Drug Repurposing in Personalized Medicine: Translational Pathways and Recommendations
Bibliographic record
Abstract
This review examines the integration of drug repurposing and personalized medicine as complementary approaches to transforming healthcare delivery. Drug repurposing identifies new therapeutic uses for existing medications with established safety profiles, while personalized medicine tailors treatments to individual patient characteristics. This integration offers reduced development time and costs, expanded options for rare and complex diseases, and targeted interventions based on patient-specific biomarkers. The manuscript explores translational pathways, including drug-centric, target-centric, and disease-centric approaches, as well as emerging computational and AI methodologies. Case studies in neurological disorders, oncology, and seizure disorders demonstrate successful applications. Despite promising outcomes, challenges persist across regulatory frameworks, intellectual property protection, data integration, and the management of biological variability among patients. Recommendations include strengthening regulatory support, developing robust validation pipelines, promoting open-source, collaborative models, and leveraging AI and big data technologies. Through coordinated stakeholder efforts, drug repurposing in personalized medicine can become a cornerstone of precision healthcare, providing more effective, patient-tailored treatments.
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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 | 0.032 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".