Application of error-corrected sequencing technologies for in vivo regulatory mutagenicity assessment
Bibliographic record
Abstract
Error-corrected sequencing (ECS) is a transformative method for in vivo mutagenicity assessment, enabling direct, highly sensitive measurement of mutation frequency and spectrum. ECS addresses key limitations of the transgenic rodent (TGR) assay, including lack of integration into standard toxicity studies, restricted model availability, and limited alignment with the 3R principles. To support regulatory acceptance, an expert workgroup of the International Workshops on Genotoxicity Testing (IWGT) reviewed ECS technologies and developed consensus recommendations for its inclusion into Organisation for Economic Co-operation and Development (OECD) test guidelines. The working group agreed that ECS: produces results that are concordant with validated TGR assays; can be incorporated into standard ≥28-day repeat-dose toxicity studies; and, data interpretation should be based on overall mutation frequency compared with concurrent vehicle controls. The working group emphasized harmonized data reporting aligned with OECD principles and endorsed study designs that enable quantitative risk assessment. Overall, the working group agreed that ECS offers a significant advancement over current mutagenicity assays by enabling the use of diverse models beyond conventional TGR systems described in OECD test guideline 488. The working group fully supports the application of ECS to generate in vivo mutagenicity data for regulatory submissions and recommends its inclusion in future OECD test guidelines.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".