have applied simultaneous microdialysis/PET for the evaluation and development of mechanism-based PK-PD model. Nevertheless, considering that the combination of these sampling methodologies allow the simultaneous assessment of target site drug distribution and receptor binding properties, simultaneous microdialysis/PET may became an attractive approach for mechanism-based PK-PD modeling. In conclusion, membrane-based and imaging techniques, both used alone or simultaneously, will became the standard methodology for PK-PD modeling, enhancing gain of information during both drug development and clinical practice. ABBREVIATIONS 5-FU

=

5-Fluorouracil

AUC

=

Area under the curve

BBB

=

Blood-brain barrier

CE

=

Capillary electrophoresis

Cmax

=

Maximum concentration of drug in serum

CNS

=

Central nervous system

DACA

=

Dimethylamino)ethyl]acridine-4-carboxamide

EC50

=

Effective concentration to yield half-maximal response

EEG

=

Electroencephalogram

Emax

=

Maximal efficacy

FDG

=

18

F-fluorodeoxyglucose

FLT

=

18

F-3-deoxy-3-fluorothymidine

LC

=

Liquid chromatography

MIC

=

Minimum inhibitory concentration


MRS

=

Magnetic resonance spectroscopy

MS

=

Mass spectrometry

NMDA

=

N-methyl-D-aspartic acid

PET

=

Photon emission tomography

PK-PD

=

Pharmacokinetic-pharmacodynamic

SPET

=

Single photon emission computed tomography

TDM

=

Therapeutic drug monitoring

Höcht et al.

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Role of Inflammatory Biomarkers in Establishing PK/PD Relationships and Target Organ Toxicity Sivaram Pillarisetti* and Ish Khanna* Reddy US Therapeutics Inc., 3065 Northwoods Circle, Norcross GA 30071, USA Abstract: High levels of inflammatory cytokines and adhesion molecules are associated with many inflammatory disorders [e.g. rheumatoid arthritis, inflammatory bowel disease and lupus] as well as metabolic and cardiovascular diseases including diabetes and obesity. Examples of such markers include tumor necrosis factor [TNF], interleukins [IL-1, IL-6, IL-8 and IL-18], vascular cell adhesion molecules and markers of macrophage inflammation [e.g. MMPs]. In many preclinical disease models, levels of these markers are significantly elevated relative to normal animals. Modulation of these biomarkers with pharmaceutical agents in preclinical and clinical studies can be effectively used for concept validation, effective dose selection, and establishing good pharmacokinetics/pharcodynamics correlation. On the other hand, elevation of these markers in normal animals, following treatment with an agent, could indicate safety concerns leading to potential tissue damage. Monitoring of inflammatory markers in normal animals can be diagnostic and of high value in evaluating safety or efficacy of new molecules. The levels of biomarkers can be monitored either by high throughput microarrays/proteomics or by specific ELISA based assays. Since many of the biomarkers appear in systemic circulation, these can be monitored in blood/plasma without interference with tissues. This makes the approach particularly attractive for clinical studies. An overview of the biomarkers, potential applications and case histories linking biomarkers to PK/PD correlation from preclinical and clinical studies are discussed.

1. INTRODUCTION Inflammation is normally a localized, protective response to harmful stimuli such as pathogens or irritants. The process usually starts with an injury/insult to blood vessel wall endothelium. Exposure to a variety of pathogens can cause endothelial activation resulting in secretion of chemoattractant signals (Fig. 1). Depending on the nature of signal, circulating monocytes and other leukocytes are attracted to the site of injury and interact/bind to endothelium through a variety of receptors [e.g. selectins and vascular adhesion molecules] [1, 2]. Bound leukocytes transmigrate to the subendothelial space where they become tissue resident macrophages. The recruitment, accumulation and

*Corresponding Author: Tel: 770-446 9500; Fax: 770-446 1950; E-mail: [email protected] or [email protected] Gary W. Caldwell / Atta-ur-Rahman / Z. Yan / M. Iqbal Choudhary (Eds.) All rights reserved – © 2009 Bentham Science Publishers.


Pillarisetti and Khanna

subsequent activation of leukocytes are central events in the pathogenesis of most forms of inflammation [1, 2]. Host-mediated inflammatory response destroys, dilutes or wards off the injurious agent and the injured tissue. Commonly, inflammation occurs as a defensive response to invasion of the host by foreign, particularly microbial, agents. Responses to mechanical trauma, toxins and neoplasia can also cause inflammatory reactions. Circulating monocytes

Tethering and rolling of monocytes

Transmigration

Chemotactic signals Endothelium Activated/injured endothelium

Maturation to macrophages

Cytokines (TNF, IL-1) Effects on surrounding cells in tissue

Inflammatory diseases e.g. RA, IBD, OA

Organ toxicity

Fig. (1). Pathways leading to tissue inflammation (see text for details).

There are other forms of inflammation that often don’t involve infection and host defense. These include autoimmune responses and subclinical inflammation. In autoimmune inflammation body proteins are recognized as foreign and antibodies are produced against body’s own proteins. This is the underlying cause of autoimmune diseases such as rheumatoid arthritis and systemic lupus erythematosus. Subclinical inflammation can occur under normal physiological conditions and is often attributed to nutritional status and related oxidative stress. Excess plasma glucose [hyperglycemia] or plasma lipids [cholesterol or free fatty acids] or obesity can induce oxidative stress [reactive oxygen species or ROS] within cells via various mechanisms [3-6]. Excess oxidative stress activates ROS-sensitive transcription factors which drive the expression of inflammatory cytokines [7, 8]. Prolongation of the inflammatory process can damage tissue of origin or can lead to inflammatory diseases as diverse as diabetes, diabetic nephropathy, atherosclerosis, Alzheimer’s, fatty liver, pancreatitis and cancer [9-18]. Many cytokines and the high-sensitivity C-reactive protein [CRP] are considered markers of inflammation and have been studied in both disease progression as well as organ toxicity. This review

Role of Inflammatory Biomarkers


will focus on selective markers of inflammation that are useful in monitoring the efficacy and safety of pharmacological agents. 2. KEY INFLAMMATORY CYTOKINES AND THEIR ROLES Cytokines encompass a large number of glycoproteins involved in cell-to-cell signaling. Cytokines can be classified based on their mode of action [pro or antiinflammatory] or based on cell type of origin. e.g., T helper cell type I [Th1; for TNF and interleukin-2] and T helper cell type II [Th2; for interleukins -4, -5, -6, -10, and -13. Cytokines may also be identified based on their function such as those effecting chemoattraction [chemokines, including monocyte chemoattractant protein-1 [MCP-1]]. Although leukocytes/lymphocytes are the major sources of these cytokines, other cell types and tissues including vascular cells, liver and adipose express one or more of these cytokines and their receptors, making them targets of down stream signaling and toxicities. A brief discussion on inflammatory cytokines is outlined below. 2a. Tumor Necrosis Factor (TNF) Tumor necrosis factor-alpha [TNF] often described as the master cytokine is the major mediator of inflammation in many tissues [19-21]. It was originally described as a circulating factor that can cause necrosis of tumors, but has since been identified as a key regulator of the inflammatory response. It is also a major mediator of cell apoptosis and plays key role in immune defense against bacterial infections. Elevated levels of TNF or sustained activation of TNF signaling has been implicated in the pathogenesis of many autoimmune diseases including rheumatoid arthritis, inflammatory bowel disease, multiple sclerosis and psoriasis. In addition more recent data suggest that TNF along with other cytokines contribute to the pathogenesis of diabetes and cardiovascular disease [atherosclerosis]. TNF binds to two specific receptors, TNF-receptor type I [TNF-R1 aka p55/60] and TNF-receptor type II [TNF-R2, aka p75/80]. Signaling through these receptors is extremely complex, leading to induction of other inflammatory mediators as well as cell death and survival signals. Although signaling involves many players, nuclear transcription factor NFB appears to be critical for regulation of both inflammatory as well as cell death genes [22]. Vascular endothelial cells are highly susceptible to TNF challenge and undergo a number of pro-inflammatory changes, which increase leukocyte adhesion, transendothelial migration and promote thrombosis [23]. Thus all vascularized tissues are prone to TNF-mediated toxicity. TNF-signaling in cells also leads to induction of other pro- and anti-inflammatory cytokines including interleukins-1, -4, -6, -8 and -10 and monocyte chemoattractant protein MCP-1. The relative levels of these cytokines may determine the extent of damage/toxicity to the tissue. The central role of TNF in inflammation has been demonstrated in clinic. A number of agents that block the action of TNF have proven highly beneficial to treat a range of inflammatory conditions, including rheumatoid arthritis, ankylosing spondylitis, inflammatory bowel disease and psoriasis [24-26]. 2b. Interleukins IL-1 The IL-1 family consists of four proteins that share considerable sequence homology IL-1, IL-1, IL-1 receptor antagonist [IL-1Ra], and IL-18 [27, 28]. Mature IL-1 is deri-



ved proteolytically from pro-IL-1 by intracellular IL-1-converting enzyme [ICE or caspase-1]. IL-1 binds and induces signaling through IL-1 receptor [IL-1R]. Minute amounts [1ng/kg] of IL-1 cause significant inflammatory response in humans. The signaling cascade results in activation of nuclear factor kappa B [NF-B] and activating protein-1 [AP-1] and the transcription of a variety of pro-inflammatory genes, including autocrine amplification of IL-1 [28-30]. In addition to the IL-1RI, IL-1 may also bind to the type II interleukin-1 receptor. Binding of IL-1 to this receptor does not result in cellular activation, and IL-1RII is therefore presumed to act as a decoy that negatively regulates IL-1 activity. IL-18 has been assigned to the IL-1 family on the grounds of sequence homology [26% with IL-1] and similarity of the IL-18 receptor to IL-1R [31]. Like IL-1, IL-18 is dependent on ICE for proteolytic processing, and on nuclear translocation of NFB for transcriptional activation. IL-1 like TNF induces vessel wall inflammation. IL-1 plays a major role in a wide range of inflammatory and autoimmune diseases. These include RA, OA, chronic obstructive pulmonary disease [COPD], asthma, inflammatory bowel disease [IBD] [[both Crohn's disease [CD] and ulcerative colitis [UC]], atherosclerosis and diseases of the central nervous system such as multiple sclerosis [MS], Alzheimer's disease and stroke [32]. IL-6 The IL-6-type cytokines IL-6, IL-11, LIF [leukemia inhibitory factor], OSM [oncostatin M], ciliary neurotrophic factor, cardiotrophin-1 and cardiotrophin-like cytokine are an important family of mediators involved in the regulation of the acute-phase response to injury and infection [33]. Besides their functions in inflammation and the immune response, these cytokines also play a crucial role in hematopoiesis, liver and neuronal regeneration, embryonic development and fertility. IL-6-type cytokines exert their action via the signal transducers gp [glycoprotein] 130, LIF receptor and OSM receptor leading to the activation of the JAK/STAT [Janus kinase/signal transducer and activator of transcription] and MAPK [mitogen-activated protein kinase] cascades [34]. Although gp130 is ubiquitously expressed, the number of cells that respond to IL-6 is limited, since the expression of the other receptor subunits especially of the -receptors, is more restricted and tightly regulated. The function of the -receptors to render cells sensitive to the respective cytokinecan also be taken over by the soluble form of the -receptors. This is one of the rare situations in which a complex of cytokine and soluble receptor can act agonistically instead of antagonistically. Soluble forms of cytokine receptors in vivo are formed either by limited proteolysis [shedding] of membrane-bound receptors or by translation from an alternatively spliced mRNA [4]. In the case of IL-6, the scenario is more complex, since soluble forms for IL-6R [sIL-6R] and gp130 [sgp130] are both present in human serum. It has been demonstrated that sIL-6R potentiates the antagonistic activity of sgp130. Thus the naturally occurring combination of sIL-6R and sgp130 might act as a kind of buffer to modulate systemic responses to circulating IL-6. Because IL-6 has a wide range of biological activities on various target cells, deregulated overproduction of IL-6 causes various clinical symptoms. IL-6 plays important roles in the regulation of immune response and inflammation, and overproduction of IL-6 is involved in the pathology of inflammatory diseases such as rheumatoid arthritis [RA], Castleman’s disease, juvenile idiopathic arthritis and Crohn’s disease [35].



2c. Other Inflammatory Cytokines/Markers MCP-1 MCP-1 is a chemoattractant protein that specifically attracts blood monocytes and tissue macrophages to its source, via interaction with CCR2, its cell surface receptor [36]. Many cells produce MCP-1 in response to a variety of pro-inflammatory stimuli, and predictably, its expression has been identified in diseases which involve significant macrophage inflammation. These include atherosclerosis, rheumatoid arthritis and kidney diseases such as diabetic nephropathy. CRP CRP is an acute phase reactant that was discovered over 70 years ago. CRP is a blood protein that binds to the C-polysaccharide of pneumococci. CRP is a pentamer of 23 kDa subunits and is mainly produced by liver. CRP levels are usually low in normal individuals but can rise 100- to 200-fold or higher with acute systemic inflammation. CRP levels are high in patients with rheumatoid arthritis [RA] and cardiovascular disease [CVD]. In patients with CVD levels of CRP predict future myocardial infarction, stroke and peripheral vascular disease. IL-1, IL-6, and TNF, all can stimulate liver production of CRP. It is not entirely clear if CRP is just a biomarker for inflammation or CRP could have a more direct pro-inflammatory role. Modulation of CRP levels by drugs appear to correlate with disease severity [37, 38]. 3. CYTOKINES IN DISEASE Although elevated cytokines are seen in almost all inflammatory diseases the causal relationship has been established in autoimmune diseases such as rheumatoid arthritis [RA], inflammatory bowel disease [IBD], and psoriasis. 3a. Cytokines and Autoimmune Diseases Rheumatoid arthritis [RA] is a chronic disease characterized by synovial inflammation that leads to the destruction of cartilage and bone. Proinflammatory cytokines such as TNF, IL-1 and IL-6 play important role in initiating and perpetuating inflammatory and destructive processes in the rheumatoid joint [20, 27, 31, 34]. These cytokines regulate many NFB inducible genes that control expression of other cytokines, cell adhesion molecules, immunoregulatory molecules, and proinflammatory mediators. The expression of cyclooxygenase-2 and inducible nitric oxide synthase [iNOS] and thereby production of prostaglandins [PG] and NO are regulated by these cytokines. The prostaglandin PGE2 and nitric oxide [NO] further promote inflammation and likely participate in destructive mechanisms in the rheumatoid joint. Although the actions of IL-1 and TNF-alpha show a large degree of overlap, some differences have been observed in animal models [45, 47]. As indicated in Table 1 [39], inflammatory cytokines [TNF-, IL-1, IL-6] are significantly elevated in synovial fluids of RA patients relative to OA [40-44]. This seems particularly relevant for IL-6 where high levels in synovial fluid and serum correlate with disease progression. The preclinical studies suggest that IL-1 expression occurs later in the disease relative to TNF- [45, 46]. The reported data suggests that IL-1 has significant effect on cartilage destruction where as TNF- influences joint swelling [46, 47]. In RA, the cytokines such as IL-4, IL-10 and IL-13, detected in synovial fluid of



patients, have protective, anti-inflammatory effects. In patients with active rheumatoid arthritis, blockade of TNF-, IL-1 or IL-6 results in improvement in clinical and radiographic scores. TNF-alpha antagonists are currently the most effective treatments for early and late RA. Use of these agents provides relief of RA symptoms and improvements in American College of Rheumatology [ACR] criteria. ACR criteria is indicated as ACR 20, ACR 50, or ACR 70 and measures improvement in tender or swollen joint counts and improvement in three of five parameters [acute phase reactant such as erythrocyte sedimentation rate, patient assessment, physician assessment, pain scale and disability questionnaire]. Table 1.

Cytokine Levels in Patients with RA and OA

Cytokine

Disease

Concentration [pg/ml]

Fold Change

Reference

TNF

RA OA

95 ± 25 39-69±6-32

2.4

[40, 41]

IL-1

RA OA

130±22 27±4.5

4.6

[42]

IL-6

RA OA

1610 89-398

18

[41, 43]

IL-1 antagonism also has additional benefits on cartilage and bone erosion [48]. In vitro studies suggest that IL-1 can cause cartilage destruction by stimulating the release of matrix metalloproteinases and other degradative products, and it can increase bone resorption by stimulating osteoclast differentiation and activation. In animal models of RA, blocking the effects of IL-1 with either IL-1 receptor antagonist [IL-1Ra; endogenous], anti-IL-1 monoclonal antibodies significantly reduced cartilage destruction and bone erosion. More recently, IL-6 antagonists have shown good efficacy in autoimmune diseases. Tocilizumab, a humanized antihuman IL-6 receptor antibody recognizes both the membrane-bound and the soluble form IL-6R and specifically blocks IL-6 actions [49]. Tocilizumab has been shown to be effective not only for improving signs and symptoms but also for preventing joint destruction of RA. Significantly elevated levels of TNF-, and other cytokines were observed in other autoimmune diseases such as Crohn’s disease, psoriasis and systemic lupus erythematosus and TNF antagonists are effective in relieving symptoms. In summary, these data strongly show that elevated cytokines not only serve as a biomarker to predict disease severity but blockingtheir function can effectively reduce disease severity in many autoimmune diseases. 3b. Cytokines and Cardiovascular Disease Cardiovascular disease is currently the leading cause of death in developed countries. Atherosclerosis - a progressive disease characterized by the accumulation of lipids in the



large arteries - constitutes the single most important contributor to this growing burden of cardiovascular disease [50, 51]. Macrophage-mediated inflammation plays an important role in the initiation and progression of atherosclerosis and the development of atherosclerotic events. The macrophages can avidly take up lipids within the lesion leading to lipid loaded foam cells. Inflammatory cytokines can weaken the protective fibrous cap of the atheroma by stimulating the expression degradative enzymes such as MMP-2 and -9. Loss of fibrous cap results in thrombosis and the occurrence of acute coronary syndromes such as unstable angina pectoris and myocardial infarction [MI]. Serum levels of cytokines are elevated in many cardiovascular pathologies including coronary heart disease, aortic aneurysm and acute MI [52-60]. Of these cytokines, IL-6 has been identified as an important marker of inflammation in coronary atherosclerotic plaques. Serum levels of IL-6 increase in response to acute MI, unstable angina, percutaneous coronary intervention, and late restenosis [58-60]. IL-6 levels, which were undetectable [i.e., 5), moderate (~5) or weak (90% of whom were receiving enzyme-inducing ACs, irinotecan, SN-38 and SN38G AUC values were approximately 40%, 25% and 25%, respectively, of the corresponding values in patients with metastatic colorectal cancer who were not receiving chronic dexamethasone and enzyme-inducing ACs (Fig. 10) [47]. It has been recommended that the dose of irinotecan be increased by 50% in patients who are being treated concomitantly with enzyme-inducing ACs, in order to achieve adequate exposure to irinotecan and SN-38 [29]. Pretreatment of patients with barbiturates has been found to stimulate docetaxel metabolism to a significant extent [48]. In any case, the American Academy of Neurology’s position is that ACs are not effective in preventing first seizure in patients with newly diagnosed brain tumor, and should not be used routinely [49]. St John’s wort has become one of the world’s most popular herbal preparations, particularly among cancer patients, because of its putative activity against mild to moderate depression. However, in cancer patients treated with irinotecan, concomitant administration of St John’s wort reduced the AUC of its active metabolite, SN-38, by 42%, which may have a deleterious impact on treatment outcome (Fig. 11) [50]. Cigarette smoke contains several constituents that are known to interact with drugmetabolizing enzymes. Among patients receiving irinotecan, irinotecan clearance was about 18% faster among smokers than among non-smokers, and systemic exposure to SN-38 was almost 40% lower in smokers [51]. In the same study, hematological toxicity was considerably less prevalent among smokers, which may reflect a less favorable therapeutic outcome [51]. CYP3A4 is the major enzyme responsible for metabolism of erlotinib, an epidermal growth factor receptor tyrosine kinase inhibitor [52]. Oral clearance of erlotinib has been shown to be 24% faster in smokers compared with nonsmokers [53], and this may affect overall survival in patients with non-small-cell lung cancer [51]. Tamoxifen is converted to the active metabolites 4-hydroxytamoxifen and 4-hydroxyN-desmethyltamoxifen (endoxifen), which is more than 100 times potent than tamoxifen with respect to its anti-estrogen effect [54, 55]. Plasma endoxifen steady-state concentrations are, on average, 6-10 times higher than corresponding 4-hydroxytamoxifen concen-

Important Drug Interactions


N = 15 p = 0.004

A AUC (ng・h/mL)

80

60

40

20 without AC

B

N = 15 p = 0.04

1200

CL (mL/min)

with AC

1000 800 600 400 200 0 without AC

with AC

C N = 14 p = 0.01

CL (mL/min/m2)

40 30 20 10 0 without AC

with AC

Fig. (9). AUC (A) and clearance (B) of vincristine [45] and clearance (C) of etoposide [46] when administered alone or concomitantly with various anticonvulsants to patients with advanced cancer. Dose used were: (A,B) vincristine 1.4mg/m2 (max 2mg), (C) etoposide 960-1500mg/m2. CL, clearance. Error bars represent standard deviation.


A

Ishiguro et al.

p < 0.0001

AUC (ng・h/mL)

30000

Glioma

(N = 32)

25000 20000

Non-CNS tumor (N = 163)

p < 0.0001

15000 p < 0.0001

10000 5000 0 CPT

SN-38 (x10)

SN-38G (x10)

p < 0.0001

B

20

CL

(L/h/m2)

40

0 Glioma (N = 32)

Non-CNS tumor (N = 163)

Fig. (10). AUC of irinotecan (CPT) and its metabolites (A) and clearance of CPT (B) in patients with glioma and non-central nervous system tumors [47]. Dose used were: irinotecan 125mg/m2 . CL, clearance; CNS, central nervous system. CL, clearance. Error bars represent standard deviation.

trations in women receiving 20 mg/day tamoxifen in the long term, and endoxifen has been shown to be the metabolite most responsible for tamoxifen activity [54, 55]. Plasma concentrations of endoxifen are lower in patients who are also taking paroxetine, a selective serotonin re-uptake inhibitor (SSRI) that is commonly prescribed for the nonhormonal treatment of hot flashes, than in patients who are not taking paroxetine (Fig. 12) [56]. Tamoxifen-treated patients with impaired CYP2D6 metabolism arising from the presence of one or two CYP2D6*4 alleles or the concomitant administration of a moderate or potent CYP2D6 inhibitor have a nearly two-fold higher risk of breast cancer recurrence, independent of standard prognostic factors [54].



A

AUC (μM・h)

80

without SJW with SJW

p = 0.14

60 p = 0.033

40

N=5

20 0 CPT

B

SN-38 (x10)

N=5 p = 0.11

80

CL (L/h)

60 40 20 0 without SJW

with SJW

Fig. (11). AUC (A) and clearance (B) of irinotecan (CPT) when administered alone or concomitantly with St John’s wort (SJW) in patients with cancer [50]. AUC data for SN-38 are also shown. Dose used were: irinotecan 350mg/m2, SJW 300mg three times a day. CL, clearance; SJW, St John’s wort. Error bars represent standard deviation.

Cyclophosphamide is a prodrug that requires bio-activation to become cytotoxic. The first step in this activation process is the cytochrome p450 isoenzyme-catalyzed hydroxylation of cyclophosphamide to 4-hydroxycyclophosphamide. ThioTEPA, an agent used in high-dose chemotherapy regimens, strongly inhibits the bio-activation of cyclophosphamide, resulting in AUC values that are decreased by 26%, presumably resulting in decreased efficacy [57]. Therefore, the sequence and scheduling of these two agents in high-dose chemotherapy regimens is likely to be extremely important [57]. Effect on Warfarin Pharmacokinetics Imatinib is a strong inhibitor of CYP2C9, which is the principal enzyme required for inactivation of warfarin [58]. In a phase 1 clinical trial of docetaxel/estramustin/imatinib


Ishiguro et al.

administered to prostate cancer patients who were also receiving warfarin prophylaxis, two out of thirteen patients experienced grade 3 prolongation of prothrombin time, which was attributed to the interaction between imatinib and warfarin [59]. 200

Endoxifen concentration (nM)

N = 34

N=6

N=3

150

100

50

0

None

Paroxetine

None

Wt/Wt

Wt/Wt

Null/Null

SSRI Genotype

Fig. (12). Plasma endoxifen concentrations in patients with breast cancer concurrently taking commonly used antidepressants, paroxetine and tamoxifen 20mg/day, according to CYP2D6 genotype [56]. Error bars represent standard deviation. SSRI, selective serotonin re-uptake inhibitor; Wt, wild type.

An adverse interaction between FU and warfarin has been reported, which may be due to FU impairing vitamin K absorption or interfering with warfarin absorption, vascular transport, degradation, or excretion, or FU interfering with metabolism in general [4]. Several studies have shown that a 1 mg/day (mini-dose) of warfarin reduces catheter-related thrombosis without causing an alteration in prothrombin time or activated partial thromboplastin time, or causing bleeding. However, Masci et al. reported that there was a warfarin INR elevation of more than 1.5 in 33% of patients receiving fluorouracil-based chemotherapy, with 19% having an INR value of more than 3.0 [60]. Eight percent of patients also experienced bleeding. These authors also observed an INR elevation in 28% of patients treated with a de Gramont regimen (FU and folinic acid), 26% treated with a FU, folinic acid and irinotecan (FOLFIRI) regimen and 49% treated with a FU, folinic acid and oxaliplatin (FOLFOX) regimen. There was a significantly higher incidence of INR elevation in the patients treated with FOLFOX regimen. Oxaliplatin is largely bound to plasma protein and especially to albumin (85%), therefore it was originally suggested that displacement of warfarin from plasma protein could explain the prolongation of INR in that study [60] but other factors are now thought to be more important.



There is a significant pharmacokinetic interaction between capecitabine and warfarin, resulting in exaggerated anticoagulant activity. During capecitabine treatment, the AUC for a single dose of warfarin increased by 57%, with a 51% longer elimination half-life and an INR that was increased 2.8-fold relative to warfarin without capecitabine [61]. Copur et al. described two patients who presented with bleeding most likely caused by an adverse interaction between capecitabine and warfarin after 6 weeks of concomitant therapy [62]. In both cases, there was a marked elevation in INR value (to >10), with subsequent gastrointestinal bleeding. Since the exact mechanism by which this interaction occurs is unknown, close monitoring of coagulation parameters is recommended for all patients receiving concomitant warfarin and capecitabine, with appropriate adjustment of warfarin dosage as necessary [62]. Excretion For approximately one-third of anticancer agents, 30% or more undergoes renal clearance as an active or toxic compound. For example, methotrexate and bleomycin are mostly excreted unaltered in the kidney. Thus, even a minor decrease in renal function in patients undergoing chemotherapy can have profound effect on renal clearance, leading to significant toxicity [29]. When the cumulative dose of cisplatin is greater than 300 mg/m2, the total plasma clearance of a renally excreted drug such as bleomycin is reportedly decreased by 50% [63]. Thus, close monitoring of renal function should be performed in patients receiving concurrent cisplatin, such as in the MVAC regimen for bladder cancer and the BEP regimen for germ cell tumors [29]. Since fatal pulmonary toxicity has been associated with delayed bleomycin elimination as a result of cisplatininduced renal failure, the dose of bleomycin should be reduced when creatinine clearance is less than 60 mL/min [64]. Methotrexate is not only filtered but is also actively secreted by the renal tubules [65]. Concurrent administration of certain non-steroidal anti-inflammatory drugs or penicillin may increase the serum concentration of methotrexate via competition in renal tubular secretion [65, 66]. Further, when urine becomes acidified, methotrexate may precipitate within the renal tubules, which delays its elimination, causing further toxicity. Therefore, medications that potentially acidify urine, such as furosemide and thiazide diuretics as well as high dose vitamin C, should be avoided in patients receiving methotrexate treatment [67, 68]. Patients being treated with high doses of methotrexate must be kept hydrated and with a urine pH above 7.0 by using acetazolamide [67, 68]. SUMMARY In the field of oncology, drug-drug interactions are a major concern in both day-today practice and drug development. The US Food and Drug Administration has issued guidelines detailing which in vitro and in vivo drug interaction studies should be conducted during drug development [1]. However, data arising from studies of this type are not available for many drugs that are already on the market, meaning that data must be garnered from case studies. Further compounding the problem is that data from clinical trials that are terminated due to unexpected toxicities are not always reported in full in the literature. Further, drug-drug interactions that result in a lower level of efficacy but no increase in toxicity have not been given much attention until recently. The interaction between tamoxifen and SSRIs such as paroxetine is one famous example.


Ishiguro et al.

For most anti-neoplastic drugs, especially cytotoxic drugs, the recommended dose is close to the maximum tolerated dose in order to achieve maximum clinical benefit. Although CYP3A4 inhibitory activity leading to a less than two-fold increase in AUC is classified as “weak” inhibition, for anti-neoplastic drugs even a minor increase in AUC (e.g. 30%) can result in severe dose-limiting toxicity. Conversely, in cancer patients a reduction in AUC because of drug interactions might result in a lower level of efficacy, potentially leading to disease recurrence or progression. This phenomenon is particularly important for adjuvant chemotherapy, when there is no evaluable disease that might be used to monitor drug efficacy. Therefore, knowledge of drug interactions is more important for clinical oncologists than for any other specialty. Medical oncologists must not only control patient compliance via education, but also take charge of controlling the many factors affecting the pharmacokinetic and pharmacodynamic variables controlling response to treatment (Fig. 13).

Fig. (13). Schematic diagram showing the factors affecting the pharmacokinetics and pharmacodynamics of drugs.

ABBREVIATIONS 5-FU

=

5-Fluorouracil

AUC

=

Area under the concentration-time curve

Cmax

=

Maximum plasma concentration



CYP

=

Cytochrome P450

INR

=

International normalization ratio

MIC

=

Minimum inhibitory concentration

UGT

=

Uridine diphosphate-glucuronosyltransferase

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Bjornsson, T. D.; Callaghan, J. T.; Einolf, H. J.; Fischer, V.; Gan, L.; Grimm, S.; Kao, J.; King, S. P.; Miwa, G.; Ni, L.; Kumar, G.; McLeod, J.; Obach, S. R.; Roberts, S.; Roe, A.; Shah, A.; Snikeris, F.; Sullivan, J. T.; Tweedie, D.; Vega, J. M.; Walsh, J.; Wrighton, S. A. J. Clin. Pharmacol., 2003, 43, 443-69. Monahan, B. P.; Ferguson, C. L.; Killeavy, E. S.; Lloyd, B. K.; Troy, J.; Cantilena, L. R. Jr. JAMA, 1990, 264, 2788-90. Roden, D. M. N. Engl. J. Med., 2004, 350, 1013-22. Beijnen, J. H.; Schellens, J. H. Lancet Oncol., 2004, 5, 489-96. Arsenic trioxide (Trisenox) Package Insert (Japanese). In Nippon Shinyaku CO., LTD.: 2007. Amphotericin B (Fangizone) Package Insert (Japanese). In Bristol-Myers Squibb Company: 2007. Tobramycin (Tobracin) Package Insert (Japanese). In J-DOLPH Pharmaceutical CO., LTD.: 2008. Cheung, Y. W.; Cradock, J. C.; Vishnuvajjala, B. R.; Flora, K. P. Am. J. Hosp. Pharm., 1987, 44, 12430. Pujol Cubells, M.; Prat Aixela, J.; Girona Brumos, V.; Duran Pou, S.; Villaronga Flaque, M. Pharm. World Sci., 1993, 15, 34-6. Cisplatin (Randa) Package Insert (Japanese). In Nippon Kayaku CO., LTD.: 2007. Mesna (Uromitexan) Package Insert (Japanese). In Shionogi & CO., LTD.: 2005. Mitomycin C (Mitomycin) Package Insert (Japanese). In Kyowa Hakko Kogyo CO., LTD.: 2006. Gianni, L.; Vigano, L.; Locatelli, A.; Capri, G.; Giani, A.; Tarenzi, E.; Bonadonna, G. J. Clin. Oncol., 1997, 15, 1906-15. Holmes, F. A.; Madden, T.; Newman, R. A.; Valero, V.; Theriault, R. L.; Fraschini, G.; Walters, R. S.; Booser, D. J.; Buzdar, A. U.; Willey, J.; Hortobagyi, G. N. J. Clin. Oncol., 1996, 14, 2713-21. Millward, M. J.; Webster, L. K.; Rischin, D.; Stokes, K. H.; Toner, G. C.; Bishop, J. F.; Olver, I. N.; Linahan, B. M.; Linsenmeyer, M. E.; Woodcock, D. M. Clin. Cancer Res., 1998, 4, 2321-9. Baker, A. F.; Dorr, R. T. Cancer Treat. Rev., 2001, 27, 221-33. D'Incalci, M.; Schuller, J.; Colombo, T.; Zucchetti, M.; Riva, A. Semin. Oncol., 1998, 25(6 Suppl 13), 16-20. Venturini, M.; Lunardi, G.; Del Mastro, L.; Vannozzi, M. O.; Tolino, G.; Numico, G.; Viale, M.; Pastrone, I.; Angiolini, C.; Bertelli, G.; Straneo, M.; Rosso, R.; Esposito, M. J. Clin. Oncol., 2000, 18, 2116-25. Rowinsky, E. K.; Gilbert, M. R.; McGuire, W. P.; Noe, D. A.; Grochow, L. B.; Forastiere, A. A.; Ettinger, D. S.; Lubejko, B. G.; Clark, B.; Sartorius, S. E. J. Clin. Oncol., 1991, 9, 1692-703. Levofloxacin (Clavit) Package Insert (Japanese). In Daiichi Sankyo & CO., LTD.: 2008. Preston, S. L.; Drusano, G. L.; Berman, A. L.; Fowler, C. L.; Chow, A. T.; Dornseif, B.; Reichl, V.; Natarajan, J.; Corrado, M. JAMA, 1998, 279, 125-9. Mercaptopurin (Leukerin) Package Insert (Japanese). In Ohara Pharmaceutical CO., LTD.: 2007. Gefitinib (Iressa) Package Insert (Japanese). In AstraZeneca: 2006. Erlotinib (Tarceva) Package Insert (Japanese). In Chugai Pharmaceutical CO., LTD.: 2007. Benet, L. Z.; Hoener, B. A. Clin. Pharmacol. Ther., 2002, 71, 115-21. Thompson, M. E.; Highley, M. S. Ann. Oncol., 2003, 14, 500. Le, A. T.; Hasson, N. K.; Lum, B. L. Ann. Pharmacother., 1997, 31, 1006-8. Hall, G.; Lind, M. J.; Huang, M.; Moore, A.; Gane, A.; Roberts, J. T.; Cantwell, B. M. Postgrad. Med. J., 1990, 66, 860-1. Lam, M. S.; Ignoffo, R. J. J. Oncol. Pharma. Pract., 2003, 9, 45. Fujita, K. Curr. Drug Metab., 2006, 7, 23-37. Bosque, E. Ann. Intern. Med., 2001, 134, 427. Bohme, A.; Ganser, A.; Hoelzer, D. Ann. Hematol., 1995, 71, 311-2. Gillies, J.; Hung, K. A.; Fitzsimons, E.; Soutar, R. Clin. Lab. Haematol., 1998, 20, 123-4. United States Food and Drug Administration: Campostar label.

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Kehrer, D. F.; Mathijssen, R. H.; Verweij, J.; de Bruijn, P.; Sparreboom, A. J. Clin. Oncol., 2002, 20, 3122-9. Engels, F. K.; Ten Tije, A. J.; Baker, S. D.; Lee, C. K.; Loos, W. J.; Vulto, A. G.; Verweij, J.; Sparreboom, A. Clin. Pharmacol. Ther., 2004, 75, 448-54. Bruno, R.; Hille, D.; Riva, A.; Vivier, N.; ten Bokkel Huinnink, W. W.; van Oosterom, A. T.; Kaye, S. B.; Verweij, J.; Fossella, F. V.; Valero, V.; Rigas, J. R.; Seidman, A. D.; Chevallier, B.; Fumoleau, P.; Burris, H. A.; Ravdin, P. M.; Sheiner, L. B. J. Clin. Oncol., 1998, 16, 187-96. Murray, L. S.; Jodrell, D. I.; Morrison, J. G.; Cook, A.; Kerr, D. J.; Whiting, B.; Kaye, S. B.; Cassidy, J. Clin. Oncol., (R Coll. Radiol.), 1998, 10, 35-8. Harvey, V. J.; Slevin, M. L.; Dilloway, M. R.; Clark, P. I.; Johnston, A.; Lant, A. F. Br. J. Clin. Pharmacol., 1984, 18, 421-30. Bardakji, Z.; Jolivet, J.; Langelier, Y.; Besner, J. G.; Ayoub, J. Cancer Chemother. Pharmacol., 1986, 18, 140-4. Tolcher, A. W.; Cowan, K. H.; Solomon, D.; Ognibene, F.; Goldspiel, B.; Chang, R.; Noone, M. H.; Denicoff, A. M.; Barnes, C. S.; Gossard, M. R.; Fetsch, P. A.; Berg, S. L.; Balis, F. M.; Venzon, D. J.; O'Shaughnessy, J. A. J. Clin. Oncol., 1996, 14, 1173-84. Kerr, D. J.; Graham, J.; Cummings, J.; Morrison, J. G.; Thompson, G. G.; Brodie, M. J.; Kaye, S. B. Cancer Chemother. Pharmacol., 1986, 18, 239-42. Fedeli, L.; Colozza, M.; Boschetti, E.; Sabalich, I.; Aristei, C.; Guerciolini, R.; Del Favero, A.; Rossetti, R.; Tonato, M.; Rambotti, P. Cancer, 1989, 64, 1805-11. Chang, S. M.; Kuhn, J. G.; Rizzo, J.; Robins, H. I.; Schold, S. C., Jr.; Spence, A. M.; Berger, M. S.; Mehta, M. P.; Bozik, M. E.; Pollack, I.; Gilbert, M.; Fulton, D.; Rankin, C.; Malec, M.; Prados, M. D. J. Clin. Oncol., 1998, 16, 2188-94. Villikka, K.; Kivisto, K. T.; Maenpaa, H.; Joensuu, H.; Neuvonen, P. J. Clin. Pharmacol. Ther., 1999, 66, 589-93. Rodman, J. H.; Murry, D. J.; Madden, T.; Santana, V. M. J. Clin. Oncol., 1994, 12, 2390-7. Friedman, H. S.; Petros, W. P.; Friedman, A. H.; Schaaf, L. J.; Kerby, T.; Lawyer, J.; Parry, M.; Houghton, P. J.; Lovell, S.; Rasheed, K.; Cloughsey, T.; Stewart, E. S.; Colvin, O. M.; Provenzale, J. M.; McLendon, R. E.; Bigner, D. D.; Cokgor, I.; Haglund, M.; Rich, J.; Ashley, D.; Malczyn, J.; Elfring, G. L.; Miller, L. L. J. Clin. Oncol., 1999, 17, 1516-25. Royer, I.; Monsarrat, B.; Sonnier, M.; Wright, M.; Cresteil, T. Cancer Res., 1996, 56, 58-65. Glantz, M. J.; Cole, B. F.; Forsyth, P. A.; Recht, L. D.; Wen, P. Y.; Chamberlain, M. C.; Grossman, S. A.; Cairncross, J. G. Neurology, 2000, 54, 1886-93. Mathijssen, R. H.; Verweij, J.; de Bruijn, P.; Loos, W. J.; Sparreboom, A. J. Natl. Cancer Inst., 2002, 94, 1247-9. van der Bol, J. M.; Mathijssen, R. H.; Loos, W. J.; Friberg, L. E.; van Schaik, R. H.; de Jonge, M. J.; Planting, A. S.; Verweij, J.; Sparreboom, A.; de Jong, F. A. J. Clin. Oncol., 2007, 25, 2719-26. Ling, J.; Johnson, K. A.; Miao, Z.; Rakhit, A.; Pantze, M. P.; Hamilton, M.; Lum, B. L.; Prakash, C. Drug Metab. Dispos., 2006, 34, 420-6. Lu, J. F.; Eppler, S. M.; Wolf, J.; Hamilton, M.; Rakhit, A.; Bruno, R.; Lum, B. L. Clin. Pharmacol.. Ther., 2006, 80, 136-45. Goetz, M. P.; Knox, S. K.; Suman, V. J.; Rae, J. M.; Safgren, S. L.; Ames, M. M.; Visscher, D. W.; Reynolds, C.; Couch, F. J.; Lingle, W. L.; Weinshilboum, R. M.; Fritcher, E. G.; Nibbe, A. M.; Desta, Z.; Nguyen, A.; Flockhart, D. A.; Perez, E. A.; Ingle, J. N. Breast Cancer Res. Treat., 2007, 101, 11321. Lim, H. S.; Ju Lee, H.; Seok Lee, K.; Sook Lee, E.; Jang, I. J.; Ro, J. J. Clin. Oncol., 2007, 25, 383745. Jin, Y.; Desta, Z.; Stearns, V.; Ward, B.; Ho, H.; Lee, K. H.; Skaar, T.; Storniolo, A. M.; Li, L.; Araba, A.; Blanchard, R.; Nguyen, A.; Ullmer, L.; Hayden, J.; Lemler, S.; Weinshilboum, R. M.; Rae, J. M.; Hayes, D. F.; Flockhart, D. A. J. Natl. Cancer Inst., 2005, 97, 30-9. Huitema, A. D.; Kerbusch, T.; Tibben, M. M.; Rodenhuis, S.; Beijnen, J. H. Cancer Chemother. Pharmacol., 2000, 46, 119-27. Imatinib (Glivec) Package Insert (Japanese). In Novartis: 2008. Lin, A. M.; Rini, B. I.; Derynck, M. K.; Weinberg, V.; Park, M.; Ryan, C. J.; Rosenberg, J. E.; Bubley, G.; Small, E. J. Clin. Genitourin. Cancer, 2007, 5, 323-8. Masci, G.; Magagnoli, M.; Zucali, P. A.; Castagna, L.; Carnaghi, C.; Sarina, B.; Pedicini, V.; Fallini, M.; Santoro, A. J. Clin. Oncol., 2003, 21, 736-9. Camidge, R.; Reigner, B.; Cassidy, J.; Grange, S.; Abt, M.; Weidekamm, E.; Jodrell, D. J. Clin. Oncol., 2005, 23, 4719-25.

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122


Pharmacogenomic Considerations in Breast Cancer Management Hiroshi Ishiguro1,*, Ikuko Yano2 and Masakazu Toi3 1

Translational Research Center, Department of Clinical Trial Management / Outpatient Oncology Unit; 2Department of Pharmacy and 3Breast Surgery Department, Kyoto University Hospital, 54 Shogoinkawaharara-cho,Sakyo-ku, Kyoto-city, 606-8507, Japan Abstract: Many factors affect an individual’s response to a drug, and large inter-ethnic, intra-ethnic, and even intra-individual variations exist. These variations may affect both the therapeutic response to a drug, or the side effects that the patient experiences. Since anti-neoplastic drugs often have a very narrow therapeutic range, it is very desirable to be able to predict these variations in response or to ensure that these variations are as small as possible. It has recently become possible to predict some extreme responses, such as severe side effects, using pharmacogenomic approaches; for example, the uridine diphosphate-glucuronosyltransferase (UGT) 1A1 genetic polymorphism is a predictor of irinotecan toxicity. Further, adding pharmacokinetic and pharmacodynamic information may increase the accuracy of response prediction. Both severe toxicity and clinical benefit can be predicted using a combination of pharmacogenomic and pharmacokinetic information. For example, the clinical benefit obtained from adjuvant treatment with tamoxifen is reduced in patients who have either a particular Cytochrome P450 (CYP) 2D6 genetic polymorphism or who are taking paroxetine, a strong inhibitor of CYP2D6. It is possible to monitor pharmacodynamic parameters, such as serum estrogen levels, as a measure of the therapeutic effect of aromatase inhibitors. In this review, we summarize current knowledge in the field of pharmacogenomics as it relates to breast cancer, focusing particularly on clinical data.

INTRODUCTION Pharmacogenomics, the intersection of pharmacology and genomics, is an approach often used in drug development to assess the genetic determinants of drug response on a genome-wide basis. Pharmacogenetics, a subset of pharmacogenomics, is slightly more narrowly defined as the study of the genetic basis for variation in drug response [1]. Pharmacogenomics is an important field of research because the activity of drugmetabolizing enzymes often varies widely, even among healthy people, making drug metabolism highly variable between individuals (e.g. drug elimination rates can vary up to 40-fold [2]). Genetic factors and aging appear to account for most of these variations in healthy individuals [2]. Pharmacogenetic variations can have significant clinical con*Corresponding Author: Tel: +81-75-751-4770; Fax: +81-75-751-4772; E-mail: [email protected] Gary W. Caldwell / Atta-ur-Rahman / Z. Yan / M. Iqbal Choudhary (Eds.) All rights reserved – © 2009 Bentham Science Publishers.

Pharmacogenomic Considerations


sequences; for example, patients who metabolize certain drugs rapidly may require higher and/or more frequent doses to achieve therapeutic concentrations. In contrast, patients who metabolize certain drugs slowly may need lower and/or less frequent doses to avoid toxicity, particularly for drugs with a narrow margin of safety, such as antineoplastic drugs. However, most genetically determined differences in drug metabolism cannot currently be predicted before drug administration. Also, many environmental and developmental factors, such as concomitantly administered drugs, can interact with each other and with genetic factors to affect drug response [2]. DRUG SAFETY AND PHARMACOGENOMICS Anti-neoplastic drugs, especially cytotoxic anti-neoplastic drugs, have a very narrow therapeutic window, so pharmacogenomics has become an important research topic in this context. Irinotecan, a key agent used in the treatment of colorectal and lung cancer, is also often used for advanced breast cancer. Irinotecan is in fact a prodrug, which is converted to its active but toxic metabolite, SN-38, by carboxyesterase in vivo (Fig. 1) [3]. Irinotecan is also metabolized by CYP3A4 to the inactive metabolites 7-ethyl-10-[4N-(5-aminopentanoic acid)-1-piperidino] carbonyloxycampothecin (APC) and 7-ethyl10-(4-amino-1-piperidino] carbonyloxycampothecin (NPC). Over time, SN-38 is metabolized to an inactive form, SN-38G, by uridine diphosphate-glucuronosyltransferase (UGT) 1A1 glucuronidation. The area under the concentration-time curve (AUC) of SN38G is ~7-fold larger than for SN-38, suggesting extensive conversion of SN-38 into SN-38G in vivo, but high levels of inter-patient variability have also been found [3]. Recently, a series of studies have provided evidence that the UGT1A1*28 genetic polymorphism may have an important influence on irinotecan toxicity [4]. It has been shown that there is a significant reduction in SN-38 glucuronidation activity in individuals with the *28 variant, and that this haplotype is significantly associated with a reduced SN38G/SN-38 AUC ratio [5]. In 2005, the US Food and Drug Administration required that a warning to this effect be added to the irinotecan package insert [6], which now states: “Patients homozygous/heterozygous for the UGT1A1*28 allele: Patients homozygous for the UGT1A1*28 allele are at increased risk of neutropenia; initial one-level dose reduction should be considered for both single-agent and combination regimens. Heterozygous carriers of the UGT1A1*28 allele may also be at increased risk; however, most patients have tolerated normal starting doses.” Trastuzumab, a humanized anti-HER-2/neu immunoglobulin G (IgG) 1 monoclonal antibody (mAb), is a drug that has significant benefit in terms of breast cancer prognosis, but has also been associated with cardiotoxicity. Trastuzumab binds to the extracellular domain of HER2, which is encoded by the proto-oncogene HER2 (erbB-2, neu), and is a transmembrane protein with tyrosine kinase activity but no identified physiological ligand. The HER2 gene is amplified in 30% of cases of invasive breast cancer and HER2 gene amplification is correlated with poor prognosis. The most thoroughly investigated germinal HER2 polymorphism at a clinical level occurs at codon 655 (ATC/isoleucine to GTC/valine), which encodes a residue in the transmembrane domain of the HER2 protein. In a group of 61 patients with advanced breast cancer studied by Beauclair et al., although no cardiac toxicity was found in 36 patients with the Ile/Ile genotype, 24% (5/21) of patients with the Ile/Val genotype experienced a greater than 20% reduction of their left ventricular ejection fraction (LVEF). Only a few patients carried the Val/Val genotype, and none of them developed any symptoms of cardiotoxicity [7].


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O APC HN

OH

CH2CH3 N N

O

O

Irinotecan O

CH2CH3 N

O

N

A4 P3 Y C

O N

O

N

O CH2CH3

N

OH O

CH2CH3

OH O

A4 P3 CY

Carboxyesterase

O

NH2

CH2CH3 OH

NPC

O

CH2CH3

N

N

O

O

N O

Carboxyesterase

SN-38

N O

OH O CH2CH3

UGT1A1

CH2CH3

O

N

O OH

OH O

CH2CH3 O

O

O N

OH

OH

N

OH

O SN-38G

CH2CH3

OH O

Fig. (1). Metabolic pathways of irinotecan. (Modified figure from reference [3]).

DRUG EFFICACY AND PHARMACOGENOMICS In the human genome, inter-individual differences occur approximately every 300 to 1000 nucleotides, with each individual harboring a an estimated total of 3.2 million single nucleotide polymorphisms [1]. There are thus likely to be many genetic polymorphisms within genes encoding pharmacokinetic- and pharmacodynamic-related proteins (e.g. metabolizing enzymes, transporters and receptors), and therefore variations in drug responses due to these polymorphisms are unlikely to be rare phenomena.



Cyclophosphamide is a drug that is commonly used to treat breast cancer. It is a prodrug that must be metabolized to 4-hydroxycyclophosphamide to become cytotoxic. After administration, 75-80% of the dose is bioactivated by several CYP enzymes, including (amongst others) CYP2B6, CYP3A4 and CYP3A5, of which CYP2B6 has the highest activity. 4-Hydroxycyclophosphamide and its ring-open tautomer aldophosphamide are detoxified by glutathione S-transferase (GST) and aldehyde dehydrogenase (Fig. 2) [8]. In cyclophosphamide-treated breast cancer patients with an unfavorable CYP3A/GST genotype, cyclophosphamide metabolism is affected, such that activation is slow and detoxification is rapid, leading to worse overall survival compared with patients with other genotypes (Fig. 3) [9, 10]. OO

ClH2CH2C N ClH2CH2C

N

OO

ClH2CH2C T GS

ClH2CH2C N

P

ClH2CH2C

HN SG

4-Glutathionylcyclophosphamide

OO

ClH2CH2C

CYP3A4?

P

N ClH2CH2C

HN OH

GS T

4-Hydroxycyclophosphamide

N ClH2CH2C

P NH2 O

O N

Decomposition

Aldophosphamide Cl

OH

P NH2

GS

GS T

Monochloromonoglutathionylphosphoramide mustard

O

ClH2CH2C N ClH2CH2C

O-

P

O

+

H2C

CH C

NH2

Phosphoramide mustard

HN

4-Ketocyclophosphamide

OO

ClH2CH2C

P

O

Equilibrium

OO

HN

Dechloromethylcyclophosphamide

CYP2B6 others

Cyclophosphamide

N

P

H

HN

ClH2CH2C

OO

ClH2CH2C

CYP3A4

P

H Acrolein

Fig. (2). Metabolic pathways of cyclophosphamide. (Modified figure from reference [7]).


Ishiguro et al.

Even though trastuzumab is usually only administered to patients with HER-2/neupositive breast cancer, only 25 to 30% of these patients will respond to this mAb. The mechanism by which it exerts an anti-tumor effect is not well understood, but antibodydependent cell-mediated cytotoxicity (ADCC) is thought to be involved [11]. In ADCC, the antibody binds to tumor cells and is then engaged by effecter cells via their receptors for IgG. FcR is an activating IgG fragment C receptor (FcR) that is expressed on monocytes/macrophages and natural killer cells. A polymorphism encoding valine (V) at amino acid 158 of FcR rather than phenylalanine (F) greatly increases the affinity of IgG1 to the Fc receptor, leading to a higher level of ADCC activity. The FcR158 V/V genotype is significantly correlated with a higher objective response rate and better progression-free survival in trastuzumab-treated breast cancer patients (Fig. 4) [11].

Overall survival (%)

100

Other CYP3A/GST genotype N = 86

p < 0.05

50 N=4 Unfavorable CYP3A/GST genotype

2

4

6

8

10

Years Fig. (3). Cyclophosphamide CYP3A/GST metabolic enzyme genotype versus overall survival for breast cancer patients treated with a cyclophosphamide-containing regimen. (Modified figure from references [9,10]).

TAMOXIFEN-METABOLIZING ENZYMES Tamoxifen is widely used as the standard treatment for recurrent or metastatic breast cancer, as well as in an adjuvant setting. Tamoxifen is a prodrug that requires CYP2D6catalyzed metabolic activation to 4-hydroxytamoxifen and 4-hydroxy-N-desmethyltamoxifen (endoxifen) for therapeutic pharmacological activity (Fig. 5). Compared with the parent drug, these two metabolites have 100-fold greater affinity to estrogen receptor and 30- to 100-fold greater potency in suppressing estrogen-dependent cell proliferation [12]. Steady-state plasma endoxifen concentrations have been found to be 5- to 10-fold higher than those of 4-hydroxytamoxifen [13]. Therefore, the relationships between endoxifen concentration and CYP2D6 activity, and endoxifen concentration and tamoxifen treatment efficacy and adverse events have been studied.



Progression-free survival (%)

100 V/V genotype N = 11

50

p = 0.0035

Other genotype

1

N = 43

2

3

4

5

Years Fig. (4). Progression-free survival of trastuzumab-treated breast cancer patients according to FcRIII genotype. (Modified figure from reference [11]).

N O

CH3 O

CH3

CYP2D6

H3C

N

H3C

CYP3A4

CYP3A4

4-hydroxy-tamoxifen

O

N H

CH3 O

CYP2D6 H3C

OH N-desmethyl-tamoxifen

CH3

OH

Tamoxifen

H3C

CH3

Endoxifen

Fig. (5). Metabolic pathways of tamoxifen. (Modified figure from reference [13]).

N H

CH3


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CYP2D6 GENE POLYMORPHISM AND ENDOXIFEN CONCENTRATION The plasma concentration of endoxifen appears to be influenced by a patient’s CYP2D6 genotype. The CYP2D6*4 allele results in a splicing defect, the CYP2D6*3 and *6 alleles lead to translation frameshifts, and in the CYP2D6*5 allele the entire CYP2D6 gene is deleted. None of these alleles yields a functional CYP2D6 enzyme [14]. In one study, more than 20% of a German Caucasian population had one of these four polymorphisms, compared with about 7% of the Japanese population (Fig. 6) [15]. In another study, tamoxifen-treated breast cancer patients who were heterozygous or homozygous for the null genotype had mean plasma endoxifen concentrations that were 55% and 26%, respectively, of those who were homozygous for the wild-type CYP2D6 genotype (Fig. 7A) [14]. The CYP2D6*10 allele, which produces an unstable enzyme that is associated with decreased CYP2D6 activity, is a major variant in individuals of Japanese, Korean and Chinese ethnic background [16]. This allele is seen in 40 to 50% of these populations (Fig. 6) [15]. Although the steady-state plasma concentrations of endoxifen were comparable in wild type and CYP2D6*10 heterozygotes, the concentration in CYP2D6*10 homozygotes was about 40% that of wild type (Fig. 7B) [16]. Wild

Null

Low

Others

Japanese

Caucasian

0%

20%

40%

60%

80%

100%

Percentage of population Fig. (6). Differences between German Caucasian and Japanese populations in terms of CYP2D6 gene polymorphism [15].

CONCOMITANTLY ADMINISTERED DRUGS AND ENDOXIFEN CONCENTRATION In one study of tamoxifen-treated breast cancer patients, plasma endoxifen concentrations in wild-type CYP2D6 homozygotes were found to be highly variable. However, those who were using CYP2D6 inhibitors such as paroxetine, fluoxetine, sertraline, citalopram, amiodarone and metoclopramide had plasma endoxifen concentrations that were 58% lower than concentrations in those who were not using CYP2D6 inhibitors (Fig. 7C) [14]. The plasma endoxifen concentration in subjects taking paroxetine was



p = 0.002


100

A 80 60 40 20 0 Wild/Wild

Wild/Null

Null/Null

(N = 48)

(N = 29)

(N = 3)

Endoxifen concentration (ng/mL)

CYP2D6 genotype p < 0.001

25

B

20 15 10 5 0 Wild/Wild (N = 64)

Wild/Low

Low/Low

(N = 89)

(N = 49)

CYP2D6 genotype


p = 0.0025 100

C

80

p = 0.08

60 40 20 0 -

+ Wild/Wild

(N = 34)

(N = 13)

-

+ Wild/Null

(N = 17)

(N = 11)

-

CYP2D6 inhibitor

Null/Null

CYP2D6 genotype

(N = 3)

Fig. (7). Endoxifen concentrations in breast cancer patients treated with tamoxifen, according to CYP2D6 genotype. (A) Wild-type versus null genotype [14]. (B) Wild-type versus low genotype [16]. (C) Wild-type versus null genotype with or without CYP2D6 inhibitor [14].


Ishiguro et al.

similar to that for subjects with the CYP2D6 null genotype who were not taking a CYP2D6 inhibitor [14]. Thus, not only genetic factors, but also environmental factors, including concomitantly administered drugs, can have a significant effect on a drug’s pharmacokinetics. CYP2D6 ACTIVITY AND ADVERSE EVENTS The most common request from breast cancer survivors regarding additional treatment or treatment modification is relief from hot flashes [17]. Hot flashes are the subjective sensation of heat that is thought to be related to a narrowing of the thermoneutral zone. The onset of symptoms is generally thought to occur as a result of decreased estrogen or increased gonadotropin concentrations. However, other reported precipitators include psychological stress, hot weather, alcohol and caffeine, although the physiological mechanism underlying this phenomenon is not well understood [17]. In a study conducted by Goetz et al., moderate to severe [National Cancer Institute Common Toxicity Criteria (version 1) grade 2-3] hot flashes were experienced by 20% of tamoxifen-treated breast cancer patients with either wt/wt or *4/wt CYP2D6 genotypes, but no patients with a *4/*4 genotype experienced hot flashes (Fig. 8A) [13]. Similarly, in a recent presentation, Rae et al. described how the presence of active CYP2D6 alleles predicts a higher likelihood of tamoxifen discontinuation in breast cancer patients due to treatment-related side effects* (Fig. 8B). These findings raise the question of whether the alleviation of the hot flashes is related to a reduced concentration of active metabolites. ADVERSE EVENTS AND TREATMENT EFFICACY Among breast cancer patients who took tamoxifen for adjuvant endocrine treatment, those who reported hot flashes were found to be less likely to develop recurrent breast cancer than those who did not develop hot flashes (Fig. 9A). In that study, hot flashes were a stronger predictor of breast cancer-specific outcome than age, hormone receptor status, or even disease stage [17]. These findings suggest an association between side effects, efficacy, and tamoxifen metabolism. Cuzick et al. recently reported the results of the Arimidex, Tamoxifen, Alone or in Combination (ATAC) randomized, doubleblinded, multicenter trial, in which patients with early-stage breast cancer received treatment with anastrozole alone, tamoxifen alone or the two agents in combination*. The occurrence of hot flashes was an indicator of treatment efficacy for both tamoxifen and anastrozole, with similar differences in treatment efficacy between patients with and without hot flashes for both drugs (Fig. 9B). It is likely that the occurrence of hot flashes is an indirect measurement of estrogen suppression by anastrozole and tamoxifen. CYP2D6 ACTIVITY AND TREATMENT EFFICACY In the ATAC trial, using the annual hazard rate, the benefit of anastrozole over tamoxifen was seen throughout the follow-up period, and the early recurrence (less than 3 years) rate was significantly higher for the tamoxifen group (Fig. 10A) [18]. Among patients treated with adjuvant tamoxifen, an immediate broad peak in the hazard rate was seen in patients with decreased CYP2D6 activity (Fig. 10B) [19]. In contrast, the hazard rate in patients with extensive CYP2D6 activity was reduced and did not peak until _____________________ *2007 San Antonio Breast Cancer Symposium.



Incidence of hot flashes (%)

40

A p = 0.06

20

0 Null/Null (N = 13)

Wild/Null (N = 40)

Wild/Wild (N = 137)

CYP2D6 genotype

CYP2D6 genotype

B

High (N = 170)

Intermediate (N = 87)

Low (N = 10)

0

10

20

Treatment discontinuation (%)

Fig. (8). Relationship between CYP2D6 genotype and hot flashes [13] (A) and between CYP2D6 phenotype and treatment discontinuation (B) in breast cancer patients treated with tamoxifen*. *2007 San Antonio Breast Cancer Symposium.

nearly the fourth year after treatment. These findings suggest that the peak in the hazard rate for recurrence that was seen following the initiation of tamoxifen in the ATAC trial may be due to there being a subset of patients in which tamoxifen is not fully activated. Several other randomized trials have demonstrated that administration of an aromatase inhibitor following either 2-3 or 5 years of tamoxifen treatment (switching or extension) significantly prolongs disease-free survival and overall survival compared with tamoxifen treatment for 5 years. Further research is needed to determine whether prior assessment of CYP2D6 metabolism can be used to identify which patients are most suitable for these strategies without a risk of early recurrence [19]. SUMMARY There are many factors involved in drug response, and large variations in response can occur between patients with different ethnic backgrounds, between patients with the


Ishiguro et al.

Hot flashes

100 Disease free survival (%)

N = 674

A

p = 0.01 No hot flashes

50

N = 190

2

4

6

8

10

Years

100 Hot flashes Recurrence rate (%)

B

Tamoxifen Anastrozole

No hot flashes

(N = 1091)

(N = 963)

(N = 1614)

(N = 1738)

p < 0.05

50

2

4

6

8

10

Years

Fig. (9). Relationship between hot flashes and disease-free survival in tamoxifen-treated breast cancer patients [17] (A) and hot flashes and recurrence in breast cancer patients who participated in the ATAC trial* (B). Modified figure from reference [16] and a presentation by Cuzick J. et al.* *2007 San Antonio Breast Cancer Symposium.

same ethnic background, and even for single individual at different times. Pharmacogenomics is one approach that can be used to understand these variations. Ideally, for antineoplastic drugs, we would want to refer to pharmacodynamic or pharmacokinetic data such as the international normalized ratio for warfarin or the measures of drug concentration in the blood (pharmacokinetics) that exist for anti-convulsant and immunosuppressive drugs. These will be very useful in reducing variations in drug response in the field of oncology.



A

Smoothed hazard rate

Tamoxifen Anastrozole

Poor/Intermediate metabolizer

B

Extensive metabolizer

2

4

6

8

10

Years

Fig. (10). Hazard rates for recurrence in patients treated with tamoxifen or anastrozole [18] (A) and in breast cancer patients treated with tamoxifen according to CYP2D6 activity [19] (B). Modified figure from references [18,19].

ABBREVIATIONS ADCC

=

Antibody-dependent cell-mediated cytotoxicity

APC

=

7-Ethyl-10-[4-N-(5-aminopentanoic acid)-1-piperidino] carbonyloxycampothecin

ATAC trial =

Arimidex, Tamoxifen, Alone or in Combination trial

AUC

=

Area under the concentration-time curve: AUC

CYP

=

Cytochrome P450

FcR

=

Fragment C receptor

GST

=

Glutathione S-transferase

IgG

=

Immunoglobulin G

LVEF

=

Left ventricular ejection fraction

mAb

=

Monoclonal antibody

NPC

=

7-Ethyl-10-(4-amino-1-piperidino] carbonyloxycampothecinecine

UGT

=

Uridine diphosphate-glucuronosyltransferase


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[15] [16] [17] [18] [19]

Relling, V.M.; Giacomini, MK. In Goodman & Gilman's The Pharmacological Basis of Therapeutics. eleventh ed.; McGRAW-HILL: New York, 2006, pp. 93-115. Cafiero, A.; Hussar, D.; Tarloff, B.J.; Vivian, M.E. In The Merck Manual of Diagnosis and Therapy. Eighteenth ed.; Merck Research Laboratories: Whitehouse Station, NJ, 2006, pp. 2513-44. Mathijssen, R. H.; van Alphen, R. J.; Verweij, J.; Loos, W. J.; Nooter, K.; Stoter, G.; Sparreboom, A. Clin. Cancer Res., 2001, 7, 2182-94. O'Dwyer, P. J.; Catalano, R. B. J. Clin. Oncol., 2006, 24, 4534-8. Sai, K.; Saeki, M.; Saito, Y.; Ozawa, S.; Katori, N.; Jinno, H.; Hasegawa, R.; Kaniwa, N.; Sawada, J.; Komamura, K.; Ueno, K.; Kamakura, S.; Kitakaze, M.; Kitamura, Y.; Kamatani, N.; Minami, H.; Ohtsu, A.; Shirao, K.; Yoshida, T.; Saijo, N. Clin. Pharmacol. Ther., 2004, 75(6), 501-15. United States Food and Drug Administration: Campostar label. Beauclair, S.; Formento, P.; Fischel, J. L.; Lescaut, W.; Largillier, R.; Chamorey, E.; Hofman, P.; Ferrero, J. M.; Pages, G.; Milano, G. Ann. Oncol., 2007, 18(8), 1335-41. Nakajima, M.; Komagata, S.; Fujiki, Y.; Kanada, Y.; Ebi, H.; Itoh, K.; Mukai, H.; Yokoi, T.; Minami, H. Pharmacogenet. Genomics, 2007, 17(6), 431-45. DeMichele, A.; Aplenc, R.; Botbyl, J.; Colligan, T.; Wray, L.; Klein-Cabral, M.; Foulkes, A.; Gimotty, P.; Glick, J.; Weber, B.; Stadtmauer, E.; Rebbeck, T. R. J. Clin. Oncol., 2005, 23(24), 5552-9. DeMichele, A.; Gimotty, P.; Botbyl, J.; Aplenc, R.; Colligon, T.; Foulkes, A. S.; Rebbeck, T. R. J. Clin. Oncol., 2007, 25(35), 5675-7. Musolino, A.; Naldi, N.; Bortesi, B.; Pezzuolo, D.; Capelletti, M.; Missale, G.; Laccabue, D.; Zerbini, A.; Camisa, R.; Bisagni, G.; Maria Neri, T.; Ardizzoni, A. J. Clin. Oncol., 2008, 26(11), 1789-96. Kiyotani, K.; Mushiroda, T.; Sasa, M.; Bando, Y.; Sumitomo, I.; Hosono, N.; Kubo, M.; Nakamura, Y.; Zembutsu, H. Cancer Sci., 2008, 99(5), 995-99. Goetz, M. P.; Rae, J. M.; Suman, V. J.; Safgren, S. L.; Ames, M. M.; Visscher, D. W.; Reynolds, C.; Couch, F. J.; Lingle, W. L.; Flockhart, D. A.; Desta, Z.; Perez, E. A.; Ingle, J. N. J. Clin. Oncol., 2005, 23(36), 9312-8. Jin, Y.; Desta, Z.; Stearns, V.; Ward, B.; Ho, H.; Lee, K. H.; Skaar, T.; Storniolo, A. M.; Li, L.; Araba, A.; Blanchard, R.; Nguyen, A.; Ullmer, L.; Hayden, J.; Lemler, S.; Weinshilboum, R. M.; Rae, J. M.; Hayes, D. F.; Flockhart, D.A. J. Natl. Cancer Inst., 2005, 97(1), 30-9. Fujita, K. Curr. Drug Metab., 2006, 7(1), 23-37. Lim, H.-S.; Ju, Lee, H.; Seok Lee, K.; Sook Lee, E.; Jang, I.-J.; Ro, J. J. Clin. Oncol., 2007, 25(25), 3837-45. Mortimer, J. E.; Flatt, S. W.; Parker, B. A.; Gold, E. B.; Wasserman, L.; Natarajan, L.; Pierce, J. P. Breast Cancer Res. Treat., 2008, 108(3), 421-426. Buzdar, A. U.; Cuzick, J. Clin. Cancer Res., 2006, 12(3 Pt 2), 1037s-1048s. Goetz, M. P.; Knox, S. K.; Suman, V. J.; Rae, J. M.; Safgren, S. L.; Ames, M. M.; Visscher, D. W.; Reynolds, C.; Couch, F. J.; Lingle, W. L.; Weinshilboum, R. M.; Fritcher, E. G.; Nibbe, A. M.; Desta, Z.; Nguyen, A.; Flockhart, D. A.; Perez, E. A.; Ingle, J. N. Breast Cancer Res. Treat., 2007, 101(1), 113-21.


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More Than Skin Deep: The Human Skin Tissue Equivalent as an Advanced Drug Discovery Tool Alexandra S. Bause, Sarah D. Lamore and Georg T. Wondrak* University of Arizona, College of Pharmacy, Arizona Cancer Center, Tucson, AZ, USA Abstract: Advanced human tissue equivalents are an emerging drug discovery tool that aims at minimizing costly candidate attrition in later stages of preclinical and clinical development. 3-D cellular tissue models amenable to screening in 96-well plate format and drug monitoring in real time provide more accurate compound screening in a physiologically relevant context, not achievable in traditional two dimensional, single cell type-based assays. Currently, equivalents that represent the healthy or pathological physiology of human tissues including skin, vasculature, lung, prostate, and various malignant tumors are used for rapid activity screening, toxicity profiling, and pharmacokinetic characterization of compound libraries. Human skin equivalents with intact stratum corneum reconstructed in vitro from matrix-embedded dermal fibroblasts and epidermal primary keratinocytes closely resemble the complex architecture and functional complexity of skin. Using human skin equivalents, delivery of novel topical agents for photoprotection has been studied, and toxicity and activity profiling of skin care products and FDA-approved drugs including sunscreens has been performed. Importantly, human skin equivalents are better predictors of drug activity than available animal models as mouse and guinea pig skin do not reflect the architecture of human skin in terms of physico-optical properties such as light reflectance and scattering, epidermal thickness, cellularity, and biochemical composition. Recently, cellular composition of advanced commercial skin reconstructs has been further optimized by incorporation of melanogenic melanocytes and immunomodulatory dendritic (Langerhans) cells in order to assess drug modulation of cutaneous pigmentation, inflammation, photo-immunosuppression, and photo-carcinogenesis. Continuous progress in skin equivalent engineering will ensure the expanding role of skin equivalents in disease model-based assays for rapid identification and development of novel cutaneous therapeutics.

1. INTRODUCTION. ENHANCING THE DRUG DISCOVERY PROCESS USING THREE-DIMENSIONAL RECONSTRUCTED TISSUE MODELS The paradigm of modern drug discovery is aptly represented by a complex funneling process that begins with initial identification and validation of a molecular target which

*Corresponding Author: Tel: 520-626-9017; Fax: 520-626-3797; E-mail: [email protected] Gary W. Caldwell / Atta-ur-Rahman / Z. Yan / M. Iqbal Choudhary (Eds.) All rights reserved – © 2009 Bentham Science Publishers.


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is causatively involved in a disease process and therefore selected for future pharmacological intervention as shown in Fig. (1). Further activities should then ultimately lead to market introduction of a novel drug that provides superior therapeutic benefit. In a time of ever increasing biological understanding of the molecular pathology underlying human disease states, no dearth of promising drug targets exists. However, it seems that many academic and industrial drug development projects have proven fruitful by further increasing our biological understanding of the disease process (target discovery), but fail to deliver a novel therapeutic agent that could enter the marketing phase [1-4].

target target identification & validation assay development & screening

lead identification

lead

• potency • selectivity • druglikeness • toxicological profile

lead optimization

preclinical development costliness of candidate attrition

clinical trials

drug Fig. (1). The obligatory funneling process underlying modern drug discovery. The use of human skin reconstructs as a versatile drug discovery tool throughout all developmental stages from target identification to preclinical development of cutaneous therapeutics may significantly reduce costly candidate attrition at late stages.

The reasons underlying the discrepancy between success rates of pharmaceutical target versus drug discovery are manifold and may be connected to the very nature of pharmaceutical discovery. The process of iterative selection and optimization of drug candidates that must outperform earlier therapeutic agents is intrinsically associated with high candidate attrition rates. Apart from limitations associated with high throughput screening of compound libraries with insufficient molecular diversity and inadequate coverage of chemical space, an inadequate quality of cell-based screening systems has been invoked to rationalize the high failure rate correlated with generation of promising

More Than Skin Deep


preclinical candidates. Indeed, it has been argued that cell-based screening methodologies and even advanced animal models such as mouse xenograft tumor models that are dependent on single-cell type cell culture insufficiently represent the biological complexity of the molecular target in a meaningful tissue context. After target identification and validation, development of screening assays aims at the identification of promising lead compounds that then can undergo iterative lead optimization based on preclinical efficacy, toxicity, and pharmacokinetic profiling. These efforts will then lead to the identification of clinical candidates that can be advanced through the obligatory phases of clinical testing. Obviously, a major determinant of the success rate of this funneling process is the validity and predictive power of the assay systems that are used for efficacy and toxicology screening. Advanced human tissue equivalents that mimic the exact physiology of the human target tissue to a great extent are an emerging drug discovery tool that aims at minimizing costly candidate attrition in later stages of preclinical and clinical development. Three dimensional cellular tissue models amenable to screening in 96-well plate format and drug monitoring in real time provide more accurate compound screening in a physiologically relevant context, not achievable in traditional two dimensional, single cell type-based assays [5]. Currently, equivalents that represent the healthy or pathological physiology of human tissues including skin, vasculature, lung, prostate, and various malignant tumors are used for rapid activity screening, toxicity profiling, and pharmacokinetic characterization of compound libraries and selected lead compounds during preclinical development [6,7]. As discussed in this review, the use of human skin reconstructs is now firmly established as a versatile drug discovery tool throughout all developmental stages from target identification to preclinical development of cutaneous therapeutics. Due to technological accessibility, commercial availability, and biological validity of skin tissue reconstructs, skin pharmacology is now fundamentally impacted by this technology that rapidly emerges as the novel gold standard of preclinical skin models. Skin reconstructs of variable complexity have been generated ranging from human differentiated epidermal equivalents with stratum corneum reconstructed to full thickness reconstructs that include extracellular matrix-embedded dermal fibroblasts and other skin cells of particular interest including melanocytes and Langerhans cells [8,9]. All of these closely resemble the complex architecture and functional complexity of skin. Obviously, cellular and acellular composition of skin reconstructs is highly adjustable and can be customtailored in order to provide a relevant model of a specific pathological condition. For example, photoaged skin can be reconstructed by incorporation of photo-crosslinked collagen and senescent fibroblasts during dermal reconstruction, and psoriatic skin can be reconstructed by incorporation of patient derived keratinocytes cultured from psoriatic lesions [10]. Using human skin equivalents delivery of novel topical agents for skin photoprotection and anti-psoriatic intervention has been studied, and pharmacodynamic profiling of FDA-approved drugs including sunscreens has been performed [11]. Importantly, human skin equivalents are superior predictors of drug photoprotective activity compared to available animal models, since mouse and guinea pig skin do not reflect the architecture of human skin in terms of physico-optical properties such as light reflectance and scattering, epidermal thickness, cellularity, permeability, and biochemical composition. Moreover animal models of specific skin conditions are often completely unavailable or poorly represent the human disease. For example, transgenic melanoma mo-


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dels in mice are of debatable relevance to the human disease, and drug candidates that display anti-melanoma activity in these transgenic mice may be completely inactive against human melanoma. Therefore, the commercial availability of human melanoma reconstructed skin that mimics the successive progressional stages of the disease may provide a stringent and powerful screening tool for the identification of promising chemopreventive and chemotherapeutic anti-melanoma agents as discussed below [12]. In addition to high costs associated with animal experimentation and poor representation of human skin physiology in animal models, ethical concerns and regulatory guidelines limit or completely ban the extensive use of animal models particularly in the earlier stages of the drug discovery process that would require screening in high or moderate throughput. In the European Union (EU), animal testing for cosmetic ingredients is completely banned by 2009, and ethical and legal guidelines in other countries equally create an urgent need for alternative in vitro methods that allow the cost effective and highly predictive toxicity and efficacy assessment of topical consumer products and pharmaceutical agents. In summary, the emerging role of human skin reconstructs as superior drug discovery tool is based on the following characteristics: I. adequate representation of human skin; II. high model-to-human predictability of drug action; (i.e., drug effects in the model are predictive of therapeutic efficacy in humans; III. adaptability to screening in high throughput; IV. commercial availability and cost competitiveness; V. inter-laboratory reproducibility and standardization; VI. adjustable degree of tissue complexity (e.g. simple epidermal reconstruct versus skin-type specific pigmented skin reconstructs); VII. validated tissue biomarkers that specify test compound action and can easily be measured (e.g. toxicity markers for corrosion and irritation studies). VIII. superiority over animal experimentation based on ethical, legal, economical, and operational considerations; IX. superior predictive power and biological validity over cell-based and animal-based screening methods Three major applications of reconstructed human skin can be distinguished: First, skin reconstructs serve as basic research tools for skin biology. Second, skin reconstructs serve as drug discovery tools that allow drug target identification and validation. Moreover, compound screening for efficacy, toxicity, and pharmacokinetic profiling can be performed. Thirdly, skin reconstructs are used therapeutically in the area of skin tissue replacement and reconstructive surgery, for example in burn victims. 2. THE BIOLOGICAL COMPLEXITY OF HUMAN SKIN Human skin is a complex organ composed of a variety of specialized cells, organized into three highly differentiated principal layers: the epidermis, the outermost layer, followed by the dermis (jointly referred to as cutis and separated by a basement membrane), and the deeper localized subcutis, mainly composed of adipose tissue [13]. The epidermis is a squamous epithelium derived from terminal differentiation of keratinocytes with associated hair follicles and glandular structures (sebaceous glands and sweat glands). Keratinocytes constitute approximately 80% of epidermal cells forming the multilayered differentiated epidermis comprising (-from inside out-) the stratum germinativum/basale, the stratum spinosum, the stratum granulosum, the stratum lucidum, and the stratum corneum. The avascular epidermis is characterized by self-renewal with undifferentiated basal cells proliferating and differentiating into a stratified multilayered tissue, where complete turnover of the interfollicular epidermis occurs every

More Than Skin Deep


four weeks. Multipotent stem cells within the epidermis produce daughter cells that differentiate along multiple lineages. Remarkably, hair follicle lineages, sebocytes and interfollicular epidermis originate from stem cells within the hair follicle bulge. The differentiated epidermis acts as an effective barrier against physical (UV radiation), chemical (xenobiotics), and biological (microbial) insults. The outermost acellular stratum corneum consists of dead corneocytes (cornified keratinocytes), the endproduct of epidermal terminal differentiation. Cornification involves changes in expression patterns of keratins, where basal layers express keratins 5 and 14, while the suprabasal keratinocytes produce keratins 1 and 10. Furthermore, cornification involves synthesis of involucrin, loricrin, and small proline rich proteins as structural constituents of the cornified envelope that is crosslinked through transglutaminase enzymatic activity. As another essential part of the human skin barrier, proteolysis of the structural protein filaggrin during terminal differentiation produces a specific mixture of amino acids (serine, glycine, arginine, ornithine, citrulline, alanine, histidine) and their derivatives (pyrrolidone-5-carboxylic acid, urocanic acid) called the natural moisturizing factor (NMF). Components of the NMF are crucially involved in skin hydration, pH control, antimicrobial defense, immunomodulation, and UV-photoprotection [14]. Keratinocytes produce a multitude of molecules involved in barrier formation (structural proteins including keratins, filaggrin, loricrin, and involucrin, and lipids such as ceramides and cholesterol), melanogenesis (-MSH), mitogenesis (CCL5), host defense (defensins), inflammatory signaling (IL, TNF-), angiogenesis (VEGF), and cellular differentiation and calcium homeostasis (vitamin D3 and calcitriol). In addition to keratinocytes, the pigment-producing melanocytes at the epidermal– dermal junction are interspersed among every 5–10 basal keratinocytes, forming the ‘epidermal–melanin unit’. It is thought that one melanocyte synthesizes and transports melanin-containing melanosomes to the surrounding keratinocytes for phagocytotic uptake and formation of supranuclear melanin caps, protecting keratinocyte DNA from the genotoxic effects of ultraviolet light [15]. Another important cellular constituent of human epidermis are Langerhans cells (LC), bone marrow derived dendritic cells, which circulate in the peripheral blood as progenitors, and then migrate into the suprabasal epidermis. These cells play a major role in the skin immune defense system. After exposure to exogenous antigens, LC migrate to the regional lymph nodes to present them to naive T cells. Recently, LC have emerged as crucial epidermal targets of UV-photodamage involved in induction of photo-immunosupression thought to contribute to solar photocarcinogenesis [16]. The dermis with its mesenchymal components, i.e. fibroblasts and blood vessels embedded in fibroblast-derived extracellular matrix containing collagen, elastin and glycosaminoglycans, provides mechanical and nutritional support and rigidity to the skin. At least three subpopulations of dermal fibroblasts have been identified which occupy unique niches in the dermis: papillary dermal fibroblasts (superficial dermis), reticular fibroblasts (deep dermis), and hair follicle-associated fibroblasts [17]. Importantly, keratinocytes and fibroblasts interact with each other to maintain skin integrity through paracrine cytokine loops. For example, fibroblasts modulate epidermal homeostasis and reepithelialization after epidermal damage with production of keratinocyte growth factor and TGF-1, and fibroblast–keratinocyte interactions are involved in the formation of the basement membrane [18].


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In addition to these basic skin constituents other specialized cells including sebocytes, mast cells, sensory Merkel cells, and adipocytes contribute to the fascinating functional and structural complexity of human skin. Remarkably, simple skin models can be generated from single cellular constituents using keratinocytes (epidermal reconstruct) or fibroblasts (dermal reconstruct). More advanced skin reconstructs that better represent the functional complexity of skin are characterized by incorporation of multiple skin cell types including keratinocytes, melanocytes, Langerhans cells, and fibroblasts and their genetically engineered or malignant counterparts. 3. BASICS OF HUMAN SKIN RECONSTRUCTION Historically, the development of skin reconstructs as a drug discovery tool occurred in three stages based on fundamental advances in (I) skin cell culture technology, (II) the generation of complex skin tissue models for biological studies, and (III) the optimization and validation of skin models for toxicological and pharmacological studies. (I)

Cell culture. All tissue reconstruction is based on developing the fundamental technology of culturing cells in monolayers. For human skin keratinocytes, this was achieved through serial cultivation of strains of human epidermal keratinocytes with the formation of keratinizing colonies from single cells using serum containing culture medium and a feeder-layer of pre-irradiated quiescent fibroblasts [19]. The inclusion of serum became dispensable when calcium-regulated differentiation of normal human epidermal keratinocytes in chemically defined clonal culture and serum-free serial culture was reported [20], followed by cultivation of other human skin cell types including melanocytes and Langerhans cells [21,22].

(II)

Tissue reconstructs. Cellular monolayers obviously do not represent the functional complexity of differentiated skin in vivo and are therefore poor models for molecular studies of skin biology and pathophysiology. Moreover, drug discovery targeting complex alterations of skin structure and function (barrier impairment, immunological and inflammatory dysregulation, altered epidermal differentiation and melanogenesis, carcinogenesis, photodamage, and chronological aging) depends on model systems that adequately represent the target tissue. A fully differentiated epidermal reconstruct was first generated by Pruniéras et al. who described methods for cultivation of keratinocytes at the air–liquid interface [23], and many skin reconstructs of diverse complexity have been created as discussed below. Interestingly, the differences between gene expression profiles of reconstructed human epidermis and classical cultures of keratinocytes based on cDNA expression arrays comprising 505 genes related to cutaneous biology have been examined. A comparison of gene expression between keratinocytes grown as cultures on plastic dishes or in a three-dimensional reconstruct identified six genes with considerably higher expression in the reconstruct. All of these genes (keratin 1, corneodesmosin, filaggrin, loricrin, calmodulin-like skin protein, and caspase 14) are related to keratinocyte terminal differentiation as expected in a model that adequately represents human epidermis [5]. Importantly, a major driving force behind technological advances in full thickness skin reconstruction is the use of reconstructed skin for therapeutic applications as skin substitutes [24,25]. Currently, skin replacement based on epidermal, dermal, or full-thickness skin substitutes is in clinical use to treat different types of difficult-to-heal wounds including

More Than Skin Deep


large and deep burns, trauma-induced wounds, and ulcers resistant to conventional therapies. (III)

Skin reconstruction as a pharmacological tool. Moreover, skin reconstructs have become the focus of increased research activities since it has been demonstrated that they may excellently reproduce certain aspects of the pathology of various skin diseases including skin carcinogenesis and solar photodamage. For example, various studies demonstrated the usefulness of reconstituted three-dimensional human skin as a novel in vitro model of skin carcinogenesis using established carcinogens including benzo[a]pyrene, ultraviolet B (UVB) radiation, UVA, and psoralen-UVA [11,26]. Molecular consequences of carcinogen exposure (upregulation of c-fos and p53 expression, DNA adduct formation) were similar to those reported previously in human and murine skin after carcinogenic insult demonstrating that skin reconstructs can be used as a discovery tool for assessing genotoxicity and mechanism of action of mutagens/carcinogens in human skin [26]. Recently, melanoma skin reconstruction has been achieved representing the progressional stages of the disease and a commercial model is now available as discussed below [12,15]. Remarkably, it is now possible to reconstruct human skin containing the major cellular targets for skin photodamage: keratinocytes (UVB-induced nonmelanoma skin cancer), Langerhans cells (photo-immunosuppression), melanocytes (melanoma), and fibroblasts (UVA-induced photoaging). Inclusion of human skin color- and phototype-specific melanocytes allows the reconstruction of human skin with differential inducible and constitutive pigmentation and photosensitivity [27,28]. Further progress may be facilitated by inclusion of optimized biomaterials (e.g. epidermal reconstruction on fibrin scaffolds) and genetically modified cellular components.

A representative full thickness reconstruct that has been generated according to the following standard procedure closely resembles the anatomy of human skin as shown in Fig. (2): First, dermal equivalents are prepared by mixing human dermal fibroblasts and native bovine type I collagen in a Petri dish. After lattice contraction for three days at

A

B

Fig. (2). Human reconstructed skin closely resembles the architecture of skin in situ. Human skin reconstructed in vitro (B) closely resembles normal human skin (A) displaying a well-stratified epithelium covered by a dense stratum corneum on top of a fibroblast populated dermis. However, the absence of interpapillary ridges, functionally significant extensions of the epithelium into the connective tissue, and the somewhat less pronounced thickness of the stratum corneum are apparent in reconstructed skin. (reproduced with permission from reference [64], Elsevier Ireland Ltd.).


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37°C, 5% CO2, adult human epidermal keratinocytes grown in primary culture are seeded on this support using stainless rings. The rings are then removed and the cultures are kept submerged for seven days, allowing the cells to form a monolayer. The medium used is composed of minimum essential Eagle’s medium supplemented with 10% fetal calf serum, 10 ng/mL epidermal growth factor, 8.4 ng/mL cholera toxin, 0.4 g/mL hydrocortisone. The cultures are then raised at the air–liquid interface on grids and kept one week to allow keratinocytes to stratify and differentiate completely during which time the reconstruct is fed by capillarity [11]. 4. SKIN RECONSTRUCTS AS A DRUG DISCOVERY TOOL The broad availability of reconstructs that correctly represent the complex biology of healthy human skin facilitated research that aimed at defining the toxicological effects of chemical and physical insult on human skin. Indeed, established biomarker profiles indicative of exposure-induced toxicity in human skin can easily be studied in reconstructs with high reproducibility. Reconstructed skin has therefore become an indispensable screening tool for toxicological profiling of test compounds intended for future topical application [29]. Moreover, screening of compounds that exert protective effects against environmentally relevant insults such as solar UV-radiation has rapidly become a major application of skin reconstructs [8]. Since pathological key mechanisms involved in complex diseases affecting human skin such as chronic inflammation, psoriasis and cancer are adequately reproduced in skin reconstructs, pharmaceutical screening of compound libraries with high throughput and predictive power for drug efficacy seems feasible. Usefulness of these models has been established for activity screening of topical retinoids and glucocorticoids as discussed below. In particular, reconstructed human skin has rapidly emerged as a superior experimental model for the identification of novel pharmacological and cosmeceutical agents designed to interfere with skin photodamage and chronological aging including sunscreens and topical antioxidants as discussed below. Various human epidermal reconstructs are now commercially available and have been used successfully as pharmaceutical screening tools. For example, EpiDerm™ (MatTek,Ashland, MA, USA; http://www.mattek.com) consists of normal, human epidermal keratinocytes that form a multilayered, highly differentiated model of the human epidermis on cell culture inserts. Recently, EpiDermFT, a full thickness skin model comprising normal human epidermal keratinocytes and dermal fibroblasts has been created enabling in vitro studies on fibroblast-keratinocyte cell interactions. Moreover, specialized models representing pigmented human skin (MelanoDermTM), human melanoma skin (MLNM-FT-A375), and oral epithelium (EpiOralTM and EpiGingivalTM) are available. Another common model, EPISKIN TM (SkinEthic, Nice, France; http://www. skinethic.com), consists of adult human keratinocytes cultured on a base of bovine collagen type I and III, coated with a thin layer of human collagen IV, and forming a fully differentiated epidermis with a functional horny layer. From the same manufacturer, another in vitro reconstructed human epidermis comprising normal human keratinocytes cultured in chemically defined medium on inert polycarbonate filters at the air-liquid interface can be obtained (RHE by SkinEthic). Again, more specialized models that represent pigmented epidermis (RHPE) and oral (HOE) and gingival epithelium (HGE) are available.

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5. SKIN RECONSTRUCTS AS A TOXICITY SCREENING TOOL Skin toxicity as a result of compound exposure comprises a variety of adverse effects ranging from acute corrosive tissue damage to inflammatory irritation, and many parameters including speed of onset, persistence, and reversibility define the severity of these reactions [29]. Animal skin toxicity testing is compromised both for its limited predictive capacity for human toxicity, as well as for ethical and legal restrictions associated with animal experimentation. Reconstructed human epidermis allows topical application of test compounds and identification of agents that (I) corrode the skin (‘corrosion’), (II) irritate the skin or the eyes (‘irritation’), (III) sensitize the skin (‘sensitization’), or (IV) elicit toxic responses in combination with UV-light (‘phototoxicity’) as summarized in Fig. (3) [29-33].

NH O

O O

OH

S

O O

N

N

F Na+

HO O2N

NO2

F O

O N

OH

N

Cl CnH2n+1

n=8, 10, 12, 14, 16, 18 Corrosives

Irritants

N O

N

O O

Sensitizers

Cl

S Photosensitizers

IL-1α IL-8 TNFα PGE2 Hsp27 cell death

skin damage, pain, edema, erythema, itching Fig. (3). Biomarkers of skin toxicity validated in human skin reconstructs. For abbreviations see text. Skin toxicity inducing compounds by category (from top to bottom): acrylic acid, 2-tertbutylphenol (corrosion); SDS, benzalkonium chloride (irritation); 1-fluoro-2,4-dinitrobenzene, oxazolone (sensitization); ciprofloxacine, chlorpromazine (phototoxicity).

Current international regulations require assessment of skin corrosion for all chemicals placed on the market [34]. Predictive test methods must define the toxicity profile of consumer products, single ingredients (e.g. detergents), experimental drugs, cosmeceuticals, and other agents leading to classification as irritant/slightly irritant/non-irritant or


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severely corrosive/ corrosive/ non-corrosive. In recent years, skin reconstructs have replaced more traditional animal-based test methods including the long established Draize test based on rabbit eye exposure as discussed in [29]. In the EU, the use of human skin reconstructs as predictive test models for corrosion testing of dangerous substances are now regulated through the OECD 431 guideline [35]. A common irritation protocol based on the use of human skin reconstructs has been established by the ECVAM (European Centre for the Validation of Alternative Methods) and is the subject of further validation studies [33,36,37]. 5.1. Corrosion Corrosive materials are identified by their ability to reduce cell viability below defined threshold levels at specified exposure periods [38]. Corrosive agents penetrate or erode the stratum corneum and cause cell death in the underlying cell layers. Skin damage from corrosion occurs within minutes after chemical exposure and results in irreversible tissue necrosis by physicochemical processes that occur independent of a biological damage response. An inflammatory response of the surrounding tissue is commonly a consequence of the skin corrosion process [38]. In an example test protocol and prediction model using reconstructed human epidermis, corrosion potential of test material is predicted from the mean tissue viabilities assessed after exposure times as short as 3 min [33]. Test materials are applied directly to the stratum corneum and exposure to the chemical is terminated by rinsing with phosphate buffered saline. Tissue viability is then assessed using the MTT assay with formazan solvent extraction followed by photometric analysis compared to untreated control tissue. A chemical is classified as ‘corrosive’ if the relative tissue viability is decreased below 50% after 3 min of exposure to a test material. In addition, test materials classified ‘non-corrosive’ after 3 min (viability > 50%) are classified ‘corrosive’ if the relative tissue viability is decreased below 15% after exposure for 1 h. Similar protocols have been implemented and used successfully according to ECVAM guidelines using various commercial and academic skin reconstruct models [33]. 5.2. Irritation During early development of novel cosmetic and pharmaceutical formulations designed for topical delivery, the potential for the induction of adverse reactions due to skin irritation must be assessed early on in order to exclude irritants from further development. In contrast to in vitro tests for skin corrosion, validation of in vitro tests for assessing the acute skin irritation potential of chemicals is ongoing, and consequently no generally accepted regulatory testing requirements based on the use of human skin reconstructs have been established. Skin irritation is an inflammatory damage response that occurs in the absence of cellular necrosis [29]. The principal clinical signs of cutaneous irritancy in humans are erythema, induration, and edema. Acute skin irritation occurs as a response to a single exposure to potent irritants, whereas cumulative skin irritation, the most common type of skin irritation, occurs only after repetitive exposures to mild irritants. Inflammatory events associated with irritation originate from the complex interaction between epidermal cells, dermal fibroblasts, endothelial cells and invading leukocytes that involve cytokines, lipid mediators, and other signaling molecules including reactive oxygen species as summarized in Fig. (3). Initiation of irritation can result from physi-

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cochemical properties of the test agent that leads to lipid solving effects impairing the integrity of the stratum corneum lipid barrier and damaging skin cell membranes. Apart from their role as epidermal structural components, keratinocytes are key initiators of skin inflammatory and immunological reactions involved in skin irritation in response to a chemical or physical insult. Irritation by surfactants is a well-studied mechanism of chemical-induced skin irritation, with sodium dodecyl sulfate (SDS) serving as a reference substance for assessing irritant potentials in vivo [39]. Cytokines released from keratinocytes are crucial upstream mediators of skin irritation. The proinflammatory cytokine IL is considered a key inducer of the skin inflammatory cascade. This major biomarker of skin irritation, that is contained in the cytoplasm of keratinocytes is released during inflammatory signalling in response to irritation [40], and can easily be assessed by ELISA analysis. In human skin reconstructs, release of IL in response to surfactant exposure such as benzalkonium chloride is rapidly triggered and correlates with loss of cell membrane integrity as assessed by LDH release and loss of mitochondrial energy production as assessed by the MTT assay [30,41]. Induction of acute cutaneous oxidative stress is an important consequence of irritant exposure and production of cellular ROS in keratinocytes may contribute to inflammatory signalling [42]. For example, IL synthesis and release are induced upon exposure to the irritant tributyltin, which is thought to increase generation of reactive oxygen species through impairment of mitochondrial respiration. Importantly, IL is a potent inducer of cellular signaling through the NFB proinflammatory pathway. Regulation of epidermal homeostasis and inflammatory responses are controlled by NFB transcriptional activity upregulating expression of proinflammatory cytokines including IL-6 and IL-8. Indeed, release and cytoplasmatic concentration of IL in two full thickness skin equivalents after SDS induced irritation has been documented recently [43]. Studies comparing release of IL after surfactant exposure between full thickness and epidermal skin equivalents demonstrate that keratinocytes are the exclusive source of IL in skin reconstructs. In contrast, communication between keratinocytes and fibroblasts regulates an acute inflammatory response with IL-6 and IL-8 release following irritant exposure. Chemotactic IL-8 is released from tissue reconstructs in response to irritant exposure, and the ratio of released IL-8 to IL has been suggested as a biomarker that discriminates irritants from sensitizers [44]. Another pleiotropic inflammatory cytokine, TNF-, is stored in the epidermal mast cells and also produced by keratinocytes following irritant exposure inducing the expression of endothelial adhesion molecules. In a full thickness skin model TNF- expression levels have been proposed to serve as skin irritation markers based on mRNA detection. Apart from inflammatory cytokines, inflammatory lipid metabolites derived from arachidonic acid are synthesized and released in response to skin irritant exposure as discussed in [29]. Arachidonic acid is released from membrane pools by enzymatic activity of phospholipase A2, an important downstream target activated by IL-1. Further biotransformation of arachidonic acid by cyclooxygenase and PGE synthase induces formation of strongly inflammatory metabolites including prostaglandin E2 released in irritant exposed epidermal reconstructs as well as in skin in vivo. PGE2 is therefore an important marker of irritation that can be easily assessed by ELISA analysis of tissue culture medium. Moreover, in response to the irritant benzoyl peroxide the release of leukotriene B4 and 15-hydroxyeicosatetraenoic acid from arachidonate prelabelled skin equivalents has been demonstrated.


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Discovery of novel markers indicative of skin irritation elicited by a test compound is ongoing with a focus on differential expression patterns of irritated and non-irritated skin equivalents using genomic and proteomic analysis as reviewed in [29]. For example, differential gene expression using cDNA array technology after treatment of epidermal reconstructs with SDS has been observed. Moreover, screening for differentially abundant proteins in SDS treated human skin by 2D-PAGE revealed seven potential new epidermal markers for skin irritation, among which heat shock protein 27 (Hsp27) was the most prominently up-regulated [45]. An in vitro human reconstructed epidermis model used for screening acute and chronic skin irritation potential was validated against in vivo data from skin tolerability studies in order to establish predictivity of skin reconstruct based models for acute and chronic skin irritation [46]. The cumulative irritation potential of topical agents (SDS, calcipotriol and trans-retinoic acid) was evaluated in a human clinical study. In parallel, using a reconstructed epidermis model, compoundinduced modulation of cell viability, the release and gene expression of IL-1 and IL-8, and morphological changes were evaluated during 3 days as endpoints representative of an inflammatory reaction. It was found that the topical agents that were non-irritating in the human study were non-cytotoxic and did not induce cytokine expression in the in vitro acute model. Moreover, all irritating controls exhibited specific cell viability and cytokine patterns, which were predictive of the in vivo human data. In this study, the human reconstructed epidermis model was a reliable preclinical tool for the prediction of the irritation potential of topical products. In another study, the predictive ability of reconstructed human epidermis equivalents for the assessment of skin irritation of cosmetics was assessed [30]. To this end, 22 formulations from product development test representing different cosmetic product classes were tested in vivo using the modified Frosch-Kligman soap chamber patch test with repetitive occlusive application and in vitro using a series of epidermis equivalents. In vivo, skin reactions including erythema, dryness, fissures, and transepidermal water loss were evaluated. In vitro, cell viability and the extracellular release of IL and LDH into the culture medium collected after topical application of the products were assessed. In general, in vivo and in vitro classifications as ‘irritant’ and ‘non-irritant’ of all test products was fully concordant suggesting the usefulness of the skin reconstructs for routine screening of developmental formulations before human in vivo dermatological evaluation. It is important to note that apart from cutaneous skin irritation testing performed in reconstructs, in vitro testing of intra-orally applied consumer products has been performed using a human oral epithelial reconstruct generated from primary normal human oral keratinocytes and fibroblasts [47]. In reconstructs exposed to low concentrations (0.015%) of SDS, increased epithelial thickness and proliferation based on immunohistochemical detection of the marker Ki-67 were observed, whereas reduced epithelial thickness and cell proliferation and massive induction of epithelial cell death was observed upon exposure to higher SDS concentrations (> 0.15%). 5.3. Sensitization Sensitization occurs as an immunological reaction in response to xenobiotics that form allergenic epitopes on epidermal proteins. Sensitization induces activation of immuno-competent dendritic cells (Langerhans cells) that migrate from the epidermis to the regional lymph nodes, where the processed antigen is presented to naive T-cells [48].

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Skin sensitization assessment can be performed in vivo using complex procedures including including the local lymph node assay [44]. Recently, analysis of cell viability and IL and IL-8 expression and release in reconstructed human epidermis has been documented as a reliable predictor of in vivo skin irritation and/or sensitization. This method allowed discrimination between 1-chloro-2,4-dinitrobenzene, nickel sulfate, oxazolone, 2,4-dinitrofluorobenzene, and 2,4,6-trinitrobenzenesulfonic acid as established skin sensitizers from benzalkonium chloride, benzoic acid, and SDS as established skin irritants. The epidermal reconstruct used in this study recreates many of the structural and functional features of the epidermis, but is exclusively composed of keratinocytes. However, the absence of Langerhans cells and leukocytes reduces the complexity and biological relevance of this model. Recently, a Langerhans cell containing reconstructed human epidermis has been generated and successfully employed as screening tool to evaluate sensitization potential of test compounds [16,48]. Topical exposure of these reconstructs to known allergens and allergen-inducible cytokines resulted in an activated appearance of the Langerhans cells. Concomitantly, overexpression of IL-1 and the co-stimulatory Langerhans surface epitope CD86 mRNAs were detected in the reconstructed epidermis. 5.4. Phototoxicity During topical application of chemical compounds the formation of reactive photoexcited states may occur as a result of solar exposure of skin [49]. The specific photochemical reactivity of a compound is determined by the chemical structure of its lightabsorbing chromophore and may lead to light-driven production of cytotoxic singlet and triplet states and reactive oxygen species, including singlet oxygen and superoxide radical anions. Moreover, the photoexcited compound itself may react with tissue proteins leading to the formation of antigenic epitopes in skin that illicit an immune response upon repetitive exposure. Indeed, phototoxicity of cosmetic and pharmaceutical agents that induces photo-irritation, an inflammatory skin reaction in response to exposure to the combined action of solar photons and chemical agent, is a serious adverse reaction associated with many compounds that contain a photoactive chromophore. Early assessment of the phototoxic potential of novel compounds is therefore imperative, and three-dimensional skin models have been used as efficient and cost-effective phototoxicological screening tools [50-52]. Phototoxic standards (e.g. phenothiazines and fluoroquinolones) and test compounds are applied to the reconstruct and then irradiated with UV-light (-most often noncytotoxic doses of UVA and visible light-) to trigger a phototoxic reaction. Established markers of phototoxicity in reconstructed skin include leakage of LDH and loss of viability based on the MTT assay, and increased photoinflammatory signaling through IL and IL-8 release and expression of IL-8 mRNA. Reconstruct-based phototoxicity studies are often complemented by simple cell-based and chemical assays, e.g. photobinding to human serum albumin indicative of a photoallergic potential. 6. SKIN RECONSTRUCTS AS A DISCOVERY TOOL FOR CUTANEOUS PHARMACOLOGY Experimental evidence has shown that the cutaneous pharmacology of many test compounds can be studied in three-dimensional skin reconstructs.


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6.1. Reconstruct-Based Pharmacokinetic Characterization of Test Compounds Kinetics of cutaneous absorption and residence time, permeation and penetration potentially leading to systemic availability is an important quality parameter of pharmaceutical and cosmetical test compounds and also impacts the skin toxicology of hazardous agents. Reconstructed human epidermis is an established tool for in vitro skin absorption studies of chemical compounds performed according to nationally-defined regulatory guidelines [31]. For example, according to OECD guideline 428, human skin preparations can be replaced by reconstructed human epidermis models based on experimental evidence that demonstrates equivalence with data obtained in human skin [53]. Earlier research has studied epidermal reconstructs in parallel with human and animal skin to determine percutaneous absorption and skin metabolism of glucocorticoids and glucocorticoid esters, estradiol, as well as absorption of flufenamic acid, chlorpheniramine and various other agents [31,54]. Recently, barrier function of reconstructed human epidermis was compared to human heat-separated epidermis using the Franz diffusion cell procedure. Skin permeation of caffeine and testosterone used as test compounds differed significantly between the two test systems suggesting methodological limitations for replacement of human skin for in vitro permeability experiments [55]. It has long been discussed that skin equivalents may differ from normal human skin with regard to penetration rate of substances through the stratum corneum [29,31,56,57]. More recent experimental evidence suggests the feasibility of technical improvements that will allow reconstruct-based screening tests for skin penetration with sufficient reliability and throughput [53]. 6.2. Reconstruct-Based Pharmacodynamic Studies Beneficial and adverse effects of topically applied glucocorticoid ointments have been demonstrated in a full-thickness skin model [58]. Anti-inflammatory efficacy was validated by assessing suppression of UVB-induced IL-6 and IL-8 upregulation. Glucocorticoid-induced skin atrophy with decrease of epidermal thickness and impaired dermal type I collagen synthesis, both classic adverse effects of topical glucocorticoid therapy, were closely reproduced. Reconstructed epidermis serves as a benchmark for assessment of retinoid performance [59]. For example, in reconstructed epidermis the topical retinoid tretitoin alters tissue morphology through inhibition of epidermal differentiation and modulates biomarker expression that reflects retinoid effects in normal human skin. Recently, the therapeutic benefit of topical application of acitretin, an aromatic synthetic retinoid, currently taken as an oral drug for the treatment of severe psoriasis in adults, was demonstrated in reconstructed human epidermis for local management of keratinization disorders [60]. Using a similar experimental methodology, development of topically applied cosmeceutical agents that provide health benefit to human skin can be facilitated by reconstruct technology as discussed below. 6.3. Reconstruct-Based Drug Discovery Targeting Skin Photodamage and Aging It is now well established that acute and chronic photodamage contributes to skin photoaging and photocarcinogenesis as reviewed in [61]. Photo-oxidative stress, originating from reactive oxygen species formed in sun-exposed skin, is a pathological key me-

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chanism that underlies both skin extracellular matrix protein damage (involved in wrinkle formation) and skin cell photomutagenesis (involved in carcinogenesis). Importantly, the mechanisms by which solar UV-irradiation causes skin photodamage are wavelength dependent as reviewed in [49]. UVB (290-320 nm) is thought to cause direct structural damage to DNA in the form of epidermal cyclobutane pyrimidine dimers (CPD) and other photoproducts. However, most of the solar UV energy incident on human skin derives from the deeply penetrating UVA region (> 95%, 320-400 nm) that is not directly absorbed by DNA. UVA-induced photodamage of human skin that contributes to tumorigenic initiation and progression of non-melanoma and melanoma skin cancer is thought to originate from photo-oxidative stress [62,63]. The structural and functional alterations that result from skin photooxidative stress depend on a cascade of chemical events initiated by photoexcited states of endogenous skin chromophores that act as photosensitizers. These endogenous skin components with sensitizer activity (including porphyrins, melanin precursors, riboflavin, pyridoxine, enzymatic collagen crosslinks, advanced glycation endproducts, and lipofuscin-type pigments) form redoxreactive excited states that lead to formation of reactive oxygen (ROS) and carbonyl species (RCS) by light-driven redox cycling [49]. These reactive intermediates then induce skin protein, lipid and DNA damage, and trigger alterations of redox and inflammatory signaling leading to photoaging and photocarcinogenesis. Chronic photooxidative stress also leads to skin photoaging, characterized by accumulation of senescent dermal fibroblasts, extracellular matrix remodeling with collagen crosslinking, proteasedependent collagen breakdown, overexpression of dysfunctional elastin (solar elastosis), and chronic inflammatory signaling. 6.3.1. Skin Reconstructs for Mechanistic Studies on Photodamage Induced by Solar Radiation Skin reconstructs are excellent models for mechanistic studies on molecular pathways involved in photodamage. After exposure to solar UV many important biomarkers indicative of photodamage are equally expressed in normal and reconstructed human skin as summarized in Fig. (4) [64]. Indeed, exposure of reconstructed human skin to erythemogenic doses of UVB radiation induces changes characteristic of a moderate sunburn reaction in human skin [11,65]. Cyclobutane pyrimidine dimers (CPDs), UVB-fingerprint DNA lesions, can be detected immunohistochemically in all epidermal layers of a full thickness reconstruct using a CPD antibody followed by fluorescence labeling. At 24 h after exposure, sunburn cells, apoptotic keratinocytes that are classic markers of the epidermal sunburn response, can be visualized by H&E staining of the fixed reconstruct. Moreover, other important sunburn markers including accumulation of p53 are easily detected in UV-exposed skin reconstructs. At physiologically relevant doses of deeply penetrating UVA, specific changes in the dermal layer can be detected with induction of cellular oxidative stress using the fluorigenic redox probe DCFH-DA immediately after irradiation. Moreover, UVA-induction of fibroblast apoptosis can be detected within 6 h after irradiation by TUNEL staining of the reconstruct. 48 h after UVA, loss of fibroblast cellularity in the upper layer of the dermis with no changes in the epidermal layer can be detected. Moreover, UV-induction of MMP-1 production has been studied in reconstructed skin using ELISA analysis. Under UVA exposure conditions, MMP-1 production was directly induced in the dermal fibroblasts of full thickness reconstructs and removal of the epidermal layer immediately after UVA exposure did not alter this induction suggesting that this dermal UVA effect does not require the pre-


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sence of keratinocytes. In contrast, UVB-induced MMP-1 production in dermal fibroblasts required the presence of the epidermis and was abolished when the epidermis was removed during the post-irradiation period. Using comparative studies on monolayered cultured keratinocytes and fibroblasts versus reconstructed skin that combines these two cell types, paracrine mechanisms based on epidermal release of IL-1 and IL-6 were demonstrated to cause UVB-induced MMP-1 upregulation in dermal fibroblasts [66,67]. This finding nicely exemplifies the superior experimental versatility of skin reconstructs for mechanistic studies on the cellular crosstalk involved in the molecular pathways of skin photodamage.

CPD

sunburn cells

p53

normal human skin

reconstructed human skin

Fig. (4). Biomarkers of UV-induced tissue damage in normal and reconstructed human skin. Immediately after UVB exposure (50 mJ/cm2) DNA lesions are detectable by immunohistochemical visualization of cyclobutane pyrimidine dimers (CPD). 24 h after irradiation, sunburn cells (arrows) can be observed using H&E staining. Moreover, 24 h after UVB, p53 protein accumulation becomes apparent (arrows). Remarkably, both systems display a similar pattern of UV-damage biomarkers. (reproduced with permission from reference [64], Elsevier Ireland Ltd.).

Based on their structural and functional similarities with human skin and the close concordance of photodamage biomarkers in reconstructs and human skin, the use of organotypic skin reconstructs has become a valuable discovery tool for the identification and development of pharmaceutical and cosmeceutical agents that target skin photodamage, aging, and dyspigmentation as summarized in Fig. (5) and Table (1). 6.3.2. Suncreens and Sunblockers Sunscreens and sunblockers are important photoprotective topical agents, and combinatorial use of modern broad-spectrum suncreens with complementary agents that exert skin photoprotection by other synergistic mechanisms of action has emerged as an important chemopreventive strategy targeting skin photoaging and photocarcinogenesis [61]. The U.S. FDA regulates sunscreen products as over-the-counter drugs, and appro-

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val and marketing of novel sunscreen agents is therefore a rare event. The use of skin reconstructs in photoprotection studies examining novel therapeutic agents is now firmly established and promises to accelerate the preclinical drug discovery and development process of this important class of skin protectants. A study on the protective effect of the

solar photons [UVB, UVA, visible, infrared]

O O

photoprotective agents

N H

O

O O O

HO

P HO OH

OH

HO

DNA ROS p53 IL-8 PGE2 MMP-1 damage CPD 8-oxodG

sunburn cells Langerhans cells fibroblast

depletion

sunburn & photoaging chlorpromazine

Fig. (5). Biomarkers of UV-induced tissue damage validated in human skin reconstructs and antagonized by photoprotective agents. For abbreviations see text. Photoprotective small molecule compounds (from top to bottom): octylmethoxycinnamate (UVB-sunscreen), L-prolinemethylester (quencher of photoexcited states), 2-phospho-L-ascorbate (antioxidant), MexorylTMSX (broad spectrum UV-sunscreen). Table 1.

Drug Discovery Targeting Skin Photodamage, Aging, and Dyspigmentation Using Reconstructed Human Skin Agent

Mechanism of Action

References

MexorylTM SX

Sunscreen

Bernerd et al., 2000; [11]

L-Proline-methylester

QPES

Wondrak et al., 2005; [72]

Aminoguanidine

Glycation inhibitor

Pageon et al., 2008; [75]

-Carotene

Antioxidant

Hakozaki et al. 2008; [76]

Aloesin

Skin whitener

Wang et al., 2008; [80]


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classic UVB sunscreen 2-ethylhexyl-p-methoxycinnamate and the UVA sunscreen MexorylTM SX on reconstructed human skin exposed to UVB or UVA Irradiation was able to discriminate molecular parameters in skin indicative of UVB (sunburn cell formation and CPD formation in nuclear DNA) versus UVA (fibroblast apoptosis) photoprotection [11]. Equally, measurements of the protective effect of the topically applied sunscreens Eusolex 8020, a UVA-blocking dibenzoylmethane derivative, and Eusolex 6300, a UVB-blocking benzylidene camphor-derivative was achieved using skin equivalents by measurement of residual cellular viability 24 h postirradiation using the 3-(4,5dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) test and assessment of the inflammation response by IL determination [68]. The use of cellularly more complex reconstructed human skin for the evaluation of photodamage and sunscreen efficacy has been described recently. Based on reconstructed epidermis, comprising keratinocytes, melanocytes, and Langerhans cells, UVinduced pigmentation, cellular parameters related to UV-induced immunosuppression (morphological alterations and reduction in numbers of epidermal Langerhans cells) and photoprotection achieved by topical application of a broad spectrum sunscreen were studied [8]. Similarly, assessment of the skin photoprotective capacities of an organomineral broad-spectrum sunblock containing Tinosorb M, OCM, ZnO and TiO2 was performed in ex vivo hairless rat skin and reconstructed human skin demonstrating prevention of UVA-specific damage characterized by dermal expression of the fibroblast marker vimentin [69]. In another study, the combinatorial protective effects of sunscreens and antioxidants (-tocopherol and ascorbic acid) on reconstructed epidermis with or without melanocytes 24 h after UVB, UVA, or UVA+B irradiation were compared using various markers including sunburn cell formation, CPD formation, immunohistochemical detection of total epidermal protein oxidation, and catalase and superoxide dismutase activities [70]. Moreover, using full thickness reconstructed skin as an adequate tool for the discrimination between UVB- and UVA-induced skin photodamage, it has been demonstrated that the photoprotection afforded by two sunscreen formulations having similar SPF values yet different UVA-screening efficacies is not equal with regard to prevention of dermal damage related to photoaging [71]. Based on the involvement of epidermal Langerhans cells in the mediation of UVinduced immunosuppression, an important factor in skin photocarcinogenesis, a Langerhans cell containing reconstructed human epidermis has been used to validate the efficacy of sunscreens to prevent UV-induced immunosuppression. Exposure of the reconstructed epidermis to solar simulated radiation depleted Langerhans cell numbers within 24 h of irradiation and induced dramatic morphological changes with loss of dendricity in the surviving cells, all of which were prevented by topical application of a broad spectrum UV filter before UV-exposure [16]. 6.3.3. Other Photoprotective and Anti-Aging Molecules Quenchers of photoexcited states (QPES compounds). Photooxidative stress is a key mechanism in UVA-induced skin photodamage involving photoexcited states of endogenous UVA chromophores such as porphyrins, melanin precursors, and cross-linkfluorophores of skin collagen as reviewed in [49]. Skin photodamage by endogenous UV-photosensitizers occurs by direct reaction with substrate molecules (type I photosensitization) or molecular oxygen (type II), leading to formation of reactive oxygen species. The causative role of photoexcited states in skin photodamage suggests that direct molecular antagonism of photosensitization reactions using physical quenchers of pho-

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toexcited states (QPES compounds) offers a novel chemopreventive opportunity for topical skin photoprotection [72]. QPES compounds antagonize the harmful excited state chemistry of endogenous sensitizers by physical quenching, facilitating the harmless return of the sensitizer excited state to the electronic ground state by energy dissipation. Pilot studies using reconstructed full thickness human skin exposed to solar simulated light have provided proof-of concept efficacy of QPES compounds for topical skin photoprotection [72]. QPES compounds suitable for further preclinical development were identified using a series of chemical and cell-based screening methods. First, hit compounds were identified in a broad primary screening assay based on QPES suppression of photosensitized plasmid DNA cleavage. A secondary screen then confirmed hit compounds as QPES agents based on the nonsacrificial quenching of dye-sensitized singlet oxygen (1O2) formation by electron paramagnetic resonance detection of 2,2,6,6tetramethyl-piperidine-1-oxyl, a stable free radical indicative of 1O2 formation. These initial screens identified a pyrrolidine pharmacophore with pronounced QPES activity, and L-proline and other noncytotoxic proline derivatives containing this pharmacophore were then screened for efficacy in skin cell based assays of sensitized photodamage. Final confirmation of promising QPES lead compounds for further preclinical development was obtained in photoprotection studies using a human full thickness skin reconstructs, comprising a dermal equivalent from rat tail tendon type I collagen and human skin fibroblasts with a multilayered epidermis formed from immortalized human HaCaT keratinocytes. Various QPES compounds play an increasingly important role as combinatorial agents used together with regular sunscreens in order to enhance photostability of existing sunscreen formulation in order to provide additional skin UVA photoprotection by suppression of photodamage that originates from photoexcited states in skin [49]. Glycation inhibitors. Recent research has demonstrated that during chronological and actinic aging skin proteins accumulate significant chemical damage with formation of chromophore-epitopes that can act as protein crosslinks and UV-photosensitizers [73,74]. Non-enzymatic amino-carbonyl reactions (glycation) between reactive carbonyl species (RCS) and protein-bound amino-groups induce the accumulation of crosslinks and other posttranslational epitopes called advanced glycation endproducts (AGEs) on long-lived skin proteins such as dermal collagen, elastin, laminin, and fibronectin [61,75]. AGEs induce marked changes in extracellular matrix architecture and function including extensive protein crosslinking thought to contribute to loss of dermal elasticity and wrinkle formation during chronological and actinic aging of the human skin. Due to the causative involvement of glycation reactions in skin aging and photodamage, considerable research efforts aim at developing experimental therapeutics that act as topical inhibitors of glycation reactions capable of delaying or even reversing the detrimental consequences of skin extracellular matrix damage [61,73-75]. Recently, reconstructed full thickness human skin modified by glycation of the dermal equivalent was used in order to study glycation damage during skin aging [75]. Pre-glycation of the collagen used for dermal reconstruction induced significant structural changes throughout the full thickness reconstruct with increased levels of collagen IV and laminin in the basement membrane zone and expansion of 1-integrin to suprabasal layers of the epidermis, alterations that can also be observed in chronologically aged human skin and during wound healing. Using this chronologically-aged skin reconstruct model as a powerful screening tool, the potential of small molecule glycation inhibitors to suppress


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glycation damage and glycation-induced dermal and epidermal alterations was demonstrated. Antioxidants. The causative involvement of reactive oxygen species (ROS) in UVinduced skin damage including inflammation, photoaging, and ultimately photocarcinogenesis is firmly established. Topical application of small molecule antioxidants is therefore a promising chemopreventive approach targeting skin photodamage as discussed in [49]. Improvement of dermal epidermal junction morphogenesis as well as enhanced synthesis of extracellular matrix components and basement membrane proteins by 2phospho-L-ascorbate supplementation has been demonstrated in a full thickness reconstruct [64]. Recently, a commercial human epidermal reconstruct was used to monitor UVB-induced formation of ROS in real time and to screen small molecule topical antioxidants for skin photoprotection [76]. Based on chemiluminescent visualization of spatial ROS distribution by topical application of a cypridina luciferin analog as a chemiluminescent probe using a high-performance low-light imaging luminograph system with a CCD camera the antioxidant and photoprotective effects of test agents such as ascorbate, -carotene, superoxide dismutase (SOD) were evaluated. As molecular endpoints, quantification of emitted chemiluminescence (CL) intensities, MTT assay and oxidative DNA damage using DNA 8-hydroxy-2-deoxyguanosine (8-oxodG) staining were performed. Thus, this model shows promise not only for visualizing the production of UVB-induced ROS in real-time but also for screening of topical anti-oxidants for photoprotective and anti-photo-oxidative efficacy. Similarly, reconstructed skin has been used successfully to demonstrate photoprotective and anti-photoaging effects of an antioxidant ethanolic extract of rosmarinus officinalis [77]. Importantly, a genetically modified human skin reconstruct has recently been generated in order to validate catalase as a key antioxidant enzyme in skin involved in the endogenous skin photoprotective and antioxidant network, providing an impressive example of using human skin reconstructs for drug target validation studies [78]. Epidermis was reconstructed with normal human keratinocytes overexpressing sustainably lentivirus-mediated catalase (CAT), copper/zinc superoxide dismutase (CuZnSOD) or manganese superoxide dismutase (MnSOD) enzymes. Upon exposure to UVB irradiation a significant decrease in sunburn cell formation, caspase-3 activation and p53 accumulation was observed in human reconstructed epidermis overexpressing CAT. Upon UVA exposure, UVA-induced hypertrophy and DNA oxidation (8-oxodG) were decreased by CAT overexpression. Remarkably, these effects were not achieved by overexpression of CuZnSOD or MnSOD. Thus, based on these studies using genetically engineered human skin reconstructs, vector-mediated CAT overexpression or therapeutic administration of small molecule catalase mimetics could be a novel photoprotective strategy in monogenic/polygenic photosensitive disorders characterized by ROS accumulation. Natural product anti-inflammatory agents. Due to the involvement of inflammatory signaling in the response of human skin to UV exposure, anti-inflammatory agents are promising ingredients of photoprotective topical formulations. Recently, the use of reconstructed human epidermis for screening of fragrance raw materials and essential oils with potential anti-inflammatory activity has been reported [79]. An initial screening with pig blood platelets was conducted on 900 perfumery raw materials, and active compounds which successfully reduced formation of PGE2, a key marker of keratinocyte irritancy and crucial mediator of inflammatory events in human skin, were then validated

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in a human keratinocyte cell line. Application of anti-inflammatory fragrances in a hydrogel allowed reduction of UVB-induced PGE2 formation in reconstructed epidermis. Moreover, antiperspirant-induced formation of PGE2 was equally reduced to background levels in this epidermal screening model if these novel fragrances were incorporated into the antiperspirant formulation. It was concluded that the active ingredients identified by screening in reconstructed human skin may be part of a cosmetic formulation designed for optimal product mildness. Skin pigmentation modulators. Pharmacological modulation of skin pigmentation is an important area of current skin drug discovery. Epidermal reconstructs containing melanocytes that closely mimic the melanogenic potential of human skin have been developed and are used as powerful drug discovery tools in academic and pharmaceutical research circumventing the problems associated with species extrapolation, the use of laboratory animals, and time consuming clinical trials. The MelanoDermTM Skin Model (MatTek) is based on co-culture of human keratinocytes and melanocytes, which are alternatively comprised of Black, Caucasian, or Asian cells in order to accurately reflect the ethnic complexity of human skin pigmentation. The co-cultures, produced using serum free medium without artificial stimulators of melanogenesis, undergo spontaneous melanogenesis leading to tissues of varying levels of pigmentation. Topical application of test compounds acting as skin lighteners or self-tanning agents is possible, since the cultures are grown on cell culture inserts at the air-liquid interface. Screening of pharmacological modulators of skin pigmentation is then based on analytical methods to evaluate melanocyte dendricity and viability, pigment granule transfer to adjacent keratinocytes, bulk darkening of tissue, and total melanin content and synthesis rates. Topical application of inhibitors of melanogenesis will reduce melanin production and macroscopic darkening of the reconstructs, whereas stimulants of melanogenesis including melanocyte stimulating hormone will increase melanin content and macroscopic darkening over untreated controls. Numerous studies using melanocyte containing epidermal reconstructs for the identification of pigmentation altering agents have been published [27]. The skin lightening effects of aloesin and arbutin were tested in a human pigmented skin model demonstrating dose dependent reduction of tyrosinase activity and melanin content [80]. N-acetyl glucosamine (NAG) has previously been shown to reduce skin hyperpigmentation based on glucosamine inhibition of enzymatic glycosylation and conversion of inactive human pro-tyrosinase to the active tyrosinase. To identify additional mechanisms by which NAG might affect melanin production, an in vitro genomics experiment was conducted in SkinEthicTM skin equivalent cultures, which were topically dosed with NAG vs. a vehicle control [81]. Relative to vehicle, NAG reduced melanin production, and the expression of several pigmentation-relevant genes was significantly altered (downregulated or up-regulated) by NAG treatment . The effects of all-trans retinoic acid (RA) on melanogenesis and the mechanism of its action in topical treatment have recently been studied in the pigmented skin equivalent as well as in monolayer culture of melanocytes [82]. Interestingly, suppression of melanogenesis by RA was not observed in pigmented skin equivalents and monolayer culture of murine and human melanocytes, whereas the control substance hydroquinone showed strong inhibition of melanogenesis. The results suggested that the role of RA in combinatorial bleaching treatments appears to be based on promotion of keratinocytes proliferation and acceleration of epidermal turnover.


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7. RECONSTRUCTED SKIN AS A TARGET DISCOVERY TOOL: RECONSTRUCTION OF DISEASED SKIN The commercial availability of advanced tissue reconstructs that adequately represent the complexity of functional human skin has significantly impacted the regulatory, cosmeceutical, and pharmaceutical domains of the drug discovery process. In addition to accelerating the rate at which screening of novel molecules as better consumer ingredients, cosmeceuticals, and drugs occurs, advanced skin reconstructs are currently revolutionizing the way that novel skin drug targets are identified and validated. Successful reconstruction of specialized skin models that adequately represent many aspects of the complex biology underlying important skin pathologies with unmet therapeutic needs including cancer (nonmelanoma and melanoma), immune dysregulation (psoriasis), and microbial infection (acne) has now opened the exciting opportunity to identify and validate novel molecular targets for drug discovery. 7.1. Skin Cancer As a striking example, reconstruction of human melanoma skin that represents the progressional stages of this devastating disease has now been achieved. Melanoma is a highly invasive and metastatic tumor that originates from skin melanocytes [83,84]. Alarming increases in incidence rates combined with the notorious resistance to chemotherapeutic intervention and the lack of adequate animal models that represent the human biology of this complex disease create an urgent need for better disease models that would allow rapid identification of promising preclinical candidates from compound libraries. Indeed, melanoma reconstructs that successfully reproduce early disease progression and invasion have been generated in the in commercial and academic environment [12,15]. These models would provide a tool for the identification of novel antimelanoma agents that discriminate between tumor cells and healthy tissue. Progression of cutaneous melanoma via radial growth phase (RGP) confined to the epidermis and vertical growth phase (VGP) with penetration of the basement membrane and invasion of the dermis can now be mimicked and pharmacologically manipulated using a full thickness melanoma skin model (MLNM-FT-A375, MatTek; http://www.mattek.com) as shown in Fig. (6). The MLNM-FT-A375 model consists of human malignant melanoma cells (A375), normal, human epidermal keratinocytes (with organized basal, spinous, granular, and cornified epidermal layers) and dermal fibroblasts, forming a welldifferentiated full thickness skin model with RGP, VGP, and metastatic progressional stages of melanoma dependent on the duration of tissue culture after reconstruction. The model closely parallels the progression of melanoma in vivo, thus providing a valuable tool to study the cell biology of melanoma and to screen and develop preventative and therapeutic treatments for the most serious cutaneous malignancy. It has already been stated that reconstituted three-dimensional human skin serves as a valid in vitro model for mechanistic studies on skin carcinogenesis induced by chemical carcinogens and UV exposure [26]. Recently, epidermal photocarcinogenesis has been carefully studied by reconstruction of DNA repair-deficient xeroderma pigmentosum skin in vitro that also serves as a model a model of skin hypersensitivity to UV light [85,86]. In xeroderma pigmentosum (XP), a genetic defect in nucleotide excision repair of ultraviolet (UV)-induced mutagenic lesions causes extreme photosensitivity and cancer proneness. Nucleotide excision repair is an enzyme-based mechanism of DNA repair that removes cyclobutane pyrimidine dimers and the pyrimidine-pyrimidone (6–4) pho-

More Than Skin Deep


toproducts, both signature DNA lesions caused in skin exposed to erythemogenic doses of solar ultraviolet light. After isolation and culture of several strains of XP-C keratinocytes and fibroblasts from human patients, a three-dimensional skin model in vitro comprising both XP epidermis and a XP dermal equivalent was generated. The XP model displayed repair deficiency characterized by long-lasting persistence of UV-induced DNA damage and p53 positive nuclei after UV-B exposure, closely resembling the pathological UV repair response observed in human XP skin. Genetic reintroduction of a functional XPC gene into keratinocytes and fibroblasts before reconstruction reversed various cellular consequences of UV-hypersensitivity. Reconstruction of XP skin in vitro may therefore serve as a superb model for the development of novel therapeutic strategies including gene therapy and may allow the identification of novel pharmacological agents that efficiently prevent or treat skin UV-hypersensitivity. In this context, topical delivery of DNA repair enzymes has shown great promise [87].

Fig. (6). Commercial melanoma tissue reconstruct (MLNM-FT-A375) representing progressional stages of the disease. Human Metastatic Melanoma Cells (A375) in full thickness melanoma skin model. Metastatic A375 cells develop radial growth phase (RGP) melanoma nodes at dermal/epidermal junction (day 11). With extended culture time, melanoma nodes adopt a vertical growth phase (VGP) morphology (day 18); subsequently, isolated clusters of cells invade the dermis (metastatic invasion) (day 29). Long arrows: melanoma cell clusters at the epidermal-dermal junction; short arrows: separated melanoma cell clusters infiltrating the dermis. [formalin fixed, paraffin embedded, H&E stained reconstructs; reproduced with permission, MatTek, Ashland, MA, USA; http://www.mattek.com).

7.2. Psoriasis Reconstruction of psoriatic skin, recently achieved using keratinocytes and fibroblasts derived from psoriatic patients, represents a valuable tool for drug discovery targeting this disease [10]. Psoriasis is a chronic inflammatory disease affecting the skin and joints that is associated with increased antigen presentation, T-helper (Th)-1 cytokine


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production, and T cell activity. Consistent with inflammatory dysregulation of psoriatic skin, the proinflammatory genes TNF-, IFN-, and IL-8 were expressed at high levels in the psoriatic models, whereas minimal expression was detected in reconstructs built from normal skin keratinocytes and fibroblasts. Moreover, the chemokine receptor CXCR2 was overexpressed in the psoriatic model and localized to the granular layer of keratinocytes, an expression pattern closely resembling psoriatic skin in vivo. Remarkably, models derived from uninvolved psoriatic skin showed the same gene expression profile as those derived from involved skin along with an increased proliferation rate when compared to normal models suggesting an inherent predisposition of psoriatic individuals to develop the disease phenotype even in the absence of T cells. 7.3. Microbial Skin Infection Human skin equivalents have shown promise as discovery tools to study cutaneous microbial interactions, potentially useful for the identification of novel antimicrobial therapeutics. Ethical considerations associated with the intentional inoculation of pathogenic microorganisms onto human skin and biological insufficiencies of animal models create an urgent need for skin reconstructs with microbial inoculation, representing relevant pathogen-induced skin diseases including inflammatory acne, seborrhoeic dermatitis, and wound infection. Earlier studies have demonstrated feasibility of microbial inoculation of skin reconstructs for determining the adhesiveness of staphylococcus aureus and staphylococcus epidermidis to reconstructed epidermis [88]. Moreover, inoculation of epidermal reconstructs with the human skin commensals staphylococcus epidermidis, propionibacterium acnes and malassezia furfur as well as the transient pathogen staphylococcus aureus has been reported [89]. A novel, real-time growth monitoring method was also developed, using S. aureus containing a lux cassette with light output as analytical parameter representing bacterial colonization of the reconstruct. Successful colonization of reconstructed epidermis with constitutive and pathological microbial flora can therefore serve as a model to investigate interactions between resident and transient microbial communities on skin underlying the innate responses that protect skin from colonization by opportunistic pathogens. Microbial inoculation of reconstructed epidermis will serve as a unique discovery tool for screening of novel antimicrobial chemotherapeutics targeting infection of human skin by defined pathogens. The usefulness of the reconstruct-based skin disease models described above suggests that other pathologies that involve impaired skin barrier structure and function (atopic dermatitis and ichthyosis) [90], redox and inflammatory dysregulation (vitiligo), and early stages of carcinogenesis (actinic keratosis, dysplastic nevus) can be modeled adequately in vitro. These novel reconstructs would then offer exciting possibilities for future drug discovery targeting important pathologies with unmet therapeutic needs. Continuous progress in skin tissue engineering will be achieved based on gene-modified cellular components of increasing complexity and incorporation of stem cells and progenitor cell-derived populations [7,91,92]. This will ensure the expanding role of skin equivalents in disease model-based pharmaceutical assays for rapid identification and development of novel cutaneous therapeutics. ACKNOWLEDGEMENTS Supported in part by grants from the National Institutes of Health [R01CA122484, ES007091, Arizona Cancer Center Support Grant CA023074], and from the Arizona

More Than Skin Deep


Biomedical Research Commission (ABRC 0721). Support from the Arizona Science Foundation (SL) and German Academic Exchange Service (AB) is greatly acknowledged. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]

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Across Skin Barrier: Known Methods, New Performances Krzysztof Cal* Department of Pharmaceutical Technology, Medical University of Gdansk, Hallera 107, 80-416 Gdansk, Poland Abstract: Skin is still the desirable route for the delivery of drug substances into the human body. Transdermal drug delivery offers many advantages over the conventional oral route of application, such as the elimination of hepatic first-pass effect, reduced side effects, constant concentration of a drug in the blood. Human intact skin is normally permeable for molecules with log P in the range of 1-3, smaller than 500 Da and present in a unionized form. Usually, obtained fluxes of drug substances are too low for the induction of systemic therapeutic effects. It is caused by the specific structure and composition of the outer layer of the skin – the stratum corneum. Closely packed, built as “brick” (corneocytes) and “mortar” (lipid bilayers), the stratum corneum is the most important limiter for transdermal drug delivery. Molecules that permeated the stratum corneum are easily taken up by capillary vessels present in the deeper skin layers. This chapter presents different methods used for skin permeation enhancement. Various drug forms and carriers, chemical permeation enhancers, electrically supported methods and devices, and the stratum corneum bypassing or removing methods are described, and the recent achievements in the field and possible practical use in market products are discussed. The special subchapters are dedicated to the skin disposition of one of the most often used penetration enhancers – terpenes, and the use of cyclodextrins in formulations applied onto the skin.

INTRODUCTION The application of exogenous substances onto the skin has been known for many thousands of years. The ancient Egyptians and Greeks applied olive oil, mixture of water and lead oxide in olive oil (astringent agent and occlusive barrier), and essential oils (penetration enhancers) onto the skin [1,2]. In the 16th century Fuchs [3] recommended soaking feet in water with Hyoscyamus niger for insomnia; and “witches” used the henbane containing liniments to obtain a “fly-like” feeling [3,4]. Over the years, the skin was regarded as an impermeable barrier [5]. The first conscious transdermal application can be dated in 1893, when the efficiency of salicylic acid in acute rheumatoid arthritis was demonstrated [1,6]. In the mid 20th century, through the tape-stripping technique

*Corresponding Author: Tel: +48 58 349 3183; Fax: +48 58 349 3190; E-mail: [email protected] Gary W. Caldwell / Atta-ur-Rahman / Z. Yan / M. Iqbal Choudhary (Eds.) All rights reserved – © 2009 Bentham Science Publishers.

Across Skin Barrier


and water permeability assessments, it was proven that the stratum corneum of epidermis is the major barrier in the skin permeation process [7,8]. It was confirmed by in vitro skin permeation studies with a wide range of drug substances [5,9,10]. The first, introduced on the market in 1954, transdermal preparation for the treatment of systemic disease was nitroglycerin in the form of an ointment [1,6]. Only thirty years later, FDA approved the first transdermal therapeutic system containing scopolamine [11]. Now, passive patches containing buprenorphine, clonidine, estradiol, ethinylestradiol + norelgestromin, fentanyl, flurbiprofen, indometacin, ketoprofen, levonorgestrel, lidocaine, methylphenidate, nicotine, norethisterone, oxybutynin, rivastigmine, rotigotine, selegiline and testosterone, and transdermal patches supported by iontophoresis are available in different countries [11-13]. This chapter describes the structure and composition of the skin, targets of skin applications of drugs, possible permeation pathways and different methods for skin permeation enhancement. The special subchapter is dedicated to the use of cyclodextrins in formulations applied onto the skin, because the mechanism of their action is complex and can consists of influences on drug molecules, vehicles and skin. THE BARRIER OF HEALTHY SKIN Skin is the easiest available and the largest human organ. It separates the organism from the external environment. The skin area in adults is 1.7-2 m2. In general, skin consists of epidermis with the stratum corneum and dermis. The dermis is about 0.5-2 mm thick. It is built from collagen and elastic fibers, and contains blood vessels, free nerve endings, and skin appendages (hair follicles, sweat and sebaceous glands) (Fig. 1). The main cells in the dermis are fibroblasts. Subcutaneous tissue (hypodermis) lies beneath the dermis and consists of fat lobules and muscles. The dermis is connected with the epidermis via the stratum basale. The epidermis consists of several layers of the skin cells – keratinocytes, reflecting different stages of differentiation. From the stratum basale the cells move up and undergo the keratinization process, and the cells die. The process of keratinocytes’ movement from the stratum basale to the surface of the skin, where cells are sloughed off, lasts from 2 to 4 weeks. In turn, the layer of dead cells corneocytes - is formed, the stratum corneum, that consists of 15-20 corneocytes layers. Corneocytes are composed mainly of keratin and natural moisturizing factor, and may contain from 10 to 80% of water [14-17]. Corneocytes are polygonal, elongated and flat (0.2-1.5 m thick and 34.0-46.0 m in diameter) [18]. Each corneocyte is encapsulated in an insoluble tough protein shell having a thickness of 10 nm, corneocyte envelope, which is covalently bound to an outer lipid envelope composed of a layer of ceramides [19]. Such enveloped corneocytes are embedded in a lipid matrix. This “brick” (corneocytes) and “mortar” (lipids) model of the stratum corneum was first presented by Michaels et al. [20]. The stratum corneum, according to the degree of hydration, is 10 to 50 m thick. The intercellular lipids of the stratum corneum have a unique composition compared to other epithelial lipid layers and consist of ceramides (40-50%), cholesterol (25%) and free fatty acids (10-20%). No phospholipids are present in the healthy stratum corneum [21,22]. Lipids are arranged in organized multilaminar structures, which basic unit is a bilayer. Hydrophilic polar groups (heads) of lipids are directed to each other and a monomolecular aqueous layer appears between them. Such a composition and structure


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of the stratum corneum’ lipids is considered to be the most important for skin penetration and the permeation processes [23,24]. Drug form Dissolution Diffusion

Transepidermal pathway

Intracellular route

Shunt routes

Intercellular route Lipidic region

Corneocyte

Stratum corneum

Epidermis

Dermis

Capillary blood vessels

Sweat duct

Hair follicle

Fig. (1). Skin structure and possible permeation pathways.

Blood vessels, coming across the dermis and reaching up to the stratum basale, supply the skin with oxygen and nutrients, and remove toxins and metabolites. On the other hand, these vessels deliver substances that permeate the stratum corneum, directly to the systemic blood circulation [17]. TARGETS FOR THE SKIN’S APPLICATION OF DRUGS For many years, the skin was a target of the application of only dermatological drug products that were used in the local treatment of skin diseases. Currently, drug products applied onto the skin to treat muscles and joints inflammations or which purpose is to induce systemic effects are gaining significance [1]. This effect is possible thanks to the penetration of drug substances into not only the skin, but also permeation across the skin into the tissues localized beneath this organ or even into the circulation of blood. The surface of the skin is rarely the target for drug activity, except e.g. antiseptics and antimycotics, as far as dermatological use is concerned. Most of the drug substances, applied onto the skin for treatment purposes, must undergo diffusion to viable epidermis

Across Skin Barrier


(corticosteroids) or to the dermis (regulators of collagen synthesis). A drug substance, which penetrates into the epidermis or dermis, is partially or fully absorbed into systemic circulation. This might result in the occurrence of systemic side effects and, furthermore, even toxic effects. On the other hand, many drug forms are intended for transdermal delivery and are considered as a non-invasive and alternative for the conventional route of administration, e.g. in patients that cannot take drugs orally [1,12,14,17]. There are many unquestionable advantages of transdermal drug delivery [1,11,2528]. The drug substance avoids hepatic first pass effect and variances in the gastrointestinal tract that may influence on absorption. Transdermal systems enable achieving a steady-state drug concentration in the blood. This reduces the likelihood of peakassociated side effects, and ensures that drug concentration is above the minimal therapeutic concentration. Because transdermal patches are usually applied every three days, the dosing frequency is reduced. On the other hand, transdermal administration has some limitations. Transdermal delivered drug substances should be effective in the daily dose < 10 mg, and have appropriate physicochemical properties (log P in the range 1-3, molecular weight 100-500 Da, aqueous solubility > 100 g/ml, melting point < 200oC, unionized form). Sometimes, after the application of a drug form onto the skin, sensitization or irritation reactions appear, caused by the drug substance or excipients. Transdermal systems are relatively complex drug forms, expensive to develop and manufacture, and thus limited to relatively a few manufacturers. SKIN PERMEATION PATHWAYS Penetration is the movement of a molecule into a particular layer, while permeation is the passage through one layer into another layer or compartment. Absorption is usually the uptake of a substance into the blood vessels. The ability of exogenous substances to penetrate and to permeate the skin depends on the two following processes: the substance must be released in the dissolved form from the vehicle, and then it must overcome the stratum corneum barrier (Fig. 1). Both processes are closely related and depend on the physicochemical properties of permeant, a type of carrier and penetration pathway [17,29-31]. When the drug form is present on the skin, the drug substance can permeate the skin via the transfollicular pathway - through the skin appendages (Fig. 1). Substances diffuse even 1000 times faster through the skin appendages than via the other way. Unfortunately, the transfollicular pathway is of little significance, as skin appendages occupy less than 0.1% of the skin surface (except the hairy skin of the head), and secretions of glands being eliminated in the opposite direction impede the diffusion of substances. Thus, the transepidermal pathway - through the stratum corneum - is the main pathway of substance skin permeation. As viable skin layers, epidermis and dermis, might be considered to be a protein hydrogel, where the diffusion rate of permeating substance are comparable to the diffusion rate in the high viscous solution, the dead stratum corneum makes the factual barrier for substance permeation across the skin. Within this layer, molecules diffuse mainly via the tortuous lipid intercellular pathway. Corneocytes’ accurate arrangement and laminar composition of lipids surrounding the cells allow diffusing substances through the stratum corneum over 1000 times slower than through the viable skin layers. The lipophilic character of the stratum corneum favors permeation of nonpolar, unionized, small and lipophilic substances. Permeation of high lipophilic compounds may be limited by hydrophilic properties of deeper-situated viable skin layers.


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Although it is possible for hydrophilic substances or ions to penetrate across the cells of the stratum corneum, these pathways have little part in the skin permeation process, and the permeant is subjected to a number of partitioning steps between hydrophilic corneocytes and lipophilic intercellular spaces [17,29]. It might be assumed that the stratum corneum is a homogenous lipid membrane, across which diffusion occurs according to the simply modified Fick’s first law (Eq. 1) [30]. This equation takes into consideration participation of a vehicle in the permeation process (K) and the process of diffusion across the stratum corneum.

J= where:

K D C L

(Eq. 1)

J-

steady-state flux - cumulative amount of permeating substance [g/cm2 h];

K-

partition coefficient of the substance between the stratum corneum and vehicle;

D-

diffusion coefficient of the substance in the stratum corneum [cm2/h];

C -

difference between applied concentration of substances and concentration below the stratum corneum (in vivo) or in the acceptor fluid (in vitro) [g/cm2];

L-

diffusion pathlength - thickness of the stratum corneum [cm].

Partition coefficient (K) of the substance between the stratum corneum and the vehicle is difficult to be determined, that is why the value of the partition coefficient of the substance between n-octanol and water is used for calculations. C can be replaced by applied concentration, because in normal conditions this concentration is much larger than the concentration in deeper skin layers. The real tortuous diffusion pathlength (L) is much longer (about 500 m) than the stratum corneum thickness (10-50 m); however, the stratum corneum thickness is mostly used, because it is measured more easily. STRATEGIES FOR ENHANCING SKIN PERMEATION As explained before, the stratum corneum is the principal barrier for skin absorption of drugs. The following groups of methods for increasing stratum corneum permeation are known and intensively investigated: -

methods related to the modification of drug molecules, choice of the appropriate vehicle and drug form, incorporation of drug into carrier;

-

methods related to the modification of the stratum corneum properties - hydration, chemical penetration enhancers;

-

electrically supported methods and devices - iontophoresis, electroporation, ultrasound;

-

methods related to the bypassing or removing of the stratum corneum - microneedles, jet injections, ablations.

DRUG MOLECULES, DRUG FORMS AND DRUG CARRIERS Prodrugs When the molecule has inappropriate physicochemical properties for permeation through the stratum corneum, usually lipophilicity, it can be optimized by altering its

Across Skin Barrier


chemical structure, which improves the stratum corneum/vehicle partitioning and diffusion characteristics [1]. Prodrugs are bioreversible derivatives of drug molecules that undergo an enzymatic and/or chemical transformation in vivo to release the active parent drug, which can then exert the desired pharmacological effect [32]. The skin contains appreciable non-specific esterase activity, and thus steroid esters provide greater topical anti-inflammatory activity than the parent steroids do, as the lipophilic moieties added to the steroids (e.g. the valerate in betamethasone-17-valerate) improve partitioning of the molecules into the stratum corneum compared with the parent steroid [17]. The most commonly applied prodrug strategy is to covalently bond an active substance with an inactive moiety by an ester bond [1,33]. The prodrugs were experimentally used to increase skin permeation of different groups of drug substances, such as non-steroidal antiinflammatory [34-36], beta-blockers [37], anti-cancers [38,39] and others [1]. Although about 5-7% of drugs approved can be classified as prodrugs, within preparations used onto the skin the most successful application of the prodrugs remains the steroids [1,32]. Finally, what is interesting, there are a lot of patent applications by Yu and Xu [40] concerning positively charged water-soluble prodrugs of acetaminophen, acetylsalicylic acid, diclofenac, diflunisal, ibuprofen, ketoprofen, prostaglandins and others, with a very high skin penetration rate. Ion Pairs Charged molecules do not readily penetrate lipid membrane, such as the stratum corneum [31]. The ion pairs approach utilizes oppositely charged species added to charged drugs, forming an ion pair in which the charge is temporarily neutralized. The pair of oppositely charged ions is held together by Coulomb attraction [41]. The ion pair partitions into the stratum corneum lipids, diffuses through this layer and dissociates in the viable epidermis into its charged compounds [1,31]. The good examples for ion pair are ibuprofen with triethylamine as a pairing agent [41] and equimolar mixtures of salicylic acid and various alkyl amines [42]. The recent studies on the ion pairs skin penetration enhancement method concern indapamide with organic acids [43] and aminolevulic acid with HCl/alcohol [44], but in general, the enhancement effect of ion pairs is rather modest. Thermodynamic Activity of Permeants and Supersaturation The greatest flux of a drug through a membrane occurs when the drug is at its maximum thermodynamic activity [1,31]. At saturation, equilibrium exists between the solid and liquid phase and activity equals 1. Therefore, all vehicles that contain a finely ground suspension exist as saturated solutions of the drug [1,31]. The most common method of creating a supersaturated system is the co-solvent method [1,45-49]. Saturated solutions of the co-solvent mixture are combined with a poor solvent to create a supersaturated system [35-37]. Supersaturated systems can also be obtained through the evaporation of a volatile co-solvent [50], by cooling a heated saturated solution down to skin temperature [51], or by moisture from the skin absorbing into a formulation, and acting as an antisolvent [52]. Although the drug flux from supersaturated systems increases over 10 times, such systems are ungrateful because of their instability represented by crystallization. There are many attempts to inhibit the drug’s crystallization, e.g. by using different polymers


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as antinucleants [46,47,49,53-56], however to maintain a reasonable storage period of supersaturated systems is still a challenge. Eutectic Systems The molecule can diffuse across the membrane only when it is in a dissolved form. A eutectic system is a mixture of two or more compounds, which do not interact to form a new chemical entity, but its melting point is lower than the melting points of particular compounds and the solubility increases [1]. A linear correlation was seen when the log of steady-state flux is plotted against the melting point, indicating that the lower the melting point (greater solubility) the better the penetration [1,31]. The great achievements in such systems were to launch the drug products EMLA® (1:1 eutectic mixture of lidocaine and prilocaine) and S-Caine® (1:1 lidocaine and tetracaine) on the market [1,57]. Both mixtures have a melting point below room temperature and are incorporated into a cream form. The products are intended for use on intact skin to provide local analgesia, e.g. before injections and superficial dermatological procedures, such as dermal filler injections, pulsed dye laser therapy, facial laser resurfacing, and laser-assisted tattoos removal. The disadvantages of using EMLA® are a delay in analgesia and necessity of applying occlusive conditions. The vehicle of S-Caine® forms a pliable peel on the skin when exposed to air, that can be easily removed, meaning no occlusion is required [1]. The success of the EMLA® mixture stimulated the studies on eutectic systems with other drugs: testosterone [58], ibuprofen [59] and propranolol [60]. Liposomes Liposomes are rounded, stable vesicles composed of one or more concentric lipid bilayers [1,61]. Liposomes have two compartments: an aqueous central core and a lipophilic region within the lipid bilayer. Hydrophilic drugs can be incorporated into the inner aqueous compartment, while lipophilic drugs can be incorporated within the lipid bilayers. Conventional liposomes are composed of phospholipids, and usually with cholesterol to stabilize the vesicle. The most common phospholipid is phosphatidylcholine obtained from soybean or egg yolk. The conventional liposomes are most often prepared by the film hydration method [17,62], where the liposome compounds are dissolved in a volatile solvent, next the solvent is evaporated and a thin film of lipids deposits on the wall of the container. An aqueous solution of the drug to be incorporated is added at a temperature greater than the phase transition temperature of the lipids. As a result, multilamellar vesicles (MLV, < 10 m in diameter) are formed, containing several lipid bilayers surrounding an aqueous drop. MLV can be next sonicated or extruded to obtain large unilamellar vesicles (LUV, 1-5 m in diameter), or small unilamellar vesicles (SUV, 0.1-0.5 m in diameter). The conventional liposomes for transdermal application induced the unfounded enthusiasm. Simply, they are too large and too rigid to penetrate and permeate the stratum corneum. Liposomes are often adsorbed into the skin surface and fused with the outer layer of stratum corneum, and next release the free drug substance, which independently permeates the skin [31]. The liposomal lipids can also act as a skin penetration enhancer. The great problem with liposomes is their limited stability. A range of modifications in liposome compounding are proposed to decrease their rigidity and to increase stability, e.g. Oh et al. [63] describe polysorbate-based deformable liposomes for transdermal

Across Skin Barrier


delivery of retinol (see p. Transfersomes). There are many other attempts to formulate liposomes-like vehicles, such as niosomes, transfersomes, ehtosomes, and catezomes. Niosomes Niosomes (non-ionic surfactant vehicles) are formed by the self-assembly of nonionic surfactants in an aqueous dispersion; comparing to the liposomes they are flexible and more stable [1,2,31]. However, they often reduce fluxes of drugs in comparison to the conventional liposomes [64,65]. Transfersomes The transfersomes introduced by Cevc are highly deformable (or elastic) liposomes [1,17,31,66]. Transfersomes are composed of phospholipids, usually phosphatidylcholine, and surfactant (10-25%), such as sodium cholate, deoxycholate, Span 80, Tween 80 or dipotassium glycyrrhizinate [1,67]. Surfactant molecules act as an “edge activator” and give flexibility [17]. The formulation also contains a few percent of ethanol, and with the final aqueous lipid suspension, its total lipid concentration ranges between 4 and 10% [17]. Preparation methods are similar to those for conventional liposomes. The film hydration method is used most commonly [1,67]. Ultra deformable transfersomes are claimed to be able to squeeze through pores that are 10% of the vesicle diameter (about 20 nm diameter) [17,66]. Whereas the diffusion gradient is the driving force behind the topical delivery of drugs, the osmotic gradient across the skin is thought to be responsible for driving elastic vesicles [1]. The difference in water content varies from almost 100% at the epidermal/dermal junction to approximately 20% at the skin’s surface (depending on the environment) [1,17]. After applying transfersomes onto the skin’s surface, the formulation will dry, and the vesicles start to partially dehydrate, resulting in the vesicles becoming flattened or curved [1,17]. To maintain stability, the vesicle will penetrate deeper into the stratum corneum, where water content is higher [17]. Such a hydration theory is supported by the reduction in flux observed when the skin is occluded [1,68]. Transfersomes have been successfully used as topical and transdermal carriers for number of drug substances, including retinol [63], diclofenac [69], triamcinolone [70], dexamethasone [71], methotrexate [72], ketotifen [73], zidovudine [74], ethinylestradiol [75], often resulting in an increase of skin penetration/permeation. The transfersomal form of ketoprofen is in phase III of clinical trials [76-78]. Ethosomes Conventional liposomes and transfersomes can contain up to 10% of ethanol. Ethosomes are liposomes that contain 20-45% of ethanol, and as liposomes, they are composed of phospholipids [79]. Ethosomes are often prepared by first dissolving the lipids and drug in ethanol, then adding the aqueous component as a fine stream with thorough mixing [1,80]. High ethanol content results in ethosomes being much smaller than liposomes, and thus size reduction is unnecessary. Furthermore, ethanol enhances solubility of more lipophilic drugs [1]. The mechanism of ethosome action remains unclear [17]. Both the components of ethosomes, ethanol and phospholipids, can act as penetration enhancers [1,80]. However, ethosomes were much more effective penetration enhancers than hydro-ethanolic solu-


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tions, ethanol or an ethanolic phospholipids solution [1,80]. It seems that ethanol initially disrupts the lipids within the stratum corneum, and next ethosomes, which are more flexible than liposomes, squeeze through the compromised stratum corneum [81]. Ethosomal formulations increased skin penetration of ketotifen [73], minoxidil [80], testosterone [80], acyclovir [81], trihexphenidyl hydrochloride [82], and cannabidiol [83]. The Supra-Vir cream containing acyclovir in ethosomes is available on some markets, and ethosomal erythromycin and clindamycin are clinically tested [84]. Catezomes Catezomes are a non-phospholipids vesicle with a cationic charge that makes them substantive to skin. They are used to deliver oil-soluble ingredients, like UV absorbers and ceramides or water-soluble drugs, like panthenol. Solid Lipid Nanoparticles By incorporating drugs into nanoparticles, such limitations as poor aqueous solubility or inadequate stability can be overcome [1]. Solid lipid nanoparticles can be obtained by high shear homogenization and ultrasonic dispersion, high pressure homogenization or emulsification, or vaporization methods [31,85,86]. Drug distribution within the nanoparticle is a function of particle composition (lipid, drug, and surfactant) and of the method of preparation [1]. A drug can be homogenously dispersed throughout the matrix of the nanoparticle, or it can be loaded into the core or the particle shell. The localization of the drug influences its release rate. A drug from the shell is usually released rapidly, whereas from the core – the process is more or less, prolonged [86]. Although there are many studies on the use of solid lipid nanoparticles in transdermal delivery of drugs, e.g. for isotretinoin [85], flurbiprofen [87], ketoprofen [88], naproxen [88], prednicarbate [89], retinol [90], the major chance for solid lipid nanoparticles propagations seems to be application in cosmetics, including sunscreens [91-93]. Microemulsions Microemulsions are defined as a system of water, oil and surfactants, which are transparent, a single optically isotropic and thermodynamic stable liquid [94]. Microemulsions can be considered as ideal liquid vehicles for drug delivery as they have most of the requirements for this: thermodynamic stability, ease of formulation, low viscosity, high solubilization capacity and small droplet size [95]. The major problem for microemulsions is that drugs can precipitate and crystallize as large crystals during storage or dilution. The dilution effects on the stability of the microemulsions were in most studies neglected and not considered [96]. In addition, the toxicity of the microemulsions compounds still imposes limitations in the use of this carrier [31]. Although there are many studies on skin application of drug-loaded microemulsions, and this carrier seems very promising, the obtainment of proper drug products based on microemulsions is still a challenge [94,96]. MODIFICATIONS OF STRATUM CORNEUM Hydration Hydration can be achieved by soaking the skin, applying a formulation with high water content and creating an occlusive condition [1,17]. Occlusion prevents natural

Across Skin Barrier


water loss from the skin, thus the water content of the stratum corneum equalizes with the underlying layers. Occlusion can be achieved by using dressings, hydrophobic ointments or patch-based formulations [17]. It is suggested that small amounts of water is present in the polar region of the stratum corneum lipids. The water insertion loosens lipid packing, increasing the mobility of the lipids chains [1,97], and larger amounts of water can exist as a separate phase in the intercellular spaces or can be taken up by the corneocytes [98]. Occlusion also causes an increase in temperature of the skin’s surface and dermal clearance in dermal capillaries. Unfortunately, long-term occlusion results in maceration of the skin and the high humidity of the skin favor the growth of microorganisms. Chemical Skin Penetration Enhancers Penetration enhancers are excipients that are added intentionally to the preparations for reversibly decrease the barrier of the stratum corneum against therapeutic substances [99,100]. A perfect penetration enhancer should be characterized by the following major properties [1,99,100]: -

it should be pharmacologically inactive;

-

it should be non-toxic, non-irritant, non-allergy inducing;

-

it should give a reproducible effect;

-

there should be a quick and complete reconstitution of the skin’s barrier after its removal;

-

it should be compatible with other compounds of formulation;

-

its action should be unidirectional - it should enhance drug absorption into the skin, but not promote the loss of endogenous substances;

-

it should be acceptable as far as its organoleptic properties are concerned - fragrancefree and colorless.

The most significant and characteristic feature of a safe penetration enhancer is its reversible effect, i.e. that a proper stratum corneum barrier is restored after preparation application. Elimination of a penetration enhancer from the stratum corneum may occur as a result of evaporation from the skin (e.g. ethanol or terpenes) or diffusion into deeper layers of the skin and, in consequence, into the blood circulation. The first elimination pathway, that is evaporation, is definitely favorable because it diminishes risk of penetration enhancer absorption into the blood. The mechanism of penetration enhancer action consists mainly in their polarity. These compounds, located in the stratum corneum, according to their physicochemical properties, may: -

disorder the fine arrangement of intercellular lipids within the stratum corneum;

-

increase fluidity or dissolve intercellular lipids;

-

change the hydration of lipids polar groups.

It is also a significant fact that penetration enhancers increase solubility of drug substances in the stratum corneum owing to their own solubility in the lipids of this layer, thus increasing the partition coefficient of penetrants between the stratum corneum and


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the vehicle. The most widely used enhancers nowadays are alcohols (ethanol), glycols (propylene glycol), fatty acids (oleic acid), terpenes (menthol). Ethanol Ethanol is used in many transdermal formulations and is often the solvent of choice used in patches [17,99,100]. The mechanism of ethanol action at a low concentration (25%) on the stratum corneum consists in its interaction with lipids polar groups so that the fine arrangement of lipids becomes disturbed that, in turn, results in the increase of “fluidity” of these compounds. As far as higher concentrations (> 50%) are concerned, stronger physicochemical interactions may cause conformational changes of -keratin, an increase in the number of its free sulfhydryl groups and a partial extraction of lipids. It results in the formation of hydrophilic micropores enabling the absorption of polar molecules. Ethanol increases percutaneous absorption of, inter alia, 5–fluorouracil, hydrocortisone and estradiol [99-102], which was taken advantage of in transdermal therapeutic systems containing this drug substances. On the other hand, ethanol, at concentrations exceeding 60%, decreases the percutaneous absorption of many drugs probably because of dehydration of viable skin layers. Propylene Glycol It has been suggested that the propylene glycol effect on the lipids of the stratum corneum is comparable to the ethanol action [99,100]. Propylene glycol also causes changes in the structure of -keratin, probably because of the movement of bound water and solvation of this protein. The combination of propylene glycol with oleic acid in one system (action synergism) obtains the highest activity. It has also been proposed that the presence of water in the vehicle is necessary for the effect of propylene glycol to be exerted. Fatty Acids Fatty acids, that are used as penetration enhancers, are characterized by double bond in cis conformation (e.g. oleic acid), whereas unsaturated fatty acids in trans conformation have no influence on skin absorption of drug substances [99,100]. Fatty acids in cis conformation have a non-linear spatial structure and they incorporate within alkyl chains of the lipids of the stratum corneum, what in turn disturbs their organized structure leading to looseness and an increase in the fluidity of lipids. The molecules of oleic acid reveal high affinity to the lipids of the stratum corneum, however, their affinity to keratin and other components of this layer was not discovered. The probable mechanism of oleic acid action consists in the partition of the liquid and solid phase of lipids in the stratum corneum. The transport of polar and ionic compounds is also increased in the spots where partition takes place. Terpenes, Terpenoids and Essential Oils Terpenes/terpenoids are a large group of natural compounds deriving from active isoprene [103]. The number of carbon atoms in the terpene molecule is a multiple of 5; for example, C10 (monoterpenes), C15 (sesquiterpenes) and C20 (diterpenes). They are the main constituents of essential oils - the active substances of many plants. Terpenes have their-own very broad spectrum of biological activity, which in essential oils is an effect of synergistic or antagonistic activity of each compounds. Topical preparations containing terpenes exhibit antibacterial, rubefacient, mild-analgesic and anti-inflammatory

Across Skin Barrier


activity. Essential oils and/or terpenes are present in the most of cosmetics, household chemicals and drug products. Most of drug products are intended for topical treatment or, after spreading, for inhalation. They can be in the form of solutions, ointments, emulsions, balms, gels, lotions. Unfortunately, manufacturers of drug products and cosmetics very often do not take consider that terpenes are very strong skin penetration enhancers for different classes of substances. Interest in terpenes, terpenoids and essential oils, containing these substances, as penetration enhancers has been observed in recent years. These compounds enhance penetration of lipophilic, hydrophilic as well as ionized substances. Penetration enhancement is related to increase in substance solubility in the stratum corneum. Terpenes also increase stratum corneum/vehicle partition coefficient for lipophilic compounds and disorder the fine arrangement of intercellular lipids of the stratum corneum. Terpenes are particularly active to non-steroidal anti-inflammatory drugs [99,100,104]; and the synergism of terpenes and ethanol action is often used. It is interesting that in some carriers terpenes can be inactive for drug substance [105]. The penetration enhancement by terpenes has been recently reviewed in deep by Aqil et al. [106] and Satra et al. [107]. Other Chemical Skin Penetration Enhancers There are also other groups of penetration enhancers that are experimentally tested, such as sulfoxides, azone, pyrrolidones, fatty alcohols, surfactants, phospholipids, that have been preciously reviewed by Williams and Barry [99] and Thong et al. [100]. Most of penetration enhancers are present in various commercial cosmetics and drug products for completely different reasons. Propylene glycol is used as the vehicle forming components or, like alcohol, as solvents. Surfactants solubilize active substances within the vehicle or they enable to obtain proper form of a preparation. Although great number of studies, there is still not an ideal penetration enhancer. Skin Disposition of Terpenes Skin penetration and elimination kinetics can be one of the parameters determining the in use safety of penetration enhancers. The one of better examined in this field penetration enhancers are terpenes [108]. Although most of terpenes are Generally Regarded as Safe substances (GRAS), the side effects depending on their skin absorption during usage cannot be excluded [109-113]. Physicochemical Properties of Investigated Terpenes For the skin penetration studies both acyclic monoterpenes: (±)--citronellol, (±)linalool, linalyl acetate and cyclic monoterpenes: (-)--pinene, (-)--pinene, eucalyptol (1,8-cineole) and terpinen-4-ol were chosen. All investigated compounds appear in the liquid form at room temperature. Considering their structure, the investigated terpenes represent different chemical classes: (-)--pinene and (-)--pinene are hydrocarbons; (±)-citronellol, (±)-linalool and terpinen-4-ol are alcohols; linalyl acetate is an ester and eucalyptol is an oxide. Based on aqueous solubility and log P value, the penetrants investigated were grouped as follows [114]: -

acyclic with log P value 3: (±)--citronellol and (±)-linalool;

-

acyclic with log P > 4: linalyl acetate;

-

cyclic with log P 3: eucalyptol and terpinen-4-ol;


-

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cyclic with log P > 4: (-)--pinene and (-)--pinene.

Ex Vivo Skin Penetration of Pure Terpenes Skin penetration of terpenes was determined by their application in pure form for 1, 2 and 4 h onto the full human skin placed in flow-through diffusion cell [115,116]. The system was maintained at a temperature of 37±0.5°C and the skin was occluded. At the end of experiment, the stratum corneum was separated using a tape-stripping technique. The collected stratum corneum samples and the remaining epidermis with dermis, were extracted with methanol and the extracts were analyzed by gas chromatography [115117]. The amounts of terpenes penetrating into the skin after 4 h were very large. It was in the range from 200 to 1800 μg/cm2 and depended on the type of applied terpene. The total skin penetration of pure terpenes increases in the following order: linalyl acetate < (-)--pinene < (-)--pinene = eucalyptol 4. Among terpenes with log P 3 total skin absorption was 2-3 times greater for compounds with acyclic structure than those with cyclic structure. Higher skin absorption for oxygenic derivatives (alcohols and oxides) than for ester- and hydrocarbon-type terpenes was observed [115,116]. Influence of Vehicle Type on Ex Vivo Skin Penetration of Terpenes The application of two terpenes: (±)-linalool and terpinen-4-ol, incorporated singly into commonly used dermatological vehicles (oily solution, hydrogel and oil-in-water emulsion) allows to determine the influence of the type of vehicle on the skin penetration process [118]. The vehicles used demonstrated different properties. An oily solution is a typical anhydrous vehicle, while hydrogel contained 98% water. Terpenes in oil were completely dissolved, whereas only partly dissolved and/or dispersed in the hydrogel. Emulsion had intermediate properties. In this form, the terpenes were most probably present in several forms: dissolved in the oily internal phase, emulsified with the surfactants and forming an internal oily phase, as well as dissolved in surfactant micelles. Increased absorption time from 1 h to 4 h resulted in increased amounts of terpenes in the skin layers [118]. The greatest total cumulation in the skin was observed when terpenes were applied in hydrogel. This results from the high partition coefficient of the lipophilic terpene molecule between lipophilic stratum corneum and the hydrophilic vehicle. Hydrogel and the oily solution favored penetration of terpene with higher aqueous solubility [114] - cumulation of terpinen-4-ol in the skin layers, particularly epidermis with dermis, was greater than culmination of (±)-linalool. Skin penetration of terpenes applied as an oily solution and an oil/water emulsion were comparable and lower than when applied as hydrogel, mostly because of the unprofitable partition coefficient of penetrants between the stratum corneum and the vehicle. For all the preparations investigated, the cumulation of terpenes in epidermis with dermis was comparable or greater than in the stratum corneum. Influence of Vehicle Type on In Vivo Stratum Corneum Absorption of Terpenes in Humans The in vivo stratum corneum penetration was determined for (±)-linalool and terpinen-4-ol incorporated into two vehicles - oily solution and hydrogel [119]. The prepa-

Across Skin Barrier


rations were applied onto the skin of ventral forearms in humans. After 1 h, the stratum corneum layer was tape-stripped. The highest stratum corneum penetration was observed when terpenes were applied as hydrogel, and the stratum corneum absorption of terpinen-4-ol was 2.5 times greater than (±)-linalool. The stratum corneum absorption of both terpenes after application in oily solution was similar and 1.8 times smaller for (±)linalool and 4.8 times for terpinen-4-ol than for hydrogel vehicle. Comparing in vivo and ex vivo data after 1 h application, higher in vivo than ex vivo stratum corneum absorption of (±)-linalool applied in oily solution was noted while the in vivo cumulation of this terpene applied in hydrogel and terpinen-4-ol in oily solution was smaller than ex vivo. The same in vivo and ex vivo stratum corneum absorption of terpinen-4-ol administrated in hydrogel was determined. It was also proven that (±)-linalool in vivo retention in the stratum corneum is constant and is about 10 g/cm2, even at least 2 h after removal of (±)-linalool containing preparations, while for terpinen-4-ol the stratum corneum retention is about 5 g/cm2 after this time. In conclusions, presented results indicate a distinct relationship between skin penetration of terpenes and their physicochemical properties and the type of vehicle used. Current ex vivo and in vivo experimental models are imperfect [120]; therefore, it is difficult to introduce unequivocal advice relating to the use of terpenes in practice. The fact that only a small amount of terpenes is necessary for saturation of the stratum corneum and that they are able to cumulate in large amounts in the skin layers indicate overcoming the stratum corneum barrier by terpenes and possibility of easy in vivo penetration into the blood circulation. Because a higher skin penetration of terpenes from a hydrophilic vehicle was demonstrated, such formulations can cause side effects more easily [121]. ELECTRICALLY SUPPORTED METHODS AND DEVICES Iontophoresis Iontophoresis uses electric current flowing between two electrodes (anode and cathode) located onto the skin (Fig. 2) [1,17,31,100]. Substance, that undergoes electrolytic dissociation and possessing electric charge is dissolved in proper solvent and applied to the skin beneath the electrode of the same charge. The second electrode is placed in other spot and closes the electric circuit. At the moment of induction of current flow (approximately 0.5 mA/cm2), charged particles of a drug are repulsed from the electrode close to which they were applied and they migrate to the electrode of the opposite charge. This process takes place through the whole skin thickness, including dermis, thanks to which it is possible to obtain increased absorption. It is significant that permeability of the skin increases already under the influence of electric current alone due to disturbance of the lipid arrangement in the stratum corneum. Iontophoresis enables not only percutaneous delivery of charged substances, but it also enhances percutaneous penetration of small neutral molecules via electroosmosis. The phenomenon of electroosmosis, that is migration of endogenic Na+ ions to the cathode, which is also accompanied, by migration of water molecules together with dissolved substance, is used at this point. The iontophoresis is widely used in physiotherapy for, e.g., the following substances: -

applied beneath the anode: histamine, procaine and lidocaine, calcium (as calcium chloride), zinc, epinephrine;


-

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applied beneath the cathode: diclofenac, ketoprofene (as salts), iodine, hydrocortisone. Active Electrode

Power Supply Applied Formulation

Indifferent

Electrode

Stratum corneum

Epidermis

Dermis


Fig. (2). Schematic diagram of iontophoresis.

The main advantage of iontophoresis over other transdermal enhancement strategies is its ease of control [1], because electrical current is responsible for the increased delivery [122]. Thus, by manipulating current density and duration, the dose may be tailored to an individual patient’s needs. The major successes in iontophoresis are introduction on the market LidoSite® and Ionsys™ devices. LidoSite® is iontophoretic patch delivering lidocaine-epinephrine that provides fast, effective analgesia before blood draws, venipunctures, and other superficial dermatological procedures [123]. Ionsys™ contains fentanyl, and allows patients to self-administer the drug according to their personal requirements for pain relief (maximum 6 doses per hour) [1,124,125]. The other drugs recently studied in iontophoretic condition are ketoprofen [126], dexamethasone [127], botulinum toxin [128]. Electroporation Electric voltage was utilized in electroporation for percutaneous delivery of drug substances [1,17,31,100]. The phenomenon of electroporation consists in formation of water channels (pores) in intercellular lipid bilayers of the stratum corneum under the influence of short (micro- to milliseconds) electric impulses (voltage 10-1000 V) (Fig. 3). Occurrence of the channels due to the electric voltage applied also regards other biological membranes. It is believed that voltage applied also leads to melting of the lipids of the corneal layer, additionally increasing its permeability. It is worth mentioning that

Across Skin Barrier


macromolecular substances may be delivered by electroporation. It seems to be promising method; however miniaturization of devices is essential to facilitate routine use by patients.

Pulse Electrode

Power Supply

Applied Formulation

Stratum corneum

Epidermis

Dermis


Fig. (3). Schematic diagram of electroporation.

Ultrasound Ultrasound (sonophoresis or phonophoresis) uses energy of sound waves to increase percutaneous absorption of drugs (Fig. 4) [1,17,31,100,129]. Under the influence of ultrasounds, air bubbles appear in the intercellular spaces of the stratum corneum that disorganize lipid arrangement and enlarge intercellular spaces. Additionally, tissue temperature is raised by a few degrees in the spot of ultrasound activity, caused increase drug solubility and enhances dermal clearance by increasing the local blood flow. Sonophoresis is used in physiotherapy and sport medicine, to administer drugs in neuropathic pains, degenerative diseases and inflammations of joints and muscles. Ultrasound may enhance drug penetration into the stratum corneum by a thousand-fold [31]. Other Electrically Supported Methods There are some studies on skin permeation of diamagnetic substances under influence of magnetic field (magnetophoresis) [31]. The radio waves have been tested to increase skin permeability for hydrophilic drugs [130]. A pulse from a high-powered laser was used to create stress or compression waves. This photomechanical waves can be directed onto the skin to increase permeability [1,31,131]. Combined strategies for skin


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permeation enhancement, such as: iontophoresis or electroporation with chemical enhancers, iontophoresis with electroporation, etc. were also studied [1,17]. Power Supply

Ultrasonic Transducer

Applied Formulation

Stratum corneum

Epidermis

Dermis


Fig. (4). Schematic diagram of sonophoresis.

BYPASSING OR REMOVING OF STRATUM CORNEUM It seems that bypassing or removing of the stratum corneum may allow delivering of practically any substance. Such bypassing methods as microneedles and jet injections are, in fact, similar to the dermal injections – the drug is directly applied to the viable skin layers. However, both methods are painless. Trials of safe removal of the stratum corneum with use of such methods as tape-stripping, laser ablation, suction ablation, thermal ablation, microscissioning and consecutive drug application onto the viable epidermis have been undertaken continuously. Although promising, stratum corneum removing has significant disadvantage, namely long-time local removal of the skin barrier function, what may result in unpredictable side effects. The bypassing or removing of stratum corneum does not constitute transdermal drug delivery because the permeant is not crossing intact skin [17]. Microneedles Successful trials to omit the stratum corneum barrier are patches containing microneedles of length of 100-200 m and thickness of 10-50 m, usually. The patch is placed onto the skin before application of drug form, and next the drug penetrates to viable skin layer via created microholes (Fig. 5). Microneedles do not reach the nerve endings located within the dermis. The microneedles may also be coated with drug or

Across Skin Barrier


filled with drug; they can be also soluble. Microneedles are mainly tested for delivering of insulin, vaccines, DNA [1,17,31,132,133]. Microneedles patch

Stratum corneum

Epidermis

Dermis Capillary blood vessels

Fig. (5). Schematic diagram of application of microneedles patch.

Jet Injections Jet injection is an alternative method for drug delivery into the skin with use of needle and syringe [1]. The drug is delivered as a high-pressure jet (> 100 m/s), with sufficient intensity to pierce the skin [134]. The two major types of jet injectors, liquid jet injector and powder jet injector can be differentiate [1]. As microneedles, jet injectors are studied to deliver insulin, vaccines, and hormones [1]. Tape-Stripping and other Ablation Methods Tape-stripping is a technique with use of an adhesive tape that remove the stratum corneum layer by layer. Fragments of an adhesive tape are sequentially adhered onto the same penetration area of the skin, and then stripped off [17]. Although inexpensive, tape-stripping may be unrepeatable and unacceptable for routine use in patients. The stratum corneum can be removed by laser ablation [1,17]. The suction ablation uses a vacuum to produce a small blister on the skin [1]. Next, using an epidermatome, the upper surface of the blister is removed. The thermal ablation utilizes a current at radiofrequency (100 kHz). Dermabrasion and microscissiong uses aluminium oxide particles to rub off the stratum corneum. CYCLODEXTRINS IN SKIN FORMULATIONS Physicochemical Properties of Cyclodextrins Cyclodextrins (CDs) are cyclic oligosaccharides composed of at least six D-(+)glucopyranose units linked by -(1-4) bonds (Fig. 6a) [135,136]. Natural CDs occur in


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the form of white crystalline powder, and they form stable hydrates. CDs have quite rigid structures (stabilized by hydrogen bonds between C2 and C3 hydroxyl groups) lacking free rotation in -(1-4) bonds, thus they form torus-like molecules (truncated cone) (Fig. 6b) [136]. CD molecules have a hydrophilic outer surface (all hydroxyl groups in the ring are located in the exterior of torus) and a hydrophobic interior (there are skeletal carbons with hydrogen atoms and oxygen bridges inside the cavity). The nonbonding electron pairs of the oxygen bridges are directed toward the inside cavity, thereby generating high electron density [137,138]. There are three main natural CDs: -, -, and -CD composed of six, seven, and eight glucose units, respectively. They differ in ring size and physicochemical properties [135]. It is possible to achieve higher homologues, but because of their properties – large cavity dimension, high aqueous solubility, and weak complex formation – they cannot be of practical use. The CDs contain 18 (-CD), 21 (-CD), or 24 (-CD) hydroxyl groups that can be chemically modified. To improve some physicochemical properties of natural CDs, many types of derivatives have been developed: hydrophilic (methylated, hydroxyalkylated, and branched), hydrophobic (ethylated), ionic (sulphated and phosphated) [137-140]. Derivatization of parent crystal CDs usually leads to achieving amorphous mixtures of isomers; thus, their aqueous solubility is much higher [141].

a

HOH2 C O HO

O

OH

HOH2C

HO

HO H2 C

O HO

CH OH 2 O HO

O OH O

b

O

HO

OH OH O

O

HO

O

HO O

OH

O

CH2OH

OH HO O O CH2 OH

CH OH 2

Fig. (6). Structure (a) and torus-like shape (b) of -CD molecule [136].

The most important attribute of CDs is the ability to create inclusion complexes with a large number of molecules or their portions; however, not all molecules (drugs) can form stable complexes. There are some limitations, like very high aqueous-soluble substances, that generally cannot be included [142]. The size – geometric factor of the molecule is most important because it decides whether the molecule is able to form “stable” inclusion with -, -, or - CD. If the molecule had adequate properties, it interacts with CD inside cavity without forming covalent bonds; this interaction is “guest/host” type. CD inclusion complex is mainly formed via the substitution of included water by the appropriate “guest” molecule. Release of the enthalpy-rich water molecules from the cavity decreases the energy of the system. A decrease in the energy of the system is due to reduce the contact surface area between the solvent and solute as well as solvent (highly polar water) and imperfectly solvated (hydrophobic) CD cavity. Some other fac-

Across Skin Barrier


tors, such as hydrogen bonding, changes in surface tension, van der Waals’ interactions, and ring strain release, also can have some influence on the complex formation [143145]. Skin Toxicity Natural CDs are regarded as a non-irritant to skin [137]. -CD, applied under occlusion condition onto the skin surface in humans, does not induce irritation or allergenic reaction [137,138]. Skin compatibility with CDs, both natural and a wide range of derivatives, has been summarized recently by Piel et al. [146]. All tested CDs were well tolerated by the stratum corneum, with the exception of dimethylated derivatives where changes in corneoxenometry were observed, which can indicate disruption in the lipid bilayers. Overall, natural CDs and their hydrophilic derivatives are not able to permeate skin barrier in significant amounts; thus they are safe for topical applications [138,147149]. Only lipophilic RM--CD can interact with membranes more readily but in high concentrations [146,150]. Generally, all types of above-mentioned CDs can be used in skin formulations safely and without risk of irritation; even methylated CDs in low concentrations can be safely applied. Only for aqueous solution/suspension containing high concentration of CDs there is some probability that methylated CD will interact with the stratum corneum lipids (cholesterol, triglycerides) and temporarily affect the membrane integrity [150]. Application of Cyclodextrins in Skin Formulations Practical Aspects Practical use of the obtained complex with CDs is more complicated than forming the inclusion. As presented by Szejtli [151], drug formulations with CDs are usually not bioequivalent to their reference products. Even when only better stability is required, the absorption of the drug will be usually affected in the positive or negative way. CDs are able to modify dermal application both by increasing (supergenerics) [141,152-156] or by modifying delivery (retarded or prolonged release) [141,157-159]; hence, no reference products exist for performing a comparative study. Therefore, for registration purpose, all preclinical studies are necessary, which increase developmental cost for such products, and the reduction of dose does not solve this problem [151]. Advantages For liquid preparations intended for application onto the skin, the most biocompatible solvent is water. Montassier et al. [160,161] proposed aqueous-soluble tretinoin/CD complexes as an alternative for use 60% ethanol as a solvent. The solubility of drug was the same in both cases. Organic solvents, like ethanol, are corrosive to the skin and their volatility may cause recrystallisation of the drug substance during storage. CDs can be an alternative for them. In the skin formulations, improved solubility is usually associated with enhanced bioavailability, like for: sericoside [154], piroxicam [162], celeoxib [163], tretinoin [160,161], 4-biphenylylacetic acid [164], hydrocortisone [165], and bupranolol [166]. The increase in bioavailability is mainly due to higher concentration in the site of administration, caused rather by higher aqueous solubility, and thus improved availability onto the tissue surface than enhancement activity of CDs by itself. Schoch et al. [167]


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demonstrated that modified CDs (HP--CD as well as oktakis--CD) significantly increased in vitro corneal permeability of diclofenac sodium in comparison to formulation containing Cremophor. The improvement of skin permeation is possible by increasing drug solubility, which improved availability onto the skin surface or by influence on the barrier function of stratum corneum (probably only for DM--CD) [168]. In the studies of Ventura et al. [168] on percutaneous absorption of celecoxib from 0.01% solution or suspension in presence of HP--CD and DM--CD, both CDs influenced the in vitro drug permeation through human skin by shortening the lag time from 2 h to 35 min for 5% DM--CD and to 1 h for the same concentration of HP--CD. The cumulative amount of celecoxib permeating through the skin after 24 h was up to 7–8 times greater when either HP--CD or DM--CD was present in the donor phase in contrast to the uncomplexed drug substance. Complexation of dexamethasone with -CD and HP--CD protects the drug substance against skin metabolism. Such studies performed by Lopez et al. [155] on the homogenised mouse skin during 2 h resulted in 30 and 65% degradation of dexamethasone for complexed and free drug, respectively. However, this stabilising effect is limited ex vivo and in vivo by the non-homogenized full skin because CDs are not able to penetrate into viable skin layers. Limitations One of the biggest hopes for CD application in topical formulations was using them as universal non-irritating penetration enhancer for transdermal application of drug substances. CDs are regarded by some authors as classic enhancers that are able to extract all the major lipid classes and proteins, and thus reduce skin barrier function [149,169], while others regarded their action as problematic and rather unproved [17]. Above hypotheses are based on the studies on the animal skin (hairless mice or rats) performed in 1990s by Legendre et al. [149], Vollmer et al. [169] and Bentley et al. [170]. Although Bentley et al. [170] indicated that HP--CD caused removal and possible disorganisation of the lipids in the stratum corneum and Legendre et al. [149] stated that HP--CD exhibited 2-fold higher activity in removing cholesterol from rat skin than RM--CD, it seems that only methylated CDs applied in high concentrations (10–20%) in aqueous solutions can have influence on the stratum corneum [146,166,169]. No effect of possible disruption was seen for -, HP-- and -CDs [170]. Pretreatment studies realised on the rat skin for bupranolol as drug substance showed no flux increases for 2 and 10% solution of HP--CD, while for RM--CD flux increased markedly for both concentrations with concentration dependency [166]. Shaker et al. [148] suggested that HP--CD and its inclusion complex with corticosterone do not effectively penetrate into or transport through the skin. In hairless mouse skin model, HP--CD did not change the barrier function of the stratum corneum, nor did it enhance transport of corticosterone. Similar results were obtained during studies with human skin [171-173]. Valjakka-Koskela et al. [171] reported that addition of 10% of HP--CD decreases levosimendan flux through human in vitro skin from the solution form. Preis et al. [172] reported that incorporation of hydrocortisone/HP--CD inclusion complex into gel formulation did not involves changes in the drug permeation through human ex vivo skin in comparison to preparation without the CDs. In the same studies incorporation of drug substance/-CD inclusion complex into formulation resulted in decrease release. Also in the studies by Simeoni et al. [173] with excised human skin, HP--CD had no

Across Skin Barrier


effect on the stratum corneum and epidermal concentration of sunscreen agent buthylmethoxydibenzoylmethane in comparison to free molecules applied as solution. Additionally, they found that sulfobutylether--CD markedly reduced epidermal absorption of such filter without reducing its stratum corneum penetration [173]. Some of CDs, like HP--CD, -CD or sulfobutylether--CD, may not only had no influence on barrier function but they even, in certain cases, may had protective properties against penetration of drug substances into deeper skin layers [173,174]. Ventura et al. [168] presented opposite statement. They concluded that both HP-CD and DM--CD enhanced drug flux through human stratum corneum and epidermis by means of an increase of dissolution rate of the drug as well as a direct action on the stratum corneum. The direct impact on the stratum corneum is overestimated because during 24 h experiment with 3% solution of CDs, HP--CD had no destructive effect and seems that was not able to act as penetration enhancer. Although DM--CD in above conditions caused separation of corneocytes layers and had significant influence on barrier function of the skin, its inclusion complex with the drug showed less injurious effect than DM--CD alone. Possible Applications of Cyclodextrins in Skin Formulations CDs can be used to improve lipophilic drug entrapment in the aqueous liposomal phase, and thus result in a new two-carrier system of drugs-in-CD-in-liposome formulations [175-178]. Maestrelli et al. [179] investigated such system for transdermal delivery of ketoprofen. They achieved improvement in drug entrapment for ketoprofen/HP--CD complex in equimolar ratio. Encapsulation efficacy increased with CD concentration; however, high concentration destabilised liposomal membrane. Liposomal formulations resulted in slower and prolonged drug permeation through membrane (about 40% drug permeated after 24 h) in comparison to drug solution (60% after 4 h). The CD interaction with liposome lipid membrane depends on the type of CD, complexed drug substance and lipids [176-178]. In addition, some other lipidic systems, such as microspheres, are combined with drug/CD complexes, especially for mucosal delivery [180-183]. As skin penetration enhancement is difficult by using only CDs and as the influence on drug flux depends not only on CDs and drug substance properties, but also on other formulation components, recently CDs have been applied with good efficacy as coenhancers or in combination with other methods – supersaturation, electroporation, or iontophoresis [156,184-188]. CONCLUSIONS As Morrow et al. [1] note, the most of transdermal drug formulations are based on the passive diffusion, often with use of penetration enhancers, of a low molecular weight, lipophilic, unionized drugs. Although the conception of chemical skin penetration enhancement is outstanding and still promising, thousands of studies do not result in an ideal penetration enhancer. What’s more, the activity of known penetration enhancers to the highly hydrophilic or macromolecular drugs is often disputable. On the other hand, among passive penetration enhancement strategies, a few have been successfully introduced in commercial products. Recently methods, such as iontophoresis, electroporation, microneedles, are less limiting by the physicochemical properties of permeants,


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and have been show to increase delivery of macromolecules through the skin. Iontophoretic transdermal systems are recently employed in the medical care. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39]

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189

The Tape Stripping Method as a Valuable Tool for Evaluating Topical Applied Compounds J.J. Escobar-Chávez1,2,*, L.M. Melgoza-Contreras2, M. López-Cervantes1, D. Quintanar-Guerrero1 and A. Ganem-Quintanar1 1

División de Estudios de Posgrado (Tecnología Farmacéutica), Facultad de Estudios Superiores Cuautitlán-Universidad Nacional Autónoma de México, Cuautitlán Izcalli, Estado de México, México 54740 and 2Departamento de Sistemas Biológicos, Universidad Autónoma Metropolitana-Xochimilco, Calzada del Hueso 1100, Colonia Villa Quietud, México D.F. 04960, México Abstract: Quantification of drugs within the skin is essential for topical and transdermal delivery research. Over the last two decades, horizontal sectioning, consisting of tape stripping throughout the stratum corneum, has become one of the traditional investigative techniques. Tape stripping of human stratum corneum is widely used as a method for studying the kinetics and penetration depth of drugs. The Food and Drug Administration released a draft guidance proposing a Dermatopharmacokinetic method for evaluating bioavailability and/or bioequivalence of topical dermatological drug products. As specified in this document, the method measures topically applied drug levels in the outermost layer of the skin, the stratum corneum, as a function of time post-application and postremoval of the formulation, so as to generate a stratum corneum concentration versus time profile. The stratum corneum is collected by successive application and removal of adhesive tape providing a minimally invasive technique by which the drug’s concentration in the skin can be determined. The Dermatopharmacokinetic method assumes that: (i) in normal circumstances, the stratum corneum is the rate-determining barrier to percutaneous absorption, (ii) the stratum corneum concentration of drug is directly related to that which diffuses into the underlying viable epidermis, and (iii) Stratum corneum drug levels are more useful and relevant for assessing local, dermatological efficacy than plasma concentrations. This paper shows the applications of the tape stripping technique to evaluate drug penetration through the skin as well as stratum corneum composition and physiology, underlining its versatile application in the area of topical and transdermal drugs.

*Corresponding Author: Tel: (525) 56.23.20.65; Fax: (525) 58.93.86.75; E-mail: [email protected]



Escobar-Chávez et al.

1. INTRODUCTION Tape stripping (TS) with adhesive tape is a widely accepted and used method to examine the localization and distribution of substances within the stratum corneum (SC) [1-7]. This is a minimally invasive technique to sequentially remove SC by the repeated application of appropriate adhesive tapes [8]. This technique can be used to investigate SC cohesion in vivo by quantifying the amount of SC removed [9]. Today, weighing with precision balances is the most frequently used method to determine the amount of SC removed on a tape strip. The method is also used to provide information about the kinetics of transdermal drug delivery, offering an apparently easy and quite non-invasive methodology for skin tissue sampling, and is the basis of the FDA’s so-called dermatopharmacokinetic (DPK) approach to the assessment of topical bioavailability and bioequivalence [10]. However, validation and optimization of the procedure have not come quickly and the proposed guidance document has been withdrawn for re-evaluation. More recent work has addressed at least some of the important limitations of the DPK approach [11-13] and has proposed modifications in order to incorporate it into an improved protocol. A number of excellent reviews that have been published contain detailed discussions concerning many aspects of the TS technique [14-16]. The present review shows an updated overview of the use of the TS technique in the pharmaceutical field, specifically in the area of topical and transdermal drug delivery. This focus is justified due to the magnitude of the experimental data available with the use of this technique. The use of the TS technique in experimental medicine and pharmaceutical sciences has a long history. The skin is the largest organ of the body [17-19], accounting for more than 10% of body mass, and the one that enables the body to interact more intimately with its environment. Essentially, the skin consists of four layers: The SC, that is the outer layer of the skin (non-viable epidermis), and forms the rate-controlling barrier for diffusion for almost all compounds. It is composed of dead flattened, keratin-rich cells, the corneocytes. These dense cells are surrounded by a complex mixture of intercellular lipids, namely, ceramides, free fatty acids, cholesterol, and cholesterol sulphate. Their most important feature is that they are structured as ordered bilayer arrays [20]. The predominant diffusional path for a molecule crossing the SC appears to be intercellular [21-23]. The other layers are: the remaining layers of the epidermis (viable epidermis), the dermis, and the subcutaneous tissues (Fig. 1). There are also several associated appendages: hair follicles, sweat ducts, apocrine glands and nails. In a general context, the skin’s functions may be classified as protective, homeostasis maintaining functions, or sensing [25]. The importance of the protective and homeostatic role of the skin is illustrated in one context by its barrier property. This allows survival in an environment of variable temperature and water content and presence of environmental dangers, such as chemicals, bacteria, allergens, fungi and radiation. In a second context, the skin is a major organ for maintaining the body’s homeostasis, especially in terms of its composition, heat regulation, blood pressure control and excretory roles [25]. Third, the skin is a major sensory organ in terms of sensing environmental influences, such as heat, pressure, pain, allergens and microorganism entry. Finally the skin is an organ in a continuous state of regeneration and repair. To perform each of these functions, the skin must be tough, robust, and flexible, with an effective communication between each of its intrinsic components.

TS Method as a Valuable Tool


Fig. (1). Layers of human skin.

Many agents are applied to the skin either deliberately or accidentally, with either beneficial or deleterious outcomes. The main interest in dermal absorption assessment is related to: a) Local effects in dermatology (e.g., corticosteroids for dermatitis); b) transport through the skin seeking a systemic effect (e.g., nicotine patches, hormonal drug patches, etc.); c) surface effects (e.g., sunscreens, cosmetics, and anti-infectives) [26]; d) targeting of deeper tissues (e.g., nonsteroidal anti-inflammatory agents) [7, 27-35]; and e) unwanted absorption (e.g., solvents in the workplace, pesticides or allergens) [36,37]. Fig. (2) summarizes the process of percutaneous absorption and the possible routes by which a substance penetrates through the skin. The skin became popular as a potential site for systemic drug delivery, on the one hand, because of the possibility of avoiding the problems of stomach emptying, pH effects, enzyme deactivation associated with gastrointestinal passage, and hepatic firstpass metabolism; and on the other hand, due to its capability to enable input control. 2. IN VIVO METHODS FOR PERCUTANEOUS ABSORPTION MEASUREMENT There is a persistent belief that skin viability has little importance in percutaneous absorption. This concept of skin as passive membrane has led to the domination of the study of percutaneous absorption by laws of mass action and physical diffusion. This concept has also led researchers to use skin excised from cadavers (human and animal)



Fig. (2). Processes of percutaneous absorption and transdermal delivery.

and the physically (e.g., by freezing or heat separation) and chemically isolated skin sheets or sections determining chemical diffusion across these treated tissues [38]. Fig. (3) shows a schematic representation of the penetration of a drug by passive diffusion throughout the SC.

Fig. (3). Schematic representation of the way by which a drug or chemical permeate throughout the SC by passive diffusion.

The need of studying percutaneous absorption has its reality in dermatotoxicity, by which compounds pose a threat to human health, and to dermatopharmacology, for



which drugs need to be delivered into and through the skin to treat diseases both locally and systemically. Almost all the molecules of interest and concern in dermatotoxicology and dermatopharmacology are lipophilic. The SC, the barrier to percutaneous absorption, is a lipid saturated tissue that is like a sink to topically applied lipophilic materials. The chemical and physical properties of the topical vehicle and the barrier/sink properties of the living SC determine the initial absorption of compounds into the skin. The vitality of the living skin will, in part, determine the metabolism, distribution and excretion of the compounds through the skin and the body [38]. Some of the in vivo methods used to evaluate percutaneous penetration/absorption are described in the following paragraphs. 2.1. Radioactivity in Excretions In vivo percutaneous absorption is usually determined by the indirect method of measuring radioactivity in excreta after topical application of a labeled compound. In human studies chemical plasma levels are extremely low after topical application, often below assay detection level, so it is necessary to use tracers methodology. The compound, usually labeled with 14C or tritium, is applied and the total amount of radioactivity excreted in urine plus feces is determined. The amount of radioactivity retained in the body or excreted by some route not assayed (CO2, sweat) is corrected by determining the amount of radioactivity excreted after parenteral administration. This final amount of radioactivity is then expressed as the percentage of applied dose that was absorbed [39-41]. The equation used to determine percutaneous absorption is: % = 100 (Total radioactivity after topical administration/Total radioactivity after parenteral administration) The limitation on determining percutaneous absorption from urinary or fecal radioactivity, or both, is that the methodology does not account for skin metabolism. 2.2. Radioactivity in Blood Plasma radioactivity can be measured and the percutaneous absorption determined by the ratio of the AUC from the plasma concentration versus time curves following topical and intravenous administration [42]. This method has given results similar to those obtained from urinary excretion [43], and the same limitations discussed from excreta also apply here. 2.3. Surface Recovery This method consists in determining the loss of material from the surface as it penetrates into the skin. Skin recovery from an ointment or solution application is difficult because total recovery of compound from the skin is never assured. With topical application of a transdermal delivery device, the total unit can be removed from the skin and the residual amount of drug in the device determined. It is assumed that the difference between applied dose and the residual dose is the amount of drug absorbed. 2.4. Surface Disappearance Related to the method above mentioned, it is possible to monitor the disappearance of 14C from the surface of skin using appropriate instrumentation. The limitation on this



methodology is that the disappearance is due both to movement of 14C-labeled chemical into the skin and to the quenching effect of the skin on the rays bouncing back to the instrument. The degree of quench of chemical in the various cell layers of the skin has not been defined. 2.5. Biological/Pharmacological Response Biological assay is substituted for a chemical assay, and absorption is estimated. An obvious disadvantage is that the biological responses are limited to compounds that elicit responses that can be measured easily and accurately. This method is more qualitative than quantitative [44]. 2.6. Tape Stripping Method The TS method determines the concentration of chemical in the SC at the end of a short application period and, by linear extrapolation, predicts the percutaneous absorption of that chemical for longer application periods. The chemical is applied to the skin of animal or humans, after a period of time the excess of formulation is wiped from the surface and the SC is removed by successive tape application and removal. The tapes are assayed for chemical content. The major advantages of this method are: 1) The elimitation of urinary or fecal excretions to determine absorption, and 2) The applicability of nonradiolabeled determination to percutaneous absorption, because in general, the TSs contain adequate chemical concentrations for nonlabeled assay methodology. This is an interesting methodology for which more research is needed to establish limitations [45-47]. 2.7. Absolute Topical Bioavailability The only way to determine the absolute bioavailability of a topically applied compound is to measure the compound by a specific assay in blood or urine after topical and intravenous administration. This is extremely difficult to do in plasma because concentrations after topical administration are often low. However, as advances in analytical methodology bring more sensitive assays, estimates of absolute topical bioavailability are becoming more available. 2.8. Real time In Vivo Bioavailability This method determines the bioavailability of organic solvents following dermal exposure. Breath analysis is used to obtain real-time measurements of volatile organics in expired air following exposure. Human volunteers and animal breathe fresh air via a new breath-inlet system that allows for continuous real time analysis of undiluted exhaled air [48]. 3. IN VITRO TECHNIQUES TO DETERMINE PERCUTANEOUS ABSORPTION In vitro percutaneous absorption methods have become widely used for measuring the absorption of compounds that come in contact with skin. Safety evaluations of toxic chemicals frequently rely on in vitro studies for human permeation data. Animal data



must be used cautiously for estimating human absorption due to differences in barrier properties of animal and human skin [49]. In vitro absorption studies can also be used to measure skin metabolism if viable skin is obtained for the study and if the viability is maintained in the diffusion cells [50]. The in vitro system allows for the isolation of skin so that the metabolism of the organ can be distinguished from systemic metabolism. In vitro protocols generally allow the use of either the flow-through or static diffusion cell (Fig. 4). Diffusion cells should be made of a material resistant to binding of test material, such a glass or Teflon.

Fig. (4). Schematic overview of the Franz diffusion cell for in vitro skin permeation studies.



4. TAPE STRIPPING TECHNIQUE OVERVIEW The simplest method for reducing the barrier imposed by the SC is to remove it. Theoretically, an adhesive tape removes a layer of corneocytes. In vivo, removal of the SC by TS is performed by the repeated application of adhesive tapes to the skin’s surface. In Fig. (5) we can observe a detailed procedure of the TS technique. It has been found that on the flexor surface of the forearm, about 30 tape strips are needed to strip off most of the horny layer [8]. Multiple strips remove a substantial skin barrier, as evidenced by 20 to 25-fold increases in transepidermal water loss (TEWL) [51]. Usually, the amount of SC removed by TS is not linearly proportional to the number of tapes

Fig. (5). The tape stripping technique procedure.



removed [8]. TS appears to be simple and easy to perform [52-53], however there are different parameters that can influence the quantity of SC removed by a piece of tape, and these include TS mode [51, 54], skin hydration, cohesion between cells (which increases with SC’s depth), the body site and inter-individual differences [9, 55]. The impact of these factors has been frequently investigated [8, 51-57]. After its description by Pinkus [8], TS has become a standard method in dermatological research [58]. This method can be used to obtain a more susceptible skin, e.g., prior to the application of an irritant [59] or an allergen [60-62]. Similarly, TS is performed to induce a defined disruption of the water barrier, e.g., to evaluate the effect of a subsequently applied skin care product in barrier restoration [63]. It may be also used to obtain cells for mycological culture [64,65] or to investigate SC quality [66]. In dermatopharmacology, the SC barrier function [63,67] and the bioavailability and bioequivalence of topical drugs [52,68-70] can be evaluated with the use of this technique [71,72]. Because of the limited systemic absorption of topical products, bioavailability and/or bioequivalence studies used end-point parameters or surrogate pharmacodynamic markers. However, if a drug does not penetrate or partition into de SC, a pharmacodynamic activity will not take place. The skin stripping methodology allows the determination of the uptake and elimination profile of topically applied drugs. TS appears to be simple and easy to perform. However, there are parameters which have to be defined, as they may change the outcome. Because various brands of tape differ in shape, surface area, composition and adhesive properties, the influence of the tape brand on the outcome seems apparent [51,54]. Other parameters which influence the procedure can be subsumed in the intrinsic properties of the SC [51]. Although these properties are often investigated, little is known about the anatomical sites (intrinsic factor) as well as the pressure with which the tape is applied on to the skin, the duration of pressure and the removal process (extrinsic factors) influencing SC removal. In the case of bioequivalence studies, topical bioavailability can be estimated from the drug concentration within the SC, which is expected to be related to the drug concentration at the target site (i.e., usually viable epidermis or dermis) since the SC is the rate limiting barrier for percutaneous absorption. Similarly to the determination of the drug concentration in blood and/or urine as surrogate for the concentration at the target tissue, the determination of the drug concentration in the SC may serve as a surrogate for the concentration in the viable (epi-)dermis [71]. A typical profile obtained from a skin permeation study with sodium naproxen is shown in Fig. (6). TS, which enables the removal of the SC layer by layer, is a useful DPK technique for the assessment of drug amounts in SC as a function of time [72]. 5. APPLICATIONS OF THE TAPE-STRIPPING TECHNIQUE Removal by TS of the outermost skin layer, the SC, has become a common practice in recent years [7,36,37,56,73,74]. The determination of the kinetics and penetration depth of different kind of permeants by tracing the concentration profiles in SC, has been facilitated by the use of the virtually non-invasive method of SC stripping with adhesive tape [1,2,5,56,57]. For this reason, TS also offers the possibility of evaluating bioequivalence of topical dermatological dosage forms [5]. DPK characterization of active drugs in human volunteers has been suggested to be able to replace comparative clinical trials as a means of documenting bioequivalence [72].



Fig. (6). Penetration profiles across human SC of Sodium naproxen formulated in PF-127 gels with different penetration enhancers [Azone®-Transcutol® mixture, Transcutol® applying an infinite dose, and a film with Azone®-Transcutol® mixture, Transcutol® ] (Mean± SD; n =6).

Moreover, in vitro methods are encouraged by regulatory agencies regarding the provision of percutaneous absorption data for drugs, pesticides and cosmetics [75]. All these points are emphasized in Table 1, which summarizes the research with the TS technique to determine the kinetics and penetration depth of permeants (drugs and toxic chemicals) [7, 26, 35, 37, 57, 76-110], in order to evaluate the factors that influence the physiology of the SC [55, 57, 118,119], to determine the composition of the SC [121], superficial infections in the skin [123, 124], and evaluate skin regeneration [125, 126], etc. 5.1. Kinetics and Penetration Depth of Drugs 5.1.1. Analgesic and Anti-Inflammatory Drugs Arima et al. [76] investigated the effect of hydroxypropyl-P-cyclodextrin (HP-P-CD) on the cutaneous penetration and activation of ethyl 4-biphenylyl acetate (EBA), a prodrug of the non-steroidal anti-inflammatory drug 4-biphenylylacetic acid (BPAA), from hydrophilic ointment, using hairless mouse skin in vitro. When the hydrophilic ointment containing a complex of EBA with HP-P-CD was applied to full-thickness skin, HP-PCD facilitated the penetration of EBA into the skin, the BPAA flux through the tapestripped skin was greater than that through full-thickness skin, while the activation of the prodrug in the skin was slowed by TS. Their results suggest that the enhancing effect of HP-P-CD on the cutaneous penetration of EBA would be largely attributed to an increase in the effective concentration of EBA in the ointment. Curdy et al. [35] administered piroxicam from a commercially available gel to human volunteers, both passively and under the application of an iontophoretic current. After treatment, the SC at the site of application was progressively tape-stripped and piroxicam transport into the membrane was assessed by UV-analysis of drug extracted from the tape-strips. Current application enhanced drug uptake into the SC, as indicated by both increased piroxicam concentrations in the horny layer and detectable concentrations at greater depths in the membrane. The total amount of drug recovered in the SC post-iontophoresis was significantly higher than that found following passive diffusion for each application time.


Table 1.


Research on the Tape Stripping Technique as a Method to Determine Skin Penetration of Different Kind of Permeants

Outcome

Author (Ref.) Year

Effect of Azone® and Transcutol® on skin permeation of sodium naproxen formulated in PF-127 gels.

The combination of Azone® and Transcutol® in PF-127 gels enhanced sodium naproxen penetration, with up to twofold enhancement ratios compared with the formulation containing Transcutol® only.

Escobar-Chávez et al. [7], 2005

Administration of piroxicam from a commercially available gel to human volunteers, both passively and under the application of an iontophoretic current.

The total amount of drug recovered in the SC post-iontophoresis by TS was significantly higher than that found following passive diffusion for each application time.

Curdy et al. [35], 2001

Effect of hydroxypropyl-P-cyclodextrin (HP-P-CD) on the cutaneous penetration and activation of ethyl 4-biphenylyl acetate (EBA), a prodrug of nonsteroidal anti-inflammatory drug 4biphenylylacetic acid (BPAA), from hydrophilic ointment, using hairless mouse skin in vitro.

The enhancing effect of HP-P-CD on the cutaneous penetration of EBA would be largely attributable to an increase in the effective concentration of EBA in the ointment.

Arima et al. [76], 1998

Production and characterization of monoleine (MO) dispersions as drug delivery systems for indomethacin.

Reflectance spectroscopy demonstrated that indomethacin incorporated into MO dispersions can be released in a prolonged fashion. TS experiments corroborated this finding.

Unilaminar films of Eudragit E-100 prepared from naproxen-loaded nanoparticles vs. conventional films.

In vivo penetration studies showed no statistical differences for the penetrated amount of naproxen across the SC and the depth of penetration for the two films. The films formulated from nanoparticle dispersions were shown to be effective for the transdermal administration of naproxen.

Ganem-Quintanar et al. [78], 2006

Investigation of pig ear skin as a surrogate for human skin in the assessment of topical drug bioavailability by sequential TS of the SC.

Pig ear skin ex vivo is promising as a tool for topical formulation evaluation and optimization.

Herkenne et al. [79], 2006

Research 1) Kinetics and penetration depth of drugs

Esposito et al. [77], 2005



(Table 1) contd....

Outcome

Author (Ref.) Year

Determination of DPK parameters describing the rate and extent of delivery into the skin of Ibuprofen in the ventral forearms of human volunteers.

Prediction and experimental tests agreed satisfactorily suggesting that objective and quantitative information, to characterize topical drug bioavailability, can be obtained from this approach.

Herkenne et al. [80], 2007

Examination of the diffusion of copper through human SC in vivo following application of the metal as powder on the volar forearm for periods of up to 72 h.

Copper will oxidize and may penetrate the stratum corneum after forming an ion pair with skin exudates. The rate of reaction seems to depend on contact time and oxygen availability. A marked inter-individual difference was observed in baseline values and the amounts of copper absorbed.

Hostnek et al. [81], 2006

Comparison of the bioavailability of ketoprofen in a photostabilised gel formulation without photoprotection using a new DPK TS model and an established ex vivo penetration method using human skin.

The comparison of the amount of ketoprofen in the skin after 45 min with the amount penetrated through the excised skin during 36 h, suggests a change in the thermodynamic activity of ketoprofen during exposure.

Lodén et al. [82], 2004

Penetration kinetics of SLs (sesquiterpene lactones) in Arnica montana preparations, by using a stripping method with adhesive tape and pig skin as a model.

Gel preparation showed a decrease in penetration rate, whereas the penetration rate of ointments remained constant over time. The total amount of SLs penetrated depends only on the kind of formulation and the SLs-content, but not on SLs composition or on the extraction agent used.

Wagner Steffen et al. [83], 2006

Penetration experiments investigating several incubation times with three different skin flaps, using the Saarbruecken penetration model and the lipophilic model drug flufenamic acid.

A direct linear correlation was found between the SC/water partition coefficients and the drug amounts penetrated into the SC for all time intervals tested.

Wagner Heike et al. [84], 2002

Effect of dose and application frequency on the penetration of triamcinolone acetonide (TACA) into human SC in vivo.

Considerable TACA amounts were retained within the SC, independently of dose and application frequency.

Pallenda et al. [85], 2006




Outcome

Author (Ref.) Year

Sustained bactericidal activity of chlorhexidine base loaded poly(caprolactone) nanocapsules against Staphylococcus epidermidis inoculated onto porcine ear skin.

Topical application of chlorhexidine baseloaded positively charged nanocapsules in an aqueous gel achieved a sustained release of bactericide against Staphylococcus epidermidis for at least 8 h.

Lboutounne et al. [86], 2002

Design of an all-trans retinoic acid (RA) topical release system that modifies drug diffusion parameters in the vehicle and the skin, in order to reduce the systemic absorption and side-effects associated with the topical application of the drug to the skin.

RA encapsulation not only prolongs drug release, but also promotes drug retention in viable skin.

Fresno-Contreras et al. [87], 2005

Behaviour of a skin bioadhesive film containing lidocaine in vitro and in vivo.

In vivo experiments with TS indicated that the presence of water during film application is essential to achieve not only the proper adhesion, but also an effective accumulation.

Padula et al. [88], 2003

Keratolytic efficacy of topical preparations containing salicylic acid in humans by TS, and quantification of SC removal by protein analysis.

TS combined with protein analysis was sensitive in detecting the keratolytic effect of salicylic acid within hours of application.

Bashir et al. [89], 2005

Novel synthetic technique to synthesize the co-drug retinyl ascorbate (RA-AsA) ester from all-trans-retinyl chloride (RA) and l-ascorbic acid (AsA) suspended in ethanol at low temperature.

The data suggest the potential value of RA-AsA co-drug for treating damage to skin resulting from UV-induced production of free radicals.

Abdulmajed et al. [90], 2004

Glycerol replacement corrects each of the defects in aquaporine-3 (AQP3)-null mice.

The findings establish a scientific basis for the >200-yr-old empirical practice of including glycerol in cosmetic and medicinal skin formulations due to its influence on water retention and the mechanical and biosynthetic functions of the SC.

Hara et al. [91], 2003

Determination of the cutaneous bioavailability and bioequivalence of topically applied drugs in vivo. The procedure uses serial TS and TEWL measurements to quantify the thickness of the removed SC and to determine the intact membrane thickness.

Integration of the concentration profile over the entire SC thickness provides a measure of the cutaneous bioavailability and hence can be used to assess the bioequivalence of topically applied drugs.

Kalia et al. [92], 2001




(Table 1) contd....

Outcome

Author (Ref.) Year

Contribution of SC barrier and microvascular perfusion in determining dermal tissue levels of hydrophilic drugs (aciclovir and penciclovir) in vivo.

There was no relationship between fibre depth and the amount of drug dialysed, which suggests free movement of antiviral drug on reaching the aqueous environment of the dermis.

Morgan et al. [93], 2003

Determination of the in-vitro dermal delivery of a new class of lipophilic, highly potent and uniquely selective anti-VZV nucleoside analogue compared with aciclovir.

Topical delivery of these compounds is highly promising as a new first line treatment for VZV infections.

Jarvis et al. [94], 2004

Effect of CpG oligodeoxynucleotide (CpG-ODN) on the immune response to an antigen applied to tape-stripped mouse skin by evaluating the production of cytokines and Ig isotypes.

Administration of CpG ODN through skin is a simple strategy for patients with diseases like atopic dermatitits, which is characterized by Th2dominated inflammation.

Inoue et al. [96,97], 2005, 2006

Administration of human immunodeficiency virus type-1 (HIV-1) DNA vaccine with cytokine-expressing plasmids to the skin of mice by a new topical application technique involving prior elimination of keratinocytes using TS.

Topical application is an efficient route for DNA vaccine administration and that the immune response may be induced by DNA plasmids taken in by DCs, Langerhans cells, or others such as antigen-presenting cells.

Liu et al. [98], 2002

Effect of sucrose esters (sucrose oleate and sucrose laureate in water or Transcutol®, TC) on the SC barrier properties in vivo. Impact of these molecules on the in vivo percutaneous penetration of 4-hydroxybenzonitrile (4-HB).

A combination of sucrose esters (oleate or laureate) and TC is able to temporarily alter the SC barrier properties, thereby promoting 4-HB penetration.

Ayala-Bravo et al. [99], 2003

Absorption of 4-cyanophenol (4CP) in humans using TS experiments to assess the conditions under which diffusion alters tape stripping results.

Chemical concentrations in TSs can be affected by diffusion during tape stripping, but with tTS < 0.2 tlag and an exposure time > 0.3 tlag , TS concentrations are not significantly affected by tTS.

Reddy et al. [100], 2002

Development of a sensitive method for the determination of polyethylene glycols with different molecular weights (MW) in the human SC obtained by TS.

The method showed to be suitable for studying permeability in normal and impaired skin with respect to MW in the range of 150–600 Da.

Jakasa et al. [101,102], 2004, 2007




Outcome

Author (Ref.) Year

Dependence of permeability on molecular weight with different forms of barrier disruption.

Irrespectively of the form of barrier disruption, not only higher amounts, but also more varieties of chemicals (larger molecules) may penetrate into the skin in the presence of a compromised barrier, compared with normal skin.

Tsai et al. [103], 2003

Measurement of the status of skin surface hydration of uraemic patients with the corneometer and skin surface hydrometer, the functional capacity and the urea concentration in SC by TS technique, as well as the response of eccrine sweat gland to sudorific agent (0.05% pilocarpine HCL) in 18 age-matched haemodialysis patients and 10 healthy volunteers.

The functional abnormalities of eccrine sweat glands may be account for dry skin in uraemic patients at least in part, but there is no correlation between xerosis and pruritus.

Park et al. [104], 2001

Penetration of octyl methoxycinnamate (OMC) encapsulated in poly(caprolactone) nanoparticles, into and across porcine ear skin in vitro.

Nanoparticulate encapsulation of OMC increased its “availability” within the SC.

Alvarez-Román et al. [105], 2004

In vivo distribution profile of OMC contained in nanocapsules (NCs) through the SC. Comparison with a nanoemulsion (NE) and a conventional o/w emulsion (EM).

NE increased the extent of OMC penetration relative to the penetration achieved by NCs or EM.

Olvera-Martínez et al. [26], 2005

Quantification of four common sunscreen agents, namely 2-hydroxy-4 methoxybenzophenone, 2-ethylhexyl-pmethoxycinnamate, 2ethylhexylsalicylate (octylsalicylate) and salicylic acid 3,3,5trimethcyclohexyl ester in a range of biological matrices.

A preliminary clinical study demonstrates a significant penetration of all sunscreen agents into the skin, as well as of oxybenzone and its metabolites across the skin.

Sarveiya et al. [106], 2004

Amount of sunscreen present on the skin of people at the beach.

The best protected areas were the upper arm and décolleté, but even in these areas, most volunteers had applied only 10% of the COLIPA standard amount.

Lademann et al. [107], 2004




(Table 1) contd....

Research

Outcome

Author (Ref.) Year

1) Kinetics and penetration depth of drugs Penetration of titanium dioxide (TiO2) and In vivo and in vitro penetration studies methylene bis-benzotriazoyl tetramethylshowed an absence of TiO2 penetration into butylphenol (MBBT), included in a viable skin layers through either transcorbroad-spectrum sunscreen formulation, neal or transfollicular pathways, and a into human skin in vivo, using the TS negligible transcutaneous absorption of method, and in vitro, using a compartMBBT. mental approach.

Mavon et al. [108], 2007

In vitro human skin permeation and distribution of geranyl nitrile (GN)

Systemic exposure resulting from the use of GN as a fragrance ingredient, under unoccluded conditions, would be low based on the currently reported use levels.

Brian et al. [109], 2007

Development of a method to investigate the effectiveness of reservoir closure by different formulations. Model penetrant: Patent Blue V.

Application of barrier creams cannot replace other protective measures and should be maximally used to inhibit low-grade irritants or in combination with other protectants, or in body areas where other protective measures are not applicable.

Teichmann et al. [110], 2006

Differences in the distribution and the Penetration of highly hydrophilic and localization of both dyes within the SC were lipophilic dyes into the skin using pure oil observed. These differences depend on the or water, comparing them with an o/w physicochemical properties of both the emulsion. vehicles and the dyes.

Jacobi et al. [111], 2006

2) Dermal absorption of toxic or irritant chemicals Naphthalene has a short retention time in the Development and testing of a simple, nonhuman SC and the tape stripping method, if invasive dermal sampling technique on used within 20 min of the initial exposure, human volunteers under laboratory condican be employed to measure the amount of tions to estimate acute dermal exposure to naphthalene in the SC due to a single expojet fuel (JP-8). sure to jet fuel.

Mattorano et al. [36], 2004

The amount of keratin removed with tape strips was not affected by an exposure of up Normalization of extracted concentrations to 25 min to JP-8, and there was a substanof naphthalene (as a marker for jet fuel tial decrease in the amount of keratin reexposure) from human volunteers, before moved with consecutive TSs from the same and after exposure to jet fuel (JP-8). site; thus, adjusting the amount of naphthaRemoval and quantification of keratin by lene to the amount of keratin measured in a SC-TS tape-strip sample should improve the interpretation of the amount of this analyte by using this sampling approach.

Chao et al. [37], 2004



Outcome

Author (Ref.) Year

Description of a physiologically based pharmacokinetic (PBPK) model developed to simulate the absorption of organophosphate pesticides, such as parathion, fenthion, and methyl parathion, through porcine skin with flow-through cells.

The study demonstrated the utility of PBPK models for studying dermal absorption, which can be useful as explanatory and predictive tools.

Van der Merwe et al. [112], 2006

Study of whether the sodium lauryl sulphate (SLS) penetration rate into the SC is related to an impairment of skin’s water barrier function and inflammation.

Variation in barrier impairment and inflammation of human skin depends on SLS penetration rate, which was mainly determined by SC thickness.

Jongh et al. [113], 2006

Modification and testing of a vacuuming sampler for removing particles from the skin.

Agreement between the vacuuming sampler and the TS technique.

Lundgren et al. [114], 2006

Development of a noninvasive sampling method for measuring dermal exposure to a multifunctional acrylate employing TS.

No significant difference was observed in recovery between TPGDA and UV-resin for the first tape stripping when calculated as a percentage of the theoretical amount. TS can be used to quantify dermal exposure to multifunctional acrylates

Nylander-French et al. [115], 2002

Research 2) Dermal absorption of toxic or irritant chemicals

3) Evaluation of factors that influence the physiology of the stratum corneum

Effect of gender on the physiology of the SC.

The skin of women was characterized by a significantly higher pH value (5.6±0.4) than that of men (4.3±0.4). Protein absorption was the only other parameter significantly dependent on gender.

Jacobi et al. [122], 2005

Influence of procedures inherent to each stripping protocol on changes in skin physiology.

Skin hydration was not influenced by the variables tested.

Löffler et al. [56], 2004

Efficacy of TS in removing complete cell layers from the superficial part of human SC.

Furrows in the skin can present difficulties when performing depth penetration studies. Although the largest part of the skin surface will be stripped properly, it has to be realized that small areas, represented by furrows, may still contain high concentrations of the substance applied.

Van der Molen et al. [57], 1997



(Table 1) contd....

Research

Outcome

Author (Ref.) Year

3) Evaluation of factors that influence the physiology of the stratum corneum Local changes in the ultrastructure of human skin after iontophoresis in human skin in vitro and in vivo.

No drastic changes in the ultrastructure of the SC were observed.

Fatouros et al. [123], 2006

Study of the differences in the SC lipid profile in healthy and diseased human skin relative to the SC lipid organization and to the skin barrier function in vivo.

Weerheim et al. [124], 2001

Evaluated, using attenuated total reflectance Fourier transform infrared spectroscopy, the SC bioavailability of terbinafine (TBF) following topical treatment with four different formulations, based on a vehicle consisting of 50% ethanol and 50% isopropyl myristate.

It was found that the formulation containing 5% oleic acid significantly enhanced the SC availability of TBF

Alberti et al. [125], 2001

Determined whether a structurally heterogeneous biomembrane, human SC, behaved as a homogeneous barrier to water transport.

The variation of TEWL as a function of SC removal behaved in a manner entirely consistent with a homogeneous barrier, thereby permitting the apparent SC diffusivity of water to be found.

Kalia et al. [126], 1996

4) Stratum corneum composition Establishment of a suitable analytical method for the determination of the local SC lipid composition. 5) Stratum corneum thickness

6) Determination of superficial infections and viruses New animal model for the purpose of studying superficial infections.

Evaluation of novel antimicrobial treatments for superficial infections caused by S. aureus and S. pyogenes.

Kugelberg et al. [129], 2005

Immunocompetent patients were tested for human papilloma virus (HPV) DNA in swab samples collected on top of skin tumors and in biopsies of the same tumors, obtained after stripping with tape to remove superficial layers.

HPV DNA is common in superficial layers of lesions, but is not necessarily present in tumors.

Forslund et al. [130], 2004

Tight junctions of regenerating epidermis may provide a functional barrier prior to regeneration of the corneal layer.

Malminen et al. [131], 2003

7) Skin regeneration Expression of tight junction components during the reepithelialization of suction blisters and the regeneration of the corneal layer after TS.



Outcome

Author (Ref.) Year

In vitro model for the developing skin of the premature neonate.

Porcine skin, in vitro, tape-stripped to a particular level, can provide a barrier corresponding to a specific degree of neonate maturation, and thus, can serve as a useful tool to explore whether transdermal drug delivery in this unique patient population may be beneficial.

Sekkat et al. [132], 2002

An in vivo human model was utilized to define the irritation potential of a topical agent after partial removal of the stratum corneum by cellophane TS

This model provides a method for the prediction, with exaggerated sensitivity, of chemical irritation and proclivity to enhance or retard water barrier repair.

Zhai et al. [133], 1998

Research 7) Skin regeneration

Escobar-Chávez et al. [7] determined the penetration of sodium naproxen, formulated in Pluronic F-127 gels containing Azone® and Transcutol® as penetration enhancers, through human skin in vivo by using the TS technique. It was found that the combination of Azone® and Transcutol® in PF-127 gels enhanced sodium naproxen penetration, with up to two-fold enhancement ratios compared with the formulation containing only Transcutol. These results were confirmed by TEWL and ATR-FTIR spectroscopy, suggesting a synergistic action for Azone® and Transcutol®. Esposito et al. [77] produced and characterized monoleine (MO) dispersions as drug delivery systems for indomethacin. An in vitro diffusion study was conducted using Franz cells associated to SC epidermal membrane on cubosome dispersions viscosized by carbomer. In vivo studies based on skin reflectance spectrophotometry and TS were performed to better investigate the performance of cubosome as an indomethacin delivery system. Indomethacin incorporated in viscosized MO dispersions exhibited a lower flux with respect to the analogous formulation containing the free drug in the aqueous phase and to the control formulation based on carbomer gel. Reflectance spectroscopy demonstrated that indomethacin incorporated into MO dispersions can be released in a prolonged fashion. TS experiments corroborated this finding. MO dispersions can be proposed as nanoparticulate systems able to control the percutaneous absorption of indomethacin Ganem-Quintanar et al. [78] used naproxen-loaded nanoparticles to prepare, in a one-step process, unilaminar films of Eudragit E-100. Nanoparticle films and conventional films were characterized in vitro by drug release studies through a cellulose membrane using Franz-type cells, and in vivo by penetration experiments with the TS technique. Concerning in vivo penetration studies, no statistical differences were found for the amount of naproxen penetrated across the SC and the depth of penetration for the two films.



Herkenne et al. [79] investigated pig ear skin as a surrogate for human skin in the assessment of topical drug bioavailability by sequential TS of the SC. Ex vivo experiments on isolated pig ears were compared with in vivo studies in human volunteers. Four formulations including ibuprofen in different propylene glycol (PG)-water mixtures (25:75, 50:50, 75:25 and 100:0), were compared. Derived DPK parameters characterizing the diffusion and partitioning of the drug in the SC ex vivo were consistent with those in vivo following a 30-minute application period. Furthermore, non-steady-state ex vivo results could be used to predict the in vivo concentration profile of the drug across the SC when a formulation was administered for 3 h (i.e., close to steady-state). Taken together, the results obtained suggest that pig ear skin ex vivo is promising as a tool for topical formulation evaluation and optimization. Continuing with their research, Herkene et al. [80] explored the potential of using SC-TS, post application of a topical drug formulation, to derive DPK parameters describing the rate and extent of delivery into the skin of Ibuprofen in the ventral forearms of human volunteers for periods ranging between 15 and 180 minutes. Subsequently, SC was tape-stripped, quantified gravimetrically, and extracted for drug analysis. Together with concomitant TEWL measurements, SC concentration–depth profiles of the drug were reproducibly determined and fitted mathematically. The SC-vehicle partition coefficient (K) and a first-order rate constant related to ibuprofen diffusivity in the membrane (D/L2, where L SC thickness) were derived from data-fitting and characterized the extent and rate of drug absorption across the skin. Integration of the concentration profiles yielded the total drug amount in the SC at the end of the application period. Using K and D/L2 obtained from the 30-minute exposure, it was possible to predict ibuprofen uptake as a function of time into the SC. Prediction and experiment agreed satisfactorily suggesting that objective and quantitative information, with which to characterize topical drug bioavailability, can be obtained from this approach. Hostnek et al. [81] sheded light on the long-standing controversy on whether wearing copper bangles benefits patients suffering from inflammatory conditions such as arthritis. Sequential TS was performed on healthy volunteers to examine the diffusion of copper through human SC in vivo, following application of the metal as powder on the volar forearm for periods of up to 72 h. Exposure sites were stripped 20 times, and the strips were analyzed for metal content by inductively coupled plasma-mass spectroscopy. The results indicate that, in contact with skin, copper will oxidize and may penetrate the SC after forming an ion pair with skin exudates. The rate of reaction seems to depend on contact time and oxygen availability. A marked inter-individual difference was observed in baseline values and amounts of copper absorbed. Lodén et al. [82] compared the bioavailability of ketoprofen in a photostabilised gel formulation without photoprotection using a new DPK tape stripping model and an established ex vivo penetration method using human skin. Analyses of the SC showed that during the first 45 minutes, about 12 μg/cm2 of ketoprofen were absorbed into the skin from the formulations. The area under the ketoprofen concentration–time curve (AUC0–6 h) for the photo-stabilised gel/transparent gel ratio was 73%. The rate of penetration of ketoprofen through isolated skin was approximately 0.2 μg/cm2h for both formulations. The ratio’s AUC0–36 h was 84%. Thus, the two methods did not disagree in terms of the relative efficacy of the two gels. The comparison of the amount of ketoprofen in the skin



after 45 min with the amount penetrated through the excised skin during 36 h, suggests a change in the thermodynamic activity of ketoprofen during exposure. A supersaturated formulation may have been formed initially due to evaporation of ethanol. Wagner Steffen et al. [83] studied the penetration kinetics of sesquiterpene lactones (SLs) in Arnica montana preparations; a stripping method with adhesive tape and pig skin as a model was used. For the determination of SLs in the stripped layers of the SC, a gas chromatography/mass spectrometry method was developed and validated. The penetration behavior of one gel preparation and two ointment preparations was investigated. The SLs of all preparations showed a comparable penetration and permeation through the SC, in the uppermost layer of the skin. Interestingly, the gel preparation showed a decreased penetration rate over 4 h, whereas the penetration rate of ointments remained constant over time. Moreover, they could demonstrate that the total amount of SLs penetrated depends only on the kind of formulation and the SLs-content in the formulation, but not on SLs composition or the extraction agent used. Wagner Heike et al. [84] carried out penetration experiments to investigate several incubation times with three different skin flaps using the Saarbruecken penetration model and the lipophilic model drug flufenamic acid. Drug distribution within SC was obtained by the TS technique, while the drug present in deep skin layers was determined by cryosectioning. In addition, for the lipophilic drug flufenamic acid, a direct linear correlation was found between SC/water partition coefficients and the drug amounts penetrated into the SC for all the time intervals tested. The authors concluded that SC/water-partition coefficients offer the possibility to predict drug amounts within the SC of different donor skin flaps, without a time-consuming determination of the lipid composition of the SC. 5.1.2. Corticosteroids The aim of Pellanda et al. [85] was to investigate the effect of i) dose and ii) application frequency on the penetration of triamcinolone acetonide (TACA) into human SC in vivo. The experiments were conducted on the forearms of 15 healthy volunteers, with i), single TACA doses (300 μg/cm2 and 100 μg/cm2), and ii) single (1 x 300 μg/cm2) and multiple (3 x 100 μg/cm2) TACA doses. SC samples were collected by TS after 0.5, 4 and 24 h (i) and after 4, 8 and 24 h (ii). In Experiment 1, TACA amounts within SC after application of 1 x 300 μg/cm2 compared to 1 x 100 μg/cm2 were only significantly different immediately after application, and were similar at 4 and 24 h. In ii), multiple applications of 3 x 100 μg/cm2 yielded higher TACA amounts compared to a single application of 1 x 300 μg/cm2 at 4 and 8 h. At 24 h, no difference was observed. In conclusion, by using this simple vehicle, considerable TACA amounts were retained within the SC, independently of dose and application frequency. 5.1.3. Disinfectants Lboutounne et al. [86] investigated the sustained bactericidal activity of chlorhexidine base loaded poly(-caprolactone) nanocapsules against Staphylococcus epidermidis inoculated onto porcine ear skin. The antimicrobial activity of these colloidal carriers was evaluated (i) in vitro against eight strains of bacteria, and (ii) ex vivo against Staphylococcus epidermidis inoculated for 12 h onto porcine ear skin surface treated for 3 min either with 0.6% chlorhexidine base loaded or unloaded nanocapsules suspended in hydrogel, or 1% chlorhexidine digluconate aqueous solution. Chlorhexidine absorption into



the SC was evaluated by the TS technique. The results showed that chlorhexidine nanocapsules in aqueous suspension with a 200–300 nm size and a positive charge exhibited similar minimum inhibitory concentrations against several bacteria, compared with chlorhexidine digluconate aqueous solution. Ex vivo, there was a significant reduction in the number of colony forming units from skin treated with chlorhexidine nanocapsules suspension for 3 min compared to chlorhexidine digluconate solution after an 8-h artificial contamination. Interestingly, nanocapsules were present in porcine hair follicles. Topical application of chlorhexidine base-loaded positively charged nanocapsules in an aqueous gel achieved a sustained release of bactericide against Staphylococcus epidermidis for at least 8 h. 5.1.4. Drugs for Keratinization Disorders Fresno-Contreras et al. [87] designed an all-trans retinoic acid (RA) topical release system that modifies drug diffusion parameters in the vehicle and the skin in order to reduce systemic absorption and side-effects associated with the topical application of the drug to the skin. RA, either in free form or encapsulated in SC lipid liposomes, was included in hydrogels prepared with Carbopol® UltrezTM 10 and hyaluronic acid. In vitro permeability experiments with [3H]-t-RA were carried out using a Franz-type diffusion cell in abdominal rat skin samples. Accumulation of the drug in the surface and skin layers was evaluated by both the TS technique and a dissection technique. The results show that RA encapsulation not only prolongs drug release, but also promotes drug retention in viable skin. At the same time, interaction between RA and hyaluronic acid has an obstructive effect on diffusion, which contributes to the formation of a reservoir. 5.1.5. Anesthetics Padula et al. [88] studied the behavior of a skin bioadhesive film containing lidocaine, in vitro and in vivo. Film characterization included in vitro and in vivo drug transport studies with and without iontophoresis. The release rate was compared with a lidocaine commercial gel. The permeation kinetics across the skin was not linear, but the patch acted as a matrix controlling drug delivery. Additionally, permeation rate increased with drug loading. The in vivo experiments with TS indicated that the presence of water during film application is essential to achieve not only the proper adhesion, but also an effective accumulation. The application of an electric current to the patch can further increase the amount of drug accumulated in the SC. 5.1.6. Keratolytics Bashir et al. [89] studied the keratolytic efficacy of topical preparations containing salicylic acid (SA) in humans by the TS technique, quantifying SC removal by protein analysis. In combination with TS, squamometry was used to evaluate the influence of SA on skin surface scaliness and desquamation. Furthermore, skin barrier perturbation and skin irritability were recorded and related to the dermatopharmacological effect of the preparations. In contrast to squamometry, TS combined with protein analysis was sensitive in detecting the keratolytic effect of SA within hours of application. Importantly, whereas the pH of the preparations had only a minimal influence on efficacy, local dermatotoxicity was significantly increased at an acidic pH. This indicates that the intent to increase the amount of free, non-dissociated SA is, in fact, counterproductive, as more acidic preparations resulted in skin irritation and barrier disruption.



5.1.7. Retinoids and Antioxidants Abdulmajed et al. [90] used a novel synthetic technique to synthesize the co-drug retinyl ascorbate (RA-AsA) ester from all-trans-retinyl chloride (RA) and l-ascorbic acid (AsA) suspended in ethanol at low temperature. The flux and permeation coefficient were determined using heat separated human skin membrane, and skin penetration was determined by TS using full-thickness human skin. All experiments were performed in parallel with retinyl palmitate and ascorbyl palmitate. Overall, the data suggest the potential value of RA-AsA co-drug for treating damage to skin resulting from UV-induced production of free radicals. 5.1.8. Aquaporine-3 Hara et al. [91] showed that glycerol replacement corrects each of the defects in aquaporine-3 (AQP3)-null mice. SC water content, measured by skin conductance and 3 H2O accumulation, was 3-fold lower in AQP3-null vs. wild-type mice, but was similar after topical or systemic administration of glycerol in amounts that normalized glycerol content in the SC. Orally administered glycerol fully corrected reduced skin elasticity in AQP3-null mice, as measured by the kinetics of skin displacement after suction, and the delayed barrier recovery, as measured by TEWL after TS. The analysis of [14C]glycerol kinetics indicated a reduced blood-to-SC transport of glycerol in AQP3-null mice, resulting in slowed lipid biosynthesis. These data provide functional evidence for a physiological role of glycerol transport by aquaglyceroporin, and indicate that glycerol is a major determinant of SC water retention and of mechanical and biosynthetic functions. Their findings establish a scientific basis for the >200 year old empirical practice of including glycerol in cosmetic and medicinal skin formulations. 5.1.9. Antimycotic Drugs Kalia et al. [92] presented a method to determine the cutaneous bioavailability and hence to evaluate the bioequivalence of topically applied drugs in vivo. The procedure uses serial TS and TEWL measurements to quantify the thickness of the removed SC and to determine the intact membrane thickness. Following TS, the drug is extracted from the tapes and assayed, by HPLC. This provides a drug concentration profile of terbinafine as a function of the normalized position within the SC. The data are fitted to a solution of Fick’s second law of diffusion in order to calculate characteristic membrane transport parameters. Integration of the concentration profile over the entire SC thickness, that is, the AUC, provides a measure of the cutaneous bioavailability and hence can be used to assess the bioequivalence of topically applied drugs. 5.1.10. Antiviral Drugs Morgan et al. [93] measured the contribution of SC barrier and microvascular perfusion in determining dermal tissue levels of two hydrophilic drugs (aciclovir and penciclovir) in vivo. Removal of the SC by TS resulted in a 1300-fold increase in penciclovir absorption and a 440-fold increase in aciclovir absorption, confirming that SC is the major barrier to hydrophilic drug absorption. 5.1.11. Anti-Varicella Zoster Virus Nucleoside Jarvis et al. [94] determined the in-vitro dermal delivery of a new class of lipophilic, highly potent and uniquely selective anti-Varicella Zoster virus nucleoside (VZV) ana-



logue compared with aciclovir. Three test compounds (Cf1698, Cf1743, and Cf1712) and acyclovir were formulated in propylene glycol/aqueous cream, and finite doses were applied to full-thickness pig ear skin for 48 hours in vertical Franz-type diffusion cells. Depth profiles were constructed following TS and membrane separation. All three test compounds reached the target basal epidermis in concentrations suggesting they would be highly efficacious in reducing viral load. Furthermore, the data showed that each of the test compounds would have a far superior performance than aciclovir. The dermatomal site of viral replication during secondary infection —the basal epidermis— was successfully targeted. 5.1.12. Vaccines The skin-associated lymphoid tissue, formed by powerful antigen-presenting cells (APCs), such as Langerhans cells (LCs), dermal dendritic cells (DCs), re-circulating T cells, and regional LNs, ensures the efficient presentation of antigen to immunocompetent cells and the induction of strong immune responses. LCs and dermal DCs commonly exist in the skin and are easy to target [95]. The TS technique has been used to study the effect of oligodeoxynucleotides on the immune response [96] and expression of immune receptors [97]. Inoue et al. [96] examined the effect of CpG-oligodeoxynucleotide (CpG-ODN) on the immune response to an antigen applied to tape-stripped mouse skin, by evaluating the production of cytokines and Ig isotypes. Confocal laser scanning microscopy revealed that the OVA (model antigen) and CpG-ODN easily penetrated the tape-stripped skin. Co-administration of CpG-ODN and OVA to the disrupted skin elicited an antigenspecific, Th1-predominant immune response, and enhanced the production of Th1-type cytokines, IL-12 and IFN-. On the other hand, the production of a Th2-type cytokine, IL-4, was drastically suppressed. In terms of antigen-specific antibody production, the IgG2a level, which is regulated by IFN-, was increased by CpG-ODN, but IgE production regulated by IL-4 was suppressed. Furthermore, the administration of CpG-ODN through the skin drastically attenuated the production of IgE in mice experiencing IgEtype immune response. Administration of CpG-ODN through the skin may shift the immune response from a Th2 to a Th1-like response. Continuing with their studies, Inoue et al. [97] also demonstrated that TS induces the expression of toll-like receptor (TLR)-9 in the skin, and enhances the Th1-type immune response triggered by CpG-ODN administered through the tape-stripped skin. TS induces the expression of TLR-9 and tumor necrosis factor (TNF)- in the skin, and CpGODN treatment through the tape-stripped skin enhances the migration of antigenpresenting cells to the draining lymph nodes. On the other hand, TLR-9 mRNA and TNF- mRNA were not observed in the skin when CpG-ODN was injected intradermally, or in Th1-type immune response. The transdermal application of CpG-ODN with an antigen through the tape-stripped skin is an effective way to induce a Th1-type immune response, and is also a simple, cost-effective and needle-free vaccination system. Liu et al. [98] administered human immunodeficiency virus type-1 (HIV-1) DNA vaccine with cytokine-expressing plasmids to the skin of mice by a new topical application technique involving prior elimination of keratinocytes using TS. Their results revealed that the topical application of HIV-1 DNA vaccine induced high levels of both humoral and cell-mediated immune activity against HIV-1 envelope antigen. Coadministration of the DNA vaccine with cytokine expression plasmids of IL-12 and gra-



nulocyte-macrophage colony-stimulating factor by this new method raised the levels of both the HIV-specific cytotoxic T lymphocyte (CTL) response and delayed-type hypersensitivity (DTH) and facilitated the induction of substantial immune responses by DNA vaccine. Skin biopsy sections showed significant increases of S-100 protein-positive dendritic cells (DCs). These results suggest that the topical application method is an efficient route of DNA vaccine administration and that the immune response may be induced by DNA plasmids taken in by DCs, Langerhans cells (LCs), or others such as antigen-presenting cells. This new topical application is likely to be of benefit in clinical use. 5.1.13. Other Kind of Permeants (Polyethylene Glycols, 4-Cyanophenol, Urea) Ayala-Bravo et al. [99] investigated the effect of sucrose esters (sucrose oleate and sucrose laureate in water or Transcutol®, TC) on the SC barrier properties in vivo, and examined the impact of these surfactant-like molecules on the in vivo percutaneous penetration of a model penetrant, 4-hydroxybenzonitrile (4-cyanophenol, 4CP). The effect of the enhancers on 4CP penetration was monitored in vivo using ATR-FTIR spectroscopy in conjunction with TS of the treated site. A combination of sucrose esters (oleate or laureate) and TC is able to temporarily alter the SC barrier properties, thereby promoting 4CP penetration. Results from TS experiments can be affected significantly by chemical diffusion into the SC during the time required to apply and remove all of the TSs, tTS (period of time required to completely remove the SC by TS). For this reason, Reddy et al. [100] studied dermal absorption of 4CP in humans using TS experiments to assess the conditions under which diffusion alters TS results. Mathematical models were developed to assess the effects of diffusion on parameter estimation. In an experiment with tTS > tlag (i.e., the lag time for a chemical to cross the SC), the permeability coefficient for 4CP, Psc,v, calculated including tTS, was consistent with the values from the literature. When diffusion during stripping was not included in the model, Psc,v, was 70% smaller. Calculations show that chemical concentrations in TSs can be affected by diffusion during TS, but with tTS < 0.2 tlag and an exposure time > 0.3 tlag, TS concentrations are not significantly affected by tTS. Jakasa et al. [101] developed a sensitive method for the determination of polyethylene glycols (PEGs) with different molecular weights (MW) in the human SC obtained by TS. The analysis is based on derivatization with pentafluoropropionic anhydride and gas chromatography–electron capture detection. The method showed to be suitable for studying permeability in normal and impaired skin with respect to MW in the range of 150–600 Da. In order to obtain more data to assess the barrier function of uninvolved skin in atopic dermatitis (AD) patients, Jakasa et al. [102] determined the percutaneous penetration of PEGs of various molecular sizes in vivo in AD patients and control subjects using TS of the SC. The apparent diffusion coefficient of PEGs through atopic skin was twice as high as through normal skin, and decreased with increasing MW in both groups. The partition coefficient in the skin of AD patients was half of that for normal skin, but as for normal skin, there was no MW dependence. Although atopic skin exhibited an altered barrier with respect to diffusion and partitioning, the permeability coefficients were nearly the same for atopic and normal skin. The results support the assumption of an altered skin barrier in AD patients, even if the skin is visibly unaffected by the disease.



Tsai et al. [103] further investigated the dependence of permeability on MW with different forms of barrier disruption. A series of PEGs with a MW ranging from nearly 300 to over 1000 Da were used to study the effects of TS and sodium dodecyl sulphate (SDS) treatment on MW permeability profiles of mouse skin in vitro. The total penetration of PEG oligomers across control skin and tape-stripped skin and SDS-treated to different degrees of barrier disruption progressively decreased with increasing MW. Penetration enhancement relative to control skin was more prominent with larger molecules. The MW cut-off for skin penetration increased with the degree of barrier disruption, irrespectively of the treatment applied, and was 986 Da (TS) and 766 Da (SDS treatment) at TEWL levels in the range of 10–20 g/m2 per h, compared with 414 Da for control skin. The results strongly suggest that, regardless of the form of barrier disruption applied, not only higher amounts, but also more varieties of chemicals (larger molecules), may penetrate into the skin in the presence of a compromised barrier compared with normal skin. Park et al. [104] measured the status of skin surface hydration of uraemic patients with the corneometer and skin surface hydrometer, the functional capacity and the urea concentration of SC by TS technique and the response of eccrine sweat gland to sudorific agent (0.05% pilocarpine HCL) in 18 age-matched haemodialysis patients and 10 healthy volunteers. They also performed the water sorption-desorption test to uraemic and control subjects after application of urea in various concentrations. Uraemic patient's skin showed decreased water content compared to control subjects. However, they found no correlation between dry skin and pruritus. Although the urea concentration determined by TS of the horny layer in uraemic patients was elevated compared to control subjects (28.2 g/cm2 vs 5.04 g/cm2), its moisturizing effect to relieve pruritus is questionable because its artificial application revealed no improvement of the functional capacity of horny layer in concentration 5 times higher than the physiological concentration. Uraemic patients showed decreased sweating response to sudorific agent. In conclusion, the functional abnormalities of eccrine sweat glands may be account for dry skin in uraemic patients at least in part, but there is no correlation between xerosis and pruritus. 5.1.14. UV Absorbers Alvárez-Román et al. [105] determined whether encapsulation of lipophilic compounds in polymeric nanoparticles is able to improve topical delivery to the skin. The penetration of octyl methoxycinnamate (OMC) encapsulated in poly(-caprolactone) nanoparticles, into and across porcine ear skin in vitro, was investigated using TS. Quantification of OMC in the skin using TS demonstrated that nanoparticulate encapsulation produced a 3.4-fold increase in the level of OMC within the SC. Nanoparticulate encapsulation of OMC increased its “availability” within the SC. Olvera-Martínez et al. [26] prepared polymeric nanocapsules (NCs) containing OMC, and their in vivo distribution profile through the SC was determined by the TS technique. The penetration degree of OMC formulated in NCs was compared with that obtained for a nanoemulsion (NE) and a conventional oil-in-water (o/w) emulsion (EM). In vivo percutaneous penetration, evaluated by the TS technique, demonstrated that NE increased the extent of OMC penetration relative to the penetration achieved by NCs or EM. Likewise, OMC accumulation in the skin was significantly greater with NE than with EM or NCs.



Sarveiya et al. [106] developed a reverse HPLC assay to quantify four common sunscreen agents, namely, 2-hydroxy-4-methoxybenzophenone, 2-ethylhexyl-p-methoxycinnamate, octylsalicylate and salicylic acid 3,3,5-trimethcyclohexyl ester in a range of biological matrices. This assay was further applied to study skin penetration and systemic absorption of sunscreen filters after topical application to human volunteers. The assay allows the analysis of sunscreen agents in biological fluids, including bovine serum albumin solution, plasma and urine, and in human epidermis by using the TS technique. The results from the preliminary clinical study demonstrate a significant penetration of all sunscreen agents into the skin. Lademann et al. [107] determined the amount of sunscreen present on the skin of people at the beach. The amounts of sunscreen applied to different body sites were quantitatively determined by TS. The actual amounts of sunscreen applied were compared with the COLIPA (European Cosmetic Toiletry and Perfumery Association) standard. Most volunteers had applied 10% or less of the COLIPA standard amount to all body sites assessed. Mavon et al. [108] assessed the penetration of titanium dioxide (TiO2) and methylene bis-benzotriazoyl tetramethylbutylphenol (MBBT), included in a broad-spectrum sunscreen formulation, into human skin in vivo, using the TS technique, and in vitro, using a compartmental approach. More than 90% of both sunscreens were recovered in the first 15 tape strippings. In addition, they have shown that the remaining 10% did not penetrate the viable tissue, but was localized in the furrows. Less than 0.1% of MBBT was detected in the receptor medium, and no TiO2 was detected in the follicles, the viable epidermis or the dermis. Thus, this in vivo and in vitro penetration study showed an absence of TiO2 penetration into viable skin layers through either transcorneal or transfollicular pathways, and a negligible transcutaneous absorption of MBBT. However, differences in distribution within the SC reinforced the need for a complementary approach, using a minimally invasive in vivo methodology and an in vitro compartmental analysis. This combination represents a well-adapted method for testing the safety of topically applied sunscreen formulations in real-life conditions. 5.1.15. Fragances In-vitro human skin permeation and distribution of geranyl nitrile (GN) were determined by Brian et al. [109] using epidermal membranes, following application in 70% ethanol, under non-occlusive conditions, at maximum in-use concentration (1%). Levels of GN in the epidermis (plus any remaining in SC after TS), filter paper membrane support, and receptor fluid were combined to produce a total absorbed dose value of 4.72±0.32%. The systemic exposure resulting from the use of GN as a fragrance ingredient, under unoccluded conditions, would be low based on the currently reported use levels. 5.1.16. Dyes Teichmann et al. [110] developed a method to investigate the effectiveness of reservoir closure by different formulations. Patent Blue V in water was used as a model penetrant. Its penetration, with and without barrier cream treatment, was analyzed by TS in combination with UV/VIS spectroscopic measurements. The investigations showed that the SC represents a reservoir for topically applied Patent Blue V in water. Furthermore, the barrier investigations showed that vaseline and bees wax form a 100% barrier on the



skin surface. The third barrier cream, containing waxes and surfactant, only partially showed a protective effect against the penetration of Patent Blue V in water. Strong inter-individual differences were observed for this barrier product. In conclusion, it was assumed that the application of barrier creams cannot replace other protective measures, and should be used to inhibit low-grade irritants or in combination with other protectants, or in body areas where other protective measures are not applicable. Jacobi et al. [111] studied the penetration of highly hydrophilic (Patent blue V) and lipophilic (curcumin) dyes into the skin using pure oil (o) or water (w), and comparing them with an o/w emulsion. The penetration and localization of both dyes were investigated in vivo using TS and microscopy techniques. Differences in the distribution and the localization of both dyes within the SC were observed. These differences depend on the physicochemical properties of both the vehicles and the dyes. The vehicle appears to affect, in particular, the penetration pathways. As we can observe, there is an ongoing search for the identification of testing methods to optimize topical dosage forms and to assess topical drug bioavailability. While in vitro screening continues to play an important role (and is relatively inexpensive and easy to use), regulatory approval of drug delivery systems to the skin, with few exceptions, requires clinical trials to be performed. For many drugs used topically, the problem remains unsolved, since an easily visualized pharmacodynamic response is not elicited. As a consequence, various alternative techniques have been considered, of which SC TS is being given the greatest attention [7,26,35,76-111]. While the former is technically more challenging, the potential reward is a drug concentration-time profile in a compartment presumed to be in close communication with the site of action of most dermatological drugs. The latter, in contrast, offers an apparently easy and quite non-invasive methodology for skin tissue sampling, and is the basis of the FDA’s so-called DPK. Validation and optimization of the procedure have not come quickly. The goal of the research described here is not only to contribute to further establishing the credibility of the tape stripping technique, but also to demonstrate that useful and relevant measurements can be made on a surrogate, ex vivo skin model. 5.2. Dermal Absorption of Toxic or Irritant Chemicals The rate and extent of dermal absorption are important in the analysis of risk from dermal exposure to toxic chemicals, and for the development of topically applied drugs, barriers, insect repellents, and cosmetics, and the TS technique has been widely used to determine the penetration of these kinds of substances [36, 37, 112-114]. Mattorano et al. [36] developed and tested a simple, non-invasive dermal sampling technique on 22 human volunteers to estimate acute dermal exposure to jet fuel (JP-8). Two sites on the ventral surface of each forearm were exposed to 25 μl of JP-8, and the SC was sequentially tape-stripped using an adhesive tape. The analysis of the first tape strips indicated that JP-8 was rapidly removed from the SC over the 20-min study period. On average, after 5 min of exposure, the first two tape strips removed 69.8% of the applied dose. The amount recovered with two tape strips decreased over time, to a recovery of 0.9% 20 min after exposure. By fitting a mixed-effect linear regression model to the TS data, the authors were able to accurately estimate the amount of JP-8 initially applied. This study indicates that naphthalene has a short retention time in the human SC



and that the TS technique, if used within 20 min of initial exposure, can be used to reliably measures the amount of naphthalene initially present in the SC due to a single exposure to jet fuel. Chao et al. [37] presented a TS method for the removal and quantification of keratin from the SC for normalization of extracted concentrations of naphthalene (as a marker for jet fuel exposure) from 12 human volunteers before and after exposure to jet fuel (JP8). Due to the potential for removal of variable amounts of squamous tissue from each tape strip sample, keratin was extracted and quantified using a modified Bradford method. Naphthalene was quantified in the sequential tape strips collected from the skin between 10 and 25 min after a single dose of JP-8 was initially applied. The penetration of jet fuel into the SC was demonstrated by the fact that the average mass of naphthalene recovered by a tape strip decreased with increasing exposure time and subsequent tape strips. The actual concentration of naphthalene (as a marker for JP- 8 exposure) per unit of keratin in a tape-strip sample can be determined by using this method, and may prove necessary when measuring occupational exposures under field conditions. Van der Merwe et al. [112] described a physiologically based pharmacokinetic model developed to simulate the absorption of organophosphate pesticides, such as parathion, fenthion, and methyl parathion through porcine skin with flow-through cells. Three parameters were optimized based on experimental dermal absorption data, including solvent evaporation rate, diffusivity, and a mass transfer factor. Diffusion cell studies were conducted to validate the model under a variety of conditions, including different dose ranges (6.3–106.9 μg/cm2 for parathion; 0.8–23.6 μg/cm2 for fenthion; 1.6–39.3 μg/cm2 for methyl parathion), different solvents (ethanol, 2-propanol and acetone), different solvent volumes (5–120 μL for ethanol; 20–80 μL for 2-propanol and acetone), occlusion versus open to atmosphere dosing, and corneocyte removal by TS. The study demonstrated the utility of PBPK models for studying dermal absorption. The similarity between the overall shapes of the experimental and model-predicted flux/time curves and the successful simulation of altered system conditions for this series of small, lipophilic compounds, indicated that the absorption processes described in the model successfully simulated important aspects of dermal absorption in flow-through cells. These data have a direct relevance in the assessment of topical organophosphate pesticides’ risk. Jongh et al. [113] studied whether sodium lauryl sulphate (SLS) penetration rate into the SC is related to an impairment of skin’s water barrier function and inflammation. The penetration of SLS into the SC was assessed using a non-invasive TS procedure in 20 volunteers after a 4-h exposure to 1% SLS. Additionally, the effect of a 24-h exposure to 1% SLS on the skin water barrier function was assessed by measuring TEWL. A multiple regression analysis showed that the baseline TEWL, SC thickness and SLS penetration parameters K (SC ⁄water partition coefficient) and D clearly influenced the increase in TEWL after the 24-h irritation test. They found that variation in barrier impairment and inflammation of human skin depends on SLS’ penetration rate, which was mainly determined by SC thickness. Lundgren et al. [114] modified and tested a vacuuming sampler for removing particles from the skin. The sampler was compared with two other skin and surface exposure sampling techniques. These were based on surrogate skin (a patch sampler-adhesive tape on an optical cover glass) and a TS removal procedure. All three samplers measure the mass of dust on the skin. Dust containing starch was deposited onto the skin in a whole-



body exposure chamber. Samples were taken from forearms and shoulders and analysed using optical microscopy. With the different sampling techniques, small differences in results were obtained. There was a good agreement between the vacuuming sampler and the TS technique. Nylander-French et al. [115] developed and tested a noninvasive sampling method for measuring dermal exposure to a multifunctional acrylate employing a TS technique of the SC. Samples were subsequently extracted and a gas chromatographic method was employed for quantitative analysis of tripropylene glycol diacrylate (TPGDA). This method was tested in 10 human volunteers exposed to an a priori determined amount of TPGDA or a UV-radiation curable acrylate coating containing TPGDA (UV-resin) at different sites on hands and arms. On the average, the first TS removed 94% of the theoretical quantity of deposited TPGDA and 89% of the theoretical quantity of deposited TPGDA in UV-resin 30 min after exposure. Quantities of TPGDA recovered from two consecutive TS´s accounted for all of the test agent, demonstrating both the efficiency of the method to measure dermal exposure and the potential to determine the rate of absorption with successive samples over time. In general, the amount removed by the first TS was greater for TPGDA than for UV-resin while the second TS removed approximately 6 and 21% of TPGDA and UV-resin, respectively. However, when the amounts removed with the first TS for TPGDA or UV-resin from the five different individual sites were compared, no significant differences were observed. No significant difference was observed in recovery between TPGDA and UVresin for the first tape stripping when calculated as a percentage of the theoretical amount. The results indicate that this TS technique can be used to quantify dermal exposure to multifunctional acrylates. 5.3. Evaluation of Factors that Influence the Physiology of the Stratum Corneum Many factors are known to influence the physiology of the SC. In this way, increasing age is related to decreased skin thickness [115] and a variation in skin lipids [116] and flora [117]. The anatomical region also influences lipid distribution [118, 119], microflora [120] and physical parameters such as TEWL [121]. However, there are already a few studies reporting conflicting results for the effect of gender [122] on skin physiology, as well as for the effect of anatomical site, pressure, pressure duration and tape removal rate in skin physiology [55], or the effect of skin transport technology, as iontophoresis, on human skin [123]. Jacobi et al. [122] studied the effect of gender on the physiology of the SC. The physiological parameters: TEWL, pH value, hydration and sebum content were determined on the flexor forearms of 6 female and 6 male volunteers. In addition, SC samples, removed by TS, were studied for amount, spectroscopic properties, protein content, and mass. The skin of women was characterized by a significantly higher pH value (5.6±0.4) than that of men (4.3±0.4). Protein absorption was the only other parameter significantly dependent on gender. Both effects might be caused by differences in human biology, such as hormonal status. Therefore, volunteers’ gender should be considered in dermatologic studies. Löffler et al. [56] investigated the influence of the procedures (anatomical site, pressure, pressure duration, tape removal rate) inherent to each stripping protocol on changes in skin physiology. A significant influence of all parameters on TEWL increase, as a function of tape strip number was observed. The fastest increase was demonstrated on the forehead, followed by the back and, lastly, the forearm. Rapid removal produced a



protracted increase compared with slow removal. Pressure for 10s induced a faster increase in TEWL than 2s pressure. Likewise, pressure at 330 g cm-2 induced an earlier increase than pressure at 165 g cm-2. Skin hydration was not influenced by the variables tested. Van der Molen, et al. [57] investigated the efficacy of TS in removing complete cell layers from the superficial part of the human SC. A histological section of skin that was tape-stripped 20 times, clearly showed non-stripped skin in the furrows, indicating persistent incomplete TS. Replicates of tape-stripped skin surface demonstrated that even after removing 40 tape strips, furrows were still present. They emphasize that the results from studies using the TS method have to be viewed from the perspective that cells on one tape strip of the SC may come from different layers, depending on the position of the tape strip in relation to furrow slope, and such results should be interpreted with considerable caution. Fatouros et al. [123] investigated the local changes in the ultrastructure of human skin after iontophoresis in human skin in vitro and in vivo. Human dermatomed skin was subjected to passive diffusion for 6 hours, followed by nine hours of iontophoresis at 0.5mA/cm2. In addition, iontophoresis patches were applied to healthy volunteers for 3.5h with 0.5h of passive delivery followed by 3h of iontophoresis at a current density of 0.25mA/cm2. Subsequently, a series of TSs were performed, and were visualized by freeze-fracture transmission electron microscopy. In vitro/in vivo studies suggest that iontophoresis results in the formation of intercellular water pools, and in a weakening of the desmosomal structure only in the upper part of the SC. However, no changes in lipid organization were observed in vitro and in vivo at the 0.5 and 0.25mA/cm2 current densities, respectively. Therefore, even at relatively high current densities, no drastic changes in the ultrastructure of the SC are observed. As far as structural changes in SC are concerned, iontophoresis is a safe method under the experimental conditions used. 5.4. Stratum Corneum Composition Weerheim et al. [124] established a suitable analytical method for the determination of local SC lipid composition. For this purpose, SC samples were collected by sequential stripping with Leukoplex tape in five healthy volunteers. Lipids were extracted with an ethyl acetate: methanol mixture (20:80) and separated by means of HPTLC. The results of this study revealed that the free fatty acid level is higher, and cholesterol and ceramide levels are lower in the uppermost SC layers. Levels remained unchanged in the underlying SC layers. In these layers, the ceramide level was about 60 wt % and free fatty acid and cholesterol levels were about 20 wt % each. Ceramides could be separated into seven different fractions, and the relative amounts of individual ceramide fractions did not significantly change with the SC depth. The method developed allowed to study the differences in the SC lipid profile in healthy and diseased human skin relative to the SC lipid organization and the skin barrier function in vivo. 5.5. Determination of Stratum Corneum Thickness Alberti et al. [125] evaluated, using attenuated total reflectance Fourier transform infrared spectroscopy, the SC bioavailability of terbinafine (TBF) following topical treatment with four different formulations, based on a vehicle consisting of 50% ethanol and 50% isopropyl myristate. Three of these formulations included a percutaneous penetration enhancer: either 5% oleic acid, 10% 2-pyrrolidone or 1% urea. The SC con-



centration profile of TBF was measured by repeated infrared spectroscopic measurements while sequentially stripping off the layers of this barrier membrane with adhesive tape. TEWL measurements were also performed, to permit facile estimation of SC thickness. The SC concentration profiles of TBF were fitted to the appropriate solution of Fick's second law of diffusion. This analysis enabled the efficacies of the different formulations tested to be compared to the non-enhancer control. While it was found that the formulation containing 5% oleic acid significantly enhanced the SC availability of TBF, the other formulations did not improve the apparent drug delivery. Kalia et al. [126] determined whether a structurally heterogeneous biomembrane, human SC, behaved as a homogeneous barrier to water transport. Impedance spectra (IS) of the skin and measurements of the rate of TEWL were recorded sequentially in vivo in human subjects as layers of the SC were progressively removed by the serial application of adhesive tape strips. The low-frequency impedance of skin was much more significantly affected by TS than the higher frequency values; removal of the outermost SC layer had the largest effect. In contrast, TEWL changed little as the outer SC layers were stripped off, but increased dramatically when 6-8 microns of the tissue had been removed. It follows that the two noninvasive techniques probe SC barrier integrity in somewhat different ways. After SC removal, recovery of barrier function, as assessed by increasing values of the low-frequency impedance, apparently proceeded faster than TEWL decreased to the pre-stripping control. The variation of TEWL as a function of SC removal behaved in a manner entirely consistent with a homogeneous barrier, thereby permitting the apparent SC diffusivity of water to be found. Skin impedance (low frequency) was correlated with the relative concentration of water within the SC, thus providing an in vivo probe for skin hydration. Finally, the SC permeability coefficient to water, as a function of SC thickness, was calculated and correlated with the corresponding values of skin admittance derived from IS. 5.6. Determination of Superficial Infections and Viruses Topical infections due to S. aureus and S. pyogenes are clinically relevant and cause a variety of serious symptoms, including toxic shock syndrome and skin lesions [127], that can progress to sepsis and systemic shock if they are left untreated [128]. These bacterial species are also the most common causes of impetigo in humans [128]. The TS technique has offered the possibility of studying superficial infections on the skin [129], as well as viruses in skin tumors [130]. Kugelberg et al. [129] presented a new animal model for the purpose of studying superficial infections. In this model, an infection is established by disruption of the skin barrier by partial removal of the epidermal layer by TS and subsequent application of the pathogens Staphylococcus aureus and Streptococcus pyogenes. The infection and the infection route were purely topical. Thus, the present model is considered more biologically relevant for the study of superficial skin infections in mice and humans. Established topical antibiotic treatments are shown to be effective. The procedures involved in the model are simple, a feature that increases throughput and reproducibility. This new model should be applicable to the evaluation of novel antimicrobial treatments for superficial infections caused by S. aureus and S. pyogenes. Forslund et al. [130] investigated 229 immunocompetent patients tested for human papilloma virus (HPV) DNA in swab samples collected on top of skin tumors and in biopsies of the same tumors, obtained after stripping with tape to remove superficial



layers. HPV DNA was detected on top of 69% of the lesions, and in 12% of the stripped biopsies. A difference was seen for the four types of tumors studied. Seborrheic keratosis had 79% HPV positivity on top of lesions versus 19% in biopsies; actinic keratosis had 83% HPV positivity on top of lesions versus 11% in biopsies; basal cell carcinoma had 63% on top of lesions versus 8% in biopsies; and squamous cell carcinoma had 58% on top of lesions versus 19% in biopsies. HPV DNA is common in superficial layers of lesions, but is not necessarily present in tumors. 5.7. Skin Regeneration Malminen et al. [131] investigated the expression of tight junction components during reepithelialization of suction blisters and regeneration of the corneal layer after TS. Suction blisters were induced in eight healthy volunteers, and skin biopsies were taken 4 or 6 days afterwards. The restoration of epidermal barrier function was evaluated by measuring water evaporation (WE) from the wound area. TS was performed on three volunteers to remove the corneal layer. Prior to the biopsies, WE from the blister wounds was markedly elevated compared with normal skin. In the epidermis surrounding the blister, occludin and ZO-1 (membrane-associated guanylate kinase homologue protein family) were expressed in the granular cell layer only. In the hyperproliferative zone adjacent to the border of the blister, the expression of ZO-1 was redistributed into several spinous cell layers, while occludin expression was restricted to the upper epidermis. In the leading edge of migrating keratinocytes, both proteins were expressed solely in the most superficial layer of keratinocytes. Double labelling for ZO-1 and involucrin showed expression of both proteins in the same layers of hyperproliferative keratinocytes, while the expression patterns were clearly different in migrating keratinocytes. Tight junctions of regenerating epidermis may provide a functional barrier prior to regeneration of the corneal layer. Sekkat et al. [132] developed an in vitro model for the developing skin of the premature neonate. Barriers of different levels of efficiency were produced by differential tapestripping of the SC from the skin of excised porcine ears, and were characterized by measurements of TEWL. In this way, it was possible to express the recorded TEWL as a function of percentage SC thickness (F), generating the following relationship: TEWL = 2.7+41.exp [-0.028.F]. These data were then compared to previously published in vivo measurements of TEWL obtained from a population of premature neonates at various post-conceptional ages (PCA). The former showed a remarkably parallel relationship to that found in vitro with the porcine skin model, namely TEWL = 3.3+41 exp [0.026.(PCA-160)]. Therefore, it can be suggested that the empirically adjusted PCA (i.e., PCA-160) has a close correlation with the developing thickness of the neonate’s SC. Consequently, porcine skin in vitro, tape-stripped to a particular level, can provide a barrier corresponding to a specific degree of neonate maturation, and thus, can serve as a useful tool to explore whether transdermal drug delivery in this unique patient population may be beneficial. Zhai et al. [133] used an in vivo human model to define the irritation potential of a topical agent after partial removal of the SC by cellophane TS. The tape was applied to and removed approximately 50 times (mean, 50.0 +/- 16.7) from each test site on the volar aspect of the forearm. TEWL was measured before and daily for 5 days. The TEWL values at baseline after stripping represented the point of maximal stripping barrier disruption. The barrier disruption and irritation potential were assessed with TEWL



measurements. The results showed that the model topical agent had no adverse effect on barrier repair, i.e. did not interfere with TEWL normalization. This model provides a method for the prediction, with exaggerated sensitivity, of chemical irritation and proclivity to enhance or retard water barrier repair. They believe that the model may predict the response of low irritation materials and may be more sensitive than patch testing on normal skin, particularly for products to be used on certain areas, e.g. the face, anus, etc., or even mucous membranes. CONCLUSIONS The quantification of drugs within the skin is essential for topical and transdermal delivery research. Over the last two decades, horizontal sectioning, consisting of TS, has been the traditional investigative technique. Many in vivo methods for measuring dermal absorption of chemicals are invasive (e.g., blood sampling) or slow (e.g., urine samples collected for extended periods). TS of the outermost skin layer, the SC, is a fast and relatively non-invasive technique for measuring the rate and extent of dermal absorption. The TS technique has the potential to meet the requirements for an efficient and reliable method to assess dermal exposures. Stripping with an adhesive has been widely accepted as a dermal sampling technique in dermatology. Tape stripping data have been used to estimate permeability coefficients and partition coefficients, SC mass, barrier function, drug reservoir from in vivo dermal exposures, and even to explain the SC physiology. TS has also been proposed as a method for evaluating the bioequivalence of topical dermatological dosage forms. DPK characterization of the penetration of active drugs in human volunteers has been suggested to be able to replace comparative clinical trials as means of documenting bioequivalence. It is suggested that DPK assessment of drug concentrations in the SC is comparable to blood/urine measurements of systemically administered drugs, where the concentration of a drug in the SC is expected to relate to its concentrations in viable tissue. Short-contact DPK experiments can be used to obtain diffusion and partitioning parameters that may subsequently be able to predict drug penetration into the SC following longer application periods. Although tape stripping is widely used to determine dermal absorption through the SC, several factors can influence the actual technique. Recent reviews on this topic provide updated and additional insights [134-136]. The investigation of variations in skin condition (dry versus moist skin, skin defects, etc.) to determine their potential impact on the sampling method is warranted. For these reasons, the tape stripping technique requires further development. ACKNOWLEDGMENTS José Juan Escobar-Chávez wishes to acknowledge the PROFIP/UNAM grant. The authors also thank the financial support from PAPIIT/UNAM (Reference IN213205). ABBREVIATIONS SC

=

Stratum corneum

DPK

=

Dermatopharmacokinetic

TS

=

Tape stripping



AUC

=

Area under curve

TEWL

=

Transepidermal water loss

HP-P-CD

=

Hydroxypropyl-P-cyclodextrin

EBA

=

Ethyl 4-biphenylyl acetate

BPAA

=

4-biphenylylacetic acid

MO

=

Monoleine

PG

=

Propylene glycol

SLs

=

Sesquiterpene lactones

TACA

=

Triamcinolone acetonide

TA

=

Retinoic acid

SA

=

Salicylic acid

RA-AsA

=

Retinyl ascorbate

RA

=

Retinyl chloride

AsA

=

Ascorbic acid

AQP3

=

Aquaporine-3

VZV

=

Varicella Zoster virus

APCs

=

Antigen-presenting cells

LCs

=

Langerhans cells

DCs

=

Dendritic cells

CpG-ODN

=

CpG-oligodeoxynucleotide

TLR-9

=

Toll-like receptor-9

TNF

=

Tumor necrosis factor

HIV-1

=

Human immunodeficiency virus type-1

CTL

=

cytotoxic -T- lymphocyte

TC

=

Transcutol

4CP

=

4-cyanophenol

TSs

=

Tape strippings

AD

=

Atopic dermatitis

SDS

=

Sodium dodecyl sulphate

OMC

=

Octyl methoxycinnamate

NCs

=

Nanocapsules

NE

=

Nanoemulsion

EM

=

Emulsion



COLIPA

=

European Cosmetic Toiletry and Perfumery Association

MBBT

=

Methylene bis-benzotriazoyl Tetramethylbutylphenol

GN

=

Geranyl nitrile

FDA

=

Food and Drug Administration

JP-8

=

Jet fuel-8

SLS

=

Sodium lauryl sulphate

TPGDA

=

Tripropylene glycol diacrylate

TBF

=

Terbinafine

IS

=

Impedance spectra

HPV

=

Human papilloma virus

WE

=

Water evaporation

PCA

=

Post-conceptional ages

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Methylphenidate Extended-Release Capsules: A New Formulation for Attention-Deficit Hyperactivity Disorder Pilar García-García1, Francisco López-Muñoz1,*, Juan D. Molina2, Roland Fischer3 and Cecilio Alamo1 1

Department of Pharmacology, Faculty of Medicine, University of Alcalá, Madrid, Spain; 2Acute Inpatients Unit, Dr. Lafora Psychiatric Hospital, Madrid, Spain and 3 Medice chem.-phar., Fabrik Pütter GmbH & Co.KG, Iserlohn, Germany Abstract: In recent years Attention Deficit Hyperactivity Disorder (ADHD) has been the focus of growing interest, and different drugs have been introduced for its treatment. Thus, there is a range of medication for ADHD, but new formulations are necessary for more individualized therapy. The choice will depend upon the circumstances and detailed assessment. A new extendedrelease formulation of methylphenidate (Medikinet®) has increased the drugdelivery treatment options for ADHD. Medikinet® combines the advantages of immediate-release (IR) and extended-release (ER) formulations of methylphenidate, with rapid onset and prolonged duration of action (7-8 h), in a single dose intended for once-daily administration. The concentration-time profile is achieved through the particular formulation of Medikinet®, whose hardgelatine capsule contains 50% enteric-coated and 50% uncoated pellets, providing both a first, immediate release and a second, delayed release. The coated pellets only dissolve at a pH > 5.5 and release the active drug in a sustained way into the intestine. There is no difference in the bioavailability of the IR/ER product when administration follows a normal or high-calorie breakfast. Medikinet® also shows a bioavailability comparable to that of the b.i.d. 10 mg immediate release regime, as well as a high level of efficacy and good tolerability. In this review, we describe the pharmacokinetics of Medikinet® and compare its characteristics with those of other formulations used for treating ADHD.

INTRODUCTION Attention-deficit hyperactivity disorder (ADHD) is one of the neuropsychiatric conditions that has received most attention in the scientific literature in the last few years [1]. The majority of authors acknowledge that ADHD is the most widespread problem in developmental neurology and one of the commonest reasons for neuropaediatric consultations [2-4]. The prevalence of this disorder is generally accepted to be around 5-6% in *Corresponding Author: Tel: +34 91 724 8210; Fax: +34 91 724 8205; E-mail: [email protected] Gary W. Caldwell / Atta-ur-Rahman / Z. Yan / M. Iqbal Choudhary (Eds.) All rights reserved – © 2009 Bentham Science Publishers.

Methylphenidate Extended-Release Capsules


children of school age, and from 1–6% in the adult population [3,5-9]. ADHD is, therefore, a highly prevalent disorder across the developmental spectrum [9]. The core symptoms of ADHD include attention deficit, hyperactivity and impulsiveness, which should be reviewed in the context of each age group [10]. This disorder also has a wide range of consequences, the commonest being retarded academic progress, comprehension difficulties when reading, instability of relationships with friends and classmates, low selfesteem, and disorganization. Children suffering from this disorder are more likely, as juveniles, to be involved in traffic violations and car accidents and to have early and uncontrolled sexual relations, which can lead to sexually-transmitted diseases and unwanted paternity or maternity [4,11-13]. Furthermore, they generate a total medical cost and use of medical resources far in excess of those of their peers without ADHD. Pharmacological treatment of this disorder dates back more than 50 years, with the use of psychostimulants, the type of drug that has been subjected to most research in children with ADHD, and whose efficacy has been demonstrated in a large quantity of clinical trials [4,9,14-16]. Psychostimulants constitute the primary pharmacotherapy for children diagnosed with ADHD, and between 73% and 94% of children with ADHD respond to stimulants, such as methylphenidate, dexamphetamine and pemoline [17-19]. Methylphenidate is the best known and most widely used stimulant for treating ADHD in children, accounting for at least 70% to 90% of ADHD drug therapy, while dexamphetamine and pemoline are generally regarded as second-line therapies (and other therapeutic agents are still at the development stage). Data show that dexamphetamine is equally effective as methylphenidate, but the pharmacological class of amphetamines is used more reluctantly than methylphenidate. Indeed, in most European countries, dexamphetamine is not commercially available. Pemoline is available under restricted use, as it can cause an increase in liver enzymes and, very rarely, irreversible hepatotoxicity. Therefore, it is not considered as a drug of first choice [10,12,19]. In recent years, pharmacological research in this field, rather than focusing on the search for new therapeutic drugs, has launched itself into the development of new formulations of stimulants [20-23], aimed at achieving better control of children with ADHD through a single daily dose. However, there has also been research with nonstimulant drugs, such as atomoxetine, bupropion or modafinil. Even so, it is the new formulations with methylphenidate that are arousing the most therapeutic interest, given their advantages with regard to the management of ADHD, and consequently, to the improvement of its symptoms [4,10,18,24]. ATTENTION-DEFICIT HYPERACTIVITY DISORDER The combination of inattentive, impulsive and hyperactive behaviour is recognized as a clinically relevant disorder when these symptoms are serious, when mental development is inappropriate and when there is deterioration of social relations in the school and family contexts [10]. This behaviour disorder is currently known as attention deficit disorder (ADD) or attention-deficit hyperactivity disorder (ADHD), though over the course of the history of this pathology it has been referred to by other names, such as hyperkinesis, hyperkinetic syndrome or minimal brain dysfunction (MBD). The first clinical approximation to ADHD is attributed to the German doctor and man of letters Heinrich Hoffman, who in 1854 called his son “Fidgety Philip”. Later, in 1902, British pediatrician George Frederic


García-García et al.

Still referred to mental conditions related to abnormality of moral control (referring to the cognitive capacity to distinguish good from bad) in children of normal intelligence. At the beginning of the 20th century the principal hypothesis for explaining children’s hyperactivity was based on brain dysfunction. Thus, the initial conception involved minimal brain lesion, but by the late 1950s this had become minimal brain dysfunction. In 1960, Stella Chess introduced the idea of hyperactive child syndrome, considering it to be a developmental diagnosis with good prognosis. By the 1970s the triad of symptoms that currently define ADHD had been identified: attention deficit, hyperactivity and impulsiveness. In 1972, the conceptualization of this disorder took a radical turn thanks to the contribution of Virginia Douglas, of McGill University, who proposed that its chief symptom was attention deficit, rather than hyperactivity. Finally, the Diagnostic and Statistical Manual of the American Psychiatric Association (DSM-III, 1980) recorded attention deficit disorder with and without hyperactivity [25,26]. Currently, according to the International Classification of Diseases (ICD) [27] and the DSM [28], the terms mainly used are attention-deficit hyperactivity disorder (ADHD) and hyperkinetic disorder (HKD), which can be considered synonymous [29,30]. A review of the historical development of the ADHD concept reveals that this pathology is by no means of recent discovery, though today it is receiving more attention, its diagnosis is being improved and it is becoming better understood. Thus, both the DSM-IV and the ICD-10, used as diagnostic criteria by specialists in ADHD, break this disorder down into three core symptoms: attention deficit, hyperactivity and impulsiveness [20,31]. ADHD is, therefore, a persistent pattern of inattention and/or hyperactivity/impulsiveness which is more frequent and severe than that normally observed in subjects of a similar developmental level. Some symptoms must necessarily appear before age 7, but it is only from this age onwards that the condition is usually diagnosed. Symptoms must occur in two different contexts: that of school or work and that of family. Within the different subtypes of ADHD identified, the commonest is the combined form (60%), which encompasses the inattentive (30%) and hyperactive/impulsive (10%) forms. With regard to gender, ADHD is more prevalent in boys, the boy:girl ratio being 3:1, though the literature includes ratios of up to 9:1 [32-33], and by the subtypes mentioned above the ratio is 4:1 for the hyperactive subtype and 2:1 for that of inattention [25]. According to the DSM-IV, the estimated prevalence of ADHD is 3% to 5%. However, extensive epidemiological studies have demonstrated that the prevalence of symptoms of ADHD in children is 9% to 20%, depending on the methodology used to diagnose the disorder [27,30,32,34]. As ADHD is often associated with comorbid disorders, it is important to seek evidence of other conditions, such as stress disorders and adjustment disorders. A recent comprehensive review of literature reported the rates of comorbidity for clinically referred children with ADHD at 30-50 % for conduct disorders, 15-75% for mood disorders, approximately 25% for anxiety disorders and between 10% and 90% for learning disorders [30,34-36]. There is growing consensus that ADHD continues through adolescence and often also into adulthood, although in slightly altered form. It is estimated that about 70% of children will take their ADHD into adolescence, and 10% into adulthood. Its high prevalence in childhood, combined with the follow-up results, suggests that approximately 2%



of adults may suffer from ADHD. This would make ADHD a relatively common adult disorder that may be under-identified in adult psychiatric clinics [34,37]. To diagnose ADHD, clinicians should elicit the history of specific symptoms from those who know the child best – usually the parents and teachers. Rating scales with specific ADHD symptoms have been developed, and provide a systematic approach for documenting clinical history. Additionally, various psychometric instruments can be used to rate behaviour and performance and to measure improvement of ADHD. Direct observation in clinical settings may often not confirm reports from parents or teachers. If confirmation of subjective reports is necessary, then observation in the natural settings of home or school is recommended [8,36]. Psychological tests, especially with tasks of attention/concentration and learning, are supportive methods for diagnosis [30,38], but it is still difficult to separate cause and consequence. Attempts to clarify the pathophysiology of ADHD have been made from various perspectives, but the etiology of ADHD remains unknown [32,34,39-41]. However, there would appear to exist a considerable genetic basis that makes these children vulnerable to other factors, such as obstetric and postnatal complications, as well as other environmental factors (smoking during pregnancy, toxic agents such as lead, etc.). Thus, the frequency of ADHD and of other psychiatric disorders is greater in first-order relatives of patients with this illness, though what is inherited may be not the clinical manifestations but rather a particular neurobiological vulnerability [8,25], as suggested by studies of families, comparisons of twins, and studies of adopted children, which provide some support for possible genetic transmission. Genetic influences of around 60% heredity for some forms of ADHD have been reported. Genes influencing the dopaminergic systems may play a role in genetic transmission, and it has been suggested that the dopamine transporter gene and the D4 dopamine receptor gene are associated with ADHD [32,34,41]. Current research supports a neurobiological cause of ADHD. Neurochemically, ADHD seems to be linked to an imbalance between the dopaminergic, noradrenergic and serotonergic neurotransmitter systems, characterized by decreased dopamine (DA) and increased noradrenaline (NA) [42]. The neuroanatomical location of deficits seems to be in the cortical and subcortical areas, particularly the frontal lobe and the prefrontal lobe. Positron emission tomography (PET) scans reveal decreased cerebral metabolism in the premotor and prefrontal superior cortex, as well as diminished blood flow to the corpus striatum in ADHD children [43]. Both NA and DA participate in numerous cognitive processes (attention, alertness, executive function, etc.) that appear to function defectively in ADHD. The maintenance of high levels of vigilance and attention depend on the catecholamine modulation of the prefrontal, cingulate and parietal cortices, as well as of the striate nucleus, the hippocampus and the thalamus. In all these structures there is substantial participation of catecholaminergic neurons. Although the physiopathology of ADHD has not been fully clarified, there appears to be a dysregulation of DA and NA levels that would lead to an alteration in the frontallymediated executive functions, and the adrenergic imbalance could mean that the psychological functions necessary for appropriate responses to environmental stimuli become altered. The prefrontal cortex depends on adequate dopaminergic and noradrenergic innervation for maintaining optimum functioning, as pointed out previously. Thus, dopaminergic hypofunction is the pathogenic core of ADHD, in which there is an increase in



the dopamine transporter, suggesting a dysfunctional regulation of dopamine or of its receptors, which will lead in turn to a smaller quantity of dopamine in the synaptic cleft. The consequence of catecholaminergic dysregulation is that processing of external and internal stimuli is affected, so that impulsive behaviours would indicate a failure in the system for delayed gratification, while excessive motor activity would indicate a failure in the systems of inhibition, which involve frontal and limbic structures [39,40]. THERAPEUTIC MANAGEMENT OF ADHD Two treatment modes can be distinguished for ADHD; on the one hand, the pharmacological mode, which tends to be applied in conjunction with psychological treatment, and on the other, the non-pharmacological mode, which would include diverse therapies such as educational, cognitive-behavioural and other psychological and psychiatric approaches [8,17,25,36,44]. The majority of authors recommend multimodal treatment, which combines pharmacotherapy and psychological treatment. It should be borne in mind that without appropriate treatment for each patient, ADHD can lead, as we have pointed out, to a range of circumstances – academic failure, social stigmatization, and so on. Moreover, the disorder can cause a deterioration of occupational and social functioning and can have a negative effect on emotional stability [12,17]. The treatment regimes mentioned emerged from the MTA study (Multimodal Treatment Study of Children with Attention-Deficit/Hyperactivity Disorder) carried out in the United States, one treatment arm of which was called “Community Care”, in which the treatment of ADHD was not stipulated by the study protocol. It can be assumed that treatment selection in this study arm reflects the customary treatment decisions for ADHD in the USA. Medication was given to 67% of these children. Of the 97 children with medication in this arm, 84 (87%) were treated with methylphenidate, while only 6 (6%) were treated with amphetamines. There are considerable cultural differences in the use of stimulants. In Europe, where their prescription has been restricted by custom and by law, clinical guidelines recommend initially a rigorous regime of multiple psychosocial interventions, such as behaviour modification, cognitive therapy, family therapy and teacher consultation; in North America, where the prescription of stimulants has been generally accepted for decades, clinical guidelines recommend an initial pharmacological therapeutic regime [12,17,19,44]. The principal conclusions of the MTA study suggest that a careful medication regime (mostly methylphenidate) is superior to behavioural treatment alone. Medication therapy combined with behavioural treatment appears to be the most beneficial treatment for ADHD, especially in children with additional anxiety disorders [12,17,45]. Thus, the drug most widely used in these patients is methylphenidate, whose efficacy is endorsed by numerous studies and indeed by its consistent use over five decades [25,45,46]. At the same time, it is important to stress that the treatment of ADHD patients is complex and multidisciplinary, being conditioned by a large number of factors, some intrinsic, such as the individual characteristics of each patient, sex, age, etc., and some extrinsic, such as family and social environment. In sum, therapy should be based on four therapeutic pillars: the family, pedagogical, psychological and pharmacological approaches. The general goal of the treatment should be to improve the child’s cognitive, behavioural and social functioning and increase his/her self-esteem, with the minimum



of side effects. Of all therapeutic approaches, the combined (or multimodal) treatment is that which, in general, appears to offer the best results at the present time. METHYLPHENIDATE The various guidelines for the treatment of ADHD concur on the fact that stimulants of the central nervous system are extremely effective in the improvement of ADHD symptoms. Prominent among such stimulants is methylphenidate (MPH), as already mentioned. Indeed, the pharmacology of MPH has become a focus of major interest in the wake of the development of different controlled-release formulations with particular drug-delivery and pharmacokinetic profiles [10,16,18,19,47-51]. The mechanism by which MPH produces psychostimulant effects appears to depend prominently upon the facilitation of catecholaminergic neurotransmission. MPH is thought to act as an indirect sympathomimetic substance, but predominantly via dopamine (DA) transmission, and less via the norepinephrine (NE) or serotonin (5-HT) systems. Recognizing that MPH binds with high affinity to the DA transporter or uptake channel, it has been proposed that such binding blocks the synaptic clearance of impulse-released DA, leading to prolonged post-synaptic neurochemical mediation [44,52]. Methylphenidate hydrochloride (methyl 2-phenyl-2-(2-piperidyl)acetate hydrochloride) is a basic ester of phenylacetic acid (Fig. 1). It is formulated as freely soluble hydrochloride salt. The molecular structure of MPH contains a basic phenylethylamine moiety which is common to psychostimulant agents such as amphetamines, and is thought to be responsible for its amphetamine-like action profile. The presence of two chiral centres in the structure of MPH allows four possible stereoisomers, but all current MPH products, as well as the product-marketing authorization applied for, contain the drug in the racemic form, a 50:50 mixture of the threo-R,R (+)- and threo-S,S (-)isomers. The threo-R,R (+)-stereoisomer appears to be almost exclusively responsible for the catecholaminergic effects of racemic MPH [53].

* N H

*

CH3O

* HCl O

Fig. (1). Molecular structure of methylphenidate hydrochloride.

Pharmacokinetics of Methylphenidate The pharmacokinetic properties of MPH hydrochloride, shown in Table 1, are thoroughly described in the literature [10,25,44,54-57]. As known from other psychoactive drugs, there is a considerable range of inter-individual variation in MPH serum levels. In addition, serum levels of MPH show intra-individual variations – that is, a given subject may have different serum levels on different days. Therefore, plasma levels have not proven useful in standard clinical practice. Data regarding the correlation between serum levels of MPH and clinical response are contradictory [25,55,58].


Table 1.


Pharmacokinetic Parameters of the Different Formulations of Methylphenidate

Parameters

Concerta® 1 Capsule of 18 mg

Rubifen® 1 Capsule of 10 mg*

Medikinet ® 1 Capsule of 20 mg

% immediate release

22

100

50

% extended release

78

0

50

Cmax (ng/mL)

3.7

9

6.4

6.8

1-2

2.75

AUC0-inf (ng.h.ml )

41.8

Interindividual variability

48.9

T1/2 (h)

3.5

2

3.2

Duration of effect (h)

12

3-4 h

7-8

Tmax (h) -1

*One dose of Concerta is equal to three doses of Rubifen; Cmax: Maximum concentration; Tmax; time necessary for attaining maximum concentration; AUC: area under the curve; t1/2: half-life of elimination.

Methylphenidate is a basic drug, and as such is poorly bound (approximately 15%) to plasma protein. Low plasma binding makes it highly amenable to crossing the bloodbrain barrier. Typical therapeutic doses of MPH provide a Tmax of 1.5 - 2.5 hours, reaching a Cmax of 6 - 15 ng/ml with a T1/2 of 2 - 3.5 hours. Pharmacokinetic parameters for children and adults appear to be comparable [10,59]. Animal studies (rats) have shown that MPH accumulates in easily perfused tissues, favouring kidney > lung > brain > heart > liver. Brain concentration of MPH is about eight times that of serum. Methylphenidate is rapidly metabolized and excreted in urine, small amounts also appearing in the faeces [10,60,61]. The major metabolic route is the hydrolysis of the ester function to the corresponding carboxylic acid, commonly known as ritalinic acid (2-phenyl-2-piperidyl acetic acid), which accounts for approximately 80% of the dose. Minor metabolites include p-hydroxy, oxo, and conjugated derivatives of MPH. A small portion of MPH is metabolized by the hepatic microsomal oxidase system. Less than 1% appears in the urine as unchanged MPH [10,61]. METHYLPHENIDATE FORMULATIONS There are three pharmaceutical formulations of MPH: immediate release or shortacting formulation, sustained release or intermediate-acting methylphenidate, and extended release or long-acting methylphenidate [9,10,23,57]. Immediate-release methylphenidate (Ritalin®, Rubifen®), whose pharmacokinetic properties are shown in Table 1, releases 100% of the methylphenidate in the capsule on administration. This formulation has a therapeutic effect of 2 to 4 hours, which means that in many cases two or three administrations are necessary to achieve a sustained effect [62]. Moreover, the abrupt decrease of plasmatic concentrations of MPH can produce a rebound effect. Because of the relatively short half-life of two to (at most) four hours, in many patients a single early-morning dose of rapid-release methylphenidate



results in the ADHD symptoms increasing again during the latter part of the school morning; this also creates considerable difficulties in the family context at homework time [63]. Furthermore, rebound effects are not infrequently seen in school as the effect subsides. Many affected children, therefore, require a second pre-midday dose while still at school. D-methylphenidate (Focalin®) is a racemic variant of methylphenidate marketed in the United States. This is a development of the classic formulation, from which the relatively inactive L isomer has been removed. Thus, this product contains only the D isomer, so that with half the dose the same effect is obtained as with the conventional formulation [10,64-66]. With the aim of solving the problem of administering multiple doses of MPH, modified release systems were designed. Both in the United States and in Europe there are different modified systems of release of MPH and other drugs used in the treatment of ADHD, which means that the products have different characteristics (Table 1). Among the specific formulations of methylphenidate are the SODAS technology, the CD system, the OROS technology, the transdermic release (skin patch) system and, finally, capsules containing different types of pellets. The present review will deal in depth with the pharmacological characteristics of this last-named formulation. Methylphenidate administration with the SODAS (Spheroidal Oral Drug Absorption System) technology, whose representative is Ritalin LA®, involves the use of a capsule made up of a shell containing half the MPH dose in the form of immediate-release MPH, while the other half has an enteric protection layer that permits release of the active agent after 4 hours. In reality, the LA formulation mimetizes, in a single application, the immediate-release administration of MPH twice, separated by 4 hours. The main advantage of this formulation is convenience of administration [10,67,68]. Methylphenidate CD, whose commercial name is Metadate CD®, is characterized by the combination, in a single capsule, of 30% of immediate-release MPH and 70% of slow-release MPH. In this way a prolonged effect, sustained over 9 hours, is achieved. From the pharmaceutical perspective it uses a technique similar to the SODAS technique [10,67-69]. Methylphenidate with OROS (Concerta®) technology consists in an osmotic-release capsule. Each capsule includes a shell of immediate-release MPH and three compartments, two with MPH and another with an osmotic polymer/polymeric agent, coated with a semi-permeable membrane. After oral administration, the coating of the capsule provides immediate release of 22% of the dose. From that point on, the osmotic compartment becomes hydrated due to the passing of intestinal juices through the semipermeable membrane, and increases in volume, acting as a plug. The OROS formulation provides two-phase kinetics with two peaks of concentration, corresponding to the two periods of MPH release, with total exposure to the drug equivalent to 3 doses of immediate-release MPH. Fluctuations of plasma concentration of the drug are fewer than in the case of repeated administration of immediate-release stimulants, thus eliminating the daily variation of pharmacological effects associated with the older formulations. MPH with the OROS release system was designed to replace the three-administration regime, morning, midday and evening, since with its use the second peak of plasma concentration occurs later than with other sustained-release systems. This means that the therapeutic effect lasts 12 hours, which can result in insomnia or lack of appetite in the



evening, with the consequent negative effect on children and secondary effects on the family. In such cases it would be advisable to use a shorter release system [10,19,48, 68,70-72]. In April 2006 the Food and Drugs Administration (FDA) approved the first skin patch for MPH release (Daytrana®), though it has yet to be authorized in Europe. The patch has been patented with the so-called “DOT Matrix” technology. This dot-matrix transdermic technology uses a semi-solid suspension of microscopic cells of the drug concentrated uniformly and dispersed by a silicon glue. The steep diffusion gradient between each cell of the drug and the skin means that the drug adequately penetrates the skin. With this release system the methylphenidate passes directly into the bloodstream, providing constant levels throughout the day. The patch must remain on the skin for 9 hours, even during the child’s everyday activities, including swimming, exercise and bathing. Clinical trials have shown that the patch, fixed to the skin for 9 hours, has an effect lasting 12 hours [73-76]. METHYLPHENIDATE EXTENDED-RELEASE CAPSULES: OVERVIEW OF BIOPHARMACEUTICS The aim of the development process of the newly-developed modified-release formulation of MPH hydrochloride (Medikinet® retard modified-release capsules) (Fig. 2) was to optimize broaden the range of existing MPH modified-release formulations, as Metadate CD®, which was approved by the FDA [69,77]. Methylphenidate extended-release capsules consist of two fractions of active substance in a 1:1 ratio. The hard-gelatine Medikinet® capsules contain two types of pellets in equal proportion: 50% immediaterelease at the gastric level (white pellets) and 50% extended-release at the intestinal level, which have a gastro-resistant coating that permits the delayed release of MPH (blue pellets) (Fig. 3).

Fig. (2). Capsule of Medikinet®.

The retard pellet has two layers with different characteristics: an outer releasedelaying layer (enteric coat) and an inner methylphenidate layer. The enteric coating of the outer layer comprises (co-)polymers of (meth)acrylic acid and (meth)acrylate con-



taining carboxyl groups, thereby causing sustained release of the psychostimulant in vivo. The addition of an alkaline agent to this formulation results in a partial neutralization of the carboxyl groups of the polymer forming the enteric coating, and thus in the formation of small channels in the enteric coating which allow a slight diffusion of the MPH through the coating even at a pH of under 5.5, which is typical in the stomach of human patients. This formulation results in optimum plasma concentration in vivo.

e

Fig. (3). Pellets of Medikinet®.

The in vivo release profile of the Medikinet® capsules, as shown in Fig. (4), was designed so that the first portion would be released in the stomach’s acid medium immediately, within 30 minutes of ingestion, and the modified-release portion would be released only when pH values were higher than 5.5, this second release therefore taking place in the intestine (see Fig. 4). Studies have shown that MPH is rapidly and almost completely absorbed from the gastrointestinal tract. Medikinet® features a modified pharmacokinetic profile with prolonged Tmax and T1/2 values, offering more stable daily profiles of the active drug level, and combines the advantages of a rapid influx rate with the development of a relatively prolonged plateau phase [78]. Influence of Food The influence of food on rate and extent of absorption is a controversial issue. Various studies have shown that food does not affect the pharmacokinetics of MPH [79], though others claim that food modifies absorption rate, accelerating it. However, a more recent study showed that food produces a significant increase in the duration of absorption, but does not affect the final rate [80]. When the medication is taken in the morning after breakfast, the non-delayed release portion of the capsule is rapidly dissolved and an initial maximum concentration is achieved after a mean of 2 hours. Methylphenidate is then released from the sustainedrelease part of the capsule, and contributes to creating a plateau phase during which the concentration does not fall below 75% of the maximum achieved concentrations [81].



25

Amount released [mg]

20

15 pH 1.2

pH 6.8

10

5

0 0

20

40

60

80

100

120

140

160

Time [min]

Fig. (4). Release profile of methylphenidate from Medikinet retard® capsules.

After single daily doses of 20 mg, 30 mg and 40 mg of Medikinet®, the maximum concentrations and the areas under the concentration-time curves are proportional to the administered dose. Because of the short half-life of MPH, the modified-release principle also does not result in accumulation of the active drug when the prescribed dosing interval is maintained. In a balanced, randomized cross-over design, the bioavailability of MPH from one 10 mg Medikinet® capsule (single dose) was compared to 10 mg MPH water solution in 12 healthy volunteers, all recruited out of the 24 from the preceding study. Medikinet® is bioequivalent to the reference solution in terms of extent of absorption, but it shows a lower peak concentration at a later time point compared to the reference. Cmax is outside common bioequivalence limits, and Tmax is delayed by 1.92 hours. The plasma concentration time curve suggests that when the drug is taken with food, retardation occurs as the Cmax summit ends in a “plateau-like” phase. Nevertheless the influence of food on the kinetic behaviour of the substance was not very pronounced, but rather demonstrated a “standard” profile. Apart from this, the results match the theoretical expectations for a hybrid formulation such as Medikinet retard® with respect to Cmax and Tmax, where bioequivalence to the “immediate release” solution would not be expected. Within the assessment of this study an additional exploratory evaluation was carried out, comparing the results of the same 12 volunteers’ participation in the two studies from the 20 mg Medikinet retard® sequence (fasting), with the 10 mg Medikinet retard® sequence (highcalorie fed), normalized to 20 mg assuming linear kinetics. In order to further elucidate the influence of food intake it was attempted to explore whether the kind of food taken as breakfast does influence the bioavailability, since children (at least in Europe) most likely do not eat a high-calorie, high-fat breakfast. A “normal” child’s breakfast was now introduced in one of the sequences. In this balanced,



concentration [ng/ml]

randomized, crossover, open, four-way, single-dose design the bioavailability of MPH hydrochloride from Medikinet® modified-release 10 mg and 20 mg capsules after singledose application and 10 mg Ritalin® immediate-release after b.i.d. application following a normal breakfast were compared to single-dose application of Medikinet® capsule following a high-calorie breakfast. Both Medikinet retard® formulations (10 mg and 20 mg capsules) were now dose proportional with regard to the excipients and identical to the product-marketing authorization applied for. The study was carried out with 16 healthy male and female volunteers, all 16 of whom completed the study and were considered in the statistical analysis. For better comparability, Ritalin® was also administered after a normal breakfast, and not on an empty stomach as recommended by the designer of the original study. The results (Fig. 5) demonstrate that there is no difference in bioavailability between the Ritalin® IR in a dosage of 10 mg b.i.d. and the 20 mg Medikinet® formulation given after a normal breakfast. There is bioequivalence with regard to rate and extent of absorption. There is no difference in bioavailability of the 20 mg Medikinet® formulation given after a normal and after a high-calorie breakfast. Once again there is bioequivalence with regard to rate and extent of absorption. Nevertheless, as far as the individual curves are concerned, it becomes apparent that the typical double-peak pattern is less pronounced after the high-calorie breakfast. Dose linearity is given between Medikinet® 10 mg and Medikinet® 20 mg, both administered after a normal breakfast. 8

test 1 (Ritalin® 10mg, normal BF, tau=4h)

arithmetic means (n=16)

test 2 (Medikinet® Retard 10mg, normal BF) test 3 (Medikinet® Retard 20mg, normal BF) test 4 (Medikinet® Retard 20mg, high calory BF) 6

4

2

0 0

6

12

18

24 time [h]

Fig. (5). Plots of the mean whole blood levels of methylphenidate hydrochloride over the 24-hour sampling period (BF: breakfast).

As food intake with a normal breakfast does not alter stomach acidity to above a pH of 4, the enteric-coated pellet portion of Medikinet retard® will only release the active ingredient in the duodenum, creating the prerequisites for the retardation effect. In contrast, if children show severe anacidity of the stomach, reaching ph values > 5.5, the retardation will not come into effect, and such children should not be treated with Medikinet®.



Dosage and Frequency of Use Medikinet® modified release is a hard-gelatine capsule with different dosage options: 10, 20, 30 or 40 mg. Administration of this drug once a day is generally sufficient for achieving the therapeutic goal. Research shows that the dose for children should not exceed 60 mg per day. Thus, when the dose set by the clinician is sufficient, onset of the effect occurs in the first hour after administration of the drug, the effect being maintained until early afternoon. As mentioned earlier, duration of action of this drug is approximately 8 hours. In some cases it may be necessary to administer an additional dose of immediate-release MPH to prolong the effects in the afternoon; however, this decision should be made on the basis of the patient’s particular symptoms. In order to maintain the appropriate pharmacokinetics of Medikinet®, the drug should be administered with or after breakfast, and never on an empty stomach. Safety of the Capsule Shell One of the main problems of therapeutic compliance in children with ADHD is the difficulty for parents to administer the medication, especially in children who have trouble swallowing capsules. Although data on this matter are scarce, some authors refer to swallowing problems in 26% of patients [82,83]. In this regard, Medikinet® brings considerable advantages, especially for those patients with difficulties for swallowing the capsules, such as small children; the advantages extend to cases in which the capsule tears open or is bitten into, or in which a patient takes the capsule contents without the shell, since the capsule, as well as being swallowed whole, can be opened and its content ingested without modification of the drug’s pharmacokinetic properties. In this regard, Fischer et al. [79] carried out a study to check the bioequivalence of whole-capsule ingestion versus its content only. The resulting curve profile showed that the sustainedrelease characteristics are preserved, and that there is no immediate release of the total dose of methylphenidate (dose dumping). It can be assumed that biting the capsule, due to which some of the pellets can enter the oral cavity, does not result in a changed plasma concentration curve compared to the normal and correct practice of swallowing the entire capsule whole. Given the small size of the Medikinet® pellets it is advisable that when the drug is taken without the capsule its contents are mixed into a spoonful of semi-solid food, such as apple purée, jam or yoghurt, so as to avoid loss of pellets and the consequent decrease in efficacy [79,81]. Methylphenidate Extended-Release Capsules Versus Methylphenidate (b.i.d.) Döpfner et al. [78] carried out a study to explore whether the bioavailability of MPH extended-release capsules was equivalent to that of immediate-release MPH administered b.i.d. This multi-centre, randomized, double-blind, controlled clinical trial was carried out in children who already responded to MPH, with the aim of assessing the efficacy and tolerability of Medikinet®. A crossed design was used, comparing it with both immediate-release MPH (2 x 10mg), administered every four hours, and with placebo. The study took place in successive treatment phases of four-day observation, total observation time being limited to 2 weeks.



For the clinical assessment the SKAMP questionnaire (Swanson, Kotkin, Afler, MFlynn, and Pelham Scale) was used, and parents/caregivers assessed the children’s behaviour taking into account ADHD symptoms and rule-compliance, as well as recording the number of arithmetic problems correctly solved. Likewise, individual ADHD symptoms and aggressive behaviour, throughout the morning and afternoon activities, were assessed by means of the German questionnaires for the assessment of hyperkinetic disorders (FBB-HKS; Fremdbeurteilungsbogen für Hyperkinetische Störungen) and for social behaviour disorders (FBB-SSV; Fremdbeurteilungsbogen für Störungen des Sozialverhaltens), in accordance with Corrigan’s Agitated Behaviour Scale (ABS). In addition, the child’s activity was measured throughout the entire period (“level of agitation”) by means of an actimeter. The study included 82 schoolchildren aged 8-14, divided into groups of 5-10 patients each. The children were subjected to relatively high demands, with five concentration tests over the course of the day, but they also received throughout the study behavioural therapy and training in social adaptation, and were attended by doctors, psychologists and education experts. Dosage was identical in the two MPH treatment groups, and did not exceed 1 mg/kg of body weight. Administration of the drugs was designed in such a way that Medikinet® was taken at 9 o’clock in the morning and immediate-release MPH and placebo at 9 in the morning and 13.00 hours, using the double-simulation technique. Daily administration of a capsule of Medikinet® considerably reduced symptoms of inattention and hyperactivity in the 79 patients assessed, and the superiority of Medikinet® versus placebo (p 5

d)

number of hydrogen-bond acceptors (N and O atoms) > 10

Similar analyses have been performed that include additional descriptors such as: molar refractivity [39], counts of the number of rings, rotatable bonds, as well as hydrogen bond donors and acceptors [41, 42].


Hecht and Fogel

Recent analyses comparing drug candidates in development with marketed drugs concluded that larger, more lipophilic compounds tended to be identified from highthroughput screening of compound libraries in lead discovery [42, 43]. As compounds pass through the different stages of pre-clinical and clinical development, the mean molecular weight of drug candidates tends to converge to that of marketed drugs. Likewise, many promising lipophilic compounds are discontinued as development proceeds. Additionally it was determined that drugs developed for oral administration tended to be lighter and had fewer rotatable bonds and hydrogen bond acceptors and donors than drugs developed for other indications [43]. These observations explain a significant portion of the inefficiency and high-attrition rates of current drug discovery and development approaches. A major limitation of using predictive rules like the Rule-of-Five is that many of the “inactive” or “non-druggable” compounds in commercially available screening libraries also obey these rules. For example 68.7% of the compounds in the Available Chemical Directory (ACD) screening database (containing >2.4 million compounds) do not violate the Rule-of-Five [44]. In other words, simple ADME filters are not enough to eliminate “non-druglike” molecules from screening libraries. This often results in precious resources being spent on optimization and development of drug candidates that ultimately fail. The later the failure in the drug discovery process, the greater the cost. This has given rise to the mantra: “fail fast, fail early.” In order to address this deficiency, more sophisticated in silico models of absorption, often modeling activities in in vitro assays such as Caco-2 or MCDK and BBB, have been developed [44]. Many of these models use computational intelligence methodologies such as ANNs, EC, and fuzzy logic and will be discussed in more detail in Section 5. Several commercial software packages for the prediction of drug absorption include properties such as: aqueous solubility and partition coefficients; Caco-2 cell permeability; BBB permeability; MDCK cell and skin permeability; and cell absorption. Some representative programs (and companies) include: ADMET Predictor (Simulations-Plus, www.simulations-plus.com); Discovery Studio, TOPKAT and Accord (Accelrys, www. accelrys.com); ChemSilico (ChemSilico, www.chemsilico.com); KnowItAll (Bio-Rad, www.biorad.com); ADME boxes (Pharma-Algorithms, www.pharma-algorithms.com); Pre-ADMET (www.preadmet.bmdrc.org); QikProp (Schrödinger, www.schrodinger. com); VolSurf (Molecular Discovery, www.moldiscovery.com); VolSurf and Sybyl (Tripos, www.tripos.com); and various MDL databases and tools (Symyx, www.mdli. com). 2.2. Distribution Prediction of tissue distribution of a drug is a very important consideration in drug development. Descriptors such as logP, molecular weight, as well as acidity have been proven to be useful in modeling distribution [45]. Additional terms are usually added to account for plasma-protein binding, tissue composition, blood composition, as well as blood flow to the tissues [46-49] Modeling of plasma protein binding is also very important as bound drugs are often prevented from crossing cell membranes and getting to their intended targets [50]. On the other hand, drugs that bind to proteins tend to have a longer t [51]. Plasma proteins

Computational Intelligence Methods


that bind drugs include: albumin (for acidic drugs); 1-acid glycoproteins (for basic drugs); lipoproteins (for neutral and basic drugs) as well as erythrocytes and ,,globulins [20, 51]. Several commercially available software packages exist for prediction of multicompartment pharmacokinetic models, drug release, dissolution, and transport, elimination half-life, and plasma protein binding. Some representative examples include: GastroPlus (Simulations-Plus, www.simulations-plus.com); Pre-ADMET (www.preadmet.bmdrc. org); and KnowItAll (Bio-Rad, www.biorad.com). 2.3. Metabolism Predicting the potential interactions and metabolic pathways of a drug is extremely difficult. There is great interest in developing models for biotransformation (e.g., toxic metabolites & intermediates), enzyme and/or receptor binding and inhibition (e.g., cytochrome P450, hERG potassium channels), and synergistic/antagonistic drug-drug interactions [52, 53]. Biotransformation studies are important to identify what enzymes are metabolizing a drug, what metabolites are produced and if so, how they are cleared [54, 55]. If these metabolites are reactive, they can cause toxicity or other adverse events. Rule-based expert systems have been developed based on chemical similarities and decision trees [55]. These include: MDLI Metabolite Database (Symyx, www.mdli.com); Meteor (Lhasa, www.lhasalimited.org); MetaDrug (GeneGo, www.genego.com); MexAlert and MetabolExpert (CompuDrug, www.compudrug.com); and MetaSite (Molecular Discovery, www.moldiscovery.com). Potential cytochrome P450 interactions (as well as interactions with other metabolically important enzymes) have traditionally been studied using QSAR models [56, 57]. Because of the potential for arrhythmia and cardiac failure, there is also currently interest in developing QSAR models for potential interactions with the hERG potassium channel [58, 59]. Unfortunately there have been relatively few models of metabolic stability and its effect on t, or on potential drug-drug interactions [54]. Because of the complexity of modeling metabolism and metabolic pathways, there is currently great interest in applying computational intelligence methodologies. This will be discussed in more detail in Section 5 below. 2.4. Excretion Currently very little effort has been directed towards in silico models of excretion processes [36]. While most drugs are excreted to via the kidneys or the bile to some extent, for the most part they are eliminated via other routes (e.g., they are metabolized). 2.5 Toxicology As toxicity is currently the major reason for drug candidate failure in clinical trials, there is currently considerable interest in developing predictive in silico models. These models generally fall into one of two following categories: expert systems (based on rules generated from human experts as well as the scientific literature) and QSAR models – in particular for cytochrome P450’s and hERG receptors (as discussed in Section 2.3) [60-62].


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Expert systems have been used to predict toxicological endpoints that include: rodent carcinogenicity; Ames mutagenicity; developmental toxicity potential; skin and eye irritation; acute oral toxicity LD50; acute inhalation toxicity LC50; acute toxicity LD50; acute toxicity EC50; maximum tolerated dose (MTD); chronic lowest observable adverse effect level (LOAEL); and skin sensitization [63]. Several representative examples of predictive toxicological software include: Actelion Property Explorer (Actelion, www.actelion.com); ADMET-Predictor (SimulationsPlus, www.simulations-plus.com); ChemSilico (ChemSilico, www.chemsilico.com); DEREK (Lhasa, www.lhasalimited.org); Hazard Expert (CompuDrug, www.compudrug. com); KnowItAll (Bio-Rad, www.biorad.com); LAZAR (www.predictive-toxicology. org/lazar/); MCASE, CASE, MTOX (Multicase, www.multicase.com); OncoLogic (www.epa.gov/oppt/sf/); Pre-ADMET (www.preadmet.bmdrc.org); TOPKAT (Accelrys, www.accelrys.com); ToxBoxes (Pharma-Algorithms, www.pharma-algorithms.com); and ToxScope (Lead Scope, www.leadscope.com). Recent developments of microarray technologies have completely transformed the fields of toxicogenomics and pharmacogenomics [64-71]. Not only are microarray experiments used identifying biomarkers and validating drug targets, they are also used to study the metabolic and potential toxicological effects of compounds in a highthroughput mode. The amount of data generated from these experiments is astronomical and CI approaches are routinely employed in these analyses [64-71]. 3. QSAR & QSPR QSAR models are in essence a mathematical function that relates features and descriptors generated from small molecule structures to some experimental determined activity or property. The first QSAR models introduced in 1969 were rather simple, identified the relationship between the water-octanol partition coefficient and biological activity [38]: log(1/C) = k1logP – k2(logP)2 + k3s + k4

(1)

where C is the concentration of the compound that gives a biological response, P is the water-octanol partition coefficient, and k1, k2, k3, and k4 are constants. QSPR models are used often to model and predict ADMET properties. QSAR and QSPR are very similar in that much of the same computational approaches are used in their development and optimization. The major differences arise from the activities/properties they are designed to predict. For QSAR models, relevant biological responses most often include: the concentration needed to inhibit 50% of activity (IC50); the dose required to reduce activity by 50% in cell based or animal studies (ED50); the inhibition constant, Ki; as well as the bonding constant Kd. As mentioned above, QSPR models are generated to predict physico-chemical properties and as well as biological activities relevant to ADMET. These often include the dose required to kill 50% of the cells or animals tested (LD50), solubility, lipohilicity and partition coefficients, absorption through intestinal walls, measures of cell membrane permeabilities, as well as BBB penetration. Some of the more widely used commercially available software packages for performing QSAR and QSPR include Cerius2 and Cata-



lyst (Accelrys), MOE (Chemical Computing Group), OpenEye, MDL, and Sybyl (Tripos). 3.1. QSAR and QSPR: 1D & 2D Models QSAR and QSPR models are based on molecular descriptors or features. While there are literally thousands of descriptors available, they generally fall into one of four major classes: 1) counts of features; 2) physico-chemical properties; 3) topological indices and atom connectivities; and 4) calculated intramolecular energies. The first class includes descriptors such as hydrogen bond acceptors, hydrogen bond donors, aromatic ring systems, carbonyl groups, basic nitrogens, and carboxyl groups. Descriptors based on predicted physico-chemical properties include dipole moments, volumes, polarizabilities, water-octanol partition coefficients, solubilities, molecular weights, melting points, boiling points, heat of sublimations, and molar refractivities. Topological indices and atom connectivities are based on the two- and three-dimensional structures of compounds. These include branching indices, kappa shape indices, electrotopological state indices, atom-pairs, topological torsions, as well as surface areas both polar and non-polar. Finally, there are many descriptors based on calculated intramolecular energies using both quantum mechanical as well as empirical methodologies. Numerous software packages are available for generating molecular descriptors. Some of the more popular and well known ones include: Sybyl (Tripos), Catalyst and Cerius2 (Accelrys), MOE (Chemical Computing Group), OpenEye, and Dragon. QikProp (Schrödinger) is primarily focused on generating descriptors and predicted activities relevant to ADMET. Because of the large number of descriptors available, the rate limiting step in the development of QSAR and QSPR models is often their identification, appropriate reduction, and weighting. A variety of techniques are typically employed for this purpose including multiple linear regression (MLR), partial least squares regression (PLS), and principle component analysis (PCA). MLR is perhaps the most widely used method for modeling linear correlations between descriptors and activities. For best results, the number of samples should be > 2n, where n is the number of descriptors. It is also important that descriptors used are not significantly correlated in order to avoid redundancies. PLS is useful for cases where the number of samples is small with respect to the number of descriptors. Unfortunately this is very often the case in drug discovery and development where data points are often very expensive and difficult to obtain. In PLS there is a linear transformation of the original descriptors into a new space composed of a smaller number of orthogonal variables. PCA is useful for transforming a large number of correlated descriptors into a far fewer number of orthogonal descriptors or principal components. The first principal component accounts for as much of the variability as possible, with each subsequent principal component accounting for additional variability. Development of improved and more efficient strategies is a very active area of research, and some of the more popular techniques include computational intelligence methodologies such as ANNs and evolutionary algorithms (EAs) [72-74]. In particular, ANNs have proven useful for selection of features that are nonlinearly correlated to small molecule activities [75-83]. The “genetic function approximation” (GFA) is another variation of evolutionary computing in which populations of QSAR models are generated and optimized [84].


Hecht and Fogel

This approach has become very popular and is included in Accelrys’ Cerius2 software package for QSAR model generation. 3.2. Pharmacophore Modeling, CoMFA and CoMSIA QSAR and QSPR models using pharmacophore modeling have proven to be very useful in drug discovery and development [85-87]. A pharmacophore is essentially the three dimensional substructure of an active compound or structure class that is both necessary and sufficient for bioactivity. The first step in generating a pharmacophore requires generation of a 3D structural alignment of a set of active compounds. Common structural and chemical features in the aligned structures are then identified and the distances and angles between the features are calculated. These features often include: hydrogen bond donors and acceptors, charged or polar groups, as well as aromatic groups. These models are extremely computationally efficient and large numbers of compounds (literally millions) can be screened against these models. Scoring is based on how well they fit the model. One very popular variation on pharmacophore modeling is comparative molecular field analysis (CoMFA) [85, 87]. As in pharmacophore modeling, a 3D structural alignment is performed on a set of training compounds. However, for a CoMFA model, the structural alignment is performed in a lattice of grid of points to which a molecular force field is applied [88]. Interaction energies are calculated for the molecule at each point of the lattice. These energies typically have steric, electrostatic and hydrophobic terms. Because of the large numbers of descriptors, PCS and/or PLS are typically used to reduce the number of descriptors during model development. Comparative molecular similarity indices analysis (CoMSIA), is very similar to CoMFA but is instead based on similarity [89]. 4. COMPUTATIONAL INTELLIGENCE AND MACHINE LEARNING The field of computational intelligence has many tools and techniques for building predictive models for processes that are extremely complex and where our understanding of the fundamentals is limited [76, 90, 91]. There are very few problems more complex than that of modeling biological responses in response to administration of a drug. It should not be surprising, then, that many of these tools and methodologies have been successfully applied to QSAR & QSPR modeling [22, 59, 92]. In QSAR & QSPR models, these computational intelligence approaches are used to predict experimental activities based on descriptors or features requiring a method of supervised learning. Perhaps one of the most useful applications has been that of feature selection. As mentioned previously, there are literally thousands of descriptors currently available. This section presents a brief introduction to ANNs, fuzzy logic, EC, as well as other machine learning approaches. 4.1. Artificial Neural Networks Artificial neural networks are transfer functions modeled loosely after the neural architecture of the human brain that accept some number of input features and yield some output decision. ANNs (or more commonly referred to as simply “neural networks”) are patterned after the neuronal structure of the brain as a tool for pattern recognition [9395]. Supervised learning of ANNs occurs using a training set of examples in which the neural net learns the relevant mapping of inputs to output decisions.



A typical ANN architecture consists of an input layer, one or more hidden layers, and an output layer. An example is shown in Fig. (2). Linear neural net models do not have a hidden layer: input nodes are directly connected to the output node(s). Non-linear models have at least one hidden layer with connections to both the input layer and the output layer. The number of connections between the nodes of each layer and their relative weightings will vary from model to model. For QSAR and QSPR, inputs to the ANNs are molecular descriptors and the output is a decision concerning the predicted activity or other property [77]. As was the case for use of MLR, PLS, and PCA, feature selection needs to be performed in order to select which ones to include or exclude from the model as input. Each input or feature then needs to be weighted with respect to maximizing predictive accuracy on the output decision over the training examples. The relative weights of each input are often unknown.

Output Layer

Input Layer

Hidden Layer(s)

Fig. (2). An artificial neural network architecture using five input nodes, one hidden layer with four nodes, and two output nodes. This architecture is a feed-forward multi-layer perceptron. Other architectures are possible making use of recurrence, a variable number of connections, variable number of nodes, nodes per layer, layers, processing elements internal to each node.

Optimization of the relative weights and/or the architecture of the ANN (e.g., the connections between layers) can be performed in order to minimize the mean squared error (MSE) between the predicted output and actual values over the training set. For example:

MSE =

1 N

N

(P k =1

k

Ok ) 2

(2)

where P was the predicted activity, O was the observed activity, and N was the number of patterns in the training set. Backpropagation is one of the most commonly used of the training algorithms for weight adjustment. A validation set of held-out examples (not used for training) is used to test the best model. The model can be re-designed if neces-


Hecht and Fogel

sary. In some cases a second held-out testing set of data is used to assess final predictive accuracy. 4.2. Fuzzy Systems Fuzzy systems which are based on fuzzy set theory [96, 97], attempt to build models that capture uncertainties and imprecision not easily quantified by other methods. Fuzzy algorithms have proven useful for clustering or classification in bioinformatics [98-102] where they are used to handle uncertainties in rule-based representations. For prediction of “drug-likeness” a fuzzy model representation might take the form: IF the activity score is ACTIVE and compliance to the Rule-of-Five is MOSTLY TRUE THEN the decision of drug-likeness is TRUE For prediction of toxicity a fuzzy model representation might take the form: IF the structure is SIMILAR TO a known cytrochrome P450 inhibitor and the predicted metabolite score is ACTIVE THEN the decision of toxicity is TRUE Fuzzy systems seem ideal for modeling toxicity and metabolism where the inputs used to generate the model do not cleanly separate into discrete values or are subjective. Whereas other methods would force the inputs or continuous variables into partitions on user defined discrete intervals, a fuzzy system can be designed to represent membership in vaguely defined partitions. This is useful when the discrete interval boundaries are largely subjective and/or difficult to determine empirically. There are many subdisciplines of fuzzy logic theory that have been developed to handle linguistic variables, and many of these are appropriate for use in biological problems such as prediction of toxicity or of metabolism. 4.3. Evolutionary Computation Evolutionary algorithms are designed to mimic natural evolution as a populationbased optimization process. An typical example is provided in Fig. (3). EAs use random variation and selection as a means for discovering solutions to complex problems. A typical evolutionary computation process starts with an initial set (population) of solutions. These are randomly altered (e.g., mutated and/or recombined) to generate the individuals comprising the current population which are subsequently evaluated using a fitness function (defined by the user). Based on their scores, a subset of individual solutions in the population are chosen to be parents for the next generation. The cycle then continues with random alteration, scoring with the fitness function and then selection. This continues until a halting criterion has been met, such as a specific number of generations or exceeding the available time. Methods of evolutionary computation include evolutionary programming [103], evolution strategies [104], genetic algorithms [105107], genetic programming [108], particle swarm optimization [109], ant-colony optimization [110], differential evolution [111, 112], and others. Each approach has its own advantages and disadvantages relative to specific problems. The “No Free Lunch” theorem indicates that no single optimization approach will work best over all problems [113].



4.4 Evolved Artificial Neural Networks and Evolved Fuzzy Systems One very powerful application of evolutionary computing has been the optimization of the connections (and weightings) between the input layer, the hidden layer(s), and the output layers in neural networks [114, 115]. The evolutionary algorithm creates populations of ANNs and scores each ANN based on mean squared error between the predicted and actual outputs. Likewise, evolutionary algorithms can be used to optimize any fuzzy classifiers or fuzzy inputs that are used in ANNs (e.g., fuzzy neural nets) or in fuzzy systems [116]. Evolutionary computing can also be used to evolve the selection of features to be used in a neural net model simultaneous with the optimization of that model’s architecture [117-120].

Population Initialization

Random Variation

Fitness Scoring

Parent Solutions

Process Termination

Fig. (3). A flow diagram of a standard evolutionary algorithm. The loop of variation, scoring, and, generation of parent solutions for the next “generation” of evolution continues until a termination criterion is satisfied.

4.5. Other Common Machine Learning Approaches Support vector machines (SVM) have recently been used for prediction of compound activities [121, 122]. Support vector machines represent the input descriptors/features as vectors that are projected onto higher-dimensional space. An optimal hyperplane is then constructed separating the actives and inactives. The hyperplane is used to predict the activity of new compounds that are tested [123-125]. Other techniques employed for modeling of ADMET properties include clustering and decision trees with recursive partitioning [22, 44, 59, 126]. K-means clustering is one of the oldest and most widely used clustering methods. Data are grouped by similarities in their features/descriptors. Decision trees consist of nodes where each node is connected to all the outcomes of a decision based on a single attribute. Recursive partitioning is often used to examine every attribute of the data and rank them with regards to their ability to partition the rest of the data. In general, the tree is first grown to its full size by evaluating each and every attribute and generating nodes for each outcome. The tree is then pruned back based on its predictive performance. 5. COMPUTATIONAL INTELLIGENCE AND ADMET MODELING In this section, current applications of computational intelligence to predictive ADMET and QSPR models are reviewed. These models have focused on a relatively small (but very important) subset of ADMET properties and activities reflecting the needs of drug development programs to increase the survivability of drug candidates. 5.1. Absorption Absorption is critical to the development of orally available pharmaceuticals. Models for aqueous solubility, intestinal absorption, Caco-2 permeability as well as BBB penet-


Hecht and Fogel

ration are well established and are routinely applied in lead discovery as a screen for “non-druglike” compounds. As these models predict properties and activities related to solute-solvent interactions including hydrophobic interactions, they often employ topological and surface property based descriptors such as PSA. Aqueous solubility is perhaps one of the most commonly modeled ADMET properties. Solubility refers to the maximum amount of compound that can dissolve in a given quantity of water. These models are commonly used as experimental determinations of solubility are very costly in terms of time, money and perhaps most importantly, the amount of compound used. In general, milligrams of compound are required for solubility determination. Table 1 provides representative examples of computational intelligence based aqueous solubility models. While MLR was a common approach used method early on [130-135], in recent years there has been an increased use of ANNs and EAs [120, 144152, 155-163]. When EAs are combined with other techniques such as ANNs, they are most often used for feature selection [162]. However, EAs can also be used effectively in order to evolve the ANNs themselves [120]. Table 1.

Models of Aqueous Solubility

Reference #

Method

Descriptors

127-139

MLR

calculated molecular descriptors

140, 141

MLR

topological and molecular descriptors

142

MLR

surface and calculated molecular descriptors

143

PLS

infrared spectral data

144-147

ANN


148-156

ANN


157-160

EA


161

EA


120, 162

EA & ANN


163

ANN & Fuzzy Logic

topological descriptors

164

SVM


165

EA, ANN, SVM


Experimental determinations of intestinal absorption are generally very low throughput, extremely time consuming, and require costly animal models [166]. Because of these considerations, in silico models are very commonly used – particularly early on in drug discovery and development. Table 2 provides representative examples of human intestinal absorption models. While MLR and PLS were the techniques most commonly



used in the development of these models, there are a number of models using ANNs and EA as well as decision trees and recursive partitioning [183]. In two models, EAs were used for descriptor selection and then ANNs were generated [181, 182]. While the types of descriptors used in these models varied considerably, topological and surface properties were often used as well as H-bonding terms and logP values. Table 2.

Models of Intestinal Absorption

Reference #

Method

Descriptors

167-169

MLR


170

MLR

H-bond descriptors and other calculated molecular descriptors

171

MLR

molecular groups

172

MLR

PSA

173

PLS

MolSurf

174

PLS

H-Bond descriptors and logP

175

PLS


176

PLS

atom types

177

ANN

molecular hashkeys

178

ANN

PSA, logP, and topological descriptors

152

ANN


158, 179, 180

EA


181, 182

EA & ANN


183

Recursive Partitioning


184

Decision Trees


164, 185-187

SVM


Although Caco-2 permeability studies are less costly and easier to run than other intestinal absorption models, there remains great interest in using in silico filters – especially when screening large libraries [9, 11]. As was the case for the intestinal absorption models, both MLR, PLS, and ANNs were the techniques most commonly used. Again, topological and surface property based descriptors proved to be the most useful for these models. As experimental models of BBB penetration tend to be relatively expensive and low throughput [166]. It is imperative that drugs targeted for CNS indications are able to pass through this barrier. For these reasons, in silico BBB permeability filters are applied early in drug discovery and development. Table 4 lists BBB models constructed using


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MLR, PLS, ANNs as well as EAs and SVMs. Of particular interest are models that combine computational intelligence techniques [229, 231]. Table 3.

Models of Caco-2 Permeability

Reference #

Method

Descriptors

188, 189

MLR

PSA and MW

190, 191

MLR

topological and surface descriptors

192, 193

MLR


194

MLR

H-bond and molecular descriptors

175, 195, 196

PLS


197

PLS

MolSurf descriptors

198

PLS

VolSurf descriptors

174

PLS

logP and H-bonding descriptors

181

ANN


199-201

ANN


100

ANN


101

ANN


202

EA


158

EA


203

SVM


Table 4.

Models of BBB Permeability

Reference #

Method

Descriptors

204-207

MLR

PSA, logP & molecular descriptors

208-214

MLR


215

MLR


216

MLR

Solvation energy

217

MLR

PSA, H-bond descriptors, logP

174

PLS

H-bonding descriptors and logP

218

PLS

MolSurf descriptors



Reference #

Method

Descriptors

219

PLS

surface and molecular descriptors

175, 196, 220, 221

PLS


222

PCA

VolSurf descriptors

223

ANN


58, 82, 224, 225

ANN


226

EA


227

EA, CoMFA, CoMSIA


164, 228

SVM


229

SVM & ANN


230

Decision Tree


231

ANN, SVM, Clustering, & Decision Tree


5.2. Distribution, Clearance, and Metabolism In addition to optimizing compounds for the ability to be absorbed, it is also very important to optimize compounds for their distribution to different organs and tissues, their clearance from the body, as well as their metabolic stabilities. As was the case for absorption, in silico models are used routinely to screen compounds for these purposes. Models of cytochrome P450 activity are extremely important for evaluating the potential for metabolism and reactive intermediate formation before compounds transit through the portal vein and into general circulation. As these models are designed to predict enzyme activity they often include 3D-QSAR techniques such as pharmacophore modeling [245, 246] as well as CoMFA [252, 253]. Examples of modeling techniques such as ANNs, EAs, SVMs, MLR, and PLS are also presented in Table 5. Table 5.

Models of Predicted Cytochrome P450 Activity

Reference #

Method

Descriptors

232, 233

MLR

surface descriptors, logP

234

PLS

logP

158

EA


235, 236

ANN


164, 237-242

SVM



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(Table 5) contd....

Reference #

Method

Descriptors

243

ANN, SVM


244

3D QSAR

structural fragments

245, 246

3D QSAR

pharmacophore

56, 247-251

3D QSAR


252, 253

3D QSAR

CoMFA

254

Clustering


255-257

Clustering

microarray gene expression data

As mentioned previously, once drugs enter the blood stream, they often bind to blood proteins such as albumin and may be prevented from reaching their targets. Again, in silico models to predict albumin binding have also proven to be very useful drug development [9,11]. Some representative examples of these models are presented in Table 6. A variety of techniques have been used including ant colony optimization, a type of evolutionary algorithm [264]. Table 6.

Models of Human Serum Albumin Binding

Reference #

Method

Descriptors

258

MLR


259-260

MLR

logP, topological descriptors, PSA

261

PLS


58, 262, 263

ANN


180

EA


264

EA (Ant Colony Optimization)


164

SVM


265

Expert System

pharmacophores

Although there are fewer examples, models of clearance, t and metabolic stability are also very important. Table 7 lists several based on EAs and SVMs. Examples of models based on fuzzy logic or clustering are also provided.


Table 7.


Models of Clearance & Metabolic Stability

Reference #

Method

Property

266

Fuzzy Systems

clearance time

180

EA

urinary excretion

267

EA

volume of distribution

268

Clustering

metabolic stability

269

SVM

metabolic stability

164

SVM

t and volume of distribution

5.3. Toxicity Currently toxicity is the major cause of drug candidate failure during development and clinical trials and is responsible for >90% of the drugs pulled off the market [7, 9, 10]. The ramifications of toxicity are enormous not only in terms of costs, but in actual lives. One of the most common examples is that of hERG receptor modeling. In some cases, drugs will bind to the hERG receptor and cause arrhythmia and hear failure. It is therefore important to screen out compounds with the potential for this adverse effect as early as possible. The examples presented in Table 8 utilize EAs and SVMs. Table 8.

Models of Predicted hERG Receptor Binding

Reference #

Method

Descriptors

270

EA


271

EA & CoMFA


58, 272-274

SVM


275

SVM and Clustering


Other examples of in silico toxicology models are presented in Table 9. While most of these models have been generated using molecular descriptors, there are a couple based on gene expression profiles from microarray data. 6. GENERAL TRENDS AND FUTURE DIRECTIONS Table 10 presents an analysis of the literature cited in Tables 1-9 grouping them by year and methodology. From this analysis, a couple of general overall trends for the field seem to emerge, although the data presented here is only a sampling of the available literature.


Table 9.

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Models of Toxicity

Reference #

Method

Descriptors

Toxicity

152

ANN


Ames genotxocity

276

EA & ANN


drug transfer to breast milk

277

ANN, SVM, Clustering, Decision Trees


overall predicted toxicity

278

SVM & ANN



70

Clustering

gene expression profiles


279

Clustering

gene expression profiles

hepatotoxicity

280

Clustering

structures and data in LAZAR

Ames genotxocity

281

Clustering

Structures and molecular descriptors


Prior to 2000, MLR appeared to be the preferred methodology for generating ADMET models. This changed in the early 2000s, when computational intelligence-based approaches became more popular - particularly with the use of ANNs and EAs. Since 2005, SVM and clustering approaches (including decision tree analyses) have also been used with increasing regularity. The increased use of clustering and decision tree analyses in recent years reflects the great interest in developing models of metabolism and toxicity using gene expression data coming from microarray data. Because of the astronomical quantity of data produced, it is to be anticipated that computational intelligence methodologies will continue to play a major role. Table 10. Number of References Cited Grouped by Methodology and Year

Method

1980s-1999

2000-2004

2005 -2008

ANN

6

21

7

EA

2

9

9

MLR

18

28

2

PLS

7

8

0

SVM

0

3

16

Clustering & Decision Trees

0

2

9



Again, this reflects the shift in recent years from development of newer and improved models relevant to absorbance to models of predicted metabolic activities and toxicity. Perhaps one of the largest hurdles to overcome is the amount of proprietary metabolic and toxicity data maintained in the databases of pharmaceutical companies. A promising recent development has been microarray data that has been made public (from NCBI as well as other sources). Another very important area of opportunity is the development of improved models of distribution and clearance. There are currently relatively few examples of applications of computational intelligence and this is an area of likely future application. 7. CONCLUSIONS As discussed previously, there is great interest in developing new and improved ADMET models in order to improve the efficiency and productivity of drug discovery and development. Because of the great complexities, scarce and “noisy data,” as well as overwhelming numbers of parameters involved, researchers have borrowed heavily from the field of computational intelligence and machine learning. In this paper we have reviewed applications of computational intelligence methods to the development of predictive ADMET models. There is a great opportunity for the development of novel approaches and methodologies that will increase the likelihood of survival of drug candidates through the development process. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25]

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Data Modeling and Chemical Interpretation of ADME Properties Using Regression and Rule Mining Techniques Kiyoshi Hasegawa1 and Kimito Funatsu2,* 1

Kamakura Research Laboratories, Chugai Pharmaceutical Co., LTD, 200 Kajiwara, Kamakura, Kanagawa, 247-8530, Japan and 2Department of Chemical System Engineering, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-8656, Japan Abstract: In pharmaceutical industry, in addition to high potency, good absorption, distribution, metabolism and excretion (ADME) profiles of compounds are needed for drug development. Data modeling of ADME model is a crucial step for efficient drug design. However, we have to avoid so called a situation, the black box, which might be difficult for chemical interpretation. The right balance between precision and interpretation is required for practical drug design. We review the related articles to focus several ADME modeling techniques. As regression, multiple linear regressions (MLR), partial least squares (PLS), artificial neural networks (ANN), support vector machines (SVM) are picked up and their algorithms and the representative applications are introduced. We pay attention to rule mining methods for chemical interpretation. As rule mining, rough set theory (RST) is shown as an example. Visualization is a classical but never neglected technique for easily understanding the overall behaviors of huge compounds. We especially spend more pages about kohonen neural networks (KNN) and decision trees (DT) as the representative methods. Furthermore, web application for chemists is another important aspect for practical drug design. Recent trend about this topic is shown in two industry cases. As conclusion, we will show future direction concerning in silico ADME prediction.

1. INTRODUCTION In a process of potential leads to candidates suitable for clinical trials, numerous hurdles must be overcome. In addition to adequate potency and safety, candidate compound must have optimal pharmacokinetic and metabolic properties. These properties are called as absorption, distribution, metabolism and excretion (ADME) properties in general terms [1]. Behind tight relationship between inadequate ADME properties and

*Corresponding Author: Tel: +81(03)-5841-7751; Fax: +81(03)-5841-7771; E-mail: [email protected] Gary W. Caldwell / Atta-ur-Rahman / Z. Yan / M. Iqbal Choudhary (Eds.) All rights reserved – © 2009 Bentham Science Publishers.

Data Modeling and Chemical Interpretation


failure of candidate compounds, optimizing ADME properties is a critical step in drug discovery [2]. Traditionally, pharmaceutical company has been embedded in a serial process whereby potency test is carried out first, followed by ADME tests that are essential to reveal the profile of candidates. In this scenario, if an ADME problem were to arise, it was late in the development stage and not much could be done to remedy the problem. This scenario has dramatically changed under the pressure of cost reduction. Many pharmaceutical companies have recently employed parallel processing, so that potency test and surrogate ADME tests are carried out in close proximity [3, 4]. As a result, high-throughput instruments for ADME profiles have been invested in pharmaceutical companies [5]. Early ADME studies can provide the necessary information to learn more about the fundamental mechanisms and specific properties that are used to select better compounds [6]. For example, in vitro metabolism studies in liver microsome can provide information as to which compounds in the chemical series are more metabolically stable to cytochrome P450 (CYP) enzymes. Absorption models can provide information to help rank order compounds to their ability to permeate the cell membrane and can be used as a predictor of intestinal absorption. Inhibition studies can provide valuable information on the potential for inhibition of CYP enzymes. Those kinds of information, if obtained early, can help guide chemists to modify the chemical series as means to design better candidate compounds. These studies can also help limit the number of compounds moving forward to the more time-consuming ADME studies and provide specific guidance to help better select those compounds moving forward towards drug development [4]. The advent of in vitro methods for measuring specific ADME properties have led to an increase in the availability of data on a wide range of compounds, making it possible to investigate the rules by which the chemical structure of a molecule determine its ADME properties, to build predictive models of ADME properties. The most well known is Rule-of-five [7]. Compounds are most likely to have poor absorption when molecular weight > 500, calculated octanol-water partition coefficient Clogp > 5, number of H-bond donors > 5 and number of H-bond acceptors > 10. Computation of these properties is now used as a simple but efficient ADME filter. The Rule-of-five should be seen as a qualitative absorption/permeability predictor, rather than a quantitative predictor. While calculated simple filters may be sufficient in library design, more sophisticated ADME models are required in lead optimization [8]. In silico modeling of ADME properties can be broadly divided into three categories: molecular modeling [9], physiologically based pharmacokinetic (PBPK) modeling [10] and data modeling [11, 12]. Molecular modeling approaches include quantum and classical mechanical methods, homology modeling and pharmacophore models and can be used where the underlying molecular mechanism of a property is fully understood. PBPK modeling integrates several factors responsible for ADME processes in one model and attempts to simulate the pharmacokinetics (PK) of compound in the whole organism. Data modeling is applied when the molecular mechanism of an ADME property is not clear or cannot be efficiently modeled at the molecular level and largely uses several statistical approaches. In this review, we will concentrate on data modeling techniques [13]. Other two approaches are beyond of this review and readers who are interested in should refer to the following literatures [14, 15].


Hasegawa and Funatsu

This article will review the progress made from 2000 years in the development of predictive ADME models and the current state-of-the-art [16-18]. We will discuss recent trends in four fundamental aspects (regression, rule mining, visualization and web application). Other important topics (partition of training/test sets, chemical descriptors, descriptor selection and domain of applicability), when building any predictive models, are not explicitly described here. Concerning these topics, the following review should be referred to [19]. As regression, multiple linear regressions (MLR), partial least squares (PLS), artificial neural networks (ANN), support vector machines (SVM) are picked up and their algorithms and the representative applications are introduced. We pay attention to rule mining methods for chemical interpretation. As rule mining, RST (rough set theory) is shown as an example. Visualization is a classical but never neglected technique for easily understanding the overall behaviors of huge compounds. We especially spend more pages about kohonen neural networks (KNN) and decision trees (DT) as the representative methods. Furthermore, web application for chemists is another important aspect for practical drug design. Recent trend about this topic is shown in two industry cases. As conclusion, we will show future direction concerning in silico ADME prediction. In the next section, we will introduce each method with short explanation of the algorithm followed by the representative examples. 2. REGRESSION 2.1. MLR Multiple linear regressions (MLR), called Hansch-Fujita approach, are the most widely used linear correlation method [20, 21]. Its basic assumption is that ADME property is expressed as the additive combinations of each chemical descriptor. K

y = bj x j + C

(1)

j =1

where y is ADME property. xj is descriptor variable. bj is the regression coefficient. C is the constant value. K is the number of descriptors. Least squares are used to estimate the regression coefficient bj. The great advantage of MLR is that a causal model is obtained and the physical meaning is obvious due to the simple description. However, the following severe conditions must be satisfied to apply MLR: The number of compounds should be at least five times greater than that of the descriptors. The descriptors should be independently distributed [21]. Though these statistical limitations, many MLR equations have been proposed as ADME models [22, 23]. Applications with MLR method are cited in literatures [24-35]. Hasegawa et al. have performed a quantitative structure-pharmacokinetic relationship (QSPKR) study of antifungal N-myristoyltransferase (Nmt) inhibitors [36]. For predicting rat elimination half-life (t1/2) values, they have constructed a comprehensive multivariate statistical analysis based on various chemical descriptors. The t1/2 values of 105 inhibitors were obtained by cassette dosing experiments in a high-throughput manner. The 30 physicochemical descriptors were generated for an entire 3D structure as well as for an individual fragment (Table 1). The correlation between the t1/2 values and the chemical descriptors was examined by a stepwise MLR based on the F values. They have obtained a significant MLR model with just only three variables.


Table 1.


Chemical Structures and Observed Three PK Parameters in the First Series. Cited from Table 1 in Ref. [36] N O

N

O

O

R

1-60

No.

-R

In (t1/2)

In (Cl)

In (Vss)

-0.386

2.104

1.946

-0.274

2.208

2.079

0.501

2.241

2.901

0.663

2.219

2.986

1188

-0.105

1.099

2126

0.916

3.040

2.454

0.470

3.016

2.518

0.262

2.827

CH3

1 O O

O

2

CH3

O

CH3 CF3

3 O O

CF2CF2H

4 O O

5

O

CF3 CF3

O CF3

6 O

CF3 O CF2CF3

7 O O

CF2CF2CF3

8 O O


Hasegawa and Funatsu (Table 1) contd....

N O

N

R O 61-83

No.

-R

In (t1/2)

In (Cl)

In (Vss)

0.4332

1.131

1.841

0.621

1.194

1.960

1.008

1.099

2.282

0.476

1.723

2.407

0.647

1.825

2.639

0.673

1.569

2.416

0.779

1.194

2.067

-0.994

1548

0.833

O

27

O

F

28

F

O

29 O

CF3 F

30

F

F

31

F F F

32

F

F F

33

F F F

34

N



ln(t1/2) = 0.011*PNSA(157.04) + 0.198*AlogP(63.09) – 0.081 *Rotlbonds(6.57) – 2.330 (n = 105, R = 0.843, Q = 0.827)

(2)

In Eq. 2, the numbers in parentheses are the F value of each variable, n is the number of compounds, R is the correlation coefficient and Q is the cross-validated version of R derived from the leave-one-out (LOO) procedure. The most important factor influencing the model is PNSA according to the F value. PNSA means the sum of the solventaccessible surface areas of negatively charged atoms such as oxygen, nitrogen, and halogen atoms. The model means that PNSA should be kept to a maximum value by introducing negatively charged groups to the terminal part of a chain from the C-2 position. The high lipophilicity (AlogP) of the compound is an advantage for long t1/2. Fewer rotational bonds (Rotlbonds) are also beneficial for the compound to remain in the plasma. The authors have stressed that the combination of high-throughput cassette dosing experiments and the multivariate statistical approach is helpful in designing new Nmt inhibitors with a long t1/2 value. Colmenarejo et al. have proposed the MLR model for predicting binding affinities to human serum albumin (HSA) [37]. They have determined the binding affinities to HSA of 95 diverse drugs by high-performance affinity chromatography. All the compounds but one (Captopril) in the database were used to generate the MLR model. The dataset was split into two subsets: a training one (84 molecules) and an external validation one (10 molecules). Genetic algorithm (GA) was used to exhaustively search and select multivariate equations, starting from 53 chemical descriptors. The resulting best model was logK’hsa = -0.607873 + 0.06784*(HbondDon – 3)2 – 9*10-6*(JursTPSA) – 0.028261*(EHOMO + 7.4076)2 + 0.005697*(AM1dip2) + 0.182595*(ClogP) + 2.33529*(6ring) (n = 84, R2 = 0.83, Q2 = 0.79)

(3)

Both the goodness of the fit and the predictive power of the model from the cross validation (CV) was achieved. When applying the logK’hsa of the external validation set, significant correlation was observed (Rpred2 = 0.82). Fig. (1) displays the descriptor usage versus the number of generations in the GA evolution. It can be seen that good convergence actually is achieved. (Especially, ClogP, HbondDon, 6ring and AM1dip) From the equation, an increase of hydrophobicity (ClogP) within a series of compounds is expected to result in an increased HSA binding. 6ring is a sixth-order, ring type Kier and Hall topological index. The equation indicates that molecules with non-substituted six-member rings are expected to bind more tightly to HSA. JursTPSA is the sum of solvent-accessible surface area of atoms with the absolute value of partial charges greater than or equal to 0.2. The equation indicates the logK’hsa is inversely proportional to this descriptor. Therefore, binding is favored for molecules with large non-polar surfaces. Other additional factors, like the number of hydrogen bond donors (HbondDon), HOMO energy (EHOMO) and AM1 dipole moment (AM1dip) can be important in determining HSA binding to some extent. The binding to HSA turns out to be determined by a combination of hydrophobic forces together with some modulating shape factors. Yoshida et al. have studied to construct a predictive model for human oral bioavailability (BA) of 232 structurally diverse drugs [38]. The oral BA was assigned one of four categories and analyzed in relation to chemical descriptors by the ORMUCS (ordered multi-categorical classification) method. ORMUCS is a modified form of discriminant MLR analysis using a simplex algorithm. Two lipophilicity measures, expressed as the distribution coefficient at pH 6.5 (logD6.5), and the difference between the fractions of the



usage vs. # of crossovers 1.2 Variable x103 #

ClogP use Hbond donor use K i e r C h i 6 use Jurs-TPSA use AM-1 dipole use HOMO MOPAC use Jurs-RNCG use AlogP use Rotlbonds use Jurs-TASA use

1.0

O f t i m e s v a r i a b l e

0.8

0.6

0.4

i s u s e d

0.2

0.0 0.0

0.5

1.0

1.5

# of crossovers

2.0

2.5

3.0 x10 5

Fig. (1). Variable usage vs genetic algorithm generations for the second global model. Selection of model descriptors during evolution can be seen, as well as the achieved convergence. Cited from Fig. (6) in Ref. [37].

neutral form at two given pH values (logD = logD6.5 - logD 7.4) were found to be significant factors influencing BA. The addition of 15 structural descriptors relating primarily to well-known metabolic processes and the squared term of logD6.5 yielded a satisfactory equation. The list of 18 descriptors is presented in Table 2. The final model with 18 descriptors has a correct classification rate of 71% and a Spearman rank correlation coefficient (Rs) of 0.851. In LOO, an average of 67% of drugs are correctly classified with Rs of 0.812. The predictive power of the model was evaluated using a separate test set of 40 compounds, of which 60% are correctly classified. The important role of lipophilicity terms in the final model is shown by their contribution index (CI) values. From the final equation, the optimum logD6.5 is -0.3 and a progressive negative impact on BA are seen as values move away from this level. The logD descriptor might adjust the logD of a


Table 2.


The QSAR Model. Cited from Table 5 in Ref. [38] S(X) = wisi

No.

Descriptors Si

Weight wi

CIa

nb

1

(log D6.5)

-0.27

0.05

232

-0.046

0.25

232

0.370

0.23

232

2

2

(log D6.5)

3

log D (log D6.5 – log D 7.4) c

4

phenolic OH (excluding di-ortho-subst)

-1.032

0.45

22

5

SO2NH 2

-1.014

0.17

7

6

alcoholic OH (excluding tert-OH)d

-0.177

0.09

59

7

hydrolysis: esters, lactones, -lactams, alkyl carbamates

-1.074

0.37

24

-0.599

0.26

33

-0.235

0.12

47

-0.201

0.09

13

8 9

e

f

aromatic p-hydroxylation g

ArCH2-R (excluding di-ortho subst Ar) h

10

allylic oxidation (C-C=C)

11

tert-alicyclic amine (no ring heteroatoms)i

-0.340

0.10

24

12

XCCNR (R = Me, Et; X = N,O, Ar, C=C)j

-0.410

0.15

28

13

readily oxidized moieties: thiols, dihydropyridines

-1.137

0.24

11

-0.493

0.10

15

k

14

ketones

15

NO2 on a benzene ring (excluding ortho subst)

-0.148

0.03

7

16

ArNH2, ArNHNH2 , ArCONHNH2, ArC(=NH) NH2 as pKa value1

-0.034

0.04

16

17

HOCCNH tert-alkyl, HOCCN < (cyclic rings)

0.210

0.05

16

18

benzodiazepine (with no additional fused rings)

0.231

0.05

10

constant

4.358

n- 232 (four classes); boundaries, 2.0, 3.0, 4.0; recognition, nmis = 67 (8), Rs = 0.851 (p < 0.0001); leave-one-out, nmis = 76 (10), Rs = 0.812 (p < 0.0001) a Contribution index. The product of the weighting coefficient and standard deviation for each descriptor. b n = total number of each of the descriptors used in the analysis. c Except with o-CO2H, o-CONH2 and o-CH2OH substituents, which can undergo intramolecular hydrogen bonding with the phenolic OH group. d For steroids this descriptor is 0 for 11--OH substituents (steric hindrance) and 2 for 17--OH substituents (unless tert) due to high susceptibility to first-pass metabolism. e Weighting is 0.5, where the carbon to the carbonyl is tertiary or where the carbonyl can undergo intramolecular hydrogen bonding with a nearby group. f Applies where there is an open para position with respect to the activating groups OR, N(R)R1, NHC(=O) R (R, R1 = H, alkyl, aryl, aralkyl) with no ring substituents beyond one ortho to the activating group. The activating group and ortho substituent may part of a fused ring. g R = H, CH2X, where X = C or H and is not attached to a polar atom. h Excluding steroidal ring A dienones and allylic substructures of types C=C-C-X and C=C-C-C-X, where X is a polar atom. i Excluding ring systems with a bridged N atom such as quinine and quinidine. j Weighted by 2 for O=CCNR; see the . k Excluding , unsaturated dienones, diaryl-ketones, and ketones with a heteroatom attached at the -position. Corrected (-0.5) for branching on the aliphatic side and , -unsaturation. i If a molecule contains two or more amino groups the most basic group with no ortho substitution is selected.



compound caused by the change in pH following intestinal absorption. The presence of phenolic OH and SO2NH2 groups have marked and highly significant effects in reducing oral BA. Hydrolytic cleavage of susceptible functions such as esters, lactones, -lactams, and alkyl carbamates is a strong and highly significant contributor in reducing BA. Hydroxylation of an activated aromatic ring has the largest effect in reducing BA, with aryl methyl and allylic groups contributing to a lesser extent. The model has the advantage of transparency for chemical interpretation. This means which factors may affect BA and the extent of that effect, thus providing the basis for designing improved compounds. 2.2. PLS As mentioned in the section of MLR, MLR has severe statistical limitations when applying. In order to overcome theses limitations, partial least squares (PLS) have been invented and developed in chemometrics [39]. PLS finds some latent variables on which to perform regression. These latent variables are chosen to simultaneously satisfy two conditions: (a) that they are highly correlated with ADME property and (b) that they model as much of variations among descriptor variables as possible. In PLS, the descriptor variables X and ADME property y are modeled by the latent variable t. A

X = t h p hT + E

(4)

h =1

A

y = t h qh + f

(5)

h =1

where ph and qh are the loadings for X and y blocks in the h-th component, respectively. E is a matrix of X residuals, and f is a vector of y residuals. A is the optimum number of components determined by the LOO procedure. The latent variable t is a linear combination of the descriptor variables X and the weight vector w.

t = Xw

(6)

If Eq. 6 is substituted into Eq. 5, then the MLR-like model that can be directly comparable to the Hansch equation is obtained.

y = XW ( PW ) 1 q = Xb

(7)

where W, P and q are the weight matrix, loading matrix and coefficient vector, respectively. b is the regression coefficient of the MLR-like model equation. Applications with PLS method are cited in literatures [40-51]. Gleeson et al. have constructed the model for human volume of distribution (Vss) of 199 marketed drugs [52]. They have conducted in parallel with model building for rat Vss based on a data set of 2086 in-house compounds. The two data sets were randomly partitioned into training and test sets, 75%/25% for the human data and 80%/20% for rat. The 123 descriptors, which broadly describe lipophilicity, size, topological, geometrical, and electronic features of molecules, were calculated. The results of the human Vss model in fit and prediction using PLS were 0.641 of R2, 0.597 of Q2 and 0.587 of Rpred2, respectively. The first component in the human model describes 51% of the variance and the second 13%. Component 1 (t1) essentially describes the lipophilicity/molecular



weigh dependency of Vss, and the second component (t2) describes the charge state or charge distribution. Fig. (2) shows the overall normalized/scaled coefficients. Basic moieties/large positive charge and lipophilicity based descriptors have positive coefficients. In contrast, acidic/negative charge based descriptors have negative coefficients. The results of the rat Vss model in fit and prediction using PLS were 0.519 of R2, 0.506 of Q2 and 0.463 of Rpred2, respectively. t1 (45% of the explained variance) of the PLS model relates to lipophilicity/molecular weigh, t2 (5% of the explained variance) to charge state/charge distribution, and t3 (2% of variance) to descriptors related to aromatic molecular features. Fig. (3) shows the overall normalized/scaled coefficients. Basic/positive charge and lipophilicity based descriptors have a positive coefficient. In contrast, acidic/negative charge based descriptors have negative coefficients. Comparing Figs. (2) and (3), the two trends of coefficients are relatively similar. So, the authors have attempted to predict the rat data using the human model and vice versa. The human model predicted the rat data with root mean squares error (RMSE) of 0.38. The rat model predicted the human data with RMSE of 0.50. To understand this difference, PCA was performed against the combined data set. Fig. (4) shows the PCA score plot. From the plot, it can be seen that while the majority of human compounds have rat nearneighbors, the converse is not true. The chemical space of human data is larger than that of rat data. This may explain why the human model predicts the rat results but not vice versa. This is the first reported design and application of entirely in silico models for the prediction of an in vivo PK parameter. The authors have pointed out that the predictive

Variable Coetficient

0.20

0.10

0.00

-0.10

Pos_ioniz

MaxRing2

Neg_ioniz

Lipinski

HYBOT_max_donor

HMO_reson _energy

VDW_POL_AREA

Aver_neg _charge_G_H

NPat

POSCH

MWNPat

NEGCHARGED

MM_ZAP_PCR1

MM_VDW_EP_P_VAR

MM_MAXPOS

MM_SOLVNRG

MM_HDSA

MM_HASA

MM_FHASA

ACDLogP _v70

ACDLogD65_v70

Amine2

Amine3

-0.20

Variable ID

Fig. (2). Coefficients derived from the descriptor human Vss PLS model. (r2 = 0.64, q2 = 0.60, comp = 2). Red descriptors are acid/negative charge descriptors/indicators, blue descriptors are base/positive charge descriptors/indicators, black are lipophilicity/size based descriptors, gray are size/aromaticity based descriptors, and yellow are others. Cited from Fig. (7) in Ref. [52].



0.20

Variable Coetficient

0.10

0.00

Posioniz

MineV1

Negioniz

MaxRing3

Max_neg_charge_Gast

KierChi4p

KierChi5p

Charge_range_G_H

HMO_HOMO_energy

Aver_neg_charge_G_H

Aver_neg_charge_Gast

POSCH

POSCHARGED

NEGCH

NEGCHARGED

MWNPat

M2M

CLOGP

HAROM

Amine3

-0.20

CHARGED

-0.10

Variable ID

Fig. (3). Coefficients derived from the descriptor rat Vss PLS model. (r2 = 0.52, q2 = 0.51, comp = 3). Red descriptors are acid/negative charge descriptors/indicators, blue descriptors are base/positive charge descriptors/indicators, black are lipophilicity/size based descriptors, gray are size/aromaticity based descriptors, and yellow are others. Cited from Fig. (8) in Ref. [52]. 12 10

Component Two (t2)

8 6 4 2 0 -2 -4

Human Rat

-6

Component One (t1)

Fig. (4). PCA model showing the relationship between the human and rat data sets using 23 key physicochemical descriptors. The first two components are shown, describing 61% of total variance in the data set: component 1 (35%) and component 2 (26%). The human data set differs significantly from the rat based data set on component 1. Cited from Fig. (12) in Ref. [52].



ADME models together with suitable high-quality data sets may prove useful in the early stages of drug discovery prior to resource-intensive chemical synthesis and data acquiring.

1.35 0.00 -1.35 -2.69 -4.04 -5.38

BBB BBB

-8.07

-6.73

PLS score (Latent Variables 2)

2.69

4.04

5.38

Crivori et al. have developed a quantitative model for blood brain barrier (BBB) permeation [53]. All compounds were classified into two categories: either brain penetrating (BBB+) or no brain penetrating (BBB-). The chemical descriptors were the originally developed VolSurf descriptors that are calculated from 3D interaction energy with chemical probes. They have searched for a relation between the 3D structure and the BBB permeation of the dataset consisting of 229 compounds and 72 VolSurf descriptors. PLS discriminant analysis was carried out, assigning to the BBB+ compounds a score +1, and to the BBB- compounds a score -1. Two significant latent variables emerged from the PLS model. The PLS t1-t2 score plot of the resulting model is shown in Fig. (5). The model can distinguish well between the BBB+ and BBB- compounds. The model correctly predicted more than 90% of the BBB permeation data. Since the prediction error of the discriminant PLS was 0.6 units, a confidence interval was built in the t1t2 space between the BBB+ and BBB- regions, as shown in Fig. (5). In this interval, BBB prediction can be borderline and doubtful. The coefficient plot of the model (Fig. (6)) reports the contribution of all VolSurf descriptors. Hydrophilic region descriptors

-12.74

-10.19

-7.64

-5.10

-2.55

0.00

2.55

5.10

7.64

10.19

12.74

PLS scores (Latent Variable 1) Fig. (5). Discriminant PLS t1-t2 score plot for the global model. The model offers a good discrimination between the BBB+ and BBB- compounds, since it assigned a correct BBB profile to > 90% of the compounds. A confidence interval is built in the t1-t2 space, where BBB prediction can be borderline and doubtful. , BBB+ compounds; , BBB- compounds. Cited from Fig. (4) in Ref. [53].



0.04 BBB

0.03

0.01

Hydrophobic regions

Integy Moments

0.02

Critical Packing

Size & Shape

0 -0.01 -0.02 Amphiphilic Moments

-0.03

H-Bonds

-0.04

Hydrophilic regions

Capacity Factors

-0.05

Fig. (6). PLS coefficient plot for the global model (training and test sets combined) for the correlation of VolSurf descriptors with blood-brain barrier permeation. Shading refers to the different energy levels used. Cited from Fig. (5) in Ref. [53].

refer to polar water-accessible surface areas, indicating the BBB permeation decreases when the polar surface increases. Capacity factor descriptors refer to polar interactions per surface unit. While diffuse polar regions are tolerable for BBB permeation, dense and localized polar regions are markedly detrimental. An increase in H-bonding capacity is known to be detrimental for permeation. The contribution of the integy moment demonstrates that, besides the number of H-bonds, their 3D distribution also influences BBB permeation. The descriptors of hydrophobic interactions are directly correlated with BBB permeation, but their role appears smaller than that of the polar descriptors. The size and shape descriptors have no marked impact on BBB permeation. In contrast, critical packing and the hydrophilic-lipophilic balance are important descriptors. The authors have pointed out the advantages of VolSurf descriptors. VolSurf descriptors are independent of the alignment of molecules and relatively independent of conformational sampling and averaging. VolSurf descriptors are also fast to compute and easy to interpret. Singh et al. have developed a rapid semi-quantitative model for evaluating the relative susceptibilities of different sites on 50 drug molecules by CYP3A4 [54]. The model is based on the energy necessary to remove hydrogen radical from each site, plus the surface area exposure of the hydrogen atom. The energy can be estimated by AM1 semiempirical molecular orbital calculation. However, AM1 calculations take too long to be a practical usage. Therefore, they have developed a statistical trend vector model, which is used to estimate the AM1 hydrogen abstraction energy. That is, in their study, y is hydrogen abstraction energy and X is trend vector matrix in Eq. 7. Trend vectors can



capture the local topological environment of the hydrogen. The trend vectors are of the form H-AT1, H-AT1-AT2, H-AT1-AT2-AT3, H-AT1-AT2-AT3-AT4, where AT1 is the type of atom to which the hydrogen is directly bonded, AT2 , the atom two bonds away, etc. After PLS analysis, the significant model with 14 components were obtained. The R2 for 3984 hydrogens was 0.98, and the standard error of the prediction was 2.06 kcal/mol. A plot of the estimated lowest energy sites (circles) and metabolic site energies (asterisks) versus surface area is shown in Fig. (7). It is evident from Fig. (7) that none of the hydrogens with surface area exposure < 8 Å2 are susceptible to metabolism (no overlap of circles and asterisks). If we consider all hydrogens with hydrogen abstraction energies < 27 kcal/mol and surface area exposure > 8 Å2, these hydrogens include a major metabolic site in the CYP3A4 substrates 78% of the time. The authors have concluded that this simple thumb can suggest likely sites of metabolism for compounds that could potentially be CYP3A4 substrates. 50 CYP3A4 Substrates

Solvent accessible surface area (A * *2)

25.0

Trend vector hydrogen abstraction energy vs. surface area

20.0

15.0

10.0

5.0 Low est energies Metabolic energies Surface area line at BA

0.0 0.0

10.0

20.0

30.0

40.0

50.0

60.0

TV predicted hydrogen abstraction energy (Kcal/mol)

Fig. (7). Plot of trend vector predicted hydrogen abstraction energy versus the surface area. Cited from Fig. (5) in Ref. [54].

Kaneko et al. have proposed ICA (independent component analysis) approach and applied it to a quantitative structure-property relationship (QSPR) analysis of aqueous solubility [55]. ICA is a method that extracts mutually independent components from descriptor variables. The public aqueous data set (logS) was divided into a training set of 878 molecules and a test set of 412 molecules. After ICA, 169 independent components were extracted from 173 chemical descriptors. The relationship between the 169 components and logS was modeled by using MLR with the least-squares manner. The R2, Q2,



and Rpred2 values were 0.937, 0.868, and 0.894, respectively. Fig. (8) shows the regression coefficient values for each independent component. There are three components whose absolute value is larger than 0.2, and their numbers are 52, 101, and 105. Fig. (9) shows the weight values for these components (w52, w101 and w105). Weight values give information on contribution of each chemical descriptor to each independent component. Table 3 shows chemical descriptors corresponding to each weight whose absolute values are larger than 1.0. The value of w52 corresponding to SSS(12-1_dCH-) is positive, and that of bSy corresponding to s52 is negative. SSS(12-1_dCH-) contributes to logS negatively, because a positive value times a negative value equals a negative value. Molecule is insoluble in water if the number of aromatic bonds in the molecule is large, because substructure =CH- of SSS(12-1_=CH-) is included mostly in aromatic rings. Other factor, WTPT3 also plays significantly role in water solubility. WTPT3 is the descriptor that represents like reciprocal size of molecules. This descriptor contributes to logS positively from two signs of bSy and w101. Molecule is soluble in water if the size is small. Table 3 shows that logP also contributes to logS significantly. The authors have concluded that more simple and interpretable model could be constructed by selecting important independent components. 0.3

value of b

0.2 0.1 0 -0.1 -0.2 -0.3 0

20

40

60

80

100

120

140

160

independent component Fig. (8). Value of b to each independent component. Cited from Fig. (7) in Ref. [55].

2.3. ANN Artificial neural networks (ANN) are computer-modeled system containing a number of nodes that are connected into net-like structure. ANN can handle the non-linear relationships where the standard linear MLR and PLS are useless. The network consists of input, hidden and output layer nodes which are schematically drawn as circles and connected by bonds with connection weights as shown in Fig. (10) [56, 57]. Each input layer node obtains descriptor variables X. Similarly, the output layer node produces ADME property y. Each of the input and hidden layers has an additional bias node for accommodating non-zero offset in the data modeling. The bias node obtains a signal with intensity 1.0 and distributes the signal to the next layer. If descriptor variables are given in the input layer, the input nodes only serve as distributors of input signals to the hidden layer. The net input sa is calculated by Eq. 8 in the a-th hidden node and it is transformed by the sigmoidal functions f(sa) by Eq. 9 to give the output ta of the hidden node.



1.2 1

W52

0.8 0.6 0.4 0.2 0 -0.2 0

20

40

60

80

100

120

140

160

180

120

140

160

180

120.0

140.0

160.0

180.0

W101

descriptor 9 8 7 6 5 4 3 2 1 0 -1 -2 -3 -4 -5 -6 0

20

40

60

80

100

W105

descriptor 4 3 2 1 0 -1 -2 -3 -4 -5 -6 0

20

40

60

80

100.0

descriptor

Fig. (9). Value of w to each independent component on that the value of b is larger than 0.2. Cited from Fig. (8) in Ref. [55].

D

s a = Wap X p + a

(8)

t a = f ( s a ) = (1 + exp( s a ) / T )) 1

(9)

p =1


Table 3.


Descriptors to Each w Whose Absolute Values are Larger than 0.1. Cited from Table 2 in Ref. [55]

Abbreviation

Descriptor Name

NO

w52

w101

w105

The number of oxygens

-1.6

2.0

NN

The number of nitrogens

-4.8

2.6

NS

The number of sulfurs

-1.5

NF

The number of fluorines

-1.2

Ncl

The number of chlorines

-3.2

NSB

The number of single bond

-1.1

NBR

The number of basis rings

-1.0

WTPT3

Sum of path lengths starting from heteroatoms

7.9

logP

Calculate of logP

MDE 34

Molecular distance edge between all tert quat C

2SP2

Doubly bound carbon bound to two other carbons

-2.2

2SP3

Singly bound carbon bound to two other carbons

-1.4

SSS(-C)

Count of substructure

1.1

SSS(-O)


-1.6

1.6

SSS(-O-)


-1.3

1.1

SSS(-C(O)-)


-1.8

SSS(12-1_=CH-)


4.0 1.2

1.0

y

Output v1

Hidden

vA

t1

θ

tA

w11 w12

bias θ1

θA

Input

bias 1

2

-5.6

d

Fig. (10). Three-layer neural networks. Cited from Fig. (1) in Ref. [56].



where D is the number of input nodes and Wap is the connection weight between hidden node a and input node p. a is the bias connecting hidden node a to the input layer bias node. T is an adjustable parameter determining the shape of the sigmoidal curve and referred to as a computational temperature. The output node produces the calculated ADME property by a linear equation. A

A

a =1

a =1

y = Va t a + = Va f ( s a ) +

(10)

where Va is the connection weight between the output node and hidden node a. is the bias connecting the output node to the hidden layer bias node. Applications with ANN method are cited in literatures [58-66]. The back-propagation (BP) algorithm has been widely used for ANN optimization [56]. The BP algorithm is based on a gradient descent method to minimize an error function E with respect to the connection weights (Wap, Va) and biases (a, ) from the top output layer toward the bottom input layer. McElroy et al. have presented QSPR model to predict aqueous solubility of heteroatom-containing organic compounds [67]. Before descriptor generation and model building, compounds were placed into three subsets: a training set, a CV set, and an external prediction set. The training set compounds are used in nonlinear model training phase. CV set compounds are used in nonlinear modeling phase to prevent over-training of the ANN. The prediction set compounds are never used in model building and are used to demonstrate predictive ability of a model. Data set contained a 298-member training set, a 50-member cross-validation set, and a 51member prediction set. A total of 229 descriptors were calculated for each compound. Of those, 141 are topological, 30 are geometric, 10 are electronic, and 48 are hybrid descriptors. After pre-selection, 98 descriptors remained for subsequent ANN analysis. The 11-5-1 architecture was selected as the final model with GA and ANN routine. The best model gave a training set error was 0.576 (R2 = 0.90) log units. Cross-validation set error was 0.587 (Q2 = 0.88) log units. The prediction set error was 1.223 (Rpred2 = 0.53) log units. These results show that this model is effective at predicting aqueous solubility values for both oxygen- and nitrogen-containing compounds. A list of the descriptors can be seen in Table 4. Many of the descriptors in the model give some insight into the solute-solvent interaction when placing organic compounds in water. Branching information held in indices (KAPA-6), weighted path (WTPT-3), and distance-edge descriptors (MDE-44) allow for more topological detail. Molecular shape, which affects packing and solvent interactions, can be described through geometry dependent descriptors (GEOM-1, SYMM-25). The charged partial surface areas of the molecule, which play an important role in solvent-solute interactions, are described with DPSA-2 and SAAA-3 descriptors. The authors have stressed that aqueous solubility for a wide range of compounds could be predicted accurately based solely on molecular structure, with no corrective factor for physical state or the use of other data. Classical ANN can handle non-linear problems, but are prone to over-training, have problems with network optimization and model selection and are not efficient in dealing with high-dimensional data without pre-selection of descriptors. Bayesian neural network (BNN) represent a special type of neural net which overcome the problems of conventional ANN [57]. BNN is based on a probabilistic interpretation of network training. Network weights are found by Bayesian inference that gives an objective solution to the



network optimization. This approach also provides the model predictions as probability distributions and therefore permits evaluation of the confidence in prediction [68-70]. Lee et al. have developed highly predictive classification models for human liver microsomal (HLM) stability using the intrinsic clearance (Clint) as the end point [71]. They Table 4.

a

Descriptors of the Nonlinear Type III Model for the Combined Data Set. Cited from Table 7 in Ref. [67]

Descriptor

Type

Range

Explanationa

KAPA-6

topo

0.0-18.1

Kappa index – atom corrected

NO

topo

0-16

number of oxygens

NN

topo

0-6

number of nitrogens

NDB

topo

0-6

number of double bonds

WTPT-3

topo

2.35-43.2

Sum of path wts from heteroatoms

MDE-44

topo

0.0-83.3

distance edge between 4o and 4o carbons

SYMM-25

topo

0.05-1.0

geometrical symmetry

GEOM-1

geom

0.66-48.4

first geometric moment

DPSA-2

comb

64.3-4334

difference in partial surface areas

CHDH-1

comb

0.0-2.32

charge on donatable hydrogens

SAAA-3

comb

0.10-62.1

surface area of acceptor atoms 34

KAPA-6, Kappa index of three-bond counts, corrected for atom type: NO, the number of oxygens in the molecule; NN, the number of nitrogens in the molecule; NDB, number of double bonds in the molecule; WTPT-3, sum of all path weights starting from heteroatoms;36 MDE-44, distance-edge between all quaternary carbons;39 SYMM-25, geometric consideration of (number of unique atoms/total atoms); [(-SAi)];47 CHDH-1, sum of charges on donatable hydrogen; SAAA-3, (SAacc)/SAtot .

only used HLM data that were measured using an internally harmonized in vitro assay protocol. The total 14557 molecules were assigned as HLM stable and unstable compounds according to a cutoff of Clint = 20 ul/min/mg. The data set was partitioned into training (11646 compounds) and test (2911 compounds) sets randomly. Another data set was collected to validate the predictive power of the model (276 compounds). As chemical descriptors, the extended connectivity fingerprints of maximum diameter 6 (ECFP_6) were used. The ECFP_6 fingerprints and the HLM clearance values were used to build a classification model based on BNN algorithm. BNN model was able to correctly classify 78% of the 2911 compounds in the test set. For the validation set, BNN model was able to correctly classify 69% of the 276 compounds in the test set. The fact that predictions are worse for the validation set than the test set may suggest that the model should be updated continuously with new data. The ability of BNN classification model to discriminate between stable and unstable compounds was evaluated with a bimodal histogram of the test dataset, shown in Fig. (11). This histogram shows how BNN scores obtained for stable compounds are distributed along the positive range, while unstable compounds tend to have negative values. It can also be seen that a grey area



could be defined between -10 and 0, where there is some overlap between both classes of compounds. An advantage of the ECFP_6 fingerprints is that they are easy to translate into 2D sub-structural sketches. This allows for a structural analysis of the molecular 18% 16%

% of samples

14% 12% 10% 8% 6% 4% 2% 0% -100

-80

-20 -40 0 -60 HLM Bayesian score

20

40

Fig. (11). Bimodal histogram representing the distribution of Bayesian scores for stable (squares) and unstable (diamonds) compounds in the test set. Cited from Fig. (2) in Ref. [71].

features that contribute most to HLM stability. Fig. (12) shows the ECFP_6 fragments that the BNN classifier found most frequently in the stable molecular set. Similarly, Fig. (13) summarizes the most common fragments in the unstable set. By comparing Figs. (12) and (13), it can be seen for example that amide groups in stable compounds are surrounded by non-aromatic cyclic systems, while in unstable compounds those chemical groups are not sterically hindered. If comparisons would be extended to the hundreds of fingerprint features obtained for each class of compounds, the atomic environments most favorable for HLM stability could be easily identified. The authors have stressed that this classification model can be used in the design of new compounds with stable property. 2.4. SVM Support vector machines (SVM) are based on the structural risk minimization principle from statistical learning theory. SVM constructs a hyperplane, which separates the two classes of vectors with a maximum margin. Separating the classes with a large margin minimizes a bound on the expected generalization error. In many cases, SVM has been found to be consistently superior to other supervised learning methods and less prone to over-fitting [72, 73]. In linearly separable cases, SVM searches vector w and parameter b that minimizes ||w||2 and satisfies the following conditions:

wxi + b +1 , for y i = +1 Class 1 (positive)

(11)



A

A

NH

A

A

NH

NH

O N

N

NH

A

N

O

N

A A

A

A

O

A

A

NH

NH

N A

A

A

A

A

A

A

A

A

O

A

A

N

A

N A

A

A

O

A

A

A

N N

O

A

A

A

NH

A

N A

A

O

A

A

A

NH

O

A

A

A

A

A A

A

O OH N

A

N A

A A

N

A

A

OH

O

N

A

OH

O

A A

O

A

OH

OH

A

A

O

A

A

A

A

A A

A

A A

N N

A

N

N

A

A

A

A

A

A

A

A

A A

A

A

A N

O

N A

A

O

A

A

N

N

A

O A A

A

N

A N

A

O A

A

A

A N

N

A

A

A

A

A A

O

N

A

N

A

OH

A

O

A

A

O

A

A OH

A

A A

N

O

A

A

A

O

A

O A

A

Fig. (12). ECFP_6 sub-structural features most frequently observed in stable compounds. Cited from Fig. (3) in Ref. [71].

wxi + b 1 , for y i = 1 Class 2 (negative)

(12)

where yi is the class index, w is a vector normal to the hyperplane, |bj|/||w|| is the perpendicular distance from the hyperplane to the origin, and ||w||2 is the Euclidean norm of w. These relationships are schematically shown in Fig. (14). After the determination of w and b, a given vector xi can be classified by decision function ‘sign[(wix) + b]’. In nonlinear cases, SVM maps the input variable into a high dimensional feature space using a kernel function such as K(xi,xj) = exp(-||xj-xi||2/2ó2). Linear SVM is then applied to this feature space, and then, the decision function is given by

L f ( x) = sign i0 y i K ( x, xi ) + b i =1

(13)

where L is the number of support vectors. The coefficients i0 and b are determined by maximizing the following Langrangian expression:



A

A A A

N

N

N

N

A

O

A

A

A

A

A

A

A

A A

A A A A A

A

A A A

A N

NH

A

A

N

A N

NH

O

A A

A

N

N

A

A

O

A N

A

O

A

A A

A

N

A

A

N

A

A

A

A

A

A

A

A A

A O

A

O

A

A

F

F

F

A

F

O A

A

A

A A

AA N

A

F

F

O

A

AA N

A

A

A

NH O

A

N

A

N

NH

A

A N

N

A

A

A A

O

O

A

A

A

A

A

A O

O A

A

A A

O

A

A

A

A

A

A

A

A

Fig. (13). ECFP_6 sub-structural features most frequently observed in unstable compounds. Cited from Fig. (4) in Ref. [71]. L

i i =1

1 L L i j yi y j K ( xi , x j ) 2 i =1 j =1

(14) L

under the following conditions: 0 < i < C and

i =1

i

y i = 0 . A positive or negative

value from Eq. 13 indicates that the vector x belongs to the positive or negative classes, respectively. Applications with SVM method are cited in literatures [74-76]. Yap et al. have developed filters for predicting substrates/non-substrates of three CYP isoenzymes, CYP3A4, CYP2D6 and CYP2C9 [77]. Classification for inhibitors/non-inhibitors was not described here for avoiding redundancy. GA based descriptor selection method was used to select relevant chemical descriptors for SVM classification of the substrates. The consensus SVM classification system by using multiple descriptor



Fig. (14). Schematic illustration of SVM algorithm.

sets generated from GA was employed. The dataset were composed of 368 substrates and 334 non-substrates for CYP3A4, 198 substrates and 504 non-substrates for CYP2D6, and 144 substrates and 558 non-substrates for CYP2C9, respectively. Substrates of an isoenzyme were denoted as belonging to positive class (P+) of the isoenzyme, and non-substrates were denoted as belonging to the negative class (P-) of the isoenzyme. Representative training and validation sets were constructed from the datasets according to their distribution in the chemical space. Any pair of compounds of similar chemical features was evenly assigned into separate datasets. As chemical descriptors, a total 1497 1D, 2D, and 3D molecular descriptors were derived from the 3D structure of each compound. The SVM classification system was composed from the 81 SVM classification models. The accuracies for classification of substrates and non-substrates of CYP3A4, CYP2D6, and CYP2C9 were 98.2 and 90.9%, 96.6 and 94.4%, and 85.7 and 98.8%, respectively. Because composite descriptors encode multiple physicochemical and structural aspects of the molecule, it is difficult to extract from these descriptors information about which specific molecular characteristics are important for the substrates of these CYP isoenzymes. The authors have tried to roughly distinguish between substrates/non-substrates from the values of six selected descriptors, S, nHAcc, nHDon, MLOGP, MW, and SPH. S is the combined dipolarity/polarizability, nHAcc and nHDon are the number of acceptor and donor atoms for hydrogen bonds, respectively, MLOGP is the Moriguchi’s LogP, MW is the molecular weight, and SPH is the spherosity. The average values of these six descriptors for P+ and P- compounds of all of the various datasets are given in Table 5. Substrates of CYP3A4 are generally larger in size, less spherical in shape, more hydrophobic, and have more hydrogen bonding sites than nonsubstrates. Substrates of CYP2D6 are generally smaller in size, more hydrophobic than non-substrates, and contain one hydrogen bond donor. Substrates of CYP2C9 generally are more hydrophobic than non-substrates but are smaller in size and have lesser hydro-



gen bonding capacity. The consensus SVM model generally gives better accuracies than single SVM classification models. This model is potentially useful for developing filters for the prediction of substrates of CYP isoenzymes. Table 5.

Differences in the Values of Descriptors Importance for Distinguishing between P+ and P- Compounds. Cited from Table 8 in Ref. [77] Average Valuea

Dataset

CYP

Descriptor P+

P-

S

2.56 (1.15)

2.29 (1.17)

nHAcc

5.53 (3.45)

4.91 (3.14)

nHDon

1.72 (1.99)

1.44 (1.75)

MLogP

2.20 (1.99)

1.60 (2.06)

MW

379 (157)

315 (137)

SPH

0.76 (0.13)

0.78 (0.13)

S

2.19 (1.08)

2.53 (1.18)

nHAcc

4.10 (2.13)

5.68 (3.58)

nHDon

1.15 (1.22)

1.76 (2.07)

MLogP

2.51 (1.74)

1.68 (2.11)

MW

320 (100)

360 (166)

SPH

0.78 (0.14)

0.77 (0.13)

S

2.52 (1.26)

2.41 (1.14)

nHAcc

4.69 (2.52)

5.38 (3.48)

nHDon

1.03 (1.14)

1.73 (2.01)

MLogP

2.05 (2.04)

1.88 (2.05)

MW

326 (112)

354 (160)

SPH

0.75 (0.14)

0.78 (0.13)

3A4

Substrates/ Nonsubstrates

2D6

2C9

a

Values in parentheses are the standard deviations.

Xue et al. have modeled the binding affinities to HSA with the chemical descriptors [78]. This data set is the same as used in Colmenarejo’s study [37]. The training and test sets are also same. About 600 descriptors were calculated for each compound. After the heuristic reduction, the pool of the descriptors was reduced to 243. A variety of subsets sizes were investigated to determine the optimum number of the descriptors in model. The influences of the number of the descriptors on R2, Q2, and s2 are shown in Fig. (15).



From Fig. (15), it can be seen that seven descriptors appear to be sufficient for a successful regression model. The list of the selected seven descriptors is summarized in Table 6. The obtained model had R2 = 0.86 with Q2 of 0.63. With the test set, the prediction result 0.9 2

R 0.8

R s

R

2

0.7

2 cv

2

0.6

R

2 cv

0.5 0.4

s

2

0.3 0.2 0.1

0

1

2

3

4

5

6

7

8

9

10

number of descriptors Fig. (15). Influence of the number of descriptors on R2, R2CV, and s2 of the regression models. Cited from Fig. (1) in Ref. [78]. Table 6.

a

Seven-Descriptor Linear Model for the Binding Affinitya. Cited from Table 2 in Ref. [78]

Descriptor

Chemical Meaning

Coefficient

t-Test

(constant)

intercept

-2.513 ± 0.388

-6.472

HDCA-2

HA dependent HDCA-2 [Zefirov’s PC]

-0.401 ± 0.078

-5.136

MSA

molecular surface area

0.007 ± 0.001

12.801

NO

number of O atoms

-0.149 ± 0.017

-8.877

RNR

relative number of rings

9.210 ± 1.395

6.605

RNN

relative number of N atoms

-3.945 ± 0.663

-5.950

BI

Balaban index

0.403 ± 0.097

4.147

RNCS

relative negative charged SA (SAMNEG*RNCG) [quantum-chemical PC]

-0.045 ± 0.013

-3.392

R2 = 0.86; s2 = 0.050; rms = 0.212; n = 84; F = 63.89; R2cv = 0.63.



was obtained, Rpred2=0.71. By interpreting the descriptors in the regression model, it is possible to gain some insight into factors that are likely to govern the binding affinities to HSA. According to the analysis of the corresponding regression coefficient (Table 6), molecular surface area (MSA), relative number of rings (RNR), and the Balaban index (BI) present positive contributions for binding affinity, whereas HA dependent HDCA-2, number of O atoms (NO), relative number of N atoms (RNN), and relative negative charges surface area (RNCS) present negative contributions. From MSA, binding is favored for the molecules with large molecular surface area. From RNR, molecules with a larger number of rings are expected to bind more tightly to HSA. From BI, the large degree of branching for molecules is in favor of the binding. From HDCA-2, NO, RNN, and RNCS, the hydrogen bonding might not be favorable in protein binding. Heuristic method (HM) was not sufficiently accurate and the prediction ability was not satisfactory, showing the factors influencing the binding affinities of these compounds are complex and not all of them are linear correlations with the binding affinity. So, after the establishment of the linear model by HM, the authors have built the nonlinear model based on the same seven descriptors by SVM. The final SVM model through optimization of the parameters, gave R2 of 0.94 for the training set, Rpred2 of 0.89 for the prediction set, respectively. The authors have reported that the successive application of HM and SVM is good modeling strategy for ADME properties. 2.5. Other Regression Techniques k-Nearest Neighbors (kNN) kNN is a non-parametric approach and does not require any concrete function in advance. In this approach, a single data point is left out of the training set and its ADME property is predicted as a weighted average of the ADME values of its nearest neighbors. k equal to 1 or can be a larger number when looking for a set of k nearest neighbors. The metric used to search the nearest neighbors is usually the Euclidian distance [79-82]. Gaussian Process (GP) GP is another new promising method based on a Bayesian approach. GP is equivalent to ANN with a single hidden layer containing an infinite number of nodes. There are four main advantages in GP. (a) GP does not require subjective a priori determination of parameters such as variable importance or network architectures. (b) Since the algorithm minimize the log marginal likelihood, which directly prevents the model from overtraining, GP does not need internal CV. (c) GP does work well for a big pool of descriptors. (d) GP can easily identify domain of applicability. This means that GP can estimate the reliability of a given prediction. The disadvantage of GP is the black box. Then, the model is difficult to be interpreted [83, 84]. Random Forest (RF) RF is an ensemble of decision trees (DT) using bootstrap samples of the training data set and random variable selection in the tree induction. Each tree provides a classification. RF chooses the classification having the most votes. Compared to single DT, RF generally performs better in terms of prediction accuracy. Additional nice features of RF are its out-of-bag performance estimate that can be used in place of CV, and a measure of descriptor importance. [85-87].



Classification and Regression Trees (CART) CART is a non-parametric statistical method, which uses decision trees (DT) to solve classification and regression problems. A CART analysis generally consists of three steps. In a first step an over-grown tree is build. This tree is called the maximum tree and is grown using a binary split-procedure. In a next step the over-grown tree, which shows over-fitting, is pruned. During this procedure, a series of less complex trees is derived from the maximum tree. In the final step, the tree with the optimal tree size is selected using CV. In CART, the variable selection is part of the methodology. This means that data modeling can be started with an extended set of descriptors [88-91]. 3. RULE MINING 3.1. RST Recently, for the discovery of the particular patterns or rules from data set, what we call rule mining method has been developed and has been widely used. Rough set theory (RST) has been introduced in chemometrics [92]. The essential part of RST is construction of minimal subset of descriptors, “reducts”, which can distinguish samples belonging to different class, and extraction of rules using these reducts. For easily understanding, we will show small data set as an example. In Table 7, a1, a2 and a3 are variables and d is ADME property, each of which is discretized into three categories (1, 2 and 3). The procedure to extract the reducts and rules from Table 7 are explained step by step. First, we construct the discernibility matrix from Table 7. Discernibility matrix is the matrix that has the same numbers of columns and rows as the number of samples. In discernibility matrix, (i,j) element is filled with the variables which are needed to discern the sample Xi and Xj (Table 8). For example, X1 and X2 are discernible each other by a1 or a2 or a3, so (i, j) element is filled with ‘a1 a2 a3’. Samples labeled same class attributes Table 7.

Decision Table Using All Variables. Cited from Ref. [93] U

a1

a2

a3

d

X1

2

1

3

1

X2

3

2

1

2

X3

2

1

3

1

X4

2

2

3

2

X5

1

1

4

3

X6

1

1

2

3

X7

3

2

1

2

X8

1

1

4

3

X9

2

1

3

1

X10

3

2

1

2


Table 8.


Discernibility Matrix. Cited from Ref. [93]

1

2

3

4

5

6

7

8

9

1

-

2

a1a2a3

-

3

-

a1a2a3

-

4

a2

-

a2

-

5

a1a3

a1a2a3

a1a3

a1a2a3

-

6

a1a3

a1a2a3

a1a3

a1a2a3

-

-

7

a1a2a3

-

a1a2a3

-

a1a2a3

a1a2a3

-

8

a1a3

a1a2a3

a1a3

a1a2a3

-

-

a1a2a3

-

9

-

a1a2a3

a2

a2

a1a3

a1a3

a1a2a3

a1a3

-

10

a1a2a3

-

-

-

a1a2a3

a1a2a3

-

a1a2a3

a1a2a3

10

-

each other cannot be distinguished, so the value in the element is missing. In addition, (n, m) and (m, n) should have the same value, so the upper triangle matrix is not necessary. Second, we can obtain reducts from this matrix. Reduct means that the minimal subset of variables to discern all samples each other which are not classified to the same class. In order to obtain reducts, we have to define a special function f(D), the kind of Boolean equations. The function f(D) is calculated from the discernibility matrix by multiplying all elements. (Eq. 15) After Boolean’s manipulations, we can obtain two reducts, {a1, a2} and {a2, a3}. (Eq. 16) This means that we can discern all samples by using either {a1, a2} or {a2, a3}. f(D) = (a1+a2+a3)a2(a1+a3)(a1+a3)… (a1+a3)(a1+a2+a3)(a1+a2+a3) =a1a2+a2a3

(15) (16)

Finally, we can extract rules by using either of these reducts. The procedure is the same as above. Preparing decision table, calculating discernibility matrix, and extraction rules by discernibility function. In this case, we focus on one reduct {a1, a2}. Table 9 represents the decision table based on reduct {a1, a2}. Table 10 represents the discernibility matrix derived from reduct {a1, a2}. Table 10 is just the rule: If a1 = 2 and a2 = 1 then d = 1 If a2 = 2 then d = 2 If a1 = 1 then d = 3

(17)

Koyama et al. have applied RST to the oxidation sites data in CYP2C9 and extracted useful rules which atoms are oxidized [93]. The data set is the same as used in Sheridan’s study [85]. As for 50 drugs, the primary and secondary oxidation sites were experimentally identified. The total number of heavy atoms was 1031 and among them, 96


Table 9.


Decision Table Using the Reduct {a1, a2}. Cited from Ref. [93]

U

a1

a2

d

X1

2

1

1

X2

3

2

2

X3

2

1

1

X4

2

2

2

X5

1

1

3

X6

1

1

3

X7

3

2

2

X8

1

1

3

X9

2

1

1

X10

3

2

2

Table 10. Discernibility Matrix Using the Reductu {a1, a2}. Cited from Ref. [93] U

a1

a2

d

X1

2

1

1

X2

*

2

2

X3

2

1

1

X4

*

2

2

X5

1

*

3

X6

1

*

3

X7

*

2

2

X8

1

*

3

X9

2

1

1

X10

*

2

2

atoms were assigned as oxidation atoms. For each heavy atom, descriptor variables were calculated taking account of the nearest environments. Four types of descriptors were used. 1) SS descriptors: substructure descriptors to describe local chemical environments; 2) PE descriptors: physicochemical descriptors through bond distance to atom property; 3) SPAN descriptors: whether an atom is at the end or the middle of a molecu-



le in a topological sense; 4) HYDROGENAREA descriptor: surface areas attached to hydrogens. Fig. (16) shows the substructure descriptors in a small example molecule. For example, a symbol ‘CX2sp3-NX3sp3’ means that central sp3 carbon is connected to sp3 nitrogen with three neighbors. Totally, 1981 descriptors were used for describing each heavy atom. RST was applied to the data matrix comprising of 1031 samples and 1981 descriptors. RST could successfully produce many useful rules. Two major rules for CYP2C9 oxidation are described here: Rule 1: If CX1sp3 = 1 & 6_11 = 0 & 6_3 = 2 & SPAN_ DISTFURTHESTNEIGHBOR = 3 then oxidation_site

(18)

An atom is metabolized if the molecule is somewhat long and does not have an atom labeled ‘6’ at the 11 bond distance from the sp3-carbon and has two atoms labeled ‘6’ at the 3 bond distance from the sp3-carbon. The representative examples obeying Rule 1 are shown in Fig. (17). This rule corresponds to demethylation of methoxy group frequently observed in CYP2C9 oxidation. Rule 2: If CX2sp3 = 0 & 3_4 = 1 & 6_6 = 2 & HYFROGENAREA_ SUMAREA = 2 then oxidation_site

(19)

An atom are metabolized if the molecule has somewhat small solvent accessible surface area and has an atom labeled ‘3’ at the 4 bond distance from the focused atom and has two atoms labeled ‘6’ at the 6 bond distance from the focused atom. The representative examples obeying Rule 2 are shown in Fig. (18). This rule corresponds to epoxidation on aromatic ring frequently observed in CYP2C9 oxidation. The rule mining method such as RST is useful for chemical interpretation. Because only RST cannot achieve high statistical performance, the combination use of Random forest or ADAboost is recommended. 3.2. Rules of Thumb Gleeson has generated a set of simple and interpretable rules for 15 different ADMET assays [94]. (solubility, permeability, oral BA, volume of distribution, plasma protein binding, central nervous system penetration, brain tissue binding, P-gp efflux, in vivo clearance, hERG inhibition, and CYP 1A2/2C9/2C19/2D6/3A4 inhibitions) More than 30000 diverse molecules were collected from in-house database and PCA was performed with 12 commonly used physicochemical descriptors. Result of PCA indicated that three essentially orthogonal molecular descriptors (molecular weight, ionization state, ClogP) are enough to describe chemical space. The likelihood of a molecule having a particular ADMET parameter above average, average, or less than average is reported in Table 11. Only result of neutral compound is shown in Table 11. If molecular weight of compound increases, the solubility would decrease, the permeability would decrease, and an increase in protein binding and increase CYP inhibition on average would be observed. (Table 11) These rules are consistent with chemists’ intuition and can be used to supplement the more complex, predictive in silico models. Martin has also proposed similar approach called BA score and applied it to the real projects in Abbott Laboratories [95]. 4. VISUALIZATION Numerous mapping or projection methods have been investigated in chemometrics [96]. Only the commonly used methods are described here.



6 6 6 3

N

7

6 N

6

6

1

6

6

6

6

SPAN descriptor Longest through-bond distancc in molecule MAXDISTMOL=8 Longest through-bond distancc from indicatcd atom DISTFURTHESTNEIGHBOR=5 RATIO= 5/8= 0.625 SS descriptor CX2sp3 CX2sp3-CX3sp2a5 CX2sp3-NX3sp3A6 CX2sp3-CX3sp2a5-CX2sp2a5 CX2sp3-CX3sp2a5-NX2sp3a5 CX2sp3-NX3sp3A6-CX2sp3A6 CX2sp3-CX3sp2a5-CX2sp2a5-CX2sp2a5 CX2sp3-CX3sp2a5-NX2sp3a5-CX2sp2a5 CX2sp3-NX3sp3A6-CX2sp3A6-CX2sp3A6

Frequency 1 1 1 1 1 2 1 1 2

SS-A descriptor CX2sp3 CX2sp3-CX3sp2 CX2sp3-NX3sp3 CX2sp3-CX3sp2-CX2sp2 CX2sp3-CX3sp2-NX2sp3 CX2sp3-NX3sp3-CX2sp3 CX2sp3-CX3sp2-CX2sp2-CX2sp2 CX2sp3-CX3sp2-NX2sp3-CX2sp2 CX2sp3-NX3sp3-CX2sp3-CX2sp3

1 1 1 1 1 2 1 1 2

SS-B descriptor n n-A6 n-A6-A6 n-A6-A6-A6 n-a5 n-a5-a5 n-a5-a5-a5

1 1 2 2 1 2 2

PE descriptor 6_0 1_1 7_1 3_2 6_2 6_3 6_4 6_5

1 1 1 1 3 4 1 1

Fig. (16). The topological substructure descriptors (SS, SS-A, SS-B) and physicochemical environment (PE) descriptors for an atom (indicated by arrow) in an example molecule. The number near each atom is the physicochemical type (1 = cation, 3 = H-bond donor, 6 = hydrophobe, 7 = other). Also indicated is the ratio for the SPAN descriptor that determines whether an atom is at the end or middle of a molecule based on its topology. Cited from Fig. (1) in Ref. [85].



O Cl

Cl

O Cl

O

Cl NH

N

O

O

O

N

OH

methoxychlor harmine

indomethacin

Fig. (17). Examples derived from rule 1. Red circle means the experimentally determined oxidation site. Cited from Ref. [93]. O

HO O

O

O

H N O

NH NH HN

O

OH

Cl

Cl

O

etodolac

phenytoin aceclofenac

Fig. (18). Examples derived from rule 2. Red circle means the experimentally determined oxidation site. Cited from Ref. [93].

4.1. KNN Kohonen neural networks (KNN) are based on the idea that human brains tend to compress and organize sensory data, spontaneously. KNN can be used to generate a projection of objects from a higher-dimensional space onto a two dimensional space. In other words, this method enables a decrease in dimension while conserving the topology of the information as much as possible [97-99]. KNN is typically made up from two layers of neurons (input and output layers). The input layer contains m neurons corresponding to m variables describing objects. The output layer is a two-dimensional geometri-



cal arrangement of n neurons and the topology is usually defined as a ‘torus’. ‘Torus’ means that the right or top edge of map is continued to its left on low edge, respectively, and vice versa. The m neurons of the input layer are all connected to each of the n neurons of the output layer as shown in Fig. (19). The network is trained by adjustment of the connection weight in two phases, the competitive learning and self-organization phases. After two phases, the objects that are close to each other in the multi-dimensional descriptor space remain neighbors on the KNN map. Applications with KNN method are cited in literatures [100, 101]. Table 11. Indication of How Changes in Key Molecular Properties will Affect a Range of ADMET Parametersa. Cited from Table 3 in Ref. [94] Neutral Molecules

MWT < 400 and clogP < 4

MWT > 400 and/or clogP > 4

solubility

average

lower

permeability*

higher

average/higher

bioavailability

average

lower

volume of Dist.**

average

average

plasma protein binding

average

higher

CNS penetration***

higher/average

average/ lower

brain tissue binding

lower

higher

P-gp efflux

average

higher/average

in-vivo clearnce

average

average

hERG inhibition

lower

lower

P450 inhibition****

lower 2C9, 2C19, 2D6 & 3A4 inhibition

higher 2C9, 2C19 & 3A4 inhibition

P450 inhibition****

higher 1A2 inhibition

lower 1A2 inhibition

P450 inhibition****

average 2D6 inhibition

a

Expressed relative to the mean value of the data sets. MWT and clogP cut-offs of 400 and 4, respectively, are used. *Optimum clogP bin is 3-5 with respect to permeability. **Average to high volumes rather than high, low, or average generally considered optimum. *** Low CNS considered optimum, although for targets in the brain, this will be reversed. **** Some isoforms show a nonlinear relationship with clogP and/or MWT. These are guides only. For greater detail, look at the individual ADMET ANOVA graphs found in the text or the tables reported in the Supporting Information.

Balakin et al. have developed KNN model for P-glycoprotein (P-gp) substrates and non-substrates [99]. As chemical descriptors, a wide range of molecular descriptors of different types was calculated. These descriptors included electronic, topological, spatial, structural, and thermodynamic descriptors. A total of more than 1000 descriptors were calculated for each compound. To reduce the number of descriptors that could contain redundant information, PCA was performed. Five descriptors maximally contributing to



the first significant PC, were selected and were used as input parameters in KNN model. (BELp3: eigenvalues of burden matrix/weighted by atomic polarization, H3u: H autocorrelation/unweighted, CIC0: complementary information content, RDF025v: radial distribution function/weighted by atomic van der waals volumes, HTu: H total index/unweighted) The KNN map was generated for the entire P-gp training dataset including 96

m input Iayer

xi

x

w ij * weight vector w j*

r

output layer j*

n

n Fig. (19). Architecture of KNN. Cited from Fig. (2) in Ref. [97].

P-gp substrates and 79 non-substrates. Fig. (20) separately shows the sites of distribution of these compound categories. Obviously, their positions on the KNN map are quite different. The results of a leave-10%-out experiment indicated that the developed model is general enough and can be used for prediction purposes. On average, 77.4% of P-gp substrates (+) and 80.6% of P-gp non-substrates (-) compounds were correctly classified with this model. The authors have reported other ADME models using KNN method and their classification performances are relatively good. Korolev et al. have developed KNN models for CYP substrates and reaction products [102]. The substrates consisted of 485 compounds and the products consisted of 523 compounds. Sixty molecular descriptors describing the important molecular properties, such as lipophilicity, charge distribution, topological features, steric and surface parameters were explored. The number of descriptors was reduced to 26 by the omission of the low variable and highly correlated descriptors. To further reduce the descriptor space, PCA was performed. Eventually, seven descriptors were selected as the most relevant. (logD7.4: logP at pH 7.4, HOMO: highest occupied molecular orbital, Jurs-PPSA-1: partial negative surface area, Jurs-TPSA: total solvent-accessible surface area, MW: mole-



cular weight, HBA: number of H-bond acceptors, HBD: number of H-bond donors) The KNN map for CYP substrates was generated using the selected seven descriptors. (Fig. (21a)) The CYP substrates are distributed throughout the map as the irregularly shaped islands, with a clearly defined trend toward the right side of the map. The area occupied by the substrates is relatively large, which reflects the broad substrate specificity against CYPs. For the comparison, the CYP products were mapped into the same KNN map. (Fig. (21b)) This data set occupies distinct areas on the map substantially different from the regions of the substrates localization. On the basis of these distributions, the authors 6

6 0 1 2 3 4 5 6 7 8

5

5

4

4

3

3

2

2

1

0 1 2 3 4 5 6 7 8

1 2

1

3

5

4

6

2

1

3

(a)

5

4

6

(b)

Fig. (20). Kohonen map generated for the entire P-gp training set (167 compounds). The areas of substrates (a) and non-substrates (b) are shown separately. Cited from Fig. (8) in Ref. [99]. 10

10 0 0.5 1 1.5 2

8

8

6

6

4

4

2

2

2

6

4

(a)

8

10

0 0.5 1 1.5 2 2.5 3

2

4

6

8

10

(b)

Fig. (21). (a) 10*10 Kohonen network trained with seven selected descriptors for cytochrome substrates (485 compounds). (b) Final cytochrome reaction products (523 compounds) processed within the same map. The data have been smoothed. Cited from Fig. (1) in Ref. [102].



have built the smoothed contour plots of the occurrences of these two compound categories within the KNN map. (Fig. (22)) The area of substrates is marked in green, the area of products is in blue, and the low-populated area is in brown. The model correctly classified 76.7% of substrates and 62.7% of products, as defined by their localization in the corresponding areas of the KNN map. Although the general classification power of the model is moderate, it reasonably discriminates between CYP substrates and nonsubstrates. The enhancement factors for the areas of substrates and non-substrates were equal to 7.17 and 4.21, respectively. The enhancement factor is a ratio between the fractions of correctly and incorrectly classified compounds within the corresponding areas on the map. This number exceeds the random expectation. The authors have concluded that the developed model is useful in assessment of compound’s ability to be a CYP substrate.

10

Substrates Products

8

6

4

2

2

4

6

8

10

Fig. (22). Smoothed contour plots of the occurrences of substrates and final products within the Kohonen map. The area of substrates is depicted in green, the area of products is in blue, and the low-populated area is in brown. The contours correspond to at least 1.5% of compounds, from a particular category, per node. Cited from Fig. (2) in Ref. [102].

4.2. DT Decision trees (DT), also called recursive partitioning, create a branching structure. The branch taken at each node is determined by a rule that is derived from the molecular descriptors [103]. Thus, the data set is iteratively split into smaller and more homogeneous subsets. If a leaf in the tree contains predominantly compounds of one category, the path to this particular node provides rules for molecular properties that are associated with this specific category. The Student’s t-test is used as the split criterion. The Student’s t-test is computed according to the following formula:



t = ( X - Y ) / ( (1 / M + 1 / N ) * ( SSX + SSY ) /( M + N 2))

(20)

M

SSX = ( X i X ) 2

(21)

i =1 N

SSY = (Yi Y ) 2

(22)

i =1

where X1, X2, …., XM are the ADME property values of the compounds in the first subset, and Y1, Y2, …., YN are the ADME property values of the compounds in the second subset. M and N are the numbers of the compounds in these two subsets, respectively.

X and Y are the averaged ADME property value in each subset, respectively. SSX and SSY are the variance of X and y, respectively. The molecular descriptor that gives the largest t value is chosen as the descriptor for the splitting. Applications with DT method are cited in literatures [104-108]. Burton et al. have developed two DT models for the prediction of CYP2D6 and CYP1A2 inhibitions [109]. Dataset was extracted from in-house structured databases that contain precise measures and detailed experimental protocols. For CYP2D6, the number of inhibitors and non-inhibitors were 78 and 85 as the class thresholds of Ki = 10 uM, respectively. For CYP1A2, the number of inhibitors and non-inhibitors were 41 and 40 as the class thresholds of Ki = 30 uM, respectively. Two types of descriptors were used for building the DT models. First, 114 two-dimensional descriptors were calculated for all the compounds. This parameter set contains information about the molecules such as molecular weight, number of a given atom, number of H-bond acceptors/donors, lipophilicity, etc. A second type of descriptors was created with 32 P_VSA parameters. They are based on the approximation at atomic level of the molecular van der Waals surface area, VSAi, along with several other molecular properties, Pi. Pi considered are the molar refractivity, logP(o/w), and the electrostatic properties or pharmacophore characteristics. The DT model for CYP2D6 inhibitors with 2D and P_VSA descriptors is shown in Fig. (23a). The + and – signs mean that the class assigned to a leaf is inhibitor or non-inhibitor, respectively. The model accuracy was 90% for CYP2D6 inhibition. The DT model for CYP1A2 inhibitors with P_VSA descriptors is shown in Fig. (23b). The model accuracy was 89% for CYP1A2 inhibition. To confirm the performance of two models, two external test sets of 34 and 58 molecules related to the CYP2D6 and CYP1A2 Ki experiments were collected. The validation result for CYP2D6 inhibition was 89% accuracy. The validation result for CYP1A2 inhibition was 81% accuracy. The average values of the selected descriptors for inhibitors and non-inhibitors of CYP2D6 dataset are gathered in Table 12. These descriptors indicate the hydrophobicity, shape, and electrostatic contributions. The average values of the selected descriptors for inhibitors and non-inhibitors of CYP1A2 dataset are gathered in Table 12. It is found that the influence of hydrophilicity expressed by logP is large for CYP1A2 inhibition. Yamashita et al. have proposed multi-objective DT model [110]. The metabolic stability values for 161 drugs involving 6 CYP isoforms (1A2, 2C9, 2C19, 2D6, 2E1, and 3A4) was used. The averaged information gains for multiple objective variables were



CYP2D6

Training set: 163 compounds

CYP1A2

Training set: 81 compounds SMR_VSA6

a_hyd bpol VDisEq

PEOE_RPC+

chi l SMR_VSA5

SlogP_VSA7 PEOE_ VSA-3

PEOE_PC+ PEOE_VSA _EPNEG

(4/0)

(0/0) (0/0) (0/0)

(4/0) (4/0)

SlogP_ VSA5

SlogP_VSA9 PEOE_VSA+4

SlogP_VSA9

(1/1)

(16/3)

(10/1) (0/1) (0/0)

(3/4) (8/1)

(6/0) (4/2)

(4/1) (19/1)

Fig. (23). Two of the best models from the CYP2D6 (Ki dataset, 10 uM threshold, 2D and P_VSA descriptors) and CYP1A2 (Ki dataset, 30 uM threshold, P_VSA descriptors) datasets. Both models have been validated with an external dataset of 34 and 58 compounds for CYP2D6 and VYP1A2, respectively. + and – signs mean that the class assigned to a leaf is inhibitor or non-inhibitor, respectively. The distribution of molecules of the test set in each leaf is positioned between brackets (number of correctly classified compounds/number of misclassified compounds). Cited from Fig. (4) in Ref. [109]. Table 12. Differences in the Values of the Selected Descriptors for CYP2D6 and CYP1A2 Inhibitors Classification. Cited from Table 6 in Ref. [109] CYP2D6

CYP1A2

Average Value Descriptor

Average Value Descriptor

Inhibitor

Noninhibitor

Inhibitor

Noninhibitor

A_hydr

18.0

14.2

SMR_VSA6

15.1

56.2

bpol

30.4

25.9

SlogP_VSA7

124.6

78.4

Chil

11.8

9.7

SlogP_VSA9

63.3

79.4

VdistEq

3.3

3.0

PEOE_VSA4

2.3

3.8

PEOP_RPC+

0.2

0.3

SMR_VSA5

166.0

127.9

used as a quality-of-split criterion instead of the standard Student’s t test. This is like to find the best balanced-compounds taking account of various ADME properties including biological activities. After the tree was fully grown, pruning of the tree was performed with reference to the misclassification rate determined by the leave-some-out procedure. The number of terminal groups giving the minimum misclassification rate was regarded as optimal. Molecular descriptors of each compound were calculated by ADMET Predictor, which include constitutional descriptors, topological and electrotopological descriptors, and descriptors relating to hydrophobicity, electronic properties, hydrogen bon-



ding, and molecular ionization. When leave-some-out prediction was performed, the misclassification rate was minimal at a terminal group number of 9. Fig. (24) shows a classification tree model. The model consists of 8 splitting rules and 9 terminal groups (misclassification rate = 9.63%). Fig. (24) also presents the distribution of CYP metabolism levels in each terminal group. To obtain intuitive understanding of the trends of the tree structure, a visual image of the hierarchically structured data was presented. (Fig. (25)) The 6-colored square icons indicate the compounds studied, where the color and its brightness represent metabolic susceptibility toward each CYP isoform. The trends found were as follows: (a) CYP2C9 and CYP2E1 substrates mostly belong to Groups 1 and 2, respectively; (b) CYP2D6 substrates belong to Groups 5-8; and (c) CYP3A4 substrates are detected in almost all groups, while CYP3A4 substrates belonging to Groups 3 and 4 are highly susceptible to the enzyme. The QAvgNeg descriptor is the population average across all ionized species of the net formal negative charge calculated at pH 7.4. Considering that most of the CYP2C9 substrates belong to Group 1, it seems that CYP2C9 preferentially metabolizes anionizable compounds. On the other hand, many CYP2D6 substrates belong to Groups 5-8, in which the QAvgPos, i.e., the population average across all ionized species of the net formal positive charge calculated at pH 7.4, is greater than 0.7912. In contrast to CYP2C9 substrates, many CYP2D6 substrates appear to be cationic compounds. CYP2E1 substrates belong to Group 2, for which the QA vgNeg >0.133

0.7912

182.6

1.5

0.12209

2 >3 3

No of H-Acceptors:

< 450

MW:

>5 > 10

5 10 450 to 550

140

130 to 140

> 550

(a) ADAPT- Amgen’s Data Analysis Prediction Tools (4.08.10) File Fdit Table Data Access Analysls Visualization Prediotion Tools Window Help R

name

MW

structure

PSA

cLogP

NheavyAtoms

Rolbon

N

1

N

MDDR327280

N

S

N N

S

N

450.6186

4.2930

50.0800

31.0000

237.2943

2.3730

30.7100

16.0000

528.4254

3.7520

119.3900

35.0000

C1

MDDR327280

N

2

MDDR147076

S

F

N N

MDDR147076

N

S

3

MDDR197003

O F

N F

N

F

O

F

N

F

F

O O

O

MDDR197003 F N S

(b) Fig. (26). Color Legend window (a) and a table containing structures and colored calculated properties (b). Cited from Fig. (3) in Ref. [116].

We have developed web application for medicinal chemists [117]. Two software are mainly used for web application. For calculation of ADME properties, ADMET Predictor is employed as the best quality of product [118, 119]. ADMET Predictor has the own global models such as pKa, ClogP, human intestinal absorption, and water solubility etc. Furthermore, recently, ADMET Predictor can provide CYP kinetics values, Km and Vmax against five CYP isoenzymes. Because there are many statistical tools implemented, the local model limiting chemical skeleton can be easily constructed. When chemists



ADAPT - Amgen’s Data Analysis Prediction Tools (4.08.10) File Edit Table Data Access Analysis Visualization Prediction Tools Window Help

Similazrity Se

Absorption Index

name

name

psa

clogp

index

1

MFCD03645423

1

MFCD03645423

43.860001

4.093000

2

MFCD03645423

2

MFCD03645423

43.860001

4.093000 4.619000

PSA vs. Clogp

3.906000

ClogP 8.00

3.258000

7.14

2.692000

6.29

2.996000

5.43

2.246000

4.57

3.536000

3.750000

4.480000

3.71

1.292000

2.86

1 Selected

2.00 1.14 0.29 -0.57 -1.43

N

-2.29

N

-3.14 -4.00 0.00

N

133.33

66.67 PSA

O

O S O

2 MFCD01936602

Fig. (27). Absorption index calculation example using a set of ACD compounds. PSA vs. ClogP plot is shown, and the structure of a data point colored in red is displayed. Cited from Fig. (7) in Ref. [116].

submitting ISIS/Draw structure, ChimePro automatically translates 2D image structure into 2D mol file. ADMET Predictor receives 2D mol file, and then calculates several ADME properties based on global or local models. The necessary values are extracted from the output file using Perl script. These values are displayed in web page using CGI protocol. (Fig. (28)) For prediction of CYP oxidation sites, MetaSite is employed [120]. (Fig. (29)) Submitting chemical structure from ISIS/Draw, MetaSite calculates a metabolism score to each heavy atom. The higher score value means that the corresponding heavy atom is more likely oxidized. The output page displays the 3D structure with atom



labels and scores. Now, web page supports the prediction of metabolites against three specific organs (liver, skin, brain) that user can choose freely.

Query Structure

Prediction

O

F

Results

molname

NoName

Acid pKa

4.03

Base pKa 3.67405 >

logP (caution: not ClogP) Human jejunal permeability (cm/s*10 4) >

O

9.35438

MDCK permeability (cm/s* 10 7)

163.902

Water solubility (mg/ml)

0.0148024

BBB penetration

Undecided

Percent unbound to blood plasma proteins (%) 1.35346 Volume of distribution (L/kg)

0.159123

Km value against CYP1A2

39.0677

Km value against CYP2C19

64.7945

Km value against CYP2C9

14.0065

Km value against CYP2D6

87.6499

Km value against CYP3A4

192.301

Fig. (28). Web page of ADMET Predictor.

6. CONCLUSION Concerning in silico ADME prediction, there are three main issues that we have to be addressed. 1) to improve the quality and quantity of data; 2) to develop predictive model with high accuracy 3) to develop visualization tool for chemical interpretation. There is little doubt that significant improvements in the quality and size of ADME database will lead to significant improvements in the quality of ADME predictions. In pharmaceutical companies, many kinds of ADME databases are integrated and the prediction tools are being developed routinely [2]. Furthermore, recent progress of in vitrobased technologies corresponding to each ADME process would facilitate PBPK modeling [10]. Absorption of drugs from the gastrointestinal tract is complex and can be influenced by many factors. The factors would fundamentally be classified into three categories; physicochemical factors (pKa, solubility, stability, diffusivity, lipophilicity, and salt forms), physiological factors (gastroinstetinal pH, gastric emptying, small and large bowel transit times, active transport and efflux, and gut wall metabolism), and formulation factors (particle size and crystal form, and dosage forms such as solution, tablet, capsule, suspension, emulsion, gel, and modified release). Kinetic parameters for drug metabolism such as Km, Vmax etc. are obtained from in vitro experiments using liver



microsomes and hepatocytes. Physicochemical properties of compound could be predicted from molecular structure alone. The in vitro data and physicochemical properties can serve as inputs for simulation to predict PK parameters, such as BA, CL, Vss. The PBPK approach will help to realize the goal of fully characterizing PK of a compound early in the drug discovery process. GastroPlus seems to be a promising product as PBPK tool. GastroPlus has unique physiological models of gastrointestinal tract for human, beagle dog, rat, mouse, rabbit, cat, and monkey. Furthermore, it is flexibly linked to ADME calculation product ‘ADMET Predictor’ [121, 122].

F

O

Input molecule:

O

Atom number Atom name Atom type Score C16 C1 C2 C3 C4

2

C2

C.ar 11.000

1

C1

C.ar 5.500

3

C3

C.ar 5.500

16

C16

C.3 3.667

4

C4

C.ar 2.517

All data (csv file)

Fig. (29). Web page of MetaSite.

Combinatorial QSAR (CQSAR) has been used for developing the predictive models [123-125]. There exists no gold standard QSAR approach that guarantees the best model for every data set. CQSAR explores various combinations of optimization methods and descriptor types and includes rigorous and consistent validation. The exploratory nature of CQSAR helps in identifying highly predictive models for particular data set, whereas a conventional approach to QSAR studies using only one method and one type of descriptors has a higher chance to fail. CQSAR allows full automation, and this is highly suited to the recent drug discovery situation. The domain of applicability is another useful concept for developing the predictive models [126, 127]. This allows one can use the distance to model space to estimate the reliability of a given prediction. This is particular important for local model since it is relatively easy to stray outside the predictive domain of the model with a small number of chemistry iterations.



With virtual ADME there is a growing need for significantly improved data handling. Given that many screening and ADME predictors generate dozens of data points for each molecule, there is a clear need to develop far more sophisticated data reduction and visualization. Spotfire has become popular since it has many unique visualization modules such as heat maps, hierarchical clustering diagrams and color-coded symbols [128, 129]. We can imagine future of in silico ADME embedded in drug discovery. A dream system is presented in Fig. (30) [130-133]. A drug candidate is first presented to the QSAR and metabolic fate prediction modules. The system also predicts the structure of the first-pass metabolites and transition states. Predicted structures are again fed to the QSAR module. Metabolites are prioritized based on relative predicted reaction rates. Valuable information about metabolic fate and major metabolites is then placed into the context of pathways analysis. Information on predicted structures, their biological activity and the enzymes involved is merged with information on pathways. Simultaneously, metabonomic and toxicogenomic data are mapped onto the pathways. After going through many filters, we can select most promising compounds virtually. This kind of infrastructure system has been developed in pharmaceutical companies and ADME models play more important roles in drug discovery process.

QSAR module

Predict Binder and/or non-binder, Substrate and/or non-substrate, Inhibitor and/or Inducer

Prioritize metabolites

CH3 NH

NCEs

NH O

Model reaction module

O CH3

CH3

Select most promising compounds

Visualize and analyze Interference with normal metabolism. (possible bottlenecks, side-effects, important SUPs)

Visualization tools

CH3

Predict metabolic routes and metabolite structures

Pathway database and/or maps Toxicity arrays

Fig. (30). Proposed functionality of the next-generation ADME/Tox platform. Cited from Fig. (4) in Ref. [130].



ACKNOWLEDGEMENT We would like to thank Dr. Arakawa and Mr. Kaneko, and Mr. Koyama at The University of Tokyo for their helps when preparing this review article. ABBREVIATIONS ADAAPT

=

Amgen’s Data Access Analysis Prediction Tools

ADME

=

Absorption, distribution, metabolism and excretion

ANN

=

Artificial neural networks

BA

=

Bioavailability

BBB

=

Blood brain barrier

BNN

=

Bayesian neural networks

BP

=

Back propagation

CART

=

Classification and regression trees

CI

=

Contribution index

CQSAR

=

Combinatorial QSAR)

CV

=

Cross-validation

CYP

=

Cytochrome P450

DT

=

Decision tress

GA

=

Genetic algoritm

GP

=

Gaussian process

HLM

=

Human liver microsome

HM

=

Heuristic method

HSA

=

Human serum albumin

ICA

=

Independent component analysis

kNN

=

k-Nearest neighbors

KNN

=

Kohonen neural networks

LOO

=

Leave-one-out

MLR

=

Multiple linear regressions

NLM

=

Non-linear mapping

Nmt inhibitor

=

N-Myristoyltransferase inhibitor

ORMUCS

=

Ordered multi-categorical classification

PBPK modeling

=

Physiologically based pharmacokinetic modeling

PCA

=

Principal component analysis

P-gp

=

P-Glycoproteins



PK

=

Pharmacokinetics

PLS

=

Partial least squares

QSAR

=

Quantitative structure-pharmacokinetic relationship

QSPKR

=

Quantitative structure-pharmacokinetic relationship

QSPR

=

Quantitative structure-property relationship

RF

=

Random forest

RMSE

=

Root mean squares error

RST

=

Rough set theory

SVM

=

Support vector machines

t1/2

=

Elimination half-life

Vss

=

Volume of distribution

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A Review on Virtual Reality and Haptics Approaches in Drug Design and Discovery Susana K. Lai-Yuen* Department of Industrial & Management Systems Engineering, University of South Florida, Tampa, FL 33620-5350, USA Abstract: Virtual reality interfaces and haptics are rapidly becoming a powerful technology to enable researchers to interactively manipulate and evaluate potential drug molecules in an immersive virtual environment to accelerate the drug design process. Virtual reality refers to a computer-generated and interactive three-dimensional environment that immerses people into a virtual world while haptic devices are electromechanical devices that exert forces on users giving the illusion of touching something in the simulated environment. As molecular forces play a major role in determining the successful docking of drug molecules, virtual reality and haptics can provide researchers with invaluable human-computer interface tools for visualizing, manipulating, and “feeling” complex molecular systems in real time. The force feedback provided by haptic devices can direct researchers towards favorable drug molecule positions and orientations increasing the understanding of key forces during molecular interactions and enabling new kinds of drug design exploration. However, the main difficulty of modeling molecular systems through virtual reality and haptics is that visualization models and simulations need to be processed rapidly to satisfy the update requirements needed for real-time visualization and sense of touch. Any time delay between a user action and the corresponding update of the virtual object can lead to unrealistic visualization, unstable force response, and simulation sickness. This paper reviews some of the research advances for addressing these computational challenges ranging from new graphical representations of molecules for effective haptic force feedback calculation to virtual reality algorithms and devices for modeling complex molecular systems in a real-time virtual environment.

1. INTRODUCTION The design and discovery of new drugs is one of the most complex, time-consuming, and cost-intensive process in the pharmaceutical industry [1, 2]. Advances in computer technology and scientific visualization techniques have been vital in speeding the development of new drugs while reducing development costs. Computational tools increase current molecular modeling capabilities providing new insights into the properties of

*Corresponding Author: E-mail: [email protected] Gary W. Caldwell / Atta-ur-Rahman / Z. Yan / M. Iqbal Choudhary (Eds.) All rights reserved – © 2009 Bentham Science Publishers.


Susana K. Lai-Yuen

macromolecules and their interactions with ligands. Molecular interactions can be accurately represented by computer simulation methods such as molecular dynamics. These methods simulate very detailed molecular motions based on physical laws. However, current tools are computationally intensive and cannot account for every part of a problem as complex as drug design without the knowledge and intuition of the researcher. Moreover, these tools mainly provide a two-dimensional visual graphic display limiting the visualization and understanding of three-dimensional molecular structures and their interactions. These shortcomings can be addressed by developing new computational approaches that incorporate immersive sensory experiences to speed molecular interaction simulations and to enable researchers to visualize, feel, and interact with the simulation in real-time. An immersive and interactive virtual environment can provide researchers with a more meaningful modeling process to identify key molecular forces based on physical and chemical principles and can be an invaluable design tool for drug design and discovery. Virtual reality is rapidly becoming a powerful technology that enables people to visualize, manipulate, and interact with a simulated environment for solving today’s realworld problems. Virtual reality can be defined as a “high-end user-computer interface that involves real-time simulation and interactions through multiple sensorial channels. These sensorial modalities are visual, auditory, tactile, smell, and taste” [3]. The combination of these sensorial modalities provides an immersive and interactive environment for the user where his/her instantaneous position and orientation are tracked. This enables the user to manipulate and interact with scientific data and complex engineering systems. The most common applications of virtual reality can be found in the areas of medicine, education, entertainment, design, manufacturing, and military [4-8]. For example, virtual surgical simulators are being developed to train medical students to operate on realistic models of biological tissues and organs. One of the emerging areas of virtual reality application is in drug design and discovery where virtual reality has shown to enhance the ability to understand properties of proteins and molecular docking [9-11]. Fig. (1) shows a research chemist at the National Institute of Standards and Technology (NIST) immersed in a 3D environment for studying the behavior of “smart gels” and for understanding the binding of their components [12].

Fig. (1). A virtual reality environment for studying molecular interactions at the National Institute of Standards and Technology (NIST) [12]. Reprinted with permission. Copyright Robert Rathe.

A Review on Virtual Reality


The majority of virtual reality simulations have focused on incorporating visual and auditory modalities in the form of 3D stereo displays and sound while smell and taste feedback are still at the early stage of research. In recent years, there is a growing interest to incorporate tactile modality in virtual environments by enabling users to touch and feel virtual objects using haptic devices. Haptic devices are electromechanical devices that exert forces on users giving the illusion of touching or feeling something in the simulated environment as shown in Fig. (2). The incorporation of haptics into a virtual environment enables a more immersive experience that can greatly increase the effectiveness of real-world based simulation. In drug design and discovery, haptics can enable users to manipulate virtual molecules and feel the molecular forces providing an important design and visualization tool to speed the drug design process. The development of new mechanisms for haptics and their demonstrated potential to make the virtual experience more realistic and interactive have made these devices a key component of virtual reality systems and an emergent area of research.

Fig. (2). Example of a haptic device for molecular docking.

In drug design, recent studies have shown that interaction between molecules can be better understood with the aid of a haptic device than by having a visual display alone [11, 13]. Experiments showed that chemists could perform the positioning of a rigidbody ligand inside the binding site of a receptor up to twice as fast with haptic feedback compared to visual display alone [14]. Scientists have also stated that the forces provided by a haptic device increased their understanding of how the drug fits into the receptor. Below is a list of the benefits of using virtual reality and haptics in drug design and discovery: • Increase understanding of molecular systems and their interactions through 3dimensional visualization and interactive force feedback. • Accelerate molecular simulations through the incorporation of researcher’s knowledge and intuition via haptic devices. The researcher can steer a simulation towards better molecular conformations or specify alternate starting points for the molecular docking process.


Susana K. Lai-Yuen

• Facilitate an interaction of the researcher with a molecular system by manipulating molecules as if they were solid physical models. This achieves better solutions than either the computer or the researcher could obtain alone. • Provide an immersive and intuitive human-computer interaction system to bring the molecular system to life. • Enable multiple researchers to work together. • Improve productivity in the pharmaceutical industry. Although there have been remarkable advances in virtual reality and haptics applications in drug design and discovery, there still remains a lot of challenges to achieve a complete virtual reality system for interacting with a molecular simulation. A convincing immersive and interactive virtual environment requires an extensive combination of high-performance computer hardware and software. An effective virtual reality display requires a minimum display rate of 10 frames per second although frame rates of 30 per second are preferred [15]. Consequently, “there is at most 0.1 second of time available to compute an instance of a molecular simulation, send the data over the network, and display the molecular model” [9]. Most current computational tools for modeling molecular interactions are unsuitable for real time applications as they are computationally intensive. Therefore, the main challenge is the need for modeling and simulation techniques to represent molecular interactions and behavior in a physically correct manner while satisfying the strict time constraints of virtual reality and haptic rendering. This paper presents an overview of the state-of-the-art in virtual reality and haptics for drug design and discovery. It begins with a brief description of the different types of commercially available virtual reality systems and haptic devices in Section 2. Section 3 provides the current issues and challenges for incorporating virtual reality and haptics in the drug design and discovery process. Section 4 presents an overview of representative research advances on virtual reality and haptics for facilitating the understanding of molecular interactions and for speeding the design and discovery of pharmaceutical drugs. Section 5 concludes with a discussion on some areas of future research work for achieving a complete immersive and interactive virtual system for drug design and discovery. 2. OVERVIEW OF VIRTUAL REALITY AND HAPTIC DEVICES Virtual reality interfaces and haptics are rapidly becoming a powerful technology to enable researchers to interactively manipulate and evaluate potential drug molecules in an immersive virtual environment to accelerate the drug design process. Virtual reality refers to a computer-generated and interactive three-dimensional environment that immerses people into a virtual world while haptic devices are electromechanical devices that exert forces on users giving the illusion of touching something in a simulated environment. A virtual reality system requires four crucial technologies [3]: a visual, aural and haptic display, a graphics rendering system to generate the constantly changing images, a tracking system to collect data on the user’s position and orientation, and a database construction and maintenance system to generate realistic virtual models. Fig. (3) shows the overall system using virtual reality and haptic devices for drug design and discovery. The user utilizes one of the several types of virtual reality devices that provides him/her with a 3-dimensional image of the molecular simulation. Virtual reality devices track the user’s position and orientation to provide adequate images to the



user. At the same time, the user uses a haptic device to manipulate molecules in a simulation (i.e., dock a ligand into a receptor, etc.) while feeling the molecular forces in real time.

Fig. (3). Overview of the application of virtual reality and haptic devices in drug design and discovery. * Picture courtesy of Dr. Christoph Sensen, Sun Center of Excellence for Visual Genomics [16]. ** Reproduced by permission of Immersion Corporation. Copyright © 2008 Immersion Corporation. All rights reserved [17].

The following sections briefly introduce different types of virtual reality and haptics devices that are currently commercially available to familiarize the reader with these types of devices. These devices will be referred throughout the paper as their specific applications in drug design and discovery are described in Section 4. This review does not intend to be a thorough description of all available virtual reality and haptic devices as the main focus of this paper is on their applications in drug design and discovery. For more detailed information, the reader is referred to the provided references. 2.1. Virtual Reality Devices Virtual reality is a powerful technology involving computers and various peripherals that enables users to visualize, manipulate and interact with simulated environments to


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solve complex problems. Although there are many different types of virtual reality systems, they can be categorized based on the level of immersion provided. The major component for a virtual reality system is the display as it provides images and immersive presence within the environment. Users can look at a display such as that provided by a computer or be surrounded by several displays to experience a semi or fully immersive environment. Tracking devices are necessary to determine the position and orientation of the user(s) to display graphics accordingly. Users also utilize haptic devices to feel and interact with the virtual environment. Following is a description of the most commonly used displays for virtual environments. More detailed information on virtual reality hardware and software can be found in [3, 4, 18-20]. Head-Mounted Displays (HMD) A HMD is a set of goggles worn on the head or mounted on a helmet that generate images through small monitors placed in front of each eye as shown in Fig. (4). Separate views of the image are provided to the left and right eye creating a fully immersive virtual environment. A tracking device is attached to the HMD to generate a stereoscopic view that changes as the user’s head position and orientation change.

Fig. (4). Example of a head-mounted display. VR1280 picture courtesy of Virtual Research Systems, Inc. [21].

Projection Displays Projection displays provide field sequential stereoscopic images to a static screen. These displays allow more than one person to see the virtual environment enabling collaborative work. Field sequential stereoscopic systems sequentially display the left and right eye views of an object while the user wears special stereo-glasses to view images. The stereo-glasses are synchronized with the display screen to ensure that each eye of the user receives the correct view of the image. As shown in Fig. (5), there are three types of projection-based display systems: workbench systems, wall systems, and immersive rooms [20]. As shown in Fig. (5a), a workbench has the image projected towards the surface of a table where users stand around the table using shutter glasses and interact with the images using haptic devices. In wall systems, images are projected onto a large vertical screen where many users can see the screen as shown in Fig. (5b). Finally, immersive room systems are a multiple-person theater with surround view and sound. The images are rear projected to the walls of a theater by multiple projectors as shown in Fig. (5c). These systems provide full immersion into scientific data as they enclose the user.



(a) Example of a workbench display. Picture courtesy of Dr. Russell M. Taylor II, University of North Carolina at Chapel Hill [22].

(b) Example of a wall system display. Picture courtesy of Barco, Inc. [23].

(c) Example of an immersive room system. Picture courtesy of Dr. Christoph Sensen, Sun Center of Excellence for Visual Genomics [16]. Fig. (5). Categories of virtual reality displays.


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2.2. Haptic Devices “Haptics” comes from the Greek word haptesthai that means “to touch”. Haptics is a technology that enables users to touch, feel and manipulate virtual objects in a simulated environment. It is a computer-human interface that enables bidirectional communication between human and machine through touch in response to user movements. Haptics is being increasingly used as a key part of virtual reality systems as it adds tactile and force feedback to the visualization making virtual experience more realistic and interactive. Tactile feedback enables the user to feel surface geometry, smoothness, and slippage while force feedback reproduces the object’s hardness, weight, and inertia [24, 25]. In drug design, force feedback is useful in providing the user with a feeling of molecular forces created between molecules during molecular docking. It also prevents the user from penetrating virtual objects such as atoms. Haptic devices measure the position of a user’s hand or other parts of the body to provide force and torque feedback to the user. There are different types of haptic devices available in the market as well as those developed by laboratories. The devices vary based on structural frame, workspace, stiffness, maximum vs. sustained force and torque, among others. This section briefly describes the different types of haptic devices that are commercially available. More detailed information on various commercial and laboratory haptic devices can be found in [25-28]. Haptic devices can be ground-based (grounded to a desk or floor) or body-based (attached to parts of the body): Ground-Based Haptic Devices Haptic devices that are grounded to a desk (or desktop devices) are the most widely used nowadays due to their easiness for installation and commercial availability. Ground-based devices are used with elbow or wrist support and controlled through the fingers, hand, arm or a handle. Fig. (6) shows an example of these devices that consists

Fig. (6). Example of a desktop haptic device. SensAble PHANTOM® Desktop™ haptic device [29]. © Copyright SensAble Technologies, Inc. PHANTOM, PHANTOM Desktop, SensAble, and SensAble Technologies, Inc. are trademarks or registered trademarks of SensAble Technologies, Inc. (Reprinted with permission).



of a handle interface with a frame. Some devices can provide force feedback in six degrees of freedom (6DOF) providing the user with both force and torque feedback, which are useful in molecular docking for facilitating proper alignment of molecules. Users control virtual objects through the handle or fingertip interface and receive force or force and torque feedback at a single point through the devices.

Fig. (7). Example of a wearable haptic device. Reproduced by permission of Immersion Corporation. Copyright © 2008 Immersion Corporation. All rights reserved. [17].

Body-Based Haptic Devices This type of haptic devices can be attached to parts of the body so the user has a larger work volume. An example of these devices is shown in Fig. (7), which is a handcentric device for animating hand movements and controlling virtual objects through hands. This type of devices is suitable when the virtual reality simulation requires the use of multiple contact points, freedom of motion, and data on texture and grasping. 3. VIRTUAL REALITY AND HAPTICS FOR DRUG DESIGN AND DISCOVERY: ISSUES AND CHALLENGES Virtual environments have found acceptance in drug design and discovery as they enable scientists to have a visual and tactile experience with a molecular system while manipulating the simulation in real-time. This overcomes the limitation of most traditional molecular modeling environments that provide static 3D models by immersing the scientist in a realistic and dynamic representation of a molecular system [18]. In order to provide a convincing virtual reality and haptics experience, the system requires a high degree of performance for computer hardware and software. An effective virtual reality display requires a minimum display rate of 10 frames per second although frame rates of 30 per second are preferred to avoid any flicker and image jumps [9].


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Consequently, there is at most 0.1 second to compute a particular instance of a molecular simulation and to display the molecular model. On the other hand, haptic feedback requires much faster refresh rates than visual feedback as the human sense of touch is more sensitive than the sense of sight. For this reason, while an image needs to be updated at 30 frames per second, the sensation provided to the user needs to be updated 1000 times per second or more to provide a realistic tactile experience [24, 27]. The strict time constraints on visual and tactile response impose high demands on the performance of the hardware and software for simulating molecular systems. Any time delay between the user action and the corresponding update of the virtual object in the simulation leads to unrealistic visualization, unstable force feedback, and simulation sickness [30]. Limited time is available to compute changes in the molecules, display them, and provide the corresponding force feedback to the user. Several of the current virtual reality and haptic systems for drug design and discovery have represented the molecules as rigid bodies as this simplifies the calculation of forces. However, as molecular flexibility is critical for understanding the principles that govern in the binding of molecules, it is necessary to represent molecules as flexible bodies. Modeling flexible molecules in real-time is computationally expensive due to the exponential explosion in complexity as large numbers of variables are considered to represent the molecule’s conformations. This computational complexity is the main challenge in modeling flexible molecules in real-time to achieve a fully virtual immersive and interactive environment. In addition, to provide a haptic feedback, the forces acting on the molecules need to be calculated as the molecules change conformations. Modeling the physical changes and interactions between molecules require effective graphical molecular representations and efficient force computation to achieve visual and tactile feedback for a virtual environment [25]. 3.1. Graphical Representation of Molecules Given the refresh rate requirements for virtual reality and haptics, the time for rendering molecules and their motions is essential. The realistic physical modeling of molecules significantly contributes to the user’s sense of immersion and interactivity. Molecules are represented graphically in the form of stick model, ball-and-stick model, and CPK space-filling model as shown in Fig. (8a). The simulation program that updates the atom positions sends the new data for updating the models for visualization. Some programs use different number of polygons to represent the atoms at different levels of detail. When the molecules are far from the user, less polygons are used to speed the rendering process. Other systems use solvent-accessible surface (SAS) [31, 32] to make the rendering more efficient and to omit the display of interior atoms as shown in Fig. (8b) [33]. SAS representation can also be used for surface matching between the ligand and the receptor. The molecular representation should also facilitate the display and change of molecular conformations in real time. One of the main challenges in modeling molecules in real-time is the requirement for rapid identification and display of feasible molecular conformations. Most of the research work so far has modeled both the ligand and receptor molecules as rigid bodies in order to achieve the virtual reality and haptic refresh rates. However, as molecules are very “flexible” in nature and can adopt different conformations, new approaches are necessary to identify feasible conformations and to enable their display in real time.



(a) Ligand and receptor represented as spheres with van der Waals radii.

(b) Molecule represented by the solvent-accessible surface (SAS). Reprinted from [33], Journal of Molecular Graphics and Modelling, Vol. 17, Anderson, A. and Weng, Z., VRDD: Applying virtual reality visualization to protein docking and design, pp. 180-189, 1999, with permission from Elsevier. Fig. (8). Examples of molecular representations used in virtual reality and haptic applications for drug design and discovery.

The increase in power of computer hardware has led to the development of methods for modeling the flexibility of molecules. Some of the reviewed work has modeled the molecule as an articulated body or kinematic chain with torsional bonds. Based on the location of the torsional bonds, atoms are clustered into groups and their relationship is established to reduce the computation time and to decrease inaccuracies in update calculations during conformational search [34]. Wriggers and Birmanns [35] presented a new multiresolution visualization approach to facilitate the fitting of molecules into lowresolution density maps. A vector quantization method is used to replace the molecule with a number of codebook vectors in order to reduce the modeling of molecular data as


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shown in Fig. (9). This molecular representation is also used to calculate the haptic force feedback in real time. Although there have been many great advances towards the display of molecules and their conformations in real time, further research is still needed to incorporate the receptor’s flexibility, which increases the problem complexity exponentially.

Fig. (9). Molecule represented by a set of 10 codebook vectors. Picture courtesy of Dr. Willy Wriggers, D.E. Shaw Research, and Dr. Stefan Birmanns, University of Texas Health Science Center at Houston [36].

3.2. Real-Time Force and Torque Computation Interactions between molecules are represented by the potential energy generated between them. This energy consists of the electrostatic potential, van der Waals potential, hydrogen bond potential, etc. As users manipulate a ligand molecule to dock it into a receptor in a virtual reality environment, the interactive molecular forces need to be calculated in real time to provide realistic force feedback through the haptic device. If the haptic device provides 6DOF force feedback, then it is also necessary to calculate the torque feedback. Both the modeling and force calculation need to satisfy the virtual reality and haptic refresh rates. However, molecular forces are computationally expensive to determine as many atom pairs are considered for the calculation. The total force acting on the ligand is determined by adding all the forces acting on each ligand atom by the receptor atoms.

(a) Potential force calculation Fig. (10). Haptic force calculation.

(b) Collision force calculation



Fig. (10a) shows that as a ligand approaches the receptor, it is subject to the intermo lecular force Fpotential acting from the receptor. This force needs to be provided to the user through the haptic device anytime the force is created between the ligand and the receptor. Once the user moves the ligand towards the receptor’s surface, a force that is high enough needs to be transmitted through the haptic device. This is necessary to provide a sense of touching and to avoid molecular interpenetration. This “collision” force Fcollision will depend on the atoms in contact and those in the neighborhood that will determine the magnitude and direction of the force vector as shown in Fig. (10b). In order to calculate the torque feedback, a “pivot point” Ppvt can be defined at the center of the ligand as shown in Fig. (11). The accumulated force is assumed to be ap plied through the pivot point of the molecule so the torque is around an axis that pas ses the pivot point. In Fig. (11), the torque 1 is induced at the pivot point by a collision point P1 when a force is applied on it.

Fig. (11). Haptic torque calculation.

Moreover, as the forces and torques are created, both the ligand and the receptor should be allowed to change conformations leading to a continuous change in force magnitude and direction as the user manipulates the ligand. The new forces and torques need to be updated satisfying the haptic refresh rates to provide a continuous and realistic feedback. Therefore, approaches have been proposed and used to speed the calculation of force and torque to achieve real-time visualization and haptic feedback. One approach to compute molecular forces in real time is the 3D grid method [37, 38]. This method consists of enclosing a molecule such as the protein in a 3D grid where each grid point stores the potential energy generated by surrounding protein atoms at that point. For each 3D grid, all the protein atoms located within a predefined range from the current grid center are used in the potential energy calculation. In this way, when a ligand is introduced in the vicinity of the protein, all the grids occupied by the ligand atoms are used in calculating the total interaction energy between the ligand and the protein as shown in Fig. (12). Atoms that fall outside the reasonable range 3D grid are assigned a large value to avoid incorrect selection of molecules that are far away from the receptor. The resultant energy is then converted into a force vector that is transmitted to the user through the haptic device.


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Fig. (12). 3D grid method for real-time energy and force calculation.

Another approach is to use parallel computing to distribute and perform the calculations for the potential energy function among several processors. The function evaluation is performed in parallel using the master/slave model thus allowing the simulation of the physical and chemical properties of the molecular system in real time. For example, atom coordinates needed to calculate the potential energy can be stored in each processor so that calculations are carried out independently and in parallel. Results are then exchanged between processors to determine the molecular forces and provide them through the haptic device [9]. Real-time force and torque computation is an ongoing area of research in haptics as further factors need to be incorporated in the simulation. Scaling of force feedback based on the orientation of the ligand as it collides with the receptor, continuous force feedback as molecules change conformations, and consideration of receptor’s flexibility are some of the factors being considered. All these need to be calculated subject to the haptic update rate to provide realistic and continuous force and torque feedback to the user. 4. STATE-OF-THE-ART The first application of haptic devices for molecular docking was presented by OuhYoung et al. from the University of North Carolina at Chapel Hill [11, 14]. They designed and built a haptic device system called the “Docker” to simulate the interaction between a rigid drug molecule and a rigid receptor as shown in Fig. (13). The manipulator provides forces and torques acting on the drug to the user as he/she docks the drug into the receptor’s binding site. The interactive molecular energy and force are approximated by the electrostatic and van der Waals energies and are calculated in real-time using a 3D grid method [37, 38]. As the user moves the drug with the manipulator, the



user can feel the interactive molecular forces pulling the drug towards local energy minima. The researchers also carried out experiments with chemists to analyze the effectiveness of having both a visual and haptic feedback compared to having a visual feedback only. Experiments showed that chemists could identify the lowest energy configuration of the rigid ligand up to twice as fast with the haptic feedback compared to the visual display alone.

Fig. (13). A user guiding a drug molecule towards the receptor’s binding site with haptic force feedback. Reprinted with permission from [22], Taylor II, R. M., “Scientific applications of force feedback: molecular simulation and microscope control,” Course Notes of ACM SIGGRAPH’99, Los Angeles, CA, 1999.

A Virtual Biomolecular Environment (VIBE) for interactive molecular modeling was developed by researchers from the University of Illinois at Chicago, Florida State University and the Argonne National Laboratory [9]. VIBE uses parallel computing to simulate a molecular system that is displayed through a Cave Automatic Virtual Environment (CAVE) [39] while the data is exchanged through a high-speed network. The CAVE is a type of immersive room that surrounds users with stereoscopic images while the users wear shutter glasses and tracking devices so the images are displayed based on the users’ position and orientation. The user interacts with the molecular model and the simulation by manipulating the drug molecule around the receptor with a wand-like device. The simulation is based on molecular dynamics, which provide specific atomic interaction. In this way, a molecular scientist can visualize and control the molecular dynamics simulation in a real-time virtual environment and obtain immediate quantitative and qualitative information about a particular molecular system. Researchers at Argonne National Laboratory introduced a virtual reality system for molecular docking called Stalk [10]. The Stalk system combines parallel and distributed computing, genetic algorithms, high-speed networking, and virtual reality to simulate the interaction of a rigid-body ligand docking into a rigid receptor. Parallel computing and high-speed networking enable the rapid display of the molecular simulation while genetic algorithm is used to search for ligand conformations with minimum interaction ener-


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gy. As shown in Fig. (14), the molecular simulation is displayed through a CAVE and the user is able to manipulate the molecules’ position to guide the energy minimization search while observing the docking process.

Fig. (14). A user docking a ligand into a receptor using Stalk. Reprinted with permission from [10], Levine, D.; Facello, M.; Hallstrom, P.; Reeder, G.; Walenz, B. and Stevens, F., “Stalk: An interactive system for virtual molecular docking,” Computing in Science and Engineering, 4(2), April-June 1997, pp. 55-65. © 1997 IEEE.

Researchers at the Fraunhofer Institute for Computer Graphics in Germany developed a virtual environment for interactive molecular dynamics simulation called RealMol [40]. RealMol can run on different virtual reality hardware such as CAVE, head mounted displays, and wall systems. The user wears a cyberglove to select a molecule and move it to a different location, which is used as the starting point for the molecular dynamics simulation. The simulation is run through a molecular dynamics program called NAMD [41] and any change in the energy of the molecular system triggers a sound to the user. The user can interactively start/stop the simulation by using his/her hand with the cyberglove to make modifications to the simulation while looking for the best binding site of the drug molecule. Anderson and Weng [33] developed an interactive molecular docking program called VRDD. Molecules are represented by solvent-accessible surface models and displayed through an Immersakesk™ workbench system that provides a multiviewer, semiimmersive virtual reality experience. The user utilizes a wand to manipulate a ligand around the receptor while a Monte Carlo algorithm is used to perform an energy local search around the user-specified ligand orientation. The binding energies are computed in real-time using a previously calibrated energy function. VRDD also provides auditory feedback to indicate to the user when atoms overlap and their interaction energy changes.



Researchers at the University of North Carolina at Chapel Hill developed the Steered Molecular Dynamics (SMD) system to interactively steer forces (or place restraints) in a running molecular dynamics simulation for observing particular molecular behaviors [42, 43]. The SMD system was applied to the extraction of small ligands from proteins and can be used with a virtual 3D environment. The virtual environment was provided through the Protein Interactive Theater (PIT) built at UNC Chapel Hill [44]. The PIT is a dual-screen, stereo display system for two users who are seated at a table as shown in Fig. (15). The users wear shutter glasses with a tracking sensor and observe the image displayed across the table. The user steers the dynamics simulation by selecting an atom with a pointer device and moving it. Thus, the PIT system simplifies the placement of the restraints and facilitates the visualization of the dynamics by multiple users.

Fig. (15). Protein Interactive Theater (PIT). Reprinted with permission from [44], Arthur, K.; Preston, T.; Taylor II, R. M.; Brooks Jr., F. P.; Whitton, M. C. and Wright, W. V., “Designing and building the PIT: a head-tracked stereo workspace for two users,” Technical Report TR98-015, University of North Carolina at Chapel Hill, 1998.

Stone et al. [45] implemented a system called Interactive Molecular Dynamics (IMD) that allows the manipulation of molecules in molecular dynamics simulations with real-time visual and force feedback. IMD consists of a haptic device, a molecular dynamics simulation program called NAMD [41], and a visualization program named VMD [46] as shown in Fig. (16). NAMD is a fast and scalable program that implements the Charmm force field for molecular dynamics. VMD provides the user graphic interface for displaying the molecular dynamic simulation and is connected to the haptic device, which provides the force feedback. The use of NAMD parallel molecular dynamics program and high-speed computers were key to overcome the challenge of incorporating force feedback to molecular dynamics simulations. In subsequent work, IMD was applied to study the membrane channel protein GlpF and the unbinding pathways of enzyme glycerol kinase [47]. The IMD simulation incorporates the researcher’s intuition to adjust molecular simulations in order to significantly accelerate processes that are otherwise too slow to model within reasonable time.


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Fig. (16). Interactive Molecular Dynamics (IMD) framework. Reprinted from [45], Stone, J. E.; Gullingsrud, J. and Schulten, K., “A system for interactive molecular dynamics simulation,” ACM Symposium on Interactive 3D Graphics, 2001, pp. 191-194. http://doi.acm.org/10.1145/364338. 364398. © 2001 ACM, Inc. Reprinted by permission.

Bayazit et al. at Texas A&M University [48] presented a framework called OBPRM that combines a path planning approach with haptic devices to locate the ligand’s binding site on the protein. OBPRM uses a fully automated motion planner that generates ligand configurations close to the protein’s surface to create a roadmap for identifying potential binding sites in the protein. The generated roadmap indicates accessible potential binding sites to the ligand from an outside location. As the motion planner has difficulty sampling configurations between high potential energy areas, a haptic device is introduced to enable the user to explore the energy landscape and to identify key ligand configurations to aid the motion planner. Nagata et al. [49] presented a protein-ligand docking simulator using haptic devices. This system allows the user to feel the electrostatic forces of a protein in real time through a globular probe that has electrostatic charge. The probe is controlled by a haptic device and is used to search for regions with highly attractive forces that can indicate potential binding sites. Researchers from the National Institute of Standards and Technology proposed a new method for smoothing haptic forces between rigid molecules as they are docked or assembled [50]. Spatial constraints were introduced to provide stable forces to the user as molecules approach each other until they are in contact using the Lennard-Jones forcefield as shown in Fig. (17). The darker and lighter color arrows indicate the resultant force and torque vectors, respectively. A virtual wall is presented that impede atoms from penetrating beyond the wall providing the user with a smoother haptic force feedback.



Fig. (17). Resultant force and torque vectors experienced by a ligand as it approaches a receptor. Reprinted from [50], Computer-Aided Design, Vol. 36, No. 1, Lee, Y.-G. and Lyons, K. W., “Smoothing Haptic Interaction using Molecular Force Calculations,” pp. 75-90, 2004, with permission from Elsevier.

Birmanns and Wriggers from the University of Texas Health Science Center at Houston introduced a new real-time interactive fitting strategy to reduce the search complexity during molecular docking [51]. The proposed strategy applies virtual reality and haptic devices to assist users in orienting and positioning molecules in 3D space relative to each other. Fig. (18) shows the graphical user interface of the system, which Sculptor File

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Fig. (18). Graphical user interface for fitting molecules using SenSitus. Picture courtesy of Dr. Willy Wriggers, D.E. Shaw Research, and Dr. Stefan Birmanns, University of Texas Health Science Center at Houston [51].


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was implemented in a virtual reality visualization program called SenSitus. The generated haptic force and torque feedback guide the user towards the best fit of molecules into low-resolution electron microscopy (EM) density maps. The force and torque calculation complexity is reduced by vector quantization to achieve the necessary refresh rates for realistic haptic perception. This work was extended to model molecular docking at various levels of resolution through a hybrid modeling technique [36]. The proposed hybrid model consists of a dynamic mesh simplification algorithm to balance the computer processor load between the haptic and visual rendering for achieving both haptic and visual refresh rates. The virtual reality environment is a self-built, low-cost back-projection system with projectors, mirrors, tracking system, glasses, and a 6DOF haptic device. Researchers at the Lawrence Berkeley National Laboratory developed a tool for interactive molecular docking called DockingShop [52]. DockingShop enables users to manipulate a ligand or protein to an approximated binding site of a receptor with real-time visual feedback, and side chain and backbone flexibility as shown in Fig. (19). The main objective is to develop a graphical interface for integrating human knowledge and intuition and accelerating the molecular docking process. DockingShop provides adjustable scoring functions that indicate the quality of the molecular configuration and guide the docking process. The resultant ligand-protein or protein-protein complex can be input into a more detailed molecular docking algorithm or optimization process for further refinement. DockingShop File

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Fig. (19). Graphical user interface for DockingShop. Reprinted with permission from [52], Lu, T.C.; Max, N. L.; Ding, J.; Bethel, E. W. and Crivelli, S. N., “DockingShop: A tool for interactive molecular docking,” Lawrence Berkeley National Laboratory, Paper LBNL-58170, April 24, 2005.



(a) Graphical user interface and 5DOF haptic device for molecular docking.

(b) Ligand’s force and torque vectors provided by the haptic device as the ligand approaches the receptor. Fig. (20). Haptic system for molecular docking with flexible ligand and rigid receptor.

Lai-Yuen and Lee [53, 54] presented a system for docking flexible ligand molecules into a rigid receptor using a 5DOF haptic device as shown in Fig. (20a). As the user manipulates the ligand around the surface of the receptor, an energy minimization technique searches for a ligand conformation with lower energy while the user feels the force and torque feedback in real-time. Fig. (20b) shows the force (black arrow) and torque (blue arrow) vectors that are transmitted to the user through the haptic device as the user manipulates the ligand around the receptor. During the exploration, the user can apply a simulator called NanoDAS (Nano-scale Docking and Assembly Simulator) that explores the surroundings of the ligand to identify accessible paths to potential binding sites of the receptor [55]. NanoDAS uses potential field analysis and local search techniques to construct a search tree to “explore” and “exploit” the search space of the ligand towards various potential binding sites. Daunay et al. [56] proposed a new method for a six degrees of freedom (DOF) haptic device that allows real-time virtual interaction with molecular docking simulations as


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shown in Fig. (21). The method consists of applying wave variables to achieve stable simulation and force and torque feedback as the user manipulates a flexible ligand around the binding site of a flexible receptor. As molecular simulators used in pharmaceutical applications do not achieve real-time performance but are very accurate, the proposed method enables the user to feel the forces and torques during molecular docking using any molecular simulator. Subsequent work introduced an energy minimization process based on an energy equation with known analytic derivation [57]. The energy obtained from the minimization process is approached by a potential containing two distinct terms for the interaction force and torque. This provides forces and torques that look stable in a particular case regardless of the molecule’s displacement value.

Fig. (21). A ligand being manipulated around a receptor using virtual reality and a 6DOF haptic device. Reprinted with permission from [56], Daunay, B.; Micaelli, A. and Régnier, S., “6 DOF haptic feedback for molecular docking using wave variables,” IEEE International Conference on Robotics and Automation, Roma, Italy, April 10-14, 2007, pp. 840-845. © 2007 IEEE.

Subasi and Basdogan at Koç University in Turkey [58] presented a new humancomputer interaction approach for docking a rigid ligand molecule into a rigid receptor using an Active Haptic Workspace (AHW) visualization technique. The AHW enables the user to explore the receptor’s surface in high resolution with haptic force feedback allowing the visualization of complex 3D surfaces and overcoming the workspace limitations of commercially available haptic devices. Once the user identifies the true binding site with the haptic device, the final ligand configuration is calculated off-line using time-stepping molecular dynamics simulations and a proposed distance error minimization approach. The system creates a visual copy of the ligand to inform the user when a low-energy configuration is found. Virtual reality and haptics have also been used to teach students chemistry and molecular biology. Researchers at the University of Washington and The Scripps Research Institute used augmented reality on top of physical molecular models to facilitate the visualization of molecular structures of different sizes and complexity [59]. The approach uses haptic devices and a user display such as a head-mounted display to provide electrostatic force feedback and force field visualization on top of the physical molecular models as a new intuitive learning interface. The physical molecular models were fabricated through 3D printing, which is a type of rapid prototyping process that builds physical models in a layer-by-layer process [60]. The models are then integrated into the



augmented reality environment for teaching, research, and collaboration in molecular biology. 5. DISCUSSION This paper presented an overview of recent advances in virtual reality and haptics applications for drug design and discovery. Current research work has demonstrated that a virtual reality environment can be a powerful human-computer interface for visualizing and manipulating complex molecular systems in real time. This immersive and interactive computational tool can significantly increase researchers’ understanding of molecular docking by providing a 3-dimensional view of the system and enabling the researcher to feel the molecular forces acting on the ligand through haptic devices. Moreover, it can accelerate molecular simulations that are otherwise too slow to model within a reasonable time by facilitating the incorporation of the researcher’s knowledge and intuition into the simulation. However, the strict update requirements needed to provide a realistic visualization and tactile experience still limit the scope of the molecular simulations developed so far. It is expected that as computational power increases and becomes available, more thorough and accurate simulations can be achieved in real time. New approaches will be necessary for the realization of a complete and realistic immersive and interactive environment for drug design and discovery. Below are some of the areas of future work needed to achieve this goal: • Modeling the physical changes and interactions of molecules in real time is computationally expensive, particularly as both the size of the molecule and the number of interacting molecules increase. As both the ligand and receptor are allowed to change conformations, new algorithms are needed to speed the display of molecular conformations and to effectively identify collisions between the molecules for virtual reality and haptics applications. • The effect of the solvent in the molecular system is also required for a more complete modeling and for providing the adequate force feedback to the user. • New techniques for parallel computing will be useful for distributing the various processes involved in the display and force calculation of the molecular system in order to achieve the virtual reality and haptic update requirements. • Further experiments on the effect of the virtual reality environment on scientists are necessary as new approaches are developed. This can lead to changes in graphical user interfaces and to novel virtual reality and haptics equipment to make the environment more intuitive to the user. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8]

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Contributors

Contributors Alamo, C.

Department of Pharmacology, Faculty University of Alcalá, Madrid, Spain

of

Medicine,

Axarli, I.

Laboratory of Enzyme Technology, Department of Agricultural Biotechnology, Agricultural University of Athens, 75 Iera Odos Street, GR-11855-Athens, Greece

Bause, A.S.

University of Arizona, College of Pharmacy, Arizona Cancer Center, Tucson, AZ, USA

Bramuglia, G.F.

Cátedra de Farmacología, Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires, Junín 956, (C1113AAD) Buenos Aires, Argentina

Cal, K.

Department of Pharmaceutical Technology, Medical University, of Gdansk, Hallera 107, 80-416 Gdansk, Poland

Campillo, N.E.

Instituto de Química Médica (CSIC), Juan de la Cierva, nº 3, 28006-Madrid, Spain

Charcosset, C.

Laboratoire d’Automatique et de Génie des Procédés, Université de Lyon, UMR CNRS 5007, ESCPE-Lyon, 43 Bd du 11 Novembre 1918, 69 622 Villeurbanne Cedex, France

Coluzzi, F.

I.C.O.T. – Polo Pontino, Dept. Anaesthesiology, Intensive Care Medicine and Pain Therapy, University of Rome “La Sapienza”, Rome, Italy

Escobar-Chávez, J.J.

División de Estudios de Posgrado (Tecnología Farmacéutica), Facultad de Estudios Superiores CuautitlánUniversidad Nacional Autónoma de México, Cuautitlán Izcalli, Estado de México, México 54740 AND Departamento de Sistemas Biológicos, Universidad Autónoma Metropolitana-Xochimilco, Calzada del Hueso 1100, Colonia Villa Quietud, México D.F. 04960, México

Fessi, H.

Laboratoire d'Automatique et de Génie des Procédés, Université de Lyon, UMR CNRS 5007, ESCPE-Lyon, 43 Bd du 11 Novembre 1918, 69622 Villeurbanne Cedex, France

Fischer, R.

Medice chem.-phar., Fabrik Pütter GmbH & Co.KG, Iserlohn, Germany

Contributors


Fogel, G.B.

Natural Selection, Inc., 9330 Scranton Rd., San Diego, CA 92121, USA

Funatsu, K.

Kamakura Research Laboratories, Chugai Pharmaceutical Co., LTD, 200 Kajiwara, Kamakura, Kanagawa, 247-8530, Japan

Ganem-Quintanar, A.

División de Estudios de Posgrado (Tecnología Farmacéutica), Facultad de Estudios Superiores CuautitlánUniversidad Nacional Autónoma de México, Cuautitlán Izcalli, Estado de México, México 54740

García-García, P.


Hasegawa, K.

Department of Chemical System Engineering, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 1138656, Japan

Hecht, D.

Southwestern College, 900 Otay Lakes Rd., Chula Vista, CA 91910, USA

Höcht, C.

Cátedra de Farmacología, Instituto de Fisiopatología y Bioquímica Clínica, Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires, Junín 956, (C1113AAD) Buenos Aires, Argentina

Hsieh, Y.

Department of Drug Metabolism and Pharmacokinetics, Schering-Plough Research Institute, 2015 Galloping Hill Road, K-15-3700, Kenilworth, NJ 07033, USA

Ishiguro, H.

Kyoto Univercity Hospital, Translational Research Center, Department of Clinical Trial Management / Outpatient Oncology Unit, 54 Shogoinkawaharara-cho, Sakyo-Ku, Kyoto-City, 606-8507, Japan

Khan, M.T.H.

Department of Pharmacology, Institute of Medical Biology, Faculty of Medicine, University of Tromsø, 9037 Tromsø, Norway

Khanna, I.

Reddy US Therapeutics Inc., 3065 Northwoods Circle, Norcross GA 30071, USA

Labrou, N.E.


of

Medicine,


Contributors

Lai-Yuen, S.K.

Department of Industrial & Management Systems Engineering, University of South Florida, Tampa, FL 33620-5350, USA

Lamore, S.D.


López-Cervantes, M.


López-Muñoz, F.


Mattia, C.

I.C.O.T. – Polo Pontino, Dept. Anaesthesiology, Intensive Care Medicine and Pain Therapy, University of Rome “La Sapienza”, Rome, Italy

Mayer, M.


Melgoza-Contreras, L.M.

Departamento de Sistemas Biológicos, Universidad Autónoma Metropolitana-Xochimilco, Calzada del Hueso 1100, Colonia Villa Quietud, México D.F. 04960, México

Molina, J.D.

Acute Inpatients Unit, Dr. Lafora Psychiatric Hospital, Madrid, Spain

Opezzo, J.A.W.


Páez, J.A.

Instituto de Química Médica (CSIC), Juan de la Cierva, 3, 28006-Madrid-Spain

Pillarisetti, S.

Reddy US Therapeutics Inc., 3065 Northwoods Circle, Norcross GA 30071, USA

Platis, D.


of

Medicine,

Contributors


Quintanar-Guerrero, D.


Skopelitou, K.


Taira, C.A.


Toi, M.

Kyoto Univercity Hospital, Breast Surgery Department, 54 Shogoinkawaharara-cho, Sakyo-ku, Kyoto-city, 606-8507, Japan

Wondrak, G.T.


Yano, I.

Kyoto Univercity Hospital, Department of Pharamcy, 54 Shogoinkawaharara-cho, Sakyo-ku, Kyoto-city, 606-8507, Japan

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