Nanoparticulate drug delivery systems(NDDS)have revolutionized modern medicine by significantly improving drug targeting,bioavailability,and therapeutic efficacy.Despite the clinical success of over 90 approved nanome...Nanoparticulate drug delivery systems(NDDS)have revolutionized modern medicine by significantly improving drug targeting,bioavailability,and therapeutic efficacy.Despite the clinical success of over 90 approved nanomedicines,the development of NDDS remains challenging due to the complexity of formulation design,optimization,and characterization processes.Artificial intelligence,particularly machine learning(ML),offers powerful data analytics and predictive capabilities that can address these challenges.This review systematically summarizes recent advances in ML applications across various NDDS formulations,including polymeric nanoparticles,lipid nanoparticles,liposomes,solid lipid nanoparticles,nanostructured lipid carriers,nanoemulsions,nanosuspensions,lipid-based hybrid NDDS,self-emulsifying drug delivery systems,niosomes,and nanocrystals.We also summarize how ML algorithms could help predict critical quality attributes of NDDS,such as particle size,shape,surface properties,drug encapsulation efficiency,drug loading efficiency,drug release behavior,and stability.Furthermore,we discuss existing challenges and prospects for the formulation development empowered by ML in NDDS.In conclusion,this review provides a comprehensive overview of the transformative potential of ML in improving the formulation development of nanomedicines,ultimately accelerating their clinical translation.展开更多
基金supported by the National Natural Science Foundation of China(No.82104402)the Regulatory Science Research Project of the Guangdong-Hong Kong-Macao Greater Bay Area Center for Drug Evaluation and Inspection of National Medical Products Administration(No.GBA-JGKX-2507,China)+1 种基金Traditional Chinese Medicine Research Fund of Guangdong Provincial Bureau(No.202405061017183700,China)the Natural Science Foundation of Guangdong Province(No.2023A1515010720,China).
文摘Nanoparticulate drug delivery systems(NDDS)have revolutionized modern medicine by significantly improving drug targeting,bioavailability,and therapeutic efficacy.Despite the clinical success of over 90 approved nanomedicines,the development of NDDS remains challenging due to the complexity of formulation design,optimization,and characterization processes.Artificial intelligence,particularly machine learning(ML),offers powerful data analytics and predictive capabilities that can address these challenges.This review systematically summarizes recent advances in ML applications across various NDDS formulations,including polymeric nanoparticles,lipid nanoparticles,liposomes,solid lipid nanoparticles,nanostructured lipid carriers,nanoemulsions,nanosuspensions,lipid-based hybrid NDDS,self-emulsifying drug delivery systems,niosomes,and nanocrystals.We also summarize how ML algorithms could help predict critical quality attributes of NDDS,such as particle size,shape,surface properties,drug encapsulation efficiency,drug loading efficiency,drug release behavior,and stability.Furthermore,we discuss existing challenges and prospects for the formulation development empowered by ML in NDDS.In conclusion,this review provides a comprehensive overview of the transformative potential of ML in improving the formulation development of nanomedicines,ultimately accelerating their clinical translation.
