Inhibitory effects and mechanisms of phenolic compounds in rapeseed oil on advanced glycation end product formation in chemical and cellular models in vitro.

Sinapic acid (SA), canolol (CAO) and canolol dimer (CAO dimer) are the main phenolic compounds in rapeseed oil. However, their possible efficacy against glycation remains unclear. This study aims to explore the impacts of these substances on the formation of advanced glycation end products (AGEs) based on chemical and cellular models in vitro. Based on fluorescence spectroscopy results, three chemical models of BSA-fructose, BSA-methylglyoxal (MGO), and arginine (Arg)-MGO showed that SA/CAO/CAO dimer could effectively reduce AGE formation but with different abilities. After SA/CAO/CAO dimer incubation, effective protection against BSA protein glycation was observed and three different MGO adducts were formed. In MGO-induced HUVEC cell models, only CAO and CAO dimer significantly inhibited oxidative stress and cell apoptosis, accompanied by the regulation of the Nrf2-HO-1 pathway. During the inhibition, 20 and 12 lipid mediators were reversed in the CAO and CAO dimer groups compared to the MGO group.

Inhibitory mechanism of phenolic compounds in rapeseed oil on α-amylase and α-glucosidase: Spectroscopy, molecular docking, and molecular dynamic simulation.

Developing naturally active components to control α-amylase/α-glucosidase activity is highly desired for preventing and managing type 2 diabetes. Rapeseed oil is rich in active phenolic compounds and seed oil is a major source of liposoluble inhibitors to these enzymes. However, it remains unclear about the interaction of phenolic compounds in rapeseed oil with α-amylase/α-glucosidase. This study found that the important phenolic compounds from rapeseed oil (Sinapic acid, SA; canolol, CAO; canolol dimer, CAO dimer) possessed effective inhibition performance against α-amylase and α-glucosidase. CAO showed the lowest and highest inhibitory effect, respectively. In the kinetics studies, the inhibition mechanism of SA/CAO/CAO dimer against α-glucosidase was non-competitive, exhibiting a different way from α-amylase. Fluorescence quenching spectra implied that the static processes were responsible for the spontaneous binding between the compounds and enzymes. Fourier-transform infrared spectroscopy (FT-IR) displayed these compounds-induced conformation alterations of α-amylase/α-glucosidase. Molecular docking revealed that SA/CAO/CAO dimer decreased the catalytic efficiency of α-amylase/α-glucosidase through hydrogen bonds, hydrophobic force, or π-π interaction. Molecular dynamics matched well with the experimental and docking results regarding the inhibitory behaviors and interactions toward α-amylase/α-glucosidase. These results demonstrated the potential benefits of phenolic compounds from rapeseed oil in antidiabetic-related activities.

Discovery and optimization of a novel series of N-arylamide oxadiazoles as potent, highly selective and orally bioavailable cannabinoid receptor 2 (CB2) agonists.

The CB2 receptor is an attractive therapeutic target for analgesic and anti-inflammatory agents. Herein we describe the discovery of a novel class of oxadiazole derivatives from which potent and selective CB2 agonist leads were developed. Initial hit 7 was identified from a cannabinoid target-biased library generated by virtual screening of sample collections using a pharmacophore model in combination with a series of physicochemical filters. 7 was demonstrated to be a selective CB2 agonist (CB2 EC50 = 93 nM, Emax = 98%, CB1 EC50 > 10 microM). However, this compound exhibited poor solubility and relatively high clearance in rat, resulting in low oral bioavailability. In this paper, we report detailed SAR studies on 7 en route toward improving potency, physicochemical properties, and solubility. This effort resulted in identification of 63 that is a potent and selective agonist at CB2 (EC50 = 2 nM, Emax = 110%) with excellent pharmacokinetic properties.

Classification of kinase inhibitors using a Bayesian model.

The use of Bayesian statistics to model both general (multifamily) and specific (single-target) kinase inhibitors is investigated. The approach demonstrates an alternative to current computational methods applied to heterogeneous structure/activity data sets. This approach operates rapidly and is readily modifiable as required. A generalized model generated using inhibitor data from multiple kinase classes shows meaningful enrichment for several specific kinase targets. Such an approach can be used to prioritize compounds for screening or to optimally select compounds from third-party data collections. The observed benefit of the approach is finding compounds that are not structurally related to known actives, or novel targets for which there is not enough information to build a specific kinase model. The general kinase model described was built from a basis of mostly tyrosine kinase inhibitors, with some serine/threonine inhibitors; all the test cases used in prediction were also on tyrosine kinase targets. Confirming the applicability of this technique to other kinase families will be determined once those biological assays become available.