Exploring Ensemble Selection for Model Averaging in Model-informed Precision Dosing: A Tacrolimus Case Study Extended to Cefepime and InfliximabExploring Ensemble Selection for Model Averaging in Model-informed Precision Dosing: A Tacrolimus Case Study Extended to Cefepime and Infliximab

Model averaging combines the predictions of several population pharmacokinetic models to support model-informed precision dosing, but how to select ensemble models remains unexplored. Using tacrolimus as a case study, we first evaluated all possible ensembles of 27 models in 37 kidney and liver transplant recipients with full pharmacokinetic profiles at occasions 1 and 5 and trough concentrations at occasions 2, 3 and 4. Models were weighted by individual objective function value. Predictive performance for the occasion 5 area under the curve was assessed with individual parameters estimated from the occasion 5 trough concentration or from occasion 1 to 4 observations. Then, seven ensembles, selected a priori from relevance and external evaluation scores or as the best-performing ensembles previously identified, were compared across ten sets of observations, from a single trough concentration to dense sampling. Mean imprecision per ensemble size reached a plateau beyond 3-4 models. The best ensembles depended on the set of observations, comprising three models with the occasion 5 trough concentration and seven or eight models with occasions 1 to 4 observations. Across all sets, the ensemble optimised on the occasion 5 trough concentration was among the most precise, and score-based ensembles remained consistently precise, whereas the ensemble optimised on occasions 1 to 4 generalised poorly. Only a limited sampling strategy at occasion 5 kept imprecision below 20% for all ensembles. The plateau effect generalised to infliximab and cefepime. Ensemble composition matters more than ensemble size. Model averaging should rely on well-chosen ensembles rather than all available models.