Chemogenomics: Knowledge-Based Approaches to Drug Discovery

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Author: Edgar Jacoby (Ed.)

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WSPC/Book-329: Chemogenomics: An Emerging Strategy for Rapid Target and Drug Discovery

Contents

A Personal Introduction

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Chapter 1

Small Molecules for Chemogenomics-based 1 Drug Discovery Edgar Jacoby, Ansgar Schuffenhauer, Kamal Azzaoui, Maxim Popov, Sigmar Dressler, Meir Glick, Jeremy Jenkins, John Davies and Silvio Roggo

Chapter 2

Mapping the Chemogenomic Space Jordi Mestres

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Chapter 3

Natural Product Scaffolds and Protein Structure Similarity Clustering (PSSC) as Inspiration Sources for Compound Library Design in Chemogenomics and Drug Development Frank J. Dekker, Stefan Wetzel and Herbert Waldmann

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Chapter 4

A Reductionist Approach to Chemogenomics in the Design of Drug Molecules and Focused Libraries Roger Crossley and Martin Slater

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Chapter 5

In silico Screening of the Protein Structure Repertoire and of Protein Families Didier Rognan

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Chapter 6

New Methods for Similarity-based Virtual Screening Jérôme Hert, Peter Willett and David J. Wilton

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Chapter 7

Structural Informatics: Chemogenomics In silico Derek A. Debe, Kevin P. Hambly and Joseph F. Danzer

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Contents

Chapter 8

Index

Construction of a Homogeneous and Informative In vitro Profiling Database for Anticipating the Clinical Effects of Drugs Nicolas Froloff, Valérie Hamon, Philippe Dupuis, Annie Otto-Bruc, Boryeu Mao, Sandra Merrick and Jacques Migeon

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Following the sequencing of the human genome, a recognized key scientific challenge for the 21st century consists in the systematic identification of small molecules which interact in a specific manner with the products of the genome and modulate their biological function. Progress along side this challenge will strongly contribute to the further fundamental understanding of the biological function of the individual gene products and ultimately provide a basis for the discovery of new and better therapies for human diseases. Chemogenomics addresses this scientific challenge and integrates advanced disciplines like chemistry, genetics, chemo- and bioinformatics, structural biology, and biological screening in phenotypic and target-based assays. Complementary to previous publications on chemogenomics focusing on the individual component disciplines, this review book provides a general knowledge-centric overview of the different chemical, biological and informatics components. This new book is unique in that it provides an integrated review of the recent works of leaders in the various different disciplines and sheds light on strategies how these disciplines interact efficiently for the rapid discovery of new targets and their effector molecules simultaneously, leading toward the study of the biological pathways and circuits wherein these targets are involved. On purpose, all contributing chapter authors focus on knowledge-based approaches and show how previously generated knowledge on molecular recognition modes can efficiently be applied in systematic manners for new molecular discoveries. In the perspective of drug discovery it should be well acknowledged upfront that the primary role of chemogenomics resides in my opinion to provide starting points for future drug optimization projects which continue to rely on the classical medicinal chemistry and in vivo pharmacology-based design and selection principles. Examples of chemogenomics approaches pursued in the academia, as well as start-up biotech and pharmaceutical setups are herein provided. vii

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Chapter 1 contributed from the Novartis Molecular and Library Informatics group focuses on small molecules for chemogenomics based drug discovery and summarizes the main compound categories and selection methods of relevance for the compilation of a comprehensive discovery screening collection. Prof. Jordi Mestres from the Chemogenomics Laboratory at the University Pompeu Fabra in Barcelona summarizes in Chapter 2 mapping methodologies of the chemogenomics space and provides herewith an essential prerequisite for extracting knowledge from biochemical data. In Chapter 3, the group of Prof. Herbert Waldmann at the Max Plank Institute for Molecular Physiology provides a rationale on how natural products which play historically a predominant role in drug discovery are efficiently used in combination with protein structure similarity clustering to inspire directed compound library design and target identification. Dr. Roger Crossley and Dr. Martin Slater from the for library design pioneering BioTech company Galapagos-Biofocus, Inc. outline in Chapter 4 a reductionist approach to the design of drug molecules and focused libraries centering on the ion channel and GPCR target families. In Chapter 5, Prof. Didier Rognan from the University Louis Pasteur of Strasbourg elaborates on the basis of the GPCR target family, a concept for the in silico screening of the protein structure repertoire and of protein families in general. The group of Prof. Peter Willett from the University of Sheffield summarizes in Chapter 6 new chemoinformatics methods for similarity-based virtual screening which based on known active compounds are useful for the identification of new ligands for targets related by conserved molecular recognition. In Chapter 7, Dr. Derek D. Debe from the chemogenomics knowledgebased company Eidogen-Sertanty, Inc. demonstrates the role of 3D structural informatics for in silico chemogenomics enabling by systematic comparison of ligand binding sites the identification of new biological targets and potential side activities and directed compound design strategies. Finally in Chapter 8, Dr. Nicholas Froloff and his colleagues from Cerep SA discuss how profiling data and their integration in a homogenous and informative in vitro profiling database are becoming important for lead prioritization and design of safety pharmacology studies anticipating the clinical effects of drugs and enabling opportunistic drug discovery approaches. All chapter authors are very much acknowledged for their excellent scientific contributions and their willingness to share their insights and strategic view

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points on chemogenomics which make this book especially interesting to read. I also thank Mrs. Sook Cheng Lim and the staff of World Scientific Publishing, Co. for the invitation to edit this review book and for their commitment for completely dealing with all aspects of the production work. I’m delighted with this book and hope that you, the reader, will find it both informative and enjoyable. Edgar Jacoby Basel, January 2006

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1

Small Molecules for Chemogenomicsbased Drug Discovery Edgar Jacoby,*,a Ansgar Schuffenhauer,* Kamal Azzaoui,* Maxim Popov,* Sigmar Dressler,* Meir Glick,† Jeremy Jenkins,† John Davies† and Silvio Roggo*

1. Introduction The compound collections used within chemogenomics target familyoriented screening and chemical biology screening of whole cell systems to detect particular phenotypes include a diversity of sources.1 Typically the major pharmaceutical companies have large sets of handcrafted medicinal chemistry compounds that were generated in large quantities as crystalline samples with good water solubility in lead-optimization projects and which include design input to address the ca. 500 molecular targets investigated to date in drug discovery.2 Since the beginning of the 1990s, these sources have been enriched by compound acquisition projects where the worldwide academic organic and medicinal chemistry pools (in particular in the former Soviet Union) have become an invaluable compound source; this resulted in a successful business activity for suppliers of screening compounds. Since the mid 1990s, when combinatorial chemistry became a dominant technology driven approach, compounds from combinatorial and parallel synthesis projects have been included. The first libraries were purely chemistry and number driven. Initial reports claimed to include easily 1 000 000 or more compounds based on a few chemotypes, usually peptide-based. This approach, however, did not deliver on its promise and resulted in typically higher molecular weight and lipophilicity compounds.

1

∗ Novartis Institutes for BioMedical Research, Novartis Pharma AG, Lichtstrasse 35, Basel, CH-4056, Switzerland. † Novartis Institutes for BioMedical Research, Inc. 250 Massachusetts Avenue, Cambridge, MA 02139, USA. a Corresponding author. E-mail: [email protected]. Tel.: +41 61 32 46186; Fax: +41 61 32 46261.

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Subsequently, a quantity-to-quality paradigm shift took place.3−7 Today most combinatorial and parallel synthesis approaches are used to generate three types of libraries, viz., 1) Diverse/random libraries based on diverse scaffolds of lead and drug-like molecules for HTS (high-throughput screening); 2) Targeted libraries aiming at specific target families, or molecular interaction modes for HTS or MTS (medium throughput screening); and 3) Focused libraries for hit-to-lead and lead optimization projects where for a given chemical lead candidate, subtle modifications in the substituents are probed to optimize the pharmacodynamic and pharmacokinetic properties.1,8 The advent of combinatorial chemistry immediately triggered molecular design approaches such as chemoinformatics and computational library design in order to cope with the large number of compounds and to extract SAR (structure-activity relationship) information based on HTS data. The approaches are able to efficiently address the concepts of molecular diversity and, more recently, molecular complexity.9−13 This chapter summarizes the typical compound categories used in chemogenomics and chemical biology research together with current knowledge-based design and selection criteria aimed at systematically discovering small molecule ligands for interaction at binding sites on the target proteins of the genome.14−21 Emphasis is given to the systematic principles which allow many different types of targets of interest to be addressed in an efficient manner. The role of molecular information systems in integrating the chemical and biological knowledge spaces will be emphasized.

2. Compound Categories 2.1. Natural products and derivatives For obvious reasons, natural principles play a predominant role in the history of drug discovery.22 Diverse classes of natural products, including carbohydrates, steroids, fatty acids, polyketides, peptides, terpenoids, flavonoids, alkaloids, and many other products were isolated initially from herbs and later from various micro- and higher organisms for structure and activity characterization.23,24 All compounds produced by living organisms are generally defined as natural products. In contrast to primary metabolites, which are responsible for homeostasis and energy balance of living organisms, secondary metabolites are not required per se by their producers for these basic life functions, but they confer evolutionary advantages to

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ensure their survival and dominance over other species. Natural products are a major source of innovative tool compounds for the elucidation of signaling pathways and new medicines for most indications, such as lipid disorders, cancer, infectious diseases, and immunomodulation; the latter three applications are in many cases explored in parallel and are targeting conserved anti-cellular proliferation pathways with distinct cell type specificities. Between 1981 and 2002, 5% of the around 870 new chemical entities approved by the FDA (US Food and Drug Administration) were natural products, and another 23% were molecules derived from natural products.25 Many natural products inspired the development of important drugs classes, as for instance illustrated by the low-molecular weight biogenic amines. These molecules derived from enzymatic decarboxylation and subsequent oxidation of aromatic proteinogenic amino acids provided the guiding principles for the development of around 50% of all GPCR drugs. Immunosuppressive natural products, like Cyclosporin A, FK-506 (Tacrolimus), Rapamycin (Sirolimus) and its innovative derivative RAD001 (Everolimus), 15-Desoxy-spergualin, Mizoribine, or mycophenolic acid (see Fig. 1) revolutionized transplantation medicine.26 A key challenge for natural product drug discovery is the elucidation of the targets and the molecular mode of action of phenotypically and physiologically active molecules. As the description of biodiversity is by no means complete, the chemical and structural knowledge of natural products space is only emerging and many exciting discoveries from new sources can be expected. The majority of marine organisms probably have not yet been described, and most of those already described have not been fully examined chemically. For flowering plants, about 250 000 species have been described, of which perhaps 10% were analyzed for their chemical content. The number of insect species described is about 1 000 000, and many more have never been described. Only a very small percentage of soil bacteria have ever been cultured, to get DNA directly out of the soil and put it into host organisms.27 Natural products offer a wealth of new structures far beyond the classical repertoire of synthetic compounds. The current most comprehensive summary on the chemical and biological information of around 200 000 isolated natural products is provided in a couple of literature databases, viz., the Chapman & Hall DNP (Dictionary of Natural Products), the AntiBase database, and the CNPD (China Natural Products Database) — for detail see Table 1.

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E. Jacoby et al. OH HO HO

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15-Desoxy-spergualin Figure 1. Natural products which were breakthrough discoveries for transplantation medicine.

A number of studies have investigated the structural characteristics of natural products as compared to synthetic organic compounds. Natural products often contain a greater proportion of oxygen than nitrogen heteroatoms. Typically the natural products have a higher number of stereocenters; a higher density of functionalization and pharmacophore sites; a higher number of rings; and more skeletal diversity. Natural products exemplify macro- and polycyclic scaffolds beyond the imagination of the classical synthetic medicinal chemist.28−30 Conversely, examples also exist of very simple natural product structures with biological activity. The structural repertoire can be extended by genomics approaches to natural products. For instance, genetic pathway engineering of the epothilone biosynthesis gene cluster of S. cellulosum allowed access to new epothilones: The

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Small Molecules for Chemogenomics-based Drug Discovery Table 1 Commercially and Publicly Available Compound Databases and Molecular Informatics Resources (see Section 5) for Chemogenomics Research. The list is not exhaustive, but rather constitutes a representative compilation of selected examples in this field.

Internet Resource URL/Source

Specification of Data and Information Available

http://www.chemnavigator.com/

The iResearch Library is ChemNavigator’s compilation of commercially accessible screening compounds. The database tracks over 21.7 million samples from around 150 vendors based on 14 million unique structures, including both physically available and virtual compounds.

http://www.mdli.com/products/ experiment/screening_compounds/ index.jsp

MDL Screening Compounds Directory (formerly ACD-SC) contains over 3.5 million structures (including 3D models), comprising nearly 6 million products from 46 chemical suppliers of compounds for HTS.

http://www.warr.com/ links.html#chemlib

List of screening compound vendors updated by Wendy Warr & Associates.

http://www.chemnetbase.com/

The DNP (Chapman & Hall/CRC Dictionary of Natural Products) is a comprehensive literature database of around 170 000 isolated natural products from various sources and provides names, chemical structures, CAS registry numbers, extensive source data, uses and applications.

http://www.neotrident.com/ neotrident_def4_2.htm

CNPD (China Natural Products Database) provides for around 10 000 natural products isolated in China, 2D and 3D chemical structures, CAS registry numbers, integrated with related therapeutic uses in TMC (Traditional Chinese Medicine).

http://www.wiley.com/

AntiBase 2005 is a comprehensive database of 31 022 natural compounds from micro-organisms and higher fungi based on curated literature reports. In addition to descriptive chemical data, biological data (e.g. pharmacological activity, toxicity) and information on origin and isolation are included.

http://www.genome.ad.jp/kegg/

The KEGG (Kyoto Encyclopedia of Genes and Genomes) LIGAND database provides chemical structures for around 12 000 chemical compounds and drugs with biological information; around 2000 compounds are annotated to enzymatic pathways.

(Continued )

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http://www.brenda.uni-koeln.de/

The BRENDA database provides a comprehensive information system for enzymes. Beyond chemical information on substrates, co-substrates and co-factors, cross-links to structural, biological and disease information are annotated.

http://www.ebi.ac.uk/chebi/

ChEBI (Chemical Entities of Biological Interest) is a freely available dictionary of chemical compounds, with IUPAC and NC-IUBMB endorsed terminology. Currently three data sources have been incorporated into ChEBI, namely, KEGG Ligand, IntEnz, and Chemical Ontology.

http://chembank.med.harvard.edu/ http://pubchem.ncbi.nlm.nih.gov/

ChemBank at Harvard University and Pubchem at the NCBI are chemoinformatics databases for small molecules and their biological activities. Both systems are supported by the NCI’s initiative for chemical genetics.

http://www.sunsetmolecular.com/

The WOMBAT database contains 117 007 entries (104 230 unique SMILES), totaling over 230 000 biological activities on 1021 unique targets based on literature data.

http://www.mdl.com/products/ knowledge/medicinal_chem/ index.jsp

The MDL CMC (Comprehensive Medicinal Chemistry) database provides 3D models and important biochemical properties, including drug class, LogP, and pKa values for over 8400 pharmaceutical compounds. The MDDR (MDL Drug Data Report) is a database covering the patent literature, journals, meetings and congresses. The database contains over 132 000 biologically relevant compounds and well-defined derivatives.

http://scientific.thomson.com/ products/wdi/

The WDI (World Drug Index) contains chemical, biomedical and synonym data for over 58 000 marketed and development drugs worldwide.

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http://www.aureus-pharma.com/ http://www.eidogen-sertanty.com/ http://www.evolvus.com/ http://www.gvkbio.com/ http://www.inpharmatica.com/ http://www.jubilantbiosys.com/

A growing number of chemogenomics knowledge-based companies, like Aureus-Pharma, Eidogen-Sertanty, Evolvus, GVKBio, Inpharmatica and Jubilant Biosys are developing molecular information systems which integrate in a comprehensive manner for specific target families data from patents and selected literature together with chemical and biological search engines.

http://www.geneontology.org/

The GO (Gene Ontology) project provides a controlled vocabulary to describe gene and gene product attributes in any organism. Annotated are the biological process, the molecular function, and the cellular component of gene products.

http://www.genego.com/

MetaBase is a curated database of human protein-protein and protein-DNA interactions, transcriptional factors, signaling, metabolism and bioactive molecules. MetaCore provides intuitive tools for data visualization, mapping and exchange, multiple networking algorithms and data mining.

gene was cloned and sequenced and after destruction of the EpoF gene, epothilone C and D producing strains were generated.31 Natural products were excluded from Lipinski’s Rule-of-5 observation, which was established based on the analysis that synthetic drugs display characteristic distributions and limits for molecular weight, lipophilicity, and polarity as essential properties that enable biological membrane permeability and water solubility.32,33 Despite the fact that the distribution profiles of natural products are indeed broader compared to synthetic compounds, their fraction with two or more Rule-of-5 violations is equal to that of synthetic drugs. One interpretation of this finding might be that evolutionary optimization has coded, in addition to these essential properties, other biocharacteristics which still need to be deciphered. A number of marketed natural products based drugs are not orally available, but uniquely address a number of therapeutic applications. Compared to synthetic drug candidates, natural products — although they were not generated by nature in the perspective of therapeutic medicines — have an a priori biological advantage in that they have

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been optimized in close co-evolution with interacting cellular structures and protein binding sites that determine their mode of action. Given the formidable progress organic synthetic chemistry made during the last fifty years, especially regarding asymmetric synthesis and catalysis, almost any of the natural products can by now be made by organic chemists either through total synthesis, or using semi-synthesis approaches at an industrial scale.24 Despite this, one key dilemma for natural products drug discovery is that although the primary HTS hit rates in the micromolar affinity range are 5–10 times higher than the hit rates for synthetic compounds, the take-up rate of the compounds by chemists for follow-up lead optimization is significantly lower. This finding is most probably due to the higher structural complexity and challenges related to the chemical structure elucidation and synthesis. A promising trend to broaden the scope of natural products is given by making small combinatorial libraries from natural products and natural product like scaffolds.34,35 A systematic extension of such libraries based on protein structure similarity clustering (PSSC) was proposed by the Waldmann group and is described in further detail in Chapter 2.36,37 The principles of this approach consider the domain organization and conservation of proteins and the corresponding needs for conservatisms of the architectures and interaction modes of their ligands. In view of these practically unlimited opportunities, natural products deserve a dedicated place in the drug discovery process. This position was challenged in the last decade by the progress of synthetic compounds which at first glance have the apparent advantage of being HTS friendly, and rapid and cheap for hit-to-lead identification and chemical development. However, given the not fully met expectations of the high throughput technology driven paradigms for many discovery programs and drug targets, including especially protein-protein interactions, natural products are expected to continue to be a significant source of drugs. Virtual screening approaches focusing on natural products were recently recognized as an additional route to explore these precious and costly compounds.38 2.2. Primary metabolites, co-substrates, co-factors, and marketed drugs Primary metabolites, co-factors, and marketed drugs form additional sets of biologically relevant and validated compounds that constitute an essential component of a comprehensive screening collection.

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Primary metabolites which are key intermediates of cellular metabolisms and which interact with key enzymes and cellular regulatory receptor systems, are systematically included in deorphanization libraries of orphan targets. Databases like KEGG, BRENDA, or CheBI (see Table 1) have organized the relevant chemical and biological information. Hits from such libraries allow the elucidation of the functional relevance of a new potential target protein. The recent discovery via HTS that key intermediates of the Krebs cycle like succinate and α-ketogluterate activate extracellular GPCR systems with key regulatory roles in energy homeostasis provides a relevant example.39 More classically, the structural elucidation of enzyme substrate, or enzyme co-substrate/co-factor complexes provide an invaluable insight into the understanding of the essential molecular interactions of a given binding site, as successfully demonstrated by the design of many enzyme inhibiting drugs like sialidase inhibitors, protease inhibitors, and others.40 Combinatorial libraries around these principles provide molecular tool boxes for the systematic exploration of the roles of the individual members of a target family conserving a given interaction mode, such as the ATP cosubstrate binding site of protein kinases.41−43 A pioneering example here is the discovery of selective protein kinase inhibitors developed on the basis of trisubstituted purines to target the ATP-binding site of the human CDK2 (cyclin-dependent kinase 2).41 By iterating chemical library synthesis and biological screening, potent inhibitors of the human CDK2-cyclin A kinase complex and of the Saccharomyces cerevisiae Cdc28p kinase were identified. Given the large number of purine-dependent cellular processes, purine libraries may serve as a rich source of inhibitors for many different protein targets. The detailed comparative structural analysis of co-substrate and co-factor binding sites show that co-substrate and co-factor analogues open a very wide target window. For instance, the common structural framework for adenine and AMP (adenosine mono-phosphate) binding is conserved in 12 unrelated protein families, including different folds, which demonstrates that ligand recognition principles have a stronger conservation than protein fold conservation, thereby providing the basis for efficient systems-based inhibitor design strategies.44 The work of Sem et al. on the oxidoreductase gene family provides a first detailed analysis which divides the global family into structural subfamilies termed pharmacofamilies, which share pharmacophore features in their cofactor binding sites.45 The presence of the conserved NAD(P) (nicotinamide adenine dinucleotide-(phosphate)) cofactor binding site (approximately 15% of all known enzyme functions

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utilize NAD(P) for catalytic function) coupled with the modular nature of this gene family, has led to the development of a highly parallel approach to inhibitor design. In this chemical proteomic strategy, focused chemical libraries are tailored to subfamilies of large gene families to produce high affinity inhibitors for multiple members of the subfamily (Fig. 2). Last but not least, marketed drugs and derivative libraries are an important and invaluable compound source and provide the basis for the SOSA (Selective Optimization of Side Activities) approach.46,47 The SOSA approach consists of testing old drugs on new pharmacological targets. The aim is to subject to pharmacological screening, a limited number of drug molecules that are structurally and therapeutically very diverse and that

Figure 2. Pharmacofamilies of the NADH cofactor (structure shown in panel A) binding to oxidoreductases. Panel B shows an overlay of a subset of NAD(P)(H) geometries obtained from 288 crystal structures of oxidoreductases. The two largest pharmacofamilies are shown, corresponding to the two-domain Rossmann fold enzymes in pharmacofamilies 1 (anti) and 2 (syn). Panel C shows the corresponding pharmacophores with all protein heteroatoms indicated that are within hydrogen bonding distance of atoms in the cofactor. (Figure adapted with permission from original work of Sem et al.45).

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have known safety and bioavailability in humans, thereby potentially shortening the time and cost needed for hit optimization. The SOSA approach proceeds in two steps. First, a limited set of a few thousand carefully chosen, structurally diverse drug molecules are screened. Since bioavailability and toxicity studies have already been performed for those drugs and since they have proven their usefulness in human therapy, all hits are per definition drug-like. In the second stage, the hits are optimized by means of traditional, parallel, or combinatorial chemistry in order to increase the affinity for the new target and decrease the affinity for the other targets. The objective is to prepare analogues of the hit molecule in order to transform the observed “side activity” into the main effect and to strongly reduce or abolish the initial pharmacological activity. Successful examples of the application of the SOSA principle include, for instance, optimization projects started from the antidepressant Minaprine. Minaprine has low affinity for the muscarinic M1 GPCR (Ki = 17 µM); the optimization of this side effect yielded high affinity nanomolar M1 partial agonists. Minaprine is also a very weak acetylcholinesterase (AChE) inhibitor (Ki = 600 µM); separate optimization of this side activity resulted in nanomolar AChE inhibitors.46 The SOSA approach can be enhanced by virtual screening methods which use reference compound sets and molecular descriptors together with advanced chemoinformatics methods to compare and rank the similarity of considered candidate molecules. 2.3. Peptides and peptido-mimetics Peptide-protein molecular interactions constitute the most ubiquitous mode for controlling and modulating cellular function, intercellular communication, and signal transduction pathways. Hormones, neurotransmitters, antigens, cytokines and growth factors represent key classes of such peptide ligands.48 Physiologic peptides, such as insulin, oxytocin and calcitonin are used directly as drugs. In many cases, antagonists of the native ligands are searched for and here the endogenous peptide is per se not suitable. Peptides are key components of chemogenomics discovery libraries and are especially useful for the characterization of orphan targets. A number of successful deorphanizations, especially in the GPCR field, are based on peptides, resulting in new drug discovery projects. New peptides for such libraries are discovered using HPLC fractionations of tissue extracts together with random or designed peptide libraries based

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on the bioinformatics analysis of putatively secreted peptides and protein hormones defined in the genome.49 Peptide synthesis was revolutionized after the major breakthrough in peptide chemistry in 1963 when Merrifield published a historic paper describing the principles and the applications of solid-phase peptide synthesis (SPPS).50 Limiting factors of peptide-based drugs are directed by the number of amide bonds which determine properties like a high PSA (polar surface area); a low membrane permeability; and a potentially high proteolytic degradation; resulting in quite poor ADME (absorption, distribution, metabolism, and excretion) properties. Mainly because of these reasons, robust strategies for the design of peptide mimetics have been successfully developed and peptide-based drug discovery approaches offer a truly systematic route for chemogenomics with peptide secondary structure mimetics.51−53 The use of β-amino acid building blocks was recently discovered by Seebach to overcome limitations of α-peptidic structures such as low bioavailability and easy proteolysis and to leverage the tendency of small β-peptides to form stable secondary structures.54 Robust peptide-derived approaches aim to identify a small drug-like molecule to mimic the peptide interactions. The primary peptide molecule is considered in these approaches as a tool compound to demonstrate that small molecules can compete with a given interaction. A variety of chemical, 3D structural and molecular modeling approaches are used to validate the essential 3D pharmacophore model which in turn is the basis for the design of the mimics. The chemical approaches include in addition to N- and C-terminal truncations a variety of positional scanning methods. Using alanine scans one can identify the key pharmacophore points; D-aminoacid or proline scans allow stabilization of β-turn structures; cyclic scans bias the peptide or portions of the peptide in a particular conformation (α-helix, β-turn and so on); other scans, like N-methyl-amino-acid scans and amide-bond-replacement (depsi-peptides) scans aim to improve the ADME properties.48 Peptide and protein mimetics libraries including β-turn/α-helix mimetics are recognized to be of central importance in chemogenomics.52,53 A number of important hormones, like angiotensin, bradykinin, CCK (cholecystokinin), MSF (melanocyte stimulating factor) and SST (somatostatin) make their key recognition via specific β-turn motifs. Others, like CRF (corticotrophin releasing factor), PTH/PTHrP (parathyroid hormone/parathyroid hormone related protein), NPY (neuropeptide Y), VIP (vasoactive intestinal peptide), or GHRF (growth hormone releasing factor)

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interact via α-helix motifs.52 Whereas the design of organic drug-like αhelix mimetics is still in its infancy, the design of drug-like active β-turn mimetics based on organic drug-like scaffolds, or based on cyclic α-peptides or β/γ-peptides has advanced to a quite routine methodology.55 The work of Garland and Dean,56,57 defining a set of triangular distance constraints that the substitution points of a scaffold have to satisfy in order to mimic the specific Cα atoms of the peptide template, provided a generalized frame for the design of novel β-turn mimetic scaffolds and was in combination with database searches successfully applied for the design of CCK and SST antagonists.58 Targeted combinatorial libraries around such scaffolds are an essential component of a chemogenomics discovery library. Recent examples of successful peptide-ligand based discoveries of drug-like peptidomimetics include the discovery of SST antagonists,59 or the discovery of non-peptidic antagonists of the recently deorphanized urotensin II receptor at Sanofi-Aventis.60 As illustrated in Fig. 3, Flohr et al. used 3D models of the NMR solution structure of cyclic peptide derivatives of Urotensin II as a template for virtual 3D pharmacophore searches which resulted into non-peptidic candidates for lead optimization.

Figure 3. Discovery of Urotensin II GPCR antagonist by peptide mimetic approaches. 3D models of the NMR solution structure of cyclic peptide derivatives were used as templates for virtual 3D pharmacophore searches and resulted into non-peptidic hits.60 (Figure reproduced with permission from review of Klabunde and Hessler.61)

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2.4. Diversity oriented synthesis molecules DOS (Diversity Oriented Synthesis), as opposed to the traditional TOS (Target Oriented Synthesis) chemistry approach, was introduced by Schreiber for forward chemical genetic screening in order to mimic the structural complexity and the skeletal and stereochemical diversity of natural products.62,63 In contrast to a convergent synthesis strategy resulting from the logic of retrosynthetic analysis of the target molecules, DOS, in the ideal state, allows the application of a diverse set of reagents and structural transformations on each synthesis intermediate; this results in diverging synthesis pathways that create a broad diversity of target molecules with different scaffolds. The situation mimics in sort the early history of structural chemistry of natural products where, for instance, the exploration of terpenoids under different conditions, including pH, resulted into chemical rearrangements and the generation of new structures from the same starting materials. DOS compounds clearly share a number of characteristics with natural products, including most notably the scaffold diversity and stereochemical complexity. The questions remains, however, whether these products of pure chemist imagination capture the evolutionary advantages of natural products and natural product-based compounds. The DOS planning strategy allows, by enumeration over a larger number of steps, the genesis of truly novel structures which itself is an innovative concept — see Panel A of Fig. 4. In practice, DOS combinatorial libraries focus on leveraging information about existing biologically active molecules in order to address the biologically relevant regions of chemical space. Three types of DOS libraries are currently distinguished: 1) Libraries based on the core scaffold of an individual natural product (see above); 2) Libraries based on specific structural motifs that are found across a class of natural products; and 3) Libraries that emulate the characteristics of natural products in a more general sense — which are most directly related to the theoretical definition of DOS.64,65 DOS libraries are not directed towards a single biological target and aim to provide diverse discovery libraries. DOS has increased the need for exceptionally efficient, stereoselective and chemoselective reactions, including multicomponent reactions, that can be applied to a broad range on substrates. A number of recent success stories prove that DOS compounds provide invaluable tools for target validation — see Panel B of Fig. 4. The validation of the ADMET (absorption, distribution, metabolism, excretion, and toxicology) and in vivo properties of these compounds and their value as

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therapeutics remain, however, to be proven. Comparable to natural products, as a result of the structural complexity, a key challenge is expected in the lead optimization phase and for the industrial chemical development of the final compounds.

3. Designing Comprehensive Chemogenomics Screening Compound Collections The industrial and emerging academic screening centers have put significant investments into screening compound collection enrichment

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and enhancement projects.69−71 The main purpose of large screening collections, ranging typically from 100 000–2 000 000 compounds, is to supply the discovery pipeline with hit-to-lead compounds for today’s and future portfolio of drug discovery programs and to provide tool compounds for the chemogenomic investigation of novel biological pathways and circuits.72−74 A screening collection is a dynamic entity which aims to integrate continuously novel chemical structures. As such, it integrates design-focused and diversity-based compound sets from the synthetic and natural paradigms generated via corporate medicinal chemistry and combinatorial compound synthesis and external compound acquisition projects. The different compound categories mentioned earlier are included. The assessment of the likelihood of a molecule to bind to a molecular target is important. Both protein structure-based approaches like HTD (highthroughput docking), and ligand-based similarity and diversity approaches are applied in a rapid manner to the physically existing and virtually designed compound sets typically available for selection campaigns.5,10,75,76 The structural diversity is of particular importance to the general screening collection enhancement projects. Not only exact duplication needs to be avoided, but a general diversity in terms of chemical classes, lead-likeness, and drug-likeness needs to be achieved. Screening collection design processes are typically informatics, chemistry, and biology driven. The currently applied process within our group in outlined in Fig. 5 and consists of two steps.77 In the first step, the candidate compounds are filtered and grouped into three priority classes on the basis of their individual structural and computed physicochemical properties. Substructure and computed physicochemical filters, similar to those published by others, are used both to eliminate and to penalize compound classes.78−80 The similarity of the remaining structures to selected reference ligands of proven druggable target families of interest is then computed, and the compounds similar to drugs and known actives are prioritized for the following diversity analysis. In the second step, the compounds are compared with the archive compounds and a diversity analysis is performed. This is done separately for the compounds prioritized as similar to known drugs and actives, the drug like regular compounds, and the penalized compounds with increasingly stringent dissimilarity criteria. The automated analysis is followed by manual review of the compounds to assess more complex structural properties like the chemical derivatization potential. One major role of chemoinformatics is thus recognized in the need to reduce the number of potential candidates to a humanly

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Figure 5. Schematic illustration of the NIBR compound selection process. The candidate compounds are first filtered using substructure and computed physicochemical filters. The remaining compounds are divided iteratively into three orthogonal categories. Penalized compounds are defined based on additional substructure filters identifying particularly abundant chemotypes. Using both Unity 2D fingerprints and Similog keys, the remaining compounds are compared with selected known reference drugs and actives of the main target families of interest to NIBR, and are separated into the category of similar to known drugs and the category of drug-like diverse compounds. The candidate compound sets are then compared in an incremental manner to the existing collections and the previously made selections. This diversity selection process starts with the candidate set of similar to known drugs and ends with the penalized compound set. The incremental diversity selections are done with decreasing similarity thresholds and compound densities. For further detail see Ref. 77.

tractable number.79 The approach is thus comparable to the ones described by Oprea and others, emphasizing serendipity and the inclusion of compounds beyond strict drug-like/lead-like filters.72−74 Regarding the assessment of chemical diversity, recent advances in clustering techniques are noteworthy.81,82 They make possible the co-clustering of very large commercial compound collections and reference sets with the entire corporate collection and allow the application of constraints for the number of compounds to be selected per cluster. The ideal library size is currently a subject of scientific debate.83,84 Whereas theoretical rationales are emerging, pragmatic considerations are prevailing and focus on the diversity of chemotypes rather than on larger and larger numbers of individual compounds per scaffold85 ; the latter should, however, be such as to enable the detection of SAR from the screening data.6 As an increasing number of

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commercially available screening compounds are prepared by combinatorial or parallel synthesis, the evaluation and selection based on the scaffolds is a reasonable alternative. This is especially valid if the compounds have not yet been prepared and one is given the opportunity to prioritize the synthesis proposals. Scaffold novelty within the corporate collection and compared to the patented chemistry space can be ensured by substructure searching. As the number of attractive scaffolds is limited, the selection of the most promising ones can be done manually, although computational methods for the evaluation of scaffold diversity are emerging.86−88 Reference repertoires of privileged structures are a pragmatic guide in this process.89−92 The critical in silico and in vitro evaluation of scaffold-based prototypes is also recommended to create best added value.6 The implementation of efficient and updated 2D and 3D structure databases is one major challenge in molecular diversity management. In addition to the compound design and selection criteria used for the compilation, the quality of the compound storage, manipulation, and logistics systems used for the management of the collection are key factors to yield reproducible results.93

4. Essential Properties and Selection Processes along the Discovery Pipeline The pre-clinical drug discovery process is typically a sequential selection and optimization process focusing, as summarized in Table 2, on different essential properties at each step.94−99 Tool compounds required for early in vitro or in vivo target validation typically do not need to satisfy the same stringent in vivo efficacy and safety criteria as clinical candidates and drugs, which again depend on the targeted therapeutic indication.100 In order to reduce later attrition,101,102 the transitions between the stages include continuous iterations returning to the previous stage when the criteria of the following stage are not reached for a given candidate compound and when appropriate redesign of the compounds is required. No further redesign of the compound is possible after transition into clinical phases. As in the target-based drug discovery paradigm, the objective is to find compounds selective for one particular target or for a spectra of targets in a specific disease relevant signaling pathway, the ligand potency and efficacy are clearly among the most important properties. Because primary assays, especially cellular assays, can result in hit rates as high as 1%, a primary HTS

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— Nanomolar potency on isolated target — Submicromolar activity in functional assays — Demonstrated activity on paralogue targets in species for animal testing — Desired selectivity profile on key anti targets and safety pharmacology targets

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Table 2 Essential Properties of Small Molecules at Different Stages of Pre-clinical Drug Discovery from Screening Compound to Investigational Drug Candidate. Adapted from Refs. 94–97 Information regarding the principles of the clinical selection and approval process can be found at the FDA Center for Drug Evaluation and Research (http://www.fda.gov/cder/ ). CYP450 (cytochrome P450), DMPK (drug metabolism and pharmacokinetics), DMSO (dimethylsulfoxid), GMP (good manufacturing practice), IP (intellectual property), PAMPA (partial artificial membrane permeability assay).

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— Physicochemical characterization: LogP, LogD, pKa, solubility, aggregation — Assessment of membrane permeability using: CACO-2, PAMPA — Assessment of metabolic characteristics: CYP inhibition in major isoforms to assess drug-drug interaction liabilities and intrinsic clearance in rat and human liver microsomes

Lead Compound — Understanding of key membrane transport mechanisms — Desired metabolic characteristics — Appropriate clearance, volume of distribution and half life in rat — Evaluation of genotox: AMES bacterial mutagenicty — Evaluation of HERG interference

Drug Candidate — Identification of appropriate gallenic form for testing in animals — Metabolite profiling for each compound and assessment for reactive metabolite formation — Mammalian cell mutagenicity data — Understanding of in vivo ADMET properties, including tissue distribution an elimination properties — Dose escalation experiments and maximum tolerable dose in appropriate species — Decision for safe testing in human without impairing vital functions


ADMET/DMPK — Good water and DMSO solubility — Adequate permeability to reach site of action

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hit list triaging process is typically applied in order to select compounds for dose-response dependent validation. The objective is to select and enrich from a given primary HTS data set, those compounds which have the best potential to become hit-to-lead compounds, and to explore at maximum the chemical diversity represented in a primary hit list and other sources of information relevant for the specific biology project. As compound selection and filtering is a subject of intense scientific debate,103,104 the computational analysis process applied in our group uses in a first step data pipelining tools to annotate the different possible decision criteria to the compound.105 Because of the legacy of the screening collection, compounds violating the standard substructure filters used in the design of the newer screening sets need to be applied. In addition, project specific substructure, scaffold, and physicochemical filters are applied to the primary hit list in order to maximize the chemical attractiveness of the resulting hit list. Based on the chemist-dependent information of chemical attractiveness, Naïve Bayesian classifiers, or other machine learning techniques can be applied to translate this information into predictive computational models. In a similar manner, empiric information about the promiscuity, or cell-toxicity of the hits can be integrated using reference lists compiled over comparable assays of the same format or same target family. Input from maximum common substructure clustering methods is used to track quickly chemical scaffolds that are over represented in a hit list and to reduce large hit lists by cherry picking a representative set from each cluster, preserving the most active compounds.106 The summary of the different annotation criteria can then be used to qualify the chemical and biological hit attractiveness using simple additive point-based scoring schemes. The annotated and scored primary hit lists are then discussed within the project team for a final decision. Water solubility and compound self-aggregation properties turn out to be key properties essential at the hit-finding stage. Poorly soluble and self-aggregating compounds are a major cause for drop-out at further validation stages and resulting potentially in flat SAR of the derived compound series.107,108 Also compounds with the potential for cross-reaction with nucleophilic amino acid side chains of proteins are not desired. Both in silico prediction tools and high-throughput experimental methods for testing of these properties are currently used to either exclude or penalize compounds for the compound selection processes.109−111 A number of criteria are essential for the decision to take up a hit compound singleton or compound series to the hit-to-lead stage. The criteria,

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which need to be handled flexibly, are chemical and biological in nature, including: The compound structure and purity are confirmed; true singletons are supported by small sets of analogues; and, absence of undesired functionalities and attractiveness for parallel optimization. The latter two objectives are rather subjective and depend on the expertise and experience of the medicinal chemist to be able to cope with latent hits.112 Ideally, at the hit-to-lead phase, structural biology is in place to include guidance by molecular modeling. The biological criteria include dose-dependent response validation in a secondary assay relevant to compound optimization together with the characterization of the binding kinetics,113 and some further potency criteria. The potency criteria, however, need to be balanced again with chemical tractability of a series, since interesting low potency hits can be transformed into high potent compounds.114,115 The early hitto-lead criteria also include early in vitro ADME characterization of the compounds. Key physicochemical properties guiding the optimization are determined, including solubility, LogP, LogD, pKa and passive membrane permeability. Plasma protein binding, CYP450 inhibition in the major isoforms, and intrinsic clearance in hepatocytes are included at this stage, to address limitations in the pharmacokinetic properties and liabilities for drug-drug interactions. The in vivo pharmacodynamic and pharmacokinetic aspects become in the later stages of the discovery chain more and more important and form the essence of the art of medicinal chemistry which is characterized by detail and dynamic complexity.96,116,117 Given the limitations in extending predictions to later stages (including cross species translations), it is clear that a successful project will investigate at each stage a couple of different chemical series, including a couple of representative multi-objective optimized compounds.96,117 Noteworthy, in the context of designing screening collections, are the concepts of drug- and lead-likeness.118 Drug-likeness is a general description of the potential of a small molecule to become a drug. As we have attempted to summarize in Table 2, many chemical and biological characteristics of a compound have to be met to make a compound a drug. In a provocative statement, Lipinski estimated that currently only about 10 000 drug-like compounds exist which are sparsely, rather than uniformly, distributed through chemistry space.32,33 True — immediately useful — diversity does not, in this sense, exist in experimental chemistry screening libraries! Because of the evolutionary pressures on ADMET to deal with endobiotics and exobiotics, the ADMET property space is of low dimensionality, whereas biological receptor activity is higher dimensional in chemistry space.

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Compounds highly potent against a drug target may not be efficacious because of pharmacokinetic problems; they may be toxic, or may unfavorably interact with other drugs. Various studies have analyzed the statistical distribution of molecular properties of drugs and especially orally available drugs in order to be able to derive predictive models of drug-likeness. Experimental and, as shown in Fig. 6, computed essential properties show distinct distribution profiles for the different compound sets of a screening collection. However, as ADMET is hard to predict for large data sets,115 because it is typically multi-mechanism related and the predictions get worse as more data accumulates, these methods are mostly descriptive and focus mainly on the prediction of absorption or bioavailability. The level of permeability or solubility needed for oral absorption is related to needed potency. Based on the analysis of 2245 compounds from the WDI which were investigated in Phase II and later clinical trials, Lipinski’s Rule-of-5 predicts

Figure 6. Distributions of essential computed molecular properties defining drug-likeness for selected compound sets. Shown are the fraction of compounds vs. the properties. Orange: NIBR historical medicinal chemistry collection. Brown: Compilation of combinatorial chemistry libraries. Dark Green: Drugs (launched or Phase III listed in MDDR or CMC). Brown: Compilation from combinatorial libraries. Pink: Natural products of DNP. Light Green: HTS hits of NIBR 2004 screens. All properties were calculated with Pipeline Pilot software (www.scitegic.com).

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that poor absorption or permeation is more likely when the number of Hbond donors >5; the number of H-bond acceptors >10; the MW (molecular weight) > 500; and the CLogP (calculated Log P) > 5.32,33 Veber et al. have used SmithKlineBeecham data of oral rat bioavailability of 1100 drug candidates to analyze the importance of molecular properties related to drug-likeness.119 They found that the commonly applied MW cutoff at 500 does not itself significantly separate compounds with poor oral bioavailability from those with acceptable values in their data set. Their observations suggest that compounds which meet a number of rotatable bonds ≤10 and a PSA ≤ 140 Å2 (or 12 or fewer H-bond donors and acceptors) will have a high probability of good oral bioavailability in the rat. These findings were, however, challenged by an analysis of 434 Pharmacia compounds, from which it was concluded that generalizations on complicated endpoints are difficult and dangerous for prospective selections.120 Vieth et al. analyzed the characteristic physical properties and structural fragments of oral drugs vs. drugs of other routes of administration and found that oral drugs tend to be lighter and have fewer H-bond donors, acceptors, and rotatable bonds.121 Martin, in responding to a demonstrated need to forecast in silico the permeability and bioavailability properties of compounds, has developed a score that assigns the probability that a compound will have a bioavailability >10% in the rat.122 Neither the Rule-of-5, LogP, LogD, nor the combination of the number of rotatable bonds and PSA, has successfully categorized compounds. Instead, different properties govern the bioavailability of compounds, depending on their predominant charge at biological pH. The fraction of anionic compounds with a bioavailability >10% falls from 85% if the PSA is ≤75 Å2 , to 56% if 75 < PSA < 150 Å2 , to 11% if PSA is ≥150 Å2 . Conversely, whereas 55% of the neutral, zwitterionic, or cationic compounds that pass the Rule-of-5 have a bioavailability >10%, only 17% of those that fail have a bioavailability >10%. This same categorization distinguishes compounds that are poorly permeable from those that are permeable in Caco-2 cells. These sometimes controversial reports, which are based on different datasets, are evidence that accurate predictions of drug-likeness are quite difficult123 ; nevertheless, these data in combination with different statistical modeling and machine learning techniques, can be used with caution for the evaluation of vendor database or virtual compound libraries, or for categorizing compounds sets and conditions for experimental testing.124−126 In regards to filtering out potentially toxic compounds, structure-based methods are often employed that primarily draw from mutagenicity,

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carcinogenicity, and acute toxicity databases.127 Expert systems such as DEREK, TOPKAT, MCASE, or corporate internal developments are used to evaluate virtual compounds for multiple toxicity endpoints.127−129 Again, it is a strategic decision at what stage of the discovery process these methods are used as hard filters, or more simply they should be used as awareness indicators directing further experimental investigation and optimization. As lead optimization is quite an artistic activity, it was pointed out that screening library design should focus on lead-likeness rather than druglikeness. Teague et al. pointed out that there are differences between drugs and leads.4,130 Leads may be classified into three categories: 1) Low-affinity compounds with low MW and ClogP (e.g. endogenous compounds such as histamine); 2) High affinity and high MW compounds (e.g. peptides and natural products) that need improved pharmacokinetic profiles; and 3) Low affinity with drug-like MW (300–500) and CLogP (3–5). Most of the HTS hits belong to the third category and optimization often adds hydrophobic groups to increase the potency of the compounds. The conclusion was then drawn that chemical screening libraries should focus on low MW and lipophilic compounds. Low complexity compounds seem to have, in addition to the higher attractiveness to the chemist, a higher probability for successful detection in screening.131,132 Follow-up studies by Oprea which analyzed more systematically lead-drug pairs,133,134 recommended that lead-likeness libraries should have as characteristics: MW −5; number of rotatable bonds 20 Million BLAST Sequence Alignments

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Figure 3. The data and algorithms in Eidogen-Sertanty’s Target Informatics Platform human database. Since structure determination, structure alignment, and site alignment require significantly more computation time than sequence alignment, the database has been designed to store the results of these calculations and automatically initiate the appropriate new calculations when new experimentally derived sequence or structure data is uploaded. This approach enables us to easily update the database as new structures are deposited in the PDB, and allows users to modify the structural informatics data and classifications in realtime. Currently, the sequence layer of the database contains more than 50 000 sequences from the International Protein Index (IPI) human protein sequence database.5 At this time, there is a publicly available crystal structure for just over 2300 of these human sequences. For those sequences without an experimental structure, we built a structural model using STRUCTFAST, our proprietary homology modeling approach. STRUCTFAST builds an accurate model for approximately 2/3 of the human sequences, resulting in more than 30 000 models. When applied to each of the structures in the database, our site annotation algorithm, SiteSeeker, yields more than 90 000 binding sites, approximately half of which are (continued on facing page)

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resource of this kind, LigBase.14 The LigBase database was created by coupling site annotations from the co-crystal record in the PDB along with the CE structure alignment algorithm, yielding multiple alignments for known binding sites. Recently, another site comparison database has become available, CavBase.15 A distinguishing feature of CavBase is that it contains additional similarities between binding sites from proteins that do not share any structural homology, since the binding sites are directly aligned using a clique detection algorithm,16 not a structure alignment algorithm. At Eidogen-Sertanty, we have developed a site alignment algorithm, SiteSorter, which uses a weighted-clique detection approach17 to directly overlay binding sites and avoid the requirement for structure homology. By integrating SiteSorter with fully automated homology modeling (STRUCTFAST) and site annotation (SiteSeeker), TIP goes an additional step beyond LigBase and CavBase, providing intra- and inter family binding site for the entire proteome, not just for those proteins whose structures have been resolved experimentally. Since closely related binding sites are more likely to bind to the same small molecules, binding site similarity analysis allows us to infer important cross-reactivity information. During lead discovery for a new target, finding a cross-reactivity to a target for which there are already leads enables the fast discovery of new leads via target-hopping.18 –21 With the potential of shortcircuiting the lead discovery process on a genomic scale, target hopping is an important chemogenomic application of structural informatics. Figure 5

Figure 3. (continued from previous page) novel sites with no experimental precedent. The total number of similarity relationships stored in the database is approaching 100 million, with more than 20 million BLAST sequence alignments,6 28 million StructSorter structure alignments, and 39 million SiteSorter site alignments (the StructSorter and SiteSorter algorithms were developed internally at EidogenSertanty and have yet to be published). For the ∼2300 human co-crystals in the database, more than one million binding mode alignments have been determined using our site-ligand contacts (SLiC) approach, which will be described later in this chapter. The completion of these calculations for the human proteome requires more than 3 months on a 128-node Linux cluster (3 GHz processors). Currently, we are calculating other drug discovery relevant proteomes, such as mouse and rat, and various pathogenic species. Storing the calculation results allows users to export, in real time, multiple proteins from the database based on sequence, structure, binding site and binding mode similarities. These database exports are utilized by a visualization and analysis package we have developed called EVE (the Eidogen visualization environment). Within EVE, users can examine all of the similarity relationships with a variety of 2D and 3D visualization techniques, and upload docked molecules for binding mode alignment.

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Figure 4. TIP’s structure and binding site coverage for the major drug target families that comprise the druggable human genome.7 TIP’s structural coverage across the major target families is very high, with the only exception being membrane-bound targets (due to the distinct lack of membrane bound crystal structures). Also shown in the chart is the total number of sites annotated for each of the major target classes. SiteSeeker annotates multiple potential small molecule binding sites for a given target structure, spanning substrate, co-factor, proteinprotein interaction, inhibitor, and allosteric sites.

shows an example of intra-family target hopping, while Fig. 6 shows an example of inter-family target hopping. While the potential for target hopping exists when two binding pockets are highly similar, a second set of applications emerges from a detailed understanding of the differences between two similar binding pockets. During lead optimization, where the goal is a highly selective binder, understanding the detailed mechanism of cross-reactivity between targets is critical for modifying existing leads to enhance their affinity for the desired target. Figure 7 shows an example of an undesirable inter-family crossreactivity found in the TIP database, and proposes a mechanism for an optimized lead series to avoid the undesirable off-target. In addition to enabling the optimization of known leads, structural informatics offers the possibility of mining the proteome for interesting drug discovery opportunities that are likely to succeed because binding site similarity analysis reveals an opportunity to design a highly selective binder. Figure 8 shows

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Figure 5. An example of intra-family target hopping within kinases. According to SiteSorter, Braf kinase, the primary target for the clinical compound BAY 43-9006, is one of the 10 most similar kinases to c-Kit, which has also been shown to bind BAY 43-9006 with sub-micromolar affinity22 (60% of the binding site residues are conserved and colored blue; non-conserved positions are colored yellow). This cross-reactivity cannot be predicted based on the sequence similarity of the Braf and c-Kit kinase domains, since approximately one-sixth of the human kinome is more sequence similar to Braf than c-Kit.

an example of opportunity mining in the kinases, and Fig. 9 shows an example of opportunity mining in the area of anti-infectives. Once one or more projects have been mined, structural informatics can also be used to prioritize the projects by their expected feasibility. Figure 10 shows an example of a project whose feasibility has been adversely affected because the target’s binding site is very different in mice, the animal model of choice.

6. Ligand Binding Mode Relationships While it has long been a common practice in structure-based drug design to examine the binding modes of co-crystalized ligands to gain insight into the

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Figure 6. An example of inter-family target hopping between human and viral aspartyl proteases. The aspartyl protease active site is located at a homodimer interface in HIV and within a single domain in Cathepsin D, so sequence and structure alignments between these proteins cannot be constructed. By using an approach independent of sequence or structure homology to directly align the sites, SiteSorter finds that the HIV protease and Cathepsin D substrate sites are highly similar (identical chemical groups within 1 Å are colored dark blue). It has been verified experimentally that Cathepsin D is susceptible to inhibition by HIV-protease inhibitors.23

important principles for binding, methods for the fully automated analysis of ligand binding modes have only recently emerged in the literature.28 –30 These methods play a crucial role in structural informatics by enabling similarity based classification of the rapidly expanding database of co-crystal structural data. In the TIP database, a binding mode similarity score is determined for each of the co-crystal binding site overlays using an approach called SLiC (site-ligand contacts), which is similar to the SIFt (structural interaction fingerprint) methodology developed by Singh and co-workers at Biogen.29 In the SIFt and SLiC approaches, the types of contacts that a ligand makes with each of the residues of the binding pocket are coded into a bit string. Aligning the binding pocket residues also aligns the bit strings, enabling a Tanimoto similarity to be calculated (Fig. 11).

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Figure 7. Binding site similarity analysis can reveal unwanted off-target cross-reactivities. Searching the TIP database with the geldanamycin site on Hsp90 retrieves a SiteSeeker predicted site on ATP citrate lyase in the top 0.08% of all human binding sites, and a SiteSeeker predicted site on ADE2 in the top 0.2%. Both of these targets have been shown to bind Hsp90 inhibitors.24,25 This figure shows geldanamycin (red) positioned in the ADE2 pocket according to the SiteSorter overlay between HSP90 and ADE2 pockets. Three important hydrogen bonds to the geldanamycin are preserved in the ADE2 pocket, even though ADE2 does not share any sequence or structure homology with HSP90. Interestingly, positioning 17AAG (yellow), a geldanamycin analog and clinical candidate, into the ADE2 pocket reveals steric hindrance that may account for 17AAG’s reduced toxicity relative to geldanamycin (inside the green circle).

By converting the interactions important for binding into onedimensional bit strings, the SiFT and SLiC approaches can be coupled with small molecule docking approaches to find new molecules that are capable of making the same interactions. In this manner, automated binding mode analysis can be used to significantly enhance docking based approaches for inferring small molecule activity (Fig. 12). Recently, scientists at Vertex have published the BREED approach for determining new compounds with a high likelihood of activity based on three-dimensional binding mode similarity (Fig. 14).31 When coupled with the automated SiteSorter site alignment calculations in TIP, BREED

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Figure 8. Structural informatics can be used to mine the best opportunities for selectivity within important drug target families such as kinses. Recently, Cohen et al. developed highly selective, irreversible p90 ribosomal S6 Kinase (RSK) inhibitors by exploiting an exposed and highly non-conserved cysteine in the ATP binding pocket.26 This figure shows an EVE comparative binding site analysis of the four RSK’s along with the most highly similar ATP binding sites in the human kinome. None of the most similar kinases share the cysteine found in the RSK’s.

becomes an important chemogenomic approach for quick determination of large slices of active chemical space for the important drug target families in the human genome.

7. Summary Structural informatics is an emerging field that promises to provide a significant amount of chemogenomic knowledge as the amount of experimental structural data continues to increase. A unique aspect of this rational approach to obtaining chemogenomic information is its potential to answer

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Figure 9. Structural informatics can be used to mine anti-infective opportunities that cannot be discovered by comparative genomics. Because lanasterol demethylase (CYP51), the target for diflucan and other azole drugs, is a highly conserved enzyme that is not at all unique to the fungal genome, it would be completely disregarded as a potential anti-fungal target by comparative genomics methods. In contrast, TIP identifies CYP51 as a target with significant anti-infective potential by revealing important differences between the human and fungal binding pockets. This figure shows an EVE overlay of the azole-binding sites in the STRUCTFAST models for the CYP51 enzyme from the pathogenic fungi Candida albicans (red), and human (cyan). A fluconazole molecule is shown in its predicted binding mode. The bulky Met487 and Arg133 residues in the human enzyme close off a portion of the binding pocket that is accessible in the fungal enzyme, which has serine and histidine residues in the corresponding positions. The shallower human binding pocket does not accommodate the binding of fluconazole or other multiply substituted azole compounds.

the question “What is the mechanism of action?” as soon as it answers the question “Is this molecule active?” Hence, regardless of whether structural informatics is used to generate original activity or cross-reactivity data prior to other emerging high-throughput experimental methodologies, its place as an important technology for resolving the detailed mechanisms behind the primary chemogenomic observations is certainly assured.

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Figure 10. Binding site analysis of different species can uncover potential problems with animal models for a given target. The Cathepsin S inhibitor JNJ 10329670 (exact molecule not shown) has an activity of 34 nM in humans, but shows sub-micromolar activity in dog, monkey, and cattle, and only micromolar activity in mice.27 These activity differences can be explained by the fact that in the dog, monkey and bovine Cathepisin S pockets, only two of the residues are non-conserved, while four of the residues are non-conserved in mice.

Figure 11. The SLiC similarities for a set of CDK2 co-crystals as presented in EVE. EVE employs a 4-color coloring scheme to provide an easy to understand visual representation of the various interaction bit strings used in the SLiC scoring. Residues participating in a hydrogen bond with the ligand are colored blue, residues participating in a polar interaction are colored red, while residues participating in a hydrophobic interaction that do not have either h-bond or polar interaction are colored yellow. Residues that participate in both an h-bond and a polar interaction with the ligand are colored purple. The top line, highlighted in blue and labeled “Composite1,” is a composite SLiC (cSLiC) that represents the average of the ligand interactions made by the various co-crystals. In EVE, users can build one or more cSLiCs and use them as an alternative or supplement to energy-based affinity scoring functions for the purposes of docking pose selection and affinity ranking.

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50 45

% of Actives Recovered

40 35 30 25 Ideal

20

Co-Crystal cSLiC+Docking Co-Crystal cSLiC

15

Docking Random

10 5 0 0

1

2

3

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% of Database Screened

Figure 12. Docking-based virtual ligand screening has emerged as an important workflow in computational lead discovery. This slide demonstrates the enrichment enhancements obtained when the cSLiC shown in Fig. 11 is used to re-rank a set of docking results for CDK2. To generate these results, 91 compounds with 95% pure. The remaining compounds are >80% pure and are primarily compounds purified from natural extracts. The value of the BioPrint dataset is achieved from a combination of high quality in vitro data generated for each compound, and in vivo data extracted from public medical literature (see below). Relating both types of information supports the bioinformatics applications of the database. Also of value is the diversity of compounds, both chemical and biological, which are indicated for a large array of therapeutic areas. This diversity provides a good training set to develop and test various QSAR methods, and supports the cheminformatics applications of the database (Fig. 1).

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Construction of a Homogeneous and Informative In vitro Profiling Database

3. In vitro Pharmacological Data: Content and Validation 3.1. Assay selection The BioPrint pharmacological profile is designed for optimal assessment of “biological” diversity. Assay selection is based on several considerations including: • Diversity of targets based on the concept of the “druggable” proteome and on phylogenetic analysis. • Important therapeutic target classes. • Cerep’s experience and skills in drug profiling. • Assay number and quality. 3.1.1. “Druggable” proteome The BioPrint profile represents a subset of the “druggable” genome, more specifically a subset of the “druggable” proteome. This is illustrated by clustering the target proteins by sequence homology (Fig. 2) and by the different molecular functions of the assay targets.8−10 Receptors and enzymes are the major classes represented. Of the 168 targets in the current BioPrint in vitro pharmacological profile, 97 are receptors and 43 are enzymes (Table 1). To ensure continued diversity of the BioPrint profile, additions of new targets to the panel of assays are analyzed in relation to recent and detailed phylogenetic analysis of human G protein-coupled receptors (GPCRs) and kinases.11,12 3.1.2. Important therapeutic target classes Receptors (mainly GPCRs) and enzymes represent the majority of therapeutic targets in current drug discovery.9,13,14 Ion channels, transporters and nuclear receptors are also important targets. The BioPrint profile reflects this emphasis: 58% are receptors which include nuclear receptors; 26%, enzymes; 13%, ion channels; and 4%, transporters (Table 1). Among the receptors, G protein-coupled receptors are the most diverse and well-represented class of the targets (91 representatives). Monoamines, and more generally neurotransmitter receptors, are also highly represented in the BioPrint profile (Table 1). Many enzymes included in the profile are proteases and kinases, i.e. 13 and 8 targets, respectively (Table 1). These enzymes are involved in diseases such as cancer and inflammation, and are targets of interest for drug

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Figure 2. A cross-section of the druggable proteome taken from Ref. 10. Proteins in close proximity in this dendrogram are members of the same gene family and share sequence similarity and structure similarity in regulatory and ligand-binding domains. 92 proteins that are part of the BioPrint in vitro pharmacological profile are shown in pink and cover a representative portion of the druggable proteome.

discovery. Recently, kinases have emerged from drug discovery projects as new targets because of both the large number of family representatives (not all of which have yet been characterized), and the recent proof-of-concept studies on kinase inhibitors as a new class of anticancer drugs.15 We also include older targets that are still of interest and for which low levels of off-target effects are expected. Examples of this are enzymes involved in arachidonic acid metabolism (cyclo-oxygenases) and phosphodiesterases. 3.1.3. Experience in pharmacological profiling The BioPrint profile is enriched by 15 years of knowledge and experience in pharmacological profiling.16 This experience includes an in-depth

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Construction of a Homogeneous and Informative In vitro Profiling Database Table 1 Classification and Description of the Different Targets included in the In vitro Pharmacological BioPrint Profile. The number of assays per each family is detailed. Among the receptors there are 91 G-protein coupled receptors.

Non-peptide receptors

Class

Peptide receptors

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RADIOLIGAND BINDING ASSAYS

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Family Adenosine Adrenergic Cannabinoid Dopamine GABA Histamine Imidazoline Leukotriene Melatonin Muscarinic Platelet Activating Factor Serotonin Sigma Total Angiotensin Bombesin Bradykinin Calcitonin gene-related peptide Chemokines Cholecystokinin Complement 5a Cytokines Endothelin Galanin Glucagon Growth hormone secretagogue Melanin-concentrating hormone Melanocortin Motilin Neurokinin Neuropeptide Y Neurotensin Opioid & opioid-like Somatostatin Thyroid hormone Urotensin Vasoactive intestinal peptide Vasopressin Total

Number 5 10 2 5 2 7 1 2 1 5 1 12 1 54 2 1 1 1 3 2 1 1 2 1 2 1 1 3 1 1 1 1 4 2 1 2 2 3 40

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Nuclear receptors (steroids)

Table 1 (Continued ).

Glucocorticoid Estrogen Androgen

Channels

Total Ca2+ channels K + channels Na+ channel Cl - channel Sub-total ion channels GABA Glutamate Glycine Nicotinic Purinergic Serotonin Sub-total ligand-gated channels Total Norepinephrine Dopamine GABA Choline Serotonin Total TOTAL RADIOLIGAND BINDING ASSAYS

Amine transporters

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Monoamine metabolism Arachidonic acid metabolism NO synthases Phosphodiesterases Proteases Guanylyl cyclase Phosphatase Kinases Free radicals ATPase Miscellaneous enzymes TOTAL ENZYME ASSAYS TOTAL PHARMACOLOGICAL ASSAYS ENZYME ASSAYS

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1 1 1 3 5 3 1 1 10 2 5 1 2 1 1 12 22 1 1 1 2 1 6 125 5 2 1 7 13 1 1 8 1 1 3 43 168

understanding of the use of biological data to describe relationships between compound structures, in vitro data, and in vivo data. Targets with a clear association between a specific receptor or enzyme and an adverse effect are favored. For example, muscarinic

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acetylcholine receptors and adrenergic receptors are clearly implicated in cardiovascular effects in vivo. On the other hand, less understood targets are also of interest because compounds with well-characterized in vitro effects might help elucidate the in vivo roles of these targets. Such targets include sigma receptor, melatonin receptor, various kinases, and metalloproteinases. Finally, to increase diversity, and based on our experience, the profile includes targets with low (e.g. AT2 , B2 , ETA ), medium (e.g. AT1 , CCKB , CB2 ) and high hit rates (e.g. Alpha2C , D3, 5-HT2C ). This provides positive and negative data to effectively develop and validate predictive models with sufficient accuracy. Indeed, in the design of the database, both negative and positive results are important, and the extent of data validation is the same for both. Moreover, targets that poorly correlate to other targets can provide valuable information. 3.1.4. Assay number and quality In addition to activity on a specific therapeutic target(s), most compounds have off-target activities. Human adverse effects are mainly due to these off-target activities. Therefore the entire activity profile, not just individual assay activities, is the preferred method to correlate in vitro activities and frequency of adverse effects. The number of assays in the profile must be large enough to include sufficient information, both negative and positive, on a diverse set of biologically active chemicals. Indeed, negative results are as important as positive ones in building a knowledge database. Although it is not possible to include the main therapeutic target for every compound, this absence does not prevent data exploitation to build interesting correlations between compounds in the same biological activity class.10 Among the biological assays, preference is given to assays that measure a direct interaction between a compound and a target, making data interpretation simpler. Therefore, radioligand binding assays and isolated enzyme assays are preferred to functional or cell-based assays. For high quality data, assays that are homogeneous and robust are selected. Also, whenever possible, preference is given to assays based on human sources. Finally, miniaturized assays are preferred for their fast turnaround time and low cost.

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3.2. Compound management and testing 3.2.1. Compound management The quality of the compound collection is one key to high quality biological results. DMSO is a widely used solvent and many studies dealing with the long-term storage of compounds in this solvent have been conducted. The important parameters to maintain stability and concentration are: Freeze/thaw cycles, storage temperature and duration, container material, humidity, and oxygen.17 Compounds frozen in DMSO are generally stable. However, the number of freeze/thaw cycles must be kept to a minimum to avoid compound loss. Compound loss is mainly due to precipitation, whereas compound loss due to degradation is negligible (no additional peaks are detected in the HPLC analysis). It has been reported that 10−2 M stock solutions can undergo more than 10 freeze/thaw cycles with no significant effect on compound stability.18 Long-term storage of compounds at room temperature in DMSO, over a three-month period, is also possible with only minor loss of compound. Kozikowski et al.19 observed a less than 20% loss after three months in 92% of the cases. As for container material, polypropylene containers are as satisfactory as glass ones in compound stability and recovery. Since water is more harmful than oxygen to compound degradation and the hygroscopic nature of DMSO favors water uptake,17 humidity control is a key parameter to ensure compound storage stability. To ensure the integrity of the BioPrint compound collection, stringent rules for compound management are applied. The whole collection is stored as dry powders. Before each annual campaign of testing, new stock solutions are prepared at 10−2 M concentration in 100% DMSO in inert polypropylene containers. In cases of insufficient solubility, compounds are prepared at 10−2 M in 50% DMSO/50% H2 O or in 100% H2 O. Various database flags are generated to indicate compounds at the solubility limit for experimental testing. Multiple copies of 96-well microtiter plates containing the stock solutions are created to further avoid numerous freeze/thaw cycles. For screening, one copy is made for each assay of the panel and additional copies are dedicated for the selection of compounds for further screening. In this process, the number of freeze/thaw cycles for any compound does not exceed two.

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3.2.2. Compound testing Compound testing is performed in two steps. The first round of testing is performed at 10−5 M to measure percent inhibition in the binding assay. Selected compounds are further tested at several concentrations to determine an IC50 value. Defining the binding threshold for further testing should balance the objectives to minimize false negatives in which active compounds are missed, and to minimize the number of false positives that require follow-up testing. An activity threshold of 30% inhibition at 10−5 M for follow-up IC50 determination was selected based on the following considerations: • • • •

Slope break point of percent inhibition data. Success rate of IC50 determination. Compound solubility. Theoretical shape of a concentration-response curve.

Plotting the number of compounds as a function of percent inhibition at 10−5 M results in a distribution curve with a “slope break point” that corresponds to a discontinuity in the first derivative (“DFD”). A change in the distribution density suggests that there are different populations within the percent inhibition values. The DFD point is numerically calculated from the distribution curve and gives an assessment of the suitable threshold for each assay. Most often it is located in the vicinity of 30% inhibition at 10−5 M (unpublished work). The rate of confirmation between the inhibitory effect at 10−5 M in the screening and the percent inhibition obtained at the same concentration in the subsequent IC50 determination defines a success rate for each assay. This success rate is deemed satisfactory when the inhibitory effect in the primary screening step is above 50% inhibition; reasonable between 30% and 50% inhibition; but weak below 30% inhibition. A solubility assay with a detection range from 10−6 to 2 × 10−4 M is performed as a part of the BioPrint profile and shows that about one third of the BioPrint compounds are insoluble at 10−4 M. Thus, aqueous solubility limits the high-end concentration for IC50 determination to 10−4 M. The final DMSO concentration tolerated in the assay reaction medium must not exceed 1%. This also limits the high-end concentration to 10−4 M. Moreover, a standard competition binding curve with a Hill number nH = 1 goes from 10% to 90% inhibition within 2 log units of concentration.

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Since the highest testing concentration is 10−4 M, the screening value must be at least 25–30% inhibition at 10−5 M in order to obtain at least 50–70% inhibition at 10−4 M. The retained approach for compound testing is therefore a first screening at 10−5 M, in duplicate. Every compound that displays more than 30% inhibition is further tested at 8 concentrations ranging from 10−4 M to 10−11 M, in duplicate, for the IC50 determination. Based on several years of experience and analyses, some exceptions have been introduced. The Na+ /K+ ATPase assay is screened at 3 × 10−5 M because of a low hit rate. The cutoff for IC50 follow-up has been raised to 40% inhibition for CCKB , GABAA , GABAB(1b) , Kainate, Glycine (strychnine-insensitive), N (neuronal alpha bungarotoxin insensitive) and PAF assays. 3.3. Assay and data validation Each in vitro assay is fully validated using several criteria. The validation step for each assay and each experiment follows a specific, well-detailed and documented process. Strict quality standard guidelines ensure the construction of a high quality database of reliable and reproducible data. 3.3.1. Assay validation Each assay development includes establishing a concentration-response curve for the receptor or enzyme studied, determining the reaction kinetics, Kd and Vmax values for binding assays, or a substrate Km for enzyme assays, and testing known compounds as references. Results for the reference compounds are compared with those reported in the literature to assess the accuracy of the assay protocol. The robustness of an assay is evaluated by a high signal-to-noise ratio, reproducible IC50 or EC50 values of reference compounds, Hill number (nH ) close to 1, and a high Z value. All these parameters are addressed before assay validation is completed. To finalize the assay development, all the parameters are documented in a Standard Operating Procedure to be followed during production. Assay reproducibility and consistency is assured by tracking the results of the reference compounds. Throughout their lifetimes all assays are subjected to improvements. In case of an animal species change or a major protocol change, the assay is fully revalidated and all BioPrint compounds retested.

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3.3.2. Data validation Figure 3 shows the multiple levels of scientific and quality controls of the data. Validation of each experiment requires an acceptable signal value (background, non-specific signal, total signal) and an acceptable IC50 value of the reference compound. For both parameters, acceptance limits are well defined and documented. For a reference compound, the obtained IC50 value (at 8 concentrations in duplicate) should fall within one-half log unit of the historical mean value. Each compound is assayed in duplicate at each concentration and homogeneity of replicates is required with a standard deviation 3% of treated subjects. Frequent: occurring in >1% of treated subjects. Infrequent: occurring in 0.1 to 1% of treated subjects. Rare: occurring in

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