Cross-company evaluation of the human lymphocyte activation assay
Bibliographic record
Abstract
Nonclinical immunotoxicity evaluation is an important component of safety assessment for pharmaceuticals. One in vitro assay that can be applied in a weight of evidence assessment is the human lymphocyte activation (HuLA) assay, an antigen recall assay, similar in many respects to the in vivo T-cell-dependent antibody response (TDAR) in that cooperation of multiple immune cell types are needed to produce responses. This assay uses human cells and is more amenable than the TDAR to compound ranking and mechanistic studies. The HuLA assay requires less time and drug than TDAR assays, uses a relevant antigen (influenza), reflects a human immune response, and applies principles of the 3Rs to non-clinical safety assessment. Peripheral blood mononuclear cells (PBMC) from flu-immunized donors are re-stimulated with flu-vaccine in the presence of test articles, and proliferation is measured. Published data demonstrate the applicability of the HuLA assay, but it has not been evaluated for reproducibility across testing sites. To evaluate assay reproducibility, scientists from a consortium of institutions conducted the assay in parallel, using a common pool of donor PBMC, influenza vaccine, and known immunosuppressant compounds (cyclosporine A and mycophenolic acid). The HuLA assay was highly reproducible in identification of inhibition of antigen-specific responses, and there was significant agreement across testing sites in the half maximal inhibitory concentration (IC50) values. Intra-site variability was the largest contributor to the variability observed within the assay. The HuLA assay was demonstrated to be ideally suited to comparing multiple compounds (i.e. compound ranking or benchmarking) within the same assay. Overall, the data reported herein support the HuLA assay as a useful tool in mechanistic evaluations of antigen-specific immune responses.
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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 | 0.049 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".