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Record W2031110738 · doi:10.1038/mt.2010.67

A High-throughput Pharmacoviral Approach Identifies Novel Oncolytic Virus Sensitizers

2010· article· en· W2031110738 on OpenAlex
Jean‐Simon Diallo, Fabrice Le Bœuf, Frances W. Lai, Julie A. Cox, Markus Vähä‐Koskela, Hesham Abdelbary, Heather MacTavish, Katherine Waite, Theresa Falls, Jenny Wang, Ryan Brown, Jan Blanchard, Eric D. Brown, David H. Kirn, John Hiscott, Harry Atkins, Brian D. Lichty, John C. Bell

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMolecular Therapy · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsJewish General HospitalMcMaster UniversityOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsOncolytic virusThroughputVirusVirologyBiologyComputational biologyComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.306
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it