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dc.contributor.authorNikolic, K
dc.contributor.authorMavridis, L
dc.contributor.authorDjikic, T
dc.contributor.authorVucicevic, J
dc.contributor.authorAgbaba, D
dc.contributor.authorYelekci, K
dc.contributor.authorMitchell, JB
dc.date.accessioned2016-09-14T14:00:42Z
dc.date.available2016-09-14T14:00:42Z
dc.date.issued2016-06-10
dc.date.submitted2016-09-14T10:40:03.398Z
dc.identifier.citationNikolic K, Mavridis L, Djikic T, Vucicevic J, Agbaba D, Yelekci K and Mitchell JBO (2016) Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies. Front. Neurosci. 10:265. doi: 10.3389/fnins.2016.00265en_US
dc.identifier.issn1662-4548
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/15354
dc.description.abstractHIGHLIGHTS Many CNS targets are being explored for multi-target drug designNew databases and cheminformatic methods enable prediction of primary pharmaceutical target and off-targets of compoundsQSAR, virtual screening and docking methods increase the potential of rational drug design The 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 Parzen-Rosenblatt 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 D1-R/D2-R/5-HT2A -R/H3-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.format.extent265 - ?
dc.languageeng
dc.language.isoenen_US
dc.publisherFrontiersen_US
dc.rightsCC-BY
dc.subjectCNS diseaseen_US
dc.subjectQSARen_US
dc.subjectcheminformaticen_US
dc.subjectmulti-target drugsen_US
dc.subjectrational drug designen_US
dc.subjectvirtual dockingen_US
dc.subjectvirtual screeningen_US
dc.titleDrug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies.en_US
dc.typeArticleen_US
dc.rights.holder© 2016 Nikolic, Mavridis, Djikic, Vucicevic, Agbaba, Yelekci and Mitchell.
dc.identifier.doi10.3389/fnins.2016.00265
dc.relation.isPartOfFront Neurosci
pubs.author-urlhttp://www.ncbi.nlm.nih.gov/pubmed/27375423
pubs.declined2016-09-14T10:40:12.665+0100
pubs.organisational-group/Queen Mary University of London
pubs.organisational-group/Queen Mary University of London/Faculty of Science & Engineering
pubs.organisational-group/Queen Mary University of London/Faculty of Science & Engineering/Biological and Chemical Sciences - Staff
pubs.publication-statusPublished online
pubs.volume10


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