Optimal accompanying diagnosis design through external validation of population pharmacokinetic models for moxifloxacin in tuberculosis patients
Abstract
BACKGROUND: We aimed to design an optimal accompanying diagnosis, defined as a model-informed limited sampling strategy (LSS) that uses validated population pharmacokinetic (popPK) models to predict moxifloxacin exposures with minimal blood samples, facilitating therapeutic drug (TDM) while reducing patient burden, through external validation of published popPK models for moxifloxacin in patients with tuberculosis, for developing a limited sampling strategy to optimize drug monitoring. METHODS: A systematic literature review identified seven popPK models for moxifloxacin. These models were externally validated using a dataset of 1,042 moxifloxacin concentrations from 113 multidrug-resistant tuberculosis (MDR-TB) patients. The model performance was evaluated using various predictive performance metrics, including the median prediction error, mean absolute error, and root mean square error. A Bayesian forecasting analysis was conducted to determine the optimal sampling points for accompanying diagnosis. RESULTS: Most models showed acceptable predictive performance within ± 30% prediction error (F30). The model proposed by Pranger et al. demonstrated the best overall performance for external validation. Analysis of the accumulated data revealed that sampling at even-numbered weeks (2, 4, 6, 8, and 10) provided better predictive power than sampling at odd-numbered weeks. Data from the first week was insufficient for an accurate prediction. The model performance stabilized…
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