Population pharmacokinetics of ethambutol in South African tuberculosis patients

Abstract

Ethambutol, one of four drugs in the first-line antitubercular regimen, is used to protect against rifampin resistance in the event of preexisting resistance to isoniazid. The population pharmacokinetics of ethambutol in South African patients with pulmonary tuberculosis were characterized using nonlinear mixed-effects modeling. Patients from 2 centers were treated with ethambutol (800 to 1,500 mg daily) combined with standard antitubercular medication. Plasma concentrations of ethambutol were measured following multiple doses at steady state and were determined using a validated high-pressure liquid chromatography-tandem mass spectrometric method. The data comprised 189 patients (54% male, 12% HIV positive) weighing 47 kg, on average (range, 29 to 86 kg), and having a mean age of 36 years (range, 16 to 72 years). The estimated creatinine clearance was 79 ml/min (range, 23 to 150 ml/min). A two-compartment model with one transit compartment prior to first-order absorption and allometric scaling by body weight on clearance and volume terms was selected. HIV infection was associated with a 15% reduction in bioavailability. Renal function was not related to ethambutol clearance in this cohort. Interoccasion variability exceeded interindividual variability for oral clearance (coefficient of variation, 36 versus 20%). Typical oral clearance in this analysis (39.9 liters/h for a 50-kg individual) was lower than that previously reported, a finding partly explained by the differences in body weight between the studied populations. In summary, a population model describing the pharmacokinetics of ethambutol in South African tuberculosis patients was developed, but additional studies are needed to characterize the effects of renal function.

Fig. 1.

Observed concentrations obtained in patients at DP Marais SANTA Centre (left) and Brewelskloof Hospital (right). At DP Marais SANTA Centre, dosing occurred Monday to Friday, whereas the dosing was daily at Brewelskloof Hospital. In the right panel, the time points for each dose level are shifted to the right to better see the data from the four dose levels.

Fig. 2.

Goodness-of-fit plots. Observed concentrations versus the population predictions (top left) and individual predictions (top right), in which the solid line represents the line of identity. Absolute individual weighted residuals versus the individual predictions (bottom left) and conditional weighted residuals versus time after dose (bottom right).

Fig. 3.

Results from a visual predictive check (n = 1,000) applying population prediction correction. The median and the prediction interval (5th and 95th percentiles) of the observed data are shown as solid and dashed black lines together with the confidence intervals (shaded area) of the corresponding median and prediction interval for the simulated data. The data were binned on the basis of the number of observations.

Fig. 4.

Predictions by previously published models by means of visual predictive checks (n = 200) applying population prediction correction. The dashed and solid lines are the 5th, 50th, and 95th percentiles of the observed data. The confidence interval of the median and the prediction interval (5th and 95th percentiles) for the simulated data are given as the shaded areas. Predictions were obtained by employing the models developed by Peloquin et al. (27) (left panel), us (middle panel), and Zhu et al. (39) (right panel), modifying the published models by inclusion of allometrically scaled body weight on clearance and volume terms and lending variability terms from our model. Thus, the results of the simulations should mainly be interpreted with respect to the general tendency.