Ritesh Agrawal, Pratima Jain and Subodh Narayan Dikshit Pages 849 - 876 ( 28 )
Three-dimensional pharmacophore hypothesis was established based on a set of known DPP-IV inhibitor using PharmaGist software program understanding the essential structural features for DPP-IV inhibitor. The various marketed or under developmental status, potential gliptins have been opted to build a pharmacophore model, e.g. Sitagliptin (MK- 0431), Saxagliptin, Melogliptin, Linagliptin (BI-1356), Dutogliptin, Carmegliptin, Alogliptin and Vildagliptin (LAF237). 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 bearing the Score 16.971, has been opted to screen on ZincPharmer database to derive the novel potential anti-diabetic ligands. The best pharmacophore bear various Pharmacophore features, including General Features 3, Spatial Features 1, Aromatic 1 and Acceptors 2. The PharmaGist employed algorithm to identify the best pharmacophores by computing multiple flexible alignments between the input ligands. The multiple alignments are generated by combining alignments pair-wise between one of the gliptin input ligands, which acts as pivot and the other gliptin 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 pair-wise and multiple alignment methods, which have been weighted in Pharmacophore Generation process. The highest-scoring pharmacophore model has been selected as potential pharmacophore model. In conclusion, 3D structure search has 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 various thousands of Ligands hits. Those matches were screened through the RMSD and max hits per molecule. The physicochemical properties of various “ZincPharmer Database” screened ligands have been calculated by PaDELDescriptor software. The all “ZincPharmer Database” screened ligands have been filtered based on the Lipinski’s rule-of-five criteria (i.e. Molecular Weight <500, H-bond acceptor ≤ 10, H-bond donor ≤ 5, Log P ≤ 5) and were subjected to molecular docking studies to get the potential antidiabetic ligands. We have found various substituted as potential antidiabetic ligands, which can be used for further development of antidiabetic agents. In the present research work, we have covered rational of DPP-IV inhibitor based on Ligand-Based Pharmacophore detection, which is validated via the Docking interaction studies as well as Maximal Common Substructure (MCS).
Alogliptin, carmegliptin, diabetes type-II, DPP-IV inhibitor (DPP-IV), dutogliptin, gliptins, linagliptin (BI-1356) melogliptin, non-insulin-dependent diabetes mellitus (NIDDM), pharmacophore, pharmaGist, sitagliptin (MK-0431), saxagliptin, vildagliptin (LAF237).
Department of Chemistry, Government Shrimant Madhavrao Scindia, Model Science College, Jhansi Road, Gwalior, Madhya Pradesh 474001, India