Toward Rigorous Materials Production: New Approach Methodologies Have Extensive Potential to Improve Current Safety Assessment Practices

Abstract

Advanced material development, including at the nanoscale, comprises costly and complex challenges coupled to ensuring human and environmental safety. Governmental agencies regulating safety have announced interest toward acceptance of safety data generated under the collective term New Approach Methodologies (NAMs), as such technologies/approaches offer marked potential to progress the integration of safety testing measures during innovation from idea to product launch of nanomaterials. Divided in overall eight main categories, searchable databases for grouping and read across purposes, exposure assessment and modeling, in silico modeling of physicochemical structure and hazard data, in vitro high‐throughput and high‐content screening assays, dose‐response assessments and modeling, analyses of biological processes and toxicity pathways, kinetics and dose extrapolation, consideration of relevant exposure levels and biomarker endpoints typify such useful NAMs. Their application generally agrees with articulated stakeholder needs for improvement of safety testing procedures. They further fit for inclusion and add value in nanomaterials risk assessment tools. Overall 37 of 50 evaluated NAMs and tiered workflows applying NAMs are recommended for considering safer‐by‐design innovation, including guidance to the selection of specific NAMs in the eight categories. An innovation funnel enriched with safety methods is ultimately proposed under the central aim of promoting rigorous nanomaterials innovation.

Recent insights on indirect mechanisms in developmental toxicity of nanomaterials

“…Identification of the key processes in NM induced developmental toxicity will further provide a basis for improvement of in vitro test systems, which are suitable for the screening of a broad range of NMs and allow for the identification of potential hazards to pregnancy. In addition, the application of advanced in vitro models would reduce the use of experimental animals [200].…”

Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment

“…Coupling of gene expression profiling with bioinformatics-driven placement of the results into AOP descriptions has the potential for quantitative analysis of adverse effects that combines in vitro-derived mechanistic analyses with causally relevant modes-of-action and related key events [77,78]. As AOPs can span different cell types, numerous in vitro assays may need to be associated with a single one [14,76]. The details of the coupling are still being worked out by the community but mapping the results of pathway analyses to KEs is a simple alternative.…”

“…At the same time, new ML algorithms have been proposed in order to better understand and eventually predict the genomic behaviour underlying the exposure. Indeed, TGx datasets have been exploited in the context of drug repositioning [8,9], toxicity prediction [10][11][12][13], definition of adverse outcome pathways (AOP), and as a valuable source to develop new approach methodologies [14,15].…”

Rationale and decision rules behind the ECETOC NanoApp to support registration of sets of similar nanoforms within REACH

“…Dustiness is an established determinant of exposure, widely used in exposure modeling (Cherrie et al 2020), and considered as input in several risk assessment tools (Nymark et al 2020). Dustiness is a mandatory data requirement by REACH (Annex VII) for substances produced or imported above 1 ton per year.…”