AbstractFollowing intravenous administration, an adsorbed corona of blood proteins immediately forms on the surfaces of nanocarriers to confer a distinct biological identity that dictates interactions with the immune system. While the nanocarrier surface chemistry has long been the focus of protein corona formation, the influence of the nanocarrier structure has remained unclear despite well-documented influences on biodistribution, clearance and inflammation. Here, we present design rules for the combined engineering of both nanocarrier structure and surface chemistry derived from a comprehensive proteomic analysis of protein corona formation in human blood. A library of nine soft PEGylated nanocarriers that differ in their combination of morphology (spheres, vesicles, and cylinders) and surface chemistry (methoxy, hydroxyl, and phosphate) were synthesized to represent properties of commonly employed drug delivery vehicles. Using label-free proteomics and high-throughput techniques, we examined the relationship between physicochemical properties and the resulting nanocarrier biological identity, including dynamic changes in protein corona composition, differential immunostimulation and uptake by relevant immune cell populations. In human blood, non-polar spherical micelles developed a similar biological identity to polar vesicles, whereas the identities of polar spheres and cylinders resembled that of non-polar vesicles. The formed protein coronas were compositionally dynamic and morphology-dependent, and these time-dependent fingerprints altered nanocarrier complement activation as well as their uptake by human monocytes, macrophages, and dendritic cells. This comprehensive analysis provides mechanistic insights into rational design choices that impact nanocarrier fate in human blood.One Sentence SummaryWe demonstrate that not only the surface chemistry, but the combined chemical and structural properties of soft drug delivery vehicles impact the composition of blood proteins that adsorb to their surfaces, and these differences specify their interactions with and modulation of human immune cells.
Machine learning provides predictive analysis into silver nanoparticle protein corona formation from physicochemical properties
