Laboratory of Nanotechnology for Precision Medicine, Istituto Italiano Di Tecnologia, Via Morego 30, Genova, 16163, Italy.
Department of Civil, Environmental and Architectural Engineering, University of Padova, Via Marzolo 9, Padova, 35131, Italy.
Biomed Microdevices. 2023 Aug 5;25(3):29. doi: 10.1007/s10544-023-00671-1.
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.
机器学习（ML）工具与纳米粒子合成的结合有可能简化更高效、更有效的纳米药物的开发。通过微流控技术实现的纳米粒子连续流合成是 ML 工具的理想场所，其中多个工程参数——流速和混合配置、试剂的类型和浓度——以非平凡的方式共同决定纳米药物的形态和药理特性。在这里，我们介绍了将 ML 模型应用于负载模型疏水性治疗剂姜黄素的脂质体的微流控合成。通过系统地调节流速、脂质浓度、有机相与水相的混合体积比，生成了 200 多种不同的脂质体结构后，我们分别使用支持向量机模型和前馈人工神经网络来预测脂质体的分散性/稳定性和粒径。这项工作是朝着应用和培养 ML 模型来指导纳米粒子的微流体制备迈出的初步一步。
