PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool

  • Authors:

  • Joakim Nyberg;Sebastian Ueckert;Eric A. StröMberg;Stefanie Hennig;Mats O. Karlsson;Andrew C. Hooker

  • Affiliations:

  • The Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Faculty of Pharmacy, Uppsala University, P.O. Box 591, SE-751 24 Uppsala, Sweden;The Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Faculty of Pharmacy, Uppsala University, P.O. Box 591, SE-751 24 Uppsala, Sweden;The Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Faculty of Pharmacy, Uppsala University, P.O. Box 591, SE-751 24 Uppsala, Sweden;The Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Faculty of Pharmacy, Uppsala University, P.O. Box 591, SE-751 24 Uppsala, Sweden;The Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Faculty of Pharmacy, Uppsala University, P.O. Box 591, SE-751 24 Uppsala, Sweden;The Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Faculty of Pharmacy, Uppsala University, P.O. Box 591, SE-751 24 Uppsala, Sweden

  • Venue:
  • Computer Methods and Programs in Biomedicine
  • Year:
  • 2012

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Abstract

Several developments have facilitated the practical application and increased the general use of optimal design for nonlinear mixed effects models. These developments include new methodology for utilizing advanced pharmacometric models, faster optimization algorithms and user friendly software tools. In this paper we present the extension of the optimal design software PopED, which incorporates many of these recent advances into an easily useable enhanced GUI. Furthermore, we present new solutions to problems related to the design of experiments such as: faster and more robust FIM calculations and optimizations, optimizing over cost/utility functions and diagnostic tools and plots to evaluate design performance. Examples for; (i) Group size optimization and efficiency translation, (ii) Cost/constraint optimization, (iii) Optimizations with different FIM approximations and (iv) optimization with parallel computing demonstrate the new features in PopED and underline the potential use of this tool when designing experiments.