Evaluation and Benchmarking of a Bounded Data-Driven Correction for Compartmental Pharmacokinetic Models

Background/Objectives: This study applies the previously introduced structure-preserving hybrid framework for compartmental pharmacokinetic models and extends its clinical evaluation using published clinical datasets. The framework combines a mechanistic pharmacokinetic backbone with a bounded data-driven correction. The aim was to assess predictive performance in held-out subjects while keeping the main pharmacokinetic structure and avoiding a fully black-box model. Methods: This applied extension of the framework was tested in several numerical studies using published clinical pharmacokinetic datasets for polymyxin B, linezolid, and tacrolimus. For polymyxin B and linezolid, repeated subject-wise cross-validation with nested tuning was used to compare the mechanistic baseline with unconstrained and constrained hybrid corrections and a boosted-tree residual benchmark. The studies were designed to assess its behavior in a main application setting, across different drugs, under difficult fitting conditions, and under changes in correction strength and mechanistic parameters. Computational time was also assessed. Results: The results showed that the constrained correction remained close to the mechanistic baseline in the held-out analyses of polymyxin B and linezolid, but it did not significantly improve subject-level prediction. The unconstrained correction showed greater deterioration, while the boosted-tree benchmark gave mixed results and no significant subject-level improvement. The additional analyses showed that tighter correction bounds were generally selected and that the constrained hybrid still responds to changes in the mechanistic parameters in a sensible manner. Conclusions: Overall, the results suggest that the bounded data-driven correction can control the poorer performance seen with an unrestricted correction while keeping prediction close to the mechanistic baseline. It therefore provides a cautious way to combine mechanistic pharmacokinetic modeling with data-driven correction while preserving interpretability. Further external validation is still needed.