Evaluation of the Availability and Applicability of Computational Approaches in the Safety Assessment of Nanomaterials

Evaluation of the availability and applicability of computational approaches in the safety assessment of nanomaterials Final report of the Nanocomput project Andrew Worth, Karin Aschberger, David Asturiol Bofill, Joseph Bessems, Kirsten Gerloff, Rabea Graepel, Elisabeth Joossens, Lara Lamon, Taina Palosaari, Andrea Richarz 2017 EUR 28617 EN This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It aims to provide evidence-based scientific support to the European policymaking process. The scientific output expressed does not imply a policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of this publication. Contact information Name: Andrew Worth Address: Via Enrico Fermi 2749, 21027 Ispra (VA), Italy Email: [email protected] Tel.: +39 0332 78 9556 JRC Science Hub https://ec.europa.eu/jrc JRC106386 EUR 28617 EN PDF ISBN 978-92-79-68708-2 ISSN 1831-9424 doi:10.2760/248139 Print ISBN 978-92-79-68709-9 ISSN 1018-5593 doi:10.2760/911484 Luxembourg: Publications Office of the European Union, 2017 © European Union, 2017 The reuse of the document is authorised, provided the source is acknowledged and the original meaning or message of the texts are not distorted. The European Commission shall not be held liable for any consequences stemming from the reuse. How to cite this report: Worth, A., Aschberger, K., Asturiol Bofill, D., Bessems, J., Gerloff, K., Graepel, R., Joossens, E., Lamon, L., Palosaari, T. and Richarz, A., Evaluation of the availability and applicability of computational approaches in the safety assessment of nanomaterials, EUR 28617 EN, Publications Office of the European Union, Luxembourg, 2017, ISBN 978-92-79- 68708-2, doi:10.2760/248139, JRC106386. All images © European Union 2017, except: Page 11, ECHA, Figure 1.1, 2009. Source: European Chemicals Agency (ECHA), Helsinki Page 252, ECHA, Figure 4.2, 2016. Source: European Chemicals Agency (ECHA), Helsinki Page 278, Eric Wieser, Figure 4.6, 2010. Source: Wikimedia Commons [CC BY-SA 3.0] Executive Summary 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). Background information The first part of this report (Chapters 0-2) provides background information. Chapter 0 provides the terms of reference of Nanocomput, and is intended to orient the reader, linking the project objectives to different chapters in this report. Chapter 1 provides the scientific background and regulatory context to the rest of the report. It identifies the properties that drive the toxicity and fate of NMs, gives an overview of standard test methods for measuring physicochemical properties and toxicity, and explains the different kinds of alternative (non-animal) approaches that are being developed for regulatory purposes. In addition, an overview of the EU regulatory framework for NMs (REACH and other pieces of legislation) is provided. Chapter 2 presents an overview of software tools (including models and databases) that are available for predicting the toxicity and fate of NMs. The emphasis is on the different kinds of (mathematical) modelling approaches being used. Practical considerations and opportunities for developing computational models are also discussed. The chapter also includes experience in the grouping of NMs for the purpose of read-across, and proposals for NM categorisation schemes. Assessment of the availability and applicability of computational methods for NMs Chapter 3 provides a systematic review of the model landscape, based on a detailed and systematic survey of the scientific literature. This includes an analysis of current status of QSPR and QSAR models. A quantitative structure-property relationship (QSPR) is a mathematical model that uses key descriptors (chemical features or physicochemical properties) to make predictions of other physicochemical properties, whereas a QSAR is a i similar type of model except that the descriptors are used to make predictions of a biological activity (such as a toxicological endpoint). In this report, QSPRs and QSARs are treated in the same way, since they both employ statistical learning methods to identify useful descriptors and/or establish the form of the correlative model between descriptors and predicted property/activity. The development of QSPRs and QSARs for NMs is still in its infancy, and been a challenge for a number of reasons. Traditionally, QSPR and QSAR modelling has applied to substances in solution, typically undissociated molecules, rather than particles. At present, relatively few theoretical descriptors are available for particles, although experimental descriptors may be useful (provided they can be measured reliably). A further complication is that particles do not typically form a homogeneous collection of species – they may undergo aggregation/agglomeration processes, adsorb and desorb macromolecules present in the surrounding medium, and may (partially) dissolve as well, leading to a distribution of masses/sizes/shapes (i.e. polydispersity). Rather than modelling a single species, it may therefore be necessary to model a distribution / mixture of species, which is increasingly difficult the more the material deviates from monodispersity. Furthermore, as with most 'classical' chemicals, the mode of toxicological action is often unknown, making it difficult to identify, a priori, the most relevant and predictive descriptors. Finally, in the case of QSARs, a lack of reliable biological data has also hindered model development. In spite of these challenges, the development of QSPRs and QSARs for NMs has been a growing area of research. The analysis of QSPR/QSAR landscape identifies the properties and endpoints that are most often predicted, the availability of datasets for modelling, the descriptors (properties) that are most often used as predictors, as well as the statistical techniques most often applied. A detailed review of the literature identified 44 QSPRs (with solubility being the most frequently modelled endpoint) and 78 QSARs (with in vitro cytotoxicity endpoints being the most frequently modelled). It is concluded that while many of the QSPRs may be relevant for filling data gaps under REACH, very few QSARs directly predict a REACH endpoint. Some QSARs predict generic biological "endpoints", for example based on the integration of readouts from multiple in vitro methods. Thus, in general, QSARs are more likely to be useful for prioritising chemicals of concern, and for supporting read- across arguments, rather than for directly filling data gaps. The QSAR Model Reporting Format (QMRF), as a tool for documenting and reporting QSPR/QSARs, was found to be useful, but requires a few additional fields to capture relevant particle properties. Chapter 3 also includes an analysis of the current status of compartment-based models, including TK and TD models, in vitro and in vivo dosimetry models, and environmental fate models. TK models simulate the time-dependent concentration of particles (including NMs) in one or more biological compartments of an organism. These include physiologically based kinetic (PBK) models that are based on physiologically relevant compartments and processes, as well as classical toxicokinetic (CTK) models that simulate key ADME properties by aggregating compartments into simpler model structures. In the context of regulatory risk assessment, these models could be used to reduce uncertainties in extrapolating toxicity data (e.g. acute-to-chronic, within and between species, route-to-route), and therefore ii modify the assessment factors applied in the determination of Derived No Effect Levels (DNELs). The literature review revealed the availability of 19 TK models, including 13 PBK models and 6 CTK models. These models are applicable to a total of 15 different NMs including metals, metal oxides, polymeric and carbon-based nanomaterials, with metal NMs being the most commonly modelled materials (10 out of 19 models). The PBK models are of varying complexity (from

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