Insights on the Mechanism of Action of INH-C<sub>10</sub> as an Antitubercular Prodrug
Bibliographic record
Abstract
Tuberculosis remains one of the top causes of death worldwide, and combating its spread has been severely complicated by the emergence of drug-resistance mutations, highlighting the need for more effective drugs. Despite the resistance to isoniazid (INH) arising from mutations in the katG gene encoding the catalase-peroxidase KatG, most notably the S315T mutation, this compound is still one of the most powerful first-line antitubercular drugs, suggesting further pursuit of the development of tailored INH derivatives. The N ′-acylated INH derivative with a long alkyl chain (INH-C 10 ) has been shown to be more effective than INH against the S315T variant of Mycobacterium tuberculosis, but the molecular details of this activity enhancement are still unknown. In this work, we show that INH N ′-acylation significantly reduces the rate of production of both isonicotinoyl radical and isonicotinyl–NAD by wild type KatG, but not by the S315T variant of KatG mirroring the in vivo effectiveness of the compound. Restrained and unrestrained MD simulations of INH and its derivatives at the water/membrane interface were performed and showed a higher preference of INH-C 10 for the lipidic phase combined with a significantly higher membrane permeability rate (27.9 cm s –1 ), compared with INH-C 2 or INH (3.8 and 1.3 cm s –1, respectively). Thus, we propose that INH-C 10 is able to exhibit better minimum inhibitory concentration (MIC) values against certain variants because of its better ability to permeate through the lipid membrane, enhancing its availability inside the cell. MIC values of INH and INH-C 10 against two additional KatG mutations (S315N and D735A) revealed that some KatG variants are able to process INH faster than INH-C 10 into an effective antitubercular form ( wt and S315N), while others show similar reaction rates (S315T and D735A). Altogether, our results highlight the potential of increased INH lipophilicity for treating INH-resistant strains.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".