An assessment of applicability of existing approaches to predicting the bioaccumulation of conventional substances in nanomaterials

Abstract

The experimental determination of bioaccumulation is challenging, and a number of approaches have been developed for its prediction. It is important to assess the applicability of these predictive approaches to nanomaterials (NMs), which have been shown to bioaccumulate. The octanol/water partition coefficient (K ) may not be applicable to some NMs that are not found in either the octanol or water phases but rather are found at the interface. Thus the K values obtained for certain NMs are shown not to correlate well with the experimentally determined bioaccumulation. Implementation of quantitative structure-activity relationships (QSARs) for NMs is also challenging because the bioaccumulation of NMs depends on nano-specific properties such as shape, size, and surface area. Thus there is a need to develop new QSAR models based on these new nanodescriptors; current efforts appear to focus on digital processing of NM images as well as the conversion of surface chemistry parameters into adsorption indices. Water solubility can be used as a screening tool for the exclusion of NMs with short half-lives. Adaptation of fugacity/aquivalence models, which include physicochemical properties, may give some insights into the bioaccumulation potential of NMs, especially with the addition of a biota component. The use of kinetic models, including physiologically based pharmacokinetic models, appears to be the most suitable approach for predicting bioaccumulation of NMs. Furthermore, because bioaccumulation of NMs depends on a number of biotic and abiotic factors, it is important to take these factors into account when one is modeling bioaccumulation and interpreting bioaccumulation results. Environ Toxicol Chem 2018;37:2972-2988. © 2018 SETAC.

Meta‐analysis of Bioaccumulation Data for Nondissolvable Engineered Nanomaterials in Freshwater Aquatic Organisms

“…Biomagnification describes the relative increase in concentration from one trophic level to the next and can be ascribed to the accumulation of food (Neely, 1980). A BMF above one indicates the occurrence of biomagnification across the food chain (Utembe et al, 2018). With regard to ENMs, there are no established standards for acceptable levels of ENMs taken up by biota (Hund-Rinke et al, 2016), but several regulatory agencies give thresholds for traditional chemicals (Arnot & Gobas, 2006).…”

“…Bjorkland et al (2017) provided a comprehensive review on bioaccumulation of different types of carbon nanotubes (CNTs) in plants, invertebrates, and fish by summarizing the BCF and BAF values. Utembe et al (2018) listed the range of uptake and elimination rate constants for a number of nanomaterials, including ZnO, gold (Au), Ag, and CuO. These reviews have tended to focus on one type of organism, one type of experiment (e.g., mesocosms), or one type of ENM.…”

Toward a Framework for Environmental Fate and Exposure Assessment of Polymers

“…While the BCF accounts for uptake of a chemical substance only via dermal and respiratory absorption, the BAF accounts for additional uptake via ingestion (Arnot & Gobas, 2006;Mackay et al, 2013). Because the concept of BCF assumes passive diffusion, it is known to be inapplicable to nanoparticles (Kookana et al, 2014;Kühnel & Nickel, 2014) because equilibrium partitioning does not apply and active processes such as endocytosis play a significant role in nanoparticle uptake because of their size (Fröhlich, 2012;Kookana et al, 2014;Utembe et al, 2018). This is also the case for larger solids such as microplastics (von Moos et al, 2012).…”

“…This is also the case for larger solids such as microplastics (von Moos et al, 2012). The role of active processes also means that BCF and BAF may be dependent on exposure concentration, and thus differences between substances cannot only be attributed to differences in bioaccumulation (Utembe et al, 2018). It has been highlighted that parameters such as uptake and internalization rates and attachment efficiencies (α) should be identified and developed for nanoparticle bioaccumulation (Kühnel & Nickel, 2014;Praetorius et al, 2014).…”

Current Approaches and Techniques in Physiologically Based Pharmacokinetic (PBPK) Modelling of Nanomaterials

“…The model was developed from experimental data obtained from Inductively Coupled Plasma–Mass Spectrometry (ICP-MS) measurements of Ag + in different organs after digestion of tissues in nitric acid, which, according to the authors, could digest Ag NPs, metallic Ag and Ag ions and not silver precipitations such as silver chloride. Therefore, since digested silver concentrations represented concentrations of free Ag NPs and Ag + ions and not AgCl, the pharmacokinetic kinetic data could not discriminate Ag + in Ag NPs from dissolved or ionic Ag + [ 120 ].…”