Personalized Medicine In Clinical
               Drug Development: Opportunities For
                     Biomedical Infor...
Presentation Outline



         • Background
               • Personalized Medicine and Personalized Healthcare
         ...
Personalized Medicine or Personalized Healthcare


    • Based on the recognition that
      unprecedented types of inform...
M&S for PHC: Opportunities for Biomedical Informatics

       • Wide application of the new technologies to clinical trial...
Case Study 1: Identify Treatment Responders

                Treatment effect in overall patient population




          ...
Models to Predict Survival In Treatment Group




                                     24 hrs
               72 hrs
      ...
Models to Predict Survival In Placebo Group




                      120 hrs
                         72 hrs
            ...
Variable Importance Plots

             Top predictors in placebo                                  Top predictors in in tr...
Predictive Biomarkers: Most Important Variables For Survival On
Treatment But Least Important On Placebo




 9
          ...
Potential Application to Phase 3: Marker-based Vs. Traditional
 Design (with and without stratified analysis)

     Tradit...
Case Study 2: Identify Patients at High Safety Risk




     .



         Using biomarkers to predict individual patient ...
Question: Who will develop liver signals during the trial?
       Purpose: patient selection / risk stratification (tolera...
Predictive Models Using Baseline Information




13
                     Proprietary and Confidential © AstraZeneca 2009
N...
Predictive Baseline Variables for Biochemical Hy’s Law Cases
During The Trials
       Importance




                     ...
Biomarker-based Risk Stratification to Improve Patient Safety



     • Identify patients with high risk of developing liv...
M&S for PHC Integrated into a Clinical Program

               Which patients will benefit most from the therapy (i.e. w/
...
Acknowledgements

     •         AZ Biomedical Informatics Network
     •         AZ Hepatotoxicity Safety Knowledge Group...
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Personalized Medicine in clinical drug development: opportunities for Biomedical Informatics

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Presentation to AMIA 2009, San Francisco, CA, in panel S58: Applications of Biomedical Informatics in Clinical Drug Development.

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Personalized Medicine in clinical drug development: opportunities for Biomedical Informatics

  1. 1. Personalized Medicine In Clinical Drug Development: Opportunities For Biomedical Informatics Zhaohui (John) Cai AMIA 2009 San Francisco, CA 1 Proprietary and Confidential © AstraZeneca 2009 Nov 17, 2009 FOR INTERNAL USE ONLY
  2. 2. Presentation Outline • Background • Personalized Medicine and Personalized Healthcare (PHC) in AZ • Modeling and Simulation (M&S) for PHC: opportunities for Biomedical Informatics (BioMed Ix) • Case study 1: modeling for predicting treatment responders vs. non-responders for better efficacy • Case study 2: modeling for identifying patients with high safety risks • M&S for PHC Integrated into a Clinical Program 2 Proprietary and Confidential © AstraZeneca 2009 Nov 17, 2009 FOR INTERNAL USE ONLY
  3. 3. Personalized Medicine or Personalized Healthcare • Based on the recognition that unprecedented types of information will be obtainable from genetic, genomic, proteomic, imaging, etc, technologies, which will help us further refine known diseases into new categories • Managing a patient's health based on the individual patient's specific characteristics vs. “standards of care” • PHC in AZ to focus on therapies linked to diagnostics and tools to deliver superior outcomes to patients • PHC in AZ to deliver: • Disease segmentation • Patient selection • Improved dosing 3 Proprietary and Confidential © AstraZeneca 2009 Nov 17, 2009 FOR INTERNAL USE ONLY
  4. 4. M&S for PHC: Opportunities for Biomedical Informatics • Wide application of the new technologies to clinical trials has not come to reality in the pharmaceutical industry, for all kinds of reasons, such as • Limitations in trial designs • Extra cost and time • Uncertainly in regulatory and commercial consequences • A cost-effective approach is M&S using available data and technologies • The industry and FDA have now a broader use and acceptance of M&S • Cheaper, faster, and easier to integrate into clinical programs (arguable) • Many M&S application types: biological (from cell to system to disease), pharmacological (PK/PD, drug-disease/drug- patients/efficacy and safety*), clinical trial modeling and simulation, HEOR modeling, etc. 4 * Opportunities for BioMed Ix Proprietary and Confidential © AstraZeneca 2009 Nov 17, 2009 FOR INTERNAL USE ONLY
  5. 5. Case Study 1: Identify Treatment Responders Treatment effect in overall patient population Placebo Treatment Treatment effect in patient subpopulations defined by baseline biomarker levels Placebo Treatment Blue: survivors Red: non-survivors Marker A ≤ xxx Marker A > xxx 5 Proprietary and Confidential © AstraZeneca 2009 Nov 17, 2009 FOR INTERNAL USE ONLY
  6. 6. Models to Predict Survival In Treatment Group 24 hrs 72 hrs 120 hrs Baseline Random Forest models using common 46 variables across 4 time points 6 Proprietary and Confidential © AstraZeneca 2009 Nov 17, 2009 FOR INTERNAL USE ONLY
  7. 7. Models to Predict Survival In Placebo Group 120 hrs 72 hrs 24 hrs Baseline Random Forest using common 46 variables across 4 time points 7 Proprietary and Confidential © AstraZeneca 2009 Nov 17, 2009 FOR INTERNAL USE ONLY
  8. 8. Variable Importance Plots Top predictors in placebo Top predictors in in treatment group (prognosis markers) group (efficacy markers) Variables Variables 8 Nov 17, 2009 Importance Score Proprietary and Confidential © AstraZeneca 2009 FOR INTERNAL USE ONLY
  9. 9. Predictive Biomarkers: Most Important Variables For Survival On Treatment But Least Important On Placebo 9 Proprietary and Confidential © AstraZeneca 2009 Nov 17, 2009 FOR INTERNAL USE ONLY
  10. 10. Potential Application to Phase 3: Marker-based Vs. Traditional Design (with and without stratified analysis) Traditional design: Placebo Register Randomize Treatment Traditional analysis Stratified analysis Marker-based design: Placebo Marker A > cutoff Randomize Treatment Register Test marker Placebo Marker A <= cutoff Randomize Treatment Interim analysis Final analysis • Additional risk: a test with a quick turn around time for Marker A • Benefit: Better chance to demonstrate mortality improvement and allow a personalized medicine approach with this product Smaller sample size and shorter trial duration if interim analysis shows significance for Marker A <= cutoff arms. 10 More ethical if the treatment is not beneficial to patients with Marker A >cutoff Proprietary and Confidential © AstraZeneca 2009 Nov 17, 2009 FOR INTERNAL USE ONLY
  11. 11. Case Study 2: Identify Patients at High Safety Risk . Using biomarkers to predict individual patient risk of developing liver signals in response to a drug 11 Proprietary and Confidential © AstraZeneca 2009 Nov 17, 2009 FOR INTERNAL USE ONLY
  12. 12. Question: Who will develop liver signals during the trial? Purpose: patient selection / risk stratification (tolerators vs. adaptors and susceptibles) Data: Baseline Labs+ Demographics + Concomitant Medications + Medical History Classification models: Abnormals (FDA-guideline cut-offs) vs. Normals Abnormals Max(LFT) Liver Chemistry test 1xULN Normals 1 2 i-2 i-1 i 5 Visits Result (based on 5 projects, 24 studies) Baseline values Marker A Markers B+C Marker D Model Important for predicting Normals Abnormals AT>3 ALP>1.5 Bilirubin>1.5 12 Proprietary and Confidential © AstraZeneca 2009 Nov 17, 2009 FOR INTERNAL USE ONLY
  13. 13. Predictive Models Using Baseline Information 13 Proprietary and Confidential © AstraZeneca 2009 Nov 17, 2009 FOR INTERNAL USE ONLY
  14. 14. Predictive Baseline Variables for Biochemical Hy’s Law Cases During The Trials Importance Baseline variables 14 Proprietary and Confidential © AstraZeneca 2009 Nov 17, 2009 FOR INTERNAL USE ONLY
  15. 15. Biomarker-based Risk Stratification to Improve Patient Safety • Identify patients with high risk of developing liver signals • Better patient risk management • Cost-effective biomarker research • Being applied to a live project in transition to phase III • Potential applications of the predictive biomarkers • Trial protocol for close monitoring of the high-risk subpopulation (e.g. those with marker A > xxx) • New exclusion criterion for trials as appropriate (e.g. excluding those with marker A > xxx) • Warnings in product label: marker A should be obtained before starting therapy. If marker A > xxx, do not start therapy or apply close monitoring 15 Proprietary and Confidential © AstraZeneca 2009 Nov 17, 2009 FOR INTERNAL USE ONLY
  16. 16. M&S for PHC Integrated into a Clinical Program Which patients will benefit most from the therapy (i.e. w/ most effectiveness and least safety risk)? Data Model/ Historical Preclinical/ mining Hypothesis Literature trials Initial Phases 1 & 2a question Literature Biological mining interpretation Hypothesis & Candidate Biomarker(s) initial modeling /model Learn* Phase 2b Model validation Validated Biomarker(s) Design and analysis /model Phase 3 Model application Patient stratification Design and analysis Confirm Outcome * Opportunities for BioMed Ix (a PHC product) 16 Proprietary and Confidential © AstraZeneca 2009 Nov 17, 2009 FOR INTERNAL USE ONLY
  17. 17. Acknowledgements • AZ Biomedical Informatics Network • AZ Hepatotoxicity Safety Knowledge Group • AZ Clinical Project Team for AZDxxxx • AZ Clinical Information Science Leadership • AZ Discovery Information • AMIA CRI-WG 17 Proprietary and Confidential © AstraZeneca 2009 Nov 17, 2009 FOR INTERNAL USE ONLY

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