Inhalation is the prevailing route of inadvertent exposure for manufactured nanomaterials (MNs). For assessing potential adverse effects, indepth knowledge about Exposure-Dose-Response relationships is required to define a risk as a function of hazard and relevant exposure. Intrinsic (physico-chemical) and extrinsic (functional) MN properties determine the biological/toxicological properties (effects) of MNs. Predictive testing strategies are useful for comparative hazard and risk characterization against toxicologically well-defined positive and negative benchmark materials involving studies in rodents, cells, and cell-free (abiotic) assays.
Inhalation studies can be used for hazard identification as well as for hazard and risk characterization of inhaled MNs. A design to provide dose-response data is ideal, but less so if only exposure-response data are available. Information should also be provided for biokinetics and for identifying secondary targets. Bolus-type dosing (intratracheal instillation; oropharyngeal aspiration) can be useful for hazard identification and characterization, but not for risk characterization. Combining results from bolus dosing or in vitro tests with results of a subchronic inhalation study of the same group of MNs can be a suitable predictive bridging approach.
In vitro cellular assays designed to determine in vivo effects and underlying mechanisms present additional challenges. Cellular dose equivalency to in vivo is difficult to achieve because of static, mostly acute in vitro systems with no MN clearance. The dose dependency of mechanisms has to be considered as well. Still, in vitro tests are suitable for toxicity ranking against well-characterized benchmarks (Hazard ID). Regarding abiotic assays, predictive toxicity ranking using the metric of specific MN surface reactivity (ROS assays) is a promising screening tool, but requires further validation and standardization. Dynamic abiotic dissolution assays are also a promising tool for predicting in vivo dissolution rates but require standardization.
Information about MN dissolution using static (equilibrium solubility, μg/L) and dynamic (dissolution rate, ng/cm2/day) abiotic in vitro assays provide different information about the solubilization of MNs reflecting either static in vitro or dynamic in vivo conditions. Results of both assays may be useful for categorization if performed in physiologically relevant fluids. Because the in vivo dissolution rates of MNs can differ widely, it is too simplistic to group MNs just into soluble and poorly soluble materials. Static (equilibrium solubility) and dynamic (dissolution rate) abiotic assays are based on different concepts. Results from dynamic dissolution in relevant physiological fluids - rather than just water - add valuable information about the extrinsic functional characteristics of MNs, which may be considered as a grouping tool into high, moderate, low and insoluble MNs.
Systemic biodistribution of MNs depends on the point-of-entry. For example, MNs deposited by inhalation or instillation in the respiratory tract distribute differently than intravenously administered MNs; thus, biokinetic models based on data from intravenous MN administration should not be used to model biodistribution following inhalation. The significance of biodissolution for biokinetics, effects and underlying mechanisms has to be assessed in separate in vivo studies, involving biopersistence/biodurability and ultra-high resolution imaging for analysing bioprocessing and biotransformations at a sub-cellular level.
With respect to grouping, several strategies are necessary to cover all classes of MNs of different compositions and for different exposure routes, all of which are to be considered in regulatory decision-making. The suggested grouping and extrapolation framework presented in this paper could be pivotal in leveraging subchronic inhalation data with data from alternative test methods, thus leading to more efficient, cost-effective, and – in the long run – animal and cost saving methods to obtain needed input data for regulatory use.
