Evaluating the lifetime cumulative dose as a basis for carcinogenic potency of nitrosamines – a key tenet underpinning less-than-lifetime approaches for establishing acceptable intake limits
Bibliographic record
Abstract
Potential health risks associated with N-nitrosamine (NAs) impurities in pharmaceuticals have received significant attention. Regulatory guidance recommends methods to establish Acceptable Intake limits (AIs) that are protective for daily lifetime exposure. However, questions remain whether the same limit should apply to NA impurities in drug products used for less than lifetime (LTL). The ICH M7(R2) guidance addresses this for mutagenic impurities by establishing higher AIs for LTL exposures; however, this has not been adopted in current regulatory guidance for NA impurities which fall under the Cohort of Concern (potentially high potency carcinogens). The research described herein addresses one key knowledge gap: that carcinogenic potency of NAs is a function of total exposure rather than dose rate, a fundamental principle underlying the ICH M7(R2) approach for LTL. Data were evaluated from rodent carcinogenicity bioassays for eight NAs and aflatoxin B1 (another high potency carcinogen) involving exposure durations from 21 to 120 weeks. For all case studies, carcinogenic potency was found to be a function of total cumulative dose rather than daily dose, aligning with the ICH M7(R2) guidance, which posits that higher AI limits can be justified for LTL durations. Remaining knowledge gaps will be addressed in a subsequent publication.
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".