Andrea-Nicole Richarz

Toward good read-across practice (GRAP) guidance

Summary Grouping of substances and utilizing read-across of data within those groups represents an important data gap filling technique for chemical safety assessments. Categories/analogue groups are typically developed based on structural similarity and, increasingly often, also on mechanistic (biological) similarity. While read-across can play a key role in complying with legislation such as the European REACH regulation, the lack of consensus regarding the extent and type of evidence necessary to support it often hampers its successful application and acceptance by regulatory authorities. Despite a potentially broad user community, expertise is still concentrated across a handful of organizations and individuals. In order to facilitate the effective use of read-across, this document presents the state of the art, summarizes insights learned from reviewing ECHA published decisions regarding the relative successes/pitfalls surrounding read-across under REACH, and compiles the relevant activities and guidance documents. Special emphasis is given to the available existing tools and approaches, an analysis of ECHAs published final decisions associated with all levels of compliance checks and testing proposals, the consideration and expression of uncertainty, the use of biological support data, and the impact of the ECHA Read-Across Assessment Framework (RAAF) published in 2015.

An ISA-TAB-Nano based data collection framework to support data-driven modelling of nanotoxicology.

Summary Analysis of trends in nanotoxicology data and the development of data driven models for nanotoxicity is facilitated by the reporting of data using a standardised electronic format. ISA-TAB-Nano has been proposed as such a format. However, in order to build useful datasets according to this format, a variety of issues has to be addressed. These issues include questions regarding exactly which (meta)data to report and how to report them. The current article discusses some of the challenges associated with the use of ISA-TAB-Nano and presents a set of resources designed to facilitate the manual creation of ISA-TAB-Nano datasets from the nanotoxicology literature. These resources were developed within the context of the NanoPUZZLES EU project and include data collection templates, corresponding business rules that extend the generic ISA-TAB-Nano specification as well as Python code to facilitate parsing and integration of these datasets within other nanoinformatics resources. The use of these resources is illustrated by a “Toy Dataset” presented in the Supporting Information. The strengths and weaknesses of the resources are discussed along with possible future developments.

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

Highlights • A workflow for an exposure driven chemical safety assessment to avoid animal testing.• Hypothesis based on existing data, in silico modelling and biokinetic considerations.• A tool to inform targeted and toxicologically relevant in vitro testing.

Automated workflows for modelling chemical fate, kinetics and toxicity.

Automation is universal in todays society, from operating equipment such as machinery, in factory processes, to self-parking automobile systems. While these examples show the efficiency and effectiveness of automated mechanical processes, automated procedures that support the chemical risk assessment process are still in their infancy. Future human safety assessments will rely increasingly on the use of automated models, such as physiologically based kinetic (PBK) and dynamic models and the virtual cell based assay (VCBA). These biologically-based models will be coupled with chemistry-based prediction models that also automate the generation of key input parameters such as physicochemical properties. The development of automated software tools is an important step in harmonising and expediting the chemical safety assessment process. In this study, we illustrate how the KNIME Analytics Platform can be used to provide a user-friendly graphical interface for these biokinetic models, such as PBK models and VCBA, which simulates the fate of chemicals in vivo within the body and in vitro test systems respectively.

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.

Quantitative structure-skin permeability relationships

This paper reviews in silico models currently available for the prediction of skin permeability. A comprehensive discussion on the developed methods is presented, focusing on quantitative structure-permeability relationships. In addition, the mechanistic models and comparative studies that analyse different models are discussed. Limitations and strengths of the different approaches are highlighted together with the emergent issues and perspectives.

Experimental verification of structural alerts for the protein binding of sulfur-containing compounds.

As often noted by Dr. Gilman Veith, a major barrier to advancing any model is defining its applicability domain. Sulfur-containing industrial organic chemicals can be grouped into several chemical classes including mercaptans (RSH), sulfides (RSR’), disulfides (RSSR’), sulfoxides (RS(=O)R’), sulfones (RS(=O)(=O)R’), sulfonates (ROS(=O)(=O)R’) and sulfates (ROS(=O)(=O)OR’). In silico expert systems that predict protein binding reactions from 2D structure sub-divide these chemical classes into a variety of chemical reactive mechanisms and reactions which have toxic consequences. Using the protein binding profilers in version 3.1 of the OECD QSAR Toolbox, a series of sulfur-containing chemicals were profiled for protein binding potential. From these results it was hypothesized which sulfur-containing chemicals would be reactive or non-reactive in an in chemico glutathione assay and whether if reactive they would exhibit toxicity in excess of baseline in the Tetrahymena pyriformis population growth impairment assay. Subsequently, these hypotheses were tested experimentally. The in chemico data show that the in silico profiler predictions were generally correct for all chemical categories, where testing was possible. Mercaptans could not be assessed for GSH reactivity because they react directly with the chromophore 5,5’-dithiobis-(2-nitrobenzoic acid). With some exceptions, the major being disulfides, the in vitro toxicity data supported the in chemico findings.

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.