Exposure-AE-Dropout Analysis in Patients treated with pregabalin.

1. Exposure-AE-Dropout Analysis in Patients treated with pregabalin. Raymond Miller Pfizer Global Research and Development

2. Issue • A new 2 ligand (PD0332334) that has anxiolytic properties was in development. • Little was known about AE’s for this compound, however, extensive knowledge from other 2 ligands (pregabalin) available. • It is generally believed that dose titration may reduce AE’s.

3. Objective • To characterize the relationship between PD0332334 dose, patient characteristics, time, severity and frequency of dizziness and somnolence in patients with GAD.

4. Questions • Would AE frequency be different if the drug was titrated to the target dose? • How long do we need to titrate to minimize AE’s? • How many dose steps do we need to minimize AE’s?

5. Current Information • Multiple phase 3 trials with pregabalin titrated over 3 to 7 days to attain steady state dose in the treatment of GAD. • One phase 4 study with three treatment groups: placebo, pregabalin 600 mg/day fixed, 150-600 mg/day titrated..

6. Phase 3 trialsGAD patients treated with Pregabalin • 1630 patient’s information (47218 observations) was pooled from 6 clinical studies. • All studies consisted of treatment arms with a dose titration phase varying from 3 to 7 days followed by a three or five week maintenance. • Dizziness was spontaneously recorded using a daily diary as none=0, mild=1, moderate=2, and severe=3. • Dropout was recorded as such up to 3 days before scheduled conclusion of the study.

7. Objectives • To describe the exposure-longitudinal AE severity relationship following multiple doses of pregabalin. • To describe the relationship between AE and patient dropout • To explore the relationship between dose titration of pregabalin and dropout

8. Frequency of dizziness by day and dose

9. Exposure-Dizziness-Dropout in GAD patients treated with Pregabalin • Models were developed for exposure-AE as well as AE-dropout. • For AE separate models were developed for theincidence of adverse event and for the conditional severity of adverse event given that an adverse event has occurred. • The unconditional severity probability distribution was obtained by summing the joint probabilities. • Dropout was modeled using a discrete time survival model.

10. Assumption that j ~ Niid(0, 2) is violated.

11. Incidence Model • The probability of incidence of dizziness was modeled using a nonlinear logistic regression model given by the expression: • The incidence model does not contain an inter-individual random effect because AEi is observed only once for each patient • Sigmoid Emax model best describes the drug effect although γ is not well estimated

12. Observed vs. PredictedIncidence Model

13. Conditional Severity Model • The probability of each severity (none, mild, moderate, severe) was modeled with a proportional odds model. The conditional severity model given by the expression: • Drug exposure was based on the intended daily dose (titrated) of pregabalin. • Emax model with time-course placebo effect and a component with an exponential attenuation best describe the AE severity.

14. … … … … Dataset and NONMEM control stream $PRED B1=THETA(1) B2=B1+THETA(2) B3=B2+THETA(3) ;logits for Y>=1, Y>=2, Y.=3 RESP=0 A1 = B1 + RESP + ETA(1) A2 = B2 + RESP + ETA(1) A3 = B3 + RESP + ETA(1) C1=EXP(A1) C2=EXP(A2) C3=EXP(A3) ;probabilities for Y>=1, Y>=2, Y>=3 P1=C1/(1+C1) P2=C2/(1+C2) P3=C3/(1+C3) ;Probabilities for Y=0 Y=1, Y=2, Y=3 PA=1-P1 PB=P1-P2 PC=P2-P3 PD=P3

15. Observed vs. PredictedConditional Severity Model

16. Markov Model • Markov elements have been incorporated to account for the correlation between neighboring observations within a subject: • The logistic function (proportional odds model) and the same structures obtained with the conditional severity model was used.

17. … … … … … Dataset and NONMEM control stream $PRED B1=THETA(1) B2=B1+THETA(2) B3=B2+THETA(3) IF(PRE1.EQ.1) THEN B1=THETA(4) B2=B1+THETA(5) B3=B2+THETA(6) ENDIF IF(PRE1.EQ.2) THEN B1=THETA(7) B2=B1+THETA(8) B3=B2+THETA(9) ENDIF IF(PRE1.EQ.3) THEN B1=THETA(10) B2=B1+THETA(11) B3=B2+THETA(12) ENDIF RESP=0 A1 = B1 + RESP + ETA(1) A2 = B2 + RESP + ETA(1) A3 = B3 + RESP + ETA(1) .. ..

18. Observed vs. PredictedConditional Severity Model with Markov

19. Simulation Step(example: Time-course of incidence) ID=1 Probability of Incidence “Mean of trial” ID=2 N times simulations ID=3 “Summary of Mean” …….. ID=1630 Original Dataset

20. Posterior Predictive Check Distributions of the Number of the Different Transitions with Markov without Markov The vertical line in each plot represents the observed number of transition in the original dataset

21. Simulation (≥mild)Severity Model withMarkov Simulated Probabilities Are Presented By Means (lines) with 95% CI (dash lines) and 80 %CI (shades) from 100 Simulations.

22. Simulation (≥ moderate)Severity Model with Markov Simulated Probabilities Are Presented By Means (lines) with 95% CI (dash lines) and 80 %CI (shades) from 100 Simulations.

23. Simulation (≥severe)Severity Model with Markov Simulated Probabilities Are Presented By Means (lines) with 95% CI (dash lines) and 80 %CI (shades) from 100 Simulations.

24. Conclusion • The probability of experiencing dizziness during any day increases with pregabalin daily dose. • The predicted mean incidence of dizziness was around 35 % at daily dose of 200 mg/day or greater, which was at least 2 fold higher compared to those of at daily doses <150 mg/day. • The most frequently reported severity was mild to moderate. The risk of experience dizziness with any severity increases within 1 week, but decline to over the next 3 to 4 weeks. The risk of mild or moderate dizziness increases up to 25 % within 1 week, and declines to around 7 % over 3 to 4 weeks. • The proportional odds model including a Markov element could describe the time-course of probability of dizziness well.

25. Dropout Model

26. Dropout Across Doses

27. Dropout Model • Dropout was modeled using a discrete time survival model (Gompertz). • Dizziness severity was included in the model as a covariate. g(w,Yt-1) represents the hazard function

28. Simulations of dropout probabilities based on simulated severity of dizziness stratified by representative unique dose titration profiles over time. Observed (red line)

29. GOF 5th – 95th prediction interval constructed from 200 simulations using the original dataset structure as well as median model predicted dropout (grey line) and Kaplan-Meier estimates of in study-survival (black line).

30. External Validation:Pregabalin BID Add-On Titration Trial: A Randomized, Double-Blind,Placebo-Controlled, Parallel-Group, Multicenter Study in Patients With Partial Seizures (1008-157)

31. TIME TO WITHDRAWAL

32. External Validation:Observed (Kaplan Meier) dropout from an independent 12 week GAD trial (red line) with either placebo or 600 mg daily pregabalin treatment and its corresponding 5th-95th nonparametric confidence intervals at weekly increments. Gray polygon outlines a prediction interval of 5th and 95th quantiles of 1000 trial simulation using the described GAD dropout model

33. Titration Scenario’s300 mg daily ITT # Scenario 1 (1week): 50x2, 100, 150, 200, 250, 300 # Scenario 2 (2week): 50x3, 100x3, 150x2, 200x2, 250x2, 300... # Scenario 3 (3week): 50x4, 100x4, 150x4, 200x4, 250x3, 300... # Scenario 4 (4week): 50x6, 100x5, 150x5, 200x5, 250x5, 300... # Scenario 5 (6week): 50x8, 100x8, 150x8, 200x8, 250x8, 300...

34. >=mild

35. >=moderate

36. >=severe Note: y-axis scale is adjusted to enlarge the AE profile

37. Simulated GADsurvival probabilities from the combined Dizziness-dropout model. Two dosing schemes (blue) within a weeklong titration regimen differ only over 3 initial days of dosing.

38. Next Steps • Clinical Trial Simulations using different titration scenarios. • Titration over different times • Variations in the first week. • Scaling to drugs in same class

39. Acknowledgements • Kaori Ito • Bojan Lalovic • Matt Hutmacher

40. Backup

41. >=mild

42. >=moderate

43. >=severe Note: y-axis scale is adjusted to enlarge the AE profile

45. Simulation of dropout

46. Graphical Data Exploration- Nonparametric/Kaplan Meier AnalysisPoolability of Placebo Cohorts GAD number of events number at risk • At ti there are di events (dropouts) and ni individuals (“at risk”). In Splus (survfit) only accommodates categorical time-invariant covariates (strata)!

47. Comparison of Dropout Across Titration Schemes GAD RD@ Day 6 +100 mg/day RD@ Day 7 +150 mg/day 600 mg 400 mg Longer titration (time-to-attainment of randomized dose) ->lower dropout Study 87- an outlier?