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Joseph C. Fleishaker Vice President Clinical Research Pfizer
The Outline <ul><li>Why modeling and simulation? </li></ul><ul><li>M&S in Drug Discovery </li></ul><ul><ul><li>Target sele...
Why M&S? <ul><li>“ Use all the data, all the time, everywhere.”  Roy Bullingham, Clinical Pharmacology, Pharmacia </li></u...
Everybody brings their own model
The trick – using one model <ul><li>Model provides </li></ul><ul><ul><li>Common frame of reference </li></ul></ul><ul><ul>...
<ul><li>Target Selection </li></ul>
Source: J.E. Dumont, Cross Signaling, Cell Specificity, and Physiology  Am. J. Physiology, Cell. Physiology . Vol. 283, Is...
Critical nodes in signaling pathways: insights into insulin action – 2006 Cullen M. Taniguchi, Brice Emanuelli and C. Rona...
Assessing the Impact of Predictive Biosimulation on Drug Discovery and Development Journal of Bioinformatics and Computati...
The Roles of Cells and Mediators in a Computer Model of Chronic Asthma  International Archives of Allergy Immunology Autho...
Uses of M&S for Target Selection <ul><li>Synthesize available knowledge </li></ul><ul><li>Gain disease insights </li></ul>...
<ul><li>Compound Selection </li></ul><ul><ul><ul><li>Receptor Binding </li></ul></ul></ul><ul><ul><ul><li>ADME Properties ...
Structural Interaction Fingerprints – Uses for Modeling and Simulation Target-focused Virtual Library generation J. Med. C...
Calculated HLM CLint Measured HLM CLint <ul><li>95% prediction of all unstable compounds, and </li></ul><ul><li>80% for co...
M&S in Compound Selection <ul><li>It’s about enriching the collection of compounds that is likely to yield a successful co...
<ul><li>PK </li></ul><ul><li>PK/PD and Clinical Trial Simulation </li></ul><ul><li>Commercial Assessment </li></ul>
PK – SimCyp  <ul><li>Physiological based PK model </li></ul><ul><li>Used for  </li></ul><ul><ul><li>Predicting PK in human...
 
M&S PK <ul><li>Useful for final compound selection </li></ul><ul><li>Initial guide to dose selection in first in human stu...
Estimating Dose-Response in Humans Using Pre-Clinical Data Experience with Gabapentin, Pregabalin and Related Compounds (L...
Step 1 : Estimate Relative Potency of Pregabalin and Gabapentin UNCERTAINTY in Potency Ratio Estimate Range = 2-4, best gu...
 
Assumption: Similar Concentration-Response Shape  PK from Phase 1 <ul><li>Relative potency </li></ul><ul><li>Based on pre-...
What Can We Do With This Model? <ul><li>Run “virtual trials” - determine if a given study design is informative </li></ul>...
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 0.0...
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 Distri...
M&S PK/PD - Impact on Drug Development <ul><li>Pre-clinical PK-PD models were “validated” by correctly predicting dose-eff...
Combining diverse data to obtain overall view of drug value
Clinical Utility Index (CUI) <ul><li>CUI is an integrated measure of benefit/risk  </li></ul><ul><li>CUI is determined as ...
Hybrid Conjoint Model Low  High Size of bubble reflects relative importance based on hybrid conjoint. Efficacy Safety/Side...
<ul><li>Based on team discussion, clinical difference (normalize different scales), weights determined based on desired at...
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 <ul><li>Assess the contribution of multiple factors in compound attractiveness </li></ul><ul>...
Conclusions <ul><li>Modeling and simulation are applicable to all phases of drug development </li></ul><ul><li>Using all t...
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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 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
  • Transcript of "From In Silco to In Vivo – Modeling and Simulation Technologies, a Tool for Optimized Drug Development - Joseph Fleishaker, Pfizer Inc."

    1. 1. Joseph C. Fleishaker Vice President Clinical Research Pfizer
    2. 2. The Outline <ul><li>Why modeling and simulation? </li></ul><ul><li>M&S in Drug Discovery </li></ul><ul><ul><li>Target selection/validation </li></ul></ul><ul><ul><ul><li>Systems Pharmacology/Biology </li></ul></ul></ul><ul><ul><li>Compound selection </li></ul></ul><ul><ul><ul><li>Potency </li></ul></ul></ul><ul><ul><ul><li>ADME </li></ul></ul></ul><ul><li>M&S in Development </li></ul><ul><ul><li>Pharmacokinetics </li></ul></ul><ul><ul><li>PK/PD </li></ul></ul><ul><ul><li>Clinical Trial Simulation </li></ul></ul><ul><li>Conclusions </li></ul>
    3. 3. Why M&S? <ul><li>“ Use all the data, all the time, everywhere.” Roy Bullingham, Clinical Pharmacology, Pharmacia </li></ul><ul><li>“ It’s all part of the same experiment.” Sandy Allerheilgen, Merck, referring to any new drug development program. </li></ul><ul><li>Really about using all of the available data to inform </li></ul><ul><ul><li>Target selection </li></ul></ul><ul><ul><li>Compound Selection </li></ul></ul><ul><ul><li>Dose selection </li></ul></ul><ul><ul><li>Study design </li></ul></ul>
    4. 4. Everybody brings their own model
    5. 5. The trick – using one model <ul><li>Model provides </li></ul><ul><ul><li>Common frame of reference </li></ul></ul><ul><ul><li>Utilizes everybody’s models and assumptions </li></ul></ul><ul><ul><li>Puts everything on the table </li></ul></ul><ul><ul><li>Allow all information to be combined, including new data that’s generated </li></ul></ul><ul><li>Simulation allows </li></ul><ul><ul><li>Assumptions to be tested </li></ul></ul><ul><ul><li>What if scenarios to be tested </li></ul></ul><ul><ul><li>Multiple dry-runs to be done in silico </li></ul></ul><ul><ul><li>Reduce animal experimentation </li></ul></ul><ul><ul><li>Reduce clinical study burden </li></ul></ul>
    6. 6. <ul><li>Target Selection </li></ul>
    7. 7. Source: J.E. Dumont, Cross Signaling, Cell Specificity, and Physiology Am. J. Physiology, Cell. Physiology . Vol. 283, Issue 1, C2-C28, July 2002
    8. 8. 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
    9. 9. 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
    10. 10. 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
    11. 11. Uses of M&S for Target Selection <ul><li>Synthesize available knowledge </li></ul><ul><li>Gain disease insights </li></ul><ul><ul><li>Lack of sole role for eosinophils in airway inflammation </li></ul></ul><ul><ul><li>Explained failure of IL-5 antibody </li></ul></ul><ul><li>Utilize to assess role of other targets in treating asthma </li></ul><ul><li>Update model when node in the pathway has been assessed </li></ul><ul><li>Enrich the collection of validated targets to prosecute in drug development. </li></ul>
    12. 12. <ul><li>Compound Selection </li></ul><ul><ul><ul><li>Receptor Binding </li></ul></ul></ul><ul><ul><ul><li>ADME Properties </li></ul></ul></ul>
    13. 13. Structural Interaction Fingerprints – Uses for Modeling and Simulation Target-focused Virtual Library generation J. Med. Chem.; 2006; 49(2); 490-500
    14. 14. Calculated HLM CLint Measured HLM CLint <ul><li>95% prediction of all unstable compounds, and </li></ul><ul><li>80% for compounds not in training set </li></ul>In Silico Model for HLM has been Useful in the Design of Program X Analogs <ul><li>70% prediction of all stable compounds, and </li></ul><ul><li>60% for compounds not in training set </li></ul><ul><li>Compounds predicted to be stable were stable 95% of the time, and </li></ul><ul><li>85% of the time for compounds not in training set </li></ul>In training set
    15. 15. M&S in Compound Selection <ul><li>It’s about enriching the collection of compounds that is likely to yield a successful compound </li></ul><ul><li>It’s about time; electrons move faster that lab scientists </li></ul><ul><li>It’s not about finding the one </li></ul><ul><li>Use all available information to inform model </li></ul>
    16. 16. <ul><li>PK </li></ul><ul><li>PK/PD and Clinical Trial Simulation </li></ul><ul><li>Commercial Assessment </li></ul>
    17. 17. PK – SimCyp <ul><li>Physiological based PK model </li></ul><ul><li>Used for </li></ul><ul><ul><li>Predicting PK in humans </li></ul></ul><ul><ul><li>Simulating drug-drug interactions </li></ul></ul><ul><ul><li>Special population PK </li></ul></ul><ul><ul><li>Effects of formulation on PK </li></ul></ul>
    18. 19. M&S PK <ul><li>Useful for final compound selection </li></ul><ul><li>Initial guide to dose selection in first in human studies </li></ul><ul><li>Guide clinical studies needed in drug development </li></ul>
    19. 20. 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) <ul><ul><li>Steps </li></ul></ul><ul><ul><ul><li>Step 1: Estimate preclinical pregabalin/gabapentin potency ratio </li></ul></ul></ul><ul><ul><ul><li>Step 2: Develop drug/disease model for gabapentin pain scores using previous Phase 2/3 trials </li></ul></ul></ul><ul><ul><ul><li>Step 3: Apply potency ratio and gabapentin drug disease model to pregabalin </li></ul></ul></ul><ul><ul><ul><li>Step 4: Simulate expected response for different clinical trial designs </li></ul></ul></ul>
    20. 21. 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 <ul><li>Range of pre-clinical models </li></ul><ul><li>Receptor binding data </li></ul>
    21. 23. Assumption: Similar Concentration-Response Shape PK from Phase 1 <ul><li>Relative potency </li></ul><ul><li>Based on pre-clinical data </li></ul>Baseline pain score Placebo effect Drug effect
    22. 24. What Can We Do With This Model? <ul><li>Run “virtual trials” - determine if a given study design is informative </li></ul><ul><ul><li># doses, # subjects, etc </li></ul></ul><ul><li>Test what happens if the assumptions we make are not correct (model uncertainties)? </li></ul><ul><ul><li>Assumptions about the biology (potency of drug, toxicity, disease progression) </li></ul></ul><ul><ul><li>Assumptions about the trial (medication non-compliance, dropouts) </li></ul></ul><ul><li>What if….. </li></ul><ul><li>A good study design will answer the key question(s) even when our assumptions are not quite right </li></ul>
    23. 25. 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)
    24. 26. 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)
    25. 27. M&S PK/PD - Impact on Drug Development <ul><li>Pre-clinical PK-PD models were “validated” by correctly predicting dose-efficacy relationship in clinical trials </li></ul><ul><ul><li>valuable information for pre-clinical pharmacology </li></ul></ul><ul><li>Provided more confidence in making decisions based on PK-PD data with this drug class and disease </li></ul>
    26. 28. Combining diverse data to obtain overall view of drug value
    27. 29. Clinical Utility Index (CUI) <ul><li>CUI is an integrated measure of benefit/risk </li></ul><ul><li>CUI is determined as a function of clinically/ commercially relevant endpoints for an optimal sleep compound </li></ul><ul><li>Weights assigned based on quantitative market research </li></ul><ul><li>CUI can be defined over entire dose range </li></ul>Ouellet et al.,Clinical Pharmacology & Therapeutics (2009); 85 , 3, 277–282
    28. 30. 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
    29. 31. <ul><li>Based on team discussion, clinical difference (normalize different scales), weights determined based on desired attributes </li></ul>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%
    30. 32. Median CUI and 80% CI Ouellet et al.,Clinical Pharmacology & Therapeutics (2009); 85 , 3, 277–282
    31. 33. PD-200390 vs PD-299685 Ouellet et al.,Clinical Pharmacology & Therapeutics (2009); 85 , 3, 277–282
    32. 34. M&S in Commercial Assessment <ul><li>Assess the contribution of multiple factors in compound attractiveness </li></ul><ul><li>Allows decisions regarding compound progression to Phase III </li></ul>
    33. 35. Conclusions <ul><li>Modeling and simulation are applicable to all phases of drug development </li></ul><ul><li>Using all the data, all the time, everywhere </li></ul><ul><li>Early, it’s about enrichment </li></ul><ul><ul><li>Target space </li></ul></ul><ul><ul><li>Compound Space </li></ul></ul><ul><li>Later, it’s about </li></ul><ul><ul><li>Guiding clinical development </li></ul></ul><ul><ul><li>Trial design </li></ul></ul><ul><ul><li>Decision making </li></ul></ul>
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