Predicting the time of entry of nanoparticles in cellular membranes

The understanding of the molecular interactions between nanoparticles (NPs) and biological systems is crucial for the systematic advance in many high-impact fields, such as biomedicine and nanotechnology. A key aspect to understand and predict the biological effect of NPs, e.g ., cytotoxicity, bioavailability, is their interaction with membranes, specifically the mechanisms that regulate passive transport, which controls the permeation of most small molecules. In this paper, we introduce a new streamlined theoretical model that is able to predict the interactions between NPs and biological membranes (average permeation time), by separating the NPs’ characteristics ( i.e ., size, shape, solubility) from the membrane properties (density distribution). This factorization allows the inclusion of data obtained from both experimental and computational sources, as well as rapid estimation of large sets of permutation in new membranes. We validated our approach, by comparing our prediction for the interactions between different carbonaceous NPs and lipid bilayers with both experiments of measuring graphene quantum dot leakage encapsulated in lipid vesicles and time of entry from MD simulations.