Leveraging machine learning strategies for nonlinear mixed effects model
selection (using pyDarwin)
By Robert R. Bies, Pharm.D. Ph.D. FISoP
Professor of Pharmaceutical Sciences, Member Institute for Artificial
Intelligence and Data Science. University at Buffalo, Buffalo NY
Wednesday, May 17, 2023 at 9 am -10 am PDT
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Abstract: The application of machine learning approaches to pharmacokinetic and
pharmacodynamic measurements is becoming more widespread. Most of these
approaches are used to predict these measurements without providing inferential
insights. This presentation focuses on the application of machine learning for
nonlinear mixed effects PK model selection. It was recognized almost three
decades ago that there are significant interactions between structural,
statistical and covariate models that result in very different inferences with
respect to model structure, covariate and random effects (Wade 1994).
Classical stepwise approaches to model development are particularly susceptible
to these interactions. Previous applications of a genetic algorithm-based
model search strategy illustrated superiority of this approach to stepwise
model evaluation based on typical model fitness considerations (Bies 2006,
Sherer 2012, Sale 2015). The machine learning strategies described in this
talk present an alternative means of evaluating the model search space that may
provide greater insight into these interactions while optimizing the numerical
characteristics of the solutions obtained. This is illustrated using the
implementation of these techniques (Random Forest, Gaussian Process/Bayesian
Optimization, Genetic Algorithm among others) in the open source pyDarwin
software.
Wade, J.R., Beal, S.L. & Sambol, N.C. Interaction between structural,
statistical, and covariate models in population pharmacokinetic analysis.
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https://doi.org/10.1007/BF02353542
Sale M, Sherer EA. A genetic algorithm based global search strategy for
population pharmacokinetic/pharmacodynamic model selection. Br J Clin
Pharmacol. 2015 Jan;79(1):28-39. doi: 10.1111/bcp.12179. PMID: 23772792; PMCID:
PMC4294074.
Sherer EA, Sale ME, Pollock BG, Belani CP, Egorin MJ, Ivy PS, Lieberman JA,
Manuck SB, Marder SR, Muldoon MF, Scher HI, Solit DB, Bies RR. Application of a
single-objective, hybrid genetic algorithm approach to pharmacokinetic model
building. J Pharmacokinet Pharmacodyn. 2012 Aug;39(4):393-414. doi:
10.1007/s10928-012-9258-0. Epub 2012 Jul 6. PMID: 22767341; PMCID: PMC3400037.
Bies RR, Muldoon MF, Pollock BG, Manuck S, Smith G, Sale ME. A genetic
algorithm-based, hybrid machine learning approach to model selection. J
Pharmacokinet Pharmacodyn. 2006 Apr;33(2):195-221. doi:
10.1007/s10928-006-9004-6. Epub 2006 Mar 28. PMID: 16565924.