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(Unpublished)
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Unpublished |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: |
|
| ETD Committee: |
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| 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 2021 05:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/38546 |
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