Predictive Performance of pyDarwin-Selected Versus Expert-Developed Paediatric Tobramycin Population Pharmacokinetic Models for Model-Informed Precision Dosing.

PyDarwin is an open-source Python package integrating machine learning with NONMEM for automated model selection; however, its utility in model-informed precision dosing (MIPD) remains unvalidated. This study aims to evaluate pyDarwin for population pharmacokinetics (PopPK) model development in MIPD compared with expert-developed models, using paediatric tobramycin as a representative case. The model-building dataset included 450 plasma concentrations from 63 children with cystic fibrosis (CF). One group manually developed a model using stepwise covariate selection, while another applied pyDarwin within a predefined 27-dimensional model space, using a fitness function that incorporated a penalty based on 24-h area under the curve (AUC24h). External validation was performed using 1011 concentrations from 144 children, including evaluation using all therapeutic drug monitoring (TDM) data and clinically relevant MIPD scenarios (a priori [AP], Bayesian 1, Bayesian 2). Both models included weight-based scaling, a maturation function, and estimated glomerular filtration rate as covariates on clearance, yielding comparable clearance estimates. The pyDarwin-selected model was a two-compartment model with fixed allometric scaling exponents, whereas the manual model was a one-compartment model with estimated exponents. Both models showed comparable probability of target attainment for steady-state AUC24h. In external validation using all TDM data, the pyDarwin-selected model demonstrated better predictive performance for CF patients than manual model (prediction-based MPE -14.6% vs -41.6%; MAPE 34.0% vs 54.4%). In MIPD scenarios, the pyDarwin-selected model showed superior performance in the AP setting, with differences diminishing after Bayesian updating. These findings support pyDarwin-based automated PopPK model selection as a reliable and efficient framework for MIPD.