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Development of QSAR model for immunomodulatory activity of natural coumarinolignoids

Authors Yadav DK, Meena A, Srivastava A, Chanda D, Feroz Khan F, Chattopadhyay S

Published 30 July 2010 Volume 2010:4 Pages 173—186

DOI https://doi.org/10.2147/DDDT.S10875

Review by Single-blind

Peer reviewer comments 5


Dharmendra K Yadav, Abha Meena, Ankit Srivastava, D Chanda, Feroz Khan, SK Chattopadhyay
Metabolic and Structural Biology Department, Central Institute of Medicinal and Aromatic Plants, Council of Scientific and Industrial Research, PO-CIMAP, India

Abstract: Immunomodulation is the process of alteration in immune response due to foreign intrusion of molecules inside the body. Along with the available drugs, a large number of herbal drugs are promoted in traditional Indian treatments, for their immunomodulating activity. Natural coumarinolignoids isolated from the seeds of Cleome viscose have been recognized as having hepatoprotective action and have recently been tested preclinically for their immunomodulatory activity affecting both cell-mediated and humoral immune response. To explore the immunomodulatory compound from derivatives of coumarinolignoids, a quantitative structure activity relationship (QSAR) and molecular docking studies were performed. Theoretical results are in accord with the in vivo experimental data studied on Swiss albino mice. Immunostimulatory activity was predicted through QSAR model, developed by forward feed multiple linear regression method with leave-one-out approach. Relationship correlating measure of QSAR model was 99% (R2 = 0.99) and predictive accuracy was 96% (RCV2 = 0.96). QSAR studies indicate that dipole moment, steric energy, amide group count, lambda max (UV-visible), and molar refractivity correlates well with biological activity, while decrease in dipole moment, steric energy, and molar refractivity has negative correlation. Docking studies also showed strong binding affinity to immunomodulatory receptors.

Keywords: coumarinolignoids, immunomodulation, docking, QSAR, regression model

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