2013 - Glasgow - Scotland

PAGE 2013: Study Design
Andrew Hooker

Platform for adaptive optimal design of nonlinear mixed effect models.

Andrew C. Hooker (1), J. G. Coen van Hasselt (2)

(1) Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. (2) Department of Clinical Pharmacology, Netherlands Cancer Institute, Amsterdam, Netherlands.

Introduction: Recent years have seen an increasing interest in adaptive trial design methodologies. With the growing use of nonlinear mixed effect (NLME) models to support clinical development, adaptive optimal design (AOD) approaches have also become increasingly
relevant. A recent survey indicated that out of 10 major European pharmaceutical companies, the importance of AOD for NLMEM was ranked, 4 on a scale of 5, on average [1]. The usefulness of AOD approaches for NLME models has been previously demonstrated for PET occupancy studies [2], bridging studies [3] and pediatric PK studies [4].  

Aims: To develop a general computational platform for adaptive optimal study design in the context of NLME models. 

Methods: A general algorithm for implementing AOD methodology was created using the optimal design software package PopED [5,6] which links to NONMEM [7] and Perl speaks NONMEM [8] for the estimation steps. The proposed AOD methodology consisted of the following steps:
i) definition of prior NLME model(s);
ii) study design optimization of an initial cohort of subjects based on the prior NLME model(s);
iii) collection and estimation of a cohort of data using the optimized study design (alternatively, stochastic simulation and re-estimation);
iv) updating of the prior NLME model(s) from the estimation step.
Steps ii-iv are repeated (and might change between each iteration) until a predefined stopping criteria has been reached.

Results: An initial implementation of the AOD platform was successfully implemented, allowing the evaluation of feasibility and the identification of technical challenges. The AOD platform has a modular setup and a generalized and flexible design, allowing modifications for specific study design characteristics. As proof-of-concept, an application of adaptive optimal design of a pediatric PK bridging study supported by a maturation model [9] was implemented, in which study designs were optimized for age-cohorts and sampling times.

Conclusion: We successfully developed an initial implementation of an AOD computational platform, which will be available as freeware when released.  

[1] Mentre et al. "Current use and developments needed for optimal design in pharmacometrics: a study performed amongst DDMoRe's EFPIA members." CPT:PSP, 2013.
[2] Zamuner et al. "Adaptive-optimal design in PET occupancy studies." CPT, 2010.
[3] Foo, Duffull. "Adaptive Optimal Design for Bridging Studies with an Application to Population Pharmacokinetic Studies." Pharm. Res., 2012.
[4] Dumont et al. "Optimal two-stage design for a population pharmacokinetic study in children." PAGE, 2012.
[5] Foracchia et al. "POPED, a software for optimal experiment design in population kinetics." CMPB, 2004.
[6] Nyberg et al. "PopED: an extended, parallelized, nonlinear mixed effects models optimal design tool." CMPB, 2012.
[7] Beal et al. "NONMEM User's Guides." (1989-2009), Icon Development Solutions.
[8] Lindbom et al. "Perl-speaks-NONMEM (PsN) - A Perl module for NONMEM related programming." CMPB, 2004.
[9] Anderson et al. "Mechanism-based concepts of size and maturity in pharmacokinetics." Annu. Rev. Pharmacol. Toxicol., 2008.

Reference: PAGE 22 (2013) Abstr 2952 [www.page-meeting.org/?abstract=2952]
Poster: Study Design