Comparison of sub‐2‐μm particle columns for fast metabolite ID
Bibliographic record
Abstract
The use of sub-2-microm particle columns for fast high throughput metabolite ID applications was investigated. Three LC-MS methods based on different sub-2-microm particle size columns using the same analytical 3 min gradient were developed (Methods A, B, and C). Method A was comprised of a 1.8 microm particle column coupled to an MS, methods B and C utilized a 1.7 microm particle column (BEH 50 x 2.1 mm2 id) and 1.8 microm particle column coupled to a Q-TOF MS. The precision and the separation efficiency of the methods was compared with repeated standard injections (N=10) of reference compounds verapamil (VP), propranolol, and fluoxetine. Separation efficiency and MS/MS spectral quality were also evaluated for separation and detection of VP and its two major metabolites norverapamil (NVP) and O-demethylverapamil (ODMVP) in human-liver microsomal incubates. Results show that 1.8 microm particle columns show similar performance for separation of VP and its major metabolites and comparable spectral quality in MS(E) mode of the Q-TOF instrument compared to 1.7 microm particle columns. Additionally, the study also confirmed that sub-2-microm particle size columns can be operated with standard analytical HPLC but that performance is maximized by integrating column in UPLC method with reduced void volumes. All the methods are suitable for the determination of major metabolites for compounds with high metabolic turnover. The high throughput metabolite profile analysis using 384-well plate format of up to 48 compounds in incubates of human-liver microsomes was discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".