Abstract
Population modeling using nonlinear mixed-effects (NLME) frameworks is fundamental to pharmacometrics, enabling rigorous quantification of inter- and intra-individual variability in pharmacokinetics (PK) and pharmacodynamics (PD). Estimation algorithms such as the stochastic approximation expectation–maximization (SAEM), implemented in widely used platforms including Monolix and NONMEM, provide robust inference but often use predefined structural models and parametric assumptions. Recent advances in neural ordinary differential equations (neural ODEs) extend deep learning to continuous-time dynamical systems [1], offering flexible and data-driven representations of temporal processes. So-called variational autoencoders (VAEs) [2] provide a scalable alternative to sampling-based inference, e.g., SAEM, by replacing iterative posterior estimation with a parameterized posterior learned jointly with the model. In this context, the objectives of the present study are: 1. To formalize the conceptual relationship between SAEM and VAE-based population modeling. 2. To assess, through simulation studies, the impact of an empirical Bayes prior on the recovery of correlated latent effects and covariate relationships. 3. To evaluate predictive performance on clinical PK data relative to a previously published neural ODE benchmark [3].