Probabilistic risk assessment of gold nanoparticles after intravenous administration by integrating in vitro and in vivo toxicity with physiologically based pharmacokinetic modeling

Abstract

This study aimed to conduct an integrated and probabilistic risk assessment of gold nanoparticles (AuNPs) based on recently published in vitro and in vivo toxicity studies coupled to a physiologically based pharmacokinetic (PBPK) model. Dose–response relationships were characterized based on cell viability assays in various human cell types. A previously well-validated human PBPK model for AuNPs was applied to quantify internal concentrations in liver, kidney, skin, and venous plasma. By applying a Bayesian-based probabilistic risk assessment approach incorporating Monte Carlo simulation, probable human cell death fractions were characterized. Additionally, we implemented in vitro to in vivo and animal-to-human extrapolation approaches to independently estimate external exposure levels of AuNPs that cause minimal toxicity. Our results suggest that under the highest dosing level employed in existing animal studies (worst-case scenario), AuNPs coated with branched polyethylenimine (BPEI) would likely induce ∼90–100% cellular death, implying high cytotoxicity compared to <10% cell death induced by low-to-medium animal dosing levels, which are commonly used in animal studies. The estimated human equivalent doses associated with 5% cell death in liver and kidney were around 1 and 3 mg/kg, respectively. Based on points of departure reported in animal studies, the human equivalent dose estimates associated with gene expression changes and tissue cell apoptosis in liver were 0.005 and 0.5 mg/kg, respectively. Our analyzes provide insights into safety evaluation, risk prediction, and point of departure estimation of AuNP exposure for humans and illustrate an approach that could be applied to other NPs when sufficient data are available.

Keywords:

• Gold nanoparticle
• computational nanotoxicology
• physiologically based pharmacokinetic modeling
• probabilistic risk assessment

Acknowledgements

We would like to thank Drs Ronette Gehring, Majid Jaberi-Douraki and Miao Li in the ICCM at Kansas State University for helpful discussions. We also would like to acknowledge our NICKS colleagues Dr Parwathy Chandran, Dr Yang Li, Dr Kyoungju Choi, and Dr Maria T. Ortega whose previously published in vitro toxicity data were essential for our analysis.

Disclosure statement

The authors declare no conflict of interest.

Additional information

Funding

This work was supported by The Kansas Bioscience Authority funds to the Institute of Computational Comparative Medicine (ICCM) and Nanotechnology Innovation Center of Kansas State (NICKS), the K-State Mentoring Fellowship, and the New Faculty Start-up funds at Kansas State University.