Quinoxalin-2(1H)-one based design and synthesis produced several series of aldose reductase (ALR2) inhibitor candidates. In particular, phenolic structure was installed in the compounds for the combination of antioxidant activity and strengthening the ability to fight against diabetic complications. Most of the series 6 showed potent and selective effects on ALR2 inhibition with IC50 values in the range of 0.032-0.468 μM, and 2-(3-(2,4-dihydroxyphenyl)-7-fluoro-2-oxoquinoxalin-1(2H)-yl)acetic acid (6e) was the most active. More significantly, most of the series 8 revealed not only good activity in the ALR2 inhibition but also potent antioxidant activity, and 2-(3-(3-methoxy-4-hydroxystyryl)-2-oxoquinoxalin-1(2H)-yl)acetic acid (8d) was even as strong as the well-known antioxidant Trolox at a concentration of 100 μM, verifying the C3 p-hydroxystyryl side chain as the key structure for alleviating oxidative stress. These results therefore suggest an achievement of multifunctional ALR2 inhibitors having both potency for ALR2 inhibition and as antioxidants.

Sulfonyl group-containing compounds constitute an important class of therapeutical agents in medicinal chemistry presumably because of the tense chemical structure and functionality of the sulfonyl, which could not only form hydrogen bonding interactions with active site residues of biological targets but also, as incorporated into core ring structure, constrain the side chains and allowed their specific conformations that fit the active sites. This review focuses on sulfonamides and sulfones, which cover more than 40 series and are associated with at least 10 potential pharmaceutical targets in pathways of glucose metabolism and insulin signaling. A large number of such compounds have been reported as pharmaceuticals every year in the last decade. In particular, increasing studies suggest that sulfonamides and sulfones play a key role in the design of pharmaceutical agents with potential application for the treatment of diabetes and its complications. First, they are inhibitors of a variety of enzymes including 11β-hydroxysteroid dehydrogenase type 1, α- glucosidase, carnitine palmitoyltransferase and cytosolic phosphoenolpyruvate carboxykinase, and in turn involved in the regulation of the metabolism of glucose. In addition, they are active as activators of glucokinase and as antagonists of ghrelin receptors. These enzyme and receptors are tightly associated with the regulation of glucose metabolism and the improvement of insulin resistance. Secondly, sulfonamides and sulfones act in the insulin secretion. As agonists, they activate insulin receptor tyrosine kinase and thus increase insulin sensitivity. Moreover, they as inhibitors suppress protein tyrosine phosphatase 1B and dipeptidyl peptidase IV, and thus normalize the insulin signaling pathway. Finally, a number of sulfonamides and sulfones are inhibitors of aldose reductase, which have been linked to diabetic complications.

A series of novel benzothiadiazine 1,1-dioxide derivatives were synthesized and tested for their inhibitory activity against aldose reductase. Of these derivatives, 17 compounds, having a substituted N2-benzyl group and a N4-acetic acid group on the benzothiadiazine, were found to be potent and selective aldose reductase inhibitors in vitro with IC50 values ranging from 0.032 to 0.975 μM. 9m proved to be the most active in vitro. The eight top-scoring compounds coming from the in vitro test for ALR2 inhibition activity were then tested in vivo, whereby three derivatives, 9i, 9j, and 9m, demonstrated a significantly preventive effect on sorbitol accumulation in the sciatic nerve in the 5-day streptozotocin-induced diabetic rats in vivo. Structure-activity relationship and molecular docking studies highlighted the importance of substitution features of N4-acetic acid group and halogen-substituted N2-benzyl group in the benzothiadiazine scaffold and indicated that substitution with hallogen at C-7 had a remarkably strong effect on ALR2 inhibition potency.

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Support Vector Machines (SVM) is a machine learning method used for classifying the system. It analyses and identifies the categories using the trained data. It is widely used in medical field for diagnosing the disease. The proposed method consists of four phases. They are lung extraction, segmentation of lung region, feature extraction and finally classification of normal, benign and malignancy in the lung. Threat pixel identification with region growing method is used for segmentation of focal areas in the lung. For feature extraction gray level co-occurrence Matrix (GLCM) is been used. Extracted features are classified using different kernels of Support Vector Machine (SVM). The experimentation is performed with the help of real time computer tomography images.