3D-QSAR studies on CCR2B receptor antagonists: Insight into the structural requirements of (R)-3-aminopyrrolidine series of molecules based on CoMFA/CoMSIA models.

Objective: Monocyte chemo attractant protein-1 (MCP-1) is a member of the CC-chemokine family and it selectively recruits leukocytes from the circulation to the site of inflammation through binding with the chemotactic cytokine receptor 2B (CCR2B). The recruitment and activation of selected populations of leukocytes is a key feature in a variety of inflammatory conditions. Thus MCP-1 receptor antagonist represents an attractive target for drug discovery. To understand the structural requirements that will lead to enhanced inhibitory potencies, we have carried out 3D-QSAR (quantitative structure-activity relationship) studies on (R)-3-aminopyrrolidine series of molecules as CCR2B receptor antagonists.

Materials And Methods: Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of (R)-3-aminopyrrolidine derivatives as antagonists of CCR2B receptor with Sybyl 6.7v.

Results: We have derived statistically significant model from 37 molecules and validated it against an external test set of 13 compounds. The CoMFA model yielded a leave one out r(2) (r(2) (loo)) of 0.847, non-cross-validated r(2) (r(2) (ncv)) of 0.977, F value of 267.930, and bootstrapped r(2) (r(2) (bs)) of 0.988. We have derived the standard error of prediction value of 0.367, standard error of estimate 0.141, and a reliable external predictivity, with a predictive r(2) (r(2) (pred)) of 0.673. While the CoMSIA model yielded an r(2) (loo) of 0.719, r(2) (ncv) of 0.964,F value of 135.666, r(2) (bs) of 0.975, standard error of prediction of 0.512, standard error of estimate of 0.180, and an external predictivity with an r(2) (pred) of 0.611. These validation tests not only revealed the robustness of the models but also demonstrated that for our models r(2) (pred), based on the mean activity of test set compounds can accurately estimate external predictivity.

Conclusion: The QSAR model gave satisfactory statistical results in terms of q(2) and r(2) values. We analyzed the contour maps obtained, to study the activity trends of the molecules. We have tried to demonstrate structural features of compounds to account for the activity in terms of positively contributing physicochemical properties such as steric, electrostatic, hydrophobic, hydrogen bond donor, and acceptor fields. These contour plots identified several key features, which explain the wide range of activities. The results obtained from models offer important structural insight into designing novel CCR2B antagonists before their synthesis.