Something from nothing? Ensuring the safety of chemical mixtures

Headlines - Humans and the environment are exposed to a cocktail of chemicals from different sources. - Combined exposure to multiple chemicals can lead to health/ environmental effects even if single substances in the mixture do not exceed safe levels. - The assessment and management of mixtures is only partly covered by current legislation, which focuses on single substances in isolated sectors. - Methodology to address mixture risks is available, yet many knowledge gaps need to be filled. In particular, real co-exposure patterns are mostly unknown. - JRC is performing research on new strategies to assess the combination effects of chemicals.JRC.F.3-Chemicals Safety and Alternative Method

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

EURL ECVAM Status Report on the Development, Validation and Regulatory Acceptance of Alternative Methods and Approaches (2016)

Replacement, Reduction and Refinement of animal testing is anchored in EU legislation. Alternative non-animal approaches facilitate a shift away from animal testing. Cell-based methods and computational technologies are integrated to translate molecular mechanistic understanding of toxicity into safety testing strategies.JRC.F.3-Chemicals Safety and Alternative Method

The application of molecular modelling in the safety assessment of chemicals: A case study on ligand-dependent PPARγ dysregulation.

The aim of this paper was to provide a proof of concept demonstrating that molecular modelling methodologies can be employed as a part of an integrated strategy to support toxicity prediction consistent with the mode of action/adverse outcome pathway (MoA/AOP) framework. To illustrate the role of molecular modelling in predictive toxicology, a case study was undertaken in which molecular modelling methodologies were employed to predict the activation of the peroxisome proliferator-activated nuclear receptor γ (PPARγ) as a potential molecular initiating event (MIE) for liver steatosis. A stepwise procedure combining different in silico approaches (virtual screening based on docking and pharmacophore filtering, and molecular field analysis) was developed to screen for PPARγ full agonists and to predict their transactivation activity (EC50). The performance metrics of the classification model to predict PPARγ full agonists were balanced accuracy=81%, sensitivity=85% and specificity=76%. The 3D QSAR model developed to predict EC50 of PPARγ full agonists had the following statistical parameters: q(2)cv=0.610, Nopt=7, SEPcv=0.505, r(2)pr=0.552. To support the linkage of PPARγ agonism predictions to prosteatotic potential, molecular modelling was combined with independently performed mechanistic mining of available in vivo toxicity data followed by ToxPrint chemotypes analysis. The approaches investigated demonstrated a potential to predict the MIE, to facilitate the process of MoA/AOP elaboration, to increase the scientific confidence in AOP, and to become a basis for 3D chemotype development

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

Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology

Nanotechnology and the production of nanomaterials have been expanding rapidly in recent years. Since many types of engineered nanoparticles are suspected to be toxic to living organisms and to have a negative impact on the environment, the process of designing new nanoparticles and their applications must be accompanied by a thorough exposure risk analysis. (Quantitative) Structure-Activity Relationship ([Q]SAR) modelling creates promising options among the available methods for the risk assessment. These in silico models can be used to predict a variety of properties, including the toxicity of newly designed nanoparticles. However, (Q)SAR models must be appropriately validated to ensure the clarity, consistency and reliability of predictions. This paper is a joint initiative from recently completed European research projects focused on developing (Q)SAR methodology for nanomaterials. The aim was to interpret and expand the guidance for the well-known “OECD Principles for the Validation, for Regulatory Purposes, of (Q)SAR Models”, with reference to nano-(Q)SAR, and present our opinions on the criteria to be fulfilled for models developed for nanoparticles

Frameworks and tools for risk assessment of manufactured nanomaterials

Commercialization of nanotechnologies entails a regulatory requirement for understanding their environmental, health and safety (EHS) risks. Today we face challenges to assess these risks, which emerge from uncertainties around the interactions of manufactured nanomaterials (MNs) with humans and the environment. In order to reduce these uncertainties, it is necessary to generate sound scientific data on hazard and exposure by means of relevant frameworks and tools. The development of such approaches to facilitate the risk assessment (RA) of MNs has become a dynamic area of research. The aim of this paper was to review and critically analyse these approaches against a set of relevant criteria. The analysis concluded that none of the reviewed frameworks were able to fulfill all evaluation criteria. Many of the existing modelling tools are designed to provide screening-level assessments rather than to support regulatory RA and risk management. Nevertheless, there is a tendency towards developing more quantitative, higher-tier models, capable of incorporating uncertainty into their analyses. There is also a trend towards developing validated experimental protocols for material identification and hazard testing, reproducible across laboratories. These tools could enable a shift from a costly case-by-case RA of MNs towards a targeted, flexible and efficient process, based on grouping and read-across strategies and compliant with the 3R (Replacement, Reduction, Refinement) principles. In order to facilitate this process, it is important to transform the current efforts on developing databases and computational models into creating an integrated data and tools infrastructure to support the risk assessment and management of MNs.Commercialization of nanotechnologies entails a regulatory requirement for understanding their environmental, health and safety (EHS) risks. Today we face challenges to assess these risks, which emerge from uncertainties around the interactions of manufactured nanomaterials (MNs) with humans and the environment. In order to reduce these uncertainties, it is necessary to generate sound scientific data on hazard and exposure by means of relevant frameworks and tools. The development of such approaches to facilitate the risk assessment (RA) of MNs has become a dynamic area of research. The aim of this paper was to review and critically analyse these approaches against a set of relevant criteria. The analysis concluded that none of the reviewed frameworks were able to fulfill all evaluation criteria. Many of the existing modelling tools are designed to provide screening level assessments rather than to support regulatory RA and risk management Nevertheless, there is a tendency towards developing more quantitative, higher-tier models, capable of incorporating uncertainty into their analyses. There is also a trend towards developing validated experimental protocols for material identification and hazard testing, reproducible across laboratories. These tools could enable a shift from a costly case-by-case RA of MNs towards a targeted, flexible and efficient process, based on grouping and read-across strategies and compliant with the 3R (Replacement, Reduction, Refinement) principles. In order to facilitate this process, it is important to transform the current efforts on developing databases and computational models into creating an integrated data and tools infrastructure to support the risk assessment and management of MNs. (C) 2016 Elsevier Ltd. All rights reserved

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

Speciation analysis of protein-bound elements in cytosols as biological markers for metabolic processes with special emphasis on metallothioneins in the brain

Bei der Aufklärung der Rolle von Spurenelementen in komplexen physiologischen oder pathologischen Stoffwechselvorgängen erlaubt die Speziationsanalyse tiefere Einblicke in die im Organismus ablaufenden Prozesse als die Bestimmung von Gesamtelementgehalten. In der vorliegenden Arbeit wurden die an unterschiedliche Proteine im Cytosol von menschlichen Geweben gebundenen Elemente untersucht. Im Vordergrund standen dabei die Metallothioneine (MT) - niedermolekulare, cysteinreiche, metallbindende Proteine, welche als an zahlreichen vitalen Stoffwechselprozessen beteiligt angesehen werden. Die Isoform MT-3 wurde vor allem im Gehirn gefunden und seit ihrer Entdeckung in Zusammenhang mit Morbus Alzheimer (AD) diskutiert. Zur Speziationsanalyse wurden Verbundverfahren aus chromatographischer bzw. kapillarelektrophoretischer Trennung der Biomoleküle und on-line gekoppelter Elementdetektion mittels Plasmamassenspektrometrie (ICP-MS) eingesetzt. Die Abtrennung des MT-3 von den anderen Isoformen war dabei für eine gesonderte Betrachtung wichtig. Die einzelnen Signale wurden verschiedenen Proteinen mittels spezifischer Nachweise im Eluat der Trennungen zugeordnet. Die Identität des MT-3-Peaks konnte sicher bestätigt werden. Neben der Größenausschlußchromatographie wurden weitere Trennverfahren verwendet, welche je nach dem Ziel der durchzuführenden Untersuchung ausgewählt werden müssen. In Kooperation mit dem GKSS Forschungszentrum, Geesthacht, wurde die dort entwickelte Kapillarzonenelektrophorese-ICP-MS-Kopplung für die Anwendung auf komplexe biologische Proben optimiert. Auftretende Probleme der Vergleichbarkeit von Signalen durch Variationen der Migrationszeiten wurden durch eine rechnerische Anpassung der Zeitachsen mittels mitlaufender Markersubstanzen gelöst. Zusätzliche Informationen wurden durch die Hintereinanderschaltung unterschiedlicher Trennmethoden erhalten. Die Betrachtung der Elementprofile von verschiedenen Organen bestätigte die Hypothese, daß unterschiedliche Organe mit auf unterschiedliche Aufgaben spezialisierten Zellarten auch verschiedene Metalloprotein-Zusammensetzungen aufweisen. Die Verteilung der proteingebundenen Elemente in einem Organ von verschiedenen Patienten zeigte ebenfalls deutliche, auf unterschiedliche pathologische Prozesse zurückzuführende Unterschiede. Um die in der Literatur uneinheitlichen Angaben zur Metallbeladung von MT-3 zu klären, wurde in einem Projekt am Center for Biochemical and Biophysical Sciences and Medicine an der Harvard Medical School, Boston, natives MT-3 aus Schweinegehirn untersucht. Es zeigte sich, daß im Cytosol mehrere, nicht trennbare Formen von MT-3 existieren, wobei es sich wahrscheinlich um unterschiedliche Metallbeladungen handelt, welche auch vom individuellen physiologischen Zustand abhängen. Die Betrachtung eines größeren Probenkollektives ist demnach aussagekräftiger. Bei der Untersuchung eines Kollektives von AD- und Kontroll-Gehirnproben wurde ein signifikanter Unterschied von Elementgehalten in weißer und grauer Masse, jedoch nicht zwischen AD- und Kontrollproben gefunden. Das Hauptaugenmerk lag auf dem Vergleich der MT-Signale in den Elementprofilen, wobei die wenig variierenden Signale der Superoxid Dismutase sich als nützlicher Bezugspunkt erwiesen. Die MT-Metall-Signale waren bei den AD-Proben niedriger. Es zeigte sich jedoch, daß der größere Anteil an oxidierten MT im Cytosol der AD-Gehirne ein entscheidenderer Unterschied zu den Kontrollen war. Dies wies auf verstärkte oxidative Prozesse im Laufe der Erkrankung hin

A Monocular Pointing Pose Estimator for Gestural Instruction of a Mobile Robot

We present an important aspect of our human-robot communication interface which is being developed in the context of our long-term research framework PERSES dealing with highly interactive mobile companion robots. Based on a multi-modal people detection and tracking system, we present a hierarchical neural architec- ture that estimates a target point at the floor indicated by a pointing pose, thus enabling a user to navigate a mo- bile robot to a specific target position in his local surroundings by means of pointing. In this context, we were especially interested in determining whether it is possible to accomplish such a target point estimator using only monocular images of low-cost cameras. The estimator has been implemented and experimentally investigated on our mobile robotic assistant HOROS. Although only monocular image data of relatively poor quality were util- ized, the estimator accomplishes a good estimation performance, achieving an accuracy better than that of a hu- man viewer on the same data. The achieved recognition results demonstrate that it is in fact possible to realize a user-independent pointing direction estimation using monocular images only, but further efforts are necessary to improve the robustness of this approach for everyday application