Comparison of<i>in vitro</i>and<i>in vivo</i>clastogenic potency based on benchmark dose analysis of flow cytometric micronucleus data
Why this work is in the frame
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Bibliographic record
Abstract
The application of flow cytometry as a scoring platform for both in vivo and in vitro micronucleus (MN) studies has enabled the efficient generation of high quality datasets suitable for comprehensive assessment of dose-response. Using this information, it is possible to obtain precise estimates of the clastogenic potency of chemicals. We illustrate this by estimating the in vivo and the in vitro potencies of seven model clastogenic agents (melphalan, chlorambucil, thiotepa, 1,3-propane sultone, hydroxyurea, azathioprine and methyl methanesulfonate) by deriving BMDs using freely available BMD software (PROAST). After exposing male rats for 3 days with up to nine dose levels of each individual chemical, peripheral blood samples were collected on Day 4. These chemicals were also evaluated for in vitro MN induction by treating TK6 cells with up to 20 concentrations in quadruplicate. In vitro MN frequencies were determined via flow cytometry using a 96-well plate autosampler. The estimated in vitro and in vivo BMDs were found to correlate to each other. The correlation showed considerable scatter, as may be expected given the complexity of the whole animal model versus the simplicity of the cell culture system. Even so, the existence of the correlation suggests that information on the clastogenic potency of a compound can be derived from either whole animal studies or cell culture-based models of chromosomal damage. We also show that the choice of the benchmark response, i.e. the effect size associated with the BMD, is not essential in establishing the correlation between both systems. Our results support the concept that datasets derived from comprehensive genotoxicity studies can provide quantitative dose-response metrics. Such investigational studies, when supported by additional data, might then contribute directly to product safety investigations, regulatory decision-making and human risk assessment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it