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Towards artificial intelligence-enabled extracellular vesicle precision drug delivery.
Greenberg, Zachary F; Graim, Kiley S; He, Mei.
Afiliação
  • Greenberg ZF; Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, FL 32610, USA.
  • Graim KS; Department of Computer & Information Science & Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL 32610, USA.
  • He M; Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, FL 32610, USA. Electronic address: mhe@cop.ufl.edu.
Adv Drug Deliv Rev ; 199: 114974, 2023 Aug.
Article em En | MEDLINE | ID: mdl-37356623
ABSTRACT
Extracellular Vesicles (EVs), particularly exosomes, recently exploded into nanomedicine as an emerging drug delivery approach due to their superior biocompatibility, circulating stability, and bioavailability in vivo. However, EV heterogeneity makes molecular targeting precision a critical challenge. Deciphering key molecular drivers for controlling EV tissue targeting specificity is in great need. Artificial intelligence (AI) brings powerful prediction ability for guiding the rational design of engineered EVs in precision control for drug delivery. This review focuses on cutting-edge nano-delivery via integrating large-scale EV data with AI to develop AI-directed EV therapies and illuminate the clinical translation potential. We briefly review the current status of EVs in drug delivery, including the current frontier, limitations, and considerations to advance the field. Subsequently, we detail the future of AI in drug delivery and its impact on precision EV delivery. Our review discusses the current universal challenge of standardization and critical considerations when using AI combined with EVs for precision drug delivery. Finally, we will conclude this review with a perspective on future clinical translation led by a combined effort of AI and EV research.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article