Bedaquiline for treatment of non-tuberculous mycobacteria (NTM): a systematic review and meta-analysis.

BACKGROUND: Non-tuberculous mycobacteria (NTM) infections are increasing in incidence and associated mortality. NTM are naturally resistant to a variety of antibiotics, complicating treatment. We conducted a literature assessment on the efficacy of bedaquiline in treating NTM species in vitro and in vivo (animal models and humans); meta-analyses were performed where possible. METHOD: Four databases were searched using specific terms. Publications were included according to predefined criteria. Bedaquiline's impact on NTM in vitro, MICs and epidemiological cut-off (ECOFF) values were evaluated. A meta-analysis of bedaquiline efficacy against NTM infections in animal models was performed. Culture conversion, cure and/or relapse-free cure were used to evaluate the efficacy of bedaquiline in treating NTM infection in humans. RESULTS: Fifty studies met the inclusion criteria: 33 assessed bedaquiline's impact on NTM in vitro, 9 in animal models and 8 in humans. Three studies assessed bedaquiline's efficacy both in vitro and in vivo. Due to data paucity, an ECOFF value of 0.5 mg/mL was estimated for Mycobacterium abscessus only. Meta-analysis of animal studies showed a 1.86× reduction in bacterial load in bedaquiline-treated versus no treatment within 30 days. In humans, bedaquiline-including regimens were effective in treating NTM extrapulmonary infection but not pulmonary infection. CONCLUSIONS: Bedaquiline demonstrated strong antibacterial activity against various NTM species and is a promising drug to treat NTM infections. However, data on the genomic mutations associated with bedaquiline resistance were scarce, preventing statistical analyses for most mutations and NTM species. Further studies are urgently needed to better inform treatment strategies.

Prediction of pyrazinamide resistance in Mycobacterium tuberculosis using structure-based machine-learning approaches.

Background: Pyrazinamide is one of four first-line antibiotics used to treat tuberculosis; however, antibiotic susceptibility testing for pyrazinamide is challenging. Resistance to pyrazinamide is primarily driven by genetic variation in pncA, encoding an enzyme that converts pyrazinamide into its active form. Methods: We curated a dataset of 664 non-redundant, missense amino acid mutations in PncA with associated high-confidence phenotypes from published studies and then trained three different machine-learning models to predict pyrazinamide resistance. All models had access to a range of protein structural-, chemical- and sequence-based features. Results: The best model, a gradient-boosted decision tree, achieved a sensitivity of 80.2% and a specificity of 76.9% on the hold-out test dataset. The clinical performance of the models was then estimated by predicting the binary pyrazinamide resistance phenotype of 4027 samples harbouring 367 unique missense mutations in pncA derived from 24 231 clinical isolates. Conclusions: This work demonstrates how machine learning can enhance the sensitivity/specificity of pyrazinamide resistance prediction in genetics-based clinical microbiology workflows, highlights novel mutations for future biochemical investigation, and is a proof of concept for using this approach in other drugs.