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2 Regulatory Toxicology and Pharmacology 68 (2014) Contents lists available at ScienceDirect Regulatory Toxicology and Pharmacology journal homepage: Establishing the level of safety concern for chemicals in food without the need for toxicity testing Benoît Schilter a, Romualdo Benigni b, Alan Boobis c, Alessandro Chiodini d,, Andrew Cockburn e, Mark T.D. Cronin f, Elena Lo Piparo a, Sandeep Modi g,1, Anette Thiel h, Andrew Worth i a Nestlé Research Centre, Vers-Chez-Les-Blanc, Lausanne, Switzerland b Istituto Superiore di Sanità, Rome, Italy c Imperial College London, London, United Kingdom d ILSI Europe, Brussels, Belgium e University of Newcastle, Newcastle, United Kingdom f Liverpool John Moores University, Liverpool, United Kingdom g Unilever, Bedford, United Kingdom h DSM Nutritional Products, Kaiseraugst, Switzerland i European Commission Joint Research Centre, Institute for Health & Consumer Protection, Ispra, Italy article info abstract Article history: Received 17 May 2013 Available online 5 September 2013 Keywords: (Q)SAR Read across Chemical grouping In silico toxicology Predictive toxicology Risk assessment Uncertainties Food Incident management There is demand for methodologies to establish levels of safety concern associated with dietary exposures to chemicals for which no toxicological data are available. In such situations, the application of in silico methods appears promising. To make safety statement requires quantitative predictions of toxicological reference points such as no observed adverse effect level and carcinogenic potency for DNAreacting chemicals. A decision tree (DT) has been developed to aid integrating exposure information and predicted toxicological reference points obtained with quantitative structure activity relationship ((Q)SAR) and read across techniques. The predicted toxicological values are compared with exposure to obtain margins of exposure (MoE). The size of the MoE defines the level of safety concern and should account for a number of uncertainties such as the classical interspecies and inter-individual variability as well as others determined on a case by case basis. An analysis of the uncertainties of in silico approaches together with results from case studies suggest that establishing safety concern based on application of the DT is unlikely to be significantly more uncertain than based on experimental data. The DT makes a full use of all data available, ensuring an adequate degree of conservatism. It can be used when fast decision making is required. Ó 2013 ILSI Europe. Published by Elsevier Inc. All rights reserved. 1. Introduction Corresponding author. Address: ILSI Europe a.i.s.b.l., Avenue E. Mounier 83, Box 6, 1200 Brussels, Belgium. Fax: address: publications@ilsieurope.be (A. Chiodini). 1 Now employed at: TPM TCS, 40 Beaconsfield, Luton LU2 0RW, United Kingdom. It has been estimated that over five million man-made chemicals are known, of which are in use today (Fowler et al., 2011). The application of continuously improving analytical methods has revealed that many of these chemicals can enter the food chain and result in measurable human exposure. Since for the vast majority of these chemicals, toxicological information is absent or limited, the assessment of their health significance is therefore difficult. This is illustrated by the issue of ink components, from packaging materials, which can migrate into food. For example, isopropyl thioxantone (ITX), a photoinitiator of UV-cured inks was found to migrate at unexpectedly high levels in food products (EFSA, 2005a). Since only very limited toxicological data were available, agreement on the level of safety concern was difficult to achieve and consequently the development of management options was challenging. It is now considered that about 5000 nonevaluated ink components are currently used in Europe (Eupia, 2011). Therefore issues similar to ITX will most certainly emerge again and again. Other sources of emerging chemical safety issues are process-related chemicals. For example the EU-sponsored project HEATOX identified about 800 chemicals potentially resulting from heat treatment of food (Cotterill et al., 2008). From a toxicological perspective, most of these chemicals are not characterized. Safety concern may be also raised regarding the presence of low /$ - see front matter Ó 2013 ILSI Europe. Published by Elsevier Inc. All rights reserved.

3 276 B. Schilter et al. / Regulatory Toxicology and Pharmacology 68 (2014) levels of impurities or side products in for example synthetic additives or functional ingredients and other examples may be taken from the research and development (R&D) field. It appears likely that the food sector will increasingly face cases of emerging issues associated with chemicals for which no or little toxicological data are available. Because such cases may evolve into crisis, triggering not only heavy management actions (e.g., public recalls) but also alarm resulting in loss of consumer confidence for the food supply chain, emerging chemical issues require to be managed properly. As a pre-requisite, management of chemicals in food requires the possibility to establish the level of safety concern in order to ensure adequate consumer protection without undue over-conservatism. This is necessary even if toxicological data are inadequate or absent. Solutions to this general issue are not straightforward. Obviously the generation of new experimental toxicology data is not a practical tool to deal with situations requiring fast decision-making. Furthermore, even if sufficient facilities to perform toxicological testing within a relevant time frame were available, it still can be questioned whether testing every substance would be a rational and practical approach. The challenge is to develop alternative strategies to identify chemicals of safety concern in the absence of specific toxicological data, and to prioritize the need for toxicity testing. In this context, in silico predictive models have obvious advantage in terms of time, cost and also animal protection (JRC, 2010; Benfenati et al., 2009). In silico strategies are already proactively and successfully used for preclinical screening in pharmaceutical discovery pipelines where an early identification of toxicological hazard is a clear competitive advantage allowing the exclusion of chemicals which could potentially produce unacceptable adverse effects in further regulatory toxicology tests (Benfenati et al., 2009). The situation of the food industry is different. The most likely application of computational toxicology models would be in the establishment of the level of safety concern associated with the inadvertent/accidental presence of chemicals in products. This requires not only qualitative information on the potential hazardous properties of the chemical (e.g., carcinogenicity, hepatotoxicity) but also quantitative information (e.g., carcinogenic potency, No Observed Adverse Effect Level) allowing a comparison with estimated exposure. Another significant difference with the pharmaceutical industry is the fact that chemicals found in foods present a high degree of structural diversity and that in general chemical space (which contains and defines all possible molecules) is wider. In a recent review (JRC, 2010), a number of computational toxicology tools have been identified to be potentially applicable to the food context. In the present initiative, a decision tree (DT) has been designed as a structured approach to integrate exposure information and toxicological prediction values obtained with the available computational tools. Its application should enable optimization of the use of any information and data thought to be relevant in determining the level of human safety concern associated with exposure to food-borne chemicals for which no or very limited toxicological data is available. The application of the DT should be seen as a practical aid to provide toxicological advice within a relatively fast timeframe as determined by problem formulation. The outputs of the DT are expected to be communicated to risk managers in a pragmatic and actionable format aiming at rapid decision making about development and implementation of action plans. This implies the provision of quantitative elements of risk together with levels of confidence based on an analysis of uncertainties and the quality of the data used. Up to now, the threshold of toxicological concern (TTC) concept has been considered as the key assessment tool to handle situations where no toxicological data is available. Guidelines on the application of the TTC concept have been developed (Kroes et al., 2004). The approach described in the present manuscript is not intended to replace previous well recognized methods such as the TTC. The present DT is a tool which is applicable independently or in support of the TTC. The main improvements brought by the DT over the TTC are the ability to deal with larger range of exposures, to exploit better all data available and to provide a better insight on the domain of applicability of the tools used. 2. Computational methods Computational toxicology, also called in silico toxicology, is based on scientific knowledge gained from different scientific fields and on the premise that the toxicity of a chemical, depending on its intrinsic nature, can be predicted from its molecular structure, and inferred from the properties of similar compounds whose activities are known. It is a broad and rapidly developing discipline that relies on a diverse range of methods, including techniques that can be used to develop models from complex toxicological datasets, molecular modeling approaches for the study of intermolecular interactions, as well as the application of existing ( off-the-shelf ) predictive models and grouping read across approaches based on chemical structure. For the purposes of this report, the emphasis is on methods that can be used in a shorttime frame (days) in order to support a chemical safety assessment. Thus, research tools requiring considerable time and specific experimental data to be applicable (e.g., PBPK modeling), are considered out of scope. The main approaches applicable here are therefore those limited to the use of chemical structure information, which can be separated into QSAR models/tools and approaches for grouping chemicals according to some measure of similarity and the application of read across between analogs in the group (Quantitative) structure activity relationships Structure activity relationships (SARs) and quantitative structure activity relationships (QSARs), sometimes collectively referred to as (Q)SARs, are theoretical models that relate the structure of chemicals to their biologic activities. More specifically, (Q)SARs are quantitative relationship models between the chemical structures of compounds and a given property, such as physicochemical, biological (e.g., toxicological) and fate properties (Cronin, 2010), while a SAR is a qualitative relationship between a molecular (sub)structure and the presence or absence of a given biological activity. The term substructure refers to an atom, or group of adjacently connected atoms, in a molecule. An SAR is usually based on expert knowledge that a particular toxicological effect is brought about by a functional group or substructure in a molecule. SARs often form the basis for structural alerts, that relates a fragment or particular sub-structure of a molecule to a particular activity or functionality. This, therefore, makes them amenable to the prediction of categoric (yes/no) toxicity endpoints or allows them to be used for forming categories. To achieve this, fragments within a molecule that are associated with a toxic endpoint must be identified. This is normally assumed to be from human expert judgment, thus a toxicologist may have the knowledge that a certain fragment is associated with a toxic effect. For instance, aromatic amine is a structural alert for mutagenicity (Cronin, 2011). A mitigating factor is an additional structural feature present within a molecule that removes the toxic effect. Examples include a bulky substituent near a reactive functional group that reduces reactivity by steric hindrance or a sulfate substituent on aromatic amines which removes mutagenic activity, probably as a result of increased water solubility (Enoch and Cronin, 2010).

4 B. Schilter et al. / Regulatory Toxicology and Pharmacology 68 (2014) SARs constitute an approach to develop computational models for toxicity. In particular they provide fragments associated with organ level and age-dependent toxicity, as well as mechanism/ mode of action. Ideally an SAR will have been developed from a known and recognizable mechanism/mode of action. This should be well documented and will provide confidence in the use of an SAR. Recently, there have been developments to formalize SARs into what is being termed the adverse outcome pathway (AOP). The AOP provides an opportunity to link together the chemical structural domain to the series of mechanistically based events leading to an adverse effect at the organism or population level (Schultz, 2010; Ankley et al., 2010). SARs have been historically reported in the literature. A number of computational tools have been developed that formalize and implement these relationships into systems that can make predictions from structure (Cronin, 2010). Examples of s available to predict various types of toxicities are provided in Table 1 of the appendix. As noted with structural alerts, it must be remembered that these predictions are identifiers of hazard. To make assessments of risk, toxicological data has to be combined with exposure estimate(s). This requires the quantitative prediction of toxicity endpoints relevant for risk assessment such as chronic toxicity or carcinogenicity potency. There is an increasing number of QSAR models for chronic mammalian toxicity (usually to rodent species) these and appropriate have been reviewed (Lapenna et al., 2010). In terms of chronic animal toxicity, the lowest observed adverse effect level (LOAEL) has been modeled usually. For example a predictive in silico study of more than 400 compounds based on two-dimensional chemical descriptors and multivariate analysis was reported (Mazzatorta et al., 2008). The analysis used a highly homogenous LOAEL data set restricted to chronic, oral rat studies. The analysis of the model revealed that the bioavailability of the compound drives chronic toxicity effects, constituting a baseline effect where additional toxicity is possibly described by a few specific chemical moieties. Similarly, TOPKAT (TOxicity Prediction by Komputer Assisted Technology) is a commercially available QSAR-based model to predict rat chronic toxicity (Venkatapathy et al., 2004). Further QSAR models for chronic toxicity are available from MolCode ( through the Molcode Toolbox. These may be useful, however exact documentation and description is currently lacking. When considering pharmaceuticals, human chronic effect prediction has been addressed through the modeling of the maximum recommended therapeutic dose (MRTD). This is an estimated upper dose limit beyond which a drug s efficacy is not increased and/or undesirable adverse effects begin to outweigh beneficial effects (Matthews et al., 2004a,b; Contrera et al., 2004). The MRTD is empirically derived from human clinical trials and is a direct measure of the dose-related effects of pharmaceuticals in humans. These data are of particular interest because they refer to effects in humans of biologically active molecules and are likely to represent surrogate human LOAELs. It has been considered that the application of a factor to 10 to the MRTD would correspond to a no observed effects level in human (Matthews et al., 2004a,b; Contrera et al., 2004). Lazar (LAZy structure Activity Relationships) is a freely available system to predict the MRTD ( It is based on a similarity searching methodology based on molecular fragments (Maunz and Helma, 2008). Regarding carcinogenicity, most efforts have focused on the quantitative prediction of the carcinogenic potency such as the TD 50 (dose that caused cancer in 50% of the animals in a carcinogenicity bioassay). A QSAR model for predicting the TD 50 values, only for the class of aromatic amines, has been described (Benigni et al., 2007a). The expert system Oncologic, by the US EPA, predicts ranks of carcinogenic potency. These ranks have been shown to correlate pretty well with the TD 50 values (Benigni et al., 2012). Recent publications have provided further encouraging results showing that TD 50 could be predicted reasonably through the application of QSAR technique (Bercu et al., 2010 and Contrera, 2011). Bercu et al. proposed a step-wise approach to predicting TD 50 for compounds categorized as not potent. It is based on two MultiCASE (commercial) and VISDOM (Lilly Inc. in house package). The first one is a classification model allowing the prediction of carcinogenic potency class, while the second is a regression model predicting a numerical TD 50. This promising approach is limited by the unavailability to the public of the used. More recently Contrera (Contrera, 2011) tried to reproduce and implement the model into commercially available called SciQSAR (formally MDL-QSAR, Two models providing predictions of TD 50 based on respectively rat and mouse data extracted from the Gold database are commercially available (Simulation Plus Inc.). Recently a new module to predict Carcinogenic Potency (TD 50 ) has been implemented in TOPKAT. It was built using Partial Least Square and trained using 530 mouse and 674 rat TD 50 values from the Carcinogenic Potency Database. Two different models are available, one for rat and one for mouse. The property being modeled is the log10(td 50 ) value that it s converted to mg/kg bw/day unit. A QSAR models to predict carcinogenic potency of chemicals using oral slope factor has been published and demonstrated reasonable predictive abilities (Wang et al., 2011) Grouping and read across In addition to QSAR analysis, it is possible to estimate physicochemical, biological (e.g., toxicological) and fate properties of chemicals by using a less formalized approach based on the grouping and comparison of chemicals. The application of such an approach requires significant expert knowledge and judgment. The use of existing information for one chemical, called a source chemical, to make a prediction of the same property or toxicological endpoint for another chemical, called a target chemical, is termed read across. Analogs of the target chemical are chemicals that are similar in some way to the target. The issue is that similarity is not an absolute concept, but a relative one and thus two molecules cannot be similar in absolute terms, but only with respect to some measurable key features. Indeed there are cases where a small change in chemical structure can lead to a significant change in biological activity (the so-called similarity paradox ). This suggests that there exists no single universally appropriate similarity measure, but rather its choice depends on the particular endpoint and mode of action (when it is known). Explanatory examples are compounds that share a fragment (or structural alert) associated with a mechanism of action (e.g., an electrophilic fragment such as a nitroso group) and compounds that may be able to act by the same receptor binding mode (endocrine active compounds). The concepts of grouping and read across are further explained and illustrated by Enoch and Cronin (2010). A comprehensive guidance for applying the grouping approach has been published by OECD (2007). More recently, other systematic expert-driven processes have been proposed for read across (Wu et al., 2010; Blackburn et al., 2011; Wang et al., 2011). Grouping and read across has been used within the OECD High Production Volume Chemicals Program as an alternative for experimental testing and is currently being applied under REACH. Examples of applications of grouping read across have been reported (Wu et al., 2010; Blackburn et al., 2011). The reliability of read across depends on the selection of suitable analogs associated with reliable experimental data. In some cases, it is only possible to identify one or a limited number of suitable analogs, whereas in other cases, it is possible to build up

5 278 B. Schilter et al. / Regulatory Toxicology and Pharmacology 68 (2014) a larger and more robust chemical group, called a chemical category. The group may have one or more of the following common features: A common functional/mechanistic/structural alert group, e.g., aldehyde, epoxide, ketone, Michael acceptor, nitroamines, aromatic amines. This approach can be used to predict various types of toxicity and may allow determining sites for metabolism. This approach is quite commonly used in case of skin allergy (Enoch et al., 2008) and bacterial mutagenesis (Benigni et al., 2007b). Overall chemical similarity, e.g., based on Tanimoto coefficient. If a new substance is very similar to an existing substance, it can be assumed that minor modifications to its structure are unlikely to affect its properties for risk assessment purposes, the same hazards and potencies are used. Similar breakdown or metabolic products via physical or biological processes, which may result in structurally similar chemicals, e.g., hydrolysis of esters; oxidation of primary alcohols and aldehydes to carboxylic acids. Similar physico-chemical properties (e.g., lipophilicity, molecular weight, solubility, vapor pressure, size, etc.) as some of these properties play important role in determining the bioavailability of the chemical. Incremental change across a group, e.g., carbon chain length. It is preferred to have interpolation of results within a group, rather than extrapolation. The presence of a coherent trend in a chemical category is generally associated with a common underlying mechanism of action. In general, the application of read across between analogs in a chemical category is considered to be more reliable than the application of read across in a smaller group of analogs (in which trends are not apparent). The selection of suitable analogs for the prediction of a toxicological endpoint is not a trivial exercise. The analogs should ideally be similar on the grounds of various properties, including structure, physicochemistry as well as mechanistic chemistry and/or mode-of-action (e.g., common macromolecular target / molecular pathway), but this is not always possible due to lack of fundamental knowledge (Wu et al., 2010; Blackburn et al., 2011). Care should be taken in grouping chemicals together and in determining chemical similarity since for a given toxicity endpoint some structural features are more important than others, and these structural feature s behavior differ from one toxicity endpoint to another. Chemical similarity measures each have various pitfalls and limitations (Willet, 2006; Willet et al., 1998; Wu et al., 2010) so the choice of similarity metric needs to be considered carefully. In addition the analogs need to be associated with reliable and relevant in vivo data. It is also important to consider purity/impurity profiles for each member of the category, including their likely impact on the category endpoints. Thus, the evaluation requires both chemical and toxicological expertise. In contrast with the use of QSAR tools, the application of read across is a more time-consuming and ad hoc approach involving a range of subjective choices in terms of categorization tools, similarity metrics, datasets for the retrieval of analogs, and criteria for analog selection. A broad chemical and toxicological expertise is needed to apply this approach. In order to be reproducible, all of the expert choices need to be clearly documented. One of the advantages of read across is that it can generally be applied when suitable (scientifically valid and applicable) QSARs are not available. It can also be used to supplement the results of QSAR analysis as part of a weight-of-evidence approach. 3. Problem formulation In any risk assessment, problem formulation (involving both hazard and exposure assessment) should precede the systematic scientific evaluation (Renwick et al., 2003). It is the process by which the issues/questions and context are defined and the plan agreed upon with all stakeholders (e.g., assessors, managers,...). A clear formulation of the problem is critical for ensuring the generation of an useful and actionable assessment end product. The first step to problem formulation is the identification of the issue of concern. This might arise as consequence of incidental contamination of food products or due to the acquisition of new knowledge (e.g., new process-related contaminants, chemical characterization of new ingredients,...). This should be followed by a planning dialog that clarifies the management goals, the purpose and scope of the assessment, the time constraints and the resources available to conduct the assessment. The planning dialog is an iterative process that considers: Nature of the problem and primary risks in question. Scope and purpose of the assessment. Who should be involved in the assessment and management processes. Expertise needed. How the risk assessment will provide the information necessary to support the management decision. How the risk assessment will be communicated. What levels of resources are available. What is the timeline for completing the assessment. The process of problem formulation should ensure that all relevant concerns have been addressed, and that the assessment will yield a scientifically sound and credible risk characterization. The methods to be used to evaluate the risk should be described in an analysis plan which constitutes the final product of problem formulation. Such an analysis plan should indicate the information necessary for both scientific assessors and public health managers to be satisfied that the assessment will provide the kind and quality of information necessary to make appropriate management decisions. The application of in silico computational toxicology methods in a risk assessment must be addressed in the problem formulation step. The methods and tools used as well as their performance have to be thoroughly described. Information on the endpoints predicted and on how the data will be interpreted is important to be communicated. The capability of such methods to adequately support decision making needs to be highlighted in the problem formulation step. However, it has to be kept in mind that one of the major areas of application for in silico method is to handle emergency situations in absence of toxicological data. In such cases, thorough and formal problem formulations may not be feasible and computational methods would initially be applied to assess the possibility of major safety issue very early. 4. Development of a decision tree (DT) 4.1. General principles The DT (Fig. 1) has been developed taking into account practical experience in the application of computational toxicology methods with an effort to integrate the basic logic of classical risk assessment encompassing: (1) exposure assessment (2) hazard identification (3) hazard characterization and (4) risk characterization. In this framework, the DT can be summarized as followed:

6 B. Schilter et al. / Regulatory Toxicology and Pharmacology 68 (2014) Fig. 1. Exposure assessment (Box 1). A quantitative estimate of the actual or anticipated exposure is prerequisite for the application of the DT and therefore comes first. A quantitative exposure value is mandatory for conducting risk characterization, the last step of the process where exposure is compared with toxicological information through a margin of exposure approach. Hazard identification (Boxes 2 5). Classically it is defined as the intrinsic property of a chemical agent to produce adverse health effects. Often, hazard identification uses results from toxicity studies in animal models. In the context of the DT application, little direct toxicological data on the substance of interest is expected and therefore hazard identification focuses on the use of predicted values obtained with (Q)SAR and grouping read across methods. Read across requires proper hazard identification for selected chemical analogs of the substance of interest. Hazard characterization (Box 6). It may be defined as the determination of the relationship between the magnitude of exposure to a chemical agent and the severity and/or frequency of adverse effects. Classically, hazard characterization is the process where the most appropriate toxicological data is selected in order to either establish a safe level of human exposure (e.g., tolerable daily intake) or to provide exposure levels associated with a pre-determined excess cancer risk (e.g., 1 in 10 6 risk). Because the present work deals with situations where specific experimental toxicological data are absent or insufficient, the establishment of safe levels is not considered appropriate. In this context, hazard characterization therefore refers to the selection of the most adequate prediction values to be used in the calculation of the margin of exposure. Risk characterization (Box 7). It is classically defined as the quantitative or semi-quantitative estimate, including attendant uncertainties, of the probability of occurrence and severity of adverse effects in a given population under defined exposure conditions based on hazard identification, hazard characterization and exposure assessment. In the present context, risk characterization refers to the calculation of margins of exposure (MoE) corresponding to the ratio between relevant predicted toxicological values with exposure estimates. Proper interpretation of the size of the calculated MoEs will provide insight on the level of safety concern Exposure assessment Box 1: Is there evidence for exposure to the substance? All information regarding potential exposure must be collected, including: Chemical identity of the substance of interest (called the substance). Types and corresponding intakes of potentially contaminated foods. Actual or anticipated occurrence of the substance in relevant foods, including quantitative information on potential levels (e.g., average, highest). Populations potentially affected (e.g., age-specific). Duration of exposure. Other potential sources of exposure to the substance (e.g., inhalation). The use of the information as highlighted above should result in the building of scenarios leading to the selection of quantitative exposure values to be applied in the margin of exposure calculation conducted later in the assessment (Box 7). It is expected that in general, only fragmentary and theoretical information will be available. In the initial step, conservative assumptions may be applied to define worst-case exposure scenarios. The strength of the evidence and the uncertainties regarding all aspects of exposure should be thoroughly reviewed and described. Refinement of the exposure assessment may be triggered after the completion of the DT, such as through the generation of analytical data. The conclusion with sufficient confidence of an absence of actual exposure leads to the communication indicating no safety concern associated with this particular chemical. An example of such a possibility would be for a substance identified by extraction of packaging materials which would be proven not to migrate into the relevant food. It is recognized that absence of exposure is in general difficult to prove and depends upon the performance of the analytical method used. To conclude on the absence of exposure, it should be ensured that the limit of detection available is toxicologically adequate. In absence of toxicological information, this may require the application of the TTC or the use of predicted toxicological values. Clear evidence for exposure requires proceeding to the next step.

7 280 B. Schilter et al. / Regulatory Toxicology and Pharmacology 68 (2014) Hazard identification Box 2: Structure characterization and identification of similar compounds Structure characterization. The tools to be applied over the DT are based on deriving information directly from chemical structure. For the (Q)SAR techniques to be used, the precise definition of chemical structure is particularly important. This assumes in the first instance, that a single substance is being considered. For a single substance, an accurate representation of the chemical structure is necessary. This means that there should be no area of ambiguity. In addition, limitations associated with (Q)SAR methods need to be taken into account. Many (Q)SAR tools cannot handle certain structures such as inorganics, organometallics and macromolecules (e.g., polymers, proteins and DNA). Other approaches would be required to address such chemicals. To deal with salt or ionized compounds may not be straightforward. Many (Q)SAR tools require the neutral form of the compound to be entered. Therefore the contra-ion must be removed from the salt and the remaining ionized organic chemical needs to be neutralized when possible (e.g., not applicable in case of quaternary amines). The possible implications of such a simplification of structure may need to be considered in hazard characterization (Box 6). Is the substance a true single substance, or does it comprise a mixture of, for example, isomers (e.g., stereoisomers or tautomers) are also important questions to be raised. In case of mixture of similar isomers, each isomer should be processed through the DT, keeping in mind that most of the global QSAR models will not be able to discriminate the tiny differences, in 3D structure, between the isomers. In this context read across driven by mechanism/mode of action may have a greater impact in hazard characterization (Box 6). The minimum requirements for recording chemical structure for use in the DT will depend on the tools and techniques used. The overriding requirement for chemical structure must be its accuracy and reliability. For many compounds, the structure can be obtained from a clearly defined IUPAC name. For others, the information may be obtained from the chemical abstract service (CAS) number. There are good reviews regarding the issues relating to the accuracy of chemical structure (Young et al., 2008 and Fourches et al., 2010). To record chemical structure, the SMILES notation is often used to depict 2-D structure. The user must, however, determine what file type is required to enter the structure into the (Q)SAR tools. Most of the tools have a graphical user interface to enter chemical structures in these cases, the structure drawn must be checked thoroughly. Some web sites provide useful tools for a first look of the compound of interest. ChemSpider ( is a free-to-access collection of compound data from across the web, provided by the Royal Society of Chemistry (RSC). It offers structure searching by name or CAS and provides SMILEs, mol file (containing information about atoms, bonds, connectivity and coordinates of a molecule) and calculated/experimental physicochemical properties Identification of analogs. In the absence of specific toxicological information, hazard identification can be done using data on similar compounds called analogs through application of a grouping read across approach. Box 2 refers to the identification of analogs and the collection of relevant toxicological data. Once analogs have been identified for a target chemical on the basis of a robust and transparent method, this forms a chemical grouping or category. A grouping or category can be formed independent of whether toxicological information is available, it is merely a collection of chemical structures. If the grouping or category can be populated with toxicological data, then it may be possible to interpolate, or read across activity to the target compound. In the case where numerous analogs are retrieved from a high-quality toxicological database, a ranking-by-analogy approach can be adopted, in which the analogs are sorted according to their degree of similarity (e.g., based on a Tanimoto score from the most dissimilar chemical [score of 0] to the same chemical [score of 1]) and the closest analogs are used in the read across. This kind or ranking can be performed using various tools, including OECD Toolbox and Toxmatch. At that stage SAR models should be applied in order to get data on possible toxicological and mechanistic properties of the identified chemical analogs (e.g., affinity for the estrogen receptor, reactivity). This may bring valuable information of the homogeneity of the group of analogs and provide increased confidence for the prediction of the target chemical. An expert-driven approach has been recently proposed to systematically assess the adequacy of analogs for read across purposes (Wu et al., 2010; Blackburn et al., 2011). It involves grouping analogs based on their degree of structural, reactivity, metabolic and physicochemical similarity. It requires a thorough evaluation of the differences identified between the target chemical and analogs with respect to potential toxicological and metabolic consequences. In some instances, chemicals may not be stable in food and therefore may be anticipated to occur in the form of one or several degradates. If degradates can be identified, they should be considered in the application of the DT Collection of experimental toxicological data on analogs. The chemical grouping and read across approach relies on the availability of suitable toxicological data and information as well as an assessment of their reliability and quality. The theory is that the formation of a robust category (i.e., with a strong mechanistic basis and relevant, close analogs) populated with reliable toxicological data and/or information will provide a predicted value with a high degree of confidence. Therefore, great reliance will be placed on the availability of suitable data and some criteria with which to assess their reliability. Regrettably, there is a well-recognized paucity of toxicological data for most mammalian endpoints and repeated dose toxicity in particular (Cronin, 2009). While there are number initiatives to compile data, these will be lengthy processes. Of the databases available, one of the most comprehensive is RepDose (Bitsch et al., 2006), which is available from the web-site ( and also as a database in the OECD QSAR Toolbox. Other databases are available including a database of human Maximum Recommended Therapeutic Dose (MRTD) values for pharmaceuticals (Matthews et al., 2004a,b) as well as the Carcinogenicity Potency Database (CPDB) and Munro dataset (Munro et al., 1996). A review of sources of toxicological data has been provided recently by Cronin (Cronin, 2010). The ISSTOX project provides curated databases relative to carcinogenicity, in vitro and in vivo mutagenicity (freely available at: dati/cont.php?id=233&lang=1&tipo=7). In addition, further information may become available via regulatory submissions (e.g., REACH or SIDS dossiers) and could be used to build databases. Other projects are actively trying to promote the compilation of data for chronic toxicity e.g., the OECD QSAR Toolbox and EU COS- MOS project ( The United States Environmental Protection Agency s (US EPA) ToxRefDB will likely become an important resource in the future. Table 2 in the appendix provides some key public sources of toxicological data. In addition there are several commercial databases e.g. from Leadscope ( and VITIC from Lhasa Ltd ( The problems with using these databases are that they are not freely downloadable or usable as a complete system. The substance of interest, as well as the identified analogs, and potentially degradates are further assessed through the different steps of the DT. To incorporate in the process the toxicologically

8 B. Schilter et al. / Regulatory Toxicology and Pharmacology 68 (2014) well characterized chemical analogs (if any) may provide insight on the suitability of the available models to properly predict this group of chemicals Box 3: Assessing evidence of genotoxic potential through DNA reactivity In risk assessment, a key distinction is made between those effects reasonably anticipated to exhibit a biological threshold in their dose response relationships and those that do not. In practice, the only mechanism for which it is assumed that there is no threshold, in the absence of robust data to the contrary, is genotoxicity via direct DNA reactivity. The consequences of this are genetic mutations. Other forms of genotoxicity, such as mutation by indirect mechanisms, e.g., by reactive oxygen species, and aneugenicity, are considered potentially to exhibit thresholds. Hence, in the absence of toxicological information, an important early consideration is whether a substance may be a DNA-reactive mutagen or not. The Ames/Salmonella test is a relatively reliable predictor of such potential, providing that adequate provision is made for the possibility of activation to an electrophilic species by metabolism. This is well recognized in current protocols. In the absence of mutagenicity data, for example in the Ames/Salmonella test, structural alerts for DNA-reactive mutagenicity can be used. These rely on the propensity of certain structural motifs to give rise, either directly or on metabolism, to DNA-reactive electrophiles. A number of packages are available to assist in such predictions. A similar strategy has been proposed by others, such as EFSA, in the application of the TTC approach to compounds with limited or no toxicological information, for example plant-specific metabolites of pesticides. Table 1 of the appendix provides a non-exhaustive list of (Q)SARs aimed at highlighting the ability of chemicals to react with DNA and to cause mutagenicity. Several of them also generate predictions for carcinogenicity. The list includes both freely available and commercial systems. Most of the listed systems provide yes/ no (categorical) predictions, but some (e.g., Oncologic) generate ranks of carcinogenic potency. Such models should be applied to both, the substance of interest and the analogs (including metabolites and degradates if available). Alert for carcinogenicity through DNA-reactivity requires proceeding to Box 4. Because carcinogenicity may be related in some way to other toxic effects produced by a chemical (Venkatapathy et al., 2009; Gold et al., 2003), to process chemicals with alert for genotoxicity through Box 5 may also be advisable. The absence of any evidence for genotoxicity through DNA reactivity allows proceeding to Box 5 dealing with threshold toxicity Box 4. Predicting carcinogenic potency (non-threshold effects) In cases where genotoxicity through DNA-reactive mechanism is anticipated, a non-threshold approach may have to be applied. This requires to quantitatively predicting carcinogenic potency, either through QSAR or grouping read across techniques QSAR-based carcinogenic potency prediction. A QSAR model for predicting the TD50 values for the class of aromatic amines has been described (Benigni et al., 2007b) and the expert system Oncologic predicts ranks of carcinogenic potencies well correlated with TD50 values (Benigni et al., 2012). In addition, recent publications have provided further results showing that TD 50 could be reasonably predicted through the application of QSAR approach (Bercu et al., 2010; Contrera, 2011). Although only one TD 50 -prediction model is publically available up to now (model of Benigni et al., implemented in the Toxtree ), some are commercially available including SciQSAR (formally MDL-QSAR, two from Simulation Plus Inc. and TOPKAT Read across based carcinogenic potency prediction. If analogs associated with relevant and reliable carcinogenicity data are identified (at Box 2), a grouping read across approach may be applied. Applying read across techniques with structural analogs may be more accurate with this category of chemicals since it would concern compounds acting through a similar mechanism of action. One of the main publicly available tools for grouping and read across is the OECD QSAR Toolbox ( which is being developed to support the filling of data gaps needed for the hazard assessment of chemicals. Other tools in the public domain include Toxmatch ( and AMBIT ( A framework to conduct grouping and read across has been published together with case studies (Wu et al. 2010; Blackburn et al., 2011). All of these tools and approaches are expert-driven and require a reasonable amount of chemical and toxicological competences. Because the grouping and read across approach involves a range of subjective choices, proper documentation/traceability is of particular importance to ensure reproducibility Box 5: Predicting (L)OAEL (threshold toxicity) In cases where genotoxicity through DNA-reactive mechanism can be reasonably excluded, a threshold approach may be applied. This requires the quantitative prediction of repeat dose toxicity, either through QSAR or grouping read across techniques Quantitative modeling of chronic toxicity through QSAR. There are an increasing number of published QSAR models predicting quantitatively chronic mammalian toxicity (Mazzatorta et al., 2008; Maunz and Helma, 2008; Matthews et al., 2004a,b; Contrera et al., 2004). However, unless these have been made readily available they will be of limited use for the very rapid assessment of toxicity. This is due to the fact that QSARs will need to be redeveloped, often requiring specialist and bespoke commercial. Thus, unless QSARs are readily available, it may be better utilizing commercially and publically available expert systems such as TOPKAT (Accelrys Inc.) and Lazard ( silico.ch/). Further QSAR models for chronic toxicity are available from MolCode ( through the Molcode Toolbox. These may be useful when applied in the DT, however exact documentation and description is currently lacking Grouping and read across based methods. If analogs associated with relevant and reliable toxicological data are identified (at Box 2), a grouping read across approach may be applied according to the same principles as highlighted in Box Hazard and risk characterization Box 6: Hazard characterization In this step, qualitative information (e.g., mechanistic, target organ) and quantitative values (e.g., LOAELs, TD 50 ) obtained at Box 2 5 are listed and described in details. The main information to be collected is the following: Types of predictions (QSAR, read across) and models (e.g., TOPKAT) used. Information on model validity and domain of applicability. Endpoints (organ-specific, mechanistic). Reference points (e.g., LOAEL, NOAEL, TD 50, BMDL). Species (animal species, human). Time/duration consideration (chronic, short-term). Quality of toxicological data found on analogs.

9 282 B. Schilter et al. / Regulatory Toxicology and Pharmacology 68 (2014) Several values can be selected and brought to the next step on risk characterization Box 7: Risk characterization-margin of exposure (MoE) Risk characterization is done by dividing the individual toxicological values selected at Box 6 with the exposure estimate obtained at Box1 to obtain the margin of exposure (MoE). 5. Interpretation of the margin of exposure A MoE corresponds to the distance between a dose expected to produce toxicity and actual or estimated human exposure. It is not a measure of risk per se, but its size is considered to reflect the level of concern associated with a specified exposure. Although it appears obvious that the larger the MoE, the lower the level of concern, the interpretation of MoEs may not be straight forward and should be done on a case by case basis. Final interpretation will have to consider and compare all independently calculated MoEs. This will increase the level of confidence to the whole assessment based on the application of the DT. For the target chemical, the application of the DT is likely to provide a diverse set of toxicological values. For example QSAR models will provide either animal or human predicted toxicological values. Grouping read across approach may potentially result in the identification of an array of relevant experimental toxicological data varying for example according to animal species, study duration, study design and route of administration. In addition, (Q)SAR and grouping read across methods may provide different values for the same reference point of the most relevant endpoint. To address the significance/adequacy of each calculated MoE and to select the most adequate requires a thorough review of all the available data. In absence of specific reasons to exclude one of the MoEs, as a precaution the most conservative may be considered for decision making. In the next sections, guidance is provided on how to analyze some basic elements to be covered by a MoE Quality of data Experimental toxicological data The application of grouping read across leads to the use of experimental toxicological data on structural analogs of the target chemicals. This requires assessing the quality and reliability of such data. The methods and techniques to assess toxicological data quality have been reviewed by Nendza et al. (2010). These authors provide a very comprehensive evaluation of how to assess the quality of toxicological information and data. This has resulted in the identification of the key criteria for evaluating toxicity data quality. While this is a complex process, the most commonly criteria are those proposed by Klimisch et al. (1997). The Klimisch scheme is supported by the ToxRTool to assist in the evaluation of data reliability (Schneider et al., 2009). While these approaches are able to provide some assessment of reliability, this does not, of course, equate to accuracy of toxicological data. A recent investigation into these data quality assessment schemes has demonstrated how high quality data may score poorly due to lack of supporting information (e.g., dose levels or a clear statement that the test has been performed to Good Laboratory Practice (GLP) standards) (Przybylak et al., 2011). Therefore, these schemes provide a valuable starting point, but the assessment of data quality should ultimately be a pragmatic process performed by an expert toxicologist Choice of NOAELs There may be situations where more than one NOAEL is available for the same (critical) endpoint, from different studies. It such cases, it may be possible to identify an overall NOAEL, as described by IPCS (2009). In order to utilized this approach, the different studies should be comparable, for example with respect to study design, duration, strain and species of animal used. Expert judgment will be necessary in concluding on comparability. For example, many now consider it appropriate to compare a 3 month and a 12 month study in dogs. The overall NOAEL is the highest NOAEL identified in the different studies that provides a reasonable margin (IPCS suggest P2) over the lowest LOAEL, provided that due consideration is given to the shape of the dose response curves. This NOAEL is then used as the reference point for the effect. In the event that studies are not comparable, the lowest NOAEL would be used, unless for some other choice is considered appropriate on the basis of expert judgment, in which case a clear explanation should be provided on the basis for the decision. Where data are suitable for dose response modeling, it may be possible to use the BMD approach, with study as a covariate (EFSA, 2009) Predicted toxicological data Data should be generated with models validated according to internationally recognized criteria (JRC, 2010; Benfenati et al., 2009). The validation file should be transparently documented and available. To study the suitability of the domains of applicability of the models used to cover the substance of interest is a key element to assess the quality and reliability of predicted toxicological data Dealing with inter and intra-species differences Threshold effects Since available models potentially used in the DT predict mostly animal endpoints, an extrapolation to the human situation is required to properly interpret the calculated MoEs. In classical hazard characterization based on animal data, a safe level of exposure for human is usually derived from the application of a default uncertainty factor (UF) of 100 to the no observed adverse effect level (NOAEL) in the most relevant animal study (often a chronic toxicity study to establish health based guidance values such as ADIs). This UF takes into account potential differences between the test animal and human (Interspecies UF = 10), and the variability in sensitivity to the test substance within the human population (Intraspecies UF = 10) (Renwick et al., 2003). Therefore, to conclude on a low safety concern, a MoE based on predicted chronic animal data should be >100, in order to account for potential inter and intra species differences. For interspecies differences, allometric scaling by metabolic rate may also be used. Guidance on how to use allometric scaling can be found in the REACH (ECHA, 2008 and ECETOC, 2010) guidance. EMA (2009) advices to convert the NOAEL from an animal study on the basis of the comparative surface area versus body weight. The US FDA 2005 (FDA, 2005) recommends for interspecies extrapolation to normalize the toxicological endpoints on the basis of the body surface area to obtain the Human Equivalent Dose (HED). For example, the dose level (in mg/kg bw) of a rat study would be divided by 6.2 to obtain the HED (in mg/kg bw). Almost identical figures are given by EMA 2009 in a guideline for residual solvents in drugs. Uncertainty factors for interspecies extrapolation based on allometric scaling considering oral exposures are summarized in Table 3. In the case of the availability of an adequate human NOAEL, it is generally considered that only an UF of 10 covering the potential differences between average and most susceptible human populations is used. A similar principle has been proposed when using the MRTD for safety purposes (Matthews et al., 2004a,b). To conclude a low safety concern, a MoE calculated based on human MRTD should at least cover a factor of 10 to convert the MRTD into a

10 B. Schilter et al. / Regulatory Toxicology and Pharmacology 68 (2014) Table 3 Interspecies uncertainty factors to extrapolate from animals data to human. Interspecies factor to man Species ECHA 2008 EMA 2009 FDA 2005 Rat Mouse 7 na na Hamster 5 na 7.4 Guinea pig 3 na 4.6 Rabbit Monkey Dog na: not available. no effect level. An additional factor (2 10) has been warranted to deal with potential inter-individual variability differences (Matthews et al., 2004a,b) Carcinogenic, non-threshold hazard In the context of carcinogenicity, the MoE approach has been considered as a prioritization tool, although it does not provide any numerical value of risk (EFSA, 2005b; O Brien et al., 2006). Therefore the MoE is communicated in terms of concern rather than risk. The MoE approach for genotoxic carcinogens is still under development. Although no firm guideline is currently available on the size of MoE associated with a negligible concern, previous works have provided valuable insight and information (Gold et al., 2003; EFSA, 2005b; O Brien et al., 2006). To be of low priority, a margin higher than 100,000 over a TD 50 is likely to be necessary (Gold et al., 2003). Obviously the MoE should always be accompanied by a narrative to explain the background and the uncertainties involved. Another approach to provide insight on the level of concern is to compare the exposure estimates with a virtually safe dose calculated as a 10 6 risk based on a linear extrapolation (Gold et al., 2003; Renwick et al., 2003; O Brien et al., 2006) Dose response QSAR models of animal chronic toxicity are providing LOAEL and not NOAEL. This has to be taken into account in the interpretation of the significance of the calculated MoE. For risk assessment based on experimental toxicological data, factors to convert LOAEL into NOAEL have been proposed. For example, ECHA (ECHA, 2008) recommends to apply a factor ranging from 3 to 10. A factor of 3 should be applied as a minimum and would be applicable for the majority of the cases. The highest factor of 10 would only be applicable for severe effects e.g., irreversible effects, major malformations, or offspring lethality. EFSA (2012) does not propose a default uncertainty factor in such situations but advices that this factor should be determined on a case-by-case basis, taking into account the severity and the slope of the dose response of the effects. Similar approaches have been suggested by ECETOC (2010). The interpretation of the calculated MoE based on a predicted LOAEL would requires the same factors. In cases where little or no information is available on the severity of the toxic effects, the application of a default factor of 10 is recommended. As mentioned earlier, for safety assessment purposes, MRTDs are considered as a human LOAEL. The application of an UF of 10 to the MRTD has been recommended to convert this surrogate LOAEL into a no effect level (Matthews et al., 2004a,b) Exposure duration The interpretation of the MoE requires an alignment between the duration of the human exposure to be assessed and the duration of animal treatment used for hazard characterization. For example, the application of the DT may result in the prediction of a 90-day NOAEL from read across. If this has to be used to address the safety concern of life-long exposure in humans, an extrapolation to a chronic exposure scenario is necessary. On the other hand, chemicals detected in food might be present for only a short period of time as a result of, for example, incidental contamination. Consequently, exposure to these chemicals via food is also transient. In such a situation, to properly interpret MoE, it may be appropriate to account for these short-duration/lessthan-lifetime exposures. Guidance on how to conduct relevant time adjustment is provided below Uncertainty factors to extrapolate from short-term to long-term exposure duration For substances which are not genotoxic carcinogens, different factors have been suggested to extrapolate from short-term to long-term exposures. Numerous publications comparing quantitative endpoints (NOAEL, LOAEL) derived from toxicological studies of different durations are available (Kalberlah and Schneider, 1998; Pieters et al., 1998; Kalberlah et al., 2002; Schneider et al., 2005; Bokkers and Slob, 2005; Pohl et al., 2010; Zarn et al., 2010; Batke et al., 2011) and were used by ECHA (2008), ECETOC (2010) and EFSA (2012) to derive default factors to extrapolate long-term quantitative estimate from studies with short durations. The factors proposed are often based on the geometric means of the distribution of the ratios NOAEL short-term to NOAEL longterm or LOAEL short-term to LOAEL long-term. For the purpose of designing uncertainty factors to deal with exposure duration, data analysis needs to satisfy several criteria such as: (i) comparison within the same species, (ii) the same administration route, and (iii) for defined time-frames. Consideration should be given whether the use of the geometric means would be appropriate for that purpose (Malkiewicz et al., 2009). Uncertainty factors used by different authorities are compiled in Table Extrapolation from long to short-term exposure duration Such type of extrapolation may be used for both carcinogenicity and toxicity endpoints. For substances with alert of genotoxicity through DNA-reactivity, one way to extrapolate from longer durations to shorter durations of exposure is through the use of the Haber s Law: c t = k, where c is the dose, t the exposure time and k a constant. This implies a linear relationship between the dose and the exposure time. The use of linear time-to-dose extrapolation to assess the risk from less-than-life-time exposure to genotoxic substances has been discussed by various authors (Felter et al., 2011; Callis et al., 2010; Bos et al., 2004). In addition, the question of extrapolating from long-term to short-term exposure was addressed in the context of the application of the TTC for (potential) genotoxic impurities with unknown carcinogenic potential Table 4 Uncertainty factors to extrapolate from short-term to long-term exposures. Organization Extrapolation Uncertainty/ factor EMA 2009 (residual 6-month rodents? chronic 2 solvents) 3-month rodent? chronic 5 Shorter than 3 months in 10 rodents? chronic ECHA 2008 Subacute? subchronic 3 (chemicals) Subacute? chronic 6 Subchronic? chronic 2 EFSA 2012 Subchronic? chronic 2 Subacute: 4 weeks, subchronic: 13 weeks, chronic 1 2 years.

11 284 B. Schilter et al. / Regulatory Toxicology and Pharmacology 68 (2014) Table 5 Time-Adjustment factors developed in the frame of the TTC applied to substances with alert of genotoxicity. Duration of exposure Staged TTC as endorsed by EMA 2010 and FDA 2008 Adjustment Factor a Single dose or 120 lg/day c 800 <14 days b Below 1 month 60 lg/day c 400 Up to 3 months 20 lg/day c Up to 6 months 10 lg/day c 66.7 Up to 12 months 5 lg/day c 33.3 >12 months 1.5 lg/day d 1 a Factor to adjust from a life-long to shorter exposure duration. b Single dose (EMA, 2010) or <14 days (FDA, 2008). c d based on a 10 6 cancer risk. based on a 10 5 cancer risk. in pharmaceutical products (Müller et al., 2006; EMA, 2010; FDA, 2008). Table 5 provides the proposed factors to be applied to the TTC for substances with alerts of genotoxicity. For substances without an alert for genotoxicity via DNA-reactivity, it was argued that Haber s Law would not be adequately conservative (Gaylor 2000) when extrapolating from longer-term to shorter-term exposures. It was suggested that the modified Haber s Law c n t = k (where n is a chemical specific regression coefficient) (ten Berge et al., 1986; Gaylor 2000) would be more appropriate. In the absence of data it is recommended that n should be set at 3 per default (ECHA, 2008 and AEGL, 2001) as this provides the most reasonable estimate. An approach based on comparison of either the NOAELs or the LOAELs from studies applying different exposure durations may be used as well. As discussed in the context of extrapolating long-term from short-term exposure, numerous publications were used to derive appropriate factors. Such factors could also be used in the other direction, to extrapolate from long-term to short term exposure duration. This approach for time-adjustment was suggested by Kroes et al. (2007). It was concluded that NOAELs coming from chronic studies are 3 10 times lower when compared to the NOAELs from subchronic studies. Table 6 summarizes the timeadjustment possibilities for chronic to sub-chronic/sub-acute or sub-chronic to sub-acute. Interestingly, for extrapolation from chronic data to subchronic endpoint, both approaches, the modified Haber s Law and the inverse ECHA/ECETOC approach, result in an identical factor of 2. To extrapolate from chronic data to subacute quantitative endpoint, the Haber s law seems to be more conservative and may therefore be favored to extrapolate from a chronic endpoint to a subacute endpoint (factor of 3). A factor of 1.5 may be used to extrapolate from subchronic to subacute data Route-to-route extrapolation For the purpose of this document, route-to-route extrapolation is only considered relevant for extrapolation from routes other than oral to oral exposure. It may happen that from the read-across approach N(L)OAELs from toxicological studies are available which have been conducted via the dermal or inhalation route. In such case, route-to-route extrapolation would be necessary to use these data for oral exposure. Guidance dealing with this topic is given by ECHA (2008). For systemic effects, route-to-route extrapolation is only considered possible for substances which are well absorbed, and which are not subjected to a strong first-pass or bolus effect. A strong first-pass effect would result in either over or under conservative prediction of oral N(L)OEALs according to the toxic properties of the metabolite(s) formed. In silico techniques may be valuable to address first-pass effects and therefore improve route to route extrapolation. In case the surrogate toxicological endpoint comes from an inhalation study using the rat, a concentration in air (e.g., expressed as mg/l or mg/m 3 ) needs to be converted into mg/kg bw/d. The following equation can be used: oral NðLÞOAEL ðmg=kgbw=dayþ ¼ ABSinhalation ð%þnðlþoaec ðmg=m3 ÞRV ðm 3 =hþ daily duration ðh=dayþ ABSoral ð%þbody weight ðkg bwþ ABSinhalation (%) and ABSoral (%) refer to the percentage of the compounds absorbed either through the inhalation or oral route respectively. In general absorption will not be known and a value of 100% recommended by default. The respiratory volume (RV)/ inhalation volume depends on the age of the animals and thus on the total duration of the study. For example, in 28-day, 90- day, or chronic studies, rats (both sexes combined) have inhalation volumes of 150, 175, or 250 ml/min, respectively (ECB, 2003). In average, male rats are assumed to inhale 20.5 L/h and female rats 15.7 L/h (ECHA, 2008). For other species, information on body weight and inhalation volumes according to age can be found in Table 6 Comparison of the modified Haber s law and the ECHA/ECETOC approach to extrapolate from chronic to less than chronic. Duration of exposure Method for extrapolation Multiply chronic dose by factor of Multiply subchronic dose by factor of Reference <3 months Modified Haber s law 2 a n.a. ten Berge et al. (1986) and Gaylor (2000) <1 month Modified Haber s law 3 b 1.5 c ten Berge et al. (1986) and Gaylor (2000) <3 months Inverse ECHA/ECETOC 2 d n.a. ECHA (2008); ECETOC (2010) approach <1 month Inverse ECHA/ECETOC 6 d 3 d ECHA (2008) ECETOC (2010) approach Single exposure (once per week) 3 Kroes et al. (2007), Single exposure (less than once per 10 Kroes et al. (2007), week) Single exposure 1 5, default 3 ECHA (2008) a c n t = constant with n = 3 and t = time adjustment from 2 years to 13 weeks, exposure reduction factor of 8: c n 8 = constant: reduction of exposure time by factor of 8 results in an increase in c by factor of 2. The multiplication of the chronic dose by this factor of 2 allows extrapolating a 3 month dose from a chronic one. b As above with reduction of exposure time by factor of 26 from 2 years to 4 weeks results in an increase in c by factor of about 3 (to be used to obtain a 1 month dose from a chronic dose). c As above with reduction of exposure time by factor of 3.2 from 90-day to 28-day study, results in an increase in c by factor of 1.5. d Using the factor for time extrapolation from shorter-term studies to longer-term exposure vice versa; sub-chronic to chronic = 2, sub-acute to chronic = 6, sub-acute to sub-chronic = 3.

12 B. Schilter et al. / Regulatory Toxicology and Pharmacology 68 (2014) ECB (2003) and ECHA (2008) documents. Daily duration stands for daily exposure time of the laboratory animals during the inhalation study. In most cases this would be 6 or 8 h/day. For extrapolation from dermal N(L)OAEL to oral N(L)OAEL the equation below applies. ABSdermal (%) refers to the percentage of absorption through skin. In the absence of information on extent of absorption via the skin, it is assumed that dermal absorption will not be higher than oral absorption. In addition, in the absence of information on oral absorption, the default values as defined by ECHA 2008 is 50%. oral NðLÞOAEL ðmg=kgbw=dayþ ABSdermal ð%þdermal NðLÞAOAEL ðmg=kgbw=dþ ¼ ABSoral ð%þ 5.6. Acquisition and use of toxicokinetic information Toxicokinetics refers to the absorption, distribution, metabolism and excretion (ADME) of chemicals. It is considered of primary importance to address species differences in toxicological responses and to support quantitative extrapolation to human from animal data through the use of internal dose. In addition, kinetic data may be of great value to interpret toxicological data and support mode of action analyses, as well as for high to low dose extrapolation. Route to route extrapolation is another area where kinetics play an important role. In the context of the application of the DT, ADME information may be valuable in the interpretation of the data and in the evaluation of the suitability of potential analogs for read across. In case where the identified analogs are known to possess a particular kinetics and/or metabolic property, the prediction of such property for the substance of interest would likely improve the confidence in the QSAR and read across data for toxicological endpoints. Examples of potentially relevant parameters are the intestinal absorption, bioaccumulation potential, organ distribution and bioactivation by specific enzymes. Such knowledge on analogs would allow running targeted predictions of relevant properties on the substance of interest. Good reviews of (Q)SARs to predict oral and skin absorption (and other ADME properties) have been provided (Madden, 2010a,b and Cronin, 2005). Some models dealing with ADME are provided on Table 1 in Annex. In terms of the availability of (Q)SAR models, there are many for human intestinal absorption, fewer for skin penetration and virtually none for inhalation. There are several predictive models for human intestinal absorption based on various physico-chemical properties (Wegner et al., 2004; Clark, 1999; Bai et al., 2004; Klopman et al., 2002; Zhao et al., 2001; Wessel et al., 1998). Most of these models take into consideration passive diffusion and ignore important processes such as flux through transporters. Some of the simplest models in predictive ADME are those referred to as rules of thumb. The most widely recognized of these is the rule of 5 (Lipinski et al., 1997). This was devised to provide a screening tool for identify compounds that were likely to be poorly absorbed. This type of tool found wide acceptance among users because of simplicity and ready interpretability, it is incorporated into a number of freely available pieces of e.g., the OECD QSAR Toolbox. The success of this simple approach has led to other rules of thumb being devised. Models were developed for predicting high bioavailability (Veber et al., 2002) and transport across blood brain barrier (Norinder and Haberlein, 2002). It is important to remember that the approaches described above, have been focused and developed to optimize uptake of drugs. They may provide some guidance regarding high or low intestinal absorption, but the predictions should be treated with caution. Potential metabolites can be predicted by a number of computational approaches including a small number of freely available tools and a larger (and possibly more developed) selection of commercial (Table 1 in the appendix). It should be noted that the majority of methods simply predict potential metabolites, they do not estimate the probability of a metabolite being formed or the amount. Few, or any, methods to predict metabolism have undergone any form of formal evaluation. The freely available OECD (Q)SAR Toolbox includes computational approaches to predict metabolites Considerations on mixtures Problem formulation may indicate that the issue of concern is not exposure to a single chemical, but co-exposure to a mixture of two or more chemicals. Evidence suggesting that compounds of the mixture could act similarly or interact would require assessing them as a group. Methods are available for conducting assessments of combined exposures to multiple chemicals. They are based either on toxicology of the whole mixtures or more often on the consideration of their individual components (Meek et al., 2011; SCHER, 2011). General frameworks for risk assessment of chemical mixtures have been described (Meek et al., 2011; SCHER, 2011). In the context of the application of the DT, frameworks recently developed (Meek et al., 2011; SCHER, 2011) may be applicable. In these frameworks, dose-additivity is the default assumption. Briefly, doses of the relevant components of the mixture are added after being multiplied by a scaling factor that accounts for differences in their toxicological potencies. Based on analysis of available research data for effects resulting from combined chemical exposures, such an approach has been considered conservative (Meek et al., 2011; SCHER, 2011). Experimental studies have suggested that exposure to mixtures of chemicals at levels that are non-toxic for each individual chemical generally will not result in a health risk, but dose-addition is an important exception (Meek et al., 2011; SCHER, 2011). For individual chemicals of a mixture (Q)SAR and read across tools may provide valuable information on potential similarities regarding mode/mechanism of action and/or common target organ or tissue. Therefore, such methods may be used to decide on the need to assess the compounds of the mixture as a group with joint action in order to establish the level of safety concern of the whole mixture. In case of evidence of similarities among the relevant compounds of the mixture, the method of the point of departure index (PODI) (SCHER, 2011) can be applied using predicted toxicological values such as LOAELs to account for the differences in toxic potencies of the individual chemicals. Then a MoE approach can be used for the mixture of interest. 6. Uncertainty associated with the use of the DT Any risk assessment is inherently associated with a degree of uncertainty, the magnitude of which determines how reliable the assessment should be considered. It is recognized that the characterization of the uncertainty involved in a specific risk assessment is an important element to be communicated to decision-makers/ risk managers. For classical risk assessment based on experimental data, many potential sources of uncertainty have been identified on both exposure assessment and hazard characterization (Renwick et al., 2003; EFSA, 2006; COT, 2007). Because of its design, the application of the present DT will bring uncertainties similar to those associated to classical risk assessment (e.g., on exposure

13 286 B. Schilter et al. / Regulatory Toxicology and Pharmacology 68 (2014) assessment) as well as others related to the strong reliance on predicted toxicological values for hazard characterization. The major sources of uncertainties associated with (Q)SAR and grouping read across methods are provided below Uncertainties related to predictions using (Q)SAR models Uncertainty related to structure and its representation. Uncertainties related to the endpoint considered. Higher uncertainties are associated with the prediction of complex endpoints such as chronic toxicity. Uncertainties related to the quality and variability of biological data used to build model. Models must be built on data selected based on proper experimental design to minimize the impact of intrinsic experimental uncertainties as described above. Uncertainties related to the performance of the model. The model used need to be documented as scientifically valid. By definition, (Q)SAR models cannot be more accurate than the experimental data and a predictive variance smaller than the variance in experimental data is an indication of over fitting of the model to the data. Uncertainties related to the use of the model (applicability domain). The model used needs to be applicable to the chemical of interest. Uncertainty will be larger for query compounds being less similar to the training set of the model. Unfortunately, this information is not always available Uncertainties related to read across methods Uncertainties related to the number and suitability (degree of similarity regarding structure, reactivity, metabolic, physicochemical properties and mechanism/mode of actions) of the chemical analogs identified. Uncertainties on the toxicological information found for the analogs. Uncertainties related to extrapolation of the toxicity of the substance of interest based on data on analogs Quantifying uncertainties To deal with some specific uncertainties, default factors are applied, such as to cover test animal to human extrapolation and inter-individual variability. Other factors may be applied on a case by case basis to account for potential inadequacies in the available toxicological database (e.g., from LOAEL to NOAEL). The application of the DT requires the use of a similar approach in order to interpret the significance of the magnitude of the calculated MoEs. Indeed, to ensure safety, the MOE should at least allow for the default factors established for classical risk assessment. The additional uncertainty associated with the use of predicted toxicological values is the main difference between classical risk assessment and the application of the DT. To quantify the additional uncertainty resulting from the use of predictive models is difficult and depends upon the performance and domain of applicability of the models used. Properly validated models based on internationally recognized criteria should be applied. Characterization of the performance of available models may provide insights on the uncertainty associated with their use. To predict mutagenicity such as from the Ames test is an important step in the DT. There are a number of models available predicting accurately Ames test data and for some of them (e.g., Mazzatorta et al., 2007) the predictive error appears quantitatively similar to the experimental one of Ames test data obtained in an inter-laboratory trial (Piegorsch and Zeiger, 1991). For most food-related compounds for which a complete toxicological database is available, chronic toxicity studies generally provide the most sensitive endpoint and usually the pivotal data for hazard characterization. To predict chronic toxicity is therefore considered as a first priority in the DT. Because chronic toxicity involves a number of different biological processes and mechanisms/modes of action, its prediction is extremely challenging. There are a small number of QSARs for chronic mammalian toxicity, an example is the model reported by Mazzatorta et al. (2008), for which the root mean squared error of the predictive model was calculated to be 0.73 (in a logarithmic scale) and found to be close to the observed variability of actual experimental values (0.64). Another example is the Maximum Recommended Therapeutic Doses (MRTD) model (Maunz and Helma, 2008) considered to predict human LOAELs. The model spans nearly 9 orders of magnitude of dose variation and predicts more than 82% of the compounds within 1 log unit of all experimental values (89% within the applicability domain defined by a confidence interval >0.2) and has an overall mean log error of These performances are comparable to those of other published models based on the same database and different commercial s, as reported by Matthews et al. (2004) (mean log error = 0.56) and Contrera et al. (2004) (mean log error = 0.58). The good performances of these models are an indication that it is possible to predict human endpoints reliably with QSAR techniques, and suggest the importance of the quality of the dataset over the statistical techniques used to mine it. For the carcinogenic potency model of Contrera (2011), the mean predicted TD 50 /experimental TD 50 ratio was 1.01 with a standard deviation of 0.33 for the rat and a mean ratio of 1.28 and a standard deviation of 0.71 for the mouse. In the Bercu model (Bercu et al., 2010) a majority of compounds (rat 86% and mouse 88%) had a predicted TD 50 predictions that were 65-fold the experimental value. In addition for the same model it was rare for TD 50 prediction to exceed 10-fold the original value, with 86% or 97% less than or equal to 10-fold the experimental values. These model performances have to be compared with variability obtained in experimental studies. Estimates of cancer potency from replicated two-year bioassays have been shown to vary by a factor of 4 around a median value (Gaylor et al., 1993). To provide a general and quantitative insight on the level of uncertainties associated with the application of grouping and read across is difficult. However, as suggested by recent studies (Wu et al., 2010; Blackburn et al., 2011), the systematic application of a stringent set of criteria to select and categorize analogs is likely to significantly reduce uncertainties. Using a number of blinded case study chemicals, read across results were shown to be protective when compared with actual experimental toxicological data (Blackburn et al., 2011). The discussion above indicates that relying on predicted toxicological values (instead of experimental toxicological values) for establishing level of safety concern does introduce additional uncertainty. The actual impact of this on the overall degree of conservatism of such assessment as compared to classical risk assessment is difficult to estimate. It has to be kept in mind that the errors introduced by the models are not systematic in a sense that they may result into either under- but also over-conservative predictions. In addition, the application of the DT is applying different QSAR models, established with independent training data sets (e.g., rat LOAEL, human MRTD) as well as different approaches ((Q)SAR, read across) which, when integrated, can also provide in addition of quantitative toxicological values some relevant other information such as on potential mechanisms/modes of actions involved (e.g., potential to bind nuclear receptors). These different factors are expected to reduce the overall uncertainty and/or allow ensur-

14 B. Schilter et al. / Regulatory Toxicology and Pharmacology 68 (2014) ing an adequate degree of conservatism when the DT is properly applied Dealing with uncertainties For classical risk assessment, EFSA recommends a tiered approach to analyzing uncertainties (EFSA, 2006). Each uncertainty in an assessment may be analyzed at one of three tiers: qualitative, deterministic or probabilistic. Bearing in mind that it is not practical to treat all uncertainties quantitatively, expression of uncertainty or variability in risk estimates may be qualitative or quantitative, but should be quantified to the extent that is scientifically achievable. Initially, all significant uncertainties may be analyzed qualitatively. This may be sufficient, if the outcome is clear enough for risk managers to reach a decision. Otherwise, those uncertainties that appear critical to the outcome may be analyzed deterministically or probabilistically. It is considered to apply a similar approach to address the uncertainties associated with the application of the DT. This includes a systematic examination of all parts of the assessment to list all identifiable sources of uncertainty. Because the application of the DT implies a deficiency in data, the possibility to address uncertainties quantitatively is unlikely. Instead, it is proposed to run a qualitative evaluation of all uncertainties in order to evaluate their potential impact on the overall uncertainty of the assessment and the consequent confidence associated with the assessment. 7. Conclusion There is an increasing demand for methodologies to establish the level of safety concern for dietary chemicals for which no toxicological data are available. Examples where this may be required are in the case of fast decision making e.g., in the management of incidental contamination of food, prioritization of process related contaminants for further testing or for early assessments in research and development pipelines. In addition there is an increasing pressure to reduce animal experimentation for ethical reasons. In this context, in silico predictive methods have obvious advantages. To make safety statements requires the comparison of human intake with levels of exposure either considered safe, or associated with defined adverse effects. Therefore, quantitative predictions of toxicological reference points are necessary, including N(L)OAELs, or carcinogenic potency for chemicals with an alert of DNA reactive genotoxicity. Grouping read across and (Q)SAR models have been identified as two different and promising avenues that can be used to predict the complex toxicological reference points required. Endpoints such as chronic toxicity and carcinogenicity are often considered too heterogeneous and complex to be adequately modeled and quantitatively predicted. However, recent efforts in the (Q)SAR field have resulted in the development of models providing reasonable predictions with error within the same order of magnitude than the estimated variability of experimental data. Regarding read across, current understanding indicates that the application of very stringent criteria to select suitable analogs to target chemicals would most certainly allow extrapolations with good confidence. The integration of these independent methods is likely to further reduce uncertainties associated with the reliance on predictions. The present work indicates that if integrated according to the proposed DT, in silico tools can be used to establish levels of safety concern by calculating MoEs between predicted toxicological reference points and estimated human exposure. The size of the MoEs determines the level of concern. To conclude on safety, the selected MoE should be large enough to account for a number of uncertainties, including interspecies and inter-individual differences, dose response consideration and route to route extrapolation. These uncertainties can be adequately dealt with by factors already widely applied in classical risk assessment. The analysis of the uncertainties related to in silico approaches together with results from case studies suggest that establishing safety concern based on application of the DT is unlikely to be significantly more uncertain than that based on experimental data. The DT makes a full use of all data available, ensuring an adequate degree of conservatism. The data resulting from the application of the DT can be used to provide risk-managers with scientific advice within a very short period of time. However, it has to be kept in mind that the proper application of the methodology proposed in the present paper requires significant inter-disciplinary expertise, including competences in structural chemistry, computational modeling, toxicology, mechanism/mode of action and risk assessment. 8. Case studies In the present paper, a decision tree (DT) has been designed to aid integrating exposure information and predicted toxicological reference points obtained with (Q)SAR and read-across techniques in order to establish the level of safety concern in the absence of toxicity testing. To illustrate the principles of its application, two case studies were prepared. The first one (3-carbamyl- 2,4,5-trichlorobenzoic acid, R611965) has been developed with the only aim to present the workflow of the DT with a simplified example using chemicals toxicologically well characterized. The second (Isopropylthioxantone, ITX) was derived from a real, significantly more challenging case dealing with chemicals for which little toxicological data were available. Both examples were thought to illustrate the situation of providing insight on the level of safety concern within a short period of time (e.g., <1 month). In addition, these cases allowed comparing the outcomes of assessments established either based on the application of the DT or on the consideration of experimental data. For these two exercises, analogs were chosen following expert judgment, while models were selected based on availability within the group and documented adequate performance as described in cited publications carbamyl-2,4,5-trichlorobenzoic acid (R611965) Problem formulation In this constructed example (which does not have any link with an actual case), R was assumed to be a toxicologically uncharacterized metabolite of the pesticide chlorothalonil found unexpectedly and transiently in a food product Exposure assessment BOX1: Is there evidence for exposure to the chemical? For this exercise, a transient (<1 month), worst-case exposure of 005 mg/ kg bw/day R was assumed Hazard identification BOX2: Collecting and screening information. Structural information regarding R is provided on Table 7. A number of structural analogs could be identified for R Because of the metabolic link and the availability of extensive toxicological data, focus was on chlorothalonil and 3-carbamyl-2,4,5-trichlorobenzoic acid, another chlorothalonil metabolite called SDS Chlorothalonil: Complete toxicological database is available (JMPR, 2009). No evidence of genotoxicity was observed. Some developmental toxicity observed at doses significantly higher than those producing chronic toxicity. An ADI of 0.02 mg/kg bw/day was derived from a NOAEL of 1.8 mg/kg bw/day in a 2 year study in rat. SDS-3701: Complete toxicological database is available (JMPR, 2009). No evidence of genotoxicity was observed. Some develop-

15 288 B. Schilter et al. / Regulatory Toxicology and Pharmacology 68 (2014) Table 7 Structural information. Compound Structure SMILES R Clc1c(C(N)=O)c(Cl)c(cc1Cl)C(O)=O in vivo genotoxicity assays (JMPR, 2009). No evidence of DNA-reactivity was observed. Various predictive models were applied to R and identified metabolites/analogs (Table 8). No alert for genotoxic activity was predicted. Based on both experimental data on analogs and on predictions, R was considered to be non-genotoxic allowing proceeding to BOX5. Chlorothalonil SDS-3701 Clc1c(C#N)c(Cl)c(C#N)c(Cl)c1Cl Clc1c(C#N)c(Cl)c(C#N)c(O)c1Cl BOX5. Chronic toxicity predictions: (N)LOAELs. Chronic toxicity data and predicted values most relevant for the present exercise are provided below (Table 9). Insight on the performance of the models used is provided in Mazzatorta et al. (2008) (for predicted rat LOAEL) and Maunz and Helma (2008) (human LOAEL). Table 8 Genotoxicity data. Compound Ames model a Lazar b Experimental c DSSTox Kazius-Bursi R No No No? Chlorothalonil No Nr d No No SDA-3701 No No No No a According to Mazzatorta et al., b c JMPR, 2009, d Prediction non reliable. mental toxicity observed at doses slightly higher than those producing chronic toxicity. An ADI of mg/kg bw/day was derived from a NOAEL of 0.83 mg/kg bw/day in a 1 year study in dog BOX3: Genotoxic through DNA reactivity?. The two selected metabolites/analogs were tested in a number of in vitro and Table 9 Chronic toxicity data and predicted values. Compound Predicted Rat LOAEL a Predicted Human LOAEL b Experimental LOAEL c Experimental NOAEL c mg/kg bw/day R ?? Chlorothalonil (rat) 1.8 (rat) SDS (rat) 3 (rat) a According to Mazzatorta et al., b c JMPR, 2009, (dog) 0.83 (dog) Hazard characterization BOX6. Identification of the most relevant data. Based on the experimental data on metabolites/analogs, chronic toxicity values appear the most conservative and relevant. For both chlorothalonil and SDS-3701, the predicted rat LOAELs were within a factor of 2 of the experimental values, supporting the use of the prediction for R Risk characterization BOX7. Margin of exposure. Relevant MoEs are provided below (Table 10) Results and conclusion Various relevant MoEs have been calculated based on either read-across or QSAR methods for hazard characterization. The application of the principles highlighted in the present paper to interpret the significance of the calculated MoEs would lead to the conclusion that a transient exposure of 0.05 mg/kg bw/day to R is of negligible safety concern Discussion of the R case The outcome of the exercise developed above can be compared with those resulting from the application of experimental data. A complete database is available for R (JECFA, 2010). The most conservative endpoint is derived from a sub-chronic toxicity study in dog with a NOAEL of 50 mg/kg bw/day. Using this NOAEL, a MoE of 1000 can be calculated with the estimated human exposure. This allows for inter-species (UF = 10) and inter-individual (UF = 10) uncertainties. Interestingly, a LOAEL of 500 mg/kg bw/ day was observed in a rat chronic toxicity study confirming the good performance of the rat-loael model used to predict this type of structure. For this specific case exercise, the application of the predicted rat LOAEL would lead to an outcome similar to those resulting from the use of the experimental data. The application of additional predictive models and the use of other analogs could potentially improve the confidence associated with the assessment based on the application of the DT. For chlorothalonil, evidence is Table 10 Margin of Exposure values. Toxicological value MoE a Comments R611965: - Predicted rat LOAEL 523 mg/kg bw/day R611965: - Predicted hum. LOAEL8 mg/kg bw/day Chlorothalonil: - exp rat NOAEL 1.8 mg/kg bw/day SDS-3701: - exp dog NOAEL 0.83 mg/kg bw/day MoE allows for uncertainties regarding inter-species (UF = 10) and intra-individual (UF = 10) differences as well as for a conversion of LOAEL to NOAEL (UF = 10). Predicted LOAEL could be multiplied by a factor of 6 (bringing the MoE to 60000) for the use of a chronic toxicological value to assess a human exposure < 1 month 160 MoE allows for uncertainties regarding intra-individual differences (UF = 10) and for a conversion of LOAEL to NOAEL (UF = 10). Predicted LOAEL could be multiplied by a factor of 6 (bringing the MoE to 960) to account for the use of a chronic value to assess a human exposure < 1 month 36 A MoE of 36 is calculated based on the available NOAEL for this analog, and is not sufficient to cover inter-species, interindividual uncertainties. The NOAEL could be multiplied by a factor of 6 (bringing the MoE to 216) to account for the use of a chronic value to assess a human exposure < 1 month 17 A MoE of 17 is calculated based on the available NOAEL for this analog, and is not sufficient to cover inter-species, interindividual. The NOAEL could be multiplied by a factor of 6 (bringing the MoE to 102) to account for the use of a chronic value to assess a human exposure < 1 month a Margin of exposure (MoE) = toxicological value/human exposure.

16 B. Schilter et al. / Regulatory Toxicology and Pharmacology 68 (2014) available on a key role of reactive metabolite formed from renal b- lyase activity in the induction of kidney toxicity, the pivotal toxicological effect. Additional work to predict the potential relevance of such metabolic pathway and mode of toxic action on R could provide some valuable information to explain its predicted lower toxic potency as compared to the parent compound and SDS Isopropylthioxanthone (ITX) Problem formulation ITX is a photoinitiator used as catalyst in UV-cured inks. It has been found in infant milk-based formula product packed in Tetra- Pak bricks printed through this technology (EFSA, 2005a,b,c). This case study is derived from an actual case (EFSA, 2005c) Exposure assessment BOX1: Is there evidence for exposure to the chemical? Exposure to ITX will occur in infants consuming the contaminated infant formulas, with significant variation according to age The highest exposure has been calculated for a scenario assuming formula consumption covering the 95th percentile of the consumption distribution of ready to drink product (1060 ml for a 3 month old infant weighting 6.1 kg) and considering the highest ITX level detected (250 ppb). This resulted in a maximum exposure of 46 lg/kg bw/day (EFSA, 2005c). Such an exposure was likely to decrease with age, reaching very low levels in adulthood Hazard identification BOX2: Collecting and screening information. Structural information regarding ITX and of identified analogs is provided below (Table 11). Analogs were identified by expert knowledge, the application of the SciFinder and literature searches. All four analogs are antischistosomal drugs (reviewed in Ong, 1978; Kramers et al., 1991) with similar pharmacological activity in mice. Only few and relatively old toxicological studies are available on these substances. In rodents and rabbit hycanthone was found to be fetotoxic and teratogenic (single intramuscular injection). Epidemiological evidence indicates hepatoxicity in patients treated with hycanthone. All analogs were shown to be genotoxic and mutagenic in several in vitro and in vivo test systems (Ong, 1978; Kramers et al., 1991), with relatively lower potency for IA-4 and IA-4-N-oxide. For hycanthone, proposed mechanisms have been DNA-intercalation and frameshift, while some evidence of DNA binding is available. There is very little direct evidence that hycanthone is carcinogenic in mammals. It (single intra-muscular injection) presented some tumorigenicity in mice partially hepatectomized or infected with Schistosomiasis mansoni. Schistosoma infection is considered as a risk factor for carcinogenicity. Only one study applying chronic (1 year) oral administration (drinking water) was retrieved (Lijinsky and Taylor, 1977), suggesting that lucanthone may induce the development of tumors in rat. However, because of strong methodological flaws (most animals died from dehydratation), no firm conclusion could be drawn from that study. Because of the irrelevant route of exposure (intramuscular injection), inappropriate experimental design (single injection, either in partially hepatectomized or infected mice) and poor study quality, none of the toxicological data found in the scientific literature on the analogs were considered suitable for this exercise, except for the genotoxicity assessment BOX3: Genotoxic through DNA reactivity?. Based on the data available for the analogs, and considering the predictions obtained with different models (Table 12), ITX has been assumed to be Table 11 Structural information regarding ITX and of identified analogs. Compound Structure Smiles ITX O = C1c2c(Sc3c1cccc3C(C)C)cccc2 Lucanthone O = C1c3ccccc3Sc2c1c(ccc2C)NCCN(CC)CC Hycanthone CCN(CCNc1c2c(Sc3c(C2 = O)cccc3)c(CO)cc1)CC IA-4 Clc1ccc2c3nn(c4ccc(c(Sc2c1)c34)CO)CCN(CC)CC IA-4-N-oxide CC[N+](CCN1c2c3c(Sc4c(C3 = N1)ccc(Cl)c4)c(CO)cc2)([O-])CC

17 290 B. Schilter et al. / Regulatory Toxicology and Pharmacology 68 (2014) Table 12 Genotoxicity data for ITX analogs. Compound Ames model a Lazar b Experimental c,d DSSTox c Kazius-Bursi d ITX Yes No Yes? Hycanthone Yes Yes Nr e Yes Lucanthone Yes Yes Yes Yes IA-4 Yes No Yes Yes IA-4-N-oxide na No Yes Yes a According to Mazzatorta et al. (2007). b c Ong (1978). d Kramers et al. (1991) e Prediction not reliable. genotoxic through DNA-reactivity. This required proceeding to BOX4 and BOX BOX4. Carcinogenic potency prediction TD 50. Predicted TD 50 values for ITX are provided in Table 13. The models used (publication in preparation) have been developed using Lazar (Lazy Structure Activity relationships), an automated procedure to build local models based on activities and structures of neighbors chemicals (Maunz et al., 2013). The datasets were composed from the CPDB (Gold database) entries developed by Bercu et al. (2010). The models obtained have similar performance as those previously Table 13 Predicted TD50 values for ITX. Compound TD50 (mg/kg bw/day) Mouse model a Rat model a ITX a Publication in preparation (see text). Table 14 Predicted LOAELs for ITX. Compound LOAEL (mg/kg bw/day) Rat model a Human model b ITX a According to Mazzatorta et al., b published (Bercu et al., 2010; Contrera, 2011). In the validation study, the percentage of TD50s predicted within a factor of 10 of the experimental values was 76 and 93 percent for respectively the rat and mouse models BOX5. Chronic toxicity predictions: (N)LOAELs. The predicted LOAELs for ITX are provided in Table Hazard characterization BOX6. Identification of the most relevant data. No experimental data were considered suitable to apply a read across approach. For both carcinogenicity and chronic toxicity, predicted values were available and considered suitable for calculating MoEs Risk characterization BOX7. Margin of exposure. Relevant MoEs are provided in Table Results and conclusion Based on the experimental and predicted toxicological information available, the MoE calculated over the predicted rat TD 50 appeared the most relevant and conservative. The size of this MoE is small and insufficient to conclude on low concern Discussion of the ITX case The outcome of the ITX exercise can be compared with those expected to result from the use of experimental data. Only limited toxicological information is currently available on ITX. Genotoxicity data in vitro were equivocal (some tests positive, others negative), while in vivo assays did not confirm any evidence of activity. Based on experimental data, ITX has been generally considered as non-genotoxic (EFSA, 2005c). Regarding toxicity, only a 28-day, sub-acute study is available. A LOAEL of 50 mg/kg bw/day was derived from that study, based on some hepatic effects at the lowest dose tested (EFSA, 2007). A MoE of about 1000 is calculated between the estimated LOAEL in rat and the estimated exposure. This MoE is very close to the margin obtained with the predicted rat LOAEL (326), considering a factor of 6 10 to adjust for the difference in exposure duration (from sub-acute to chronic). This suggests that ITX was accurately predicted by the rat LOAEL model. Overall, in absence of any data, the application of the DT would provide over-conservative outcome (ITX considered as a genotoxic carcinogen). However, with the experimental genotoxicity study Table 15 Relevant MoEs. Toxicological value MoE a Comments ITX: pred. rat TD mg/kg bw/day ITX: pred. mouse TD mg/ kg bw/day ITX: pred. rat LOAEL 15 mg/kg bw/day ITX: pred. human LOAEL 0.59 mg/kg bw/day 76 For genotoxic carcinogens, a MoE of over the TD 50 may be considered of negligible health concern. Because the exposure considered is of a short duration (< 6 months) and is likely to decrease with age, the application of a factor to the TD 50 could be envisaged. Because infants may be more susceptible to genotoxic carcinogens, and since the actual reduction in exposure later in life was not quantified, such a factor was not applied 496 Idem as above 326 For a genotoxic carcinogen, such a MoE would not be considered relevant, except if being more conservative than the one over the TD 50. A MoE of 1000, accounting for inter-species (UF = 10) and inter-individual (UF = 10) differences, as well as for the conversion of the LOAEL to a NOAEL (UF = 10) would be associated with low concern for this endpoint. Since the actual reduction in exposure later in life was not quantified, a factor to adjust for the anticipated short duration of exposure could not applied 13 For a genotoxic carcinogen, such a MoE would not be considered relevant, except if being more conservative than the one over the TD 50. A MoE of , accounting for conversion of LOAEL to NOAEL (UF = 10) and inter-individual differences (UF = 2 10) would lead to the conclusion of a low concern for this endpoint. Since the actual reduction in exposure later in life was not quantified, a factor to adjust for the anticipated short duration of exposure could not applied a Margin of exposure (MoE) = toxicological value/human exposure.

18 B. Schilter et al. / Regulatory Toxicology and Pharmacology 68 (2014) as the only information available (as it was when the case happened), the application of the DT would lead to conclusion likely to be similar to those resulting from the use of the experimental sub-acute rat study. Declaration of interest The authors declare no conflict of interest. Alessandro Chiodini is employed by ILSI Europe. Acknowledgements This work was conducted by an expert group of the European branch of the International Life Sciences Institute (ILSI Europe). The authors would like to thank Dr. Ib Knudsen for his active contribution in the early phase of this project, as well as Dr. Katrin Schütte and Dr. Hervé Nordmann for their continuous support. The expert group received funding from the ILSI Europe Risk Assessment of Chemicals in Food Task Force. Industry members of this task force are listed on the ILSI Europe website at For further information about ILSI Europe, please info@ilsieurope.be or call The opinions and conclusions expressed in this publication are those of the authors and do not necessarily represent the official opinions of their institutions, including ILSI Europe and its member companies. Appendix A. Table 1 List of public databases for toxicity endpoints. Name of toxicity database, carcinogenicity and mutagenicity Brief description Web link CCRIS CPDB (The Carcinogenic Potency Database) DIMDI ISSTOX Repro and development Tox DART ICSAS Reprotox Database (US FDA) ILSI Developmental Toxicity database Chemical carcinogenesis research information system carcinogenicity, mutagenicity, tumor promotion, and tumor inhibition data provided by the National Cancer Institute (NCI) Cancer hazards from human exposures to chemicals that cause cancer in high dose rodent cancer tests Chemical and toxicological facts of approximately 500,000 substances: e.g., carcinogenic or mutagenic effects, contact allergenic properties or threshold values Freely available toxicity (mutagenicity and carcinogenicity) databases from the Istituto di Sanita, Rome Development and reproductive toxicology (DART) is a bibliographic database on the National Library of Medicine s (NLM) Toxicology Data Network (TOXNET Ò ) The ICSAS reproductive and developmental toxicity database contains data records from FDA Compiles detailed data on specific developmental and maternal endpoints in a readily accessible electronic format ccrisfs.html e.htm index.htm cont.php?id=233&lang=1&tipo=7 dartfs.html CentersOffices/CDER/ucm htm Pages/ DevelopmentalToxicityDatabase.aspx Systemic Tox HESS DB A database of the high curated results of repeat dose toxicity tests qsar/hess-e.html Munro TTC database RepDOSE Overall, the reference database consists of a range of industrial chemicals, pharmaceuticals, food substances and environmental, agricultural and consumer chemicals containing details of species and sex and/or demonstrated a range of endpoints suitable for establishing a NOEL Repeat dose study data for dog, mouse and rat. Shows effects of chemicals on target organs. Studies are rated by reliability Databases with multiple end points ACToR Aggregated ACToR (Aggregated Computational Toxicology Resource) is a collection of databases Computational Toxicology collated or developed by the US EPA National Center for Computational Toxicology Resource (NCCT) ESIS The ESIS (European chemical Substances Information System) database heterogeneous information system which provides information on chemicals pub/159e.htm actor_help_ htm esis Leadscope databases, with qsar functionality MDL Toxicity Database Chemical structure-based data on over 150,000 known toxic substances toxds.pdf NTP National Toxicology Program Bioassay On-line (NTPBSI) Database index.cfm OECD echemportal Toxnet ToxRefDB VITIC OECD echemportal gives access to data submitted to government chemical review programmes at national, regional, and international levels. Searches multiple databases simultaneously and links to government databases Databases on toxicology, hazardous chemicals, environmental health, and toxic releases, a ToxRefDB (Toxicity Reference Database) captures thousands of in vivo animal toxicity studies on hundreds of chemicals. database from LHASA (includes Gold carcinogenicity data, National Toxicology Program mutagenicity data, HERG, hepatoxicity, mutagenicity and skin sensitisation data extracted from toxicology journals and IUCLID data index?pageid=0&request_locale=en

19 292 B. Schilter et al. / Regulatory Toxicology and Pharmacology 68 (2014) Table 2 List of public for toxicity endpoints. Model Endpoint Description Web link Reference License type Gentoxicity prediction tools Toxtree Mutagenicity/ carcinogenicity computational_toxicology/qsar_tools/toxtree Freeware open source Mechanistically-based Structural Alerts (SA) for carcinogenicity, in vitro (Ames test) and in vivo (micronucleus test in rodents) mutagenicity OECD (Q)SAR Toolbox DNA binding SAs related to electrophilic reactions involved in covalent DNA binding Oncologic Carcinogenicity Structure-based Expert System. It provides probability of carcinogenicity together with estimated potency CAESAR Lazar model Derek Nexus TOPKAT Mutagenicity/ carcinogenicity Mutagenicity/ carcinogenicity Mutagenicity/ carcinogenicity Mutagenicity/ Carcinogenicity Counter-Propagation Artificial Neural Network (CP-ANN) method based on structural descriptors Multiple k-nearestneighbor (k-nn) models, based on chemical similarities calculated over fragments linked to the biological activity of interest Knowledge-based expert system composed of SAs, example compounds, and rules QSAR models derived by using a range of twodimensional molecular, electronic and spatial descriptors Rule-based system with an open knowledge base. HazardExpert Mutagenicity/ carcinogenicity CSGenoTox Mutagenicity From chem-silico, for predicting mutagenicity MultiCase Mutagenicity and carcinogenicity Data-mining models based on connectivities and SAs that represent either activating (biophore) or inactivating (biophobe) fragments 0,3746,en_2649_34379_ _1_1_1_1,00.html silico.de predictive-toxicology.html AEGL (2001)Ankley et al. (2010) Bai et al. (2004) Woo and Lai (2005) Benigni et al. (2007b) Benigni et al. (2007a) Benigni et al. (2007b) Bitsch et al. (2006) Bitsch et al. (2006) Freeware Freeware Free access on-line Free access on-line ACD/Tox Suite Genotoxicity SA-based model MolCode Toolbox Mutagenicity Suite of different QSARs Leadscope Mutagenicity and carcinogenicity Software that may predict organ level toxicity DEREK Nexus (LHASA Numerous organ level Ltd) endpoints HESS Organ level effects from repeat dose toxicity data Data mining models Knowledge-based expert system composed of SAs, example compounds, and rules A system to predict repeat dose toxicity (and identify NO(A)EL and LO(A)EL on the basis of category formation Judson (2007) hess-e.html Leadscope Human health effects Data mining models Matthews and Contrera (2007); Yang et al. (2004) In-silico first Numerous organ level endpoints Combination of DEREK, Leadscope, MultiCASE and molecular networks

20 B. Schilter et al. / Regulatory Toxicology and Pharmacology 68 (2014) Table 2 (continued) Model Endpoint Description Web link Reference License type OECD QSAR Toolbox HazardExpert/ ToxAlert (Compudrug Inc) Prediction of biological Activity Spectra for Substances (PASS) by Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences, Moscow Tox Suite from ACD Labs ADMET Predictor from Simulation Plus It is anticipated that an increasing amount of structural information on organ level toxicity will be available. It should be noted that the purpose of this is not to provide SARs, but to allow for chemical grouping Carcinogenicity, mutagenicity, teratogenicity, membrane irritation, skin sensitisation, immunotoxicity and neurotoxicity Specific organ level effects Organ-specific toxicity in various species Various ADMET end points It is anticipated that an increasing amount of structural information on organ level toxicity will be available. It should be noted that the purpose of this is not to provide SARs, but to allow for chemical grouping Software tool for initial estimation of toxic symptoms of organic compounds in humans and in animals. HazardExpert can also consider the bioavailability of the compounds PASS predicts with high accuracy (>80%) up to 3750 biological activities for your compounds Collection of modules that predict probabilities for basic toxicity endpoints. A number of expert modules including herg Inhibition, CYP3A4 inhibition, genotoxicity, acute toxicity, aquatic toxicity, eye/skin irritation, endocrine system disruption, and health effects can be choosen ADMET Predictor not only rapidly estimates a number of vital ADMET properties from molecular structures, but is also capable of building predictive models of new properties from user s data via its integrated ADMET Modeler module 0,3343,en_2649_34379_ _1_1_1_1,00.html tox/ Products.aspx?grpID=1&cID=11&pID=13 Madden (2010b) Poroikov et al. (2000) Jurgutis et al. (2007) Matthews et al., 2004a,b Freeware Open Source Freeware Open Source to predict metabolites META from Multicase Metabolites Rule based method to predict metabolites MetaboGen from Molecular Networks MetabolExpert from Compudrug Metabolites Metabolites Rule based method to predict metabolites and also has Biopath database containing molecules, reactions and pathways involved in the endogenous metabolism Predicts the most common metabolic pathways in animals, plants or through photodegradation Rule based method to predict metabolites MetaDrug from Metabolites Genego Metaprint2D Metabolic sites Rule based method to predict potential metabolic sites products.shtml~metadrug metaprint2d-react/ Stranz et al. (2008) Boyer et al. (2007) Freeware Open Source (continued on next page)