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The Paradigm Shift from Traditional to Virtual. Stephen K. Durham, PhD Department of Lead Safety Assessment. Factors Influencing Change. Technology Combinatorial Chemistry High-throughput Screens Computational Power Genomic revolution Escalating Costs. The Changing Paradigm. Traditional
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The Paradigm Shift from Traditional to Virtual • Stephen K. Durham, PhD • Department of Lead Safety Assessment
Factors Influencing Change • Technology • Combinatorial Chemistry • High-throughput Screens • Computational Power • Genomic revolution • Escalating Costs
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
What Are the Key Toxicological Liabilities Affecting Drug Development? • Genotoxicity • Carcinogenicity • Teratogenicity • Liver Toxicity • Extrahepatic Toxicity • P450 Induction
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.
Tiered Multivariate Analysis How Do We Address Safety Issues Until Virtual is a Reality?
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
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”
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
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”
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”
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