(527a) Prediction of Nanoparticles-Cell Association Based on Corona Proteins and Physicochemical Properties

The association of nanoparticles (NPs) with cells in biological fluids is impacted by the adsorption of proteins onto the NP surface. The multitude of proteins in the biological fluid form a protein corona which can then affect cellular bioactivity.  In the present work, an extensive dataset of gold NPs protein corona was utilized to investigate the relationships between protein corona fingerprints (PCFs) and NP-cellular association. The dataset consisted of a library of 84 gold nanoparticles (NPs) of 15, 30, or 60 nm cores with cationic or anionic surface ligands, and data regarding the abundance of 129 specific corona proteins and 19 NO physicochemical properties. The specific protesins of relavance to NP-cellular association were identified via quantitative structure-activity relationships (QSARs) that were developed based on both non-linear support vector regression (SVR) and linear models.  Feature selection was accomplished via sequential forward floating selection. From the initial set of 148 descriptors, the serum proteins APOB, A1AT, ANT3, and PLMN in addition to the NP zeta potential were most significant for correlating cellular association of gold nanoparticles. The non-linear model was built to a reasonable level of accuracy with only 6 serum proteins and NP zeta potential with a performance accuracy of R²=0.895. In contrast, the linear model required 11 CFs and demonstrate performance of R²=0.850.  Model robustness was validated based on Y-randomization demonstrating that the QSARs were not “chance” correlations. The present approach demonstrates that exploration of NP-cell association data, via QSAR analysis, can be particularly useful to elucidate the role of NP corona proteins and NP physicochemical properties in affecting NP-cellular. Such information is critical for supporting the design and interpretation of toxicity studies and for the design of NPs for biomedical applications.