Machine learning instructed microfluidic synthesis of curcumin-loaded liposomes

Biomed Microdevices. 2023 Aug 5;25(3):29. doi: 10.1007/s10544-023-00671-1.ABSTRACTThe 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 concentrations, organic:water mixing volume ratios, support-vector machine models and feed-forward artificial neural networks were trained to predict, respectively, the liposome dispersity/stability and size. This work presents an initial step towards the application and cultivation of ML models to instruct the microfluidic formulation of nanoparticles.PMID:37542568 | PMC:PMC10404166 | DOI:10.1007/s10544-023-00671-1

Source: Biomedical Microdevices - August 5, 2023 Category: Biomedical Engineering Authors: Valentina Di Francesco Daniela P Boso Thomas L Moore Bernhard A Schrefler Paolo Decuzzi Source Type: research