Choosing the Right Biologic for the Right Patient With Severe Asthma
Bibliographic record
Abstract
In this installment of the How I Do It series on severe asthma, we tackle the clinical conundrum of choosing the right biologic for the right patient with severe asthma. With six biologics now approved for use in this area comprising four different targeting strategies (anti-Ig E: omalizumab; anti-IL-5 and anti-IL-5-receptor: mepolizumab, reslizumab, and benralizumab; anti-IL-4-receptor: dupilumab; anti-thymic stromal lymphopoietin: tezepelumab), this question is increasingly complex. Recognizing that no head-to-head trial has compared biologics, we based our review on the expected effects of inhibiting different aspects of type 2 airway inflammation, supported whenever possible by clinical trial and real-world data. We use four variations of a case of severe uncontrolled asthma to develop concepts and considerations introduced in the previous installment ("Workup of Severe Asthma") and discuss pregnancy-related, biomarker-related, comorbidity-related, and corticosteroid dependency-related considerations when choosing a biologic. The related questions of deciding when, why, and how to switch from one biologic to another also are discussed. Overall, we consider that the choice of biologics should be based on the available clinical trial data for the desired efficacy outcomes, the biomarker profile of the patient, safety profiles (eg, when pregnancy is considered), and opportunities to target two comorbidities with one biologic. Using systemic and airway biomarkers (blood eosinophils and exhaled nitric oxide [Feno]) and other phenotypic characteristics, we suggest a framework to facilitate therapeutic decision-making. Post hoc studies and new comparative studies are needed urgently to test this framework and to determine whether it allows us to make other clinically useful predictions.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".