Development of a predictive platform for target concentration intervention of aminoglycosides.

Aminoglycosides, including gentamicin, are potent bactericidal antibiotics that are widely utilized for the treatment of severe gram-negative bacterial infections. These drugs demonstrate a concentration-dependent bactericidal effect, whereby higher concentrations result in more rapid bacterial elimination. Despite their efficacy, determining optimal dosing regimens is challenging due to significant inter-individual pharmacokinetic (PK) variability and adverse drug reactions, such as nephrotoxicity and ototoxicity. This study developed a simplified, user-friendly PK-predictive system for target concentration intervention of gentamicin. A semi-automatic platform was developed for the generation of patient-specific NONMEM PK datasets using Shiny and the estimation of individual PK parameters using maximum a posteriori Bayesian estimation. The system provides a dosing assessment based on Bayesian PK predictions, with results presented in detailed reports generated via LaTeX. The predictability of the PK model was confirmed via external validation using clinical PK data from 22 patients who underwent therapeutic drug monitoring consultations at the Asan Medical Center. In the external validation the mean prediction error was -0.19 mg/L (95% confidence interval, -0.88 to 0.41) and the root mean squared prediction error was 2.85 mg/L, and 86.4% of observed concentrations fell within the model-predicted 95% prediction interval on the individual prediction check plots. These results support the applicability of the model to this population, within the limits of precision reported, for dose individualisation. The platform architecture may also be adaptable to other drugs with a narrow therapeutic index.