Department of Chemical, Materials and Production Engineering, University of Naples "Federico II", Piazzale Tecchio 80, 80125 Naples, Italy; Interdisciplinary Research Center on Biomaterials, CRIB, Piazzale Tecchio 80, 80125 Naples, Italy; Center for Advanced Biomaterials for Health Care, CABHC, Istituto Italiano di Tecnologia, IIT@CRIB, Largo Barsanti e Matteucci 53, 80125 Naples, Italy.
Department of Chemical, Materials and Production Engineering, University of Naples "Federico II", Piazzale Tecchio 80, 80125 Naples, Italy.
Acta Biomater. 2023 Nov;171:440-450. doi: 10.1016/j.actbio.2023.09.029. Epub 2023 Sep 27.
The engineering of nanoparticles impacts the control of their nano-bio interactions at each level of the delivery pathway. Therefore, optimal nanoparticle physicochemical properties should be identified to favour on-target interactions and deliver efficiently active compounds to a specific target. To date, traditional batch processes do not guarantee the reproducibility of results and low polydispersity index of the nanostructures, while microfluidics has emerged as cost effectiveness, short-production time approach to control the nanoparticle size and size distribution. Several thermodynamic processes have been implemented in microfluidics, such as nanoprecipitation, ionotropic gelation, self-assembly, etc., to produce nanoparticles in a continuous mode and high throughput way. In this work, we show how the Artificial Neural Network (ANN) can be adopted to model the impact of microfluidic parameters (namely, flow rates and polymer concentrations) on the size of the nanoparticles. Promising results have been obtained, with the highest model accuracy reaching 98.9 %, thus confirming the proposed approach's potential applicability for an ANN-guided biopolymer nanoparticle design for biomedical applications. Nanostructures with different degrees of complexity are analysed, and a proof-of-concept machine learning approach is proposed to evaluate Hydrodenticity in biopolymer matrices. STATEMENT OF SIGNIFICANCE: Size, shape and surface charge determine nano-bio interactions of nanoparticles and their ability to target diseases. The ideal nanoparticle design avoids off-target interactions and favours on-target interactions. So, tools enabling the identification of the optimal nanoparticle physicochemical properties for delivery to a specific target are required. In this work, we evaluate the use of Artificial Neural Network (ANN) to analyse the role of microfluidic parameters in predicting the optimal size of the different hydrogel nanoparticles and their ability to trigger Hydrodenticity.
纳米颗粒的工程设计影响其在递药途径的各个水平上的纳米-生物相互作用的控制。因此，应确定最佳的纳米颗粒物理化学性质，以有利于靶向相互作用，并将有效活性化合物高效递送至特定靶标。迄今为止，传统的批量工艺不能保证结果的重现性和纳米结构的低多分散指数，而微流控技术已成为一种具有成本效益、生产时间短的方法，可以控制纳米颗粒的尺寸和尺寸分布。已经在微流控中实施了几种热力学过程，例如纳米沉淀、离子凝胶化、自组装等，以连续模式和高通量方式生产纳米颗粒。 在这项工作中，我们展示了如何采用人工神经网络（ANN）来模拟微流控参数（即流速和聚合物浓度）对纳米颗粒尺寸的影响。得到了有希望的结果，最高模型精度达到 98.9%，从而证实了所提出的方法在基于 ANN 的生物聚合物纳米颗粒设计用于生物医学应用方面的潜在适用性。分析了具有不同复杂程度的纳米结构，并提出了一种基于机器学习的概念验证方法来评估生物聚合物基质中的 Hydrodenticity。 意义声明：大小、形状和表面电荷决定了纳米颗粒的纳米-生物相互作用及其靶向疾病的能力。理想的纳米颗粒设计避免了非靶向相互作用，并有利于靶向相互作用。因此，需要能够识别用于递送至特定靶标的最佳纳米颗粒物理化学性质的工具。在这项工作中，我们评估了使用人工神经网络（ANN）来分析微流控参数在预测不同水凝胶纳米颗粒的最佳尺寸及其触发 Hydrodenticity 的能力中的作用。
