Has Toxicity Testing Moved into the 21st Century? A Survey and Analysis of Perceptions in the Field of Toxicology.

BackgroundTen years ago, leaders in the field of toxicology called for a transformation of the discipline and a shift from primarily relying on traditional animal testing to incorporating advances in biotechnology and predictive methodologies into alternative testing strategies (ATS). Governmental agencies and academic and industry partners initiated programs to support such a transformation, but a decade later, the outcomes of these efforts are not well understood.ObjectivesWe aimed to assess the use of ATS and the perceived barriers and drivers to their adoption by toxicologists and by others working in, or closely linked with, the field of toxicology.MethodsWe surveyed 1,381 toxicologists and experts in associated fields regarding the viability and use of ATS and the perceived barriers and drivers of ATS for a range of applications. We performed ranking, hierarchical clustering, and correlation analyses of the survey data.ResultsMany respondents indicated that they were already using ATS, or believed that ATS were already viable approaches, for toxicological assessment of one or more end points in their primary area of interest or concern (26-86%, depending on the specific ATS/application pair). However, the proportions of respondents reporting use of ATS in the previous 12 mo were smaller (4.5-41%). Concern about regulatory acceptance was the most commonly cited factor inhibiting the adoption of ATS, and a variety of technical concerns were also cited as significant barriers to ATS viability. The factors most often cited as playing a significant role (currently or in the future) in driving the adoption of ATS were the need for expedited toxicology information, the need for reduced toxicity testing costs, demand by regulatory agencies, and ethical or moral concerns.ConclusionsOur findings indicate that the transformation of the field of toxicology is partly implemented, but significant barriers to acceptance and adoption remain. https://doi.org/10.1289/EHP1435

Effect of Chemical Mutagens and Carcinogens on Gene Expression Profiles in Human TK6 Cells

Characterization of toxicogenomic signatures of carcinogen exposure holds significant promise for mechanistic and predictive toxicology. In vitro transcriptomic studies allow the comparison of the response to chemicals with diverse mode of actions under controlled experimental conditions. We conducted an in vitro study in TK6 cells to characterize gene expression signatures of exposure to 15 genotoxic carcinogens frequently used in European industries. We also examined the dose-responsive changes in gene expression, and perturbation of biochemical pathways in response to these carcinogens. TK6 cells were exposed at 3 dose levels for 24 h with and without S9 human metabolic mix. Since S9 had an impact on gene expression (885 genes), we analyzed the gene expression data from cells cultures incubated with S9 and without S9 independently. The ribosome pathway was affected by all chemical-dose combinations. However in general, no similar gene expression was observed among carcinogens. Further, pathways, i.e. cell cycle, DNA repair mechanisms, RNA degradation, that were common within sets of chemical-dose combination were suggested by clustergram. Linear trends in dose–response of gene expression were observed for Trichloroethylene, Benz[a]anthracene, Epichlorohydrin, Benzene, and Hydroquinone. The significantly altered genes were involved in the regulation of (anti-) apoptosis, maintenance of cell survival, tumor necrosis factor-related pathways and immune response, in agreement with several other studies. Similarly in S9+ cultures, Benz[a]pyrene, Styrene and Trichloroethylene each modified over 1000 genes at high concentrations. Our findings expand our understanding of the transcriptomic response to genotoxic carcinogens, revealing the alteration of diverse sets of genes and pathways involved in cellular homeostasis and cell cycle control

Application of Benchmark Dose Analysis to in vitro Genotoxicity Data for Compound Risk Characterisation

Genotoxic risk from exposure to pharmaceutical compounds has historically been focussed on dichotomous hazard characterisation, with little regulatory acceptance of risk assessment paradigms. The regulations focus on testing novel compounds with outdated genotoxicity test systems. Recent overwhelming support of the Benchmark Dose (BMD) methodology provides the baseline for advanced exposure risk assessments. Novel flow cytometric in vitro DNA damage response assays (MultiFlow and ToxTracker) have been developed that provide quantitative dose-response information that can be used in a high-throughput screening environment. In the following work, BMD modelling is applied to the MultiFlow and ToxTracker biomarker dose-response datasets. This work demonstrates that the MultiFlow dose-response biomarker datasets are amenable to BMD analysis for a set of clastogens and aneugens, and that the biomarker dose-responses correlate with dose-responses from the gold-standard in vitro micronucleus assay. A detailed appraisal of BMD confidence intervals (CIs) is provided for a selection of 10 clastogens requiring metabolic activation (with S9), demonstrating the criticality of using BMD uncertainty measures in comparative potency analysis. A comparative potency algorithm is developed and utilised in machine learning to distinguish four S9-dependent groupings: high and low-level potentiation, no effect, and diminution. A deep dive case study is presented for MultiFlow and ToxTracker analysis of Topoisomerase II Poisons, where BMD CI potency ranks are shown to correlate broadly with compound structural information. The Adverse Outcome Pathway (AOP) for Topoisomerase-II Poisoning is developed upon, and the Lhasa Derek Nexus alerts are mapped to the AOP. A Quantitative Structural Activity Relationship model is developed using Topoisomerase-II Poison molecular descriptors and BMD measurements from MultiFlow and ToxTracker biomarkers that correspond to Key Events relative to the Topoisomerase-II Poison AOP. This thesis provides an all-encompassing report of in vitro DNA damage response biomarker BMD analysis for compound potency ranking and read across

Profiling Chemicals Based on Chronic Toxicity Results from the U.S. EPA ToxRef Database

Attribute filters allow enhancement and extraction of features without distorting their borders, and never introduce new image features. These are highly desirable properties in biomedical imaging, where accurate shape analysis is paramount. However, setting the attribute-threshold parameters has to date only been done manually. This paper explores simple, fast and automated methods of computing attribute threshold parameters based on image segmentation, thresholding and data clustering techniques. Though several techniques perform well on blood-vessel filtering, the choice of technique appears to depend on the imaging mode.

Evaluation of the availability and applicability of computational approaches in the safety assessment of nanomaterials: Final report of the Nanocomput project

This is the final report of the Nanocomput project, the main aims of which were to review the current status of computational methods that are potentially useful for predicting the properties of engineered nanomaterials, and to assess their applicability in order to provide advice on the use of these approaches for the purposes of the REACH regulation. Since computational methods cover a broad range of models and tools, emphasis was placed on Quantitative Structure-Property Relationship (QSPR) and Quantitative Structure-Activity Relationship (QSAR) models, and their potential role in predicting NM properties. In addition, the status of a diverse array of compartment-based mathematical models was assessed. These models comprised toxicokinetic (TK), toxicodynamic (TD), in vitro and in vivo dosimetry, and environmental fate models. Finally, based on systematic reviews of the scientific literature, as well as the outputs of the EU-funded research projects, recommendations for further research and development were also made. The Nanocomput project was carried out by the European Commission’s Joint Research Centre (JRC) for the Directorate-General (DG) for Internal Market, Industry, Entrepreneurship and SMEs (DG GROW) under the terms of an Administrative Arrangement between JRC and DG GROW. The project lasted 39 months, from January 2014 to March 2017, and was supported by a steering group with representatives from DG GROW, DG Environment and the European Chemicals Agency (ECHA).JRC.F.3-Chemicals Safety and Alternative Method

Environmental risk assessment in the mediterranean region using artificial neural networks

Los mapas auto-organizados han demostrado ser una herramienta apropiada para la clasificación y visualización de grupos de datos complejos. Redes neuronales, como los mapas auto-organizados (SOM) o las redes difusas ARTMAP (FAM), se utilizan en este estudio para evaluar el impacto medioambiental acumulativo en diferentes medios (aguas subterráneas, aire y salud humana). Los SOMs también se utilizan para generar mapas de concentraciones de contaminantes en aguas subterráneas simulando las técnicas geostadísticas de interpolación como kriging y cokriging. Para evaluar la confiabilidad de las metodologías desarrolladas en esta tesis, se utilizan procedimientos de referencia como puntos de comparación: la metodología DRASTIC para el estudio de vulnerabilidad en aguas subterráneas y el método de interpolación espacio-temporal conocido como Bayesian Maximum Entropy (BME) para el análisis de calidad del aire. Esta tesis contribuye a demostrar las capacidades de las redes neuronales en el desarrollo de nuevas metodologías y modelos que explícitamente permiten evaluar las dimensiones temporales y espaciales de riesgos acumulativos

Chemical Similarity and Threshold of Toxicological Concern (TTC) Approaches: Report of an ECB Workshop held in Ispra, November 2005

There are many national, regional and international programmes – either regulatory or voluntary – to assess the hazards or risks of chemical substances to humans and the environment. The first step in making a hazard assessment of a chemical is to ensure that there is adequate information on each of the endpoints. If adequate information is not available then additional data is needed to complete the dataset for this substance. For reasons of resources and animal welfare, it is important to limit the number of tests that have to be conducted, where this is scientifically justifiable. One approach is to consider closely related chemicals as a group, or chemical category, rather than as individual chemicals. In a category approach, data for chemicals and endpoints that have been already tested are used to estimate the hazard for untested chemicals and endpoints. Categories of chemicals are selected on the basis of similarities in biological activity which is associated with a common underlying mechanism of action. A homologous series of chemicals exhibiting a coherent trend in biological activity can be rationalised on the basis of a constant change in structure. This type of grouping is relatively straightforward. The challenge lies in identifying the relevant chemical structural and physicochemical characteristics that enable more sophisticated groupings to be made on the basis of similarity in biological activity and hence purported mechanism of action. Linking two chemicals together and rationalising their similarity with reference to one or more endpoints has been very much carried out on an ad hoc basis. Even with larger groups, the process and approach is ad hoc and based on expert judgement. There still appears to be very little guidance about the tools and approaches for grouping chemicals systematically. In November 2005, the ECB Workshop on Chemical Similarity and Thresholds of Toxicological Concern (TTC) Approaches was convened to identify the available approaches that currently exist to encode similarity and how these can be used to facilitate the grouping of chemicals. This report aims to capture the main themes that were discussed. In particular, it outlines a number of different approaches that can facilitate the formation of chemical groupings in terms of the context under consideration and the likely information that would be required. Grouping methods were divided into one of four classes – knowledge-based, analogue-based, unsupervised, and supervised. A flowchart was constructed to attempt to capture a possible work flow to highlight where and how these approaches might be best applied.JRC.I.3-Toxicology and chemical substance

Model-based Imputation of Below Detection Limit Missing Data and Group Selection in Bayesian Group Index Regression

Investigations into the association between chemical exposure and health outcomes are increasingly focused on the role of chemical mixtures, as opposed to individual chemicals. The analysis of chemical mixture data required the development of novel statistical methods, one of these being Bayesian group index regression. A statistical challenge common to all chemical mixture analyses is the ubiquitous presence of below detection limit (BDL) data. We propose an extension of Bayesian group index regression that treats both regression effects and missing BDL observations as parameters in a model estimated through a Markov Chain Monte Carlo algorithm that we refer to as Pseudo-Gibbs imputation. The Pseudo-Gibbs approach enables the estimated parameters of the health effects model to inform the missing data imputations and vice versa, as well as accounting for the true variance of the BDL imputations. We conduct a simulation study showing greater power to detect chemical indices significantly associated with an outcome and sensitivity for identifying important chemicals within indices at high levels of BDL missing data. We apply our model to a case-control study on the effects of chemical exposure on childhood leukemia. We next address a problem specific to group index models: how to partition a given set of chemicals into groups to form the requisite indices. We first proposed a novel variable clustering algorithm using a variant on the traditional PCA algorithm called Robust PCA. We compared this clustering method with other variable clustering methods from the literature using a simulation study. Finally, we extended the variable clustering method identified previously to incorporate information from an outcome variable. This semi-supervised clustering extension incorporates the ability to constrain clusters based on the direction of association of individual chemicals with the outcome of interest. We apply both unsupervised variable clustering and semi-supervised clustering methods identified to a case-control study on the effects of chemical exposure on non-Hodkin’s lymphoma