Comparative reactivity of human IgE to cynomolgus monkey and human effector cells and effects on IgE effector cell potency
Bibliographic record
Abstract
BACKGROUND: Due to genetic similarities with humans, primates of the macaque genus such as the cynomolgus monkey are often chosen as models for toxicology studies of antibody therapies. IgE therapeutics in development depend upon engagement with the FcεRI and FcεRII receptors on immune effector cells for their function. Only limited knowledge of the primate IgE immune system is available to inform the choice of models for mechanistic and safety evaluations. METHODS: The recognition of human IgE by peripheral blood lymphocytes from cynomolgus monkey and man was compared. We used effector cells from each species in ex vivo affinity, dose-response, antibody-receptor dissociation and potency assays. RESULTS: We report cross-reactivity of human IgE Fc with cynomolgus monkey cells, and comparable binding kinetics to peripheral blood lymphocytes from both species. In competition and dissociation assays, however, human IgE dissociated faster from cynomolgus monkey compared with human effector cells. Differences in association and dissociation kinetics were reflected in effector cell potency assays of IgE-mediated target cell killing, with higher concentrations of human IgE needed to elicit effector response in the cynomolgus monkey system. Additionally, human IgE binding on immune effector cells yielded significantly different cytokine release profiles in each species. CONCLUSION: These data suggest that human IgE binds with different characteristics to human and cynomolgus monkey IgE effector cells. This is likely to affect the potency of IgE effector functions in these two species, and so has relevance for the selection of biologically-relevant model systems when designing pre-clinical toxicology and functional studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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 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".