Machine learning instructed microfluidic synthesis of curcumin-loaded liposomes.
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 multip...
| Publicado en: | Biomedical Microdevices Vol. 25; no. 3; pp. 1 - 14 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=169799474&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 169799474 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13872176 ODN jtl: Biomedical Microdevices issn: 13872176 maglogo: N pubinfo: dt: Sep2023 vid: 25 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 169799474 10.1007/s10544-023-00671-1 169799474 ppf: 1 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine learning instructed microfluidic synthesis of curcumin-loaded liposomes. aug: au: Di Francesco, Valentina Boso, Daniela P. Moore, Thomas L. Schrefler, Bernhard A. Decuzzi, Paolo affil: Laboratory of Nanotechnology for Precision Medicine, Istituto Italiano Di Tecnologia, Via Morego 30, 16163, Genova, Italy sug: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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