Evaluation of the standard battery of in vitro genotoxicity tests to predict in vivo genotoxicity through mathematical modeling: A report from the 8th <scp>International Workshop</scp> on <scp>Genotoxicity Testing</scp>
Bibliographic record
Abstract
In human health risk assessment of chemicals and pharmaceuticals, identification of genotoxicity hazard usually starts with a standard battery of in vitro genotoxicity tests, which is needed to cover all genotoxicity endpoints. The individual tests included in the battery are not designed to pick up all endpoints. This explains why resulting data can appear contradictory, thereby complicating accurate interpretation of the findings. Such interpretation could be improved through application of mathematical modeling. One of the advantages of mathematical modeling is that the strengths and weaknesses of each test are taken into account. Furthermore, the generated predictions are objective and convey the associated uncertainties. This approach was explored by the working group "Predictivity of In Vitro Genotoxicity Testing," convened in the context of the 8th International Workshop on Genotoxicity Testing (IWGT). Specifically, we applied mathematical modeling to a database with publicly available in vitro and in vivo data for genotoxicity. The results indicate that a mammalian in vitro clastogenicity test and a mammalian cell gene mutation test together provide strong predictive weight-of-evidence for evaluating genotoxic hazard of a substance, although they are better in predicting absence of genotoxic potential than in predicting presence of genotoxic potential. Remarkably, the bacterial reverse mutation (Ames) test did not significantly change these predictions when used in combination with in vitro mutagenicity and clastogenicity tests using cells of mammalian origin. However, in case only data from a bacterial reverse mutation test are available for the assessment of genotoxic potential, these do bear weight of evidence and thus can be used. Genotoxicity assays are generally executed in tiers, in which the bacterial reverse mutation test often is the starting point. Thus, it is reasonable to suspect that early in development test results from the bacterial reverse mutation test have influenced the composition of the database studied here. We performed several tests on the robustness of the database used for the analyses presented here, and the forthcoming results do not indicate a strong bias. Further research comparing in vitro genotoxicity data with in vivo data for additional compounds will provide more insights whether it is indeed time to reconsider the composition of the standard in vitro genotoxicity battery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| 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".