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Myint, Kyaw Zeyar (2012) QSAR METHODS DEVELOPMENT, VIRTUAL AND EXPERIMENTAL SCREENING FOR CANNABINOID LIGAND DISCOVERY. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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G protein coupled receptors (GPCRs) are the largest receptor family in mammalian genomes and are known to regulate wide variety of signals such as ions, hormones and neurotransmitters. It has been estimated that GPCRs represent more than 30% of current drug targets and have attracted many pharmaceutical industries as well as academic groups for potential drug discovery. Cannabinoid (CB) receptors, members of GPCR superfamily, are also involved in the activation of multiple intracellular signal transductions and their endogenous ligands or cannabinoids have attracted pharmacological research because of their potential therapeutic effects. In particular, the cannabinoid subtype-2 (CB2) receptor is known to be involved in immune system signal transductions and its ligands have the potential to be developed as drugs to treat many immune system disorders without potential psychotic side-effects. Therefore, this work was focused on discovering novel CB2 ligands by developing novel quantitative structure-activity relationship (QSAR) methods and performing virtual and experimental screenings. Three novel QSAR methods were developed to predict biological activities and binding affinities of ligands. In the first method, a traditional fragment-based approach was improved by introducing a fragment similarity concept that enhanced the prediction accuracy remarkably. In the second method, pharmacophoric and morphological descriptors were incorporated to derive a novel QSAR regression model with good prediction accuracy. In the third method, a novel fingerprint-based artificial neural network QSAR model was developed to overcome the similar scaffold requirement of many fragment-based and other 3D-QSAR methods. These methods provide a foundation for virtual screening and hit ranking of chemical ligands from large chemical space. In addition, several novel CB2 selective ligands within nM binding affinities were discovered. These ligands were proven to be inverse agonists as validated by functional assays and could be useful probes to study CB2 signaling as well as potential drug candidates for autoimmune disesases.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Myint, Kyaw Zeyarkym3@pitt.eduKYM3
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairDay, Billy Wbday@pitt.eduBDAY
Thesis AdvisorXie, Xiang-Qunxix15@pitt.eduXIX15
Committee MemberBahar, Ivetbahar@pitt.eduBAHAR
Committee MemberLangmead, Christopher
Date: 19 September 2012
Date Type: Publication
Defense Date: 20 August 2012
Approval Date: 19 September 2012
Submission Date: 30 August 2012
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 186
Institution: University of Pittsburgh
Schools and Programs: School of Medicine > Computational and Systems Biology
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Computational biology, cheminformatics, QSAR, GPCR, Cannabinoid, CB2, fingerprint, artificial neural networks, fragment, bioactivity prediction, hit ranking.
Date Deposited: 19 Sep 2012 17:14
Last Modified: 15 Nov 2016 14:03


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