Using Mass Spectrometry for Drug Metabolism Studies

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USING MASS SPECTROMETRY FOR DRUG METABOLISM STUDIES

EDITED BY

Walter A. Korfmacher

CRC PR E S S Boca Raton London New York Washington, D.C. Copyright © 2005 CRC Press, LLC

File: {Books}4354-Korfmacher/Revises-II/4354-FM.3d Creator: iruchan/cipl-un1-3b2-1.unit1.cepha.net Date/Time: 25.10.2004/7:29pm Page: 4/12

Library of Congress Cataloging-in-Publication Data Using mass spectrometry for drug metabolism studies/edited by Walter A. Korfmacher. p. cm. Includes bibliographical references and index. ISBN 0-8493-1963-3 (alk. paper) 1. Drugs–Metabolism. 2. Drugs–Spectra. 3. Mass spectrometry. [DNLM: 1. Pharmaceutical Preparations–metabolism. 2. Drug Design. 3. Drug Evaluation, Preclinical–methods. 4. Spectrum Analysis, Mass–methods. QV 38 U85 2004] I. Korfmacher, Walter A. II. Title. RM301. U85 2004 6150 .7–dc22

2004050306

This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the consequences of their use. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming, and recording, or by any information storage or retrieval system, without prior permission in writing from the publisher. All rights reserved. Authorization to photocopy items for internal or personal use, or the personal or internal use of specific clients, may be granted by CRC Press, provided that $1.50 per page photocopied is paid directly to Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923 USA. The fee code for users of the Transactional Reporting Service is ISBN 0-8493-1963-3/04/$0.00+$1.50. The fee is subject to change without notice. For organizations that have granted a photocopy license by the CCC, a separate system of payment has been arranged. The consent of CRC Press does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from CRC Press for such copying. Direct all inquiries to CRC Press, 2000 N.W. Corporate Blvd., Boca Raton, Florida 33431. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation, without intent to infringe.

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Copyright © 2005 CRC Press, LLC


Preface

The impetus for this book came from a combination of factors, but can be summarized by the statement that we live in exciting times. It is an exciting time to be a drug metabolism specialist involved in drug discovery efforts and it is an exciting time for mass spectrometry. I feel fortunate to have been able to live during these times and I look forward to what the future holds in store for all of us. This book was designed to be a resource book for professionals in both mass spectrometry and drug metabolism areas, but will also be helpful to medicinal chemists interested in learning more about drug metabolism issues in new drug discovery. The chapters were written so that scientists in these fields could benefit from the state-of-the-art expertise and knowledge that is contained in each chapter and the references cited by each chapter’s author. While each chapter was written so that it could be read separately from the other chapters, I have inserted notes into most of the chapters referring to another chapter for more information on a given topic. The book has chapters on general topics as well as specific areas of interest. There are also specific chapters devoted to newer technology that has more recently been introduced and appears to have great potential. I would like to thank all of the authors of these chapters for their efforts and attention to detail that have allowed this book to become a reality. I also thank ScheringPlough Research Institute management for their support of this effort. Finally, I would like to thank my family for their continuing support, especially Madeleine, my wife. Walter A. Korfmacher February 14, 2004

v



Editor

Walter A. Korfmacher, Ph.D. Dr. Korfmacher is a director of exploratory drug metabolism at ScheringPlough Research Institute in Kenilworth, New Jersey. He received his B.S. in chemistry degree from St. Louis University in 1973. He then went on to obtain his M.S. in chemistry in 1975, and Ph.D. in chemistry in 1978, both from the University of Illinois in Urbana. In 1978, he joined the FDA and was employed at the National Center for Toxicological Research (NCTR) in Jefferson, Arkansas. While at the NCTR, he also held adjunct associate professor positions at the College of Pharmacy in the University of Tennessee (Memphis) and the Department of Toxicology in the University of Arkansas for Medical Sciences (Little Rock). After 13 years at the NCTR, Dr. Korfmacher joined Schering-Plough Research Institute as a principal scientist in October, 1991. He is currently a Director and the leader for a group of fifteen scientists. His research interests include the application of mass spectrometry to the analysis of various sample types, particularly metabolite identification and trace organic quantitative methodology. His most recent applications are in the use of HPLC combined with atmospheric pressure ionization mass spectrometry and tandem mass spectrometry for both metabolite identification as well as nanogram/ml quantitative assay development for various pharmaceutical molecules in plasma. He is also a leader in the field of developing strategies for the application of new MS techniques for drug metabolism participation in new drug discovery and is frequently invited to speak at scientific conferences. In 1999–2000, Dr. Korfmacher was the chairperson of the North Jersey Mass Spectrometry Discussion Group and in 2002, Dr. Korfmacher received the New Jersey Regional Award for Achievements in Mass Spectrometry. Dr. Korfmacher has over 100 publications in the scientific literature and has made over 75 presentations at various scientific forums. vii



Contributors

University of Geneva Geneva, Switzerland

Bradley L. Ackermann, Ph.D. Senior Research Scientist Drug Disposition Eli Lilly and Company Indianapolis, Indiana

Yunsheng Hsieh, Ph.D. Principal Scientist Exploratory Drug Metabolism Department of Drug Metabolism and Pharmacokinetics Schering-Plough Research Institute Kenilworth, New Jersey

Richard M. Caprioli, Ph.D. Professor of Biochemistry and Director Mass Spectrometry Research Center Department of Biochemistry Vanderbilt University Nashville, Tennessee

Daniel B. Kassel, Ph.D. Senior Director Analytical Discovery & Development Syrrx, Inc. San Diego, California

Kathleen Cox, Ph.D. Associate Director Exploratory Drug Metabolism Department of Drug Metabolism and Pharmacokinetics Schering-Plough Research Institute Kenilworth, New Jersey

Walter A. Korfmacher, Ph.D. Director Exploratory Drug Metabolism Department of Drug Metabolism and Pharmacokinetics Schering-Plough Research Institute Kenilworth, New Jersey

Jean-Marie Dethy, MSc. Senior Scientist Department of Toxicology and Drug Metabolism Lilly Development Center Mont-Saint-Guibert, Belgium

Hong Mei, Ph.D. Associate Principal Scientist Exploratory Drug Metabolism Department of Drug Metabolism and Pharmacokinetics

Ge´rard Hopfgartner, Ph.D. Professor School of Pharmacy ix



x

Schering-Plough Research Institute Kenilworth, New Jersey Michelle L. Reyzer, Ph.D. Research Fellow Department of Biochemistry Vanderbilt University Nashville, Tennessee Thomas N. Thompson, Ph.D. Consultant 12328 Noland Overland Park, Kansas Sam Wainhaus, Ph.D. Associate Principal Scientist Exploratory Drug Metabolism


Contributors

Department of Drug Metabolism and Pharmacokinetics Schering-Plough Research Institute Kenilworth, New Jersey Xiaoying Xu, Ph.D. Associate Principal Scientist Exploratory Drug Metabolism Department of Drug Metabolism and Pharmacokinetics Schering-Plough Research Institute Kenilworth, New Jersey Manfred Zell Senior Scientist F. Hoffmann-La Roche, Ltd. Basel, Switzerland


Contents

Chapter 1

Bioanalytical Assays in a Drug Discovery Environment Walter A. Korfmacher, Ph.D.

Chapter 2

Drug Metabolism In Vitro and In Vivo Results: How do these Data Support Drug Discovery? Thomas N. Thompson, Ph.D.

35

High Throughput Strategies for In Vitro ADME Assays: How Fast Can We Go? Daniel B. Kassel, Ph.D.

83

Chapter 3

1

Chapter 4

Matrix Effects: Causes and Solutions Hong Mei, Ph.D.

103

Chapter 5

Direct Plasma Analysis Systems Yunsheng Hsieh, Ph.D.

151

Chapter 6

Acyl Glucuronides: Assays and Issues Sam Wainhaus, Ph.D.

175

Chapter 7

Utilizing Higher Mass Resolution in Quantitative Assays Xiaoying Xu, Ph.D.

203

Chapter 8

Special Requirements for Metabolite Characterization Kathleen Cox, Ph.D.

229

Chapter 9

APPI: A New Ionization Source for LC and MS/MS Assays Yunsheng Hsieh, Ph.D.

xi


253


Contents

xii

Chapter 10

Q Trap MS: A New Tool for Metabolite Identification Ge´rard Hopfgartner, Ph.D. and Manfred Zell

Chapter 11

MS Imaging: New Technology Provides New Opportunities Michelle L. Reyzer, Ph.D. and Richard M. Caprioli, Ph.D.

305

Understanding the Role and Potential of Infusion Nanoelectrospray Ionization for Pharmaceutical Bioanalysis Bradley L. Ackermann, Ph.D. and Jean-Marie Dethy, MSc.

329

Chapter 12


277

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Chapter 1 Bioanalytical Assays in a Drug Discovery Environment Walter A. Korfmacher

1.1

Introduction

The challenge of working in the pharmaceutical industry during this time of rapid expansion of our knowledge of the causes and potential cures for many diseases is both exciting and formidable. It is exciting because we are now learning how to make potent drugs that can target specific receptors in order to relieve symptoms or block the progression of a disease. It is formidable because the number of potential targets is large and the size of our chemical libraries that need to be screened against these targets is in the millions and growing even larger. While ultra-high throughput screening effectively reduces these numbers by screening out the inactive compounds, the numbers of compounds that need to be screened through drug metabolism studies can still be overwhelming. As shown in Figure 1.1, the amount of effort in terms of compound screening, lead optimization and attrition is a daunting task. Of the two million compounds that might be screened for activity, perhaps 10,000 are selected and optimized in the drug discovery stage. Next, 20 compounds might be selected for development and five of these may survive the toxicity testing and be suitable for phase I clinical screening. At current rates of success, one of the five compounds would become an approved drug. In a 2003 report by the 0-8493-1963-3/05/$0.00+$1.50 ß 2005 by CRC Press


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Using Mass Spectrometry for Drug Metabolism Studies

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Figure 1.1 Schematic chart showing the compound attrition in drug discovery to development to drug approval. The X axis is the stage or point in the process. The Y axis is the number of compounds at that point.

Tufts Center for the Study of Drug Development, the cost of bringing a new drug to market was estimated to be $897 million [1]. By the time this book is published, the average cost may well be $1 billion or more. Over the last 12–15 years, mass spectrometry (MS) has played an increasingly important role in all phases of drug discovery and drug development. In that same time, mass spectrometry has undergone tremendous changes. Mass spectrometers have become more sensitive, easier to use and have been applied to multiple areas of drug metabolism activity. At the same time, new types of mass spectrometers have been introduced. Figure 1.2 shows four of the most widely used types of mass spectrometers; of these four types, the triple quadrupole mass spectrometer (QqQ MS) has become the ‘‘gold standard’’ for quantitative assays in the drug metabolism arena. The focus of this chapter will be on the use of liquid chromatography combined with tandem mass spectrometry (LC–MS/MS) for drug metabolism participation in new drug discovery, specifically in support of in vivo pharmacokinetic (PK) screens and studies.

1.2

Review of Recent Literature

While medicinal chemists will continue to search for in silico programs and in vitro (for more details on in vitro assays, see Chapter 3) techniques to predict animal and human pharmacokinetics [2–12], the need to obtain experimental PK data from laboratory animals early in the discovery paradigm is still paramount [4, 13–15] (for more details on how to use PK data, see Chapter 2). Several review articles have been published in the last few years on the use of mass spectrometry when assaying samples from in vivo PK studies in support of Copyright © 2005 CRC Press, LLC


Bioanalytical Assays in a Drug Discovery Environment

3

Figure 1.2 Four types of mass spectrometer that are used for various drug metabolism assays. Figure provided by Jerry Pappas and used with the permission of Thermo-Finnigan Instruments.

new drug discovery and development [15–28]. Therefore, this review will cover, primarily, recent publications dealing with the use of LC–MS/MS for the analysis of PK samples in a drug discovery environment. While there will be some overlap with other chapters in this book, many citations of interest that are not included here will be found in the other chapters. One important theme that can be found in multiple citations is the need for speed when working in a discovery setting. This is important because, in a drug discovery setting, the goal is to learn as much as possible about many compounds of interest in a short time. Thus, a fast turnaround time from sample receipt to the PK report provides one important set of information about the potential lead drug—often producing ‘‘go/no go’’ feedback to chemists. For this reason, much of the recent literature discusses how best to speed up the LC–MS/MS assay. For example, Shou et al. [29], discuss the use of packed silica columns to provide rapid analysis of polar analytes. They have found that silica columns can be operated at 4 mL/min or more, which can turn a 4-min runtime into a 30-s runtime. As shown in Figure 1.3, an assay for midazolam and its two hydroxy metabolites is completed in 30 s. As shown in Figure 1.4, the authors also demonstrated that this new, ultrafast assay provided data equivalent to the standard, high-performance liquid chromatography–tandem mass spectrometry (HPLC–MS/MS) assay, which was performed at a flow rate of 0.6 mL/min. Chromatography is an important part of the LC–MS/MS system [30–33]. Romanyshyn et al. [34] compare the advantages of ultrafast gradients (also Copyright © 2005 CRC Press, LLC


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Figure 1.3 LC–MS/MS chromatograms showing a high-speed assay with a 0.5-min duration for midazolam and its two hydroxy metabolites. Source: Shou et al. [29]. With permission.

called ballistic gradients) with fast isocratic chromatographic systems. They conclude that the gradient systems provide better chromatographic separation of the analyte and its metabolites with much less development time needed. They discuss the potential for glucuronide metabolites to interfere with the analysis of the dosed compound, therefore, they stress the need for good chromatography even when high-speed assays are being developed. For Copyright © 2005 CRC Press, LLC



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Figure 1.4 Comparison of the PK (concentration vs time after dose) data obtained using the standard (0.6 mL/min) HPLC conditions (solid line) or the high speed (4.5 mL/min) HPLC conditions (dotted line). Source: Shou et al. [29]. With permission.

example, as shown in Figure 1.5, in less than 2 min, they have complete chromatographic separation of the dosed compound and two of its glucuronide metabolites [34]. Naidong et al. [35] discuss the importance of selecting the right injection solvent when developing LC–MS/MS methods. Zhao et al. [36] described the importance of selecting the proper mobile phase buffer when setting up an LC–MS/MS assay. In an article by Tiller and Romanyshyn [37], the authors compare ultrafast gradients with fast isocratic gradients in terms of avoiding matrix effects. While they concluded that both systems have trouble with very dirty samples, such as rat bile or urine, they stated that ultrafast gradients were better at keeping the column clean, due to the mobile phase gradient. They also pointed out the importance of using a divert valve after the HPLC column to send the first portion of the chromatographic eluant (typically 20% of the gradient time) to waste instead of going into the MS source. In another article by Hsieh et al. [38], the authors describe the use of a fast gradient in combination with MS/MS for the analysis of drug discovery PK samples. In this report, the authors use the postcolumn infusion system to test for the extent of the matrix effect (see MillerStein et al. [39] and King et al. [40] for a discussion of post-column infusion techniques). The authors reported that while matrix effects were observed in both fast gradients and standard gradients, if properly understood, either technique could be used for discovery PK assays. As shown in Figure 1.6, while the assay time was reduced from 4 min to 1 min, good chromatographic peak shapes for both the analyte and internal standard were maintained [38]. Copyright © 2005 CRC Press, LLC


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Figure 1.5 LC–MS/MS chromatogram showing a fast assay (less than 2 min) where complete chromatographic separation of the parent (dosed) compound and two of its glucuronide metabolites (potential interferences in this assay) was achieved. Source: Romanyshyn et al. [34]. With permission.

Figure 1.6 LC–MS/MS chromatograms showing the use of a high-speed gradient; the upper trace shows the standard assay for an analyte and its internal standard (IS) with a 4-min run time, while the lower trace shows the fast assay with a minibore column for the same two compounds with a 1-min run time. While the assay time was reduced from 4 min to 1 min, good chromatographic peak shapes for both the analyte and internal standard were maintained in the higher speed assay. Source: Hsieh et al. [38]. With permission.

The authors also reported that while atmospheric pressure chemical ionization (APCI) was less affected by matrix effects, electrospray ionization (ESI) could also be utilized as long as one was careful to ensure that the analyte and internal standard eluted in the matrix ion suppression-free region of the Copyright © 2005 CRC Press, LLC



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Figure 1.7 Comparison of the PK (concentration vs time after dose) data obtained using the standard HPLC conditions or the high speed gradient (minibore) conditions shown in Figure 1.6. The assay was performed in each case with an APCI source and an ESI source. For the data set labeled A, the samples were from a monkey PK study dosed using a 20% hydroxypropylbetacyclodextrin (HPBCD) formulation. For the data set labeled B, the samples were from a monkey PK study dosed with the same compound but with a 0.4% methylcellulose (MC) formulation. Source: Hsieh et al. [38]. With permission.

chromatogram (see Chapter 4 for more information on this topic). As a final test that either ESI or APCI could be used in the fast gradient mode, results were compared from a monkey i.v./p.o. PK study for a discovery compound when the samples were assayed by a standard gradient and a fast gradient (with a minibore column) using either APCI or ESI. As shown in Figure 1.7, this four-way comparison resulted in very similar data being produced by each assay methodology. Another approach for speeding assay throughput has been the use of parallel HPLC columns feeding into one MS/MS system [41–48]. For example, Jemal et al. [42] showed that by connecting two parallel HPLC systems with a ‘‘T’’, one could double the throughput of an assay simply by staggering the injection times of the samples. Their two-column system, as shown in Figure 1.8, was able to reduce the sample assay times from 5 min per sample to 2.5 min per sample, using this procedure [42]. This concept has been commercialized in the development of the Aria LX4Õ (Cohesive Technologies) system, which was described and tested by King et al. [43] In this system, four HPLC pumps, a specialty autosampler and various switching valves are all under the control of a single computer which has software to determine the timing of all the events so that a minimum amount of the MS acquisition time is needed for each sample that is injected. The result is an increase in sample throughput, while maintaining good chromatographic conditions for each sample. Copyright © 2005 CRC Press, LLC



8

!

!!

! "

"T

"

"

Figure 1.8 Schematic diagram showing a two-column LC–MS/MS system that can be used to double the sample throughput. Source: Jemal et al. [42]. With permission.

Another approach for increasing throughput has been to add additional ESI sprayers to the MS/MS system. Hiller et al. [44] described a dual ESI source that could be used for performing two separate assays at one time. As Hiller discusses, there were some disadvantages to this approach in that careful preselection of the analytes was needed so that the two assays did not interfere with each other. As described by Bayliss et al. [45], another commercial solution to the throughput issue was provided by the MUXÕ (Micromass, Inc.) interface. This interface allows one to attach four parallel HPLC columns to one triple quadrupole MS/MS system. Each column feeds an independent ESI sprayer; as shown in Figure 1.9, and each sprayer is sampled sequentially by a rotating interface device. Bayliss et al. [45] reported that ‘‘cross-talk’’ between sprayers was minimal and that one could assay 120 plasma samples per hour using four 50 1 mm columns. Deng et al. [46] showed an impressive use of the MUX system for high throughput assays. As shown in Figure 1.10, four parallel monolithic HPLC columns were hooked up to the MUX system using a four-injector autosampler [46]. The chromatographic run time for each column was 2 min per sample; since four samples were injected at once, that provided a sample throughput of 30 s per sample. In another report, Deng et al. [49] tested the utility of the MUX system for analyzing samples from a drug discovery PK study. They found equivalent results could be obtained whether the samples were assayed in the four-sprayer mode or the single-sprayer mode. In the four-sprayer mode, they reported inter-channel (between sprayers) ‘‘cross-talk’’ of less than 0.1%. The authors also reported a four-fold higher value for the lower limit of quantitation (LLOQ) on the four-sprayer MUX system than was obtained for the same compound on a standard single sprayer system. Several reports have described various studies looking at different chromatographic parameters in order to assess their effect on increasing Copyright © 2005 CRC Press, LLC



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Figure 1.9 Schematic diagram of the MUXÕ (Micromass, UK) ESI source design showing four ESI sprayers and an indexed sample rotor that allows ions from one sprayer at a time to enter the MS ion sampling region. Diagram provided by and used with the permission of Micromass, UK.

Figure 1.10 Schematic diagram showing a four-injector autosampler and four monolithic HPLC columns feeding a MUX ESI source to provide a four-fold increase in sample throughput. Source: Deng et al. [46]. With permission.



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sample throughput [19, 50, 51]. Murphy et al. [50] studied the effect that increasing the mobile phase flow rate had on analyte signal and assay cycle time; the authors reported that signal peak area and cycle were both reduced as the flow rate increased in a gradient system set to assay discovery PK samples after protein precipitation. The assays were performed on a triple quadrupole (QqQ) MS/MS system operated in the ESI mode. The authors attributed the reduction in signal to the concentration-dependent nature of the ESI source because the peak widths were kept constant, therefore the analyte concentration was reduced as the flow rate was increased. The authors also noted that protein precipitation was their sample preparation method of choice for drug discovery PK samples. In addition, they stated that they use methanol instead of acetonitrile in the mobile phase because methanol tends to provide an enhanced signal as compared to acetonitrile. Jemal [19] and Jemal and Hawthorne [52] have also stated that methanol in the mobile phase can provide significant signal enhancement in the positive ESI mode as compared to acetonitrile in the mobile phase. Under negative ESI conditions, Jemal [19] reported no response difference when using either methanol or acetonitrile in the mobile phase. In a recent presentation by Seliniotakis et al. [53], the authors reported that mixing methanol 1:1 with the HPLC effluent and then splitting the flow 1:1 improved the MS signal in test samples. New chromatographic column types have also gained attention as possible ways to enhance sample throughput in LC–MS/MS assays. Several authors have described the potential advantages of the monolithic HPLC columns [54– 61]. In general, monolithic columns offer the possibility of using mobile phases at very high flow rates (5–10 mL/min), which can produce very fast assays. For example, Wu et al. [54] describe the utility of using a monolithic column as part of an LC–MS/MS system in a drug discovery environment. In their report, they used 96-well plate solid phase extraction (SPE) for sample preparation. The authors noted that good chromatographic separation is still important, in order to separate the analyte from endogenous matrix components as well as for the need to provide separation from potential metabolites. They also noted that ESI is primarily a concentration-dependent detector, therefore good peak shape is also an important factor for a successful assay. For their evaluation of the monolithic column, they used a mixture of three analytes and one internal standard; the chromatography was a gradient system and positive ESI was the ionization mode. As shown in Figure 1.11, the authors demonstrated that good separation and peak shape were maintained while changing the flow rate from 1 mL/min to 6 mL/min for the same mixture of four compounds. At a flow rate of 6 mL/min, the eluant was split so that 0.4 mL/min entered the MS source. The authors found that the peak area dropped significantly as the flow rate increased, but the absolute ion abundance (peak height) decreased by only a factor of 2. The authors also reported that the signal–noise ratio (S/N) was unaffected by the increase in flow rate. Finally, by testing a sample with two known metabolites, the authors were able to demonstrate that the monolithic columns still demonstrated good separation power even at a flow rate of Copyright © 2005 CRC Press, LLC



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Figure 1.11 LC–MS/MS chromatograms showing the use of a monolithic column to shorten the assay time by increasing the mobile phase flow rate. The flow rates are 1 mL/min, 3 mL/min and 6 mL/min for the bottom, middle and top traces, respectively. Good peak shape and analyte separation was still seen at the 6-mL/min flow rate. Source: Wu et al. [54]. With permission.

6 mL/min. As a final test, the authors stated that the column was used successfully to analyze 600 plasma extracts in one overnight test. Hsieh et al. [57] have also described the utility of monolithic columns for use in drug discovery PK assays. In this work, the authors developed an assay for a compound and its metabolite. The authors made a standard curve in plasma that contained both the dosed compound and the metabolite of interest. The authors then showed that by using a flow rate of 4 mL/min, the assay time could be reduced to less than 1 min per sample. Finally, the authors demonstrated that the high flow rate assay provided assay results for the dosed drug and metabolite that were equivalent to those obtained using standard flow rate LC–MS/MS conditions. In another article, Hsieh et al. [62] have recently described the possible utility of using zirconia-based HPLC columns for drug discovery PK assays. One advantage of the zirconia-based HPLC column is that it can be heated to 200 C. The authors stated that the ability Copyright © 2005 CRC Press, LLC


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to heat the column allows one to increase the flow rate of the mobile phase without exceeding the pressure limits of the column. There has been much interest in documenting the need to obtain good chromatographic separation in order to avoid the potential issue of one or more metabolites showing up in the same selected reaction monitoring (SRM) transition that is selected for the parent (dosed) compound [63]. The basis for this potential problem is that in-source fragmentation can occur for some types of molecules and that this fragmentation can produce ions that are identical to those formed as [MH]þ ions (positive ionization mode) from the parent compound, thus these ions would produce a signal in the SRM transition for the parent compound. The most commonly cited metabolite class that can produce this effect is glucuronides. While this problem is well known to occur in APCI sources, it is sometimes assumed to not be an issue when using ESI sources. Yan et al. [64] studied the problem, specifically looking at in-source fragmentation of glucuronides in an ESI source. They tested over 100 N- and O-glucuronides in both the positive and the negative ESI mode and varied source temperature and cone voltage to see what effect, if any, these parameters had on the extent of in-source fragmentation. They noted that source temperature had little effect on the amount of in-source fragmentation and that at normal (25–40 V) cone voltage, in-source fragmentation was detected for all glucuronides; at lower cone voltages, the in-source fragmentation was reduced or eliminated. Figure 1.12 [64] shows an example of this effect for an assay of a compound, 7 and its two N-glucuronides, 7-GI and 7-GII. In trace A1 and A2, the cone voltage was set to 29 V and two extra peaks can be seen in the channel for the parent compound, 7 — one of them causing a significant shoulder on the peak for the parent compound. These extra peaks were not observed when the cone voltage was reduced to 18 V (trace B1 and B2). Liu and Pereira [65] reported that both carbamoyl glucuronides and acyl glucuronides, in-source fragmentation was a problem in both ESI and APCI modes of ionization. They stressed that this was a potential issue when using fast gradient chromatographic systems. As an example, as shown in Figure 1.13, the SRM trace for the parent (dosed) compound (a) shows a significant shoulder; this shoulder is separated when a more shallow gradient system was used (b) to assay the same sample [65]. The shoulder peak was found to be caused by a partially co-eluting carbamonyl glucuronide metabolite of the dosed compound. The need to separate acyl glucuronide metabolites from the parent compound to avoid this assay problem has been highlighted in several papers [63, 66–68]. (See Chapter 6 for more discussion of acyl glucuronides.) In papers by Tong et al. [69] and Ramanathan et al. [70], the issue of insource fragmentation by N-oxide metabolites is investigated. In the first paper, they showed that for two N-oxide compounds studied, both [MH]þ and [MH 16]þ ions were formed under APCI conditions, but not ESI conditions. In their second report, they demonstrated that in APCI and ESI sources that utilize a heated transport capillary tube, elevating the temperature of the heated transport capillary tube caused thermal deoxygenation leading to Copyright © 2005 CRC Press, LLC



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Figure 1.12 LC–MS chromatograms showing the potential interference from glucuronide metabolites. The upper two traces (A1, A2) are from a single assay with the ESI source cone voltage set to 29 V; the lower two traces (B1, B2) are from a single assay with the ESI source cone voltage set to 18 V. The sample being assayed is a microsomal incubation sample containing test compound 7 and two glucuronide metabolites of 7, 7-GI and 7-GII. The A1 and B1 channels are for the glucuronide metabolites; the A2 and B2 channels are set to monitor the [MH]þ for the test compound. At 29 V, the interferences can be seen in the A2 trace; this problem is resolved by setting the cone voltage to 18 V as shown in trace B2. Source: Yan et al. [64]. With permission.



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Figure 1.13 LC–MS/MS SRM chromatograms demonstrating the potential for interference in the assay of a test compound, I, from a co-eluting carbamonyl glucuronide metabolite of the compound, I-CG. The internal standard is labeled as IS. The upper traces (a) show the results from a fast chromatography system, while the lower traces show the results from a longer assay for the same sample. The shoulder peak in the analyte trace (a) was found to be caused by a partially co-eluting carbamonyl glucuronide metabolite of the dosed compound that was resolved when the longer assay was used as shown in trace (b). Source: Liu and Pereira [65]. With permission.




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Table 1.1 Putative metabolites of drugs of different chemical structures and the SRM transitions for the metabolites vis-a-vis the SRM transitions of the drug Drug type

Drug SRM

Metabolite

Metabolite SRM

Carboxylic acid

[M þ H]þ ! Pþ

Acylglucuronide

g or d-Hydroxycarboxylic acid Lactone

[M þ H]þ ! Pþ

Lactone

[M þ H]þ ! Pþ

Hydroxy acid

(a) [M þ H þ 176]þ ! [M þ H]þ (b) [M þ H þ 176]þ ! Pþ (a) [M þ H 18]þ ! [M þ H]þ (b) [M þ H 18]þ ! Pþ (a) [M þ H þ 18]þ ! [M þ H]þ (b) [M þ H þ 18]þ ! Pþ (a) [M þ H þ 176]þ ! [M þ H]þ (b) [M þ H þ 176]þ ! Pþ (a) [M þ H þ 80]þ ! [M þ H]þ (b) [M þ H þ 80]þ ! Pþ (a) [M þ H þ 176]þ ! [M þ H]þ (b) [M þ H þ 176]þ ! Pþ (a) [M þ H þ 16]þ ! [M þ H]þ (b) [M þ H þ 16]þ ! Pþ (a) [M þ M 1]þ ! [M þ H]þ (b) [M þ M 1]þ ! Pþ (a) [M þ H þ 16]þ ! [M þ H]þ (b) [M þ H þ 16]þ ! Pþ

þ

þ

Alcohol or phenol

[M þ H] ! P

Alcohol or phenol

[M þ H]þ ! Pþ þ

þ

Amine

[M þ H] ! P

Amine

[M þ H]þ ! Pþ þ

þ

Thiol (sulfhydryl)

[M þ H] ! P

Sulfide

[M þ H]þ ! Pþ

O-Glucuronide O-Sulfate N-Glucuronide N-Oxide Disulfide S-Oxide

The SRM transitions shown are for ESI in the positive ion mode. M is the monoisotopic mass of the drug. P is the product ion in the SRM transition used for quantitation of the drug. For each drug type, the fragmentation exhibited by the metabolite SRM transition designated as (a) can potentially take place within the source of the mass spectrometer as well. If such in-source fragmentation occurs and there is no chromatographic separation between the drug and the metabolite, the concentration of the drug determined by using the [M þ H]þ ! Pþ transition would be inflated. A similar list of SRM transitions can be prepared for negative ESI, and for atmospheric pressure chemical ionization in the positive or negative ion mode. (Reprinted, with permission, from Jemal et al. Rapid Commun. Mass Spectrom., 16, 1545, 2002.)

[MH 16]þ ions for three N-oxide compounds that were studied. The authors stated that while this could be a problem when performing quantitative assays for dosed compounds that have N-oxide metabolites, it could also be useful for metabolite identification purposes when trying to distinguish between isobaric metabolites that could be either N-oxides or hydroxylated species. In a report by Jemal et al. [71], the authors list a series of putative metabolites that have the potential to interfere with an assay for the dosed drug. As shown in Table 1.1, this list shows drug types and potential metabolites that could be formed that could, through in-source fragmentation provide false signals in the parent selected reaction monitoring (SRM) chromatogram [71]. The authors then propose a strategy for pretesting important samples to avoid being misled by these potential problem metabolites if they are in the samples. For their example compound, they have a drug with a carboxylic acid moiety and they test to see if one or more acyl glucuronide metabolites are in the samples (for more on acyl glucuronide metabolites, see Chapters 6 and 8). Tiller and Romanyshyn [66] discuss the value of monitoring metabolites in discovery PK studies. These authors give a rat PK example in which six metabolites were monitored along with the dosed drug. They also discuss a Copyright © 2005 CRC Press, LLC


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Figure 1.14 The assay results from a dog PK study. The results are plotted as amount vs time after dosing. The graph shows the amounts for the dosed compound, C, as well as a monitored monohydroxy metabolite, C OH. It can be seen that the levels of the monohydroxy metabolite, C OH, were much higher that the levels of the dosed compound, C, for both dogs. Source: Tiller and Romanyshyn [66]. With permission.

dog PK study where they monitored a monohydroxy as well as a dihydroxy metabolite. In the dog study, as shown in Figure 1.14, the hydroxylated metabolite (C OH) was found to be at concentrations higher than the dosed drug throughout the PK profile [66]. The pharmacodynamic (PD) observations from this dog study correlated well with the hydroxlylated metabolite— therefore, it was very helpful to the project team to get this type of data early in the discovery stage. Kostiainen et al. [26] reviewed the use of LC–MS/MS for drug metabolism studies including metabolite identification and Cox et al. [72] and Clarke et al. [73] have described general procedures for metabolite characterization in a drug discovery setting. Recently, Ramanathan et al. [74], Nassar and Adams [75], and Jemal et al. [76] have all described strategies for rapid metabolite identification for in vitro samples. (For more details on metabolite identification, see Chapter 8.) Off-line sample preparation has received a great deal of attention in the literature. The most common procedures are liquid–liquid extraction [77–79], solid phase extraction [80–85], and protein precipitation [86–88]. Of these three, the most common procedure, in a drug discovery environment, is protein precipitation because it is easy to implement and easy to semi-automate [88]. Most semi-automation procedures are based on the use of 96-well plates. One of the first steps that needs to be done is to transfer an aliquot of the plasma into the proper well of the 96-well plate. Ideally, this step should be performed using a robot to make the transfer; Copyright © 2005 CRC Press, LLC



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one of the practical problems is that thrombin clots tend to form in the plasma, and this can be a problem for a robotic system [89, 90]. Sadagopan et al. [91] investigated the merits of using EDTA as the anticoagulant instead of the more commonly used heparin. They found that neither anticoagulant was a problem for the LC–MS/MS assay, but EDTA was superior in that it was better in the prevention of thrombin clots relative to heparin, therefore they recommended using EDTA as the anticoagulant when collecting samples to be assayed by LC–MS/MS. Berna et al. [90] also found that EDTA was better than heparin in reducing the formation of thrombin clots in the plasma samples. In addition, they studied a special polypropylene 96-well filter plate that could be used to store and filter plasma samples as another means of avoiding the problem of thrombin clots. Mallet et al. [92, 93] have described a low elution volume 96well solid phase extraction (SPE) plate that was designed for low volume plasma studies (50-mL samples). The plate was designed to work with a Quadra 96 (Tomtec, Hamden, CT) robotic liquid handler. The authors state that this new low-elution volume SPE plate should be useful for drug discovery PK studies. Eerkes et al. [77] discuss an automated liquid/liquid extraction (L/L) procedure based on a 96-well plate format. There has also been a lot of activity in terms of on-line extraction procedures (see, for example, Ackermann et al. [94], Wu [95], Kerns et al. [96] and Cass et al. [97]). A more complete discussion of this topic can be found in Chapter 5. As sample throughput increases, so does the number of compounds that can be studied each week. Another area of interest, therefore, is automated MS/MS method development. Higton [98] has shown an MS and MS/MS automated method building system that can create SRM methods for new test compounds at a rate of close to 30 per hour. Whalen et al. [99] described the Autoscan software that can be used to obtain MS as well as MS/MS conditions for assaying 96 compounds in 1 h. In a series of articles, Watt et al. [89] and Locker et al. [100] have described the utility and application of an automated sample preparation system designed for drug discovery PK samples. In the more recent of these two articles Locker et al. [100] describe an integrated robotic system that not only makes the standard curves, but also precipitates the samples and develops an optimized MS/MS procedure for each analyte. The issue of matrix ion suppression, often called matrix effects, has received increasing attention in the literature recently [38, 40, 101–106]. Miller-Stein et al. [39] discuss some of the issues regarding the matrix effect problem and provide a procedure for evaluating matrix effects in a given assay by using post-column infusion of the analyte of interest. Muller et al. [107] studied the effect of various sample preparation techniques in terms of the observed matrix effect in the described assay; they concluded that matrix effects could be avoided when using standard chromatographic systems, but could be a problem for high throughput applications. Avery [108] suggested trying more than one potential internal standard and looking at several lots of plasma when evaluating an analytical method. Both Schuhmacher et al. [104] and Shou and Naidong [109] discussed the potential problems of the dosing formulation Copyright © 2005 CRC Press, LLC



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vehicle in terms of potential matrix effect issues; in both articles, PEG 400 was cited as causing matrix effect problems. Mei et al. [103] described a study of the potential for sample tubes to cause matrix effect issues. While not commonly available in a drug discovery setting, it has generally been assumed that the use of a stable-isotope labeled (SIL) internal standard will eliminate any matrix effect problem; a recent article by Jemal et al. [110] showed an example of a matrix effect problem that was observed even with the use of an SIL internal standard. A complete discussion of matrix effects can be found in Chapter 4. Another area of interest is the development of new technology with new capabilities. One example of this advance is the development of a higher mass resolution triple quadrupole mass spectrometer. Jemal and Ouyang [111] evaluated an enhanced mass resolution triple quadrupole mass spectrometer in terms of utility, stability and reproducibility. They demonstrated the potential utility of this new technology and also suggested ways to utilize it properly to minimize problems. Yang et al. [112] studied the stability of an enhanced mass resolution triple quadrupole mass spectrometer and found it to be suitable for typical bioanalytical applications. Xu et al. [113] compared the results of a conventional triple quadrupole mass spectrometer with those of an enhanced mass resolution triple quadrupole mass spectrometer and found that in some cases, improved lower limits of quantitation could be obtained from the enhanced mass resolution triple quadrupole mass spectrometer. Additional discussion of enhanced mass resolution mass spectrometers can be found in Chapters 7 and 8. Other new technologies that may be advantageous and are therefore important to follow are: atmospheric pressure photoionization (APPI) as discussed by Hsieh et al. [62, 114], Raffaelli and Saba [115] and Yang and Henion [116] (see also Chapter 9); the quadrupole linear ion trap mass spectrometer (see Xia et al. [117] and Chapter 10); MS imaging for small molecules (see Chapter 11); and nanospray/chip technologies (see Dethy et al. [118], Kapron et al. [119], and Chapter 12).

1.3

Current Practices

As shown in Figure 1.15, drug discovery PK analyses include multiple steps, which need to be performed in sequence so that the PK results can be distributed to the drug discovery project team as well as entered into a database for future reference. Many talks and papers have discussed speeding up drug discovery PK assays; most of these articles have focussed on one step in the process—typically the LC–MS/MS assay step. It is important to consider the whole process from start to completion when trying to determine how best to decrease the time it takes to get PK results back to the drug discovery project team. Figure 1.16 shows the major steps in the discovery bioanalytical process as a sequential series with the point that any one step can be the bottleneck. Over the last several years, these steps have been streamlined so that what used to Copyright © 2005 CRC Press, LLC



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Figure 1.15 Discovery PK analysis flowchart showing the multiple steps that are involved from the dosing to the assay to the report preparation and electronic delivery to the discovery team.

Figure 1.16 Potential bottlenecks in PK assays based on LC–MS/MS.



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Figure 1.17 Semi-automated sample preparation procedure used in the CARRS assay. Adapted from Korfmacher et al. [87]. With permission.

take 4–5 weeks can now be performed in a few days. Sample tracking can be performed using either an ExcelÕ (Microsoft Corp.) -based tracking system or a laboratory LIMS system such as WATSONÕ (InnaPhase Corp., www. innaphase.com). Standard curve preparation can be readily performed using robotic sample handling systems (e.g., Packard MultiPROBEÕ ) that can not only make dilutions of the standard stock solution, but also add the required amount of these solutions to the plasma matrix to make the plasma standards that are required for the assay. As discussed above, many researchers have described ways to speed up the sample preparation process. One of the best ways is to use 96-well plates for all of the sample handling steps. One can then use semi-automated sample preparation via protein precipitation and a liquid handling robot (e.g., TOMTEC Quadra 96Õ ) to add the acetonitrile solution including the internal standard (see Figure 1.17). This procedure has greatly improved the efficiency of the process—a chemist can now prepare 96 samples in less than 20 min; previous manual procedures based on single vials for each sample were very laborious—it was common to need up to 4 h to prepare 96 samples when each sample was handled individually. Robotics can not only save time, but if properly set up and maintained, should be more reproducible than manual procedures. The LC–MS/MS assay itself has been the focus of many recent articles regarding speeding up the process (vide supra). By using high-speed HPLC systems, one can now routinely assay plasma samples using 2-min cycle times per sample. Cycle times are the amount of time from injection of one sample Copyright © 2005 CRC Press, LLC



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to the injection of the next sample. Typical setups utilize short (2–3 cm) narrow-bore (1–2 mm, i.d.) HPLC columns with flow rates up to 1 mL/min or more. Often a divert valve is built into the LC–MS/MS system and can be used to divert the first 20% of the total sample cycle time; this allows much of the ‘‘junk’’ to be diverted to waste, thereby keeping the mass spectrometer source cleaner than it would be without the divert valve in use. The most commonly used mass spectrometer for bioanalytical applications is the triple quadrupole instrument. By using the SRM mode, a skilled operator can set up very specific MS/MS methods for the analyte and internal standard. In the positive ionization mode, this would typically be based on selecting the [MH]þ ions using the first quadrupole (Q1); the [MH]þ ions are then focussed into the collision cell (q2) where they are fragmented using collisioninduced dissociation (CID) into various product ions; one of the product ions is selected using the third quadrupole (Q3) and only ions of that specific m/z are sent to the detector. The highly specific nature of the SRM use of the triple quadrupole mass spectrometer was first noted by Brotherton and Yost [120] in 1983. The basic analytical principle that Brotherton and Yost described in their landmark article [120] was that the multiple stages of selection in the MS/MS system reduced the noise faster than the signal, thereby creating a net improvement in the S/N ratio. More recently, Korfmacher et al. [86] described the basic principles for using LC–MS/MS for drug metabolism support of new drug discovery applications. These principles include the use of SRM, whereby multiple analytes including the internal standard, can be monitored in a single LC–MS/MS assay; these basic principles are still in use today. By spiking the analyte of interest into plasma from the same species as the samples to be assayed, one can compare the response ratio of the analyte to the internal standard (a separate compound that is added in the sample preparation process) to make the calibration curve and then use this to determine the concentration of the analyte in the plasma samples. The assay data calculations are typically performed using the mass spectrometer vendor’s software, but can also be performed using other software with linear (or other smooth curve functions, e.g., power curve or quadratic as needed) regression capabilities (e.g., WATSON or Excel). For assays over a range of three orders of magnitude or more, it is common to use weighting when performing the standard curve regression—typical weighting parameters are 1/x or 1/x2. Simple PK calculations (AUC, Cmax, Tmax) can be performed using Excel or similar software. For more complicated PK calculations (e.g., clearance, volume of distribution, mean residence time), WATSON or other PK calculation programs are required. WATSON has the advantage that it is able to export sample lists to major vendors’ mass spectrometer systems and import tabular results from such systems—this is an important capability in that it avoids having to type summary assay data into the computer used for the PK report calculations. Once the PK reports are completed, they can then be saved into a database or reformatted into Excel reports, which can be issued via e-mail to the discovery project team Copyright © 2005 CRC Press, LLC


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Figure 1.18 Stages in new drug discovery. A large number of compounds are screened out by each stage. The levels I–IV refer to the assay rules outlined in this chapter.

that is awaiting the data. Thus, the whole procedure outlined in Figures 1.16 and 1.17 can be expedited through careful evaluation of each step in the process, resulting in a higher throughput operation by utilizing a combination of robotics, state-of-the-art LC–MS/MS equipment and smart software tools. One way to view the drug discovery process is that it is a series of stages through which compounds must pass in order to qualify for being a development compound. These stages represent various in vitro and in vivo tests that are performed on a series of compounds in order to select those few compounds that have the correct properties for the desired activity. As shown in Figure 1.18, there are multiple stages that involve measuring various drug metabolism and pharmacokinetic (DMPK) parameters. In terms of in vivo tests that require bioanalytical assays, there have been no clear guidelines to follow until a compound enters the development stage where most of the bioanalytical assays are required to be performed under good laboratory practice (GLP) regulations [121, 122]. Before the development stage, one could envisage a series of assay requirements that become stricter as one approaches the development stage. As shown in Figure 1.18, various levels (I–IV) have been assigned to the stages leading up to and including the development stage. As shown in Table 1.2, these drug stages have been assigned assay types (level I to level IV). level I is the screening stage, level II is lead optimization, level III is lead qualification and level IV is development. Screening can be defined as the stage where a larger number of compounds are tested in order to select a smaller number for optimization. In the optimization phase, the lead compound Copyright © 2005 CRC Press, LLC



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Table 1.2 Assay levels for bioanalytical methods Drug stage Screening Lead optimization Lead qualification Development

Assay type Level Level Level Level

I II III IV

Summary of major rules

GLP

Use a two-point standard curve Use a multi-point standard curve but no quality control Use a multi-point standard curve plus quality control GLP rules

No No No Yes

structures are varied until an optimum structure is selected (see Chapter 2 for more on this topic). In lead qualification, the optimized structure undergoes lower throughput testing (e.g., single rising dose PK, multiple dose rat enzyme induction study, etc.). Compounds that show the acceptable DMPK properties after all of these assays have been completed are then considered as candidates for development. Table 1.2 also lists the major rules for each assay level in our laboratory. These rules were designed to become stricter as the compounds move from level I to level III. Level I assays are designed to be easy to implement in a higher throughput manner. Table 1.3 lists in detail the rules for Table 1.3 Rules for discovery (non-GLP) screen assays (level I) 1. Samples should be assayed using HPLC–MS/MS technology. 2. Sample preparation should consist of protein precipitation using an appropriate internal standard (IS). 3. Samples should be assayed along with a standard curve in duplicate (at the beginning and end of the sample set). 4. The zero standard is prepared and assayed, but is not included in the calibration curve regression. 5. Standard curve stock solutions are prepared after correcting the standard for the salt factor. 6. The standard curve should be three levels, typically ranging from 25 to 2500 (they can be lower or higher as needed for the program) ng/mL; each standard is 10 the one below (thus, a typical set would be 25, 250 and 2500 ng/mL). The matrix of the calibration curve should be from the same animal species and matrix type as the samples. 7. QC samples are not used and the assay is not validated. 8. After the assay, the proper standard curve range for the samples is selected; this must include only two concentrations in the range that covers the samples. A one order of magnitude range is preferred, but two orders of magnitude is acceptable, if needed to cover the samples. 9. Once the range is selected, at least three of the four assayed standards in the range must be included in the regression analysis. Regression is performed using unweighted linear regression (not forced through zero). 10. All standards included in the regression set must be back calculated to within 27.5% of their nominal values. 11. The limit of quantitation (LOQ) may be set as either the lowest standard in the selected range or as 0.4 times the lowest standard in the selected range, but the LOQ must be greater than three times the mean value for the back-calculated value of the two zero standards. 12. Samples below the LOQ are reported as zero. 13. If the LOQ is 0.4 times the lowest standard in the selected range, then samples with backcalculated values between the LOQ and the lowest standard in the selected range may be reported as their calculated value provided the S/N for the analyte is at least 3. 14. Samples with back-calculated values between 1.0 and 2.0 the highest standard in the selected range are reportable by extending the calibration line up to 2 the high standard. 15. Samples found to have analyte concentrations more than 2 the highest standard in the regression set are not reportable; these samples must be reassayed after dilution or along with a standard curve that has higher concentrations so that the sample is within 2 the highest standard.



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Figure 1.19 A schematic diagram showing how one 96-well plate can be used to hold all of the samples and standards needed to assay six compounds in the CARRS assay. Source: Korfmacher et al. [87]. With permission.

a level I assay. A good example for a level I assay is the cassette-accelerated rapid rat screen (CARRS) assay for higher throughput analyses of plasma sample from multiple rat PK screen studies [87]. Because this assay only requires a two-point calibration curve, as shown in Figure 1.19, it is possible to assemble all the standards and samples for six mini-PK studies onto one 96-well plate [87]. Due to the two-point linear calibration curve, it is also easy to perform the calculations using an Excel-based template. The template can also be used to summarize the PK data and make it available to the drug discovery team as an e-mail attachment. The reason that this simple assay is still accurate is that triple quadrupole mass spectrometers are generally linear over at least one to two orders of magnitude. In addition, the rules allow one to estimate above and below the upper and lower standards, respectively; thus, if the standards used are 25 and 250 ng/mL, the useful quantitation range is 10 to 500 ng/mL, as long as rules 11 and 13 are followed (see Table 1.3). Level II assays are required for lead optimization studies (e.g., rat, dog or monkey PK studies); in these studies, there are higher numbers of samples (30–60) for each compound and the goal is to obtain enough data to be able to calculate several PK parameters (e.g., clearance, half-life, AUC, volume of distribution). Therefore, these assays need to be more rigorous. As shown in Table 1.4, the rules for the level II assays are more extensive than for level I assays. The biggest change is the need for a multipoint standard curve (a minimum of five concentrations is required). Because the level II assay can be several orders of magnitude (typically three to four), both weighted and nonlinear regression is allowed. Typical weighting parameters are 1/x and 1/x2; these are needed to make the low end of the calibration curve fit correctly. A power curve fitting is a very useful nonlinear fit; it is based on the equation, Y ¼ mxc þ b, where Y is the area ratio (analyte/internal standard), m is the slope, x is the analyte Copyright © 2005 CRC Press, LLC



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Table 1.4 Rules for discovery (non-GLP) full PK assays (level II) 1. Samples should be assayed using HPLC–MS/MS technology. 2. Sample preparation should consist of protein precipitation using an appropriate internal standard (IS). 3. Samples should be assayed along with a standard curve in duplicate (at the beginning and end of the sample set). 4. The zero standard is prepared and assayed, but is not included in the calibration curve regression. 5. Standard curve stock solutions are prepared after correcting the standard for the salt factor. 6. The standard curve should be 10–15 levels, typically ranging from 1 to 5000 or 10,000 (or higher as needed) ng/mL. The matrix of the calibration curve should be from the same animal species and matrix type as the samples. 7. QC samples are not used. 8. After the assay, the proper standard curve range for the samples is selected; this must include at least five (consecutive) concentrations. 9. Once the range is selected, at least 75% of the assayed standards in the range must be included in the regression analysis. 10. Regression can be performed using weighted or unweighted linear or smooth curve fitting (e.g., power curve or quadratic), but is not forced through zero. 11. All standards included in the regression set must be back calculated to within 27.5% of their nominal values. 12. The regression r2 must be 0.94 or larger. 13. The limit of quantitation (LOQ) may be set as either the lowest standard in the selected range or as 0.4 times the lowest standard in the selected range, but the LOQ must be greater than three times the mean value for the back-calculated value of the two zero standards. 14. Samples below the LOQ are reported as zero. 15. If the LOQ is 0.4 times the lowest standard in the selected range, then samples with backcalculated values between the LOQ and the lowest standard in the selected range may be reported as their calculated value provided the S/N for the analyte is at least 3. 16. Samples with back-calculated values between 1.0 and 2.0 the highest standard in the selected range are reportable by extending the calibration curve up to 2 the high standard as long as the calibration curve regression was not performed using quadratic regression. 17. Samples found to have analyte concentrations more than 2 the highest standard in the regression set are not reportable; these samples must be reassayed after dilution or along with a standard curve that has higher concentrations so that the sample is within 2 the highest standard. 18. The assay is not validated. 19. The final data does not need to have quality assurance (QA) approval. This is an exploratory, non-GLP study.

concentration, b is the intercept and c is a curve fitted power value usually between 0.9 and 1.1. The need for a nonlinear curve fit is based in part on the fact that LC–MS/MS assays (especially those based on ESI) often have a nonlinear response over a range of three or more orders of magnitude. These rules for level II assays have been tested on thousands of compounds over several years and have been found to work well. Good assays meet the rules and poor ones do not. As shown in Table 1.5, for level III assays, the main change is the use of quality control (QC) samples. This additional level of analytical rigor was put in place for those assays that are used on the smaller number of studies that are performed on compounds that are close to development. The addition of QC samples provides additional confidence in the results that are obtained with these assays. Copyright © 2005 CRC Press, LLC



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Table 1.5 Additional rules for discovery (non-GLP) PK assays requiring QC samples (level III) 1. Use all the rules for full PK (level II) assays (except rule 7) plus the following rules. 2. Quality control (QC) standards are required, and a minimum of six QCs at three concentrations (low, middle, high) are to be used. The QC standards should be frozen at the same freezer temperature as the samples to be assayed. 3. The QC standards need to be traceable to a separate analyte weighing from the one used for the standard curve standards. 4. The standard curve standards should be prepared on the same day the samples are prepared for assay. The standard curve solutions needed for this purpose may be stored in a refrigerator until needed for up to 6 months. 5. At least 2/3 of the QC samples must be within 25% of their prepared (nominal) values. 6. If dilution of one or more samples is required for this assay, then an additional QC at the higher level must be prepared, diluted and assayed along with the sample(s) needing dilution. This QC should be run in duplicate and at least one of the two assay results must meet the 25% criteria.

1.4

Conclusions

The current practice for the use of LC–MS/MS systems for bioanalytical assays in a drug discovery environment is to make use of the special capabilities of triple quadrupole mass spectrometers in a high throughput manner to provide high quality assays without following all the requirements for having validated (as per GLP regulations) assays. It is important to view the assay as merely one step in the process that must take place when one is asked to provide high quality data in a high throughput manner to support new drug discovery needs. As both mass spectrometry and sample robotic instrumentation improve, there will continue to be opportunities for increasing the throughput of these discovery pharmacokinetic studies.

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77. Eerkes, A., Shou, W.Z., and Naidong, W., Liquid/liquid extraction using 96-well plate format in conjunction with hydrophilic interaction liquid chromatography– tandem mass spectrometry method for the analysis of fluconazole in human plasma, J. Pharm. Biomed. Anal., 31(5), 917, 2003. 78. Chen, Y.L. et al. Determination of ketoconazole in human plasma by highperformance liquid chromatography–tandem mass spectrometry, J. Chromatogr., B: Anal. Technol. Biomed. Life Sci., 774(1), 67, 2002. 79. Brignol, N. et al. High-throughput semi-automated 96-well liquid/liquid extraction and liquid chromatography/mass spectrometric analysis of everolimus (RAD 001) and cyclosporin a (CsA) in whole blood, Rapid Commun. Mass Spectrom., 15(12), 898, 2001. 80. Naidong, W. et al. Liquid chromatography/tandem mass spectrometric bioanalysis using normal-phase columns with aqueous/organic mobile phases—a novel approach of eliminating evaporation and reconstitution steps in 96-well SPE, Rapid Commun. Mass Spectrom., 16(20), 1965, 2002. 81. Chen, Y.L. et al. Simultaneous determination of hydrocodone and hydromorphone in human plasma by liquid chromatography with tandem mass spectrometric detection, J. Chromatogr., B: Anal. Technol. Biomed. Life Sci., 769(1), 55, 2002. 82. Shou, W.Z. et al. An automatic 96-well solid phase extraction and liquid chromatography–tandem mass spectrometry method for the analysis of morphine, morphine-3-glucuronide and morphine-6-glucuronide in human plasma, J. Pharm. Biomed. Anal., 27(1–2), 143, 2002. 83. Shou, W.Z. et al. A highly automated 96-well solid phase extraction and liquid chromatography/tandem mass spectrometry method for the determination of fentanyl in human plasma, Rapid Commun. Mass Spectrom., 15(7), 466, 2001. 84. Schuster, A. et al. Quantitative determination of the HIV protease inhibitor atazanavir (BMS-232632) in human plasma by liquid chromatography–tandem mass spectrometry following automated solid-phase extraction, J. Chromatogr., B: Anal. Technol. Biomed. Life Sci., 788(2), 377, 2003. 85. Yang, L. et al. Validation of a sensitive and automated 96-well solid-phase extraction liquid chromatography–tandem mass spectrometry method for the determination of desloratadine and 3-hydroxydesloratadine in human plasma, J. Chromatogr. B. Anal. Technol. Biomed. Life Sci., 792(2), 229, 2003. 86. Korfmacher, W.A. et al. HPLC-API/MS/MS: a powerful tool for integrating drug metabolism into the drug discovery process, Drug Discov. Today, 2, 532, 1997. 87. Korfmacher, W.A. et al. Cassette-accelerated rapid rat screen: a systematic procedure for the dosing and liquid chromatography/atmospheric pressure ionization tandem mass spectrometric analysis of new chemical entities as part of new drug discovery, Rapid Commun. Mass Spectrom., 15(5), 335, 2001. 88. Shou, W.Z. et al. Development and validation of a liquid chromatography/tandem mass spectrometry (LC/MS/MS) method for the determination of ribavirin in human plasma and serum, J. Pharm. Biomed. Anal., 29(1–2), 83, 2002. 89. Watt, A.P. et al. Higher throughput bioanalysis by automation of a protein precipitation assay using a 96-well format with detection by LC-MS/MS, Anal. Chem., 72(5), 979, 2000. 90. Berna, M. et al. Collection, storage, and filtration of in vivo study samples using 96well filter plates to facilitate automated sample preparation and LC/MS/MS analysis, Anal. Chem., 74(5), 1197, 2002.



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Chapter 2 Drug Metabolism In Vitro and In Vivo Results: How Do these Data Support Drug Discovery? Thomas N. Thompson

2.1 2.1.1

Introduction Scope

The application of drug metabolism and pharmacokinetic (DMPK) principles to drug design is hardly a new concept. Throughout the past three decades, numerous reviews have documented examples of how DMPK data have influenced drug design [1–7]. Several recent reviews have put this concept in the context of current drug discovery in the new era of combinatorial chemistry and high-throughput screening (HTS) [8–18]. The purpose of this chapter is two-fold: (1) to summarize some of key points relating drug structure to DMPK properties that have been made by these earlier reviews, and (2) to review selected examples of new technologies that will facilitate the evaluation of DMPK properties as part of the lead optimization process. The emphasis of this review is on experimental techniques, particularly those that utilize LC–MS as the mode of analysis. Therefore, although so-called in silico techniques are making strides towards becoming a very important tool in the effort to optimize DMPK properties, they will not be reviewed here. The reader is referred to two excellent recent reviews for more 0-8493-1963-3/05/$0.00+$1.50 ß 2005 by CRC Press


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information on in silico methods [19, 20]. Likewise, other kinds of metabolism studies which address the potential for drug interactions, including enzyme inhibition, enzyme induction and enzyme mapping studies (also known as reaction phenotyping), have recently been reviewed elsewhere [21, 22], so they will not be treated in any detail here. 2.1.2

Perspective on modern drug discovery

The process by which drugs are developed from discovery to regulatory approval is inherently inefficient. By one estimate, 90% of all drugs in clinical development fail to make it to the market place [23]. As shown in Figure 2.1, among the reasons for this are poor pharmacokinetics (40%), poor clinical efficacy (30%), toxicity (animals or humans, 20%) or other unspecified causes (10%). Given the inherent inefficiency of the development process, research programs have a mandate to continually improve the discovery process to ensure a higher quality in the prospective drugs that make it through to clinical development, thereby improving the ultimate rate of successful submission [24]. One solution is the ‘‘sheer numbers’’ approach whereby increasingly more compounds are driven through the process. While this approach presumably results in more drugs with suitable clinical efficacy surviving to NDA submission, it does little to improve the efficiency of this process. Moreover, it does nothing to address the failure rate accounted for by PK and toxicity factors. Thus, it has been recognized that the ability to improve the DMPK profiles of leads is a strategic necessity in order to help minimize the number failed leads [8]. Although it is understandable that many drugs fail because of toxicity or lack of efficacy, it is not immediately obvious why in Prentis’ study the single largest factor for failure in clinical development was due to poor

Figure 2.1

Common reasons for drugs to fail in clinical trials [8].



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pharmacokinetics. One possible explanation is that toxicity or lack of efficacy is easier to detect in preclinical development. Ironically, another explanation may lie in the previously mentioned improvements in the drug discovery process. As the endeavor to find new drugs progressed from empirical discovery to rational design, a disconnect began to develop between the intrinsic activity of a drug towards its biochemical target in vitro and biological activity in vivo. The best explanation for this is that, in empirical drug discovery, drugs are discovered after they are observed to be effective in an animal model of disease. Of necessity, this demanded at least some level of a useful pharmacokinetic profile. However, the desire for a more rational approach drove the demand for ever-increasing amounts of data in order to derive structure activity relationships. In turn, this led to an increasing reliance on in vitro methods to provide the amount of data with the appropriate cycle time to feed the iterative design process [11]. To further complicate the picture, chemists either did not yet appreciate the importance of pharmacokinetics for in vivo activity, or, if they did, were resigned that little could be done to influence pharmacokinetic properties. As a result, too many drug candidates were developed based solely on their ability to inhibit an enzyme or interact with a receptor with optimized in vitro affinity, only to fail in the clinic because of unfavorable pharmacokinetic parameters [25]. 2.1.3

A rational approach to early screening for DMPK properties

Drug discovery teams today have an impressive array of biological targets, biochemical techniques to refine and exploit those targets and synthetic, analytical and computational chemistry tools to design and prepare new molecules. However, it is only comparatively recently have we been able to automate pharmacokinetic screening to evaluate many potential drug candidates in parallel [8, 9, 11, 26, 27]. For the first time, significant tools are now available to help define DMPK properties either at the very point of drug design, or at least during lead optimization. The availability of these tools has led to the realization that it is now feasible to optimize the pharmacokinetic properties of drug candidates with rational application of DMPK principles. For example, an in vivo efficacy problem (lack of potency or short duration of action) can often be redefined as a pharmacokinetic problem (e.g., low oral bioavailability, short plasma half-life) in relevant in vitro or in vivo models. Of course, in order to use this information to solve the problem, one has to assign selection criteria such as threshold intrinsic clearances (CLint), inhibition constants (Ki or IC50) or permeability coefficients (Papp) for a given series of compounds. While this takes extra time and other resources, it is obviously a far preferable position than to have to fail molecules with poor DMPK properties later in development. As stated by Tarbit and Berman, it is better to ‘‘fail fast, but fail cheap’’ [28]. Copyright © 2005 CRC Press, LLC



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2.1.3.1

Strategic considerations for incorporating early DMPK data

Powerful new high-throughput techniques notwithstanding, without a careful, comprehensive strategy for their use, there is a danger that discovery scientists will be inundated with more information than they can use in a timely, rational manner. The same conclusion has been reached by other authors who have recently written about opportunities to bring DMPK data into the discovery process at an earlier point [11]. For example, Rodrigues called for a rational HTS strategy based on automation, validation and integration of in vitro absorption–metabolism (AM) models and database management (AVID) [26, 27]. Tarbit and Berman make a similar point when they refer to implementation of a strategy with potentially several iterations through a ‘‘virtuous cycle’’ of drug design, automated screens, data capture and data analysis [28]. This iteration allows optimization of drug design with respect to DMPK properties as well as biological activity. There is a trade-off when using pharmacokinetics to select drug candidates. The time spent optimizing PK properties may come at the expense of time spent optimizing affinity for the primary target. Chemists may need to accept hand-in-mitten fits between their synthetic ligands and their targets rather than hand-in-glove fits. As a result, we may learn less about special interactions between ligands and receptors that might lead to high-affinity ligand–receptor complexes, but the payoff will be in improved activity in vivo [25]. Eddershaw and Dickins [21] discussed at some length the question of whether the resources required to apply high-throughput techniques to optimizing PK properties is worth the effort. As they point out, there has been some debate over whether high-throughput DMPK screening is even a good idea. The charge is that such approaches ‘‘de-intellectualize’’ the process of candidate optimization and should therefore be resisted. However, these authors maintain that this viewpoint fails to appreciate the enormous opportunities provided by such systems for increasing our understanding of the fundamental physicochemical and enzymatic factors that govern drug metabolism. If we accept the challenge to study large, diverse compound sets using well-defined and controlled methods, this in turn will provide reliable data that can be used to develop computational models that describe various aspects of drug metabolism. In this way, the drug metabolism scientist can have a much greater ‘‘intellectual’’ influence on the drug design process than has hitherto been possible. 2.1.3.2

Selection of the right drug metabolism tools suitable for early DMPK studies

If the first major decision is one of strategy for using DMPK data, the second major decision involves selection of the proper tool(s) at the proper time [8, 11]. Because many of these techniques have been recently discussed elsewhere [16, 29, 30], few experimental details will be presented here. Table 2.1 serves as a reminder that a continuum of techniques is available ranging from theoretical Copyright © 2005 CRC Press, LLC

Human in vivo

Physiological relevance

Compound throughput

Time needed

Cost

Comment

Most

Lowest

Most

Most

Least

Highest

Least

Least

Need regulatory approval, toxicology, formulation and bulk drug Still considered best predictor, yet expensive and increasingly controversial Time-consuming, requires animal or human donor Generally considered reliable, in vitro–in vivo correlations are improving, immortal cell lines available Generally considered reliable, in vitro–in vivo correlations are improving, immortal cell lines available Requires, animal or human donor, but enables higher throughput Now readily available, necessary for today’s high throughput assays

Animal in vivo Isolated whole organ Cellular Subcellular Isolated enzyme/receptor Recombinant enzyme/receptor

39



Mode


Table 2.1 Comparison of the predictive value of various models for metabolic stability studies



40

calculations to in vivo studies in animals or humans. Among these techniques, one intuitively realizes a gradient in the degree of confidence and level of validation. As a consequence, it is reasonable to believe that the most directly applicable and most highly validated information comes from the animal or human studies. However, these are also the most expensive, time-consuming experiments with the least capacity for compound throughput. In marked contrast, theoretical calculations are ultimately the cheapest experiments with potentially the highest throughput and could be applied at the earliest point in the process, yet they are the least validated. Luckily, a single choice of which technique to use does not have to be made. A series of studies can be rationally chosen to provide an appropriate degree of information at every step of the way [8, 11]. 2.1.3.3

The importance of integration of early DMPK data with other HTS data

Although multiple tools/screens are available, the decision to employ a screen within a drug discovery project must come from a rational appraisal of the project requirements, rather than simply because that screen is capable of providing the needed throughput. Furthermore, the point must be made that any improvements in throughput are worthless unless they are supported by rigorous and continued validation of the overall screen performance [21]. The integration element of rational HTS is very critical and ties together a number of issues (Figure 2.2). It is not sufficient to conduct one kind of DMPK screen without integrating them with other DMPK screens and with HT

Figure 2.2

Integration of in vitro ADME data with other HT screens in the discovery process.




41

pharmacology screens. In other words, solving a metabolic stability problem may not necessarily lead to a compound with an overall improvement in activity or even PK properties if the compounds with improved metabolic stability have absorption problems [11]. 2.1.3.4

The evolving role of DMPK studies in drug design

By the end of the 1980s, it was becoming common practice to obtain PK/ metabolism data, if not in the design stage, then at least before the compound(s) advanced far into development. Initially, the PK/metabolism data collected was predominantly whole animal data. As shown in Table 2.1 there is a natural inclination to this approach. In general, while whole animal studies are considered more physiologically relevant, they are also more expensive and time consuming than in vitro studies. Gradually, as the correlations to in vivo data became evident, in vitro metabolism (and other DMPK) data have become more widely accepted. Because in vitro studies generally allow for higher throughput at less cost than in vivo studies, they have now become an important part of modern drug discovery [8, 11]. Today, drug discovery is a highly driven, fast moving and iterative process. Medicinal chemists are constantly refining structural features in search of the elusive ‘‘ideal’’ molecule. In order to have an impact, metabolism data must be generated and interpreted rapidly, often in a matter of days or, at most, weeks. Usually, several iterations of metabolism studies and molecular redesign are necessary. Furthermore, experience has shown that in the absence of timely, real, metabolism data, the chemists will resort to the use of empirical data, i.e., structure–metabolism rules, literature precedent, or even anecdotal information. These realities dictate that minimal experimental design, rapid throughput analysis, and expedient data calculation/management are imperative [11]. Ideally, at the earliest stages, the so-called lead identification or hit finding stage, the chemists need to know the metabolically vulnerable moieties within a molecule. This enables them to know what changes they can make to impart improved DMPK properties. Once chemists are armed with this information, they can embark on a lead optimization campaign. At this point, it quite helpful to get feedback on the effect that various structural changes have on metabolic stability even as the pharmacological activity is being optimized. The challenge for the metabolism groups that support drug discovery is to generate data that are rigorous enough to make reliable assessments of modifications the chemists should make. Yet, at the same time, acquisition of the data should not be so rigorous as to be untenable for a large number of compounds or impede multiple iterations of the design process [11].

2.2

Pharmacokinetic Principles used in Drug Discovery

Medicinal chemistry now has decades of extensive experience in understanding structure–activity relationships with the 500 or so favorite targets of enzymes, Copyright © 2005 CRC Press, LLC



42

receptors and other macromolecules. Any progress that will be made in optimizing DMPK properties in the drug design stage will rely on the generation of a comparable understanding of the relation of absorption, distribution, metabolism, and excretion (ADME) properties to chemical structure. By understanding the factors involved in the interaction with membranes and drug metabolizing enzymes or transporter proteins, medicinal chemists can capitalize on experience they already possess [8]. A second key point to make is that both the pharmacokinetic and pharmacodynamic properties are linked to the molecular properties of drugs. Predictably, each usually has its own unique structure–activity relationship. Experience tells us that modification of the structure to improve absorption, metabolic stability or distribution may, and often does, adversely impact intrinsic pharmacological activity and vice versa [2, 6]. Thus, the chemist must think in terms of the optimal intersection of multiple parameters to ultimately ensure activity in vivo. Some of the key factors and relationships between structure and DMPK properties have been assembled from existing reviews and selected examples from primary literature and are summarized below. The reader is directed to several of these excellent review articles and the references therein for more detail [1–3, 5, 6, 31–33]. As our understanding of drug disposition at the theoretical and experimental levels improves, a pattern begins to emerge that permits some degree of prediction of the two arguably most relevant pharmacokinetics properties to pharmacologic activity, i.e., bioavailability (F) and half-life (t1/2). As Figure 2.3 indicates, these two key properties are related to more basic PK properties of fraction absorbed ( fa), clearance (CLsys) and volume of distribution (Vd). These intermediate properties are, in turn, derived from basic drug properties that can be measured in vitro in a modern drug metabolism laboratory [8, 13]. 2.2.1

Oral bioavailability

Oral bioavailability (F ) is important because, along with intrinsic pharmacological activity, it determines the dose level required to achieve the desired

Figure 2.3 properties.

The relationship between early DMPK screening data and pharmacokinetic




43

effect. Oral bioavailability of drugs is defined as the fraction of the ingested dose that is available to the systemic circulation after both absorption and first pass clearance. Mammalian anatomy dictates that during and after absorption, the drug encounters the intestinal wall, liver and lung, all of which may metabolize or excrete the drug before it reaches systemic circulation. Thus, oral bioavailability can be estimated as F ¼ fa fG fH fL ,

ð2:1Þ

where fa is the fraction absorbed across the intestinal wall, and fG fH fL is the product of the fractions escaping clearance by the gastrointestinal tract, liver and lung. Generally speaking, intestinal and liver metabolism are the major determinants of first pass clearance and are usually the only tissues modeled in DMPK screens [10, 12]. Figure 2.4 depicts the anatomical arrangement of intestine and liver in first pass clearance and illustrates the processes of permeation, efflux and metabolism, all of which will be discussed later in this chapter. An alternative estimate of bioavailability may be obtained as the ratio of the systemic clearance (CLsys) to the apparent oral clearance (CLoral),

Figure 2.4

Anatomical barriers to drug bioavailability.




44

as follows [10]: F ¼ CLsys =CLoral :

2.2.2

ð2:2Þ

Half-life

The other key property, half-life (t1/2), is defined as the time needed to clear the blood compartment of 50% of the initial drug level. The half-life of any drug is related to its apparent volume of distribution (Vd) and its systemic clearance (CLsys) as: t12 ¼ 0:693ðVd =CLsys Þ:

ð2:3Þ

Thus, the half-life of any drug is a function of blood and tissue binding of the drug as well as its total clearance and is a derived parameter from CLsys and Vd [2, 12]. 2.2.3

Fraction absorbed

Fraction absorbed ( fa) is the fraction of dose that traverses from the luminal to the serosal side of the intestinal wall, taking into account both unchanged and metabolized drug. Fraction absorbed can be computed from PK determinations of clearance and oral bioavailability using the following relationship: fa ¼ F=ð1 CLH =QH Þ,

ð2:4Þ

where fa is the fraction absorbed, F is the oral bioavailability, CLH is the hepatic clearance, and QH is the hepatic blood flow in that species [13]. Methods to determine fraction absorbed can range from simple permeability studies in vitro to measurements across the gut wall in situ or, ultimately, to in vivo comparison of total radioactivity profiles after intravenous and oral administration. 2.2.4

Clearance

Clearance is defined as the volume of blood that must be cleared of drug in a unit of time in order to account for the rate of drug elimination. Thus, clearance is the ratio of elimination rate of the drug to the drug concentration in blood entering the organ. It is well known that total systemic clearance (CLsys) of a drug is estimated as the ratio of dose to area under the curve (AUC) following intravenous administration of the drug [12, 13]: CLsys ¼ doseðivÞ =AUCðivÞ :


ð2:5Þ



45

The total clearance is the sum of all individual organ clearances that occur in sequence: CLsys ¼ CLG þ CLH þ CLr ,

ð2:6Þ

where CLG is clearance by the gut wall, CLH is hepatic clearance and is the sum of liver metabolism and biliary excretion, and CLr is renal clearance. If there is significant clearance by lung tissue, then an extra CL factor must also be added to account for lung clearance. 2.2.5

Volume of distribution

The third intermediate PK property, volume of distribution (Vd), is a measure of the extent of drug distribution and is determined by the binding of the drug in plasma as well as tissues. Volume of distribution is the proportionality constant relating the drug concentration in blood or plasma to the amount of drug in the body and is affected by plasma protein binding: Vd ¼ Vp þ Vt ð fp =ft Þ,

ð2:7Þ

where Vd is the volume of distribution, Vp is the plasma volume, Vt is the extravascular tissue space volume, fp is the unbound fraction in plasma and ft is the unbound fraction in tissues [2, 7, 13].

2.3

Absorption

By far, the oral route is the primary route of administration for most drugs [34]. Consequently, absorption from the gastrointestinal tract (GIT) is an important determinant of drug action. In order to be absorbed, a drug must undergo transit through the GIT, dissolution from a tablet form, diffusion through an aqueous environment, and finally, permeation through the intestinal wall [6, 13, 18]. A drug can permeate through the intestinal wall either between the junction of intestinal cells (paracellular) or through the intestinal cells (transcellular). Transcellular permeability may occur by passive diffusion through intestinal cell membranes, in which case it is governed by the physiological environment of the GIT (intestinal motility and pH) and the physicochemical properties of the drug (molecular weight, polar surface area, lipophilicity and pKa). Alternately, diffusion may be due to active transport through the intestinal cells via one of several transporter proteins. In that case, permeability is governed by structure–activity relationships particular to the given transporter. Lipinski et al. have summarized several properties which appear to be common to compounds which are well absorbed [35]. According to the Lipinski rule of five, as these properties have come to be known, well absorbed Copyright © 2005 CRC Press, LLC



46

compounds typically have a molecular weight

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Using Mass Spectrometry for Drug Metabolism Studies

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