Development and characterization of a unique anti‐IgE mouse monoclonal antibody cross‐reactive between human and canine IgE
Bibliographic record
Abstract
BACKGROUND: The efficacy assessment of human anti-IgE monoclonal antibodies (mAbs) in animal models before clinical trials is hampered due to the lack of cross-reactivity of anti-IgE mAbs between species. OBJECTIVE: We developed CRE-DR (an anti-dog IgE monoclonal antibody), an anti-IgE mouse mAb that recognizes canine and human IgE, and then examined its IgE specificity and cross-reactivity between three animal and human species. METHODS: ), we generated a hybridoma producing CRE-DR. The CRE-DR purified from the ascites of hybridoma-inoculated mice was used for ELISA and Western blot analysis to examine reactivity to dog, human, and rodent IgEs as well as recombinant bovine serum albumin (BSA)-conjugated to canine, human, and rodent IgE amino acid peptides corresponding to the immunizing sequence. We then performed enzyme-linked immunosorbent assays (ELISAs) for dog IgE using sera from dogs with atopic dermatitis (AD) after inhibition with canine IgE and IgG. The amino acid sequence recognized by CRE-DR was identified by ELISA using synthetic peptides. RESULTS: CRE-DR is a monoclonal mouse IgG1κ specific for dog IgE, and the ELISA values in atopic dog sera were inhibited by dog IgE, but not dog IgG. The binding of CRE-DR to human IgE was relatively maintained, but not to rodent IgEs, which results were confirmed with the BSA-conjugated IgE peptides of the various species. The CRE-DR reactivity was supported by the comparison of amino acid sequence of CRE-DR epitope, DWIEGETYYC, in dog IgE; one, two, and three amino acids were substituted in the human, rat, and mouse IgE epitopes, respectively. CONCLUSIONS AND CLINICAL RELEVANCE: CRE-DR is a mAb cross-reactive to dog and human IgEs, which can allow the use of a dog model of allergy to test the efficacy of a CRE-DR-derived anti-IgE therapeutic mAb before human clinical trials.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".