Fractional mass filtering as a means to assess circulating metabolites in early human clinical studies
Bibliographic record
Abstract
Recent changes in the regulatory environment have led to a need for new methods to assess circulating human drug metabolites in early clinical studies with respect to their potential toxicological impact. The specific goals of such studies are to determine if the metabolites present in human plasma following administration of a drug candidate also are observed in plasma from the animal studies employed for preclinical toxicological evaluation, and to estimate corresponding exposure margins (animal:human) for the major metabolites. Until recently, the accepted best practice for the characterization of circulating drug metabolites utilized liquid chromatography/tandem mass spectrometry (LC/MS/MS)-based methodologies, in conjunction with authentic chemical standards, for the detection and quantitative analyses of metabolites predicted from both animal studies and experiments with human liver preparations in vitro. While this approach is satisfactory for anticipated biotransformation products, metabolites that were not expected to circulate in human plasma frequently escape detection. Current accurate mass instruments enable the use of the technique of fractional mass filtering to detect both expected and unexpected metabolites in a rapid, less resource-intensive and more robust manner. Application of this technology to several clinical development programs at Merck Research Laboratories has demonstrated the value of fractional mass filtering in the assessment of circulating drug metabolites in early clinical trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".