Quantitative Structure-Activity Relationship Analysis of Selective Rho Kinase Inhibitors as Neuro-regenerator Agents.

Abstract

To understand the role of Rho (serine/threonine) kinases in the treatment of neurological segments, attempts have been made to find potent inhibitors of Rho enzyme by a 2D quantitative structure-activity relationship (QSAR) model. QSAR studies were executed on urea-based scaffolds from aniline and benzylamine analogues, which were aligned for generation of a chemometric-based model. Multivariate statistical approaches were applied including linear and nonlinear analysis such as multiple linear regression, partial least square and artificial neural network for the generation of model, and also an application of (in silico) absorption, distribution, metabolism, excretion studies was performed to ascertain the novelty and drug-like properties of the intended molecules. Ligand based analysis was implemented and showed excellent statistical relevance such as S value=0.38, F value=48.41, r=0.95, r²=0.91, and r²cv=0.86. Five illuminating variables, i.e., vesicle-associated membrane protein (VAMP) polarization YY component (whole molecule), VAMP dipole Y component (whole molecule), VAMP dipole Z component (whole molecule), Kier ChiV6 path index (whole molecule), and moment of inertia 2 size (whole molecule), were found and they have a profound influence on the potency of the compounds. The values of standard statistical parameters reveal the predictive power and robustness of this model and also provide valuable insight into the significance of five descriptors. The acquired physicochemical properties (electronic, topological, and steric) show the important structural features required for activity against Rho kinase. After performing Lipinski's rule of five on urea-based derivatives no molecule was violating the rule. Therefore, these features can be effectively employed for the modeling and screening of active neurological agents as novel Rho kinase inhibitors.