In silico study of natural antioxidants

Antioxidants are the body's defense system against the damage of reactive oxygen species, which are usually produced in the body through various physiological processes. There are various sources of these antioxidants such as endogenous antioxidants in the body and exogenous food sources. This chapter provides important information on methods used to investigate antioxidant activity and sources of plant antioxidants. Over the past two decades, numerous studies have demonstrated the importance of in silico research in the development of novel natural and synthesized antioxidants. In silico methods such as quantitative structure-activity relationships (QSAR), pharmacophore, docking, and virtual screenings are play critical roles in designing effective antioxidants that may be synthesized and tested later. This chapter introduces the available in silico approaches for different classes of antioxidants. Many successful applications of in silico methods in the development and design of novel antioxidants are thoroughly discussed. The QSAR, pharmacophore, molecular docking techniques, and virtual screenings process summarized here would help readers to find out the proper mechanism for the interaction between the free radicals and antioxidant compounds. Furthermore, this chapter focuses on introducing new QSAR models in combination with other in silico methods to predict antioxidants activity and design more active antioxidants. In silico studies are essential to explore largely unknown plant tissue, food sources for antioxidant synthesis, as well as saving time and money in such studies.

N-Heterocyclic (4-Phenylpiperazin-1-yl)methanones Derived from Phenoxazine and Phenothiazine as Highly Potent Inhibitors of Tubulin Polymerization

We report here a series of 27 10-(4-phenylpiperazin-1-yl)methanones derived from tricyclic heterocycles which were screened for effects on tumor cell growth, inhibition of tubulin polymerization, and induction of cell cycle arrest. Several analogues, among them the 10-(4-(3-methoxyphenyl)piperazine-1-carbonyl)-10H-phenoxazine-3-carbonitrile (16o), showed excellent antiproliferative properties, with low nanomolar GI₅₀ values (16o, mean GI₅₀ of 3.3 nM) against a large number (93) of cancer cell lines. Fifteen compounds potently inhibited tubulin polymerization. Analysis of cell cycle by flow cytometry revealed that inhibition of tumor cell growth was related to an induction of G2/M phase cell cycle blockade. Western blotting and molecular docking studies suggested that these compounds bind efficiently to β-tubulin at the colchicine binding site. Our studies demonstrate the suitability of the phenoxazine and phenothiazine core and also of the phenylpiperazine moiety for the development of novel and potent tubulin polymerization inhibitors.

Molecular features related to the binding mode of PPARδ agonists from QSAR and docking analyses

Diabetes affects approximately 4% of world's population and metabolic syndrome has been directly related to obesity. There is a class of nuclear receptors, peroxisome proliferator-activated receptors (PPARs), which controls the metabolism of carbohydrates and lipids. It has been considered an attractive target to treat diabetes and metabolic syndrome. Accordingly, the primary objective of this study was to employ molecular modelling techniques to understand the factors involved in PPARδ activation. The QSAR models obtained showed good internal and external consistency and presented good validation coefficients (QSAR: q(2) = 0.83, r(2) = 0.87; HQSAR: q(2) = 0.73, r(2) = 0.90; CoMFA: q(2) = 0.88, r(2) = 0.94). The selected properties and the contour maps described the possible interactions between the PPARδ receptor and its agonists. From these findings, it is possible to propose molecular modifications to design new compounds with improved biological properties.

Docking alignment-3D-QSAR of a new class of potent and non-chiral indole-3-carboxamide-based renin inhibitors

Using generated conformations from docking analysis by CDOCKER algorithm, some 3D-QSAR models; CoMFA region focusing (CoMFA-RF) and CoMSIA have been created on a series of a new class of potent and non-chiral renin inhibitors. The satisfactory predictions were obtained by CoMFA-RF and CoMSIA based on docking alignment in comparison to CoMFA. Robustness and predictability of the models were further verified by using the test set, cross validation (leave one out and leave ten out), bootstrapping, and progressive scrambling. All-orientation search (AOS) strategy was used to acquire the best orientation and minimize the effect of the initial orientation of aligned compounds. The results of 3D-QSAR models are in agreement with docking results. Moreover, the resulting 3D CoMFA-RF/ CoMSIA contour maps and corresponding models were applied to design new and more active inhibitors.