Review of Data Sources, QSARs and Integrated Testing Strategies for Skin Sensitisation

This review collects information on sources of skin sensitisation data and computational tools for the estimation of skin sensitisation potential, such as expert systems and (quantitative) structure-activity relationship (QSAR) models. The review also captures current thinking of what constitutes an integrated testing strategy (ITS) for this endpoint. The emphasis of the review is on the usefulness of the models for the regulatory assessment of chemicals, particularly for the purposes of the new European legislation for the Registration, Evaluation, Authorisation and Restriction of CHemicals (REACH), which entered into force on 1 June 2007. Since there are no specific databases for skin sensitisation currently available, a description of experimental data found in various literature sources is provided. General (global) models, models for specific chemical classes and mechanisms of action and expert systems are summarised. This review was prepared as a contribution to the EU funded Integrated Project, OSIRIS.JRC.I.3-Consumer products safety and qualit

On the Usefulness of Graph-theoretic Descriptors in Predicting Theoretical Parameters. Phototoxicity of Polycyclic Aromatic Hydrocarbons (PAHs)

Two main aspects on the use of theoretical approaches in predicting biological endpoints are addressed. The first concerns the appropriateness of theoretical methods and the misbelief that more sophisticated approaches produce better results. We demonstrate that the simple graph theoretic HMO approach produces results as good as »high level« ab initio calculations using only 10–4 to 10–7 of the time. Secondly, we investigate the feasibility of using a priori »mechanistic insights« in order to select the variables to be included in a QSAR model. As the majority of biological response measures are not specific for one particular mechanism, the use of this approach may well produce unrealistic results. The black box approach with its many descriptors and a posteriori interpretation of the results is much more appropriate in such cases where the biological response is the result of several mechanisms involving distribution and metabolism. All these aspects are analysed on the basis of the toxicity of PAHs after photoactivation by UV radiation

Ability of non-animal methods for skin sensitisation to detect pre- and pro-haptens: Report and recommendations of an EURL ECVAM expert meeting

Significant progress has been made in the development, validation and regulatory acceptance of in chemico and in vitro test methods for skin sensitisation. Although these methods have been shown to perform relatively well (about 80% accuracy in predicting Local Lymph Node Assay (LLNA) classifications) a concern was raised on the regulatory acceptability of negative results since it was questioned whether these methods are able to predict chemicals that need to be activated to act as sensitisers. In order to inform ongoing discussions at the regulatory level in the EU, EURL ECVAM held an expert meeting on 10-11 November 2015 to analyse the extent to which in chemical and in vitro methods are able to correctly identify chemicals that need to be activated either through abiotic activation (pre-haptens) and/or through biotic (enzyme-mediated) mechanisms (pro-haptens) to acquire skin sensitisation potential. The expert group analysed a list of 127 chemicals, with available LLNA and in vitro data, 22% of which were considered to be pre- and/or pro-haptens. The pre-haptens, constituting the vast majority of chemicals requiring activation, where mostly correctly identified by both the in chemico and in vitro assays whereas the pro-haptens which represent a small subset of sensitising chemicals, were generally identified correctly by one of the cell-based assays. As a result, the expert group recommended that negative in vitro data should be accepted unless there is a compelling scientific argument that a substance is likely to be an exclusively metabolically activated pro-hapten.JRC.I.5-Systems Toxicolog

Applying Adverse Outcome Pathways (AOPs) to support Integrated Approaches to Testing and Assessment (IATA)

Chemical regulation is challenged by the large number of chemicals requiring assessment for potential human health and environmental impacts. Current approaches are too resource intensive in terms of time, money and animal use to evaluate all chemicals under development or already on the market. The need for timely and robust decision making demands that regulatory toxicity testing becomes more cost-effective and efficient. One way to realize this goal is by being more strategic in directing testing resources; focusing on chemicals of highest concern, limiting testing to the most probable hazards, or targeting the most vulnerable species. Hypothesis driven Integrated Approaches to Testing and Assessment (IATA) have been proposed as practical solutions to such strategic testing. In parallel, the development of the Adverse Outcome Pathway (AOP) framework, which provides information on the causal links between a molecular initiating event (MIE), intermediate key events (KEs) and an adverse outcome (AO) of regulatory concern, offers the biological context to facilitate development of IATA for regulatory decision making. This manuscript summarizes discussions at the Workshop entitled “Advancing AOPs for Integrated Toxicology and Regulatory Applications” with particular focus on the role AOPs play in informing the development of IATA for different regulatory purposes.publishedVersio

Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research

Research in environmental health is becoming increasingly reliant upon data science and computational methods that can more efficiently extract information from complex datasets. Data science and computational methods can be leveraged to better identify relationships between exposures to stressors in the environment and human disease outcomes, representing critical information needed to protect and improve global public health. Still, there remains a critical gap surrounding the training of researchers on these in silico methods. We aimed to address this gap by developing the inTelligence And Machine lEarning (TAME) Toolkit, promoting trainee-driven data generation, management, and analysis methods to “TAME” data in environmental health studies. Training modules were developed to provide applications-driven examples of data organization and analysis methods that can be used to address environmental health questions. Target audiences for these modules include students, post-baccalaureate and post-doctorate trainees, and professionals that are interested in expanding their skillset to include recent advances in data analysis methods relevant to environmental health, toxicology, exposure science, epidemiology, and bioinformatics/cheminformatics. Modules were developed by study coauthors using annotated script and were organized into three chapters within a GitHub Bookdown site. The first chapter of modules focuses on introductory data science, which includes the following topics: setting up R/RStudio and coding in the R environment; data organization basics; finding and visualizing data trends; high-dimensional data visualizations; and Findability, Accessibility, Interoperability, and Reusability (FAIR) data management practices. The second chapter of modules incorporates chemical-biological analyses and predictive modeling, spanning the following methods: dose-response modeling; machine learning and predictive modeling; mixtures analyses; -omics analyses; toxicokinetic modeling; and read-across toxicity predictions. The last chapter of modules was organized to provide examples on environmental health database mining and integration, including chemical exposure, health outcome, and environmental justice indicators. Training modules and associated data are publicly available online (https://uncsrp.github.io/Data-Analysis-Training-Modules/). Together, this resource provides unique opportunities to obtain introductory-level training on current data analysis methods applicable to 21st century science and environmental health

Evidence-based Toxicology for the 21st Century: Opportunities and Challenges

The Evidence-based Toxicology Collaboration (EBTC) was established recently to translate evidence-based approaches from medicine and health care to toxicology in an organized and sustained effort. The EBTC held a workshop on “Evidence-based Toxicology for the 21st Century: Opportunities and Challenges” in Research Triangle Park, North Carolina, USA on January 24-25, 2012. The presentations largely reflected two EBTC priorities: to apply evidence-based methods to assessing the performance of emerging pathwaybased testing methods consistent with the 2007 National Research Council report on “Toxicity Testing in the 21st Century” as well as to adopt a governance structure and work processes to move that effort forward. The workshop served to clarify evidence-based approaches and to provide food for thought on substantive and administrative activities for the EBTC. Priority activities include conducting pilot studies to demonstrate the value of evidence-based approaches to toxicology, as well as conducting educational outreach on these approaches

Evidence-based Toxicology for the 21st Century: Opportunities and Challenges

The Evidence-based Toxicology Collaboration (EBTC) was established recently to translate evidence-based approaches from medicine and health care to toxicology in an organized and sustained effort. The EBTC held a workshop on “Evidence-based Toxicology for the 21st Century: Opportunities and Challenges” in Research Triangle Park, North Carolina, USA on January 24-25, 2012. The presentations largely reflected two EBTC priorities: to apply evidence-based methods to assessing the performance of emerging pathwaybased testing methods consistent with the 2007 National Research Council report on “Toxicity Testing in the 21st Century” as well as to adopt a governance structure and work processes to move that effort forward. The workshop served to clarify evidence-based approaches and to provide food for thought on substantive and administrative activities for the EBTC. Priority activities include conducting pilot studies to demonstrate the value of evidence-based approaches to toxicology, as well as conducting educational outreach on these approaches

Identifying xenobiotic metabolites with in silico prediction tools and LCMS suspect screening analysis

Understanding the metabolic fate of a xenobiotic substance can help inform its potential health risks and allow for the identification of signature metabolites associated with exposure. The need to characterize metabolites of poorly studied or novel substances has shifted exposure studies towards non-targeted analysis (NTA), which often aims to profile many compounds within a sample using high-resolution liquid-chromatography mass-spectrometry (LCMS). Here we evaluate the suitability of suspect screening analysis (SSA) liquid-chromatography mass-spectrometry to inform xenobiotic chemical metabolism. Given a lack of knowledge of true metabolites for most chemicals, predictive tools were used to generate potential metabolites as suspect screening lists to guide the identification of selected xenobiotic substances and their associated metabolites. Thirty-three substances were selected to represent a diverse array of pharmaceutical, agrochemical, and industrial chemicals from Environmental Protection Agency’s ToxCast chemical library. The compounds were incubated in a metabolically-active in vitro assay using primary hepatocytes and the resulting supernatant and lysate fractions were analyzed with high-resolution LCMS. Metabolites were simulated for each compound structure using software and then combined to serve as the suspect screening list. The exact masses of the predicted metabolites were then used to select LCMS features for fragmentation via tandem mass spectrometry (MS/MS). Of the starting chemicals, 12 were measured in at least one sample in either positive or negative ion mode and a subset of these were used to develop the analysis workflow. We implemented a screening level workflow for background subtraction and the incorporation of time-varying kinetics into the identification of likely metabolites. We used haloperidol as a case study to perform an in-depth analysis, which resulted in identifying five known metabolites and five molecular features that represent potential novel metabolites, two of which were assigned discrete structures based on in silico predictions. This workflow was applied to five additional test chemicals, and 15 molecular features were selected as either reported metabolites, predicted metabolites, or potential metabolites without a structural assignment. This study demonstrates that in some–but not all–cases, suspect screening analysis methods provide a means to rapidly identify and characterize metabolites of xenobiotic chemicals

Alternative approaches for acute inhalation toxicity testing to address global regulatory and non-regulatory data requirements: an international workshop report

Inhalation toxicity testing, which provides the basis for hazard labeling and risk management of chemicals with potential exposure to the respiratory tract, has traditionally been conducted using animals. Significant research efforts have been directed at the development of mechanistically based, non-animal testing approaches that hold promise to provide human-relevant data and an enhanced understanding of toxicity mechanisms. A September 2016 workshop, “Alternative Approaches for Acute Inhalation Toxicity Testing to Address Global Regulatory and Non-Regulatory Data Requirements”, explored current testing requirements and ongoing efforts to achieve global regulatory acceptance for non-animal testing approaches. The importance of using integrated approaches that combine existing data with in vitro and/or computational approaches to generate new data was discussed. Approaches were also proposed to develop a strategy for identifying and overcoming obstacles to replacing animal tests. Attendees noted the importance of dosimetry considerations and of understanding mechanisms of acute toxicity, which could be facilitated by the development of adverse outcome pathways. Recommendations were made to (1) develop a database of existing acute inhalation toxicity data; (2) prepare a state-of-the-science review of dosimetry determinants, mechanisms of toxicity, and existing approaches to assess acute inhalation toxicity; (3) identify and optimize in silico models; and (4) develop a decision tree/testing strategy, considering physicochemical properties and dosimetry, and conduct proof-of-concept testing. Working groups have been established to implement these recommendations

In silico toxicology protocols

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information