Bibliographic record
Abstract
From the steady stream of drug repurposing patent applications published under the Patent Cooperation Treaty (PCT), we have selected fifteen documents that first became available during the first quarter of 2025. As in each installment, some of these claims are truly surprising. Few researchers would have expected that SSRI antidepressants such as sertraline and indatraline could exhibit pronounced anticancer effects. Equally unexpected is the disclosure that sitagliptin, the first antidiabetic agent from the DPP-4 inhibitor class, may be used for the treatment of glioblastoma. Another striking example is the report that artemisinin derivatives, well known for their use against malaria, may induce differentiation in undifferentiated erythroid and myeloid cells in patients with myelodysplastic syndrome. In addition, the compound bucillamine—relatively obscure in Western medicine but long used for the treatment of rheumatoid arthritis in East Asia—has been proposed for potential benefit in organophosphate poisoning. These highlights exemplify the breadth of innovation currently shaping the drug repurposing landscape. The reviewed patent applications originate from a diverse range of jurisdictions, including France, Spain, Greece, Slovenia, South Korea, China, Japan, Canada, and the United States, illustrating the global nature of ongoing research efforts in this field.
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.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.110 | 0.063 |
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".