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.

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

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  • 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Predictive Models Using Baseline Information 13 Proprietary and Confidential © AstraZeneca 2009 Nov 17, 2009 FOR INTERNAL USE ONLY
  • 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. 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. 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. 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