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 (MFX+RIF) in tuberculosis (TB) patients. Population pharmacokinetic (popPK) models were developed for MFX (n = 77) and MFX+RIF (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 MFX+RIF (mean –10%, SE 2.5%). The Bayesian LSSs for MFX and MFX+RIF (both 0 and 6 h) slightly underestimated drug exposure (MFX mean –4.8%, SE 1.3%; MFX+RIF mean –5.5%, SE 3.1%). The multiple linear regression LSS for MFX (0 and 4 h) and MFX+RIF (1 and 6 h), showed mean overestimations of 0.2% (SE 1.3%) and 0.9% (SE 2.1%), respectively. LSSs were successfully developed using the Bayesian approach (MFX and MFX+RIF; 0 and 6 h) and multiple linear regression (MFX, 0 and 4 h; MFX+RIF, 1 and 6 h). These LSSs can be implemented in clinical practice to facilitate TDM of moxifloxacin in TB patients.

Population Pharmacokinetics and Bayesian Dose Adjustment to Advance TDM of Anti-TB Drugs

“…This allows individual patients plasma drug exposures to meet the PK/PD target [ 17 ]. The advantages of the Bayesian approach are the flexible timing of samples, as the population PK model can correct for deviations, and that it takes a number of covariates into account leading to a more accurate estimation of AUC 0–24 h [ 18 ]. The advantage of multiple linear regression-based LSSs is that these do not require modeling software and AUC 0–24 h can be easily estimated using only an equation and the measurement of drug concentrations.…”

“…The advantage of multiple linear regression-based LSSs is that these do not require modeling software and AUC 0–24 h can be easily estimated using only an equation and the measurement of drug concentrations. The disadvantage is that samples should be taken as close as possible to predefined schedules, and the population of interest should be comparable to the population in which the multiple linear regression has been performed because patient characteristics are not included in the equations to estimate drug exposure [ 18 ].…”

“…Therapeutic drug monitoring is helpful to ensure adequate exposure after dose adjustments of levofloxacin due to kidney failure or in the case of drug–drug interactions. For instance, moxifloxacin exposure is approximately 25% decreased if concomitantly used with rifampicin owing to an increase of moxifloxacin clearance [ 18 ]. Four population PK models were identified for levofloxacin and six for moxifloxacin (Table 1 ).…”

“…To reduce the burden and costs of TDM, LSSs can be applied. The under the concentration–time curve from time 0 to 24 h can be adequately estimated using a population PK model and two optimally timed samples for levofloxacin (0 and 5 h) and for moxifloxacin (0 and 6 h, either with or without rifampicin) [ 18 , 89 ].…”

Prospective evaluation of improving fluoroquinolone exposure using centralised therapeutic drug monitoring (TDM) in patients with tuberculosis (PERFECT): a study protocol of a prospective multicentre cohort study

“…22 and Bayesian dose optimisation in MWPharm++ (V.1.7.3; Mediware, Groningen, The Netherlands).Dosing is optimised based on AUC 0-24 /MIC or AUC 0-24 (in case MIC is unknown), taking into consideration comorbidities (HIV, diabetes and immunosuppression), the persistence of TB symptoms and response to treatment so far. The Bayesian dosing software uses sex, age, height, weight and renal function in addition to drug dose and measured drug concentrations to forecast the drug exposure after a dose change.…”