A novel combinatorial approach integrating experimental and computational analysis of antioxidant activity: Evaluating catechin and L-ascorbic acid in serum.

Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality globally, with oxidative stress playing a pivotal role in its progression. Free radicals produced via oxidative stress contribute to lipid peroxidation, leading to subsequent inflammatory responses, which then result in atherosclerosis. Antioxidants inhibit these harmful effects through their reducing ability, thereby preventing oxidative damage. In this study, we introduce computational models simulating hydrophilic and hydrophobic serum environments. We optimized the Ferric Reducing Ability of Plasma (FRAP) assay at a microscale level to evaluate the antioxidant activity of L-ascorbic acid (vitamin C) and catechin, a phytochemical found in green tea, in normal and hypertriglyceridemic serum. Hypertriglyceridemic serum, characterized by increased hydrophobic lipid content, provides a model to examine the impact of serum triglycerides on antioxidant activity. Additionally, we employed computational models using the Gaussian software to simulate the hydrogen atom transfer (HAT) mechanism, calculating free energy changes and bond dissociation energy (BDE) to assess the antioxidant potency of the studied compounds in both hydrophilic and hydrophobic environments. The computational results align with the experimental finding offering a unique combinatorial approach to assess antioxidant activity in both normal and hypertriglyceridemic serum, with potential implications for clinical interventions.