ArticleJournal of pharmacokinetics and pharmacodynamics2026
The evolution of nonlinear mixed effects modeling in pharmacometrics: toward AI-based variational autoencoders.
Article in Journal of pharmacokinetics and pharmacodynamics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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3 authors.
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Abstract
The evolution of nonlinear mixed effects (NLME) modeling reflects a continuous cycle of innovation based on advances in numerical methods and computational power. This commentary outlines the evolution of NLME modeling that began with linearization-based approaches in the 1980s, progressed through sampling-based methods in the 2000s, and is now entering a new phase shaped by AI. Variational autoencoders bridge classical NLME modeling with AI-based methods allowing the development and application of AI-augmented PMX models. This opens the route for integrating multimodal data and addressing increasingly complex modeling challenges.
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