ArticleJAC-antimicrobial resistance2024
Prediction of pyrazinamide resistance in
Article in JAC-antimicrobial resistance, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 21 citations in OpenAlex.
- The structural context of mutations in proteins predicts their effect on antibiotic resistance.eLife · 2026Article
- Molecular Mechanisms Underlying Antimicrobial Resistance in Mycobacteria.International journal of molecular sciences · 2026Review
- Predicting pyrazinamide resistance in Mycobacterium tuberculosis using a graph convolutional network.BMC microbiology · 2026Article
- Pyrazinamide susceptibility testing inFrontiers in cellular and infection microbiology · 2026Article
- Key Proteins and Enzymatic Mechanisms Underlying Drug Resistance in Mycobacterium tuberculosis.Advances in experimental medicine and biology · 2026Review
- Article
- Minimum Inhibitory Concentrations of Anidulafungin as a Potential Predictor of Biocide Susceptibility of Clade 1 Candidozyma (Candida) auris Isolates.MicrobiologyOpen · 2025Article
- FuNTB: a functional network clustering tool for the analysis of genome-wide genetic variants in Mycobacterium tuberculosis.Bioinformatics (Oxford, England) · 2025Article
- An improved catalogue for whole-genome sequencing prediction of bedaquiline resistance inMicrobial genomics · 2025Article
- Predicting rifampicin resistance inERJ open research · 2025Article
- A simplified pyrazinamidase test forJournal of clinical microbiology · 2024Article
- Quantitative measurement of antibiotic resistance in Mycobacterium tuberculosis reveals genetic determinants of resistance and susceptibility in a target gene approach.Nature communications · 2024Article
- Quantitative measurement of antibiotic resistance inResearch square · 2023Article
- Bedaquiline and clofazimine resistance in Mycobacterium tuberculosis: an in-vitro and in-silico data analysis.The Lancet. Microbe · 2023Article
- How quickly can we predict trimethoprim resistance using alchemical free energy methods?Interface focus · 2020Article
- How To Optimally Combine Genotypic and Phenotypic Drug Susceptibility Testing Methods for Pyrazinamide.Antimicrobial agents and chemotherapy · 2020Article
- Combining structure and genomics to understand antimicrobial resistance.Computational and structural biotechnology journal · 2020Review
- Article
- Predicting Kinase Inhibitor Resistance: Physics-Based and Data-Driven Approaches.ACS central science · 2019Article
- Tuberculosis drug resistance profiling based on machine learning: A literature review.The Brazilian journal of infectious diseases : an official publication of the Brazilian Society of Infectious DiseasesReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 3 institutions in 3 countries.
Funding
Abstract
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 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 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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.