SynthesisBMC infectious diseases2025
Optimal accompanying diagnosis design through external validation of population pharmacokinetic models for moxifloxacin in tuberculosis patients.
Synthesis in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundWe 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.
methodsA 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.
resultsMost 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 between two and eight weeks of accumulated data.
conclusionThis study suggests that popPK modeling with a limited sampling strategy can effectively predict moxifloxacin concentrations in patients with tuberculosis. Optimal accompanying diagnosis can be achieved with data collected within 1 month, preferably at even-numbered weeks, thereby reducing the burden of frequent blood sampling. These findings have implications for improving therapeutic drug monitoring in the treatment of multidrug-resistant tuberculosis, potentially improving patient compliance and treatment outcomes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.