Retrospective mining of toxicology data to discover multispecies and chemical class effects: Anemia as a case study

N-acetylation of toxic aromatic amines by fungi: Strain screening, cytotoxicity and genotoxicity evaluation, and application in bioremediation of 3,4-dichloroaniline

Aromatic amines (AA) are one of the most commonly used classes of compounds in industry and the most common pollutants found in both soil and water. 3,4-Dichloaniline (3,4-DCA) is a persistent residue of the phenylurea herbicide in the environment. In this study, we used a colorimetric method as a new approach to screen 12 filamentous fungal strains of the genera Aspergillus, Chaetomium, Cladosporium, and Mucor to assess their capacity to perform AA N-acetylation since it is considered a potential tool in environmental bioremediation. Subsequently, the selected strains were biotransformed with different AA substrates to evaluate the product yield. The strains Aspergillus niveus 43, Aspergillus terreus 31, and Cladosporium cladosporioides showed higher efficiencies in the biotransformation of 3,4-DCA at 500 µM into its N-acetylated product. These fungal strains also showed great potential to reduce the phytotoxicity of 3,4-DCA in experiments using Lactuca sativa seeds. Furthermore, N-acetylation was shown to be effective in reducing the cytotoxic and genotoxic effects of 3,4-DCA and other AA in the immortalized human keratinocyte (HaCaT) cell line. The isolated products after biotransformation showed that fungi of the genera Aspergillus and Cladosporium appeared to have N-acetylation as the first and main AA detoxification mechanism. Finally, A. terreus 31 showed the highest 3,4-DCA bioremediation potential, and future research can be carried out on the application of this strain to form microbial consortia with great potential for the elimination of toxic AA from the environment.

A read-across case study on chronic toxicity of branched carboxylic acids (1): Integration of mechanistic evidence from new approach methodologies (NAMs) to explore a common mode of action

This read-across case study characterises thirteen, structurally similar carboxylic acids demonstrating the application of in vitro and in silico human-based new approach methods, to determine biological similarity. Based on data from in vivo animal studies, the read-across hypothesis is that all analogues are steatotic and so should be considered hazardous. Transcriptomic analysis to determine differentially expressed genes (DEGs) in hepatocytes served as first tier testing to confirm a common mode-of-action and identify differences in the potency of the analogues. An adverse outcome pathway (AOP) network for hepatic steatosis, informed the design of an in vitro testing battery, targeting AOP relevant MIEs and KEs, and Dempster-Shafer decision theory was used to systematically quantify uncertainty and to define the minimal testing scope. The case study shows that the read-across hypothesis is the critical core to designing a robust, NAM-based testing strategy. By summarising the current mechanistic understanding, an AOP enables the selection of NAMs covering MIEs, early KEs, and late KEs. Experimental coverage of the AOP in this way is vital since MIEs and early KEs alone are not confirmatory of progression to the AO. This strategy exemplifies the workflow previously published by the EUTOXRISK project driving a paradigm shift towards NAM-based NGRA.

ToxRefDB version 2.0: Improved utility for predictive and retrospective toxicology analyses

The Toxicity Reference Database (ToxRefDB) structures information from over 5000 in vivo toxicity studies, conducted largely to guidelines or specifications from the US Environmental Protection Agency and the National Toxicology Program, into a public resource for training and validation of predictive models. Herein, ToxRefDB version 2.0 (ToxRefDBv2) development is described. Endpoints were annotated (e.g. required, not required) according to guidelines for subacute, subchronic, chronic, developmental, and multigenerational reproductive designs, distinguishing negative responses from untested. Quantitative data were extracted, and dose-response modeling for nearly 28,000 datasets from nearly 400 endpoints using Benchmark Dose (BMD) Modeling Software were generated and stored. Implementation of controlled vocabulary improved data quality; standardization to guideline requirements and cross-referencing with United Medical Language System (UMLS) connects ToxRefDBv2 observations to vocabularies linked to UMLS, including PubMed medical subject headings. ToxRefDBv2 allows for increased connections to other resources and has greatly enhanced quantitative and qualitative utility for predictive toxicology.

Moving towards a holistic approach for human health risk assessment - Is the current approach fit for purpose?

It is recognised that new scientific improvements and their integration in risk assessment, as outlined in the National Academies of Sciences, Engineering and Medicine 2017 report, have the potential to improve human health risk assessments by enabling a mechanistic understanding of adverse effects and more accurate predictions of biological responses. Here, I discuss why such improvements are needed and can ease a paradigm shift in human health risk assessment. The current approach to human health risk assessment is limited by several elements: (1) the relevance of data is debatable, as they are largely based on in vivo animal models that are poorly predictive for complex endpoints, raise challenges with regard to interspecies extrapolations, and are seldom informative of the mechanism underlying the observed effects; (2) lack of flexibility in data requirements by regulators, which limits the uptake of new scientific developments in a timely manner; and (3) lack of data accessibility, which makes data integration difficult. However, mechanistic-based assessments are currently conducted for the identification of endocrine disruptors and are developed for addressing developmental neurotoxicity. Such assessments can serve as examples for changing the paradigm of risk assessment. There are several opportunities for improvement, such as: make regulatory standard requirements less prescriptive; enhance and use the opportunities for read-across; analyse and quantify uncertainties in order to benchmark new approach methods to the current system; better integrate screening methods early in regulatory assessments and decision-making; and develop more adverse outcome pathways in order to link new approach methods with the current approach and ultimately make it possible to base regulatory decisions on early key events of a toxicity pathway.

The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency

The U.S. Environmental Protection Agency (EPA) is faced with the challenge of efficiently and credibly evaluating chemical safety often with limited or no available toxicity data. The expanding number of chemicals found in commerce and the environment, coupled with time and resource requirements for traditional toxicity testing and exposure characterization, continue to underscore the need for new approaches. In 2005, EPA charted a new course to address this challenge by embracing computational toxicology (CompTox) and investing in the technologies and capabilities to push the field forward. The return on this investment has been demonstrated through results and applications across a range of human and environmental health problems, as well as initial application to regulatory decision-making within programs such as the EPA's Endocrine Disruptor Screening Program. The CompTox initiative at EPA is more than a decade old. This manuscript presents a blueprint to guide the strategic and operational direction over the next 5 years. The primary goal is to obtain broader acceptance of the CompTox approaches for application to higher tier regulatory decisions, such as chemical assessments. To achieve this goal, the blueprint expands and refines the use of high-throughput and computational modeling approaches to transform the components in chemical risk assessment, while systematically addressing key challenges that have hindered progress. In addition, the blueprint outlines additional investments in cross-cutting efforts to characterize uncertainty and variability, develop software and information technology tools, provide outreach and training, and establish scientific confidence for application to different public health and environmental regulatory decisions.

In Silico Prediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs) as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This review has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, have been developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given.