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Adaptive designs as enabler for personalized medicine

Adaptive designs as enabler for personalized medicine. Michael Krams M.D. Janssen Pharmaceuticals, Titusville, NJ mkrams@its.jnj.com Thanks to Donald A Berry MDAnderson Cancer Center, Houston, TX. Borrowing information across compounds. Compound X. Compound X. Compound X. Indication.

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Adaptive designs as enabler for personalized medicine

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  1. Adaptive designs as enabler for personalized medicine Michael Krams M.D. Janssen Pharmaceuticals, Titusville, NJ mkrams@its.jnj.com Thanks to Donald A Berry MDAnderson Cancer Center, Houston, TX

  2. Borrowing information across compounds Compound X Compound X Compound X Indication Compound Y Compound Y Compound Y Indication Compound O Compound O Compound N Compound N Compound A Compound A Compound B Compound B Target B Target B Compound M Compound M Compound C Compound C Target A Target A - Compound L Compound L Target C Indication Compound D Compound D Compound K Compound K Target D Target D Compound F Compound F Target E Target E Compound G Compound G Compound J Compound J Compound E Compound E Compound H Compound H Compound I Compound I Traditional “mapping” is one-dimensional Compound Indication Desired approach is multi-dimensional Indication TargetCompounds

  3. uses accumulating data to decide on how to modify aspects of the study without undermining the validityandintegrityof the trial Validitymeans providing correct statistical inference (such as adjusted p-values, estimates and confidence intervals) assuring consistency between different stages of the study minimizing operational bias Adaptive design - definition • Integritymeans • providing convincing results to a broader scientific community • preplanning, as much as possible, based on intended adaptations • maintaining confidentiality of data

  4. Biomarkers as “necessary condition” with early readout Can be used to adapt treatment allocation (drop a dose or stop for futility) • Biomarkers Are Critical • to enable efficient decision making within clinical trials Subjects with late readout Subjects with early readout Number of subjects with information Subjects recruited x x Time

  5. E X A M P L E T R I A L

  6. Population of patients Outcome: Tumor shrinkage? Standard Phase II Cancer Drug Trials Experimental arm RANDOMIZE Population of patients Experimental arm Outcome: Longer time disease free Standard therapy

  7. ADAPTIVELY RANDOMIZE I-SPY2 TRIAL Experimental arm 1 Experimental arm 2 Outcome: Complete response at surgery Population of patients Experimental arm 3 Experimental arm 4 Experimental arm 5 Standard therapy

  8. ADAPTIVELY RANDOMIZE I-SPY2 TRIAL Experimental arm 1 Experimental arm 2 Outcome: Complete response at surgery Population of patients Experimental arm 3 Experimental arm 4 Experimental arm 5 Standard therapy Arm 2 graduates to small focused Phase III trial

  9. ADAPTIVELY RANDOMIZE I-SPY2 TRIAL Experimental arm 1 Outcome: Complete response at surgery Population of patients Experimental arm 3 Experimental arm 4 Experimental arm 5 Standard therapy Arm 3 drops for futility

  10. ADAPTIVELY RANDOMIZE I-SPY2 TRIAL Experimental arm 1 Outcome: Complete response at surgery Population of patients Experimental arm 4 Experimental arm 5 Standard therapy Arm 5 graduates to small focused Phase III trial

  11. ADAPTIVELY RANDOMIZE I-SPY2 TRIAL Experimental arm 1 Experimental arm 6 Outcome: Complete response at surgery Population of patients Experimental arm 4 Standard therapy Arm 6 is added to the mix

  12. ADAPTIVELY RANDOMIZE I-SPY2 TRIAL Experimental arm 1 Goal: Greater than 85% success rate in Phase III, with focus on patients who benefit Experimental arm 6 Outcome: Complete response at surgery Population of patients Experimental arm 4 Standard therapy Arm 6 is added to the mix

  13. Adaptive designs for dose-finding:Background and Case Study Scott Berry Gary Littman ParvinFardipour Michael Krams

  14. Berry et al. Clin Trials 2010; 7: 121–135

  15. Adaptive Study Design (1) • Investigational drug: oral anti-diabetic • What is the impact of study drug on a) FPG (week 12) b) body weight (week 24) • Two-stage adaptive design • Stage 1: • Compound M, low and medium dose • PBO • Active Comparator • Stage 2: • Compound M very low, low, medium, high dose • PBO • Active Comparator • Selection of dose range and study design informed by preclinical toxicology findings • Study powered to compare FPG at Week 12 • Enrollment to high doseconditional on evidence of safety&efficacy at medium dose; to very low dose conditional on evidence of efficacy at low dose

  16. Adaptive Study Design (2) • Bayesian decision algorithm • The algorithm analyzes the full dose-response curves of all treatment arms utilizing all available data during the treatment period • Every week, the algorithm provided probability estimates and recommendations to the Data Monitoring Committee as to whether enrollment should continue or be terminated • Three formal interim analyses to review emerging benefit-risk profile • 100 subjects with at least 4 weeks of Rx • 240 subjects randomized • 375 subjects randomized • Additional interim analyses may be requested by the Data Monitoring Committee

  17. Adaptive Study Design (3) Two stages – up to 500 subjects treated for 24 weeks Compound M very low dose Placebo Active Comparator (AC) Compound M low dose Compound M medium dose Compound M high dose Earliest transition point to Stage 2 after 100 subj. treated for at least 4 weeks The decision to initiate Stage 2 requires that Compound M medium dose be at least comparable to Active Comparator in decreasing FPG

  18. GO: Open very high dose STOP for futility Compound M medium dose– AC Compound M medium dose – AC [ ] ] [ Compound M medium dose– AC GO: Open very high dose [ ] Decision Criteria for Opening Stage 2 Difference in FPG changes at 12 weeks: 0 Continue with Stage 1 (up to 240 randomized subjects)

  19. Dealing with Partial Data • Regression model to estimate week 12 data for patients who have not yet reached that point • Initial model based on historical data from another compound • Model is refined as we accumulate data from the present study • As more patients complete 12 weeks, we become more confident in the results for two reasons • The percentage of actual observed (rather than model-based) data increases • The model becomes better as we learn from this trial • Therefore, we need to be cautious about making an early decision

  20. The real study

  21. Overview and times of interim analysis findings • Review of DMC findings: • Per protocol interim analysis: 14-Sep • No safety/tolerability issues necessitating early stop • Model on verge of futility recommendation • DMC recommended continuing stage 1 enrollment • Weekly analyses: 21-Sep, 4-Oct • Futility threshold crossed twice • Ad hoc Interim Analysis: 4-Oct • Baseline characteristics • Key Efficacy Results • Safety/Tolerability Conclusions • DMC recommends stopping trial for futility on 4-Oct • Executive Steering Committee reviewed the DMC recommendation to terminate the study for futility and agreed on 23-Oct

  22. Mar 29 24 Subjects Probability (> AC) bad good Placebo Low Medium Active Comparator Low Medium

  23. Apr 11 28 Subjects Probability (> AC) bad good Placebo Low Medium Active Comparator Low Medium

  24. Apr 30 36 Subjects Probability (> AC) bad good Placebo Low Medium Active Comparator Low Medium

  25. May 8 37 Subjects Probability (> AC) bad good Placebo Low Medium Active Comparator Low Medium

  26. May 30 49 Subjects , 1 complete Probability (> AC) bad good Placebo Low Medium Active Comparator Low Medium

  27. Jun 8 57 Subjects, 1 complete Probability (> AC) bad good Placebo Low Medium Active Comparator Low Medium

  28. Jun 21 65 Subjects , 8 complete Probability (> AC) bad good Placebo Low Medium Active Comparator Low Medium

  29. Jul 9 71 Subjects , 9 complete Probability (> AC) bad good Placebo Low Medium Active Comparator Low Medium

  30. Jul 25 90 Subjects , 18 complete Probability (> AC) bad good Placebo Low Medium Active Comparator Low Medium

  31. Aug 5 95 Subjects , 25 complete Probability (> AC) bad good Placebo Low Medium Active Comparator Low Medium

  32. Aug 15 109 Subjects , 35 complete Probability (> AC) bad good Placebo Low Medium Active Comparator Low Medium

  33. Sep 5 133 Subjects , 46 complete Probability (> AC) bad good Placebo Low Medium Active Comparator Low Medium

  34. Sep 14 147 Subjects , 53 complete Probability (> AC) bad good Placebo Low Medium Active Comparator Low Medium

  35. STOP Oct 1 171 Subjects , 61 complete Probability (> AC) bad good Placebo Low Medium Active Comparator Low Medium

  36. STOP Oct 4 179 Subjects , 63 complete Probability (> AC) bad good Placebo Low Medium Active Comparator Low Medium

  37. STOP Oct 15 190 Subjects , 70 complete Probability (> AC) bad good Placebo Low Medium Active Comparator Low Medium

  38. STOP Oct 18 199 Subjects , 71 complete Probability (> AC) bad good Placebo Low Medium Active Comparator Low Medium

  39. Learning in real time - Early stopping Berry et al. Clin Trials 2010; 7: 121–135

  40. Organizational structure Sponsor Steering Committee Study Team Lead Members Blinded Endpoint Adjudication Committee Study Team Blinded Unblinded Adjudicated endpoints Reports Independent Statistical Center Queries and requests for additional reports Reports Clinical data Clinical Data DMC Liaison Queries and requests for additional reports Reports Recommendations Data Monitoring Committee

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