1. Influence of the size of the cohorts in adaptive design for nonlinear mixed effect models: an evaluation by simulation for a pharmacokinetic (PK) and pharmacodynamic (PD) model in oncology Giulia Lestini, Cyrielle Dumont, France Mentré IAME UMR 1137, INSERM, University Paris Diderot, Paris, France PODE 2014 1September 11, 2014

2. Outline Context Objectives Methods Simulation Study Results Conlusion and Perspectives 2

3. Context: Optimal design in NLMEM Choosing a good design for a planned study is essential – Number of patients – Number of sampling times for each patient – Sampling times (allocation in time) Optimal design depends on prior information (model and parameters) D-optimality criterion – Local Designs – Robust designs Atkinson, Optimum Experimental Designs. (1995) Dodds et al., J Pharmacokinet Pharmacodyn. (2005) Pronzato and Walter, Math Biosci. (1988) 3

4. Context: Adaptive design AD: clinical trial designs that use accumulating information to decide how to modify predefined aspects of the study – Areas of interest: predicting clinical data; Phase 1 studies – ADs are useful to provide some flexibility but were rarely used for NLMEM Two-stage designs could be more efficient than fully adaptive design (not yet tested in NLMEM) Dumont et al. implemented two-stage AD in NLMEM AD questions: – How many adaptations? (e.g stages) – How many patients in each cohort? (i.e. cohorts size) 4 Foo et al., Pharm Res. (2012) Mentré et al., CPT Pharmacometrics Syst Pharmacol. (2013) Fedorov et al., Stat Med. (2012) Dumont et al., Commun Stat. (2014)

5. Objectives 1.To compare by simulation one and two-stage designs using a PKPD model in oncology 2.To study the influence of the size of each cohort in two-stage designs 3.To test extensions of two-stage adaptive design as three- and five-stage adaptive designs 5

6. Methods: Basic mixed effect model 6

7. Methods: Basic population design 7

8. Methods : Fisher Information Matrix (FIM) Mentré et al., Biometrika (1997) Bazzoli et al., Comput Methods Programs Biomed. (2010) Mentré et al., PAGE Abstr 3032 (2014) Dumont et al., Commun Stat. (2014) www.pfim.biostat.fr 8

9. Method: K-stage Adaptive Design 9 Design Optimisation Design Optimisation Estimation... Estimation Design Optimisation...

10. Concentration Simulation Study: PKPD Model CL/V kout ksyn Effect ka Gueorguieva et al., Comput Methods Programs Biomed. (2007) Gueorguieva et al., Br J Clin Pharmacol. (2014) Bueno et al., Eur J Cancer. (2008) 10

11. Simulation Study: Parameters PK Parameters 22 100 4010 00 0.49 00 0.2 PD Parameters 20.2 0.3 0.49 0.2 00 11

12. Simulation Study: Evaluated designs 12

13. Simulation Study: Clinical Trial Simulation 13

14. Results: 1-stage vs 2-stage balanced design Relative Estimation Error (REE) for PK parameters Ka and CL Ka RB 0.40.91.01.10.5 RB 1.61.7 1.8 CL 14

15. Results: 1-stage vs 2-stage balanced design Relative Estimation Error (REE) for PD parameters Kout and IC50 Kout RB 0.62.634.23.23.7 RB -0.5-0.353.101.5 IC50 15

16. Results: 1-stage vs 2-stage balanced design Relative Root Mean Squared Error (RRMSE) for PD parameters 16

17. Results: 1-stage vs 2-stage balanced design Relative Root Mean Squared Error (RRMSE) for PD parameters 17 * RRMSEs standardized to (best 1-stage design)

18. Results: Cohort size influence in 2-stage design Relative Root Mean Squared Error (RRMSE) for PD parameters 18

19. Results: Cohort size influence in 2-stage design Relative Root Mean Squared Error (RRMSE) for PD parameters 19 * RRMSEs standardized to (best 1-stage design)

20. Results: 2-stage vs 3- and 5-stage adaptive designs Relative Root Mean Squared Error (RRMSE) for PD parameters 20

21. Results: 2-stage vs 3- and 5-stage adaptive designs Relative Root Mean Squared Error (RRMSE) for PD parameters 21 * RRMSEs standardized to (best 1-stage design)

22. Results: 2-stage vs 3- and 5-stage adaptive designs Relative Root Mean Squared Error (RRMSE) for PD parameters 22 * RRMSEs standardized to (best 1-stage design)

23. Conclusions 23

24. Thank you for your attention ! 24 The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n° 115156, resources of which are composed of financial contributions from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution. The DDMoRe project is also financially supported by contributions from Academic and SME partners

25. Back up 25

26. Results: Cohort size influence in 2-stage design Relative Estimation Error (REE) for PK parameters Ka and CL Ka RB 0.50.80.50.70.9 RB 1.7 1.8 CL 26

27. Results: Cohort size influence in 2-stage design Relative Estimation Error (REE) for PD parameters Kout and IC50 Kout RB 5.95.03.78.910.4 RB 8.35.31.58.812.7 IC50 27

28. 28 2 nd Stage3 rd Stage4 th Stage5 th Stage Designs Two-stage 1224 835 649 647 645 Three-stage 1227571 1228661 Five-stage 1228760469476