Expediting treatments in the 21st century: orphan drugs and accelerated approvals
Bibliographic record
Abstract
BACKGROUND: In response to activated patient communities' catalyzation, two significant efforts by the FDA to expedite treatments have now been in place for multiple decades. In 1983, the United States Congress passed the Orphan Drug Act to provide financial incentives for development of drugs for rare diseases. In 1992, partly in response to the HIV epidemic, the FDA implemented Accelerated Approval (AA) to expedite access to promising new therapies to treat serious conditions with unmet medical need based on surrogate marker efficacy while additional clinical data is confirmed. The uses of these regulatory approaches over time are assessed in this study. METHODS: The following U.S. FDA CDER published lists were used in this analysis: 1. all orphan designations and approvals; 2. all AA and their details updated through December 31, 2022; new molecular entities (NMEs). RESULTS: Orphan drug designations and approvals have increased several-fold over the past four decades. The largest increase recently has been in therapies targeting oncological diseases (comprised of both oncology and malignant hematology). Although orphan drug approvals based on NMEs are the minority of orphan drug designations, the count of approved orphan drug NMEs has increased in recent years. The characteristics of orphan drug approvals show notable differences by disease area with rare diseases and medical genetics (49%) having a relatively large fraction of orphan drug approvals with NMEs compared to the oncological diseases (32%). Similar to the use of orphan drug designation, oncological disease therapies have been the largest utilizers of AA. Many therapies targeting these diseases address unmet medical need and can leverage surrogate markers that have previously been used in similar trials. The timings of conversion of AA (confirmed or withdrawn) were assessed and found to be consistent across decades and to have some dependency upon the broad disease area (when assessed by three large groups: HIV conversions were fastest; followed by oncology; followed by all others). By the end of 2022, 98% of the first 105 (approved in 2010 or earlier) AA had been converted to confirmed or withdrawn. CONCLUSIONS: Although the typical timings for AA to be confirmed or withdrawn has not changed significantly over the decades, the disease areas utilizing orphan drug designation and AA have changed significantly over time. Both programs have had increases in their use for therapies targeting oncological diseases. The re-use of surrogate markers for oncological diseases has been an advantage in a way that may not be scientifically feasible in many other disease areas that have greater differentiation across disease etiology. For non-oncological diseases, applicability of AA is, in part, dependent upon greater focus on characterization and acceptance of novel surrogate markers.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".