Oxidative Stress Mediated Idiosyncratic Drug Toxicity
Bibliographic record
Abstract
The following describes a novel screening method for "new chemical entities" (NCEs), suitable for ADMET studies, that measures ability to form prooxidant radicals on metabolism and their ability to induce oxidative stress in intact cells. The accelerated molecular cytotoxic mechanism screening (ACMS) techniques used with isolated rat hepatocytes showed that cytotoxicity is usually initiated as a result of macromolecular covalent binding or macromolecular oxidative stress. While P450 is likely responsible for drug metabolic activation in the liver, intestine, lung, and in other nonhepatic tissues, where P450 levels are low, peroxidases including prostaglandin synthetase peroxidase can catalyze xenobiotic one-electron oxidation to form prooxidant free radicals that may cause toxicity or carcinogenesis. Inflammation markedly activates H2O2, generating NADPH oxidase and peroxidase of certain immune cells when they infiltrate tissues including the liver. Myeloperoxidase and NADPH oxidase in the Kupffer cells (resident macrophages of the liver) also become activated during inflammation. The addition of noncytotoxic concentrations of peroxidase/H2O2 to the hepatocyte incubate markedly increased drug cytotoxicity and prooxidant radical formation as shown by glutathione or lipid oxidation. Many drugs that have hepato- or gastrointestinal (GI) toxicity problems or were withdrawn from the market for safety problems, e.g., troglitazone, tolcapone, mefenamic acid, diclofenac, and phenylbutazone, were markedly more toxic and prooxidant in this inflammation model system, whereas other drugs, e.g., entacapone, were not toxic in this inflammation model. Some of the idiosyncratic hepatotoxicity responsible for recent drug withdrawals may therefore result from commonplace sporadic inflammatory episodes during drug therapy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".