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

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.