Dissertations, Theses, and Capstone Projects
Date of Degree
6-2024
Document Type
Dissertation
Degree Name
Ph.D.
Program
Biochemistry
Advisor
Stephen Redenti
Committee Members
Avi Ma’ayan
Julio Gallego-Delgado
Liang Zhao
Pratyusha Mandal
Subject Categories
Biochemistry | Molecular Biology
Keywords
Extracellular Vesicles(EV); Retinoblastoma; Cancer metastasis; QSAR-ML; Drug repurposing
Abstract
Extracellular vesicles (EVs) are lipid-enclosed particles that are generated by most types of cells and have been identified as an important mechanism for cell communication aside from cell direct contact. EVs contain a variety of functional macromolecules including proteins, lipids, RNA, which can be transferred from the host cells to target cells at a distance. The molecular pathways of EV biogenesis have been studied intensely in recent decades and the process involves several key genes and proteins. Cancer cell released EVs target distant tissues and upregulate signals that are attractive to cancer cell homing, playing a role in tumor progression and metastasis. Blocking the release of EVs from cancer cells with targeted drugs is predicted to reduce metastatic signaling and disease pathogenesis, with the potential to reduce recurrence. In this study, we first created a focused database of genes and drugs that have been used by researchers to inhibit EV release. Second, we developed a QSAR-ML model to identify novel drugs with functional properties similar to existing EV release-inhibition drugs. Third, QSAR-ML identified novel drugs were validated in vitro for inhibition of EV release from childhood ocular cancer retinoblastoma cells. This work provides a novel computational tool and a novel drug library that may optimize therapeutic strategies to reduce EV-mediated signaling and retinoblastoma pathogenesis.
Recommended Citation
Huang, Kunhui, "Machine-Learning QSAR-Based Novel Drug Identification to Inhibit Retinoblastoma Extracellular Vesicle Release and Metastatic Signaling" (2024). CUNY Academic Works.
https://academicworks.cuny.edu/gc_etds/5825
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