Machine learning instructed microfluidic synthesis of curcumin-loaded liposomes.

ABSTRACT: The association of machine learning (ML) tools with the synthesis of nanoparticles has the potential to streamline the development of more efficient and effective nanomedicines. The continuous-flow synthesis of nanoparticles via microfluidics represents an ideal playground for ML tools, where multiple engineering parameters - flow rates and mixing configurations, type and concentrations of the reagents - contribute in a non-trivial fashion to determine the resultant morphological and pharmacological attributes of nanomedicines. Here we present the application of ML models towards the microfluidic-based synthesis of liposomes loaded with a model hydrophobic therapeutic agent, curcumin. After generating over 200 different liposome configurations by systematically modulating flow rates, lipid

SUBMITTER: Di Francesco V 

PROVIDER: S-EPMC10404166 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature