Amortized In-Context Mixed Effect Transformer Models: A Zero-Shot Approach for Pharmacokinetics

Accurate dose–response forecasting under sparse sampling is central to precision pharmacotherapy. We present the Amortized In-Context Mixed-Effect Transformer (AICMET) model, a transformer‑based latent‑variable framework that unifies mechanistic compartmental priors with amortized in‑context Bayesian inference. AICMET is pre‑trained on hundreds of thousands of synthetic pharmacokinetic trajectories with Ornstein-Uhlenbeck priors over the parameters of compartment models, endowing the model with strong inductive biases and enabling zero‑shot adaptation to new compounds. At inference time, the decoder conditions on the collective context of previously profiled trial participants, generating calibrated posterior predictions for newly enrolled patients after a few early drug concentration measurements. This capability collapses traditional model‑development cycles from weeks to hours while preserving some degree of expert modelling. Experiments across public datasets show that AICMET attains state‑of‑the‑art predictive accuracy and faithfully quantifies inter‑patient variability—outperforming both nonlinear mixed‑effects baselines and recent neural ODE variants. Our results highlight the feasibility of transformer‑based, population‑aware neural architectures as offering a new alternative for bespoke pharmacokinetic modeling pipelines, charting a path toward truly population-aware personalized dosing regimens.