Protocols of In Vitro Protein Covalent Binding Studies in Liver
Bibliographic record
Abstract
Xenobiotics, including therapeutic agents, can produce a variety of beneficial, as well as adverse, effects in mammals. One potential source of drug-mediated toxicity stems from metabolic activation of the parent compound, typically catalyzed by one or more members of the cytochrome P450 family of enzymes. The resulting electrophile, if not quenched by low molecular weight endogenous nucleophiles, can form covalent adducts to cellular proteins, potentially resulting in enzyme inactivation, cell death, or formation of an immunogenic species. The toxicological consequences of exposure to such reactive intermediates range from mild inflammation to organ failure, anaphylaxis, and death. At Merck Research Laboratories, the potential of drug candidates to bind covalently to proteins is evaluated at the lead optimization stage of drug discovery by incubating a radiolabeled analog of the compound in question with liver microsomal preparations (under oxidative conditions) or whole cells (full cellular metabolic capability), typically derived from rat and human liver. A semi-automated method based on the Brandel Harvester technique then is used to measure the formation of covalent adducts of the test compound to liver proteins. This assay is viewed as an important component of drug discovery programs, since the findings are employed to guide specific efforts to abrogate bioactivation issues through informed structural modification of lead compounds.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.010 |
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".