Mass Spectrometry-based Anaylsis to Investigate the Pharmacokinetics and Proteomic Properties of a Viral Sensitizer
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».