The new era of add-on asthma treatments: where do we stand?
Bibliographic record
Abstract
Abstract Globally, a small proportion (5–12%) of asthma patients are estimated to have severe disease. However, severe asthma accounts for disproportionately high healthcare resource utilization. The Global Initiative for Asthma (GINA) management committee recommends treating patients with asthma with inhaled corticosteroids plus long-acting β 2 -agonists and, when needed, adding a long-acting muscarinic receptor antagonist or biologic agent. Five biologics, targeting different effectors in the type 2 inflammatory pathway, are approved for asthma treatment. However, biologics have not been compared against each other or add-on inhaled therapies in head-to-head clinical trials. As a result, their positioning versus that of current and anticipated small-molecule strategies is largely unknown. Furthermore, with the emergence of biomarkers for predicting response to biologics, a more personalized treatment approach—currently lacking with inhaled therapies—may be possible. To gain perspective, we reviewed recent advances in asthma pathophysiology, phenotypes, and biomarkers; the place of biologics in the management and personalized treatment of severe asthma; and the future of biologics and small-molecule drugs. We propose an algorithm for the stepwise treatment of severe asthma based on recommendations in the GINA strategy document that accounts for the broad range of phenotypes targeted by inhaled therapies and the specificity of biologics. In the future, both biologics and small molecules will continue to play key roles in the individualized treatment of severe asthma. However, as targeted therapies, their application will continue to be focused on patients with certain phenotypes who meet the specific criteria for use as identified in pivotal 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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.019 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".