Review of monoclonal antibody therapies in asthma and allergic diseases - a new paradigm for precision medicine
Bibliographic record
Abstract
BACKGROUND: Elucidation of the critical immune pathways involved in allergic inflammation has identified, apart from IgE, therapeutic targets in the cytokine network suitable for intervention by biological therapies. OBJECTIVE: The drugs that target the cytokine networks pertinent to asthma and allergic diseases are reviewed and some illustrative case histories presented. The overview proposes a framework to use when deciding which monoclonal antibody (mAb) to select for treatment of severe asthma based on total IgE concentration, peripheral blood eosinophil count, induced sputum analysis and measurement of fractional exhaled nitric oxide (FENO). METHODS: Internet-based literature search including PubMed for studies on biological therapies targeting IgE and the cytokine network in allergic inflammation focusing on asthma with and without rhinosinusitis and nasal polyposis, eczema, urticaria and food allergies. Lists of pivotal trials published in the peer reviewed literature and pertaining to their own mAb products were also provided by GSK, AstraZeneca and Sanofi. Therapeutic agents licensed or in advanced stages of development (Phase 2b and 3) were selected for discussion. RESULTS: The survey identifies a number of mAbs with substantial potential for the future targeted treatment of asthma with and without rhinosinusitis and nasal polyposis, eczema, urticaria and food allergies uncontrolled by existing therapies. A pragmatic framework is proposed for selecting the optimal mAb for initial use in individual patients with severe asthma. CONCLUSIONS: Launch of these new biologicals may revolutionise the treatment of allergic diseases if employed in an endotype-specific fashion, heralding an unprecedented era of personalised medicine.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".