REPURPOSING OF FDA APPROVED COMPOUNDS TO INHIBIT OSM/OSMR SIGNALING IN GLIOBLASTOMA
Bibliographic record
Abstract
Abstract Repurposing of FDA approved compounds to inhibit OSM/OSMR signaling in glioblastoma OSM/OSMR signalling is important in glioblastoma, yet specific inhibitors to suppress this signaling pathway remain to be explored. Here we conducted HTS screening of FDA approved compounds to identify lead compounds that can suppress OSM signalling via OSMR to inhibit STAT3 phosphorylation. The strategy of drug repurposing that have already been approved by FDA, with known pharmacological and safety profiles, offers several advantages including bypassing development costs and rapid and effective translation into phase 2 clinical trials. Importantly, 99% of newly developed drugs fail in clinical trials, further highlighting the significance of drug repurposing. Our HTS screen revealed 456 compounds with > 40% potency. We analyzed the compounds based on their mode of action and physicochemical properties. Our HTS discovered 63 G-protein coupled receptor modulators including 5-Hydroxy tryptamine (5-HT) and Dopamine receptor D2 (DRD2) modulators as OSM/OSMR inhibitors. Following a counter-screen we focused on compounds with highest potency as well as their potential to cross BBB using physicochemical attributes and the safety profiles previously established in human patients. These analyses revealed 7 lead compounds which are presently being tested in vivo in combination with present standards of care of glioblastoma patients.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".