Combined In vitro Experimental and Computational Approaches for Predicting the Identity and Behaviour of Protein Corona-Adsorbed Nanoparticles

尽管多年来进行了出色的个体研究，但我们根据其工程性质预测纳米颗粒（NPs）在体内行为的能力仍然有限。该NSERC发现研究计划的目的是在血浆蛋白表面吸附后，对NP与不同细胞和非细胞生物屏障的相互作用进行全面了解，并生成这些不同相互作用的预测模型。 在将静脉内注射的NP的工程化性质（NP的广泛应用）与NP通过细胞外基质的转运（在离开循环后）、NP细胞毒性和NP的细胞摄取中的任一个相关联方面存在不一致。这部分与NP与血浆的相互作用有关，导致蛋白质的冠沉积在NP表面上。在模拟生理环境的条件下，对特定工程化NP设计特性的NP冠形成动力学的理解有限。然而，已经表明NP电晕形成的动力学决定了NP在体内的行为。在这项研究计划中，我的团队将进行全面的调查，以解开工程NP设计特性，血浆蛋白沉积动力学（冠形成）和随后通过细胞外基质和细胞相互作用的运输之间的关系。这些关系尚未在文献中描述。我将指导和培训我的团队使用模拟动态生理环境的体外实验和机器学习方法的组合来开发这些关系。我的研究计划分为两个研究主题：NPs与血浆蛋白相互作用的机制理解，以及使用体外模型和建模算法研究蛋白质冠状吸附NPs的细胞外基质和细胞相互作用。这些拟议的科学努力将产生关键的新的见解电晕形成的决定因素及其在随后的行为的NP的作用。没有以前的工作已经证明了使用模拟动态生理环境的体外模型来分离和准确预测电晕吸附的NP的不同个体功效终点的能力。将其与机器学习方法相结合，将产生经过验证的结果，并观察到传统方法不明显的微妙关系。我们将提供新的知识，使工程师能够开发新一代的NP，既有效又安全。

Despite years of excellent individual investigations, our ability to predict the behaviour of nanoparticles (NPs) in the body based on their engineered properties is still limited. The aim of this NSERC Discovery research program is to develop a global understanding of the interaction of NPs with different cellular and acellular biological barriers following surface adsorption of plasma proteins, and generate predictive models of those different interactions. There is inconsistency in relating the engineered properties of NPs injected intravenously (vast applications of NPs) to either of NP transport through the extracellular matrix (after exiting the circulation), NP cytotoxicity and cell uptake of NPs. This is partly related to the interaction of NPs with the blood plasma resulting in a corona of proteins being deposited on the NP surface. There is a limited understanding of the kinetics of NP corona formation for specific engineered NP design properties in a condition simulating the physiological environment. However, it has been shown that the kinetics of NP corona formation determines the behaviour of NPs in the body. In this research program, my team will undertake a comprehensive investigation to unravel the relationships between engineered NP design properties, kinetics of plasma protein deposition (corona formation) and subsequent transport through the extracellular matrix and cell interactions. These relationships have not been described in literature. I will guide and train my team to use a combination of in vitro experiments that mimic the dynamic physiological environment, and machine learning approaches in order to develop these relationships. My research program is divided into two research themes: mechanistic understanding of the interaction of NPs with plasma proteins, and studying the extracellular matrix and cell interactions of protein corona-adsorbed NPs using in vitro models, and modelling algorithms. These proposed scientific endeavours will generate critical new insights into the determinants of corona formation and its role in the subsequent behaviour of NPs. No previous work has demonstrated an ability to separate and accurately predict the different individual efficacy end-points of corona-adsorbed NPs using in vitro models that mimic the dynamic physiological environment. Combining this with machine learning approaches will result in validated results and observation of subtle relationships that are not obvious by traditional approaches. We will provide new knowledge that will enable engineers to develop new generations of NPs that are both effective- and safe-by-design.