Résumé
Tucuxi, a Swiss-developed Model-Informed Precision Dosing (MIPD) software, aims to support clinical dosage decision-making to achieve therapeutic concentration targets. This study assessed its predictive accuracy compared to NONMEM, a gold-standard tool for Bayesian PK predictions. A panel of models was created to mimic various pharmacokinetic scenarios following oral, bolus, or intravenous administration. For each scenario, a virtual population of 4000 patients receiving doses ranging from 10 to 120 mg every 24 h was created. Sparse and rich profiles were simulated, with either one or four samples taken per patient. Tucuxi and NONMEM predicted concentrations at sampling times, trough (Cmin) and peak (Cmax) concentrations, and area under the curve (AUC0-24h) were compared by calculating their relative differences, mean prediction error (MPE) and relative root mean square error (RMSE). The bioequivalence criterion was additionally applied to compare AUC0-24h, Cmin, and Cmax. All the outcomes predicted by Tucuxi closely matched those predicted by NONMEM. A median of 99.8% of predicted concentrations at sampling times presented relative errors smaller than 0.1%. For all outcomes predicted, MPE and relative RMSE were 0% (−0.09, 0.07) and 0.82% (0%, 18.79%) respectively. The bioequivalence criterion, calculated for AUC0-24h, Cmin, and Cmax, was verified for all models, with median values of 100%. This project highlights Tucuxi's excellent predictive accuracy compared to NONMEM, demonstrating its reliability and potential for adoption in clinical practice.