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Abstract
Human histamine H1 receptor (HHR1) is one of the receptors through which histamine exerts its allergic reactions, and inhibition of this receptor is an important strategy in treatment of inflammatory diseases. In this study, classical and 3D quantitative structure–activity relationship (QSAR) studies were carried out using 36 inverse agonists of which 27 diverse compounds were used in training set. The MOE® and SYSTAT® programs were used in descriptor calculation and step-wise multiple regression analysis. A predictive and statistically significant QSAR model based on four descriptors was developed and cross-validated with the leave-one-out (LOO) method. The statistical data of this model were r = 0.908, F = 25.703, s = 0.445, . Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) studies were carried out using the same training set compounds. Pharmacophore-based molecular alignment was used to develop models using different fields. The best CoMFA model was the one developed using the CoMFA indicator fields (r
2 = 0.998,
), and the best CoMSIA model was the one with CoMSIA steric fields (r
2 = 0.862,
). As the models explain well the observed variance in HHR1 binding affinities in both the training and the test set, they can be used in designing future antihistamines.
Acknowledgements
This research was supported by Basic Science Research Program (2009-0073267), Pioneer Research Center Program (2009-0081539) and Management of Climate Change Program (2010-0029084) through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (MEST) of Republic of Korea. This work was also supported by the Next-Generation BioGreen 21 Program (PJ008038) from Rural Development Administration (RDA) of Republic of Korea. The authors would like to thank Central Drug Research Institute, Lucknow, India, for financial support. Dr A.K. Saxena, Dr Anshuman Dixit and A.S. Kushwaha are gratefully thanked for their assistance during this work.