Green Toxicology

Maertens, Alexandra; Anastas, Nicholas D.; Spencer, P.J.; Stephnes, Martin; Goldberg, Alan M.; Hartung, Thomas

doi:10.14573/altex.1406181

Cited by 46 publications

(51 citation statements)

References 36 publications

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“…In parallel with this shift toward more structured toxicity screening strategies, in the recent ECHA guidance document on QSARs and grouping for chemical and nanomaterials recommendations are included for grouping based on biological (re)activity . Thus, current risk assessment frameworks are moving toward integration of alternative methods to reduce unnecessary testing on animals and ease costs due to novel Safe by Design strategies . The use of biological pathways, such as those in the GO and WikiPathways databases, could well reflect the above recommendations, since apart from capturing biological information they also reflect the key mechanistic events independent of single genes, a flexibility which is demonstrated by the increased implementation of GSEA …”

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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Section: Discussionmentioning

confidence: 99%

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

et al. 2017

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“…In the context of research on new chemical entities (drugs, pesticides, etc. ), such early testing has been used to guide chemical design strategies: For instance, it implied the development of alternative structures, which have the same desired effect, while avoiding the toxic effect identified in mechanistic in vitro tests, an approach called Green Toxicology (Maertens et al 2014;Crawford et al 2017). A challenge of these is the adequate integration of metabolic activation and detoxification into the testing strategy to perform adequate in vitro-to-in vivo extrapolations (IVIVE).…”

Section: Design Of Mechanistic Test Methodsmentioning

confidence: 99%

Adverse outcome pathways: opportunities, limitations and open questions

et al. 2017

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Adverse outcome pathways (AOPs) are a recent toxicological construct that connects, in a formalized, transparent and quality-controlled way, mechanistic information to apical endpoints for regulatory purposes. AOP links a molecular initiating event (MIE) to the adverse outcome (AO) via key events (KE), in a way specified by key event relationships (KER). Although this approach to formalize mechanistic toxicological information only started in 2010, over 200 AOPs have already been established. At this stage, new requirements arise, such as the need for harmonization and re-assessment, for continuous updating, as well as for alerting about pitfalls, misuses and limits of applicability. In this review, the history of the AOP concept and its most prominent strengths are discussed, including the advantages of a formalized approach, the systematic collection of weight of evidence, the linkage of mechanisms to apical end points, the examination of the plausibility of epidemiological data, the identification of critical knowledge gaps and the design of mechanistic test methods. To prepare the ground for a broadened and appropriate use of AOPs, some widespread misconceptions are explained. Moreover, potential weaknesses and shortcomings of the current AOP rule set are addressed (1) to facilitate the discussion on its further evolution and (2) to better define appropriate vs. less suitable application areas. Exemplary toxicological studies are presented to discuss the linearity assumptions of AOP, the management of event modifiers and compensatory mechanisms, and whether a separation of toxicodynamics from toxicokinetics including metabolism is possible in the framework of pathway plasticity. Suggestions on how to compromise between different needs of AOP stakeholders have been added. A clear definition of open questions and limitations is provided to encourage further progress in the field.

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“…One of the most important goals is to increase the testing throughput in order to improve -the availability of regulatory test data for many chemicals, -the effectiveness of substitution of the more hazardous chemicals by providing reliable data for an ample set of potential alternative chemicals, also in the low tonnage production range or even for green chemical engineering (Maertens et al, 2014), -the assessment of mixture toxicity, -the assessment of environmental media including the use of bio-analytics to complement chemical analytics (Schroeder et al, 2016), -approaches to cross-species extrapolation for ample coverage of environmental toxicity (Groh et al, 2015), -the assessment of the multitude of nanomaterial compositions, forms and size distributions, -the possibility to retest chemicals according to progress in the development of scientific and toxicological understanding. In addition to these practical needs, a critical mass of concern regarding the scientific uncertainties of animal test results and their regulatory utility also has been steadily accumulating in recent years (e.g., Basketter et al, 2012;Paparella et al, 2013;Hartung, 2013;Leist et al, 2014;NAS, 2015).…”

Section: ) Foster the Interest In Improved And Newly Defined In Silimentioning

confidence: 99%

Uncertainties of testing methods: What do we (want to) know about carcinogenicity?

Paparella

Colacci

Jacobs

2017

ALTEX

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Green Toxicology

Abstract: Summary Historically, early identification and characterization of adverse effects of industrial chemicals was difficult because conventional toxicological test methods did not meet

Cited by 46 publications

References 36 publications

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

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

Adverse outcome pathways: opportunities, limitations and open questions

Uncertainties of testing methods: What do we (want to) know about carcinogenicity?

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