Ab initio chemical safety assessment: A workflow based on exposure considerations and non-animal methods

Vision of a near future: Bridging the human health-environment divide. Toward an integrated strategy to understand mechanisms across species for chemical safety assessment

There is a growing recognition that application of mechanistic approaches to understand cross-species shared molecular targets and pathway conservation in the context of hazard characterization, provide significant opportunities in risk assessment (RA) for both human health and environmental safety. Specifically, it has been recognized that a more comprehensive and reliable understanding of similarities and differences in biological pathways across a variety of species will better enable cross-species extrapolation of potential adverse toxicological effects. Ultimately, this would also advance the generation and use of mechanistic data for both human health and environmental RA. A workshop brought together representatives from industry, academia and government to discuss how to improve the use of existing data, and to generate new NAMs data to derive better mechanistic understanding between humans and environmentally-relevant species, ultimately resulting in holistic chemical safety decisions. Thanks to a thorough dialogue among all participants, key challenges, current gaps and research needs were identified, and potential solutions proposed. This discussion highlighted the common objective to progress toward more predictive, mechanistically based, data-driven and animal-free chemical safety assessments. Overall, the participants recognized that there is no single approach which would provide all the answers for bridging the gap between mechanism-based human health and environmental RA, but acknowledged we now have the incentive, tools and data availability to address this concept, maximizing the potential for improvements in both human health and environmental RA.

One science-driven approach for the regulatory implementation of alternative methods: A multi-sector perspective

EU regulations call for the use of alternative methods to animal testing. During the last decade, an increasing number of alternative approaches have been formally adopted. In parallel, new 3Rs-relevant technologies and mechanistic approaches have increasingly contributed to hazard identification and risk assessment evolution. In this changing landscape, an EPAA meeting reviewed the challenges that different industry sectors face in the implementation of alternative methods following a science-driven approach. Although clear progress was acknowledged in animal testing reduction and refinement thanks to an integration of scientifically robust approaches, the following challenges were identified: i) further characterization of toxicity pathways; ii) development of assays covering current scientific gaps, iii) better characterization of links between in vitro readouts and outcome in the target species; iv) better definition of alternative method applicability domains, and v) appropriate implementation of the available approaches. For areas having regulatory adopted alternative methods (e.g., vaccine batch testing), harmonised acceptance across geographical regions was considered critical for broader application. Overall, the main constraints to the application of non-animal alternatives are the still existing gaps in scientific knowledge and technological limitations. The science-driven identification of most appropriate methods is key for furthering a multi-sectorial decrease in animal testing.

A Computational Workflow for Probabilistic Quantitative in Vitro to in Vivo Extrapolation

A computational workflow was developed to facilitate the process of quantitative in vitro to in vivo extrapolation (QIVIVE), specifically the translation of in vitro concentration-response to in vivo dose-response relationships and subsequent derivation of a benchmark dose value (BMD). The workflow integrates physiologically based pharmacokinetic (PBPK) modeling; global sensitivity analysis (GSA), Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC) simulation. For a given set of in vitro concentration and response data the algorithm returns the posterior distribution of the corresponding in vivo, population-based dose-response values, for a given route of exposure. The novel aspect of the workflow is a rigorous statistical framework for accommodating uncertainty in both the parameters of the PBPK model (both parameter uncertainty and population variability) and in the structure of the PBPK model itself recognizing that the model is an approximation to reality. Both these sources of uncertainty propagate through the workflow and are quantified within the posterior distribution of in vivo dose for a fixed representative in vitro concentration. To demonstrate this process and for comparative purposes a similar exercise to previously published work describing the kinetics of ethylene glycol monoethyl ether (EGME) and its embryotoxic metabolite methoxyacetic acid (MAA) in rats was undertaken. The computational algorithm can be used to extrapolate from in vitro data to any organism, including human. Ultimately, this process will be incorporated into a user-friendly, freely available modeling platform, currently under development, that will simplify the process of QIVIVE.

Relevance and Application of Read-Across - Mini Review of European Consensus Platform for Alternatives and Scandinavian Society for Cell Toxicology 2017 Workshop Session

Structure activity relationship (SAR)-based read-across is an effective approach for addressing data gaps in human health risk assessment for 'data-poor' chemicals. In read-across, available data on chemical structural analogues are used to predict the toxicity potential of the data-poor chemical. This approach has long been recognized by regulatory agencies and used by industry to evaluate the hazards of chemicals for which there are limited direct data. Construction of a scientifically robust SAR-based read-across hazard assessment is a complex and iterative process involving multiple considerations in each step. Traditional in vivo data generated using regulatory guideline compliant study designs typically forms the basis for read-across assessments. Recently, however, new data streams have been explored and incorporated to enhance read-across predictivity. These in vitro and omics data streams may be used in different ways involving identification of hazards or to provide insight into modes of action for observed toxicological responses. These 'new approach methods' can also enable comparison of biological responses across analogues or a category of structurally related chemicals in order to establish a pattern of biological similarity in addition chemical similarity and/or to help address potency differences across a category. The purpose of this workshop session was to inform on practical considerations in conducting SAR-based read-across assessments and to review recent activities related to application of new approach methods to the practice of read-across.

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.