Modelling of mycobacterial load reveals bedaquiline's exposure-response relationship in patients with drug-resistant TB

Elin M Svensson, Mats O Karlsson, Elin M Svensson, Mats O Karlsson

Abstract

Background: Bedaquiline has been shown to reduce time to sputum culture conversion (SCC) and increase cure rates in patients with drug-resistant TB, but the influence of drug exposure remains uncharacterized.

Objectives: To investigate whether an exposure-response relationship could be characterized by making better use of the existing information on pharmacokinetics and longitudinal measurements of mycobacterial load.

Methods: Quantitative culture data in the form of time to positivity (TTP) in mycobacterial growth indicator tubes obtained from a randomized placebo-controlled Phase IIb registration trial were examined using non-linear mixed-effects methodology. The link to individual bedaquiline exposures and other patient characteristics was evaluated.

Results: The developed model included three simultaneously fitted components: a longitudinal representation of mycobacterial load in patients, a probabilistic component for bacterial presence in sputum samples, and a time-to-event model for TTP. Data were described adequately, and time to SCC was well predicted. Individual bedaquiline exposure was found to significantly affect the decline in mycobacterial load. Consequently, the proportion of patients without SCC at week 20 is expected to decrease from 25% (95% CI 20%-31%) without bedaquiline to 17% (95% CI 13%-21%), 12% (95% CI 8%-16%) and 7% (95% CI 4%-11%), respectively, with half the median, median and double the median bedaquiline exposure observed in patients with standard dosing. Baseline bacterial load and level of drug resistance were other important predictors.

Conclusions: To our knowledge, this is the first successful description of bedaquiline's exposure-response relationship and may be used when considering dose optimization. Characterization of this relationship was possible by integrating quantitative information in existing clinical data using novel models.

© The Author 2017. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy.

Figures

Figure 1.
Figure 1.
VPC (n = 1000) of final logistic model describing the probability of having a positive sample given the estimated underlying mycobacterial load over time on treatment for the placebo and bedaquiline arms.
Figure 2.
Figure 2.
VPC (n = 100) of time to positivity in MGIT per week on treatment. The solid lines represent the observed time to positivity and the shaded areas the 95% prediction intervals based on model simulations of time to positivity.
Figure 3.
Figure 3.
Posterior predictive check of time to sputum culture conversion (SCC). The solid lines represent the observed time to SCC and the shaded areas the 95% prediction intervals based on time to SCC calculated from model simulations of time to positivity. The vertical dashes represent censoring events.
Figure 4.
Figure 4.
Typical impact of bedaquiline exposure on sputum culture conversion (SCC) in MDR-TB patients based on simulations from the final model. OBR, optimized baseline regimen. This figure appears in colour in the online version of JAC and in black and white in the print version of JAC.

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