A Bayesian approach to estimate the probability of resistance to bedaquiline in the presence of a genomic variant

PLoS One. 2023 Jun 14;18(6):e0287019. doi: 10.1371/journal.pone.0287019. eCollection 2023.

Abstract

Background: Bedaquiline is a core drug for treatment of rifampicin-resistant tuberculosis. Few genomic variants have been statistically associated with bedaquiline resistance. Alternative approaches for determining the genotypic-phenotypic association are needed to guide clinical care.

Methods: Using published phenotype data for variants in Rv0678, atpE, pepQ and Rv1979c genes in 756 Mycobacterium tuberculosis isolates and survey data of the opinion of 33 experts, we applied Bayesian methods to estimate the posterior probability of bedaquiline resistance and corresponding 95% credible intervals.

Results: Experts agreed on the role of Rv0678, and atpE, were uncertain about the role of pepQ and Rv1979c variants and overestimated the probability of bedaquiline resistance for most variant types, resulting in lower posterior probabilities compared to prior estimates. The posterior median probability of bedaquiline resistance was low for synonymous mutations in atpE (0.1%) and Rv0678 (3.3%), high for missense mutations in atpE (60.8%) and nonsense mutations in Rv0678 (55.1%), relatively low for missense (31.5%) mutations and frameshift (30.0%) in Rv0678 and low for missense mutations in pepQ (2.6%) and Rv1979c (2.9%), but 95% credible intervals were wide.

Conclusions: Bayesian probability estimates of bedaquiline resistance given the presence of a specific mutation could be useful for clinical decision-making as it presents interpretable probabilities compared to standard odds ratios. For a newly emerging variant, the probability of resistance for the variant type and gene can still be used to guide clinical decision-making. Future studies should investigate the feasibility of using Bayesian probabilities for bedaquiline resistance in clinical practice.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bayes Theorem
  • Genomics*
  • Probability
  • Uncertainty

Substances

  • bedaquiline

Grants and funding

This work was supported by the Research Foundation Flanders (FWO) [grant numbers G0F8316N and 1S39119N], VLIR-UOS support Degefaye and Trang to follow the master of epidemiology course at university of Antwerp. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.