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pyDarwin: A Machine Learning Enhanced Automated Nonlinear Mixed-Effect Model Selection Toolbox

  • Xinnong Li
  • , Mark Sale
  • , Keith Nieforth
  • , Kristin L. Bigos
  • , James Craig
  • , Fenggong Wang
  • , Kairui Feng
  • , Meng Hu
  • , Robert Bies
  • , Liang Zhao
  • SUNY Buffalo
  • Certara
  • Johns Hopkins University
  • United States Food and Drug Administration

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

pyDarwin is an open-source Python package for nonlinear mixed-effect model selection. pyDarwin combines machine-learning algorithms and NONMEM to perform a global search for the optimal model in a user-defined model search space. Compared with traditional stepwise search, pyDarwin provides an efficient platform for conducting an objective, robust, less labor-intensive model selection process without compromising model interpretability. In this tutorial, we will begin by introducing the essential components and concepts within the package. Subsequently, we will provide an overview of the pyDarwin modeling workflow and the necessary files needed for model selection. To illustrate the entire process, we will conclude with an example utilizing quetiapine clinical data.

Original languageEnglish
Pages (from-to)758-773
Number of pages16
JournalClinical Pharmacology and Therapeutics
Volume115
Issue number4
DOIs
StatePublished - Apr 2024

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