Application of Toxicology Databases in Drug Development (Estimating potential toxicity)

1. 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 Contrerajf@cder.fda.gov

2. THE REVOLUTION IN PHARMACEUTICAL DEVELOPMENT 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

3. The Need for Rapid and Effective Screening Methods to Identify and Prioritize Potential Toxicity • 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

4. 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

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

6. 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

7. Information ApplicationsComputational 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

8. Computational Toxicology; E-ToxThe application of computer technology to analyze, model and predict toxicological activityE-ADMEThe application of computer technology to analyze, model and predict absorption, distribution, metabolism and excretion

9. 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

10. 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 Major FDA/CDER Carcinogenicity Database Fields

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

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

13. 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

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

15. 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

16. 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

17. Modeling Biological DescriptorsMajor 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

18. The Representation of Molecular DiversityThe 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

19. The Representation of Biological ActivityTwo 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

20. The Representation of Biological ActivityModeling 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

21. The Representation of Biological ActivityWeight 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

22. Assignment of Carcinogenic PotencyCompounds 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, 111-118.

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

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