Predicting the Impact of Biocorona Formation Kinetics on Interspecies Extrapolations of Nanoparticle Biodistribution Modeling

Abstract

Aim: To assess the impact of biocorona kinetics on expected tissue distribution of nanoparticles (NPs) across species. Materials & methods: The potential fate of NPs in vivo is described through a simple and descriptive pharmacokinetic model using rate processes dependent upon basal metabolic rate coupled to dynamics of protein corona. Results: Mismatch of time scales between interspecies allometric scaling and the kinetics of corona formation is potentially a fundamental issue with interspecies extrapolations of NP biodistribution. The impact of corona evolution on NP biodistribution across two species is maximal when corona transition half-life is close to the geometric mean of NP half-lives of the two species. Conclusion: While engineered NPs can successfully reach target cells in rodent models, the results may be different in humans due to the fact that the longer circulation time allows for further biocorona evolution.

Keywords::

• allometry
• biocorona
• interspecies extrapolation
• in vitro–in vivo correlation
• nanomaterials
• nanoparticle biodistribution

Financial & competing interests disclosure

This work was partially supported by the Kansas Bioscience Authority funds to the Institute of Computational Comparative Medicine at Kansas State University (USA), and funds from the Nanotechnology Innovation Center of Kansas State University. No additional external funding was received for this study. The sponsors had no role in study design, data collection and analysis, decision to publish, or preparation of this manuscript. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Additional information

Funding

This work was partially supported by the Kansas Bioscience Authority funds to the Institute of Computational Comparative Medicine at Kansas State University (USA), and funds from the Nanotechnology Innovation Center of Kansas State University. No additional external funding was received for this study. The sponsors had no role in study design, data collection and analysis, decision to publish, or preparation of this manuscript. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript.