Development of the adverse outcome pathway “alkylation of <scp>DNA</scp> in male premeiotic germ cells leading to heritable mutations” using the <scp>OECD</scp>'s users' handbook supplement
Bibliographic record
Abstract
The Organisation for Economic Cooperation and Development's (OECD) Adverse Outcome Pathway (AOP) programme aims to develop a knowledgebase of all known pathways of toxicity that lead to adverse effects in humans and ecosystems. A Users' Handbook was recently released to provide supplementary guidance on AOP development. This article describes one AOP-alkylation of DNA in male premeiotic germ cells leading to heritable mutations. This outcome is an important regulatory endpoint. The AOP describes the biological plausibility and empirical evidence supporting that compounds capable of alkylating DNA cause germ cell mutations and subsequent mutations in the offspring of exposed males. Alkyl adducts are subject to DNA repair; however, at high doses the repair machinery becomes saturated. Lack of repair leads to replication of alkylated DNA and ensuing mutations in male premeiotic germ cells. Mutations that do not impair spermatogenesis persist and eventually are present in mature sperm. Thus, the mutations are transmitted to the offspring. Although there are some gaps in empirical support and evidence for essentiality of the key events for certain aspects of this AOP, the overall AOP is generally accepted as dogma and applies broadly to any species that produces sperm. The AOP was developed and used in an iterative process to test and refine the Users' Handbook, and is one of the first publicly available AOPs. It is our hope that this AOP will be leveraged to develop other AOPs in this field to advance method development, computational models to predict germ cell effects, and integrated testing strategies.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.032 |
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".