QT Evaluation Studies: Pharmacometric Considerations

1. QT Evaluation Studies: Pharmacometric Considerations Leslie Kenna, Peter Lee and Yaning Wang Office of Clinical Pharmacology and Biopharmaceutics CDER/FDA Clinical Pharmacology Subcommittee Meeting November 17, 2003

2. Outline • Overarching question • Challenges • Methods: Clinical Trial Simulation • Preliminary results

3. What do we want to know? Drug effect on QT interval: “Worst-case” scenario

4. Challenges Variation in response > response of interest

5. Challenges Variation in response > response of interest • Wide intra-individual variability e.g. #1: within day variability

6. 15 msec Historical Baseline QTc Data for Drug “X”Subject i: 10 ECGs / time QTcF (msec) Time (hr)

7. Challenges Variation in response > response of interest • Wide intra-individual variability e.g. #1: within day variability e.g. #2: between day variability

8. Smooth Day 1 Smooth Day 2 Smooth through Days 1:4 Smooth Day 3 Smooth Day 4 Intraindividual Variability in Baseline QTc: Subject i: 4 Days of Measurement QTCF (msec) Time (hr)

9. Challenges Variation in response > response of interest • Wide intra-individual variability e.g. #1: within day variability e.g. #2: between day variability • Wide inter-individual variability

10. Interindividual Variability in Baseline QTc Subject i Subject k QTCF (msec) Time (hr)

11. Observations in Recent Submissions Diverse study designs: e.g. duration, timing, # replicates

12. “Baseline” for Six QT Evaluation Studies

13. Observations in Recent Submissions Diverse study designs: e.g. duration, timing, # replicates Different response to same positive control • Case 1 Moxifloxacin 400 mg 8 msec DQTcF • Case 2 Moxifloxacin 400 mg 13 msec DQTcF

14. Observed Response to Moxifloxacin in Two Recent QT Evaluation Studies What role did study design play in the discrepancy in response?

15. Observations in Recent Submissions Diverse study designs: e.g. duration, timing, # replicates Different observed response to same positive control • Case 1 Moxifloxacin 400 mg 8 msec DQTcF • Case 2 Moxifloxacin 400 mg 13 msec DQTcF Observed response sensitive to analysis method • Mean vs. outlier analysis

16. Mean vs. Outlier Analysis

17. Mean vs. Outlier Analysis

18. Observations in Recent Submissions Diverse study designs: e.g. duration, timing, # replicates Different observed response to same positive control • Case 1 Moxifloxacin 400 mg 8 msec DQTcF • Case 2 Moxifloxacin 400 mg 13 msec DQTcF Observed response sensitive to analysis method • Mean vs. outlier analysis • Definition of baseline

19. Definition of Baseline Influences Analysis(# Outliers) “Baseline” Dose-response appears shallow

20. Definition of Baseline Influences Analysis(# Outliers) “Baseline” “Baseline” Response directly proportional to dose

21. Goal Use available data to aid in the prospective design of QT studies

22. Specific Aims • • Assemble a QT database from submissions • • Resample the data and use Clinical Trial Simulation to evaluate: • (a) clinical trial designs • (b) data analysis methods

23. Clinical Trial Simulation Approach

24. Simulation Study Overview Create true data Sample from historical baseline QT data Choose models and parameters for study design, PK, PD Add baseline response to simulated response to treatment Sample true data according to study design Estimate response Metrics in Concept Paper, in submissions Repeat “many” times at given set of study design parameters Compute Performance Power: Fraction of simulations yielding insignificant effect

25. Simulation Study Overview Create true data Sample from historical baseline QT data Choose models and parameters for study design, PK, PD Add baseline response to simulated response to treatment Sample true data according to study design Estimate response Metrics in Concept Paper, in submissions Repeat “many” times at given set of study design parameters Compute Performance Power: Fraction of simulations yielding insignificant effect

26. Simulation Study Overview Create true data Sample from historical baseline QT data Choose models and parameters for study design, PK, PD Add baseline response to simulated response to treatment Sample true data according to study design Estimate response Metrics in Concept Paper, in submissions Repeat “many” times at given set of study design parameters Compute Performance Power: Fraction of simulations yielding insignificant effect

27. Simulation Study Overview Create true data Sample from historical baseline QT data Choose models and parameters for study design, PK, PD Add baseline response to simulated response to treatment Sample true data according to study design Estimate response Metrics in Concept Paper, in submissions Repeat “many” times at given set of study design parameters Compute Performance Power: Fraction of simulations yielding insignificant effect

28. Simulation Study Overview Create true data Sample from historical baseline QT data Choose models and parameters for study design, PK, PD Add baseline response to simulated response to treatment Sample true data according to study design Estimate response Metrics in Concept Paper, in submissions Repeat “many” times at given set of study design parameters Compute Performance Power: Fraction of simulations yielding insignificant effect

29. Simulation Study Overview Create true data Sample from historical baseline QT data Choose models and parameters for study design, PK, PD Add baseline response to simulated response to treatment Sample true data according to study design Estimate response Metrics in Concept Paper, in submissions Repeat “many” times at given set of study design parameters Compute Performance Power: Fraction of simulations yielding insignificant effect

30. Step 1(a): Create True Data (Sample Baseline) 1. Randomly pick a subject from the database 2. Randomly pick a day of subject i’s baseline observations Day 4 3. Randomly pick n baseline observations / time point

31. Step 1 (b): Choose Simulation Conditions • 4 treatments evaluated: 2 doses of drug, placebo, active control • Parameters to be varied 1. Crossover vs. parallel design 2. Single dose vs. steady state design 3. N 4. ECG sampling • Timing • # replicate ECGs (n): at baseline, on treatment • # days of measurement: at baseline, on treatment 5. PK/PD model for drug • Delayed or direct response • EC50; parent, metabolite 6. PK model for drug • CL; parent, metabolite • ka

32. Treatment effect Drug QTc QTc QTc + = Time Time Time Placebo QTc QTc QTc = + Time Time Time Step 1 (c): Simulate Drug Response Treatment response Baseline QTc

33. True Treatment response Baseline QTc QTc Drug QTc QTc Drug Drug QTc Time Time Time Time QTc Placebo QTc QTc Placebo Placebo QTc Time Time Time Time Step 2: Sample According to Study Design e.g. Sampled Response e.g. Sampled Baseline

34. e.g. Sampled Response e.g. Sampled Baseline Estimated Treatment Effect QTc QTc Drug Time Time - = QTc QTc Placebo Time Time - = Step 3: Estimate Response Examples of approaches to estimating treatment effect Mean(sampled response): treatment - baseline Max(sampled response): treatment - baseline # Subjects with outlying values: treatment - baseline

35. Step 4: Repeat Many Times Randomly pick baseline data ↓ Simulate response to treatment ↓ Estimate drug effect DQTcF Study 1 5 msec Study 2 8 msec … Study 1000 2 msec

36. Step 5: Evaluate Performance e.g. Power Fraction of simulations in which Ho rejected Ho: mean change from baseline for drug = placebo

37. Preliminary Results

38. Study Results: 200 clinical trials simulated QT baseline source data Resample from 72 hr QTc data in 45 subjects Individual Trial Design Randomized, parallel, with treatment and placebo arms 24 hour placebo run in; 24 hours on treatment Hourly QT sampling from 1-24 hours N: varied Treatment characteristics p.o. administration Dose = 100 mg PK: One compartment; ka: 1/hr, CL: 25 L/hr, V: 400 L, Tmax: 2-5 hours PK/PD: Linear relationship, no effect delay Drug effect on QTc is additive on top of the baseline variation Intersubject variability of PK and PK/PD are 25% Analysis 24-hr max QTc during treatment – 24-hr max QTc for baseline 24-hr mean QTc during treatment – 24-hr mean QTc for baseline 24-hr max QTc during treatment – 24-hr average QTc during baseline 24-hr max QTc during treatment (no baseline subtraction)

39. Tmax: 2-5 hours log (Conc ng / mL)) Time (hr) Rmax ~ 16 msec DQTc from Baseline (msec) Time (hr) Treatment Characteristics

40. Power to Detect a Difference Between Drug and Placebo with 95% Confidence

41. Effect of Baseline Measurement on Power Several QT samples at baseline One QT sample at baseline

42. Questions for the committee 1. What additional study design points are recommended for consideration in the analysis of PK-QT data? 2. Comment on the case studies presented and the pros and cons of using clinical trial simulation approaches to evaluate PK-QT study design. Are there other methods of analyzing PK-QT data that the FDA should consider? 3. What critical design elements influence the outcome of a PK-QT study that has as its goal to identify a meaningful change in QT?