Classification of High‐Activity Tiagabine Analogs by Binary QSAR Modeling

Jurik, Andreas; Reicherstorfer, Regina; Zdrazil, Barbara; Ecker, Gerhard F.

doi:10.1002/minf.201300020

Cited by 12 publications

(8 citation statements)

References 29 publications

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“…On the basis of a set of tiagabine analogs from literature sources, we recently investigated ligand-based structure–activity relationships of the compound class. 16 Briefly, binary QSAR allowed classification of GABA uptake inhibitors into active and inactive bins by using the degree of rigidity and polarity distribution as main descriptors. With the increasing knowledge provided by the X-ray structures of analogous transport proteins, 17 structure-based approaches for elucidating the molecular basis of drug–transporter interaction also become feasible.…”

Section: Introductionmentioning

confidence: 99%

A Binding Mode Hypothesis of Tiagabine Confirms Liothyronine Effect on γ-Aminobutyric Acid Transporter 1 (GAT1)

et al. 2015

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Elevating GABA levels in the synaptic cleft by inhibiting its reuptake carrier GAT1 is an established approach for the treatment of CNS disorders like epilepsy. With the increasing availability of crystal structures of transmembrane transporters, structure-based approaches to elucidate the molecular basis of ligand–transporter interaction also become feasible. Experimental data guided docking of derivatives of the GAT1 inhibitor tiagabine into a protein homology model of GAT1 allowed derivation of a common binding mode for this class of inhibitors that is able to account for the distinct structure–activity relationship pattern of the data set. Translating essential binding features into a pharmacophore model followed by in silico screening of the DrugBank identified liothyronine as a drug potentially exerting a similar effect on GAT1. Experimental testing further confirmed the GAT1 inhibiting properties of this thyroid hormone.

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Section: Introductionmentioning

confidence: 99%

A Binding Mode Hypothesis of Tiagabine Confirms Liothyronine Effect on γ-Aminobutyric Acid Transporter 1 (GAT1)

et al. 2015

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“…In this respect, contingency matrix and VSA descriptors turned out to be well-suited to describe the dataset. Moreover, as 2D-QSAR is a versatile method for capturing SAR information, therefore the test compounds were easily differentiated as active ones having ortho-substitution in the linker region of the derivatives of nipecotic acid from the inactive compounds (Jurik et al, 2013 ).…”

Section: Pharmacoinformatics Approachesmentioning

confidence: 99%

Structure, Function, and Modulation of γ-Aminobutyric Acid Transporter 1 (GAT1) in Neurological Disorders: A Pharmacoinformatic Prospective

Zafar

Jabeen

2018

Front. Chem.

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γ-Aminobutyric acid (GABA) Transporters (GATs) belong to sodium and chloride dependent-transporter family and are widely expressed throughout the brain. Notably, GAT1 is accountable for sustaining 75% of the synaptic GABA concentration and entails its transport to the GABAA receptors to initiate the receptor-mediated inhibition of post-synaptic neurons. Imbalance in ion homeostasis has been associated with several neurological disorders related to the GABAergic system. However, inhibition of the GABA uptake by these transporters has been accepted as an effective approach to enhance GABAergic inhibitory neurotransmission in the treatment of seizures in epileptic and other neurological disorders. Here, we reviewed computational methodologies including molecular modeling, docking, and molecular dynamic simulations studies to underscore the structure and function of GAT1 in the GABAergic system. Additionally, various SAR and QSAR methodologies have been reviewed to probe the 3D structural features of inhibitors required to modulate GATs activity. Overall, present review provides an overview of crucial role of GAT1 in GABAergic system and its modulation to evade neurological disorders.

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“…Previously, various antagonists of hGAT1, including nipecotic acid, guvacine, proline, pyrrolidine, azetidine and THPO derivatives (Dalby, 2000; Andersen et al, 2001; Clausen et al, 2005; Fülep et al, 2006; Faust et al, 2010; Hellenbrand et al, 2016; Schmidt, Höfner & Wanner, 2017; Lutz et al, 2018; Tóth, Höfner & Wanner, 2018), have been synthesized and pharmacologically tested and optimized using structure–activity relationship (SAR) data. Additionally, several ligand-based strategies including 2D QSAR (Jurik et al, 2013), CoMFA (Zheng et al, 2006) and pharmacophore models (Hirayama, Díez-Sampedro & Wright, 2001; Nowaczyk et al, 2018) have been developed to optimize small molecule inhibitors against hGAT1. However, most of these studies were class specific, focusing on nipecotic acid derivatives (Petrera et al, 2015), Tiagabine analogs (Jurik et al, 2015) and triarylnipecotic acid derivatives (Dhar et al, 1994).…”

Section: Introductionmentioning

confidence: 99%

GRID-independent molecular descriptor analysis and molecular docking studies to mimic the binding hypothesis of γ-aminobutyric acid transporter 1 (GAT1) inhibitors

Zafar¹,

Jabeen²

2019

PeerJ

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BackgroundThe γ-aminobutyric acid (GABA) transporter GAT1 is involved in GABA transport across the biological membrane in and out of the synaptic cleft. The efficiency of this Na+ coupled GABA transport is regulated by an electrochemical gradient, which is directed inward under normal conditions. However, in certain pathophysiological situations, including strong depolarization or an imbalance in ion homeostasis, the GABA influx into the cytoplasm is increased by re-uptake transport mechanism. This mechanism may lead to extra removal of extracellular GABA which results in numerous neurological disorders such as epilepsy. Thus, small molecule inhibitors of GABA re-uptake may enhance GABA activity at the synaptic clefts.MethodsIn the present study, various GRID-independent molecular descriptor (GRIND) models have been developed to shed light on the 3D structural features of human GAT1 (hGAT1) inhibitors using nipecotic acid and N-diarylalkenyl piperidine analogs. Further, a binding hypothesis has been developed for the selected GAT1 antagonists by molecular docking inside the binding cavity of hGAT1 homology model.ResultsOur results indicate that two hydrogen bond acceptors, one hydrogen bond donor and one hydrophobic region at certain distances from each other play an important role in achieving high inhibitory potency against hGAT1. Our docking results elucidate the importance of the COOH group in hGAT1 antagonists by considering substitution of the COOH group with an isoxazol ring in compound 37, which subsequently leads to a three order of magnitude decrease in biological activity of 37 (IC50 = 38 µM) as compared to compound 1 (IC50 = 0.040 µM).DiscussionOur docking results are strengthened by the structure activity relationship of the data series as well as by GRIND models, thus providing a significant structural basis for understanding the binding of antagonists, which may be useful for guiding the design of hGAT1 inhibitors.

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Classification of High‐Activity Tiagabine Analogs by Binary QSAR Modeling

Cited by 12 publications

References 29 publications

A Binding Mode Hypothesis of Tiagabine Confirms Liothyronine Effect on γ-Aminobutyric Acid Transporter 1 (GAT1)

A Binding Mode Hypothesis of Tiagabine Confirms Liothyronine Effect on γ-Aminobutyric Acid Transporter 1 (GAT1)

Structure, Function, and Modulation of γ-Aminobutyric Acid Transporter 1 (GAT1) in Neurological Disorders: A Pharmacoinformatic Prospective

GRID-independent molecular descriptor analysis and molecular docking studies to mimic the binding hypothesis of γ-aminobutyric acid transporter 1 (GAT1) inhibitors

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