Drug Repurposing in the Development of Anticancer Agents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Research into repositioning known drugs to treat cancer other than the originally intended disease continues to grow and develop, encouraged in part, by several recent success stories. Many of the studies in this article are geared towards repurposing generic drugs because additional clinical trials are relatively easy to perform and the drug safety profiles have previously been established. OBJECTIVE: This review provides an overview of anticancer drug development strategies which is one of the important areas of drug restructuring. METHODS: Repurposed drugs for cancer treatments are classified by their pharmacological effects. The successes and failures of important repurposed drugs as anticancer agents are evaluated in this review. RESULTS AND CONCLUSION: Drugs could have many off-target effects, and can be intelligently repurposed if the off-target effects can be employed for therapeutic purposes. In cancer, due to the heterogeneity of the disease, often targets are quite diverse, hence a number of already known drugs that interfere with these targets could be deployed or repurposed with appropriate research and development.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it