Bibliographic record
Abstract
The addendum to the ICH S1B (S1B (R1) guideline) is a guideline that enables consideration of whether a 2-year carcinogenicity study in rats should be conducted using an integrated approach based on the weight of evidence (WoE) evaluation. Since The S1B (R1) guideline was published on the ICH official website on August 4, 2022, the S1B (R1) guideline has been implemented in 10 out of 17 regions, including EC, FDA, Health Canada, Swissmedic, and MHLW/PMDA. In Japan, "Guidelines for Carcinogenicity Studies of Drugs"(PMSB/ELD Notification No.0310-1), which includes revisions to the S1B (R1) guideline, was issued on March 10, 2023. Also on the same day, March 10, 2023, PMDA established a new "Safety consultation for drugs (consultation regarding the S1B (R1) guideline), establishing a system to accept consultations regarding exemptions from 2-year rat carcinogenicity studies using the S1B (R1) guideline. On the other hand, the S1B (R1) guideline requires that consultations must be held with each regulatory authority to which a marketing authorization application is submitted, which may result in differing decisions being reached between regulatory authorities. Against this background, on March 12, 2024, an IWG was established to share and exchange information on each region's decision-making status for the appropriate implementation of the S1B (R1) guideline, as well as to identify factors that cause inconsistencies in WoE assessments and consider measures to increase consistency and issues that should be addressed in the Q&A. This presentation will provide the activities of the IWG and the status of S1B (R1) consultations in Japan as well as provide regulatory perspectives on points to consider in carcinogenicity assessment using an integrated approach using WoE evaluation, based on the experience of S1B (R1) consultations to date.
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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 | 0.086 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.020 |
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".