1. | 1 Application of a Bayesian strategy for monitoring multiple outcomes in early oncology clinical trials Application of a Bayesian strategy for monitoring multiple outcomes (safety and efficacy) in early oncology clinical trials

2. Phase I clinical trials in oncology ● Recommend a dose for further clinical development ● Design: ●Patients included in successive cohorts (usually n=3 in each cohort) ●All patients within the same cohort receive the same dose First cohort receive the lowest dose Primary endpoint: Dose-Limiting Toxicity After completion of each cohort, decision is made on predefined algorithm to: Escalate the dose Stay at the same dose De-escalate the dose Stop the study 2

3. Several designs ● Up-and-Down designs ● « 3+3 » ● Accelerated Titration Design ● Model-based dose-response designs ● CRM: Continual Reassessment Method ● bCRM: CRM applied on two binary outcomes (safety and efficacy) ● Designs derived from the CRM : Escalation With Overdose Control (EWOC) ● bEWOC: Escalation With Overdose Control for bivariate outcome ● Dose-response relationships ● Plane (Probability of Activity, Probability of Toxicity) ● Design 3

4. NOM DE LA PRESENTATION |4|4 Up-and-Down design Dose level (i) Enter 3 patients 0/3 DLT1/3 DLT≥ 2/3 DLT Dose level (i-1) is the MTD Add 3 patients 1/6 DLT≥ 2/6 DLT Escalate to dose level (i+1) 3+3 design 4

5. NOM DE LA PRESENTATION |5|5 Model-based dose-response relationship Target range logit[  (d)] =  + exp() log(d/d*) 5 Target level

6. NOM DE LA PRESENTATION |6|6 New drugs in development 6 ● Cytotoxic drugs ● Assumption: Monotonous Dose-Efficacy relationship ● Dose level recommended: Maximum Tolerated Dose (MTD) A dose with a probability of DLT closest to a target proportion ● Molecularly Targeted Agents (MTA) ● Non-monotonous Dose-Efficacy relationship ● Two endpoints: Toxicity and Efficacy (binary outcomes) Cytotoxic profile MTA profile

7. EWOC for bivariate outcome Dose-response models ● Safety model ● Efficacy model 7 Slope > 0 => Monotonous dose-toxicity relationship Non-monotonous dose-efficacy relationship P DLT : Probability of DLT P RESP : Probability of Tumor Response d 1 *, d 2 * : Reference doses

8. Bayesian estimation ● Gaussian a priori distributions for all parameters ● A posteriori distributions obtained by MCMC algorithm ● 3 independent chains ● Convergence checked by Brooks-Gelman-Rubin criterion ● Software tools ● R software version 2.12.2 ● BRugs package version 0.7.1 ● OpenBUGS software version 3.2.1 8

9. Dose-response curves and related credibility interval 9 DLT Probability mean estimate Tolerability threshold

10. Dose-response curves and related credibility interval 9 Response Probability mean estimate Interest threshold

11. Dose-response curves and related credibility interval 9

12. ● Binary endpoints ● DLT ● Tumor Response ● Looking for doses that are in the Targeted Area: e.g. doses such: IP(DLT)<0.35 IP(Resp)>0.5 EWOC for bivariate binary outcome IP(resp | di)=0.48 IP(DLT | di)=0.18 Plane (IP(Resp), IP(DLT)) Over-toxicity Useless Moderate Targeted Probability of Response Not safe Not active Active but not safe Safe but not active Active and safe

13. EWOC for bivariate outcome Predictions for dose level escalation decision 11 400mg/kg 300mg/kg 250mg/kg 200mg/kg 150mg/kg Dose 300mg/kg

14. Probability to be in each area 12 125 mg/kg 250 mg/kg 150 mg/kg 300 mg/kg 200 mg/kg 400 mg/kg

15. Conclusion 13 ● Limits / Next steps ● Limited data / Use PK sampling, PD-biomarkers, mechanistic modelling ● Better assessment of patient variability / Hierarchical models ● No Time-to-Event / Different kinds of toxicities (Acute and Cumulative) ● Different schedules / PK model ● Efficacy model more flexible for Molecularly Targeted Agents ● Decision and communication tool (clinician team) ● Advantages ● Assess both Toxicity and Efficacy ● Take into account uncertainty and control the over-dosing

16. References ● [1] Booth C. M., Calvert A. H., Giaccone G.,Lobbe-Zoo M. W., Seymour L. K., Eisenhauer E. A. Endpoints and other considerations in phase I studies of targeted anticancer therapy: Recommendations from the task force on methodology for the development of innovative cancer therapies. European Journal of Cancer 2008, 44, 19-24. ● [2] O'Quigley J., Pepe M., Fisher L.Continual reassessment method: a practical design for phase 1 clinical trials in cancer. Biometrics 1990, 46, 33-48. ● [3] Neuenschwander B., Branson M., Gsponer T. Critical aspects of the bayesian approach to phase I cancer trials. Statistics in Medicine 2008, 27, 2420-2439. ● [4] Thall P. F., Cook J. D., Estey E. H. Adaptive dose selection using efficacy- toxicity trade-off: Illustrations and practical considerations. Journal of Biopharmaceutical Statistics 2006, 16, 623-638. ● [5] Whitehead J., Hampson L., Zhou Y., Ledent E., Pereira A.A Bayesian approach for dose-escalation in a phase I clinical trial incorporating pharmacodynamic endpoints. Journal of Biopharmaceutical Statistics 2007, 6, 44. 14

17. Merci

18. Dose 20% 35% 60% Response (proba of DLT) Under dosing Targeted toxicity Overdosing Unacceptable toxicity Escalation With Overdose Control (Neuenschwander et. al. 2008)

19. Application of a Bayesian strategy for monitoring multiple outcomes in early oncology clinical trials | 19 Population PKPD model ● Enhanced data ● Actual doses ● Actual Administration schedule ● PK sampling ● PKPD model ● Reflects the mechanisms of the agent (Acute Toxicity, Cumulative Toxicity and Tumour Response) ● Population model ● Each patient has his own specificity ● Population parameters

20. Application of a Bayesian strategy for monitoring multiple outcomes in early oncology clinical trials | 20 Population PKPD model : simple case ● Data ● Binary outcomes (AT, CT and TR) ● Actual doses / schedule ● PK sampling ● PK model : 1 compartment + absorption ● PD model : 3 thresholds ● Threshold AT based on drug concentration ● Threshold CT and TR based on exposure

21. Application of a Bayesian strategy for monitoring multiple outcomes in early oncology clinical trials | 21 Population PKPD model

22. Population PKPD model : simple case

23. Application of a Bayesian strategy for monitoring multiple outcomes in early oncology clinical trials | 23 Probability of events and design ● 250 virtual patients drawn from the population distribution ● Probability of AT ● Probability of CT ● Probability of TR ● Dose escalation and dose allocation based on these probability (like bEWOC) ● Additional rules to take into account the CT

24. Application of a Bayesian strategy for monitoring multiple outcomes in early oncology clinical trials | 24 Priors

25. Application of a Bayesian strategy for monitoring multiple outcomes in early oncology clinical trials | 25 PKPD model ● PK model : ● PD model