Pharmacophore modeling, 3D-QSAR, and in silico ADME prediction of N-pyridyl and pyrimidine benzamides as potent antiepileptic agents. (4th May 2017)

• Record Type: Journal Article
• Authors: Malik, Ruchi; Mehta, Pakhuri; Srivastava, Shubham; Choudhary, Bhanwar Singh; Sharma, Manish
• Is Part Of: Journal of receptor and signal transduction research. Volume 37:Number 3(2017)
• Page Start: 259
• Page End: 266
• Publication Date: 2017-05-04
• Subjects: Docking -- KCNQ2/Q3 openers -- epilepsy -- 3D-QSAR -- ADME; Cell receptors -- Periodicals; Cellular signal transduction -- Periodicals; 571.6
• DOI: 10.1080/10799893.2016.1217883
• Languages: English
• ISSNs: 1079-9893

Abstract

Abstract: Biological mechanism attributing mutations in KCNQ2/Q3 results in benign familial neonatal epilepsy (BFNE), a rare form of epilepsy and thus neglected. It offers a potential target for antiepileptic drug discovery. In the present work, a pharmacophore-based 3D-QSAR model was generated for a series of N -pyridyl and pyrimidine benzamides possessing KCNQ2/Q3 opening activity. The pharmacophore model generated contains one hydrogen bond donor (D), one hydrophobic (H), and two aromatic rings (R). They are the crucial molecular write-up detailing predicted binding efficacy of high affinity and low affinity ligands for KCNQ2/Q3 opening activity. Furthermore, it has been validated by using a biological correlation between pharmacophore hypothesis-based 3D-QSAR variables and functional fingerprints of openers responsible for the receptor binding and also by docking of these benzamides into the validated homology model. Excellent statistical computational tools of QSAR model such as good correlation coefficient ( R 2 > 0.80), higher F value ( F > 39), and excellent predictive power ( Q 2 > 0.7) with low standard deviation (SD <0.3) strongly suggest that the developed model could be used for prediction of antiepileptic activity of newer analogs. A preliminary pharmacokinetic profile of these derivatives was also performed on the basis of QikProp predictions.