The use of the United States FDA programs as a strategy to advance the development of drug products for neglected tropical diseases
Bibliographic record
Abstract
Neglected tropical diseases (NTDs) are infections which are endemic in poor populations in lower- and middle-income countries (LMIC). Approximately one billion people have now or are at risk of getting an NTD and yet less than 5% of research dollars are focused on providing treatments and prevention of these highly debilitating and deadly conditions. The United States Food and Drug Administration (FDA) Orphan Drug Designation program (ODDP) provides orphan status to drugs and biologics, defined as those intended for the safe and effective treatment, diagnosis or prevention of rare diseases and/or disorders that affect fewer than 200 000 people in the United States, or that affect more than 200 000 persons but are not expected to recover the costs of developing and marketing a treatment drug. These regulations have led to the translation of rare disease knowledge into innovative rare disease therapies. The FDA Guidance for Industry on developing drugs for the treatment and prevention of NTDs describes the following regulatory strategies: Orphan Product Designation, Fast Track Designation, Priority Review Designation, Accelerated Approval and Tropical Disease Priority Review Voucher. This paper will discuss how these regulations and especially the ODDP can improve the clinical development and accessibility of drug products for NTDs.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".