Using High-Resolution Quadrupole Tof Technology in Dmpk Analyses
Bibliographic record
Abstract
Recent advances in quadrupole TOF (Q-TOF) MS have some bioanalytical scientists referring to a 'paradigm shift' in their field. They are speaking of a potential move away from workflows based upon triple-quadrupole MS. Gone would be the optimizing of numerous parameters in selected-reaction monitoring (SRM) experiments, replaced with more generic workflows provided by Q-TOF instruments with high data acquisition rates, excellent mass accuracy (≤5 ppm) and high resolving power (≥30,000). Such a move could pay real dividends for high-throughput workflows, especially in drug metabolism and pharmacokinetics analyses where quantitation and qualification studies could actually be merged. But, are modern Q-TOF-MS instruments, touted as high-resolution MS, ready for this? If not, how close is it? This article will examine these questions by reviewing recent advances in Q-TOF technology and some fascinating orthogonal technology (such as ion mobility) that modern Q-TOFs employ for even greater analytical power.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".