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An In-Silico Identification of Potential Flavonoids against Kidney Fibrosis Targeting TGFβR-1

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Fibrosis is a hallmark of progressive kidney diseases. The overexpression of profibrotic cytokine, namely transforming growth factor β (TGF-β) due to excessive inflammation and tissue damage, induces kidney fibrosis. The… Click to show full abstract

Fibrosis is a hallmark of progressive kidney diseases. The overexpression of profibrotic cytokine, namely transforming growth factor β (TGF-β) due to excessive inflammation and tissue damage, induces kidney fibrosis. The inhibition of TGF-β signaling is markedly limited in experimental disease models. Targeting TGF-β signaling, therefore, offers a prospective strategy for the management of kidney fibrosis. Presently, the marketed drugs have numerous side effects, but plant-derived compounds are relatively safer and more cost-effective. In this study, TGFβR-1 was targeted to identify the lead compounds among flavonoids using various computational approaches, such as ADME/T (absorption, distribution, metabolism, and excretion/toxicity) analysis, molecular docking, and molecular dynamics simulation. ADME/T screening identified a total of 31 flavonoids with drug-like properties of 31 compounds, a total of 5 compounds showed a higher binding affinity to TGFβR-1, with Epicatechin, Fisetin, and Luteolin ranking at the top three (−13.58, −13.17, and −10.50 kcal/mol, respectively), which are comparable to the control drug linagliptin (−9.074 kcal/mol). The compounds also exhibited outstanding protein–ligand interactions. The molecular dynamic simulations revealed a stable interaction of these compounds with the binding site of TGFβR-1. These findings indicate that flavonoids, particularly Epicatechin, Fisetin, and Luteolin, may compete with the ligand-binding site of TGFβR-1, suggesting that these compounds can be further evaluated for the development of potential therapeutics against kidney fibrosis. Further, in-vitro and in-vivo studies are recommended to support the current findings.

Keywords: kidney fibrosis; silico identification; fibrosis; targeting tgf

Journal Title: Life
Year Published: 2022

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