Prediction of Rifampin Exposure using a Single Concentration-time Point in Patients with Tuberculosis
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Background: Rifampin exhibits highly variable exposure in tuberculosis patients, leading to adverse effects or treatment failure. This study aimed to develop therapeutic drug monitoring (TDM) strategy for rifampin using a single concentration-time point to estimate the area under the concentration-time curve (AUC), with the potential to reduce the number of blood draws. Methods: Plasma concentration(Cp)-time data were obtained from tuberculosis patients by collecting serial venous blood samples after rifampin administration. The Cp timepoint (Ct) that predicts AUC best was explored using linear regression (Exploration). The accuracy and precision were evaluated using Bland-Altman plot. Physiologically based pharmacokinetic modeling approach was used to evaluate whether the single Ct point identified in Exploration provides the best prediction of the AUC (Complement). Results: Cp-time data obtained from 26 participants were evaluable for the determination of AUC by Ct. In Exploration, C4 best predicted the AUC (r2=0.91, p<0.0001), followed by C2 (r2=0.84, p<0.0001). In AUC prediction by C4, the datapoints for predicted and observed AUC pairs were randomly scattered in Bland-Altman plot with the mean bias of −0.029 μg · h/mL, and the 95% limit of agreement of −21.1 to 21.1 μg · h/mL. In Complement, C4,sim best predicted the AUC (r2=0.86, p<0.0001), which supports that C4 reliably predicted AUC. Conclusions: For improving treatment outcomes in the treatment of tuberculosis, a single concentration monitoring is applicable to rifampin TDM instead of AUC, potentially making the process less invasive, painful and cumbersome for patients, clinicians and healthcare providers.
Abstract
Background: Rifampin exhibits highly variable exposure in tuberculosis patients, leading to adverse effects or treatment failure. This study aimed to develop therapeutic drug monitoring (TDM) strategy for rifampin using a single concentration-time point to estimate the area under the concentration-time curve (AUC), with the potential to reduce the number of blood draws. Methods: Plasma concentration(Cp)-time data were obtained from tuberculosis patients by collecting serial venous blood samples after rifampin administration. The Cp timepoint (Ct) that predicts AUC best was explored using linear regression (Exploration). The accuracy and precision were evaluated using Bland-Altman plot. Physiologically based pharmacokinetic modeling approach was used to evaluate whether the single Ct point identified in Exploration provides the best prediction of the AUC (Complement). Results: Cp-time data obtained from 26 participants were evaluable for the determination of AUC by Ct. In Exploration, C4 best predicted the AUC (r2=0.91, p<0.0001), followed by C2 (r2=0.84, p<0.0001). In AUC prediction by C4, the datapoints for predicted and observed AUC pairs were randomly scattered in Bland-Altman plot with the mean bias of −0.029 μg · h/mL, and the 95% limit of agreement of −21.1 to 21.1 μg · h/mL. In Complement, C4,sim best predicted the AUC (r2=0.86, p<0.0001), which supports that C4 reliably predicted AUC. Conclusions: For improving treatment outcomes in the treatment of tuberculosis, a single concentration monitoring is applicable to rifampin TDM instead of AUC, potentially making the process less invasive, painful and cumbersome for patients, clinicians and healthcare providers.