Tymazoline Hydrochloride

Alzheimer's disease (AD) is the most common form of dementia. Although its cause and mechanism of progression are not well understood, various in vitro and in vivo experiments have proved that the decreased activity of the cholinergic neuron is responsible for the memory damage that is observed in these patients. Therefore, the nicotinic acetylcholine receptor (nAChR) is one of the possible drug targets for this disease. At present, extensive nAChR ligands have been designed, and some of the α7 nAChR agonists (e.g., DMXB-A and JN403) have been found to improve the memories and spatial abilities of animal models. However, most of the α7 nAChR agonists cannot be used therapeutically for various reasons, such as poor selectivity for nAChR subtypes, poor pharmacokinetic properties, or toxicity. In the current study, we built homology models of α7 nAChR and virtually screened possible nAChR ligands by combining molecular docking, molecular feature searches, hydrogen bond analyses, and quantitative structure activity relationship (QSAR) study. Our docking simulations and QSAR modeling were reasonably accurate and predictive of nAChR ligand affinity, and we have provided novel and reasonable computational methods for the virtual screening of possible α7 nAChR agonists that may be effectively used for the treatment of various neural disorders, particularly Alzheimer's disease. The compounds that were found in this study may be assessed in future in vitro or in vivo experiments for their affinities to nAChRs in addition to their biological functions.

A Quantitative Structure-Retention Relationship (QSRR) is proposed to estimate the chromatographic retention of 83 diverse drugs on a Unisphere poly butadiene (PBD) column, using isocratic elutions at pH 11.7. Previous work has generated QSRR models for them using Classification And Regression Trees (CART). In this work, Ant Colony Optimization is used as a feature selection method to find the best molecular descriptors from a large pool. In addition, several other selection methods have been applied, such as Genetic Algorithms, Stepwise Regression and the Relief method, not only to evaluate Ant Colony Optimization as a feature selection method but also to investigate its ability to find the important descriptors in QSRR. Multiple Linear Regression (MLR) and Support Vector Machines (SVMs) were applied as linear and nonlinear regression methods, respectively, giving excellent correlation between the experimental, i.e. extrapolated to a mobile phase consisting of pure water, and predicted logarithms of the retention factors of the drugs (logk(w)). The overall best model was the SVM one built using descriptors selected by ACO.