The numerical probability of carcinogenicity to humans of some pharmaceutical drugs: Alkylating agents, topoisomerase inhibitors or poisons, and DNA intercalators
Bibliographic record
Abstract
The nonclinical branch of regulatory pharmacology has traditionally relied on the sensitivity and specificity of regulatorily recommended bioassays. Nonetheless, any predictive testing (eg, safety pharmacology) with less than 100% sensitivity or 100% specificity is prone to deliver false positive or negative results (namely, outcomes discordant to the clinical gold standard). It was recently suggested that the statistics-based and regulatory pertinent "predictive values approach" (PVA) might help to reach a more predictive use of preclinical testing data. To resolve the associated probability of carcinogenicity to humans, the PVA was applied to 37 pharmaceuticals bearing inadequate epidemiological evidence of carcinogenicity, but identifiable as unequivocal mutagens. According to current knowledge, a 98.9% (or more) probability of carcinogenicity to humans was reckoned for those 37 genotoxic drugs. Accordingly, these pharmaceutical drugs might be either scientifically or regulatorily regarded as "carcinogenic to humans." In the USA, European Union, or Canada as examples, the great majority of these 37 pharmaceuticals are authorized for medical use in humans. From the results of the present appraisal, the following is suggested (1) for the pharmaceuticals listed in this report, to include significant carcinogenicity warnings in their prescribing information; (2) to conduct pharmacoepidemiology studies or risk-benefit analyses (if warranted), and (3) based on the respective risk-benefit analyses, to re-evaluate the authorization of hydralazine and phenoxybenzamine as antihypertensives, oxcarbazepine as an anticonvulsant, and phenazopyridine as a urinary tract antimicrobial or analgesic. For the four latter drugs (eg, phenoxybenzamine), a 99.5% probability of carcinogenicity to humans was estimated.
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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 | 0.021 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".