TY - JOUR
T1 - Machine learning instructed microfluidic synthesis of curcumin-loaded liposomes
AU - Di Francesco, Valentina
AU - Boso, Daniela P.
AU - Moore, Thomas L.
AU - Schrefler, Bernhard A.
AU - Decuzzi, Paolo
N1 - Publisher Copyright:
© 2023, The Author(s).
PY - 2023/9
Y1 - 2023/9
N2 - 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 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.
AB - 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 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.
KW - Artificial Intelligence
KW - Artificial neural network
KW - Drug delivery
KW - Microfluidics
KW - Nanomedicine
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UR - http://www.scopus.com/inward/citedby.url?scp=85166597849&partnerID=8YFLogxK
U2 - 10.1007/s10544-023-00671-1
DO - 10.1007/s10544-023-00671-1
M3 - Article
C2 - 37542568
AN - SCOPUS:85166597849
SN - 1387-2176
VL - 25
JO - Biomedical Microdevices
JF - Biomedical Microdevices
IS - 3
M1 - 29
ER -