Pharmacokinetics of the natural antibiotic negamycin
Bibliographic record
Abstract
1. Negamycin exerts its antimicrobial activity by inhibiting bacterial protein synthesis and is efficacious in animal models of infection. In order to optimize negamycin exposure for therapeutic purposes, its pharmacokinetics in pre-clinical species were determined. 2. Negamycin has a dipeptide-like structure with logD7.4 < -1, causing low permeation into Caco-2 cells, low-oral bioavailability in rats of 6% and low-plasma protein binding of 10% in mouse, rat, dog and human plasma. Negamycin degradation rates in microsomes and hepatocytes predicted low-hepatic intrinsic clearance in pre-clinical species, which was confirmed in vivo where clearance varied between 3.4 and 11.5 mL/min/kg and virtually all negamycin was cleared unchanged renally. The similar behavior in multiple animal species allowed for the prediction of systemic clearance and volume of distribution in humans using multiple-scaling methods and physiological-based pharmacokinetic modeling and simulation. 3. Only 0.05-0.25% (mol/mol) of administered negamycin was recovered as 2-(1-methylhydrazinyl)acetic acid, a potential reactive metabolite, from rat and dog urine, respectively. 4. In summary, negamycin is a very polar molecule with low-plasma protein binding and low-oral bioavailability that is slowly and exclusively cleared into the urine. Its physicochemical properties make intravenous or intramuscular administration, or a derivative thereof, for therapeutic purposes most likely.
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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 | 0.001 |
| 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".