The Adverse Outcome Pathway approach in nanotoxicology

Gerloff, Kirsten; Landesmann, Brigitte; Worth, Andrew; Munn, Sharon; Palosaari, Tarja; Whelan, Maurice

doi:10.1016/j.comtox.2016.07.001

Cited by 93 publications

(76 citation statements)

References 71 publications

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“…Especially the identification of adverse outcome pathways (AOP) has received much attention to help in vitro to in vivo extrapolation and fill knowledge gaps in nanotoxicology (Gerloff et al, 2016). However, nanotoxicology is still struggling with fundamental issues in experimental testing, giving rise to experimental data of variable quality and hampering the generation of predictive models, such as QSARs (Tantra et al, 2015).…”

Section: What Can Nanosafety Learn From Drug Development?mentioning

confidence: 99%

What can nanosafety learn from drug development? The feasibility of “safety by design”

2017

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"Safety by design" (SbD) is an intuitively appealing concept that is on the rise within nanotoxicology and nanosafety research, as well as within nanotechnology research policy. It leans on principles established within drug discovery and development (DDD) and seeks to address safety early, as well as throughout product development. However, it remains unclear what the concept of SbD exactly entails for engineered nanomaterials (ENMs) or how it is envisioned to be implemented. Here, we review the concept as it is emerging in European research and compare its resemblance with the safety testing and assessment practices in DDD. From this comparison, it is clear that "safety" is not obtained through DDD, and that SbD should be considered a starting point rather than an end, meaning that products will still need to progress through thorough safety evaluations and regulation. We conclude that although risk reduction is clearly desirable, the way SbD is currently communicated tends to treat safety as an inherent material property and that this is fundamentally problematic as it represents a recasting and reduction of societal issues into technical problems. SbD therefore faces a multitude of challenges, from practical implementation to unrealistic stakeholder expectations.

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Section: What Can Nanosafety Learn From Drug Development?mentioning

confidence: 99%

What can nanosafety learn from drug development? The feasibility of “safety by design”

2017

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“…It is also worth noting the potential relevance of the method to the recently initiated Organization for Economic and Cooperative Development program on the development of so called adverse outcome pathways (AOPs) describing a sequence of causally linked key events at different levels of biological organization, starting from molecular initiating events (MIEs) leading to adverse health outcomes . Many of the molecular‐function‐based GO ids identified when testing the BIO descriptor workflow on the PCF proteomics data set point to binding and receptor activation/inhibition, indicating that the proteins involved in the corona could play a role in the identification of nanomaterial‐associated MIE …”

Section: Discussionmentioning

confidence: 99%

Enriching Nanomaterials Omics Data: An Integration Technique to Generate Biological Descriptors

et al. 2017

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produced, and one that has introduced integration methodologies for all available information to draw a more complete view of the biological mechanisms of the disease. Along these lines, a recently published review presents a cancer research-inspired pipeline that applies high-throughput and high-content profiling technologies integrated with omics profiling for assessing toxicity of chemicals and engineered nanomaterials. [6] The authors emphasize how, in analogy to biomarker discovery in cancer research, toxicology's aim is to identify genes or pathways from the "toxome" that could predict specific toxicity effects. Indeed, the search for new approach methodologies is largely focused on "big data" such as omics, but to this date there are relatively few concrete examples of how to utilize this type of data within the currently available risk assessment frameworks. Quantitative structure-activity relationship (QSAR) modeling remains a key method in toxicology, aiming mainly to predict the dependent variable of interest ("end-point") using structural and physicochemical data, although there are recent paradigms of successful prediction with omics data. [7,8] Grouping and read across is one of the most commonly used alternative approaches for risk assessment of novel substances and materials of industrial relevance submitted under the registration, evaluation, authorization and restriction of chemicals. Particularly, the read across method has not yet gained full regulatory acceptance, however recent efforts by the European chemicals agency (ECHA) are contributing toward its consistent and transparent evaluation.Methods for integrating biological information relative to genes' functions have been developed for omics data, [9][10][11][12] but have rarely been applied to QSAR modeling or grouping. For example, pathway analysis via gene set enrichment analysis (GSEA) methods [13] or the so-called connectivity mapping models have been applied to toxicity data to identify similar modes of action. [14,15] Smalley et al. [15] presented a connectivity mapping model to compare a query gene set microarray profile from a biological system exposed to a test substance, to those in a reference compounds database. In another approach, Williams and Halappanavar [16] identified pulmonary disease-related biclusters of genes from gene expression profiles derived from mouse disease models, which were then used to conduct GSEA The interest toward omics data is growing in the field of toxicology owing to the diverse knowledge they generate, which can improve prediction and dosage profiling for more accurate safety assessment. An integration methodology is presented where high-throughput omics data are enriched with biological-pathway information to produce a novel set of biological (BIO) descriptors by decomposing omics data to meaningful clusters in terms of both their mechanistic interpretation and correlation affinity. A generalized simulated annealing algorithm is employed to estimate the optimal partition of the enriched data and ...

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“…Although recent trends in toxicology focus on toxicity pathways, mode of actions and AOPs (Raies and Bajic 2016;NRC 2007;Shukla et al 2010;Nel et al 2013), definite AOPs have not yet been established for specific NPs or classes of NPs (e.g. poorly soluble, low toxicity NPs) due to toxicity data gaps (Gerloff et al 2017), although there are areas of advancement (Vietti, Lison and van den Brule 2016). Increasingly, omics data have become more widely available given the increase in highthroughput and high content screening methods.…”

Section: Introductionmentioning

confidence: 99%

Application of Bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics

et al. 2019

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Inroads have been made in our understanding of the risks posed to human health and the environment by nanoparticles (NPs) but this area requires continuous research and monitoring. Machine learning techniques have been applied to nanotoxicology with very encouraging results. This study deals with bridging physicochemical properties of NPs, experimental exposure conditions and in vitro characteristics with biological effects of NPs on a molecular cellular level from transcriptomics studies. The bridging is done by developing and implementing Bayesian Networks (BNs) with or without data preprocessing. The BN structures are derived either automatically or methodologically and compared. Early stage nanotoxicity measurements represent a challenge, not least when attempting to predict adverse outcomes and modeling is critical to understanding the biological effects of exposure to NPs. The preprocessed data-driven BN showed improved performance over automatically structured BN and the BN with unprocessed datasets. The prestructured BN captures inter relationships between NP properties, exposure condition and in vitro characteristics and links those with cellular effects based on statistic correlation findings. Information gain analysis showed that exposure dose, NP and cell line variables were the most influential attributes in predicting the biological effects. The BN methodology proposed in this study successfully predicts a number of toxicologically relevant cellular disrupted biological processes such as cell cycle and proliferation pathways, cell adhesion and extracellular matrix responses, DNA damage and repair mechanisms etc., with a success rate >80%. The model validation from independent data shows a robust and promising methodology for incorporating transcriptomics outcomes in a hazard and, by extension, risk assessment modeling framework by predicting affected cellular functions from experimental conditions.

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The Adverse Outcome Pathway approach in nanotoxicology

Cited by 93 publications

References 71 publications

What can nanosafety learn from drug development? The feasibility of “safety by design”

What can nanosafety learn from drug development? The feasibility of “safety by design”

Enriching Nanomaterials Omics Data: An Integration Technique to Generate Biological Descriptors

Application of Bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics

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