Background and objective This study proposes a model-informed approach for therapeutic drug monitoring (TDM) of rifampicin to improve tuberculosis (TB) treatment. Methods Two datasets from pulmonary TB patients were used: a pharmacokinetic study (34 patients, 373 samples), and TDM data (96 patients, 391 samples) collected at Radboud University Medical Center, The Netherlands. Nine suitable population pharmacokinetic models of rifampicin were identified in the literature and evaluated on the datasets. A model developed by Svensson et al. was found to be the most suitable based on graphical goodness of fit, residual diagnostics, and predictive performance. Prediction of individual area under the concentration-time curve from time zero to 24 h (AUC 24) and maximum concentration (C max) employing various sampling strategies was compared with a previously established linear regression TDM strategy, using sampling at 2, 4, and 6 h, in terms of bias and precision (mean error [ME] and root mean square error [RMSE]). Results A sampling strategy using 2-and 4-h blood collection was selected to be the most suitable. The bias and precision of the two strategies were comparable, except that the linear regression strategy was more biased in prediction of the AUC 24 than the model-informed approach (ME of 9.9% and 1.5%, respectively). A comparison of resulting dose advice, using predictions on a simulated dataset, showed no significant difference in sensitivity or specificity between the two methods. The model was successfully implemented in the InsightRX precision dosing platform. Conclusion Blood sampling at 2 and 4 h, combined with model-based prediction, can be used instead of the currently used linear regression strategy, shortening the sampling by 2 h and one sampling point without performance loss while simultaneously offering flexibility in sampling times. Key Points We propose a new model-informed therapeutic drug monitoring method for rifampicin. The proposed method uses only two blood samples at 2 and 4 h after dosing. In contrast to linear regression-based methods, the proposed method allows for flexible sampling times and prediction of optimal dose adjustment.

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Background: Modeling and simulation is increasingly used to study pediatric pharmacokinetics, but clinical implementation of age-appropriate doses lags behind. Therefore, we aimed to develop model-informed doses using published pharmacokinetic data and a decision framework to adjust dosing guidelines based on these doses, using piperacillin and amikacin in critically ill children as proof of concept.Methods: Piperacillin and amikacin pharmacokinetic models in critically ill children were extracted from literature. Concentration-time profiles were simulated for various dosing regimens for a virtual PICU patient dataset, including the current DPF dose and doses proposed in the studied publications. Probability of target attainment (PTA) was compared between the different dosing regimens. Next, updated dosing recommendations for the DPF were proposed, and evaluated using a new framework based on PK study quality and benefit-risk analysis of clinical implementation.Results: Three studies for piperacillin (critically ill children) and one for amikacin (critically ill pediatric burn patients) were included. Simulated concentration-time profiles were performed for a virtual dataset of 307 critically ill pediatric patients, age range 0.1–17.9 y. PTA for unbound piperacillin trough concentrations >16 mg/L was >90% only for continuous infusion regimens of 400 mg/kg/day vs. 9.7% for the current DPF dose (80 mg/kg/6 h, 30 min infusion). Amikacin PTA was >90% with 20 mg/kg/d, higher than the PTA of the DPF dose of 15 mg/kg/d (63.5%). Using our new decision framework, altered DPF doses were proposed for piperacillin (better PTA with loading dose plus continuous infusion), but not for amikacin (studied and target population were not comparable and risk for toxicity with higher dose).Conclusions: We show the feasibility to develop model-informed dosing guidelines for clinical implementation using existing pharmacokinetic data. This approach could complement literature and consensus-based dosing guidelines for off-label drugs in the absence of stronger evidence to support pediatricians in daily practice.

Objective Plasma bedaquiline clearance is reportedly more rapid with African ancestry. We examined whether genetic polymorphisms explained between-individual variability in plasma clearance of bedaquiline, its M2 metabolite, and clofazimine in a cohort of patients treated for drug-resistant tuberculosis in South Africa. Methods Plasma clearance was estimated with non-linear mixed-effects modelling. Associations between pharmacogenetic polymorphisms, genome-wide polymorphisms, and variability in clearance were examined using linear regression models. Results Of 195 cohort participants, 140 were evaluable for genetic associations. Among 21 polymorphisms selected based on prior genome-wide significant associations with any drug, rs776746 (CYP3A5*3) was associated with slower clearance of bedaquiline (p = 0.0017) but not M2 (p = 0.25). CYP3A5*3 heterozygosity and homozygosity were associated with 15% and 30% slower bedaquiline clearance, respectively. The lowest P-value for clofazimine clearance was VKORC1 rs9923231 (p = 0.13). In genome-wide analyses, the lowest P-values for clearance of bedaquiline and clofazimine were RFX4 rs76345012 (p = 6.4x10 -7) and CNTN5 rs75285763 (p = 2.9x10 -8), respectively. Conclusions Among South Africans treated for drug-resistant tuberculosis, CYP3A5*3 was associated with slower bedaquiline clearance. Different CYP3A5*3 frequencies among populations may help explain the more rapid bedaquiline clearance reported in Africans. Associations with RFX4 and CNTN5 are likely by chance alone.