Giving Drugs a Second Chance: Overcoming Regulatory and Financial Hurdles in Repurposing Approved Drugs As Cancer Therapeutics
Bibliographic record
Abstract
The repositioning or ‘repurposing’ of existing therapies for alternative disease indications is an attractive approach that can save significant investments of time and money during drug development. For cancer indications, the primary goal of repurposed therapies is on efficacy, with less restrictions on safety due to the immediate need to treat this patient population. This report provides a high-level overview of how drug developers pursuing repurposed assets have previously navigated funding efforts, regulatory affairs, and intellectual property laws to commercialize these ‘new’ medicines in oncology. This article provides insight into funding programs (e.g. government grants, philanthropic organizations) that academic and corporate initiatives can leverage to repurpose drugs for cancer. In addition, we highlight previous examples where secondary uses of existing, FDA- or EMA-approved therapies have been predicted in silico and successfully validated in vitro and/or in vivo (i.e. animal models and human clinical trials) for certain oncology indications. Lastly, we describe the strategies that the pharmaceutical industry has previously employed to navigate regulatory considerations and successfully commercialize their drug products. These factors must be carefully considered when repurposing existing drugs for cancer to best benefit patients and drug developers alike.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".