Hydrogen Sulfide Releasing 2-Mercaptoacrylic Acid-Based Derivative Possesses Cytoprotective Activity in a Small Intestine of Rats with Medication-Induced Enteropathy

Potential role of carvedilol in intestinal toxicity through NF-κB/iNOS/COX-2/TNF-α inflammatory signaling pathway in rats

BACKGROUND

The increased use of indomethacin (IND) is associated with gastrointestinal injury. This research aims to investigate the effects of a beta-blocker, carvedilol (CAR) on a rat model of IND-induced acute intestinal damage and clarify the probable underlying protective mechanisms.

MATERIALS AND METHODS

Twenty-four male Wistar rats were divided into four groups. Control group: given vehicles; CAR-treated group: given 10 mg/kg/day CAR PO daily by gastric gavage for 10 consecutive days; IND-treated group: given a single Sc dose of 10 mg/kg IND at the end of the ninth day of the experiment; combined CAR/IND-treated group: given both IND and CAR.

RESULTS

In the rats that received IND, severe intestinal histopathological changes together with oxidative and nitrosative intestinal stress were present biochemically and immunohistochemically. Obvious inflammatory and tissue damage were represented by the significant intestinal increases in TNF-α, COX-2, and caspase-3 together with the elevated expression of VCAM-1 adhesion molecules. Intestinal gene expression of NF-kB and COX-2 was also increased. Pretreatment with CAR significantly reversed the IND-induced intestinal toxic manifestations.

CONCLUSION

CAR has beneficial intestinal protective effects. Its ameliorative action is conferred through its antioxidant, antinitrosative, anti-inflammatory, and antiapoptotic properties.

5-[4-(tert-Butyl)cyclohexylidene]-2-thioxothiazolidin-4-one

The Knoevenagel reaction is an essential synthetic tool in the organic and medicinal chemistry of thiazolidin-4-one derivatives. In the present work, the application of ethylenediamine diacetate (EDDA) as an effective catalyst for the interaction of 2-thioxothiazolidin-4-one with 4-(tert-butyl)cyclohexanone is proposed. The structure of novel synthesized 5-[4-(tert-butyl)cyclohexylidene]-2-thioxothiazolidin-4-one (yield 61%) was confirmed by 1H-, 13C-NMR, LC-MS, IR, and UV spectra. Drug-like properties of the synthesized compound were evaluated in silico using the SwissAdme, and their potential antimicrobial activity against 15 strains of Gram-positive and Gram-negative bacteria as well as yeasts was evaluated in vitro. The synthesized compound possesses satisfactory drug-like parameters and promising antimicrobial properties and presents interest as a prospective intermediate for the forthcoming design of biologically active small molecules.

Evaluation of Anticonvulsant Activity of Dual COX-2/5-LOX Inhibitor Darbufelon and Its Novel Analogues

Neuroinflammation is an integral part of epilepsy pathogenesis and other convulsive conditions, and non-steroidal anti-inflammatory drugs (NSAIDs) present a potent tool for the contemporary search and design of novel anticonvulsants. In the present paper, evaluation of the anticonvulsant activity of the potential NSAID dual COX-2/5-LOX inhibitor darbufelone methanesulfonate using an scPTZ model in mice in dose 100 mg/kg is reported. Darbufelone possesses anticonvulsant properties in the scPTZ model and presents interest for in-depth studies as a possible anticonvulsant multi-target agent with anti-inflammatory activity. The series of 4-thiazolidinone derivatives have been synthesized following the analogue-based drug design and hybrid-pharmacophore approach using a darbufelone matrix. The synthesized derivatives showed a significant protection level for animals in the scPTZ model and are promising compounds for the design of potential anticonvulsants with satisfactory drug-like parameters.

HNet-DNN: Inferring New Drug-Disease Associations with Deep Neural Network Based on Heterogeneous Network Features

Drug research and development is a time-consuming and high-cost task, pressing an urgent demand to identify novel indications of approved drugs, referred to as drug repositioning, which provides an economical and efficient way for drug discovery. With increasing volumes of large-scale chemical, genomic, and pharmacological data sets generated by the high-throughput technique, it is crucial to develop systematic and rational computational approaches to identify new indications of approved drugs. In this paper, we introduce HNet-DNN, which utilizes a deep neural network (DNN), to predict new drug-disease associations based on the features extracted from the drug-disease heterogeneous network. Instead of the straightforward concatenation of chemical and phenotypic features as the input of DNN, we used these raw features of drugs and diseases to construct a drug-drug similarity network and a disease-disease similarity network, and then built a drug-disease heterogeneous network by integrating known drug-disease associations. Subsequently, we extracted topological features for drug-disease associations from the heterogeneous network and used them to train a DNN model. Our intensive performance evaluations demonstrated that HNet-DNN effectively exploits the features of the heterogeneous network to boost the predictive performance of drug-disease associations. Compared with a couple of typical classifiers and competitive approaches, our method not only achieved state-of-the-art performance but also effectively alleviated the overfitting problem. Moreover, we ran HNet-DNN to predict new drug-disease associations and carried out case studies to verify the effectiveness of our method.