Utilizing<sup>19</sup>F NMR to investigate drug disposition early in drug discovery
Bibliographic record
Abstract
1. Nuclear magnetic resonance (NMR), a non-selective and inherently quantitative method, has not been widely used as a quantitative tool for characterizing the disposition of lead molecules prior to clinical development. As a test case, we have chosen a fluoropyrimidine compound in lead optimization phase and evaluated its disposition following oral administration to rats using 19F NMR. 2. Urine, bile and feces from individual rats were profiled and the amount of dose eliminated in each matrix was calculated. The results indicated that, in male rats, the mean dose eliminated over 0-48 h was 40%, with 28% in urine, 9% in bile and 3% in feces. In female rats, the mean dose recovered in excreta over the same period was 55%, with 40% in urine, 8% in bile and 7% in feces. 3. In addition, plasma from rats and plasma from toxicology study in dogs were also profiled and exposure of circulating entities was determined. Plasma exposure determined by 19F NMR was in good agreement with those determined by conventional LC-MS/MS method, suggesting quantitative 19F NMR can be reliably used to estimate single dose or steady-state systemic exposure of circulating entities in animals and humans.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".