From In Silco to In Vivo – Modeling and Simulation Technologies, a Tool for Optimized Drug Development - Joseph Fleishaker, Pfizer Inc.
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From In Silco to In Vivo – Modeling and Simulation Technologies, a Tool for Optimized Drug Development - Joseph Fleishaker, Pfizer Inc.

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Dr. Joseph C. Fleishaker - Pfizer Inc., Speaker at the marcus evans Discovery Summit Fall 2011, delivers his presentation on From In Silco to In Vivo – Modeling and Simulation Technologies, a Tool ...

Dr. Joseph C. Fleishaker - Pfizer Inc., Speaker at the marcus evans Discovery Summit Fall 2011, delivers his presentation on From In Silco to In Vivo – Modeling and Simulation Technologies, a Tool for Optimized Drug Development

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  • 3 Treatment of diabetic neuropathy Two treatment groups: placebo and 3600 mg/day Gabapentin 82 patients per treatment arm 4 week dose escalation phase pain scores measured daily on a 11 point ordinal scale sources of info

From In Silco to In Vivo – Modeling and Simulation Technologies, a Tool for Optimized Drug Development - Joseph Fleishaker, Pfizer Inc. From In Silco to In Vivo – Modeling and Simulation Technologies, a Tool for Optimized Drug Development - Joseph Fleishaker, Pfizer Inc. Presentation Transcript

  • Joseph C. Fleishaker Vice President Clinical Research Pfizer
  • The Outline
    • Why modeling and simulation?
    • M&S in Drug Discovery
      • Target selection/validation
        • Systems Pharmacology/Biology
      • Compound selection
        • Potency
        • ADME
    • M&S in Development
      • Pharmacokinetics
      • PK/PD
      • Clinical Trial Simulation
    • Conclusions
  • Why M&S?
    • “ Use all the data, all the time, everywhere.” Roy Bullingham, Clinical Pharmacology, Pharmacia
    • “ It’s all part of the same experiment.” Sandy Allerheilgen, Merck, referring to any new drug development program.
    • Really about using all of the available data to inform
      • Target selection
      • Compound Selection
      • Dose selection
      • Study design
  • Everybody brings their own model
  • The trick – using one model
    • Model provides
      • Common frame of reference
      • Utilizes everybody’s models and assumptions
      • Puts everything on the table
      • Allow all information to be combined, including new data that’s generated
    • Simulation allows
      • Assumptions to be tested
      • What if scenarios to be tested
      • Multiple dry-runs to be done in silico
      • Reduce animal experimentation
      • Reduce clinical study burden
    • Target Selection
  • Source: J.E. Dumont, Cross Signaling, Cell Specificity, and Physiology Am. J. Physiology, Cell. Physiology . Vol. 283, Issue 1, C2-C28, July 2002
  • Critical nodes in signaling pathways: insights into insulin action – 2006 Cullen M. Taniguchi, Brice Emanuelli and C. Ronald Kahn NATURE REVIEWS MOLECULAR CELL BIOLOGY FEBRUARY 2006
  • Assessing the Impact of Predictive Biosimulation on Drug Discovery and Development Journal of Bioinformatics and Computational Chemistry Author: S. Michelson vol 1 (1): 169-177
  • The Roles of Cells and Mediators in a Computer Model of Chronic Asthma International Archives of Allergy Immunology Author: A.K. Lewis, T. Paterson, C.C. Leong, N. Defranoux, S.T. Holgate, C.L. Stokes Vol. 124:282-286
  • Uses of M&S for Target Selection
    • Synthesize available knowledge
    • Gain disease insights
      • Lack of sole role for eosinophils in airway inflammation
      • Explained failure of IL-5 antibody
    • Utilize to assess role of other targets in treating asthma
    • Update model when node in the pathway has been assessed
    • Enrich the collection of validated targets to prosecute in drug development.
    • Compound Selection
        • Receptor Binding
        • ADME Properties
  • Structural Interaction Fingerprints – Uses for Modeling and Simulation Target-focused Virtual Library generation J. Med. Chem.; 2006; 49(2); 490-500
  • Calculated HLM CLint Measured HLM CLint
    • 95% prediction of all unstable compounds, and
    • 80% for compounds not in training set
    In Silico Model for HLM has been Useful in the Design of Program X Analogs
    • 70% prediction of all stable compounds, and
    • 60% for compounds not in training set
    • Compounds predicted to be stable were stable 95% of the time, and
    • 85% of the time for compounds not in training set
    In training set
  • M&S in Compound Selection
    • It’s about enriching the collection of compounds that is likely to yield a successful compound
    • It’s about time; electrons move faster that lab scientists
    • It’s not about finding the one
    • Use all available information to inform model
    • PK
    • PK/PD and Clinical Trial Simulation
    • Commercial Assessment
  • PK – SimCyp
    • Physiological based PK model
    • Used for
      • Predicting PK in humans
      • Simulating drug-drug interactions
      • Special population PK
      • Effects of formulation on PK
  •  
  • M&S PK
    • Useful for final compound selection
    • Initial guide to dose selection in first in human studies
    • Guide clinical studies needed in drug development
  • Estimating Dose-Response in Humans Using Pre-Clinical Data Experience with Gabapentin, Pregabalin and Related Compounds (Lockwood et al. Pharm Res 2003;20:1752-9)
      • Steps
        • Step 1: Estimate preclinical pregabalin/gabapentin potency ratio
        • Step 2: Develop drug/disease model for gabapentin pain scores using previous Phase 2/3 trials
        • Step 3: Apply potency ratio and gabapentin drug disease model to pregabalin
        • Step 4: Simulate expected response for different clinical trial designs
  • Step 1 : Estimate Relative Potency of Pregabalin and Gabapentin UNCERTAINTY in Potency Ratio Estimate Range = 2-4, best guess = 3 Pre-Clinical Data gabapentin/pregabalin EC 50 ED 50
    • Range of pre-clinical models
    • Receptor binding data
  •  
  • Assumption: Similar Concentration-Response Shape PK from Phase 1
    • Relative potency
    • Based on pre-clinical data
    Baseline pain score Placebo effect Drug effect
  • What Can We Do With This Model?
    • Run “virtual trials” - determine if a given study design is informative
      • # doses, # subjects, etc
    • Test what happens if the assumptions we make are not correct (model uncertainties)?
      • Assumptions about the biology (potency of drug, toxicity, disease progression)
      • Assumptions about the trial (medication non-compliance, dropouts)
    • What if…..
    • A good study design will answer the key question(s) even when our assumptions are not quite right
  • Improvement from placebo 0 200 400 600 800 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 Distribution of Model Predicted Trial Outcomes 0.05 0.1 0.2 0.5 0.8 0.9 0.95 Pregabalin dose (mg/day)
  • Improvement from placebo 0 200 400 600 800 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 Actual Trial Results and Predicted Outcome Distribution 0.05 0.1 0.2 0.5 0.8 0.9 0.95 Pregabalin dose (mg/day)
  • M&S PK/PD - Impact on Drug Development
    • Pre-clinical PK-PD models were “validated” by correctly predicting dose-efficacy relationship in clinical trials
      • valuable information for pre-clinical pharmacology
    • Provided more confidence in making decisions based on PK-PD data with this drug class and disease
  • Combining diverse data to obtain overall view of drug value
  • Clinical Utility Index (CUI)
    • CUI is an integrated measure of benefit/risk
    • CUI is determined as a function of clinically/ commercially relevant endpoints for an optimal sleep compound
    • Weights assigned based on quantitative market research
    • CUI can be defined over entire dose range
    Ouellet et al.,Clinical Pharmacology & Therapeutics (2009); 85 , 3, 277–282
  • Hybrid Conjoint Model Low High Size of bubble reflects relative importance based on hybrid conjoint. Efficacy Safety/Side Effect * Ouellet et al.,Clinical Pharmacology & Therapeutics (2009); 85 , 3, 277–282 High Low
    • Based on team discussion, clinical difference (normalize different scales), weights determined based on desired attributes
    Calculation of CUI Ouellet et al.,Clinical Pharmacology & Therapeutics (2009); 85 , 3, 277–282 CUI - Attribute Clinical Diff Weight Residual Effect (LEEDS) 5 points 35% WASO 25 min 25% Quality 20 points 17% LPS 15 min 13% Sleep Architecture (Stage 1, Stage 3-4) 5% 10%
  • Median CUI and 80% CI Ouellet et al.,Clinical Pharmacology & Therapeutics (2009); 85 , 3, 277–282
  • PD-200390 vs PD-299685 Ouellet et al.,Clinical Pharmacology & Therapeutics (2009); 85 , 3, 277–282
  • M&S in Commercial Assessment
    • Assess the contribution of multiple factors in compound attractiveness
    • Allows decisions regarding compound progression to Phase III
  • Conclusions
    • Modeling and simulation are applicable to all phases of drug development
    • Using all the data, all the time, everywhere
    • Early, it’s about enrichment
      • Target space
      • Compound Space
    • Later, it’s about
      • Guiding clinical development
      • Trial design
      • Decision making