An Application of a Large-Scale, Data-Driven Approach to Prioritize Compound-Targeted Conditions in the Nutraceutical Space: Case Study of QuercefitTM (Quercetin Phytosome) Toward Unbalanced Lipid Conditions in Metabolically Challenged Adults.

Abstract

In preventive healthcare, the demand for systematic identification of wellness-promoting natural compounds is rising. Quercetin, a phenolic compound found in various fruits and vegetables, is known for its antioxidant and anti-inflammatory properties, improving lipid profiles and metabolic dysfunctions in conditions like Type 2 diabetes and NAFLD. This study applies a novel adaptation of an in-silico drug repurposing methodology to quercetin, analyzing a gene expression signature library of over 800 diseases and 30 quercetin-related conditions to prioritize molecular targets. Our findings revealed a strong computational link between quercetin and hypercholesterolemia. To validate this, we conducted a proof-of-concept clinical study using a high-bioaccessibility quercetin formulation (QuercefitTM) in healthy adults with borderline metabolic profiles, confirming health benefits. This study highlights quercetin's known potential in managing hypercholesterolemia and demonstrates the power of computational methods in advancing natural compound discovery and repositioning. The integration of in-silico predictions with human studies could pave the way for more precise or alternative use of bioactive compounds in dietary supplements.