Numerical Verification of Tucuxi, a Promising Bayesian Adaptation Tool for Model-Informed Precision Dosing.

Abstract

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 (C_min) and peak (C_max) concentrations, and area under the curve (AUC_0-24h) 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 AUC_0-24h, C_min, and C_max. 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 AUC_0-24h, C_min, and C_max, 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.