Mass Spectrometry-Based Analysis to Investigate the Physiochemical and Proteomic Properties of Viral Sensitizers
Bibliographic record
Abstract
Attenuated viruses hold great potential in viral-based therapies including vaccine production and oncolytic virotherapy (OVt).Both applications are hindered by poor infection propagation as a result of genetic heterogeneity amongst target cells in regard to antiviral response functionality.Our collaborative research group has developed a novel solution to address this problem: first-in-class small molecules termed viral sensitizers (VSes) that potentiate viral infection in resistant cells.Liquid chromatography mass spectrometry (LC-MS) methods that made use of various scanning modes were developed to characterize the poor physiochemical properties of the first lead compound (VSe1).Furthermore, the LC-MS methods were applied in the screening of a large VSe library and performing quality control (QC) experiments.Subsequently, new lead compounds were identified with improved plasma stability and retained activity.In vivo pharmacokinetic (PK) and metabolomic studies using the new lead VSes revealed that glutathione stability and tumour penetrance are important considerations for future screening of VSes.MS-based and gel-based proteomic experiments made use of target identification techniques to elucidate the VSe mechanism of action (MOA).GSTp1 was studied as a potential target but multiple evidences supported that VSes act as noncovalent inhibitors.Future studies will further address the unknown MOA and in vivo behavior to further narrow the list of lead VSes to application-specific compounds.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".