Biyoinformatik ve Genetik Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12469/46

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Browsing Biyoinformatik ve Genetik Bölümü Koleksiyonu by Institution Author "Djikic, Teodora"

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    Review
    Citation - WoS: 57
    Citation - Scopus: 73
    Drug Design for Cns Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3d-Qsar and Virtual Screening Methodologies
    (Frontiers Media Sa, 2016) Nikolic, Katarina; Mavridis, Lazaros; Djikic, Teodora; Vucicevic, Jelica; Agbaba, Danica; Yelekçi, Kemal; Mitchell, John B. O.
    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 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.
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    Article
    Citation - WoS: 8
    Citation - Scopus: 9
    Human Dopamine Transporter: the First Implementation of a Combined in Silico/In Vitro Approach Revealing the Substrate and Inhibitor Specificities
    (Taylor & Francis Inc, 2019) Djikic, Teodora; Marti, Yasmina; Spyrakis, Francesca; Lau, Thorsten; Benedetti, Paolo; Davey, Gavin; Schloss, Patrick; Yelekçi, Kemal
    Parkinson's disease (PD) is characterized by the loss of dopamine-generating neurons in the substantia nigra and corpus striatum. Current treatments alleviate PD symptoms rather than exerting neuroprotective effect on dopaminergic neurons. New drugs targeting the dopaminergic neurons by specific uptake through the human dopamine transporter (hDAT) could represent a viable strategy for establishing selective neuroprotection. Molecules able to increase the bioactive amount of extracellular dopamine thereby enhancing and compensating a loss of dopaminergic neurotransmission and to exert neuroprotective response because of their accumulation in the cytoplasm are required. By means of homology modeling molecular docking and molecular dynamics simulations we have generated 3D structure models of hDAT in complex with substrate and inhibitors. Our results clearly reveal differences in binding affinity of these compounds to the hDAT in the open and closed conformations critical for future drug design. The established in silico approach allowed the identification of promising substrate compounds that were subsequently analyzed for their efficiency in inhibiting hDAT-dependent fluorescent substrate uptake through in vitro live cell imaging experiments. Taken together our work presents the first implementation of a combined in silico/in vitro approach enabling the selection of promising dopaminergic neuron-specific substrates.