Design of novel quinazolinone derivatives as inhibitors for 5HT₇ receptor

Abstract

To study the pharmacophore properties of quinazolinone derivatives as 5HT₇ inhibitors, 3D QSAR methodologies, namely Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) were applied, partial least square (PLS) analysis was performed and QSAR models were generated. The derived model showed good statistical reliability in terms of predicting the 5HT₇ inhibitory activity of the quinazolione derivative, based on molecular property fields like steric, electrostatic, hydrophobic, hydrogen bond donor and hydrogen bond acceptor fields. This is evident from statistical parameters like q² (cross validated correlation coefficient) of 0.642, 0.602 and r² (conventional correlation coefficient) of 0.937, 0.908 for CoMFA and CoMSIA respectively. The predictive ability of the models to determine 5HT₇ antagonistic activity is validated using a test set of 26 molecules that were not included in the training set and the predictive r² obtained for the test set was 0.512 & 0.541. Further, the results of the derived model are illustrated by means of contour maps, which give an insight into the interaction of the drug with the receptor. The molecular fields so obtained served as the basis for the design of twenty new ligands. In addition, ADME (Adsorption, Distribution, Metabolism and Elimination) have been calculated in order to predict the relevant pharmaceutical properties, and the results are in conformity with required drug like properties.

Keywords::

• G protein coupled receptor (GPCR)
• antidepressants
• 3D QSAR (3 dimensional quantitative structure activity relationship)
• CoMFA (comparative molecular field analysis)
• CoMSIA (comparative molecular similarity indices analysis)

Acknowledgments

We gratefully acknowledge the support for this research from the Department of Science and Technology, New Delhi, India, University Grants Commission, New Delhi, India, and the Department of Chemistry, Nizam College, Hyderabad, India. We are also greatly thankful to Dr. G. N. Sastry, Scientist, Indian Institute of Chemical Technology for Sybyl 6.9 software and his useful suggestions. We thankfully acknowledge Schrödinger Inc. for Qidprop2.5.

Declaration of interest

The author declare no conflicts of interest.