A strategy for identification of drug metabolites from dried blood spots using triple‐quadrupole/linear ion trap hybrid mass spectrometry
Bibliographic record
Abstract
Discovery stage studies that address issues of absorption, distribution, metabolism and excretion (ADME) are vital for lead optimization resulting in new drug candidates. Often pharmacokinetics (PK) is assessed in these experiments without regard for the metabolism of the compound or the potential for metabolites to circulate in vivo. This work presents a strategy for drug level determination and detection of metabolites using dried blood spots for sample collection. Initially, metabolites are detected from microsomal incubations and characterized using tandem mass spectrometry. Data dependent enhanced MS and enhanced product ion (EMS-EPI) scanning with dynamic background subtraction was used on a hybrid quadruple linear ion trap mass spectrometer. On-the-fly background subtraction greatly improved the detection of metabolites. These data were used to build a multiple reaction monitoring (MRM) method for the parent and metabolites. MRM-EPI scanning was used to analyze the extracted dried blood spots from the PK study. Circulating metabolites were detected using MRM and their identities confirmed on the basis of fragment ion spectra collected simultaneously. The use of dried blood spots provides a means for re-analysis of PK samples for metabolite identification without the need for complex sample storage and preparation. Both parent compound and metabolite information can be collected in these studies, resulting in a savings of time and resources.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".