Limited sampling strategies using linear regression and the Bayesian approach for therapeutic drug monitoring of moxifloxacin in tuberculosis patients

Abstract

Therapeutic drug monitoring (TDM) of moxifloxacin is recommended to improve the response to tuberculosis treatment and reduce acquired drug resistance. Limited sampling strategies (LSSs) are able to reduce the burden of TDM by using a small number of appropriately timed samples to estimate the parameter of interest, the area under the concentration-time curve. This study aimed to develop LSSs for moxifloxacin alone (MFX) and together with rifampin (MFXRIF) in tuberculosis (TB) patients. Population pharmacokinetic (popPK) models were developed for MFX (n 77) and MFXRIF (n 24). In addition, LSSs using Bayesian approach and multiple linear regression were developed. Jackknife analysis was used for internal validation of the popPK models and multiple linear regression LSSs. Clinically feasible LSSs (one to three samples, 6-h timespan postdose, and 1-h interval) were tested. Moxifloxacin exposure was slightly underestimated in the one-compartment models of MFX (mean –5.1%, standard error [SE] 0.8%) and MFXRIF (mean –10%, SE 2.5%). The Bayesian LSSs for MFX and MFXRIF (both 0 and 6 h) slightly underestimated drug exposure (MFX mean –4.8%, SE 1.3%; MFXRIF mean –5.5%, SE 3.1%). The multiple linear regression LSS for MFX (0 and 4 h) and MFXRIF (1 and 6 h), showed mean overestima-tions of 0.2% (SE 1.3%) and 0.9% (SE 2.1%), respectively. LSSs were successfully developed using the Bayesian approach (MFX and MFXRIF; 0 and 6 h) and multiple linear regression (MFX, 0 and 4 h; MFXRIF, 1 and 6 h). These LSSs can be implemented in clinical practice to facilitate TDM of moxifloxacin in TB patients.