Repurposing existing drugs for new uses: a cohort study of the frequency of FDA-granted new indication exclusivities since 1997
Bibliographic record
Abstract
BACKGROUND: Drug repurposing (i.e., finding novel uses for existing drugs) is essential for maximizing medicines' therapeutic utility, but obtaining regulatory approval for new indications is costly. Policymakers have therefore created temporary indication-specific market exclusivities to incentivize drug innovators to run new clinical investigations. The effectiveness of these exclusivities is poorly understood. OBJECTIVE: To determine whether generic entry impacts the probability of new indication additions. METHODS: For a cohort of all new small-molecule drugs approved by the FDA between July 1997 and May 2020, we tracked new indications added for the subset of drugs that experienced generic entry during the observation period and then analyzed how the probability of a new indication changed with the number of years since/to generic entry. RESULTS: Of the 197 new drugs that subsequently experienced generic entry, only 64 (32%) had at least one new indication added. The probability of a new indication addition peaked above 4% between 7 and 8 years prior to generic entry and then to dropped to near zero 15 years after FDA approval. We show that the limited duration of exclusivity reduces the number of secondary indications significantly. CONCLUSION: Status quo for most drug innovators is creating novel one-indication products. Despite indication-specific exclusivities, the imminence of generic entry still has a detectable impact on reducing the chances of new indication additions. There is much room for improvement when it comes to incentivizing clinical investigations for new uses and unlocking existing medicines' full therapeutic potential.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".