Adverse Outcome Pathway‐Informed Integrated Testing to Identify Chemicals Causing Genotoxicity Through Oxidative <scp>DNA</scp> Damage: Case Study on 4‐Nitroquinoline 1‐Oxide
Bibliographic record
Abstract
Adverse outcome pathways (AOPs) provide a framework to organize and weigh evidence linking molecular interactions of toxicants in cells to adverse outcomes relevant to risk assessment or regulatory decision-making. Applying this framework facilitates the interpretation of data produced using new test methods. We used an existing AOP (AOP #296) that describes how oxidative DNA damage leads to mutations and chromosomal aberrations to develop an integrated testing strategy to evaluate whether a chemical operates through this pathway. We exposed human TK6 cells to increasing concentrations of 4-nitroquinoline 1-oxide (4NQO), a tobacco mimetic that causes oxidative DNA damage, in a time-series design. We measured oxidative DNA damage and strand breaks using the high-throughput CometChip assay with and without formamidopyrimidine DNA glycosylase (Fpg), alongside analyses of micronucleus (MN) frequency by flow cytometry, and mutations by error-corrected sequencing (duplex sequencing-DS). Our analysis shows how these methods can be combined to quantify 4NQO-induced, concentration- and time-dependent increases in: (a) oxidative DNA damage (occurred early and at low concentrations); (b) strand breaks (remained elevated to 6 h post-exposure); (c) MN frequency (at 24 h); (d) mutation frequency (at 48 h); and (e) C > A transversions consistent with expected substitutions induced by oxidative DNA lesions. The time series shows the repair of oxidative DNA damage with persistent strand breaks remaining at 6 h. Overall, we provide an example of an AOP-informed testing strategy and contribute to the quantitative understanding of AOP #296. We also demonstrate the value of DS as an effective approach for mutagenicity assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".