Network Modelling and Computational insights of the Compounds from Aminothiazole Scaffold against Arthritis

Abstract

Arthritis is a general team for conditions that cause inflammation, pain and stiffness in the joints. Rheumatoid Arthritis is a specific type of arthritis. It is a chronic inflammatory autoimmune disorder where the immune system mistakenly attacks the lining of the joint capsules, causing painful swelling and eventual bone erosion. Aminothiazole is a nitrogen and sulfur containing heterocyclic compound. Their biological activities include anti- inflammatory, anticancer, antimicrobial and antiviral properties. In this study the aminothiazole scaffold are interacted with network pharmacology and molecular docking. Totally 111 compounds were retrieved from PubChem based on the physicochemical properties, pharmacokinetic properties and toxicity profiles. The compounds contain low toxicity and better pharmacokinetic properties were selected for the further studies. Network pharmacology and gene ontology enrichment were performed. The top ten targets were selected based on degree centrality using Cytohubba plugin in cytoscape. From the top ten targets molecular docking were performed for these three targets namely PIK3CA, EGFR and AKT1. The molecular docking study revealed that the compounds namely 4-(2-amino-2H- 1,3-thiazol-3-yl)-N-[(3-bromophenyl)methylidene]butanamide,5-(2-amino-1,3-thiazol-4-yl)- 2-ethoxybenzoic acid and (N-[(2-amino-1,3-thiazol-4-yl)methyl]-3-phenylprop-2-enamide) showed highest binding energy and good interaction profile. In conclusion, the result revealed that aminothiazole derivatives is one of the best option for the treatment of Rheumatoid arthritis.