MétaCan
Menu
Back to cohort
Record W2754456074 · doi:10.1016/j.tube.2017.09.003

Mechanisms of resistance against NITD-916, a direct inhibitor of Mycobacterium tuberculosis InhA

2017· article· en· W2754456074 on OpenAlexfundno aff
Matthew B. McNeil, Devon Dennison, Catherine Shelton, Lindsay Flint, Aaron Korkegian, Tanya Parish

Bibliographic record

VenueTuberculosis · 2017
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsnot available
FundersNational Institutes of HealthNational Institute of Technology DelhiNational Institute of Allergy and Infectious DiseasesBill and Melinda Gates FoundationDalCor PharmaceuticalsInha University
KeywordsINHAIsoniazidMycobacterium tuberculosisTuberculosisDrug resistanceMicrobiologyBiologyVirologyMedicinePathology

Abstract

fetched live from OpenAlex

Isoniazid inhibits Mycobacterium tuberculosis InhA and is a key component of drug regimens that treat tuberculosis. However, the high rate of resistance against isoniazid is a contributing factor to the emergence of multi-drug resistance strains of M. tuberculosis. The 4-hydroxy-2-pyridine NITD-916 is a direct inhibitor of M. tuberculosis InhA that has comparable efficacy to isoniazid in mouse models of TB infection but a lower frequency of resistance. To characterize resistance mechanisms against NITD-916 we isolated resistant mutants in H37Rv (Euro-American lineage) and HN878 (East-Asian lineage) strains of M. tuberculosis. The resistance frequency was similar in both strains. Mutations were identified in residues within or near to the active of InhA or in the fabG1inhA promoter region. All mutants were resistant to NITD-916 but were not cross resistant to isoniazid, despite homology to SNPs identified in isoniazid resistant clinical isolates.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.305
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueTuberculosisSame topicTuberculosis Research and EpidemiologyFrench-language works237,207