Towards a future regulatory framework for chemicals in the European Union - Chemicals 2.0

The body of EU chemicals legislation has evolved since the 1960s, producing the largest knowledge base on chemicals worldwide. Like any evolving system, however, it has become increasingly diverse and complex, resulting in inefficiencies and potential inconsistencies. In the light of the EU Chemicals Strategy for Sustainability, it is therefore timely and reasonable to consider how aspects of the system could be simplified and streamlined, without losing the hard-earned benefits to human health and the environment. In this commentary, we propose a conceptual framework that could be the basis of Chemicals 2.0 - a future safety assessment and management approach that is based on the application of New Approach Methodologies (NAMs), mechanistic reasoning and cost-benefit considerations. Chemicals 2.0 is designed to be a more efficient and more effective approach for assessing chemicals, and to comply with the EU goal to completely replace animal testing, in line with Directive 2010/63/EU. We propose five design criteria for Chemicals 2.0 to define what the future system should achieve. The approach is centered on a classification matrix in which NAMs for toxicodynamics and toxicokinetics are used to classify chemicals according to their level of concern. An important principle is the need to ensure an equivalent, or higher, protection level.

A scheme to evaluate structural alerts to predict toxicity - Assessing confidence by characterising uncertainties

Structure-activity relationships (SARs) in toxicology have enabled the formation of structural rules which, when coded as structural alerts, are essential tools in in silico toxicology. Whilst other in silico methods have approaches for their evaluation, there is no formal process to assess the confidence that may be associated with a structural alert. This investigation proposes twelve criteria to assess the uncertainty associated with structural alerts, allowing for an assessment of confidence. The criteria are based around the stated purpose, description of the chemistry, toxicology and mechanism, performance and coverage, as well as corroborating and supporting evidence of the alert. Alerts can be given a confidence assessment and score, enabling the identification of areas where more information may be beneficial. The scheme to evaluate structural alerts was placed in the context of various use cases for industrial and regulatory applications. The analysis of alerts, and consideration of the evaluation scheme, identifies the different characteristics an alert may have, such as being highly specific or generic. These characteristics may determine when an alert can be used for specific uses such as identification of analogues for read-across or hazard identification.

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Structural alerts are useful tools for predictive toxicology.

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12 criteria to evaluate structural alerts have been identified.

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A strategy to determine confidence of structural alerts is presented.

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Different use cases require different characteristics of structural alerts.

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A Scheme to Evaluate Structural Alerts to Predict Toxicity – Assessing Confidence By Characterising Uncertainties.

Probabilistic modelling of developmental neurotoxicity based on a simplified adverse outcome pathway network

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A developmental neurotoxicity Adverse Outcome Pathway network was simplified.

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Common key events were chosen based on topology analysis and expert judgement.

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Quantification of causal relationships was informed by key event relationships.

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Various types of information were integrated for probability prediction.

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Bayesian hierarchical modelling was applied for hazard identification.

In a century where toxicology and chemical risk assessment are embracing alternative methods to animal testing, there is an opportunity to understand the causal factors of neurodevelopmental disorders such as learning and memory disabilities in children, as a foundation to predict adverse effects. New testing paradigms, along with the advances in probabilistic modelling, can help with the formulation of mechanistically-driven hypotheses on how exposure to environmental chemicals could potentially lead to developmental neurotoxicity (DNT). This investigation aimed to develop a Bayesian hierarchical model of a simplified AOP network for DNT. The model predicted the probability that a compound induces each of three selected common key events (CKEs) of the simplified AOP network and the adverse outcome (AO) of DNT, taking into account correlations and causal relations informed by the key event relationships (KERs). A dataset of 88 compounds representing pharmaceuticals, industrial chemicals and pesticides was compiled including physicochemical properties as well as in silico and in vitro information. The Bayesian model was able to predict DNT potential with an accuracy of 76%, classifying the compounds into low, medium or high probability classes. The modelling workflow achieved three further goals: it dealt with missing values; accommodated unbalanced and correlated data; and followed the structure of a directed acyclic graph (DAG) to simulate the simplified AOP network. Overall, the model demonstrated the utility of Bayesian hierarchical modelling for the development of quantitative AOP (qAOP) models and for informing the use of new approach methodologies (NAMs) in chemical risk assessment.

A matter of trust: Learning lessons about causality will make qAOPs credible

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Quantitative AOPs are toxicodynamic models based on Adverse Outcome Pathways.

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Mathematical models, e.g. Bayesian networks, can inform the quantification of AOPs.

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Model credibility is enhanced by applying the principles of causality theory.

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A causal diagram is illustrated for an AOP for Parkinsonian motor deficits.

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Computational resources to help assess causality are listed.

Toxicology in the 21st Century has seen a shift from chemical risk assessment based on traditional animal tests, identifying apical endpoints and doses that are “safe”, to the prospect of Next Generation Risk Assessment based on non-animal methods. Increasingly, large and high throughput in vitro datasets are being generated and exploited to develop computational models. This is accompanied by an increased use of machine learning approaches in the model building process. A potential problem, however, is that such models, while robust and predictive, may still lack credibility from the perspective of the end-user. In this commentary, we argue that the science of causal inference and reasoning, as proposed by Judea Pearl, will facilitate the development, use and acceptance of quantitative AOP models. Our hope is that by importing established concepts of causality from outside the field of toxicology, we can be “constructively disruptive” to the current toxicological paradigm, using the “Causal Revolution” to bring about a “Toxicological Revolution” more rapidly.

In Silico Models for Predicting Acute Systemic Toxicity

In this chapter, we give a brief overview of the regulatory requirements for acute systemic toxicity information in the European Union, and we review structure-based computational models that are available and potentially useful in the assessment of acute systemic toxicity. Emphasis is placed on quantitative structure-activity relationship (QSAR) models implemented by means of a range of software tools. The most recently published literature models for acute systemic toxicity are also discussed, and perspectives for future developments in this field are offered.

COSMOS next generation - A public knowledge base leveraging chemical and biological data to support the regulatory assessment of chemicals

The COSMOS Database (DB) was originally established to provide reliable data for cosmetics-related chemicals within the COSMOS Project funded as part of the SEURAT-1 Research Initiative. The database has subsequently been maintained and developed further into COSMOS Next Generation (NG), a combination of database and in silico tools, essential components of a knowledge base. COSMOS DB provided a cosmetics inventory as well as other regulatory inventories, accompanied by assessment results and in vitro and in vivo toxicity data. In addition to data content curation, much effort was dedicated to data governance - data authorisation, characterisation of quality, documentation of meta information, and control of data use. Through this effort, COSMOS DB was able to merge and fuse data of various types from different sources. Building on the previous effort, the COSMOS Minimum Inclusion (MINIS) criteria for a toxicity database were further expanded to quantify the reliability of studies. COSMOS NG features multiple fingerprints for analysing structure similarity, and new tools to calculate molecular properties and screen chemicals with endpoint-related public profilers, such as DNA and protein binders, liver alerts and genotoxic alerts. The publicly available COSMOS NG enables users to compile information and execute analyses such as category formation and read-across. This paper provides a step-by-step guided workflow for a simple read-across case, starting from a target structure and culminating in an estimation of a NOAEL confidence interval. Given its strong technical foundation, inclusion of quality-reviewed data, and provision of tools designed to facilitate communication between users, COSMOS NG is a first step towards building a toxicological knowledge hub leveraging many public data systems for chemical safety evaluation. We continue to monitor the feedback from the user community at support@mn-am.com.

The role of validation in establishing the scientific credibility of predictive toxicology approaches intended for regulatory application

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The role of validation in establishing the credibility of predictive methods is discussed.

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Various assessment frameworks for predictive methods have evolved independently, being developed by different communities.

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A set of seven credibility factors is proposed as a method-agnostic means of comparing the various assessment frameworks.

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It is hoped this will facilitate communication and cross-disciplinary collaboration between method developers and users.

New approaches in toxicology based on in vitro methods and computational modelling offer considerable potential to improve the efficiency and effectiveness of chemical hazard and risk assessment in a variety of regulatory contexts. However, this presents challenges both for developers and regulatory assessors because often these two communities do not share the same level of confidence in a new approach. To address this challenge, various assessment frameworks have been developed over the past 20 years with the aim of creating harmonised and systematic approaches for evaluating new methods. These frameworks typically focus on specific methodologies and technologies, which has proven useful for establishing the validity and credibility of individual methods. However, given the increasing need to compare methods and combine their use in integrated assessment strategies, the multiplicity of frameworks is arguably becoming a barrier to their acceptance. In this commentary, we explore the concepts of model validity and credibility, and we illustrate how a set of seven credibility factors provides a method-agnostic means of comparing different kinds of predictive toxicology approaches. It is hoped that this will facilitate communication and cross-disciplinarity among method developers and users, with the ultimate aim of increasing the acceptance and use of predictive approaches in toxicology.

An Evaluation of the Proposed OECD Testing Strategy for Skin Corrosion

The use of testing strategies which incorporate a range of alternative methods and which use animals only as a last resort is widely considered to provide a reliable way of predicting chemical toxicity while minimising animal testing. The widespread concern over the severity of the Draize rabbit test for assessing skin irritation and corrosion led to the proposal of a stepwise testing strategy at an OECD workshop in January 1996. Subsequently, the proposed testing strategy was adopted, with minor modifications, by the OECD Advisory Group on Harmonization of Classification and Labelling. This article reports an evaluation of the proposed OECD testing strategy as it relates to the classification of skin corrosives. By using a set of 60 chemicals, an assessment was made of the effect of applying three steps in the strategy, taken both individually and in sequence. The results indicate that chemicals can be classified as corrosive (C) or non-corrosive (NC) with sufficient reliability by the sequential application of three alternative methods, i.e., structure-activity relationships (where available), pH measurements, and a single in vitro method (either the rat skin transcutaneous electrical resistance (TER) assay or the EPISKIN™ assay). It is concluded that the proposed OECD strategy for skin corrosion can be simplified without compromising its predictivity. For example, it does not appear necessary to measure acid/alkali reserve (buffering capacity) in addition to pH for the classification of pure chemicals.

Quantitative adverse outcome pathway (qAOP) models for toxicity prediction

The quantitative adverse outcome pathway (qAOP) concept is gaining interest due to its potential regulatory applications in chemical risk assessment. Even though an increasing number of qAOP models are being proposed as computational predictive tools, there is no framework to guide their development and assessment. As such, the objectives of this review were to: (i) analyse the definitions of qAOPs published in the scientific literature, (ii) define a set of common features of existing qAOP models derived from the published definitions, and (iii) identify and assess the existing published qAOP models and associated software tools. As a result, five probabilistic qAOPs and ten mechanistic qAOPs were evaluated against the common features. The review offers an overview of how the qAOP concept has advanced and how it can aid toxicity assessment in the future. Further efforts are required to achieve validation, harmonisation and regulatory acceptance of qAOP models.

Computational modelling for the sustainable management of chemicals

This commentary explores the contribution of computational toxicology to chemical safety assessment in the context of the broad policy challenges faced by the European Union. The state of the European Environment is considered from the perspective of chemical contributions to the burden of disease and ecosystem damage. This sets the scene for highlighting research and innovation opportunities to further develop computational approaches for assessing the human health and environmental effects of chemicals. Emphasis is placed on focus topics that are particularly relevant to the political priorities of the new European Commission. In particular, two of the six priorities are discussed - “The European Green Deal” and “A Europe fit for a Digital Age”. The former includes the zero pollution ambition for a toxic-free environment, including the need to develop safe and sustainable chemicals, while the latter includes the challenges and opportunities posed by Artificial Intelligence. This commentary is based on a presentation given at the 19th meeting of The Italian Society of Toxicology (SITOX), held in Bologna, Italy, in February 2020.

Membrane transporter data to support kinetically-informed chemical risk assessment using non-animal methods: Scientific and regulatory perspectives

Humans are continuously exposed to low levels of thousands of industrial chemicals, most of which are poorly characterised in terms of their potential toxicity. The new paradigm in chemical risk assessment (CRA) aims to rely on animal-free testing, with kinetics being a key determinant of toxicity when moving from traditional animal studies to integrated in vitro-in silico approaches. In a kinetically informed CRA, membrane transporters, which have been intensively studied during drug development, are an essential piece of information. However, how existing knowledge on transporters gained in the drug field can be applied to CRA is not yet fully understood. This review outlines the opportunities, challenges and existing tools for investigating chemical-transporter interactions in kinetically informed CRA without animal studies. Various environmental chemicals acting as substrates, inhibitors or modulators of transporter activity or expression have been shown to impact TK, just as drugs do. However, because pollutant concentrations are often lower in humans than drugs and because exposure levels and internal chemical doses are not usually known in contrast to drugs, new approaches are required to translate transporter data and reasoning from the drug sector to CRA. Here, the generation of in vitro chemical-transporter interaction data and the development of transporter databases and classification systems trained on chemical datasets (and not only drugs) are proposed. Furtheremore, improving the use of human biomonitoring data to evaluate the in vitro-in silico transporter-related predicted values and developing means to assess uncertainties could also lead to increase confidence of scientists and regulators in animal-free CRA. Finally, a systematic characterisation of the transportome (quantitative monitoring of transporter abundance, activity and maintenance over time) would reinforce confidence in the use of experimental transporter/barrier systems as well as in established cell-based toxicological assays currently used for CRA.

Unlocking the potential of in silico chemical safety assessment - A report on a cross-sector symposium on current opportunities and future challenges

In silico chemical safety assessment can support the evaluation of hazard and risk following potential exposure to a substance. A symposium identified a number of opportunities and challenges to implement in silico methods, such as quantitative structure-activity relationships (QSARs) and read-across, to assess the potential harm of a substance in a variety of exposure scenarios, e.g. pharmaceuticals, personal care products, and industrial chemicals. To initiate the process of in silico safety assessment, clear and unambiguous problem formulation is required to provide the context for these methods. These approaches must be built on data of defined quality, while acknowledging the possibility of novel data resources tapping into on-going progress with data sharing. Models need to be developed that cover appropriate toxicity and kinetic endpoints, and that are documented appropriately with defined uncertainties. The application and implementation of in silico models in chemical safety requires a flexible technological framework that enables the integration of multiple strands of data and evidence. The findings of the symposium allowed for the identification of priorities to progress in silico chemical safety assessment towards the animal-free assessment of chemicals.

The Principles of Validation and the ECVAM Validation Process

The validation of a test method is the process by which the relevance and reliability of the method are assessed for a particular purpose. It is an essential stage in the evolution of the method from its development to its acceptance and application for regulatory purposes. The principles according to which alternative tests should be validated have been agreed upon at an international level, although the actual process by which the validation process is conducted varies between different validation authorities. This paper summarises the principles of alternative test development and validation and describes how the principles have been applied to the validation of in vitro tests by the European Centre for the Validation of Alternative Methods (ECVAM).

Report of the Workshop on the Validation of QSARs and Other Computational Prediction Models

This workshop addressed current issues with regard to establishing a framework for the validation of quantitative structure–activity relationships (QSARs) and other computational prediction models. QSARs and related models attempt to associate physicochemical and structural properties of compounds to their biological activity. As such, they may permit the prediction of biological activity from chemical structure alone. As yet, no formal validation criteria have been agreed internationally for QSARs and related techniques. However, some general and preliminary criteria were discussed and agreed upon at a European Chemical Industry Council/International Council of Chemical Associations (CEFIC/ICCA) workshop on the regulatory acceptance of QSARs, held in Setubal, Portugal, in March 2002. These criteria were presented at a Fourth World Congress workshop, along with a proposal for the practical validation of computer models, such as QSARs.

Capturing the applicability of in vitro-in silico membrane transporter data in chemical risk assessment and biomedical research

Costs, scientific and ethical concerns related to animal tests for regulatory decision-making have stimulated the development of alternative methods. When applying alternative approaches, kinetics have been identified as a key element to consider. Membrane transporters affect the kinetic processes of absorption, distribution, metabolism and excretion (ADME) of various compounds, such as drugs or environmental chemicals. Therefore, pharmaceutical scientists have intensively studied transporters impacting drug efficacy and safety. Besides pharmacokinetics, transporters are considered as major determinant of toxicokinetics, potentially representing an essential piece of information in chemical risk assessment. To capture the applicability of transporter data for kinetic-based risk assessment in non-pharmaceutical sectors, the EU Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM) created a survey with a view of identifying the improvements needed when using in vitro and in silico methods. Seventy-three participants, from different sectors and with various kinds of expertise, completed the survey. The results revealed that transporters are investigated mainly during drug development, but also for risk assessment purposes of food and feed contaminants, industrial chemicals, cosmetics, nanomaterials and in the context of environmental toxicology, by applying both in vitro and in silico tools. However, to rely only on alternative methods for chemical risk assessment, it is critical that the data generated by in vitro and in silico methods are scientific integer, reproducible and of high quality so that they are trusted by decision makers and used by industry. In line, the respondents identified various challenges related to the interpretation and use of transporter data from non-animal methods. Overall, it was determined that a combined mechanistically-anchored in vitro-in silico approach, validated against available human data, would gain confidence in using transporter data within an animal-free risk assessment paradigm. Finally, respondents involved primarily in fundamental research expressed lower confidence in non-animal studies to unravel complex transporter mechanisms.

Virtual Cell Based Assay simulations of intra-mitochondrial concentrations in hepatocytes and cardiomyocytes

In order to replace the use of animals in toxicity testing, there is a need to predict human in vivo toxic doses from concentrations that cause adverse effects in in vitro test systems. The virtual cell based assay (VCBA) has been developed to simulate intracellular concentrations as a function of time, and can be used to interpret in vitro concentration-response curves. In this study we refine and extend the VCBA model by including additional target-organ cell models and by simulating the fate and effects of chemicals at the organelle level. In particular, we describe the extension of the original VCBA to simulate chemical fate in liver (HepaRG) cells and cardiomyocytes (ICell cardiomyocytes), and we explore the effects of chemicals at the mitochondrial level. This includes a comparison of: a) in vitro results on cell viability and mitochondrial membrane potential (mmp) from two cell models (HepaRG cells and ICell cardiomyocytes); and b) VCBA simulations, including the cell and mitochondrial compartment, simulating the mmp for both cell types. This proof of concept study illustrates how the relationship between intra cellular, intra mitochondrial concentration, mmp and cell toxicity can be obtained by using the VCBA.

From in vitro to in vivo: Integration of the virtual cell based assay with physiologically based kinetic modelling

Physiologically based kinetic (PBK) models and the virtual cell based assay can be linked to form so called physiologically based dynamic (PBD) models. This study illustrates the development and application of a PBK model for prediction of estragole-induced DNA adduct formation and hepatotoxicity in humans. To address the hepatotoxicity, HepaRG cells were used as a surrogate for liver cells, with cell viability being used as the in vitro toxicological endpoint. Information on DNA adduct formation was taken from the literature. Since estragole induced cell damage is not directly caused by the parent compound, but by a reactive metabolite, information on the metabolic pathway was incorporated into the model. In addition, a user-friendly tool was developed by implementing the PBK/D model into a KNIME workflow. This workflow can be used to perform in vitro to in vivo extrapolation and forward as backward dosimetry in support of chemical risk assessment.

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.

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The VCBA is a mathematical model that simulates in vitro fate of chemicals and the corresponding cellular effect.

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The VCBA has been implemented in an open access web-based KNIME platform for ease of use.

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KNIME Analytics Platform can be used to provide a user-friendly graphical interface for biokinetic models.

Exploring waiving opportunities for mammalian acute systemic toxicity tests

A survey was carried out to explore opportunities for waiving mammalian acute systemic toxicity tests. We were interested in finding out whether data from a sub-acute toxicity test could be used to predict the outcome of an acute systemic toxicity test. The survey was directed at experts in the field of toxicity testing, and was carried out in the context of the upcoming 2018 final registration deadline for chemicals under the EU REACH Regulation. In addition to the survey, a retrospective data analysis of chemicals that had already been registered with the European Chemicals Agency, and for which both acute and sub-acute toxicity data were available, was carried out. This data analysis was focused on chemicals that were administered via the oral route. The answers to the questionnaire showed a willingness to adopt waiving opportunities. In addition, the responses showed that data from a sub-acute toxicity test or dose-range finding study might be useful for predicting chemicals that do not require classification for acute oral toxicity (LD50 > 2000mg/kg body weight). However, with the exception of substances that fall into the non-classified category, it is difficult to predict current acute oral toxicity categories.

Evaluation of Non-animal Methods for Assessing Skin Sensitisation Hazard: A Bayesian Value-of-Information Analysis

This paper offers a Bayesian Value-of-Information (VOI) analysis for guiding the development of non-animal testing strategies, balancing information gains from testing with the expected social gains and costs from the adoption of regulatory decisions. Testing is assumed to have value, if, and only if, the information revealed from testing triggers a welfare-improving decision on the use (or non-use) of a substance. As an illustration, our VOI model is applied to a set of five individual non-animal prediction methods used for skin sensitisation hazard assessment, seven battery combinations of these methods, and 236 sequential 2-test and 3-test strategies. Their expected values are quantified and compared to the expected value of the local lymph node assay (LLNA) as the animal method. We find that battery and sequential combinations of non-animal prediction methods reveal a significantly higher expected value than the LLNA. This holds for the entire range of prior beliefs. Furthermore, our results illustrate that the testing strategy with the highest expected value does not necessarily have to follow the order of key events in the sensitisation adverse outcome pathway (AOP).

In Silico Models for Acute Systemic Toxicity

In this chapter, we give an overview of the regulatory requirements for acute systemic toxicity information in the European Union, and we review the availability of structure-based computational models that are available and potentially useful in the assessment of acute systemic toxicity. The most recently published literature models for acute systemic toxicity are also discussed, and perspectives for future developments in this field are offered.

Validation of Computational Methods

In this chapter, we provide an overview of how (Quantitative) Structure Activity Relationships, (Q)SARs, are validated and applied for regulatory purposes. We outline how chemical categories are derived to facilitate endpoint specific read-across using tools such as the OECD QSAR Toolbox and discuss some of the current difficulties in addressing the residual uncertainties of read-across. Finally we put forward a perspective of how non-testing approaches may evolve in light of the advances in new and emerging technologies and how these fit within the Adverse Outcome Pathway (AOP) framework.

A Tutorial for Analysing the Cost-effectiveness of Alternative Methods for Assessing Chemical Toxicity: The Case of Acute Oral Toxicity Prediction

Compared with traditional animal methods for toxicity testing, in vitro and in silico methods are widely considered to permit a more cost-effective assessment of chemicals. However, how to assess the cost-effectiveness of alternative methods has remained unclear. This paper offers a user-oriented tutorial for applying cost-effectiveness analysis (CEA) to alternative (non-animal) methods. The purpose is to illustrate how CEA facilitates the identification of the alternative method, or the combination of methods, that offers the highest information gain per unit of cost. We illustrate how information gains and costs of single methods and method combinations can be assessed. By using acute oral toxicity as an example, we apply CEA to a set of four in silico methods (ToxSuite, TOPKAT, TEST, ADMET Predictor), one in vitro method (the 3T3 Neutral Red Uptake cytotoxicity assay), and various combinations of these methods. Our results underline that in silico tools are more cost-effective than the in vitro test. Battery combinations of alternative methods, however, do not necessarily outperform single methods, because additional information gains from the battery are easily outweighed by additional costs.

Role of in silico genotoxicity tools in the regulatory assessment of pharmaceutical impurities

The toxicological assessment of genotoxic impurities is important in the regulatory framework for pharmaceuticals. In this context, the application of promising computational methods (e.g. Quantitative Structure-Activity Relationships (QSARs), Structure-Activity Relationships (SARs) and/or expert systems) for the evaluation of genotoxicity is needed, especially when very limited information on impurities is available. To gain an overview of how computational methods are used internationally in the regulatory assessment of pharmaceutical impurities, the current regulatory documents were reviewed. The software recommended in the guidelines (e.g. MCASE, MC4PC, Derek for Windows) or used practically by various regulatory agencies (e.g. US Food and Drug Administration, US and Danish Environmental Protection Agencies), as well as other existing programs were analysed. Both statistically based and knowledge-based (expert system) tools were analysed. The overall conclusions on the available in silico tools for genotoxicity and carcinogenicity prediction are quite optimistic, and the regulatory application of QSAR methods is constantly growing. For regulatory purposes, it is recommended that predictions of genotoxicity/carcinogenicity should be based on a battery of models, combining high-sensitivity models (low rate of false negatives) with high-specificity ones (low rate of false positives) and in vitro assays in an integrated manner.

An integrated approach for bioaccumulation assessment in mussels: towards the development of Environmental Quality Standards for biota

The possible use of chemical concentrations measured in mussels (Mytillus galloprovincialis) for compliance checking against Environmental Quality Standards (EQS) established for biota is analyzed with the help of an integrated model. The model consists of a 3D planktonic module that provides biomasses in the different compartments, i.e., phytoplankton, zooplankton and bacteria; a 3D fate module that provides the concentrations of contaminants in the water column and in the sediments; and a 3D bioaccumulation module that calculates internal concentrations in relevant biotic compartments. These modules feed a 0D growth and bioaccumulation module for mussels, based on the Dynamic Energy Budget (DEB) approach. The integrated model has been applied to study the bioaccumulation of persistent organic pollutants (POPs) in the Thau lagoon (France). The model correctly predicts the concentrations of polychlorinated biphenyls (PCBs) and polychlorinated dibenzodioxins and dibenzofurans (PCDD/Fs) in mussels as a function of the concentrations in the water column and in phytoplankton. It also sheds light on the origin of the complexity associated with the use of EQS for biota and their conversion to water column concentrations. The integrated model is potentially useful for regulatory purposes, for example in the context of the European Water Framework (WFD) and Marine Strategy Framework Directives (MSFD).

Computational methods to predict the reactivity of nanoparticles through structure-property relationships

IMPORTANCE OF THE FIELD

Innovative biomedical techniques operational at the nanoscale level are being developed in therapeutics, including advanced drug delivery systems and targeted nanotherapy. Given the large number of nanoparticles that are being developed for possible biomedical use, the use of computational methods in the assessment of their properties is of key importance.

AREAS COVERED IN THIS REVIEW

Among the in silico methods, quantum mechanics is still used rarely in the study of nanostructured particles. This review provides an overview of some of the main quantum mechanics methods that are already used in the assessment of chemicals. Furthermore, classical tools used in the chemistry field are described, to show their potential also in the pharmacological field.

WHAT THE READER WILL GAIN

The current status of computational methods in terms of availability and applicability to nanoparticles, and recommendations for further research are highlighted.

TAKE HOME MESSAGE

The in silico modelling of nanoparticles can assist in targeting and filling gaps in knowledge on the effects of these particular particles. Computational models of the behaviour of nanoparticles in biological systems, including simulation models for predicting intermolecular interactions and harmful side effects, can be highly valuable in screening candidate particles for potential biomedical use in diagnostics, imaging and drug delivery.

Prediction of acute toxicity to mice by the Arithmetic Mean Toxicity (AMT) modelling approach

A modelling approach based on the structural and physicochemical similarity of chemicals to their nearest neighbours is proposed for toxicity estimation. This approach, called Arithmetic Mean Toxicity (AMT) modelling, is illustrated by means of an AMT model for predicting acute rodent toxicity. The AMT approach uses one or a few pairs of nearest structural neighbours. Each pair contains a chemical with a higher descriptor value and with a smaller descriptor value compared with the chemical of interest. Arithmetic mean toxicity values of those pairs are considered as toxicity of chemical of interest. The toxicity of the chemical of interest was not included in the development of the AMT model. The approach was applied to calculate the toxicity of chemicals to mice following intravenous injection. A toxicity data set containing 10,241 organic neutral compounds was formed from the SYMYX Toxicity database. The toxicity (log (1/LD(50)), mmol/kg), where LD(50) is the median lethal dose, of 10,227 chemicals was calculated with a standard deviation +/-0.52. A cascade AMT model was applied to estimate error values in calculations of toxicity of chemicals having different number structural neighbours and level of similarity. It was found that 7085 chemicals (about 69% of all chemicals in the data set) were calculated with a standard deviation in the interval (+/-0.33)-(+/-0.48), which is comparable to the experimental error of determination. For the remaining 3142 chemicals (about 31% of the data set), the standard deviation was +/-0.64. In the regulatory assessment of chemicals, the AMT approach could be used as a means of filling data gaps when experimental data are missing.

Toward in silico approaches for investigating the activity of nanoparticles in therapeutic development

The distinctive characteristics of nanoparticles (NPs), resulting from properties that arise at the nanoscale, are stimulating the use of these particles in the biomedical sector for diagnostic and therapeutic purposes. However, these same characteristics of NPs also underlie widespread concerns regarding potential toxic effects. Given the large number of NPs that are being developed for possible biomedical use, there is a need to develop rapid screening methods based on in silico methods. This feature review provides an overview of some of the main in silico methods that are already used in the assessment of chemicals. The current status of these methods, in terms of availability and applicability to NPs, and recommendations for further research, are highlighted.

Toxmatch--a chemical classification and activity prediction tool based on similarity measures

Chemical similarity forms the underlying basis for the development of (Quantitative) Structure-Activity Relationships ((Q)SARs), expert systems and chemical groupings. Recently a new software tool to facilitate chemical similarity calculations named Toxmatch was developed. Toxmatch encodes a number of similarity indices to help in the systematic development of chemical groupings, including endpoint specific groupings and read-across, and the comparison of model training and test sets. Two rule-based classification schemes were additionally implemented, namely: the Verhaar scheme for assigning mode of action for aquatic toxicants and the BfR rulebase for skin irritation and corrosion. In this study, a variety of different descriptor-based similarity indices were used to evaluate and compare the BfR training set with respect to its test set. The descriptors utilised in this comparison were the same as those used to derive the original BfR rules i.e. the descriptors selected were relevant for skin irritation/corrosion. The Euclidean distance index was found to be the most predictive of the indices in assessing the performance of the rules.

Toxmatch-a new software tool to aid in the development and evaluation of chemically similar groups

Chemical similarity is a widely used concept in toxicology, and is based on the hypothesis that similar compounds should have similar biological activities. This forms the underlying basis for performing read-across, forming chemical groups and developing (Quantitative) Structure-Activity Relationships ((Q)SARs). Chemical similarity is often perceived as structural similarity but in fact there are a number of other approaches that can be used to assess similarity. A systematic similarity analysis usually comprises two main steps. Firstly the chemical structures to be compared need to be characterised in terms of relevant descriptors which encode their physicochemical, topological, geometrical and/or surface properties. A second step involves a quantitative comparison of those descriptors using similarity (or dissimilarity) indices. This work outlines the use of chemical similarity principles in the formation of endpoint specific chemical groupings. Examples are provided to illustrate the development and evaluation of chemical groupings using a new software application called Toxmatch that was recently commissioned by the European Chemicals Bureau (ECB), of the European Commission's Joint Research Centre. Insights from using this software are highlighted with specific focus on the prospective application of chemical groupings under the new chemicals legislation, REACH.

Publicly-accessible QSAR software tools developed by the Joint Research Centre

To promote the availability of reliable computer-based estimation methods for use in the regulatory assessment of chemicals, the European Chemicals Bureau (ECB) within the European Commission's Joint Research Centre (JRC) has developed a range of user-friendly and freely available software tools. The article gives an overview of four of these tools, explaining their main functionalities and applicability: Toxtree, Toxmatch, DART and the JRC QSAR Model Database. Toxtree predicts different types of toxicological hazard and modes of action by applying decision tree approaches; it can be used for initial hazard assessments. Toxmatch is a tool for chemical similarity assessment; it can be used to compare model training and test sets, to facilitate the formation of chemical categories and to support the application of read-across between analogues. DART (Decision Analysis by Ranking Techniques) provides a variety of Multi-criteria Decision Making (ranking) methods, and can be used to support the ranking of chemicals according to their environmental and toxicological concern. Finally, the JRC QSAR Model Database is a web-based inventory of (Q)SAR models to help industry and government authorities to identify suitable (Q)SARs for chemicals undergoing regulatory review.

Mode of action-based classification and prediction of activity of uncouplers for the screening of chemical inventories

A new approach for classification of uncouplers of oxidative and photophosphorylation, also suitable for screening of large chemical inventories, is introduced. Earlier fragment-based approaches for this mode of toxic action are limited to phenols but weak acids of extremely diverse chemical classes can act as uncouplers. The proposed approach overcomes the limitation to phenolic uncouplers by combining structural fragments with the global information of physico-chemical descriptors. In a top-down approach to reduce the number of candidate chemicals, firstly substructure definitions for the detection of weak acids were applied. Subsequently, conservative physico-chemical thresholds for the two most important properties for the uncoupling activity were defined: an acid dissociation constant (pK(a)) between 3 and 9, and a sufficiently low energy barrier for the internal permeability of anions (17 kcal/mol). The later was derived from a novel approach to calculate the distribution of compounds across membranes. The combination of structural and physico-chemical criteria allowed a good separation of active from inactive chemicals with high sensitivity (95%) and slightly lower (more than 75%) specificity. Applying this approach to several thousand high and low production volume chemicals retrieved a surprisingly small number of 10 compounds with a predicted excess toxicity above 10. Nevertheless, uncoupling can be an important mode of action as highlighted with several examples ranging from pesticide metabolites to persistent organic compounds.

An evaluation of the implementation of the Cramer classification scheme in the Toxtree software

Risk assessment for most human health effects is based on the threshold of a toxicological effect, usually derived from animal experiments. The Threshold of Toxicological Concern (TTC) is a concept that refers to the establishment of a level of exposure for all chemicals below which there would be no appreciable risk to human health. When carefully applied, the TTC concept can provide a means of waiving testing based on knowledge of exposure limits. Two main approaches exist; the first of these is a General Threshold of Toxicological Concern; the second approach is a TTC in relation to structural information and/or toxicological data of chemicals. The structural scheme most routinely used is that of Cramer and co-workers from 1978. Recently this scheme was encoded into a software program called Toxtree, specifically commissioned by the European Chemicals Bureau (ECB). Here we evaluate two published datasets using Toxtree to demonstrate its concordance and highlight potential software modifications. The results were promising with an overall good concordance between the reported classifications and those generated by Toxtree. Further evaluation of these results highlighted a number of inconsistencies which were examined in turn and rationalised as far as possible. Improvements for Toxtree were proposed where appropriate. Notable of these is a necessity to update the lists of common food components and normal body constituents as these accounted for the majority of false classifications observed. Overall Toxtree was found to be a useful tool in facilitating the systematic evaluation of compounds through the Cramer scheme.