机器学习引导的高通量纳米粒子设计

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY
Ana Ortiz-Perez, Derek van Tilborg, Roy van der Meel, Francesca Grisoni and Lorenzo Albertazzi
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引用次数: 0

摘要

由于组合空间大、结构功能关系复杂,设计具有所需特性的纳米粒子是一项极具挑战性的工作。高通量方法和机器学习方法是加速纳米粒子成分设计的具有吸引力的新兴策略。迄今为止,如何将纳米粒子配方、筛选和计算决策结合到一个有效的工作流程中还没有得到充分探索。在本研究中,我们展示了三项关键技术的整合,即基于微流控的配方、高含量成像和主动机器学习。作为一个案例研究,我们应用我们的方法设计了在人类乳腺癌细胞中具有高吸收率的 PLGA-PEG 纳米粒子。从用于模型训练的一小组纳米粒子开始,我们的方法仅用了两个机器学习指导的迭代,就将吸收率从~5倍提高到~15倍,每个迭代耗时一周。据我们所知,这是首次成功整合这三种技术,通过纳米粒子成分优化生物反应。我们的研究结果凸显了拟议平台在快速、无偏见地优化纳米粒子方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Machine learning-guided high throughput nanoparticle design†

Machine learning-guided high throughput nanoparticle design†

Designing nanoparticles with desired properties is a challenging endeavor, due to the large combinatorial space and complex structure–function relationships. High throughput methodologies and machine learning approaches are attractive and emergent strategies to accelerate nanoparticle composition design. To date, how to combine nanoparticle formulation, screening, and computational decision-making into a single effective workflow is underexplored. In this study, we showcase the integration of three key technologies, namely microfluidic-based formulation, high content imaging, and active machine learning. As a case study, we apply our approach for designing PLGA-PEG nanoparticles with high uptake in human breast cancer cells. Starting from a small set of nanoparticles for model training, our approach led to an increase in uptake from ∼5-fold to ∼15-fold in only two machine learning guided iterations, taking one week each. To the best of our knowledge, this is the first time that these three technologies have been successfully integrated to optimize a biological response through nanoparticle composition. Our results underscore the potential of the proposed platform for rapid and unbiased nanoparticle optimization.

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CiteScore
2.80
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