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

Chart2

1.00045399931.00045399938.360355317

1.00000002061.00000002067.7718537991

117.2304062646

4.678794411716.7322504846

5.895416595616.2739250416

5.723665527415.8522452777

4.114032239115.4642811662

2.353352832415.1073369523

1.497870683714.7789324219

1.356739933514.4767856681

1.25561533211.0001670174.1987972348

1.18315638891.02478752183.9430355294

1.13123728741.13123728743.7077234009

1.09403562551.30197383423.4912257913

1.067379471.49787068373.2920383749

1.04827949991.69483451223.1087771049

1.03459377341.88162689362.9401685971

1.02478752182.05399224562.7850412812

1.01776103552.21103332392.6423172606

1.0127263382.35335283242.5110048227

1.00911881972.48215066342.3901915476

In Vitro Studies

In Vivo Studies

Computational Analyses

Stage of Development

No of Compounds

Sheet1

0.10.118.16531310578.3603553171.00045399931.0004539993

0.10.126.13417119037.77185379911.00000002061.0000000206

0.10.134.67879441177.230406264611

40.143.63597138126.73225048464.67879441171

70.152.88875602846.27392504165.89541659561

80.162.35335283245.85224527775.72366552741

60.171.96971967865.46428116624.11403223911

40.181.69483451225.10733695232.35335283241

30.191.49787068374.77893242191.49787068371

30.1101.35673993354.47678566811.35673993351

31111.25561533214.19879723481.25561533211.000167017

32121.18315638893.94303552941.18315638891.0247875218

33131.13123728743.70772340091.13123728741.1312372874

34141.09403562553.49122579131.09403562551.3019738342

35151.067379473.29203837491.067379471.4978706837

36161.04827949993.10877710491.04827949991.6948345122

37171.03459377342.94016859711.03459377341.8816268936

38181.02478752182.78504128121.02478752182.0539922456

39191.01776103552.64231726061.01776103552.2110333239

310201.0127263382.51100482271.0127263382.3533528324

311211.00911881972.39019154761.00911881972.4821506634

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