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dc.contributor.authorNikolic, Katarina
dc.contributor.authorMavridis, Lazaros
dc.contributor.authorDjikic, Teodora
dc.contributor.authorVucicevic, Jelica
dc.contributor.authorAgbaba, Danica
dc.contributor.authorYelekçi, Kemal
dc.contributor.authorMitchell, John B. O.
dc.date.accessioned2020-12-29T20:58:24Z
dc.date.available2020-12-29T20:58:24Z
dc.date.issued2016
dc.identifier.issn1662-453X
dc.identifier.urihttps://doi.org/10.3389/fnins.2016.00265en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12469/3694
dc.description.abstractThe diverse cerebral mechanisms implicated in Central Nervous System (CNS) diseases together with the heterogeneous and overlapping nature of phenotypes indicated that multitarget strategies may be appropriate for the improved treatment of complex brain diseases. Understanding how the neurotransmitter systems interact is also important in optimizing therapeutic strategies. Pharmacological intervention on one target will often influence another one, such as the well-established serotonin-dopamine interaction or the dopamine-glutamate interaction. It is now accepted that drug action can involve plural targets and that polypharmacological interaction with multiple targets, to address disease in more subtle and effective ways, is a key concept for development of novel drug candidates against complex CNS diseases. A multi-target therapeutic strategy for Alzheimer's disease resulted in the development of very effective Multi-Target Designed Ligands (MTDL) that act on both the cholinergic and monoaminergic systems, and also retard the progression of neurodegeneration by inhibiting amyloid aggregation. Many compounds already in databases have been investigated as ligands for multiple targets in drug discovery programs. A probabilistic method, the ParzenRosenblatt Window approach, was used to build a "predictor" model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. Based on all these findings, it is concluded that multipotent ligands targeting AChE/MAO-A/MAO-B and also D-1-R/D-2-R/5-HT2A-R/H-3-R are promising novel drug candidates with improved efficacy and beneficial neuroleptic and procognitive activities in treatment of Alzheimer's and related neurodegenerative diseases. Structural information for drug targets permits docking and virtual screening and exploration of the molecular determinants of binding, hence facilitating the design of multi-targeted drugs. The crystal structures and models of enzymes of the monoaminergic and cholinergic systems have been used to investigate the structural origins of target selectivity and to identify molecular determinants, in order to design MTDLs.en_US
dc.description.sponsorshipEuropean Union (EU) European Cooperation in Science and Technology (COST) Ministry of the Republic of Serbia Marie Sklodowska-Curie action FP7-MC-ITNen_US
dc.language.isoEnglishen_US
dc.publisherFrontiers Media Saen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMulti-target drugsen_US
dc.subjectCNS diseaseen_US
dc.subjectQSARen_US
dc.subjectRational drug designen_US
dc.subjectCheminformaticen_US
dc.subjectVirtual screeningen_US
dc.subjectVirtualen_US
dc.subjectDockingen_US
dc.titleDrug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologiesen_US
dc.typeReviewen_US
dc.relation.journalFrontiers in Neuroscienceen_US
dc.identifier.volume10en_US
dc.identifier.wosWOS:000377492500002en_US
dc.identifier.doi10.3389/fnins.2016.00265en_US
dc.contributor.khasauthorDjikic, Teodoraen_US
dc.contributor.khasauthorYelekçi, Kemalen_US


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