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

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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.