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  • In conclusion phenolic hydroxyl was introduced not only to C

    2023-11-20

    In conclusion, phenolic hydroxyl was introduced not only to C3 side chain but also to C6 or C7 position of the quinoxalinone core, resulting in a new group of ARIs candidates exhibiting antioxidant activity. Biological activity tests suggested that compounds were not only sufficient to inhibit ALR2 but also effective for DPPH radical scavenging, which indicated success in the development of potent ARIs with the combination of the two types of activities. Compound was the most potent against ALR2 and much less active against ALR1 suggesting an excellent inhibitor with distinguished selectivity. exhibited significant antioxidant activity even comparable with Trolox at high concentrations, and was also potent in the ALR2 inhibition with excellent selectivity. The SAR studies revealed that the introduction of -hydroxyl in the C3 styryl side chain greatly increased the activity and selectivity on ALR2 inhibition, while the combination of the phenolic 3,4-dihydroxyl and vinyl spacer at the C3 position as well as C7-hydroxyl on the quinoxalinone core was more favored for achieving strong antioxidant activity. Moreover, the docking study further confirmed the importance of phenolic hydroxyl on enhancing ALR2 inhibitory activity. Thus it ON-01910 synthesis is reasonable to conclude that the introduction of phenolic hydroxyl group to C3 side chain and the quinoxalinone core could successfully enhance ALR2 inhibitory activity and add an antioxidant property, which is a beneficial strategy for the discovery of more powerful multifunctional ARIs. Acknowledgments This work was supported by the National Natural Science Foundation of China (grant no. 21272025 and grant no. 21572021), the Research Fund for the Doctoral Program of Higher Education of China (grant no. 20111101110042), and Beijing Natural Science Foundation (no. 7142096).
    Introduction Aldose reductase (AR) is an essential enzyme of the polyol pathway and plays a vital role in the development of diabetic complications. Aldose reductase normally functions to reduce toxic aldehydes in the cell to inactive alcohols; however, when the glucose concentration in the cell becomes too high, aldose reductase also reduces the glucose to sorbitol, which is later oxidized to fructose. During the process of reducing high intracellular glucose to sorbitol, aldose reductase consumes NADPH. The accumulation of sorbitol can lead to an osmotic imbalance and may contribute to the progression of diabetic complications, such as cataracts, neuropathy, and nephropathy [1–3]. The inhibition of AR is a possible prevention or treatment for these effects [4]. The inhibitory effect of aldose reductase was found in several structurally diverse classes of the compounds, including flavone coumarin, xanthine, naphthalene, flavone and quinazoline derivatives [5–8], but the flavonoid derivatives were more potent. A recent QSAR study on a data set of the inhibitory activities against the AR enzyme for the substituted flavonoids was reported using topology indices (JCIM 46, 90, 2006; JBMC 16, 7473, 2008) [9,10] and three-dimensional (3D)-QSAR methodologies (JMM 15, 841, 2009; JMC 6, 33, 2010) [11,12]. Flavonoids are a group of naturally occurring polyphenolic compounds that are ubiquitously found in fruits and vegetables [13–15]. Chemically, flavonoids are benzo-γ-pyrone derivatives, and they have potential application for a variety of pharmacological targets. The structural diversity of these compounds provides anti-hepatitis, antibacterial, anti-inflammatory, anti-mutagenic, anti-allergic, and anti-viral activities [16–18]. The increase in the speed and efficiency of drug discovery has been aided by large investments from major pharmaceutical companies, with the primary aim of reducing the cost per synthesized compound or assay. Computational models that predict the biological activity of compounds based on their structural properties are powerful tools to design highly active molecules. In this sense, quantitative structure–activity relationship (QSAR) studies have been successfully applied for modeling the biological activities of natural and synthetic chemicals [19].