An international validation study of the IL-2 Luc assay for evaluating the potential immunotoxic effects of chemicals on T cells and a proposal for reference data for immunotoxic chemicals
Bibliographic record
Abstract
To evaluate the immunotoxic effects of xenobiotics, we have established the Multi-ImmunoTox assay, in which three stable reporter cell lines are used to evaluate the effects of chemicals on the IL-2, IFN-γ, IL-1β and IL-8 promoters. Here, we report the official validation study of the IL-2 luciferase assay (IL-2 Luc assay). In the Phase I study that evaluated five coded chemicals in three sets of experiments, the average within-laboratory reproducibility was 86.7%. In the Phase II study, 20 coded chemicals were evaluated at multiple laboratories. In the combined results of the Phase I and II studies, the between-laboratory reproducibility was 80.0%. These results suggested that the IL-2 Luc assay was reproducible both between and within laboratories. To determine the predictivity, we collected immunotoxicological information and constructed the reference data by classifying the chemical into immunotoxic compounds targeting T cells or others according to previously reported criteria. When compared with the reference data, the average predictivity of the Phase I and II studies was 75.0%, while that of additional 60 chemicals examined by the lead laboratory was 82.5%. Although the IL-2 Luc assay alone is not sufficient to predict immunotoxicity, it will be a useful tool when combined with other immune tests.
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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 | 0.062 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".