Proposing novel MAO-B hit inhibitors using multidimensional molecular modeling approaches and application of binary QSAR models for prediction of their therapeutic activity, pharmacokinetic and toxicity properties

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dc.contributor.authorİş, Yusuf Serhat
dc.contributor.authorAksoydan, Busecan
dc.contributor.authorDurdağı, Serdar
dc.contributor.authorYurtsever, Mine
dc.date.accessioned2024-06-13T20:18:06Z
dc.date.available2024-06-13T20:18:06Z
dc.date.issued2018
dc.departmentMeslek Yüksekokulu, Gedik Meslek Yüksekokulu, Kimya Teknolojisi Programı
dc.description.abstractMonoamine oxidase (MAO) enzymes MAO-A and MAO-B play a critical role in the metabolism of monoamine neurotransmitters. Hence, MAO inhibitors are very important for the treatment of several neurodegenerative diseases such as Parkinson's disease (PD), Alzheimer's disease (AD), and amyotrophic lateral sclerosis (ALS). In this study, 256 750 molecules from Otava Green Chemical Collection were virtually screened for their binding activities as MAO-B inhibitors. Two hit molecules were identified after applying different filters such as high docking scores and selectivity to MAO-B, desired pharmacokinetic profile predictions with binary quantitative structure activity relationship (QSAR) models. Therapeutic activity prediction as well as pharmacokinetic and toxicity profiles were investigated using MetaCore/MetaDrug platform which is based on a manually curated database of molecular interactions, molecular pathways, gene disease associations, chemical metabolism, and toxicity information. Particular therapeutic activity and toxic effect predictions are based on the ChemTree ability to correlate structural descriptors to that property using recursive partitioning algorithm. Molecular dynamics (MD) simulations were also performed to make more detailed assessments beyond docking studies. All these calculations were made not only to determine if studied molecules possess the potential to be a MAO-B inhibitor but also to find out whether they carry MAO-B selectivity versus MAO-A. The evaluation of docking results and pharmacokinetic profile predictions together with the MD simulations enabled us to identify one hit molecule (ligand 1, Otava ID: 3463218) which displayed higher selectivity toward MAO-B than a positive control selegiline which is a commercially used drug for PD therapeutic purposes.
dc.description.sponsorshipScientific and Technological Research Council of Turkey - TUBITAK, BIDEB-2211A program
dc.identifier.doi10.1021/acschemneuro.8b00095
dc.identifier.endpage1782
dc.identifier.issn1948-7193
dc.identifier.issue7
dc.identifier.pmid29671581
dc.identifier.scopus2-s2.0-85046301839
dc.identifier.scopusqualityQ1
dc.identifier.startpage1768
dc.identifier.urihttps://doi.org/10.1021/acschemneuro.8b00095
dc.identifier.urihttps://hdl.handle.net/11501/1229
dc.identifier.volume9
dc.identifier.wosWOS:000439531400025
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorİş, Yusuf Serhat
dc.institutionauthorid0000-0003-3818-6923
dc.language.isoen
dc.publisherAmerican Chemical Society
dc.relation.ispartofACS Chemical Neuroscience
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectMAO-A
dc.subjectMAO-B
dc.subjectDocking
dc.subjectBinary Qsar Models
dc.subjectMolecular Dynamics Simulations
dc.subjectBlood-Brain Barrier Prediction
dc.subjectG-Log P Prediction
dc.titleProposing novel MAO-B hit inhibitors using multidimensional molecular modeling approaches and application of binary QSAR models for prediction of their therapeutic activity, pharmacokinetic and toxicity properties
dc.typeArticle

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