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Pain Chemogenomics Knowledgebase (PAIN-CKB) for Systems Pharmacology Target Mapping and PBPK Modeling Investigation of Opioid Drug-Drug Interactions

Shen, Mingzhe (2020) Pain Chemogenomics Knowledgebase (PAIN-CKB) for Systems Pharmacology Target Mapping and PBPK Modeling Investigation of Opioid Drug-Drug Interactions. Master's Thesis, University of Pittsburgh. (Unpublished)

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Abstract

More than 50 million adults in America suffer from chronic pain. Opioids are commonly prescribed for their effectiveness in relieving many types of pain. However, excessive prescribing of opioids can lead to abuse, addiction, and death. Non-steroidal anti-inflammatory drugs (NSAIDs), another major class of analgesic, also have many problematic side effects including headache, dizziness, vomiting, diarrhea, nausea, constipation, reduced appetite, and drowsiness. There is an urgent need for the understanding of molecular mechanisms that underlie drug abuse and addiction to aid in the design of new preventive or therapeutic agents for pain management. To facilitate pain related small-molecule signaling pathway studies and the prediction of potential therapeutic target(s) for the treatment of pain, here we present a comprehensive platform of pain domain-specific chemogenomics knowledgebase (PAIN-CKB) with integrated data mining computing tools. Our new computing platform describes the chemical molecules, genes, proteins, and signaling pathways involved in pain regulation. PAIN-CKB is implemented with a friendly user-interface for the prediction of the relevant protein targets of the query compound and analysis and visualization of the outputs based on HTDocking, TargetHunter, BBB predictor, and Spider Plot. We performed three case studies to systematically validate the integrity and accuracy of PAIN-CKB and its algorithms/tools. First, system pharmacology target mapping was carried out for four FDA approved analgesics (acetaminophen, diclofenac, fentanyl, and morphine) in order to identify the known targets and predict off-targets. Subsequently, the target mapping outcomes were applied to build physiologically based pharmacokinetic (PBPK) models for acetaminophen and fentanyl to explore the potential drug-drug interaction (DDI) between this pair of drugs. Finally, docking analysis was conducted to explore the detailed interaction pattern of acetaminophen reactive metabolite (NAPQI) and its hepatotoxicity target thioredoxin reductase (TrxR).


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Shen, Mingzhemis216@pitt.edumis2160000-0001-7461-3764
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairBertz, RichardRichard.Bertz@pitt.eduRichard.Bertz
Committee CoChairFeng, Zhiweizhf11@pitt.eduzhf11
Committee MemberXie, Xiangqunxix15@pitt.eduxix15
Committee MemberXue, Yingyix49@pitt.eduyix49
Committee MemberKirisci, Leventlevent@pitt.edulevent
Date: 9 April 2020
Date Type: Publication
Defense Date: 18 March 2020
Approval Date: 9 April 2020
Submission Date: 1 April 2020
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 89
Institution: University of Pittsburgh
Schools and Programs: School of Pharmacy > Pharmaceutical Sciences
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Pain, Knowledgebase, Opioids, NSAIDs, Computational Systems Pharmacology-Target Mapping, PBPK
Date Deposited: 09 Apr 2020 15:40
Last Modified: 09 Apr 2020 15:40
URI: http://d-scholarship.pitt.edu/id/eprint/38546

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