Emerging investigator series: the dynamics of particle size distributions need to be accounted for in bioavailability modelling of nanoparticles
We propose to include the time-dependent size distribution of dispersed and internalized nanoparticles (NPs) in the ecotoxicological evaluation of exposure of biota to NPs and to develop tools to add the particle dynamics in the bioavailability modelling of NPs. The challenges that we face are that: 1) NPs are hardly ever present in dispersions within a narrow size range but rather as size distributions. This affects the overall particle behavior as size does matter in many processes. 2) In exposure media or environmental matrices, the size distribution of NPs changes over time due to transformation and aggregation processes and subsequent sedimentation. 3) The physico-chemical properties and solubility of internalized NPs are modified during biodistribution, while the interactions between NPs and the components of biological fluids have not been well explored. This makes bioavailability modelling and hence quantifying the dose–response relationship on the basis of the actual number of bioavailable particles in the exposure medium questionable. The myriad of processes indicate that exposure concentrations of NPs are not a straightforward expression of the dose–response relationship. The classical dose–response relationship is suggested to include the fate assessment of external and internal NPs when attempting to predict the response of organisms. Various conventional ideas for modelling bioavailability and effects are discussed, and they were found to be not fully tailored to NPs. We think that currently size-dependent features still require a little more experimental data and should be verified for a broader range of specific test species and a variety of testing conditions. Understanding of the underlying processes is achievable and the first steps in developing mechanistic-based modelling can be performed. Before such mechanistic evidence becomes available, we advocate to keep the modelling as simple as it can be.