Food and Drug Administration Public Workshop Summary—Addressing Challenges in Inhaled Antifungal Drug Development
Bibliographic record
Abstract
Allergic bronchopulmonary aspergillosis and invasive fungal diseases represent distinct infectious entities that cause significant morbidity and mortality. Currently, administered inhaled antifungal therapies are unapproved, have suboptimal efficacy, and are associated with considerable adverse reactions. The emergence of resistant pathogens is also a growing concern. Inhaled antifungal development programs are challenged by inadequate nonclinical infection models, highly heterogenous patient populations, low prevalence rates of fungal diseases, difficulties defining clinical trial enrollment criteria, and lack of robust clinical trial endpoints. On 25 September 2020, the US Food and Drug Administration (FDA) convened a workshop with experts in pulmonary medicine and infectious diseases from academia, industry, and other governmental agencies. Key discussion topics included regulatory incentives to facilitate development of inhaled antifungal drugs and combination inhalational devices, limitations of existing nonclinical models and clinical trial designs, patient perspectives, and industry insights.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.021 | 0.018 |
| Insufficient payload (model declined to judge) | 0.036 | 0.013 |
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".