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 . The usefulness of AOD approaches for NLME models has been previously demonstrated for PET occupancy studies , bridging studies  and pediatric PK studies .
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  and Perl speaks NONMEM  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  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.
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