Development and characterization of a unique anti‐IgE mouse monoclonal antibody cross‐reactive between human and canine IgE
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it