Review of Transcriptomic Biomarkers That Predict In Vitro Genotoxicity in Human Cell Lines
Bibliographic record
Abstract
The current genotoxicity testing paradigm provides little mechanistic information, has poor specificity in predicting carcinogenicity in humans, and is not suited to assessing a large number of chemicals. Genomic technologies enable the characterization of genome-wide transcriptional changes in response to chemical treatments that can inform mechanisms or modes of action. These technologies provided an impetus to develop transcriptomic biomarkers that could transform genotoxicity hazard assessment for drugs, cosmetics, and environmental and industrial chemicals. In August 2022, the International Workshops on Genotoxicity Testing (IWGT) held a workshop to critically review progress in the development and application of transcriptomic biomarkers in genotoxicity testing. Here, we describe the findings of this workshop's subgroup that conducted a systematized review and analysis of in vitro transcriptomic biomarkers for evaluating genotoxicity. Although there is a multitude of published reports exploring transcriptomics in genetic toxicology, the working group identified only five in vitro transcriptomic biomarker candidates, of which three (GENOMARK, TGx-DDI, and MU2012) were independently developed with sufficiently defined context of use, validation data, and supporting case studies that warranted inclusion in the review. Although these in vitro biomarkers were developed independently and for different classes of chemicals (TGx-DDI for pharmaceuticals, GENOMARK for cosmetics, and MU2012 for medical and environmental chemicals), they all address the same shortfall of the standard in vitro genotoxicity testing battery, that is, lack of specificity by genotoxicity-induced stress response at the transcriptomic level. In this review, we discuss the development of these in vitro biomarkers, including challenges and progress toward achieving regulatory acceptance.
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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) | 0.001 | 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 | 0.000 |
| 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 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".