Detection of Covalent Adducts to Cytochrome P450 3A4 Using Liquid Chromatography Mass Spectrometry
Bibliographic record
Abstract
Protein covalent labeling can be an undesirable property of compounds being studied in drug discovery programs. Identifying such compounds relies on the use of radiolabeled material, which requires an investment in time and resources not typically expended until later in the discovery process. We describe the detection of covalent adducts to cytochrome P450 3A4, the most abundant and important P450 from a human and drug discovery viewpoint, using liquid chromatography mass spectrometry. The technique is illustrated using L-754,394 and 6',7'-dihydroxybergamottin, two known inhibitors of P450 3A4. Mass spectrometry of the intact apoprotein as well as the adducted protein is demonstrated. Such methodology may provide the means for screening compounds for covalent protein binding without the use of a radiolabel. It also provides direct information about mechanism-based inhibitors in terms of extent, stoichiometry, and nature of the adduct(s) (mass shift). This information may provide a means for understanding the mechanism of covalent labeling earlier in a drug discovery environment.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".