The In Vitro Transgenic Rodent Assay in Primary <scp>MutaMouse</scp> Hepatocytes Compared to the Mammalian Cell Gene Mutation Assay Using the <scp>HPRT</scp> Gene
Bibliographic record
Abstract
Gene mutations can be detected in mammalian cells in vitro using indicator genes such as the hypoxanthine-guanine-phosphoribosyltransferase (HPRT) gene. These assays have been adopted as OECD test guidelines (TG, e.g., OECD TG no. 476) and are used for regulatory purposes. The in vitro transgenic rodent assay (TGRA) in primary MutaMouse hepatocytes is a novel approach for the detection and quantification of gene mutations. Its methodology follows the same principles as the in vivo TGRA, an in vivo gene mutation assay with regulatory adoption (OECD TG no. 488). Although the potential of the in vitro TGRA to identify mutagens has been reported, its performance compared to an established in vitro gene mutation assay has not been reported. This study compared the in vitro TGRA with the HPRT assay using 10 known in vivo mutagens. The in vitro TGRA correctly identified all 10 mutagens, whereas the HPRT assay identified only nine. Benchmark concentration (BMC) modeling for the nine substances detected by both assays revealed overlapping confidence intervals for six compounds, indicating comparable sensitivity. For three mutagens, the HPRT assay yielded lower BMC intervals. Additionally, eight substances known to be non-mutagenic in vivo tested negative in the in vitro TGRA. While increased cytotoxicity did not induce increased mutant frequencies, it reduced DNA yield, thereby impairing mutagenicity assessment. The results of this study contribute to the understanding of the sensitivity and robustness of the in vitro TGRA and provide essential information for the validation of the assay.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".