Model-based estimates of the effects of efavirenz on bedaquiline pharmacokinetics and suggested dose adjustments for patients coinfected with HIV and tuberculosis

Abstract

Safe, effective concomitant treatment regimens for tuberculosis (TB) and HIV infection are urgently needed. Bedaquiline (BDQ) is a promising new anti-TB drug, and efavirenz (EFV) is a commonly used antiretroviral. Due to EFV's induction of cytochrome P450 3A4, the metabolic enzyme responsible for BDQ biotransformation, the drugs are expected to interact. Based on data from a phase I, single-dose pharmacokinetic study, a nonlinear mixed-effects model characterizing BDQ pharmacokinetics and interaction with multiple-dose EFV was developed. BDQ pharmacokinetics were best described by a 3-compartment disposition model with absorption through a dynamic transit compartment model. Metabolites M2 and M3 were described by 2-compartment models with clearance of BDQ and M2, respectively, as input. Impact of induction was described as an instantaneous change in clearance 1 week after initialization of EFV treatment and estimated for all compounds. The model predicts average steady-state concentrations of BDQ and M2 to be reduced by 52% (relative standard error [RSE], 3.7%) with chronic coadministration. A range of models with alternative structural assumptions regarding onset of induction effect and fraction metabolized resulted in similar estimates of the typical reduction and did not offer a markedly better fit to data. Simulations to investigate alternative regimens mitigating the estimated interaction effect were performed. The results suggest that simple adjustments of the standard regimen during EFV coadministration can prevent reduced exposure to BDQ without increasing exposures to M2. However, exposure to M3 would increase. Evaluation in clinical trials of adjusted regimens is necessary to ensure appropriate dosing for HIV-infected TB patients on an EFV-based regimen.

Figures

Fig 1

Biotransformation of BDQ to M2 and M3 metabolites (other metabolic pathways not shown).

Fig 2

Study design outlining dosing (bottom) and PK sampling (top) over time.

Fig 3

Schematic figure of developed PK model.

Fig 4

Visual predictive check showing the 5th, 50th, and 95th percentiles (lines) of observed BDQ, M2, and M3 concentration data (dots) and the 95% confidence intervals (shaded areas) of the same percentiles from model-simulated data.

Fig 5

Simulations of standard and alternative dosing regimens of BDQ evaluated as weekly exposures (AUC0–168) and maximum concentrations (Cmax) at week 24 of treatment (representative for a large proportion of the treatment period). A, standard regimen (200 mg BDQ three times weekly); B, standard regimen and concomitant EFV; C, alternative 1 (200 mg BDQ daily) and concomitant EFV; D, alternative 2 (400 mg BDQ three times weekly) and concomitant EFV.

Fig 6

Fraction of AUC observed (AUC0–336/AUCinf) for BDQ and M2 without and with coadministration of EFV.