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Current Enzyme Inhibition


ISSN (Print): 1573-4080
ISSN (Online): 1875-6662

Ligand-Based Pharmacophore Detection and Screening of Potential Glitazones

Author(s): Ritesh Agrawal, Pratima Jain, Subodh N. Dikshit

Volume 8, Issue 1, 2012

Page: [22 - 46] Pages: 25

DOI: 10.2174/157340812800228964

Price: $65


Three-dimensional pharmacophore hypothesis was built based on a set of known Protein tyrosine Phosphatase 1B (PTP1B) agonists using PharmaGist program to understand the essential structural features for Protein Tyrosine Phosphatase 1B (PTP1B) agonists. The various marketed or under development potential glitazones have been opted to build a pharmacophore model e.g. Pioglitazone, Rosiglitazone (BRL-49653), Rivoglitazone (CS-011), Darglitazone, Citaglitazone, Englitazone, Netoglitazone (MCC-555), Balaglitazone (DRF-2593) and Troglitazone. PharmaGist web based program is employed for pharmacophore development. Four points pharmacophore with the hydrogen bond acceptor (A), hydrophobic group (H), Spatial Features and aromatic rings (R) have been considered to develop pharmacophoric features by PharmaGist program. The best pharmacophore model having the Score 30.547, which has been opted to screen on ZincPharmer database to derive the novel potential antidiabetic ligands. The best pharmacophore having various Pharmacophore features, including General Features 6, Spatial Features 6, Aromatic 2, Hydrophobic 1, Donors 1, and Acceptors 2. The algorithm identifies the best pharmacophores by computing multiple flexible alignments between the input ligands. The multiple alignments are generated by combining pairwise alignments between one of the glitazone input ligand, which acts as pivot and the other glitazones as ligand. The resulting multiple alignments reveal spatial arrangements of consensus features shared by different subsets of input ligands. The best pharmacophore model has been derived using both pairwise and multiple alignment methods, which have been weighted in Pharmacophore Generation process. The highest-scoring pharmacophore model selected as potential pharmacophore model. Finally, 3D structure search have been performed on the “ZincPharmer Database” to identify potential compounds that have been matched with the proposed pharmacophoric features. The 3D ZincPharmer Database has been matched with 553 ligands hits. The physicochemical properties of 553 ligands hits have been calculated by PaDEL-Descriptor software, which have been filtered based on the Lipinski's rule of five criteria (i.e. MW < 500, H-bond acceptor ≤ 10, H-bond donor ≤ 5, Log P ≤ 5) by to get the potential antidiabetic ligands. We have found various substituted “pyrido[3',2':4,5]thieno[3,2- d]pyrimidin-4(3H)-one” as potential antidiabetic ligands, which can be used for further development of antidiabetic agents. In the present research work, we have covered rational of Thiazolidinedione's nucleus based on Maximal Common Substructure (MCS) as well as Ligand-Based Pharmacophore.

Keywords: Pharmacophore, glitazones, protein tyrosine phosphatase 1B (PTP1B), PharmaGist, PPAR-γ, selective PPAR-γ modulators, PPARs, thiazolidinediones, novel emerging targets for diabetes, diabetes, diabetes type-II, non-insulin-dependent diabetes mellitus, NIDDM, pioglitazone, rivoglitazone (CS-011), darglitazone, citaglitazone, englitazone, netoglitazone (MCC-555), balaglitazone (DRF-2593), troglitazone, rosiglitazone (BRL-49653)

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