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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 - PowerPoint PPT Presentation
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The Paradigm Shift from Traditional to VirtualStephen K. Durham, PhDDepartment of Lead Safety Assessment
Factors Influencing ChangeTechnologyCombinatorial ChemistryHigh-throughput ScreensComputational PowerGenomic revolutionEscalating Costs
The Changing ParadigmTraditional(Sequential)MTSPotencySelectivitySpecificityFunctional ActivityCurrent(Parallel)HTSPotencySelectivitySpecificityFunctional ActivityADME/PharmaceuticsSafetyFuture(Knowledge-Based)Computational Design and Screening of VirtualLibrariesIn Vitro ConfirmationDEVELOPMENT
What Are the Key Toxicological Liabilities Affecting Drug Development?GenotoxicityCarcinogenicityTeratogenicityLiver ToxicityExtrahepatic 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.
How Do We Address Safety Issues Until Virtual is a Reality?Tiered Multivariate Analysis
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In Vitro Studies
In Vivo Studies
Computational Analyses
Stage of Development
No of Compounds
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Sheet1
In Vitro Studies
In Vivo Studies
Computational Analyses
Stage of Development
No of Compounds
Sheet2
Sheet3
In Silico Predictive ToxicityComputational programs ultimately fulfill the requirement for determining liabilities at the early stages of discoveryMutagenicityCarcinogenicityReproductiveToxicity
In Silico Predictive Toxicity
Approaches to Analysis
Typical TOPKAT Output
Typical Multicase Output
Typical DEREK Output
Size Does MatterLarge Pharma AdvantagesRobust Institutional DatasetExtensive Logistical ResourcesBiotech AdvantagesFlexible and AgileRisk TolerantStrong Academic TiesQuid pro quo
Internal Evaluation ProtocolComparative computational toxicological evaluation using a pharmaceutical data setAnalysis of compounds not existing in training dataset (MCASE/ TOPKAT)Include BMS institutional data Compliance for robustness and chemical diversity
Acceptability Criteria for Computational Analysis85% ConcordanceRequire 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 AnalysisHigh concordanceRequire low false negatives and false positivesSmall compound requirementsModerate through-put with rapid results
Reliable in vitro assays are necessary to confirm computational predictions
The Changing Paradigm
Emerging Technologies
AcknowledgementsGenetic Toxicology, Drug Safety EvaluationAndrew HenwoodLarry YottiLead Safety AssessmentOliver FlintGreg PearlStructural Biology and ModelingDeborah LoughneyJonathan MasonRoy Vaz