Simulated model based adaptive optimal design of adult to children bridging study using FDA stopping criteria.
Eric A. Strömberg, Andrew C. Hooker
Department of Pharmaceutical Biosciences Uppsala University, P.O. Box 591, SE-751 24 Uppsala, Sweden
Objectives: In traditional design, the size of the study population is regularly calculated a priori using power calculations which are dependent on the prior information. Wang et al. has previously suggested a precision criteria for sample size determinations based on derived variability in clearance and volume of distribution for design of pediatric PK studies . Model based adaptive optimal design (MBAOD) has been shown to be less sensitive to initial model misspecification in the design stage. This can be useful in bridging studies where the prior information on model and parameters from the original population may differ greatly from the target population [2,3]. In this work we apply the Wang et al. precision criteria as a stopping criteria for a MBAOD of an adult to children bridging study where the estimates of variability for the parameters of interest are taken directly from the target population.
Methods: An adaptive optimal design of an adult to children bridging study was simulated 25 times using the MBAOD package in R . The true model was assumed to be a one-compartment model with linear elimination. The volume of distribution in children was scaled by a weight covariate model and clearance was driven by weight and age using a size and maturation model. Two levels of misspecification on the scaling parameters were investigated. A simulated pediatric population with children of age 3 months to 18 years was split into 6 age groups. The first cohort of children was fixed to 9 children from the age group with the oldest children. To avoid moving into age groups with poor estimates of parameters the design space for each new cohort of patients was restricted to only add children from age groups for which the stopping criteria had already been reached. In each adaptive cohort the design was optimized for which age group from which to add 2 children to the study. Once the stopping criteria were reached for all age groups, the MBAOD ended.
Results: The stopping criteria was for the small and large misspecifications reached after 2-5 and 4-8 cohorts of children, which corresponded to a total of 11-17 and 15-23 pediatric subjects.
Conclusions: The criteria for population size determination as described by Wang et al. was successfully implemented as stopping criteria for a MBAOD simulation study. The stopping criteria was reached in the MBAOD simulations with fewer individuals than required by a traditional design with the same initial misspecification in the prior information.
 Y. Wang, P.R. Jadhav, M. Lala, J. V Gobburu, Clarification on precision criteria to derive sample size when designing pediatric pharmacokinetic studies., J. Clin.
 Pharmacol. 52 (2012) 1601–6. doi:10.1177/0091270011422812.L.K. Foo, S. Duffull, Adaptive optimal design for bridging studies with an application to population pharmacokinetic studies., Pharm. Res. 29 (2012) 1530–43.
 A. Maloney, M.O. Karlsson, U.S.H. Simonsson, Optimal adaptive design in clinical drug development: a simulation example., J. Clin. Pharmacol. 47 (2007) 1231–43.
 A.C. Hooker, C. van Hasselt, Platform for adaptive optimal design of nonlinear mixed effect models., PAGE 22 Abstr 2952 [www.page-meeting.org/?abstract=2952].
Acknowledgement: This work was supported by the DDMoRe (www.ddmore.eu) project.