Regulatory assessment and risk management of chemical mixtures: challenges and ways forward.

This paper summarizes current challenges, the potential use of novel scientific methodologies, and ways forward in the risk assessment and risk management of mixtures. Generally, methodologies to address mixtures have been agreed; however, there are still several data and methodological gaps to be addressed. New approach methodologies can support the filling of knowledge gaps on the toxicity and mode(s) of action of individual chemicals. (Bio)Monitoring, modeling, and better data sharing will support the derivation of more realistic co-exposure scenarios. As knowledge and data gaps often hamper an in-depth assessment of specific chemical mixtures, the option of taking account of possible mixture effects in single substance risk assessments is briefly discussed. To allow risk managers to take informed decisions, transparent documentation of assumptions and related uncertainties is recommended indicating the potential impact on the assessment. Considering the large number of possible combinations of chemicals in mixtures, prioritization is needed, so that actions first address mixtures of highest concern and chemicals that drive the mixture risk. As chemicals with different applications and regulated separately might lead to similar toxicological effects, it is important to consider chemical mixtures across legislative sectors.

Identification and description of the uncertainty, variability, bias and influence in quantitative structure-activity relationships (QSARs) for toxicity prediction.

Improving regulatory confidence in, and acceptance of, a prediction of toxicity from a quantitative structure-activity relationship (QSAR) requires assessment of its uncertainty and determination of whether the uncertainty is acceptable. Thus, it is crucial to identify potential uncertainties fundamental to QSAR predictions. Based on expert review, sources of uncertainties, variabilities and biases, as well as areas of influence in QSARs for toxicity prediction were established. These were grouped into three thematic areas: uncertainties, variabilities, potential biases and influences associated with 1) the creation of the QSAR, 2) the description of the QSAR, and 3) the application of the QSAR, also showing barriers for their use. Each thematic area was divided into a total of 13 main areas of concern with 49 assessment criteria covering all aspects of QSAR development, documentation and use. Two case studies were undertaken on different types of QSARs that demonstrated the applicability of the assessment criteria to identify potential weaknesses in the use of a QSAR for a specific purpose such that they may be addressed and mitigation strategies can be proposed, as well as enabling an informed decision on the adequacy of the model in the considered context.

Exploring current read-across applications and needs among selected U.S. Federal Agencies.

Read-across is a well-established data gap-filling technique applied for regulatory purposes. In US Environmental Protection Agency's New Chemicals Program under TSCA, read-across has been used extensively for decades, however the extent of application and acceptance of read-across among U.S. federal agencies is less clear. In an effort to build read-across capacity, raise awareness of the state of the science, and work towards a harmonization of read-across approaches across U.S. agencies, a new read-across workgroup was established under the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM). This is one of several ad hoc groups ICCVAM has convened to implement the ICCVAM Strategic Roadmap. In this article, we outline the charge and scope of the workgroup and summarize the current applications, tools used, and needs of the agencies represented on the workgroup for read-across. Of the agencies surveyed, the Environmental Protection Agency had the greatest experience in using read-across whereas other agencies indicated that they would benefit from gaining a perspective of the landscape of the tools and available guidance. Two practical case studies are also described to illustrate how the read-across approaches applied by two agencies vary on account of decision context.

Compilation of Data and Modelling of Nanoparticle Interactions and Toxicity in the NanoPUZZLES Project.

The particular properties of nanomaterials have led to their rapidly increasing use in diverse fields of application. However, safety assessment is not keeping pace and there are still gaps in the understanding of their hazards. Computational models predicting nanotoxicity, such as (quantitative) structure-activity relationships ((Q)SARs), can contribute to safety evaluation, in line with general efforts to apply alternative methods in chemical risk assessment. Their development is highly dependent on the availability of reliable and high quality experimental data, both regarding the compounds' properties as well as the measured toxic effects. In particular, "nano-QSARs" should take the nano-specific characteristics into account. The information compiled needs to be well organized, quality controlled and standardized. Integrating the data in an overarching, structured data collection aims to (a) organize the data in a way to support modelling, (b) make (meta)data necessary for modelling available, and (c) add value by making a comparison between data from different sources possible.Based on the available data, specific descriptors can be derived to parameterize the nanomaterial-specific structure and physico-chemical properties appropriately. Furthermore, the interactions between nanoparticles and biological systems as well as small molecules, which can lead to modifications of the structure of the active nanoparticles, need to be described and taken into account in the development of models to predict the biological activity and toxicity of nanoparticles. The EU NanoPUZZLES project was part of a global cooperative effort to advance data availability and modelling approaches supporting the characterization and evaluation of nanomaterials.

Artificial Intelligence for chemical risk assessment.

As the basis for managing the risks of chemical exposure, the Chemical Risk Assessment (CRA) process can impact a substantial part of the economy, the health of hundreds of millions of people, and the condition of the environment. However, the number of properly assessed chemicals falls short of societal needs due to a lack of experts for evaluation, interference of third party interests, and the sheer volume of potentially relevant information on the chemicals from disparate sources. In order to explore ways in which computational methods may help overcome this discrepancy between the number of chemical risk assessments required on the one hand and the number and adequateness of assessments actually being conducted on the other, the European Commission's Joint Research Centre organised a workshop on Artificial Intelligence for Chemical Risk Assessment (AI4CRA). The workshop identified a number of areas where Artificial Intelligence could potentially increase the number and quality of regulatory risk management decisions based on CRA, involving process simulation, supporting evaluation, identifying problems, facilitating collaboration, finding experts, evidence gathering, systematic review, knowledge discovery, and building cognitive models. Although these are interconnected, they are organised and discussed under two main themes: scientific-technical process and social aspects and the decision making process.

A mode-of-action ontology model for safety evaluation of chemicals: Outcome of a series of workshops on repeated dose toxicity.

Repeated dose toxicity evaluation aims at assessing the occurrence of adverse effects following chronic or repeated exposure to chemicals. Non-animal approaches have gained importance in the last decades because of ethical considerations as well as due to scientific reasons calling for more human-based strategies. A critical aspect of this challenge is linked to the capacity to cover a comprehensive set of interdependent mechanisms of action, link them to adverse effects and interpret their probability to be triggered in the light of the exposure at the (sub)cellular level. Inherent to its structured nature, an ontology addressing repeated dose toxicity could be a scientific and transparent way to achieve this goal. Additionally, repeated dose toxicity evaluation through the use of a harmonized ontology should be performed in a reproducible and consistent manner, while mimicking as accurately as possible human physiology and adaptivity. In this paper, the outcome of a series of workshops organized by Cosmetics Europe on this topic is reported. As such, this manuscript shows how experts set critical elements and ways of establishing a mode-of-action ontology model as a support to risk assessors aiming to perform animal-free safety evaluation of chemicals based on repeated dose toxicity data.

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.

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

We describe and illustrate a workflow for chemical safety assessment that completely avoids animal testing. The workflow, which was developed within the SEURAT-1 initiative, is designed to be applicable to cosmetic ingredients as well as to other types of chemicals, e.g. active ingredients in plant protection products, biocides or pharmaceuticals. The aim of this work was to develop a workflow to assess chemical safety without relying on any animal testing, but instead constructing a hypothesis based on existing data, in silico modelling, biokinetic considerations and then by targeted non-animal testing. For illustrative purposes, we consider a hypothetical new ingredient x as a new component in a body lotion formulation. The workflow is divided into tiers in which points of departure are established through in vitro testing and in silico prediction, as the basis for estimating a safe external dose in a repeated use scenario. The workflow includes a series of possible exit (decision) points, with increasing levels of confidence, based on the sequential application of the Threshold of Toxicological (TTC) approach, read-across, followed by an "ab initio" assessment, in which chemical safety is determined entirely by new in vitro testing and in vitro to in vivo extrapolation by means of mathematical modelling. We believe that this workflow could be applied as a tool to inform targeted and toxicologically relevant in vitro testing, where necessary, and to gain confidence in safety decision making without the need for animal testing.

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.

Automated workflows for modelling chemical fate, kinetics and toxicity.

Automation is universal in today's 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.

Toward Good Read-Across Practice (GRAP) guidance.

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 legislations 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 aims to summarize the state-of-the-art, summarizes insights learned from reviewing ECHA published decisions as far as the relative successes/pitfalls surrounding read-across under REACH and compile the relevant activities and guidance documents. Special emphasis is given to the available existing tools and approaches, an analysis of ECHA's 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.

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.

Speciation analysis of trace elements in the brains of individuals with Alzheimer's disease with special emphasis on metallothioneins.

A speciation analysis of protein-bound elements in the cytosol of human brain was achieved by size exclusion chromatographical separation of the biomolecules and on-line detection of the metal profiles in the eluate by hyphenated inductively coupled plasma-mass spectrometry. Post-mortem samples from Alzheimer's disease brains and from brains of a control group were investigated to elucidate changes in the trace element distribution during the pathological process. Special attention was paid to the metallothioneins (MT) - cysteine-rich, metal-binding proteins of low molecular weight, existing in several isoforms. The isoform MT-3 is found especially in the brain and has a growth inhibition function on neurons. The MT peaks were identified in the element profiles. For this purpose, the metal binding capability and the heat stability of MT were taken into consideration. For verification, a comparison with pure MT-3 was carried out and further biochemical and analytical methods were applied to the fractions of the chromatographical run. A comparison between Alzheimer's disease and control brains showed a significant difference concerning the MT-1/-2 and MT-3 metal levels, leading to the assumption that there were oxidative processes having taken place in the Alzheimer's brain samples.

Determination of protein-bound trace elements in human cell cytosols of different organs and different pathological states.

In the human body, there exists over 200 different cell types, which differ in size and structure and have specialised functions in the organism. Therefore it can be assumed that these different cells also contain different proteins necessary to carry out the respective specialised functions. This supposed different metalloprotein composition in different human organs cannot be demonstrated by determination of total element concentrations. Therefore investigations of the different protein-bound forms of the elements were achieved by speciation analysis: The biomolecules were separated by size exclusion chromatography and the elements detected on-line in the eluate by a hyphenated inductively coupled plasma mass spectrometer (ICP-MS). For the interpretation of the obtained element profiles, an identification of the signals and their assignment to different metalloproteins was necessary. This identification was carried out by means of specific protein assays, i.e. enzymatic assays or immunochemical reactions, in collected fractions of the chromatographic separations. A comparison of the element binding pattern in cytosols of different human organs was then possible. The optimised method was applied to tissue cytosols of different human organs. As expected, the element patterns varied for different organs of the same patient and for the same organ of patients with different diseases. Metalloproteins and their bound metals could consequently be considered as biological markers for physiological differences or pathological changes in human tissues.

CZE-ICP-MS separation of metallothioneins in human brain cytosols: comparability of electropherograms obtained from different sample matrices.

Capillary zone electrophoresis, with its high resolution capability in the separation of different compounds, is well suited for the investigation of metal-containing proteins, especially when elemental detection is conducted using hyphenated inductively coupled plasma-mass spectrometry. A major problem in the separation of proteins in body fluids is caused by the effects of different sample matrix composition. The migration time of proteins varies significantly, depending on the nature of the matrix. Electropherograms are consequently difficult to compare and the peak identification is uncertain. Pre-analytical steps for the reduction of matrix compounds enhance the quality of the data, but the results are still unsatisfactory. This paper describes a technique for obtaining electropherograms that can be used for comparison purposes by correction of the data with the aid of time markers. A mixture of five substances (caesium chloride, arsenocholine, arsenobetaine, dimethylarsinic acid and monomethylarsonic acid) was added in a separate injection step. Ionic caesium eluted at the start of the separation and the other four markers appeared throughout and at the end of the electropherogram. All electropherograms were normalized to a reference run by recalculation of the time axis using the time markers. The method was applied to the analysis of human brain cytosols. Samples were separated after different pre-treatment steps and were compared, with special emphasis on the detection of the isoform metallothionein-3.