The Paradigm Shift from Traditional to Virtual

1. The Paradigm Shift from Traditional to Virtual • Stephen K. Durham, PhD • Department of Lead Safety Assessment

2. Factors Influencing Change • Technology • Combinatorial Chemistry • High-throughput Screens • Computational Power • Genomic revolution • Escalating Costs

3. The Changing Paradigm Traditional (Sequential) Future (Knowledge-Based) Current (Parallel) Computational Design and Screening of Virtual Libraries MTS HTS Potency Potency Selectivity Specificity Selectivity Specificity Functional Activity ADME/Pharmaceutics Safety Functional Activity In Vitro Confirmation DEVELOPMENT

4. What Are the Key Toxicological Liabilities Affecting Drug Development? • Genotoxicity • Carcinogenicity • Teratogenicity • Liver Toxicity • Extrahepatic Toxicity • P450 Induction

5. Why Do We Want to Find Out the Liabilities Early? Studies Required for an NDA: • Genotoxicity Studies (in vitro and in vivo). • Single-Dose Studies in Mice and Rats. • Two-Week, One-Month or Three-Month Studies in Rats and Dogs. • Six-Month Study in Rats. • Chronic (6 – 12 Month) Study in Dogs. • Segment I, II, and III Reproductive Toxicity Studies in Rats and/or Rabbits. • Palatability and 3-month Range-Finding Studies for Carcinogenicity Studies. • Carcinogenicity Studies in Mice and Rats. • Local Tolerance Study in Rabbits. • Antigenicity Study in Guinea Pigs. • Others as needed.

6. Tiered Multivariate Analysis How Do We Address Safety Issues Until Virtual is a Reality?

7. N N N N O O In Silico Predictive Toxicity Computational programs ultimately fulfill the requirement for determining liabilities at the early stages of discovery Mutagenicity Carcinogenicity Reproductive Toxicity

8. In Silico Predictive Toxicity

9. Approaches to Analysis

10. Typical TOPKAT Output

11. Typical Multicase Output

12. Typical DEREK Output

13. Size Does Matter • Large Pharma Advantages • Robust Institutional Dataset • Extensive Logistical Resources • Biotech Advantages • Flexible and Agile • Risk Tolerant • Strong Academic Ties “Quid pro quo”

14. Internal Evaluation Protocol • Comparative computational toxicological evaluation using a pharmaceutical data set • Analysis of compounds not existing in training dataset (MCASE/ TOPKAT) • Include BMS “institutional” data • Compliance for robustness and chemical diversity

15. Acceptability Criteria for Computational Analysis • 85% Concordance • Require low false negatives (high specificity) • Willing to accept false positives followed by rapid in vitro verification “Still looking for Utopia”

16. Post-computational Verification: Acceptability Criteria for In Vitro Analysis • High concordance • Require low false negatives and false positives • Small compound requirements • Moderate through-put with rapid results “Reliable in vitro assays are necessary to confirm computational predictions”

17. The Changing Paradigm

18. Emerging Technologies

19. Acknowledgements • Genetic Toxicology, Drug Safety Evaluation • Andrew Henwood • Larry Yotti • Lead Safety Assessment • Oliver Flint • Greg Pearl • Structural Biology and Modeling • Deborah Loughney • Jonathan Mason • Roy Vaz