Duo biologic therapy using mepolizumab and omalizumab in refractory ABPA: two cases
Bibliographic record
Abstract
BACKGROUND: Allergic bronchopulmonary aspergillosis (ABPA) presents with a wide range of symptom severity, with severe disease manifestations being harder to control through conventional inhalers. While corticosteroids remain a standard treatment option, their use is often hindered by significant adverse side effects. This case series discusses a novel treatment of duo-administration of monoclonal antibodies for two patients that reduced their exacerbations, spared the use of steroids, and improved their quality of life. CASE PRESENTATION: Both patients were diagnosed with ABPA. Before the administration of treatment, they experienced almost monthly exacerbations and infections requiring constant systemic oral corticosteroids and antibiotics. After the implementation of successive concomitant monoclonal antibody treatments, absolute eosinophil levels were brought down to normal levels, and the monthly exacerbations were eliminated. CONCLUSION: This case series describes a novel approach for ABPA therapy that holds potential in improving patient outcomes for those with severe ABPA. Duo biologic therapy may improve disease control and reduce corticosteroid reliance in patients with refractory ABPA by targeting multiple mechanistic pathways of inflammation. Mepolizumab with Omalizumab offers a potential treatment strategy to reduce exacerbation frequency and severity and has minimal adverse effects.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| 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".