Quantitative interpretation of <scp>ToxTracker</scp> dose–response data for potency comparisons and <scp>mode‐of‐action</scp> determination
Bibliographic record
Abstract
ToxTracker is an in vitro mammalian stem cell-based reporter assay that detects activation of specific cellular signaling pathways (DNA damage, oxidative stress, and/or protein damage) upon chemical exposure using flow cytometry. Here we used quantitative methods to empirically analyze historical control data, and dose-response data across a wide range of reference chemicals. First, we analyzed historical control data to define a fold-change threshold for identification of a significant positive response. Next, we used the benchmark dose (BMD) combined-covariate approach for potency ranking of a set of more than 120 compounds; the BMD values were used for comparative identification of the most potent inducers of each reporter. Lastly, we used principal component analysis (PCA) to investigate functional and statistical relationships between the ToxTracker reporters. The PCA results, based on the BMD results for all substances, indicated that the DNA damage (Rtkn, Bscl2) and p53 (Btg2) reporters are functionally complementary and indicative of genotoxic stress. The oxidative stress (Srxn1 and Blvrb) and protein stress (Ddit3) reporters are independent indicators of cellular stress, and essential for toxicological profiling using the ToxTracker assay. Overall, dose-response modeling of multivariate ToxTracker data can be used for potency ranking and mode-of-action determination. In the future, IVIVE (in vitro to in vivo extrapolation) methods can be employed to determine in vivo AED (administered equivalent dose) values that can in turn be used for human health risk assessment.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".