In-silico Prediction of Epigallocatechin-3-Gallate (EGCG) vs Retinol in Photoaging Therapy

Background: Skin aging is a cumulative damage that occurs due to complex biological processes from genetic and environmental factors that are evident in individual's appearance. Clinically photoaging causes wrinkling, telangiectasia, dryness, pigment changes and loss of elasticity. As the predominant element found in green tea, epigallocatechin-3-gallate (EGCG) exhibits an active physiological function observed in both human and animal skin. Exposure to the two components of solar UV radiation that reach the earth surface, UVA (320–400nm) and UVB (290–320nm), leads to protein oxidative damage, lipid oxidation, DNA chain damage, and depletion of antioxidant enzymes. Since 1984, all-trans retinol has been incorporated into over-the-counter (OTC) cosmetic products, yet its potential in treating photoaging continues to be investigated. Methods: Search Profile EGCG, Retinol, Hyaluronan, and then Bioactive Prediction with SAR. Predicted EGCG targets were analyzed using Comparative Toxicogenomics Database. Compound Profile Similarity with Tanimoto Similarity. Using AlphaFold model, we obtained three-dimensional configuration of Hyaluronan Synthase 1, as designated target protein in this study, from Uniprot database (https://www.uniprot.org/) with identifier Q92839. Results: Based on SAR analysis to predict potential bioactivity, it shows that EGCG has better potential than retinol as an antioxidant and free radical scavenger. Target prediction with CTD shows that in curated studies the EGCG CTD is able to target COL1A1, HAS1, NFE2L2, and MMP1. Based on tanimono similarity, the similarity between EGCG and Hyaluron is higher than Hyaluron and Retinol. Conclusions: Docking analysis shows that it is predicted that EGCG is better at interacting with HAS1 and MMP1.