First-in-class small molecule potentiators of cancer virotherapy
Bibliographic record
Abstract
The use of engineered viral strains such as gene therapy vectors and oncolytic viruses (OV) to selectively destroy cancer cells is poised to make a major impact in the clinic and revolutionize cancer therapy. In particular, several studies have shown that OV therapy is safe and well tolerated in humans and can infect a broad range of cancers. Yet in clinical studies OV therapy has highly variable response rates. The heterogeneous nature of tumors is widely accepted to be a major obstacle for OV therapeutics and highlights a need for strategies to improve viral replication efficacy. Here, we describe the development of a new class of small molecules for selectively enhancing OV replication in cancer tissue. Medicinal chemistry studies led to the identification of compounds that enhance multiple OVs and gene therapy vectors. Lead compounds increase OV growth up to 2000-fold in vitro and demonstrate remarkable selectivity for cancer cells over normal tissue ex vivo and in vivo. These small molecules also demonstrate enhanced stability with reduced electrophilicity and are highly tolerated in animals. This pharmacoviral approach expands the scope of OVs to include resistant tumors, further potentiating this transformative therapy. It is easily foreseeable that this approach can be applied to therapeutically enhance other attenuated viral vectors.
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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 | 0.001 | 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".