Quantitative structure-activity relationship models for compounds with anticonvulsant activity

The NLRP3 Inflammasome in Neurodegenerative Disorders: Insights from Epileptic Models

Neuroinflammation represents a dynamic process of defense and protection against the harmful action of infectious agents or other detrimental stimuli in the central nervous system (CNS). However, the uncontrolled regulation of this physiological process is strongly associated with serious dysfunctional neuronal issues linked to the progression of CNS disorders. Moreover, it has been widely demonstrated that neuroinflammation is linked to epilepsy, one of the most prevalent and serious brain disorders worldwide. Indeed, NLRP3, one of the most well-studied inflammasomes, is involved in the generation of epileptic seizures, events that characterize this pathological condition. In this context, several pieces of evidence have shown that the NLRP3 inflammasome plays a central role in the pathophysiology of mesial temporal lobe epilepsy (mTLE). Based on an extensive review of the literature on the role of NLRP3-dependent inflammation in epilepsy, in this review we discuss our current understanding of the connection between NLRP3 inflammasome activation and progressive neurodegeneration in epilepsy. The goal of the review is to cover as many of the various known epilepsy models as possible, providing a broad overview of the current literature. Lastly, we also propose some of the present therapeutic strategies targeting NLRP3, aiming to provide potential insights for future studies.

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

Quantitative structure–activity relationship study on prolonged anticonvulsant activity of terpene derivatives in pentylenetetrazole test

Quantitative structure–activity relationship (QSAR) study has been conducted on 36 terpene derivatives with anticonvulsant activity in timed pentylenetetrazole (PTZ) infusion test. QSAR models for anticonvulsant activity prediction of hydrazones and esters of some monocyclic/bicyclic terpenoids were developed using simplex representation of molecular structure (SiRMS; informational field [IF]) approach based on the SiRMS and the IF of molecule. Four 2D partial least squares QSAR consensus models were developed with the coefficient of determination for test sets R test 2 > 0.62 {R}_{\text{test}}^{2}\gt 0.62 . Based on the established QSAR models, we found that carvone and verbenone cores possess the most significant contribution to antiseizure action examined on the model of PTZ-induced convulsions at 3 and 24 h after oral administration of terpene derivatives. Moreover, carbonyl and hydroxy group substitution in terpenoid molecules followed by hydrazones and esters formation leads to enhancement and prolongation of antiseizure action due to the contribution of additional molecular fragments. The presented QSAR models might be utilized to predict anticonvulsant effect among terpene derivatives for their oral administration against onset seizures.

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-src Tyrosine Kinase

A prototype of a family of at least nine members, cellular Src tyrosine kinase is a therapeutically interesting target because its inhibition might be of interest not only in a number of malignancies, but also in a diverse array of conditions, from neurodegenerative pathologies to certain viral infections. Computational methods in drug discovery are considerably cheaper than conventional methods and offer opportunities of screening very large numbers of compounds in conditions that would be simply impossible within the wet lab experimental settings. We explored the use of global quantitative structure-activity relationship (QSAR) models and molecular ligand docking in the discovery of new c-src tyrosine kinase inhibitors. Using a dataset of 1038 compounds from ChEMBL database, we developed over 350 QSAR classification models. A total of 49 models with reasonably good performance were selected and the models were assembled by stacking with a simple majority vote and used for the virtual screening of over 100,000 compounds. A total of 744 compounds were predicted by at least 50% of the QSAR models as active, 147 compounds were within the applicability domain and predicted by at least 75% of the models to be active. The latter 147 compounds were submitted to molecular ligand docking using AutoDock Vina and LeDock, and 89 were predicted to be active based on the energy of binding.