An Analysis of Pharmaceutical Experience with Decades of Rat Carcinogenicity Testing
Bibliographic record
Abstract
Data collected from 182 marketed and nonmarketed pharmaceuticals demonstrate that there is little value gained in conducting a rat two-year carcinogenicity study for compounds that lack: (1) histopathologic risk factors for rat neoplasia in chronic toxicology studies, (2) evidence of hormonal perturbation, and (3) positive genetic toxicology results. Using a single positive result among these three criteria as a test for outcome in the two-year study, fifty-two of sixty-six rat tumorigens were correctly identified, yielding 79% test sensitivity. When all three criteria were negative, sixty-two of seventy-six pharmaceuticals (82%) were correctly predicted to be rat noncarcinogens. The fourteen rat false negatives had two-year study findings of questionable human relevance. Applying these criteria to eighty-six additional chemicals identified by the International Agency for Research on Cancer as likely human carcinogens and to drugs withdrawn from the market for carcinogenicity concerns confirmed their sensitivity for predicting rat carcinogenicity outcome. These analyses support a proposal to refine regulatory criteria for conducting a two-year rat study to be based on assessment of histopathologic findings from a rat six-month study, evidence of hormonal perturbation, genetic toxicology results, and the findings of a six-month transgenic mouse carcinogenicity study. This proposed decision paradigm has the potential to eliminate over 40% of rat two-year testing on new pharmaceuticals without compromise to patient safety.
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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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".