Application of Toxicology Databases in Drug Development (Estimating potential toxicity) Joseph F. Contrera, Ph.D. Director, Regulatory Research and Analysis FDA Center for Drug Evaluation and Research (CDER), Office of Testing and Research

Combinatorial Chemistry High Through-Put Screening The Human Genome The Rapidly Increasing Number and Diversity of Potential New Products The Limitations of Current Toxicology Screening Methods Increasing Demands on Regulatory Processes THE REVOLUTION IN PHARMACEUTICAL DEVELOPMENT

For lead selection of the products of high through-put technology To more efficiently assess the potential hazard of substances especially when limited experimental evidence is available As a rational basis for decisions on the nature and degree of testing Reduce animal testing The Need for Rapid and Effective Screening Methods to Identify and Prioritize Potential Toxicity

Toxicology Studies: Promise There are 6 major categories of toxicology studies: genotoxicity, acute toxicity, chronic toxicity, reproductive and developmental toxicity and carcinogenicity The design of studies in these categories is relatively standardized to meet regulatory requirements Post-GLP (Good Lab Practices;1978) studies and reviews are a potentially rich resource of good quality toxicology data

Information Applications Toxicology Databases Regulatory decision support Retrospective analysis Product development Guidance development; improving and updating regulatory standards Identifying relationships between animal toxicology and human adverse events

CDER Toxicology Databases Contributed to International Conference on Harmonization (ICH) Guidances for Pharmaceuticals ICH S1B: Testing for Carcinogenicity of Pharmaceuticals ICH S1C: Dose Selection for Carcinogenicity Studies of Pharmaceuticals ICH S1CR: Use of Limit Dose in Dose Selection for Carcinogenicity Studies ICH S4;S4B: Duration of Chronic Toxicity Testing in Animals

Information Applications Computational Toxicology; SAR; E-Tox Structure activity analysis (SAR) and predictive modeling for regulatory decision support Lead selection in drug development Estimating and prioritizing potential hazard when data is limited Hypothesis generation, identifying data gaps; prioritizing research

Computational Toxicology; E-Tox The application of computer technology to analyze, model and predict toxicological activity E-ADME The application of computer technology to analyze, model and predict absorption, distribution, metabolism and excretion

Current Database Needs and Issues Critical need for uniform compound identification; problems with multiple drug names, codes, CAS numbers for same active ingredient Better search and retrieval capability within and across databases Chemical structure similarity search and clustering capability Data entry, quality and compatibility issues Proprietary issues; Data sharing

Major FDA/CDER Carcinogenicity Database Fields Drug name *Molfile digital chemical structure 2D structure Administrative code (NDA, IND number) Clinical indication(s) Pharmacological or chemical class Species, strain Sex Route Doses Duration of dosing Tumor site, type Tumor incidence

Using Chemical Structure (Molfile) as a Key Field to Link Databases and Expand Search Capabilities Molfile “core”structure fingerprint Key Field Structural Similarity Searching, Cluster Analysis (ISIS-Base) SAR/E-Tox MCASE structural alerts Compound Names Compound Structure

Computational Toxicology E-Tox Toxicology Data Bases Clinical *ADR AERS Chemical Structure Similarity Searching (MDL Isis-Base) Chemical Structure Based Substance Inventory (MOLFILE) FDA CDER TOXICOLOGY KNOWLEDGE BASE For Decision Support and Discovery Pharm/Tox Study Summaries E-Reviews Freedom of Information Files *Clinical Post-Marketing Adverse Drug Reaction Adverse Event Reporting Systems Databases

A Knowledge Base is the Combination of Databases and Computational Methods to Discover Meaningful Relationships The CDER Toxicology Knowledge Base is a Prototype for an FDA Knowledge Base

Estimating Potential Toxicity Molecular Descriptors Biological Descriptors E-Tox/SAR Modeling Weight of Evidence Factors

Major Structure-Activity (SAR) Based Predictive Models Expert Rule Based Methods Prior expert knowledge and mechanistic hypotheses required Derek; Oncologic Statistical/Correlative Methods Little prior knowledge required. Computer generated patterns and relationships from a statistical analysis of a data set MCASE; Topkat

Representative Molecular Descriptors 2D molecular structure based clustering 2D molecular substructure clustering; molecular fragmentation 3D rigid and flexible molecular configuration clustering Physical chemical parameters, eg. Log P; homolumo constants; electrotopographic properties

Modeling Biological Descriptors Major Sources of Error Inadequate size of control data set Inadequate representation of molecular diversity (coverage) Over simplification, poor use of biological data Unbalanced representation of biological activity Inadequate validation of predictive models due to lack of studies not included in the control data set

The Representation of Molecular Diversity The Size and Diversity of Control Data Set Coverage: The FDA rodent carcinogenicity data base contains more than 1000 compounds that include both pharmaceuticals and non- pharmaceuticals Balanced representation: Approximately equal number of positive and negative studies in the FDA carcinogenicity database Validation: Availability of a large pool of new studies improves the validation process

The Representation of Biological Activity Two Year Rodent Carcinogenicity Studies Male and female dose groups Male and female untreated control groups 50+ animals/sex/group (400+ total) 40+ organ/tissue pathology analyses/animal Relatively high spontaneous age related background tumor rate Relatively high probability of some treatment related findings Sensitivity/Specificity Issues

The Representation of Biological Activity Modeling Rodent Carcinogenicity Studies Four Study Cells Male and Female Rats Male and Female Mice Each study cell can be considered an independent study More than one positive study cell is necessary to corroborate a positive finding

The Representation of Biological Activity Weight of Evidence and Data Quality Separate evaluation/modeling of male and female rat and mouse study results (4 study cells) More positive cells=greater potency and confidence A biologically relevant molecular descriptor is one that is linked to positive findings in at least two study cells The greater the number of compounds containing a molecular descriptor associated with carcinogenicity in the database, the greater the degree of confidence in the finding

Assignment of Carcinogenic Potency Compounds that induce trans-species tumors present the highest degree of risk because they adversely alter mechanisms that are conserved across species. Tennant, Mutat. Res. (1993) 286,

Review Approval THE FDA-CDER INFORMATION CYCLE NDA Reviews Non-proprietary Drug R & D Submission IND Reviews Proprietary Data Nonproprietary Databases Proprietary Databases Applications R&D Decision Support Guidances E-Tox Institutional Memory

Primary Science Labs/Patients Secondary Science eR&D in-silico computers Primary Science Secondary Science Exp. Science eR&D computers Confirmatory Science Labs/Patients NowTransition Future From Pharma 2005: An Industrial Revolution in R&D Pricewaterhouse Coopers