A dual approach to flavonoid toxicity assessment: Bridging computational and experimental paradigms

Abstract

Flavonoids form a structurally diverse group of polyphenolic compounds with high ethnopharmacological relevance, primarily attributed to their antimicrobial and anticancer activity mediated by modulation of oxidative stress, induction of apoptosis, and regulation of the cell cycle. Their translatability to the clinic is critically hindered by multifaceted toxicities involving nephrotoxicity, cardiotoxicity, and respiratory issues often traceable to conserved structural motifs. In response, we adopted an integrative dual-methodological approach that linked thorough data mining across PubMed, Google Scholar, and PubChem for pharmacokinetic parameters and SMILES-based structural information to computational toxicity prediction using ProTox 3.0 and ADMET AI in order to unravel mechanistic endpoints of toxicity.Chemical drawing utilities like ChemSketch and ChemDraw supported the structural evaluations, and cross-referring DrugBank and ClinicalTrials.gov gave validation for clinical relevance. This computational model was further validated using in vitro and in vivo model systems, guaranteeing a comprehensive evaluation of flavonoid toxicity and therapeutic potential. Although flavonoids show great antimicrobial and anticancer potential, the translational roadblock arises from discrepancies between predictive models of toxicity and empirical validation, requiring sophisticated structure–activity relationship (SAR) analysis and integrative approaches to bridge computational-experimental gaps and enhance clinical relevance. This research highlights the need for a dual investigative approach, blending in silico and experimental paradigms, to maximize the predictive validity and translational potential of flavonoid-derived therapeutics.