Generic prediction of exocytosis rate constants by size-based surface energies of nanoparticles and cells

Abstract

Nanotechnology brings benefits in fields such as biomedicine but nanoparticles (NPs) may also have adverse health effects. The effects of surface-modified NPs at the cellular level have major implications for both medicine and toxicology. Semi-empirical and mechanism-based models aid to understand the cellular transport of various NPs and its implications for quantitatively biological exposure while avoiding large-scale experiments. We hypothesized relationships between NPs-cellular elimination, surface functionality and elimination pathways by cells. Surface free energy components were used to characterize the transport of NPs onto membranes and with lipid vesicles, covering both influences by size and hydrophobicity of NPs. The model was built based on properties of neutral NPs and cells, defining Van de Waals forces, electrostatic forces and Lewis acid–base (polar) interactions between NPs and vesicles as well as between vesicles and cell membranes. We yielded a generic model for estimating exocytosis rate constants of various neutral NPs by cells based on the vesicle-transported exocytosis pathways. Our results indicate that most models are well fitted (R2 ranging from 0.61 to 0.98) and may provide good predictions of exocytosis rate constants for NPs with differing surface functionalities (prediction errors are within 2 times for macrophages). Exocytosis rates differ between cancerous cells with metastatic potential and non-cancerous cells. Our model provides a reference for cellular elimination of NPs, and intends for medical applications and risk assessment.

Nanoparticles in bodily tissues: predicting their equilibrium distributions

“…1 are sufficiently low; inhibition of transport limits tissue partitioning. 81 Macrophages (microglia) account for 10–15% of brain cells, 191 and would readily take up NPs. 192 However, the brain's blood vessels are lined with endothelial cells wedged tightly together, creating a boundary.…”

“…We calculated γ AB and γ LW (mJ/m 2 ) from effective surface energy components γ LW , and (electron acceptor and donor) for each species: NPs, membranes, serum 𝛾 + 𝛾plasma protein and water; details in [78,79], substantiated by multiple cell lines (macrophage, endothelial cancer, fibroblast, etc.) [80,81]. We take for γ + protein , γprotein , γ LW protein of serum protein 0.002, 20, and 41 mJ/m 2 , taken to resemble dry albumin at pH 7 [79,82], and for γ + plasna , γplasma , γ LW plasma of plasma 25.5, 25.5 and 21.8, mJ/m 2 , taken to resemble water.…”

“…Liver (Kuppfer) [92,95,96] and cancer cell [97,98] membranes are lectin-rich. Immunological (mucus/phagocytic/cancerous) cells have enhanced metabolism over 'tranquil'/'sluggish', e.g., endothelial cells [7,81,99,100].…”

“…We cannot always assume the barriers in Fig. 1 are sufficiently low; inhibition of transport limits tissue partitioning [81]. Macrophages (microglia) account for 10-15% of brain cells [191], and would readily take up NPs [192].…”

“…Our calculus for various NP types successfully yields partitioning in(to) many tissues/organs, by cells [40,81] and biomolecules. These data find use in PBPK modeling with extensions via scaling [201,214,221].…”