A High-throughput Pharmacoviral Approach Identifies Novel Oncolytic Virus Sensitizers
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Bibliographic record
Abstract
Oncolytic viruses (OVs) are promising anticancer agents but like other cancer monotherapies, the genetic heterogeneity of human malignancies can lead to treatment resistance. We used a virus/cell-based assay to screen diverse chemical libraries to identify small molecules that could act in synergy with OVs to destroy tumor cells that resist viral infection. Several molecules were identified that aid in viral oncolysis, enhancing virus replication and spread as much as 1,000-fold in tumor cells. One of these molecules we named virus-sensitizers 1 (VSe1), was found to target tumor innate immune response and could enhance OV efficacy in animal tumor models and within primary human tumor explants while remaining benign to normal tissues. We believe this is the first example of a virus/cell-based “pharmacoviral” screen aimed to identify small molecules that modulate cellular response to virus infection and enhance oncolytic virotherapy. Oncolytic viruses (OVs) are promising anticancer agents but like other cancer monotherapies, the genetic heterogeneity of human malignancies can lead to treatment resistance. We used a virus/cell-based assay to screen diverse chemical libraries to identify small molecules that could act in synergy with OVs to destroy tumor cells that resist viral infection. Several molecules were identified that aid in viral oncolysis, enhancing virus replication and spread as much as 1,000-fold in tumor cells. One of these molecules we named virus-sensitizers 1 (VSe1), was found to target tumor innate immune response and could enhance OV efficacy in animal tumor models and within primary human tumor explants while remaining benign to normal tissues. We believe this is the first example of a virus/cell-based “pharmacoviral” screen aimed to identify small molecules that modulate cellular response to virus infection and enhance oncolytic virotherapy.
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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 | 0.001 | 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 it