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Development and Applications of Quantitative Systems Pharmacology Methods and Tools for Drug Discovery

Pei, Fen (2020) Development and Applications of Quantitative Systems Pharmacology Methods and Tools for Drug Discovery. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

Quantitative Systems Pharmacology (QSP) is a relatively new field, which aims to determine the mechanisms of disease progression and mechanisms of action of drugs on multi-scale systems and to optimize the development of therapeutic strategies through iterative and integrated computational and experimental methods. Given the unclear mechanisms and unmet medical needs for complex diseases, there is a great need for integrated and efficient computational tools to facilitate the drug discovery process. This thesis focuses on the development and applications of computational methods for QSP-driven drug discovery, including (1) the development of an integrated and efficient chemical-protein-pathway mapping tool for polypharmacology and chemogenomics, implemented in the QuartataWeb server, (2) the development of machine learning methods for predicting protein-protein interactions (PPIs), and (3) the applications of the developed QSP methodology to Huntington’s disease, drug abuse, and non-alcoholic fatty liver disease (NAFLD) toward better understanding of disease mechanisms and facilitating the design of therapeutic strategies. To build QuartataWeb, we adopted a probabilistic matrix factorization (PMF) method using as input two databases: DrugBank v5.0 and STITCH v5, so as to predict new chemical-target associations as well as detect similarities among drugs/chemicals based on their interaction patterns with targets, as well as similarities between targets based on their interaction patterns with drugs/chemicals. Furthermore, this new tool links targets to KEGG pathways and Gene Ontology (GO) annotations, completing the bridge from drugs/chemicals to function via protein targets and cellular pathways. In the second study, we developed a methodology for automated and efficient identification PPIs using a symmetric logistic matrix factorization method. Finally, the applications have been conducted with experimental collaborators. We customized our QSP approaches based on specific disease-centric inputs and experimental resources, identified the cellular mechanisms underlying the investigated diseases or disorders, and proposed drugs to potentially serve as lead compounds for developing drugs against Huntington’s disease, drug abuse and NAFLD. Taken together, the development and applications of the QSP methodology presented here demonstrate the power of QSP-guided hypotheses as a key step required for gaining a better understanding of systems-level events underlying complex diseases/disorders and for accelerating drug discovery.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Pei, Fenfep7@pitt.edufep7
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorBahar, Ibahar@pitt.eduBAHAR
Thesis AdvisorTaylor, DLdltaylor@pitt.eduDLTAYLOR
Committee ChairSchwartz, RSrussells@andrew.cmu.edu <russells@andrew.cmu.edu>
Committee MemberGreenamyre, JTTim.Greenamyre@pitt.eduJGREENA
Committee MemberLezon, TRlezon@pitt.edu
Date: 29 December 2020
Date Type: Publication
Defense Date: 6 November 2020
Approval Date: 29 December 2020
Submission Date: 8 November 2020
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 311
Institution: University of Pittsburgh
Schools and Programs: School of Medicine > Computational Biology
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Quantitative systems pharmacology, chemogenomics, polypharmacology, drug-targe interactions, protein-protein interactions, machine learning, Huntington's disease, drug abuse, non-alcoholic fatty liver disease
Date Deposited: 29 Dec 2020 20:10
Last Modified: 29 Dec 2020 20:10
URI: http://d-scholarship.pitt.edu/id/eprint/39860

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