Cytokine release: A workshop proceedings on the state-of-the-science, current challenges and future directions
Bibliographic record
Abstract
In October 2013, the International Life Sciences Institute - Health and Environmental Sciences Institute Immunotoxicology Technical Committee (ILSI-HESI ITC) held a one-day workshop entitled, "Workshop on Cytokine Release: State-of-the-Science, Current Challenges and Future Directions". The workshop brought together scientists from pharmaceutical, academic, health authority, and contract research organizations to discuss novel approaches and current challenges for the use of in vitro cytokine release assays (CRAs) for the identification of cytokine release syndrome (CRS) potential of novel monoclonal antibody (mAb) therapeutics. Topics presented encompassed a regulatory perspective on cytokine release and assessment, case studies regarding the translatability of preclinical cytokine data to the clinic, and the latest state of the science of CRAs, including comparisons between mAb therapeutics within one platform and across several assay platforms, a novel physiological assay platform, and assay optimization approaches such as determination of FcR expression profiles and use of statistical tests. The data and approaches presented confirmed that multiple CRA platforms are in use for identification of CRS potential and that the choice of a particular CRA platform is highly dependent on the availability of resources for individual laboratories (e.g. positive and negative controls, number of human blood donors), the assay through-put required, and the mechanism-of-action of the therapeutic candidate to be tested. Workshop participants agreed that more data on the predictive performance of CRA platforms is needed, and current efforts to compare in vitro assay results with clinical cytokine assessments were discussed. In summary, many laboratories continue to focus research efforts on the improvement of the translatability of current CRA platforms as well explore novel approaches which may lead to more accurate, and potentially patient-specific, CRS prediction in the future.
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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 | 0.022 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".