21st Century Tools for Nanotoxicology: Transcriptomic Biomarker Panel and Precision‐Cut Lung Slice Organ Mimic System for the Assessment of Nanomaterial‐Induced Lung Fibrosis

Abstract

There is an urgent need for reliable toxicity assays to support the human health risk assessment of an ever increasing number of engineered nanomaterials (ENMs). Animal testing is not a suitable option for ENMs. Sensitive in vitro models and mechanism‐based targeted in vitro assays that enable accurate prediction of in vivo responses are not yet available. In this proof‐of‐principle study, publicly available mouse lung transcriptomics data from studies investigating xenobiotic‐induced lung diseases are used and a 17‐gene biomarker panel (PFS17) applicable to the assessment of lung fibrosis is developed. The PFS17 is validated using a limited number of in vivo mouse lung transcriptomics datasets from studies investigating ENM‐induced responses. In addition, an ex vivo precision‐cut lung slice (PCLS) model is optimized for screening of potentially inflammogenic and pro‐fibrotic ENMs. Using bleomycin and a multiwalled carbon nanotube, the practical application of the PCLS method as a sensitive alternative to whole animal tests to screen ENMs that may potentially induce inhalation toxicity is shown. Conditional to further optimization and validation, it is established that a combination of PFS17 and the ex vivo PCLS method will serve as a robust and sensitive approach to assess lung inflammation and fibrosis induced by ENMs.

Non‐Animal Strategies for Toxicity Assessment of Nanoscale Materials: Role of Adverse Outcome Pathways in the Selection of Endpoints

“…The resulting dataset consisting of millions of data points has also supported validation of MIEs and KEs, and identification of multi-variate biomarkers for targeted bioassay development. [26,28,35,95] Such gene set-or pathway-based biomarkers are increasingly applicable to diverse predictive modeling approaches in toxicology and have been shown to have broad potential for identification and probabilistic prediction of human toxicity and AO. [95,[106][107][108] Recently establishment and validation of a gene panel consisting of 17 individual genes (Figure 4) potentially predictive of lung fibrosis was described in a bioinformatics study by Rahman et al 2020.…”

Adverse outcome pathways as a tool for the design of testing strategies to support the safety assessment of emerging advanced materials at the nanoscale

“…In addition, as more data becomes available, criteria for evaluating the in vitro methods addressing aspects such as, what KE is assessed or predicted by the assay, how close is the measured endpoint to the response observed in vivo, how many nanomaterials have been tested using the method, does the method allow dose-response analysis and is there a standard operating protocol available, can be developed to select the assays and endpoints that demonstrate the best predictive potential. In a recent study by Rahman et al, (2020), a testing strategy involving a combination of a transcriptomic signature consisting of 17 genes (referred to as PFS17) predictive of lung fibrosis targeting different KEs in the AOP 173 and an ex vivo precision cut lung slice method was proposed as a promising alternative to assess lung inflammation and lung fibrosis induced by nanomaterials [148]. Although these strategies and specific methodologies are well described for chemicals [147], they are not readily applicable for nanomaterials due to lack of data.…”

“…The genes provide robust sets of gene signatures that can be further validated to predict nanomaterial-induced lung responses. For examples, the gene sets assigned to inflammation and fibrosis bi-clusters in [151] were further pursued and a new predictive signature (PFS17) for the assessment of the KEs in AOP 173 predictive of lung fibrosis was developed [148]. In another study, using a large publicly available toxicogenomic database specifically for liver injury Kohonen et al, defined [152,153] a toxicogenomic space covering 1331 genes packed into 14 gene sets predictive of chemical-induced liver injury, including liver fibrosis.…”

Detection of Oxidative Stress Induced by Nanomaterials in Cells—The Roles of Reactive Oxygen Species and Glutathione

“…In comparison to two-dimensional (2D) cultured cell lines, those new biogical models ought to provide accurate predictions of nanomaterials effects in vivo. Thus, some new scientific studies described the use of pulmonary fibrosis models [ 189 ], organ on-chip technology bridging the differences between 2D in vitro and three-dimensional (3D) in vivo models from skin, the lung, and the liver [ 190 , 191 ], or on-chip placenta models [ 192 ]. Despite advanced organ on-chip models, a number of concerns have to be solved to ensure the comparability to living systems in obtained outcomes [ 193 ].…”