TGx-DDI (toxicogenomic DNA damage-inducing) biomarker validation: multi-site ring trial supporting regulatory use
Bibliographic record
Abstract
Standard in vitro genotoxicity assays often suffer from low specificity, leading to irrelevant positive findings that require costly in vivo follow-up studies. The TGx-DDI (Toxicogenomic DNA Damage-Inducing) transcriptomic biomarker was developed to address this limitation by identifying DNA damage-inducing compounds through gene expression profiling in human TK6 lymphoblastoid cells. To qualify TGx-DDI as a reliable, reproducible biomarker for augmenting genotoxicity hazard assessment, a multi-site ring-trial was conducted across four laboratories using 14 blinded test compounds and standardized protocols. TK6 cells were exposed to three concentrations of each compound, followed by RNA extraction and digital nucleic acid counting using the NanoString nCounter platform. A three-pronged bioinformatics approach-Nearest Shrunken Centroid Probability Analysis, Principal Component Analysis, and Hierarchical Clustering-was used to assign DDI or non-DDI classifications. TGx-DDI demonstrated 100% sensitivity, 86% specificity, and 91% accuracy in distinguishing DDI from non-DDI compounds under validated test conditions. High interlaboratory concordance was observed (agreement coefficients ≥0.61), and transcriptomic data showed strong cross-site correlation (Pearson r > 0.84). The biomarker reproducibly classified test agents even when conducted across study sites. These results demonstrate that TGx-DDI is a robust and reproducible transcriptomic biomarker that enhances the specificity of genotoxicity testing by distinguishing biologically relevant DNA damage responses. Its integration into genotoxicity testing strategies can support regulatory decision-making, reduce unnecessary animal use, and improve the assessment of human health risks.
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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 | 0.030 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".