Drug delivery using nano materials (e.g. nanoparticle) is a promising method to achieve cell recognition. Folate receptors (FRs), which are overexpressed in many human cancer cells, have been used as ideal targets for the treatment of cancer and inflammatory diseases over several decades. In this study, we compiled a dataset consisting of 30 mono-ligand gold nanoparticles (GNPs) and 30 dual-ligand GNPs on cell recognition and uptake against four human cancer cell lines that express different levels of FR. Quantitative nanostructure toxicity relationship (QNTR) models were developed using this dataset. Specifically, we simulated the surface chemistry of GNPs by constructing virtual nanoparticles with various surface ligands. The receptor-binding affinities correlate to the important surface properties (e.g. surface shape, electron density and etc), which can be calculated by the virtual nanoparticles. Various modeling approaches (e.g. random forest, support vector machine and etc.) were applied to the resulting surface chemical descriptor set using ten-fold cross-validation procedure. The modeling results clearly indicate the relationship between nanostructure (i.e. GNPs with different ligands) and cell recognition. The validated models can be used to design new GNPs with desired cell recognition and the developed virtual nano-particle model can be used to evaluate other nanotoxicity endpoints for new nanomaterials.

Virtual nanoparticles

252nd National Meeting (2016)

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