Limited sampling strategy and target attainment analysis for levofloxacin in patients with tuberculosis

Abstract

There is an urgent need to improve and shorten the treatment of tuberculosis (TB) and multidrug resistant tuberculosis (MDR-TB). Levofloxacin, a newer fluoroquinolone, has potent activity against TB both in vitro and in vivo. Levofloxacin dosing can be optimized to improve the treatment of both TB and MDR-TB. Levofloxacin efficacy is linked primarily to the ratio of the area under the concentration-time curve for the free fraction of drug (fAUC) to the MIC. Since obtaining a full-time concentration profile is not feasible in the clinic, we developed a limited sampling strategy (LSS) to estimate the AUC. We also utilized Monte Carlo simulations to evaluate the dosing of levofloxacin. Pharmacokinetic data were obtained from 10 Brazilian TB patients. The pharmacokinetic data were fitted with a one-compartment model. LSSs were developed using two methods: linear regression and Bayesian approaches. Several LSSs predicted levofloxacin AUC with good accuracy and precision. The most accurate were the method using two samples collected at 4 and 6 h (R(2) = 0.91 using linear regression and 0.97 using Bayesian approaches) and that using samples collected at 2 and 6 h (R(2) = 0.90 using linear regression and 0.96 using Bayesian approaches). The 2-and-6-h approach also provides a good estimate of the maximum concentration of the drug in serum (Cmax). Our target attainment analysis showed that higher doses (17 to 20 mg/kg of body weight) of levofloxacin might be needed to improve its activity. Doses in the range of 17 to 20 mg/kg showed good target attainment for MICs from 0.25 to 0.50. At an MIC of 2, poor target attainment was observed across all doses. This LSS for levofloxacin can be used for therapeutic drug monitoring and for future pharmacokinetic/pharmacodynamic studies.

FIG 1

(Left) Population predicted versus population observed concentrations. (Right) Individual predicted versus individual observed concentrations.

FIG 2

Visual predictive check (VPC) for LVX concentration (μg/ml) versus time (hours) based on 1,000 Monte Carlo simulations from the final population pharmacokinetic model. The solid green line represents the 10th, 50th, and 90th percentiles (prctile) of observed data. The shaded regions represent the 90% confidence interval around the 10th, 50th, and 90th percentiles of simulated data. The blue circles are observed concentrations. Abbreviations: emp. prctile, empirical percentile; P.I, prediction interval.

FIG 3

Correlation between covariates and pharmacokinetic parameters. (A) Correlation between weight and CL/F. (B) Correlation between CL_CR and CL/F. (C) Correlation between weight and V/F. (D) Box plot of V/F by sex.

FIG 4

Observed versus predicted AUC using 2- and 6-h samples for both linear regression and Bayesian estimator.

FIG 5

Bland-Altman plots using 2- and 6-h samples. (Top) Bayesian estimator; (bottom) linear regression-based method.

FIG 6

Probability of target attainment for the different dosing regimens.