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A genetic algorithm based global search strategy for population pharmacokinetic/pharmacodynamic model selection
Author(s) -
Sale Mark,
Sherer Eric A.
Publication year - 2015
Publication title -
british journal of clinical pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.216
H-Index - 146
eISSN - 1365-2125
pISSN - 0306-5251
DOI - 10.1111/bcp.12179
Subject(s) - selection (genetic algorithm) , model selection , computer science , population , pharmacodynamics , genetic algorithm , process (computing) , genetic model , machine learning , mathematical optimization , pharmacokinetics , bioinformatics , biology , mathematics , medicine , biochemistry , environmental health , gene , operating system
The current algorithm for selecting a population pharmacokinetic/pharmacodynamic model is based on the well‐established forward addition/backward elimination method. A central strength of this approach is the opportunity for a modeller to continuously examine the data and postulate new hypotheses to explain observed biases. This algorithm has served the modelling community well, but the model selection process has essentially remained unchanged for the last 30 years. During this time, more robust approaches to model selection have been made feasible by new technology and dramatic increases in computation speed. We review these methods, with emphasis on genetic algorithm approaches and discuss the role these methods may play in population pharmacokinetic/pharmacodynamic model selection.