Mass Spectrometry-based Anaylsis to Investigate the Pharmacokinetics and Proteomic Properties of a Viral Sensitizer
Bibliographic record
Abstract
Attenuated oncolytic viruses (OVs) are a promising alternative cancer therapy to mainstream methods such as radiotherapy and chemotherapy.OV therapy takes advantage of the defective antiviral response present in most cancer cells however heterogeneity amongst target cells and attenuation of OVs to increase their safety profiles has limited the efficacy of this treatment.Our collaborative research group has developed novel small molecules named viral sensitizers (VSes) capable of enhancing viral infection and cancerspecific cell death.In this study, liquid chromatography-mass spectrometry (LC-MS) methods were developed to study the pharmacokinetic (PK) metabolic activity of VSe1-28 through in vitro time course experiments.Furthermore, glutathione (GSH) was identified as an active target for VSe1-28 and two GSH metabolites were identified in vitro.It was found that VSe1-28 has a half-life of 3.90 hrs in lysate and 4.83 hrs in growth media.Parallel to this work, proteomic experiments were conducted to confirm the molecular target and mechanism of action of VSe1-28.VSe1-28 has been suspected to inhibit the nuclear translocation of NF-kB p65 in viral resistant cancer cells through in vitro and in vivo VSe1-28 modified protein experiments.An MRM method was developed to monitor the formation of the suspected molecular target of interest and a modified tryptic digestion protocol was developed specifically for our work.These new findings will aid in the improvement VSes and progress preclinical studies one step closer to clinical use in combination with OVs.Future studies are needed to further address the suspected mechanism of action for VSe1-28 to positively confirm the binding location to p65 protein.I would like to convey my greatest gratitude and heartfelt thank you to my supervisor Dr. Jeff Smith for the incredible opportunity to pursue a rewarding master's thesis.We made it Jeff!Through floods, fires, and a pandemic.Your encouragement and graduate life-anecdotes helped grow my confidence and abilities as a scientist.I would like to convey my appreciations towards Dr. Chris Boddy and Dr. Jean-Simon Diallo for being excellent examples of hard-working successful scientists.Your support and positivity in group meetings allowed me to feel immediately integrated into a great collaborative group.Furthermore, the upmost thanks to Mike Phan for all the help in getting me on my feet and up-to-speed on the VSe projects.This master's would not have gone as smoothly or been as rewarding without your help and friendship!To the rock of CMSC, Karl Wasslen.You introduced me to MS, and I attribute a great deal of my knowledge and skills to your teachings!Thank you for answering my never-ending questions and being the character, you are.Thank you for always keeping me on my toes with your clever jokes, and continuation of "Emma smells".Without you, the CMSC would not be the incredible lab
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".