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Updated: Mar 29, 2026

Semi-Targeted Ultra-High-Performance Chromatography Coupled to Mass Spectrometry Analysis of Phenolic Metabolites in Plasma of Elderly Adults
Published on: April 22, 2022
Nanotechnology and machine learning synergies for improving the bioavailability and functional efficacy of dietary
Wenze Wu1, Bo Mei2, Yongzi Wu3
1Inner Mongolia Key Laboratory of Life Health and Bioinformatics, School of Life Science and Technology, Inner Mongolia University of Science and Technology, Baotou 014010, China; College of Life and Health Sciences, Northeastern University, Shenyang 110819, China.
Abstract:
Dietary polyphenols exhibit diverse bioactivities, but their clinical application is limited by poor bioavailability due to low solubility, rapid metabolism, and restricted absorption. This review systematically summarizes recent advances in nanocarrier-based strategies for improving dietary polyphenol delivery. Four representative nanocarrier types are analyzed, including liposomes, solid lipid nanoparticles, nanosuspensions, and polymeric micelles. We discuss how their physicochemical properties and interfacial interactions enhance solubility, stability, and targeted delivery, while also highlighting limitations such as potential toxicity, limited loading capacity, and formulation complexity. A key focus of this review is the integration of machine learning (ML) into nanocarrier design to optimize performance. Supervised models such as support vector machines, random forests, and XGBoost achieve high predictive accuracy for encapsulation efficiency, release kinetics, and biodistribution. ML further enables high-throughput screening, toxicity prediction, and iterative refinement of formulations, improving the efficacy and safety of dietary polyphenol delivery. ML-driven integration of multi-omics data provides mechanistic insights into interactions among nanocarriers, polyphenols, and biological systems, supporting biomarker discovery and precision delivery. Additionally, we present a stepwise workflow that integrates nanotechnology and ML to guide the rational development of dietary polyphenol formulations. Finally, we discuss current challenges, including data heterogeneity, model interpretability, and regulatory considerations, and outline future directions to advance ML-driven nanocarrier strategies for efficient dietary polyphenol delivery. In conclusion, this review provides a comprehensive framework and highlights the unique contribution of integrating nanotechnology and ML for designing next-generation functional foods and precision nutrition.
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