Abstract
Objective: Metabolic disorders associated with diabetic patients are a serious concern. Aldose reductase (ALR2) has been identified as first rate-limiting enzyme in the polyol pathway which catalyzes the reduction of glucose to sorbitol. It represents one of the validated targets to develop potential new chemical entities for the prevention and subsequent progression of microvascular diabetic complications. In order to further understand the intricate structural prerequisites of molecules to act as ALR2 inhibitors, ligand-based pharmacophore model, atombased 3D-QSAR and structure based drug design studies have been performed on a series of 2,4- thiazolidinedione derivatives with ALR2 inhibitory activity.
Methods: In the present study, a validated six point pharmacophore model (AAADNR) with three hydrogen bond acceptor (A), one hydrogen bond donor (D), one negative ionic group (N) and one aromatic ring (R) was developed using PHASE module of Schrodinger suite with acceptable PLS statistics (survival score = 3.871, cross-validated correlation coefficient Q2 = 0.6902, correlation coefficient of multiple determination r2 = 0.9019, Pearson-R coefficient = 0.8354 and F distribution = 196.2). In silico predictive studies (pharmacophore modeling, atom-based 3D QSAR and docking combined with drug receptor binding free energetics and pharmacokinetic drug profile) highlighted some of the important structural features of thiazolidinedione analogs required for potential ALR2 inhibitory activity. Results: The result of these studies may account to design a legitimate template for rational drug design of novel, potent and promising ALR2 inhibitors.Keywords: ALR2, diabetes, 3D-QSAR, docking, pharmacophore hypothesis, phase.
Combinatorial Chemistry & High Throughput Screening
Title:Validation of TZD Scaffold as Potential ARIs: Pharmacophore Modeling, Atom-based 3D QSAR and Docking Studies
Volume: 20 Issue: 4
Author(s): Lalita Dahiya, Manoj Kumar Mahapatra, Ramandeep Kaur, Vipin Kumar and Manoj Kumar*
Affiliation:
- University Institute of Pharmaceutical Sciences, Panjab University, Chandigarh-160014,India
Keywords: ALR2, diabetes, 3D-QSAR, docking, pharmacophore hypothesis, phase.
Abstract: Objective: Metabolic disorders associated with diabetic patients are a serious concern. Aldose reductase (ALR2) has been identified as first rate-limiting enzyme in the polyol pathway which catalyzes the reduction of glucose to sorbitol. It represents one of the validated targets to develop potential new chemical entities for the prevention and subsequent progression of microvascular diabetic complications. In order to further understand the intricate structural prerequisites of molecules to act as ALR2 inhibitors, ligand-based pharmacophore model, atombased 3D-QSAR and structure based drug design studies have been performed on a series of 2,4- thiazolidinedione derivatives with ALR2 inhibitory activity.
Methods: In the present study, a validated six point pharmacophore model (AAADNR) with three hydrogen bond acceptor (A), one hydrogen bond donor (D), one negative ionic group (N) and one aromatic ring (R) was developed using PHASE module of Schrodinger suite with acceptable PLS statistics (survival score = 3.871, cross-validated correlation coefficient Q2 = 0.6902, correlation coefficient of multiple determination r2 = 0.9019, Pearson-R coefficient = 0.8354 and F distribution = 196.2). In silico predictive studies (pharmacophore modeling, atom-based 3D QSAR and docking combined with drug receptor binding free energetics and pharmacokinetic drug profile) highlighted some of the important structural features of thiazolidinedione analogs required for potential ALR2 inhibitory activity. Results: The result of these studies may account to design a legitimate template for rational drug design of novel, potent and promising ALR2 inhibitors.Export Options
About this article
Cite this article as:
Dahiya Lalita , Mahapatra Kumar Manoj, Kaur Ramandeep , Kumar Vipin and Kumar Manoj*, Validation of TZD Scaffold as Potential ARIs: Pharmacophore Modeling, Atom-based 3D QSAR and Docking Studies, Combinatorial Chemistry & High Throughput Screening 2017; 20 (4) . https://dx.doi.org/10.2174/1386207320666170315122459
DOI https://dx.doi.org/10.2174/1386207320666170315122459 |
Print ISSN 1386-2073 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5402 |
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