Carbamate Prodrugs Restrict <i>In Vivo</i> Metabolism and Improve the Pharmacokinetics of Isoniazid
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Isoniazid (INH), an important first-line drug in tuberculosis (TB) treatment, faces significant challenges primarily due to hepatotoxicity and peripheral neuropathy as major side effects. These adverse effects often lead to poor patient compliance and treatment dropouts. The INH’s in vivo metabolism is responsible for these adverse effects. INH’s reactive terminal −NH 2 group is involved in its undesired in vivo metabolic transformations. To address this, we designed and synthesized carbamate-based prodrugs of INH by masking the −NH 2 group to reduce its metabolic activity. Herein, we report our efforts to develop such prodrugs and their impact on in vivo metabolism and the pharmacokinetic profile of free INH. The ex vivo stability, bioconversion, and in vivo pharmacokinetic profile with detailed metabolite analysis of these prodrugs were determined in mice. The lead prodrug 1d demonstrated enhanced systemic exposure of free INH (1.5-fold, AUC ≈ 3948 ng·h/mL), reduced formation of undesired metabolites, and prolonged half-life (1.3-fold, t 1/2 ≈ 0.88 h) compared to naive INH. This prodrug approach represents a promising strategy for safer and more effective TB therapy, with the potential for less frequent dosing and improved patient compliance.
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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) | 0.001 | 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".