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<p>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.</p>

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The evolution of nonlinear mixed effects modeling in pharmacometrics: toward AI-based variational autoencoders

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月8日

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  • Jan Rohleff,
  • Gilbert Koch,
  • Johannes Schropp

摘要

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.