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QSAR screening of 70,983 REACH substances for genotoxic carcinogenicity, mutagenicity and developmental toxicity in the ChemScreen project Eva B. Wedebye ∗ , Marianne Dybdahl, Nikolai G. Nikolov, Svava Ó. Jónsdóttir, Jay R. Niemelä 1 Division of Toxicology and Risk Assessment, National Food Institute, Technical University of Denmark, Mørkhøj Bygade 19, 2860 Søborg, Denmark

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Article history: Received 30 April 2014 Received in revised form 8 March 2015 Accepted 11 March 2015 Available online xxx Keywords: REACH QSAR In silico Screening Genotoxic carcinogenicity Mutagenicity Developmental toxicity

a b s t r a c t The ChemScreen project aimed to develop a screening system for reproductive toxicity based on alternative methods. QSARs can, if adequate, contribute to the evaluation of chemical substances under REACH and may in some cases be applied instead of experimental testing to fill data gaps for information requirements. As no testing for reproductive effects should be performed in REACH on known genotoxic carcinogens or germ cell mutagens with appropriate risk management measures implemented, a QSAR pre-screen for 70,983 REACH substances was performed. Sixteen models and three decision algorithms were used to reach overall predictions of substances with potential effects with the following result: 6.5% genotoxic carcinogens, 16.3% mutagens, 11.5% developmental toxicants. These results are similar to findings in earlier QSAR and experimental studies of chemical inventories, and illustrate how QSAR predictions may be used to identify potential genotoxic carcinogens, mutagens and developmental toxicants by high-throughput virtual screening. © 2015 Elsevier Inc. All rights reserved.

1. Introduction Quantitative structure–activity relationships (QSARs) are mathematical models that can be used to predict physical–chemical, biological, e.g. toxicological, and environmental fate properties of molecules based on their chemical structure. A QSAR is a mathematical model, most often a statistical correlation, relating one or more quantitative so-called chemical descriptors derived from the chemical structure to a quantitative measure of a property or activity, e.g. an (eco)toxicological endpoint. QSARs are quantitative models that yield either a continuous or categorical yes/no result [1]. A QSAR model thus links information on the chemical structure of substances with a specific property, and can subsequently be used for predicting the same property for other substances. Reliability within a defined chemical applicability domain for a model should be established by robust validation. Prediction for substances within this applicability domain can be expected to be

∗ Corresponding author. Tel.: +45 35887604. E-mail address: [email protected] (E.B. Wedebye). 1 Presently retired.

associated with the established reliability. QSAR models can be powerful tools for predicting chemically induced adverse effects, and with today’s speed of computer hardware it is possible to develop large models and screen large inventories cost-effectively. Results from QSARs may be used under the European Union regulation concerning the Registration, Evaluation, Authorisation & restriction of Chemicals, REACH, when their scientific validity has been established, the substance falls within the applicability domain, the results are adequate for classification and labelling and/or risk assessment, and adequate and reliable documentation of the method is provided. There are no fixed criteria for the regulatory acceptance of QSARs given in the REACH guidance. The industry registrant of a chemical must argue for using the QSAR data in the Registration process. Experience and common understanding should be gained by a learning-by-doing approach and by documenting the learnings [1]. QSAR predictions can in principle be applied, for example, to provide information for priority settings of chemicals, guide experimental design of experimental tests or testing strategies, improve evaluation of existing test data, provide mechanistic information, and fill data gaps needed for hazard and risk assessment, classification and labelling, and PBT (Persistent, Bioaccumulative and Toxic) or vPvB assessment (very Persistent and very Bioaccumulative) [1].

http://dx.doi.org/10.1016/j.reprotox.2015.03.002 0890-6238/© 2015 Elsevier Inc. All rights reserved.

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Other possible uses of QSAR predictions may be in hypothesis generation for making meaningful groupings and read-across. This is because QSARs are based on statistical analysis that can reveal the chemical properties that are significantly associated with the activity and others that possibly modulate the activity giving rise to a trend development over a congeneric series of chemicals. Furthermore, predictions from QSAR models for molecular and toxicokinetic endpoints may contribute in building and justifying groupings and read-across. According to the 2012 REACH progress report from ECHA, predictions and other non-standard methods can serve to build up a “fuller picture” of the substance property as part of a weight of evidence approach or for designing a testing strategy even if the property cannot be predicted adequately for REACH and the EU Regulation on classification, labelling and packaging of substances and mixtures, CLP, using the technique alone [2]. Under REACH all other options, including use of QSARs, should be considered before performing or requiring vertebrate testing [3]. This includes the need to gather all existing information on physical–chemical, toxicological and eco-toxicological properties of a substance, hereunder information generated by QSARs and other non-test methods. Especially for low tonnage substances, QSARs and other non-test methods may furthermore increase the level of information beyond the REACH requirements. The REACH endpoint guidance document states that there is a large number of potential targets/mechanisms associated with reproductive toxicity that cannot be adequately covered by a battery of QSAR models on the basis of current knowledge [4]. A negative result from current QSAR models can therefore not be interpreted as demonstrating the absence of a reproductive hazard unless there is other supporting evidence. So although REACH strongly encourages the use of non-test and in vitro methods to avoid vertebrate animal testing, QSAR results may in the near future not be adequate as a stand-alone-method to replace whole-animal reproductive toxicity testing. However, results from QSAR models may as mentioned contribute to a weight of evidence approach together with other data and they may also be used as supporting evidence in grouping and read-across approaches [4]. This way they may contribute to reducing animal testing for these endpoints [5]. Moreover, it is stated in the REACH Annexes VII–X with the tonnage-dependent standard information requirements that no testing for reproductive effects should be performed in REACH on known genotoxic carcinogens or germ cell mutagens with appropriate risk management measures implemented [3]. According to the REACH Guidance on information requirements and chemical safety assessment [6], QSAR results are among the factors that can influence the testing requirements. QSAR predictions for genotoxic carcinogens or germ cell mutagens can, if adequate also in this way contribute to reducing testing for reproductive toxicity. With particular focus on reproductive toxicity the EU FP 7 ChemScreen project aimed to make a rapid and cost-effective screening system by applying alternative existing and novel testing methods. In the present study a QSAR pre-screen for CMR effects: genotoxic carcinogenicity, mutagenicity and developmental toxicity was performed, as these effects as mentioned are of relevance in an alternative screening system to reduce in vivo testing for reproductive toxicity. Predictions for in vitro human oestrogen receptor activation and androgen receptor antagonism were furthermore included to explore their relation to the predictions for developmental toxicity. The pre-screening was sought to cover as many of the 143,835 REACH pre-registered substances (PRS) as possible, and comprised a prediction set of 70,983 discrete organic PRS for which structural information was found and was suitable for the applied QSAR models. The prediction set was run through sixteen models. To integrate the predictions and increase prediction accuracy beyond the

individual model level, decision algorithms were applied to reach overall predictions for genotoxic carcinogenicity, mutagenicity and developmental toxicity. In agreement with what is stated above that result from current QSAR models cannot in themselves demonstrate the absence of a reproductive hazard, the pre-screen was set up to produce a ‘positive list’ of potential CMR substances. The QSAR results were fed into subsequent ChemScreen activities to contribute towards the overall objective of ChemScreen to develop a framework for an innovative alternative testing strategy [7]. The positive QSAR predictions may as outlined above if accepted be used “as is” to deselect further in vitro and/or in vivo testing or be applied in weight of evidence assessments.

2. Material and methods 2.1. REACH substances included in the pre-screening To make the QSAR screening as comprehensive as possible it was based on as many as possible of the 143,835 substances, which were pre-registered between June and December 2008. It is a prerequisite for predicting the chemicals in the QSAR software that structural information in the format of e.g. SMILES (simplified molecular input line entry system, e.g. [8]) or SDF (structure-data file [9]) is available. As structural information was not required as part of the pre-registration and as no formal structure set on the PRS from ECHA exists, we had to rely on other sources for the structural information. The Computational Toxicology Group within the European Commission Joint Research Centre (JRC) has generated structure information for REACH pre-registered substances by using the ACDLabs Name-To-Structure software and validated them with a random sample [10]. In total 80,413 structures were generated, including discrete organics, inorganics, etc. The QSAR software applied in the project can handle organic chemicals with an unambiguous structure, meaning no mixtures/UVCB’s,2 organometallics or, inorganics, i.e. so-called discrete organics which for this project were defined as: • Containing at least two carbon atoms • Containing only H, Li, B, C, N, O, F, Na, Mg, Si, P, S, Cl, K, Ca, Br, or I • Substances which are not mixtures containing two or more organic components when dissociated at ionic bonds Both SMILES and SDF records supplied by JRC were processed. The SMILES version resulted in fewer accepted compounds so the SDF files were used. The records were converted to an OASIS Database Manager 1.7 database [11]. In the process a small number of invalid records and CAS duplicates were identified leaving a total of 80,394 imported records. Subsequent processing included dissociation simulation to neutralize the substances, exclusion of inorganic substances, substances with only one carbon atom, mixtures, generic structures, structures containing heavy and other inacceptable atoms or inappropriate ions, and structures with valence errors. The structures were exported as 2D SMILES which is the input format required by MultiCASE QSAR software and a check was performed in MultiCASE to identify further structures that were not accepted and these were removed. This resulted in a structure set of 70,983 REACH pre-registered substances ready for QSAR predictions.

2 Substance of Unknown or Variable composition, Complex reaction products or Biological materials.

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2.2. Applied QSAR models A number of commercial or DTU in-house QSAR models for CMR endpoints were applied in the pre-screening. The QSAR models were chosen as they were very relevant for predicting CMR effects and on the basis of their predictive performance. Although the models used are not necessarily the best predictive tools for all chemical classes, they are capable of predicting many chemical classes of substances and therefore suited to make the intended high-throughput virtual screening of the diverse PRS list. QSAR models for the following endpoints were applied: Genotoxicity in vitro: • • • •

Reverse mutation test, Ames Chromosomal aberrations in Chinese Hamster Ovaries (CHO) Chromosomal aberration in Chinese Hamster Lung cells (CHL) Mouse lymphoma cell gene mutation test Genotoxicity in vivo:

• Rodent dominant lethal test3 • Sex-Linked Recessive Lethal (SLRL) test in Drosophila melanogaster3 • Comet assay in mouse4 • Sister chromatid exchange assay in mouse bone marrow5 • Mouse mammalian bone marrow erythrocyte micronucleus test5 Carcinogenicity in vivo: • FDA Cancer models for male/female Rat and male/female Mouse - and ICSAS method to combine the four cancer models into an overall call Teratogenicity in vivo: • Teratogenic potential in humans6 Endocrine activity in vitro: • Human oestrogen receptor activation • Human androgen receptor antagonism Detailed information on the number of chemicals in the training sets of individual models as well as their accuracy as determined by robust leave-many-out cross-validation and/or external validation is given in the supplementary information: Further information on the models and/or their training sets can be found in [26–45]. Furthermore, documentation in the QMRF format7 has been made for all models and submitted to the OECD and are available from the OECD (Q)SAR Application Toolbox, which can be downloaded from the OECD homepage [13]. The training sets for all the models made by DTU have likewise been made available in the OECD Toolbox. 2.3. QSAR and the applied software The applicability domain of a QSAR should be clearly defined. When applying QSARs it is important to assure that an obtained

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Indicator test for germ cell structural chromosome aberration. Indicator test for somatic cell DNA damage/mutation. Indicator test for somatic cell structural chromosome aberration. 6 Based on epidemiological and clinical data for drugs. 7 QSAR Model Reporting Formats; a harmonised template for reporting key information on structured according to the OECD (Q)SAR validation principles [12]. 4 5

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prediction falls within the applicability domain of the models, i.e. that there is sufficient similarity regarding relevant chemical descriptors between the query substance and the substances used in the training set of the model. The definition of the applicability domain should ensure that predictions for chemicals outside the training set are made by interpolation rather than extrapolation. However, there is no single and absolute applicability domain for a given model [1]. Generally, the broader the applicability domain is defined the lower predictivity can be expected. The applicability domain definition applied in this study is described at the end of this section. A validation is a trial of the model’s performance for a set of substances independent of the training set, but within the domain of the model. The model predictions for these substances are compared with the experimentally determined values of the endpoints for the substances in order to establish the predictive performance of the model. Ideally, all models should be assessed by checking how well they predict a sufficiently large and representative set of chemicals, which were not used to make the models. This is, however, not always simple. In part valuable information may be left out by setting aside chemicals to be used in such an evaluation, reducing the applicability domain of the model. This can especially be a problem in cases when a relatively small training set is used. In part it can be difficult to assess how “external” chemicals relate to the model’s domain; that is, if they represent an even and adequate distribution within this applicability domain, thereby giving a fair picture of the predictive performance of the model. This can to a degree be addressed by using some sort of stratified rather than random selection to take out chemicals for the validation set, yet in such case the validation set is intentionally made similar and can possibly render overoptimistic prediction performances. Also, stratified selection requires many choices on method and chemical descriptors. This problem is often addressed by using one or another form of cross-validation, where a number of partial models are “externally validated” by dividing the training set into a reduced training set and a testing set. The reduced training set is used to develop a partial model, where no information from the “mother model” is transferred in the selection of descriptors, while the remaining data are used as a test set to evaluate the model predictivity. This is repeated a number of times and the results are pooled to calculate the predictivity measures for the models. For continuous models, performance measures such as the cross-validation correlation coefficient Q2 and standard deviation error of prediction, SDEP, are often used. For categorical yes/no models the predictive performance is normally calculated as sensitivity which is the ability to correctly predict positives, specificity which is the ability to correctly predict negatives and concordance which is the overall accuracy, see also e.g. [1,14] for further details. All the models applied in this project were validated with the stable leave-many-out, LMO, cross-validation approach. The training set of each model was split by random, however keeping the positive/negative balance of the training set in the subsets, into two portions of 50% of the chemicals. Models on each of the reduced sets were made, and each model predicted the other portion of the training set. This was repeated five times. Only predictions within the generically defined applicability domains, as described in the last paragraph of this section, of the models made on the reduced sets were used. Leaving out 50% of the chemicals in the partial validation models is a large perturbation of the training set. According to Gütlein et al. [15] cross-validation can reduce the variance of the results compared to external validation, as well as underestimate the performance on unseen compounds, i.e. it can produce robust and “pessimistic” measures. The models applied in this project are developed and hosted in the MultiCASE MC4PC v.2.3 software. MultiCASE is a commercial

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artificial intelligence software system [16], which can make socalled global QSAR models covering many classes of chemical substances. In the creation of a model, the programme analyses a training set of chemicals with known activity and starts by dividing each chemical into fragments containing 2–10 interconnected non-hydrogen atoms. Furthermore, so-called 2D distance descriptors which are distances between groups in the molecule are tabled. These fragments and distance descriptors are labelled with the experimental value of the parent chemical. MultiCASE determines the distribution of all fragments and distance descriptors among the chemicals in the training set. The distribution of the fragments and distance descriptors are assumed to be binomial. If a fragment or distance descriptor is over-represented in the group of active or inactive chemicals it is assumed to be relevant for the modelled activity. If the fragment or descriptor distance is not significantly overrepresented in active or inactive chemicals it will not be considered important. A fragment or distance descriptor with a statistical correlation to active or inactive chemicals is called a biophore or a biophobe, respectively. When all fragments and distance descriptors have been examined for their importance to activity, a hierarchical selection takes place, starting with the biophore with the most statistical significant result. Chemicals containing this substructure are set aside and the next biophore is found in the same manner. This is repeated until either the entire training set is used or there are no more statistically significant fragments or distance descriptors. The whole procedure is then performed for biophobes in an identical way. Each group of chemicals containing a biophore or biophobe is then analyzed to find possible modulators that either enhance or decrease the probability of the fragment being a biophore/biophobe. The modulators can be structural fragments, 2D distance descriptors or chemical properties, e.g. activating fragments, deactivating fragments, log Kow , or molecular orbital energies. Local QSAR models for each set of biophore training set chemicals are created by multiple linear regressions using the modulators found relevant. This leads to a final global model which in reality is an integrated system of local models. When a model is developed and MultiCASE predicts the activity for a chemical, the programme first looks for biophores contained in the chemical. If it identifies a biophore it then looks for modulators to calculate the activity of the chemical. When MultiCASE predicts the activity of chemicals, it produces a system of output values. These values were further processed in this project by us to define positive, negative and equivocal outcomes. Positive means that the model predicts that if this chemical would be tested, e.g. in the Ames test, the results would likely be positive for Ames mutations, i.e. the substance would be an Ames mutagen. The opposite goes for the negative predictions. Equivocal means that a definite call could not be made, as it can also happen in experimental tests. Equivocal predictions were not seen as reliable and were not used. The applicability domains for MultiCASE models as defined by the US Food and Drug Administration (FDA) [17], and implemented in the MultiCASE software were used in this project. This means that no warnings in the predictions were accepted, except warnings for one unknown fragment in chemicals predicted positive. Only positive predictions with no significant deactivating fragments were accepted.

2.4. Pre-screening 2.4.1. Genotoxic carcinogenicity QSAR models for four carcinogenicity in vivo endpoints and three genotoxicity in vitro endpoints were included in the screening performed according to the decision algorithm illustrated in Fig. 1. The algorithm was designed to identify genotoxic

Fig. 1. QSAR pre-screening decision algorithm for genotoxic carcinogenicity.

carcinogens specifically, and carcinogens acting by other mechanisms were not considered. The criterion for an overall positive cancer call was that there was a positive outcome from the ICSAS8 methodology [17] implemented in MultiCASE developed in collaboration with the U.S. Food and Drug Administration. This methodology compares the biophores identified in the individual models for male and female rat and mouse carcinogenicity modules and identifies transgender and transspecies biophores. If, for an evaluated substance, one or more positive experimental tests was present as part of the training sets for the models for any cancer endpoint, this took precedence over model predictions. The genotoxicity criterion was a positive prediction from one or more of the models for the following in vitro genotoxicity endpoints; Ames reverse mutation test, chromosomal aberrations in CHO or CHL cells, or mutations in mouse lymphoma. 2.4.2. Germ cell mutagenicity Five models predicting genotoxicity in vivo endpoints were included in the screening which was performed according to the decision algorithm illustrated in Fig. 2. Two of these endpoints, Drosophila m. SLRL and Rodent dominant lethal, relate directly to germ cell mutagenicity, and the three other endpoints measure somatic cell genotoxicity. The criterion for a positive mutagenicity prediction is that there is a positive prediction in two or more models. If one or more positive tests were part of the training sets for the models for any genotoxicity endpoint, this took precedence over model predictions. Substances with at least one positive prediction or training set chemical in either one of the models for Drosophila m. SLRL

8 The Informatics and Computational Safety Analysis Staff at the U.S. Food and Drug Administration.

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Fig. 2. QSAR pre-screening decision algorithm for mutagenicity.

Fig. 3. QSAR pre-screening decision algorithm for developmental toxicity.

and/or Rodent dominant lethal were flagged in the result file. The remaining positive calls based on two positive predictions coming solely from the models for Mouse micronucleus and/or sister chromatid exchange and/or Comet assays were flagged in the result file as originating from models predicting somatic cell genotoxicity. The EU classification guidance states that “where there is evidence of only somatic cell genotoxicity, substances are classified as suspected germ cell mutagens” [18]. 2.4.3. Developmental toxicity Three models predicting in vivo teratogenicity or foetal lethality related endpoints have been included in the screening which was performed according to the scheme illustrated in Fig. 3. The criterion for a positive developmental toxicity prediction is a positive prediction in any of the three models and without a negative prediction in the model for teratogenic risk in humans. The QSAR models applied cover certain but far from all types of harm to the unborn child as they only address malformations or foetal mortality. Two models predicting human oestrogen receptor activation and androgen receptor antagonism in vitro were furthermore included in the screening for possible additional information in relation to developmental toxicity. 3. Results and discussion We have carried out a pre-screening of as many as possible of the 143,835 REACH pre-registered substances, PRS, namely a prediction set of 70,983 discrete organic substances for which structural information were found and were suitable for the applied QSAR

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models. The QSAR pre-screen included genotoxic carcinogenicity, mutagenicity, limited developmental toxicity, as well as endocrine disruption in the form of human oestrogen receptor activitation and androgen receptor antagonism in vitro. Predictions for the full set were made with sixteen QSAR models and integrated in a database in the OASIS Database Manager 1.7 software. In order to increase prediction accuracy beyond the individual model level, decision algorithms were applied to reach overall predictions for genotoxic carcinogenicity, mutagenicity and developmental toxicity according to the schemes in Figs. 1–3. The applied algorithms are only for positive overall calls. No distinction has been made between a negative prediction for an endpoint and an unreliable prediction, i.e. a prediction outside the applicability domain of the model, which was simply discarded. Evaluated substances without positive calls for one or more of the selected effects, may have been predicted as not having this/these dangerous property(ies), or the models may not have been valid for this substance, i.e. the predictions were outside the applicability domain for these models. The individual models have individual applicability domains, and no attempt was made to define overall applicability domains for the applied combinations of models in the decisions algorithms. The numbers of substances with positive QSAR calls for the included effects are given in Fig. 4. Out of the total of 11,562 substances predicted as potential mutagens, there were 3695 substances, corresponding to 5.2% of the 70,983 predicted, with positive predictions or positive training set substances in one or both of the included two models for germ cell mutagenicity endpoints, Drosophila m. SLRL and/or Rodent dominant lethal. The remaining 7867 mutagenicity calls, corresponding to 11.1% of the 70,983 predicted, were based solely on somatic cell genotoxicity predictions. Out of the total of 8172 substances predicted positive for developmental toxicity, 5068 had a positive prediction in the teratogenic potential model and 3765 had a positive prediction in one or both of the applied genotoxicity models for Drosophila Sex-Linked Recessive Lethal and Rodent dominant lethal tests. Out of the 5068 substances predicted positive by the model for human teratogenicity potential, 661 substances (13%) were also predicted positive by one or both of the genotoxicity models. For these substances the predicted reproductive toxicity effect may be due to mutation in germ cells or mutation may be an active mechanism prior to the teratogenic effect. In many cases, a toxicological threshold is assumed to exist for reproductive toxicity. With mutagenic chemicals this may not be the case, and they may therefore be of even greater concern (e.g. [19]). Of the 4614 substances flagged for genotoxic carcinogenicity 417 substances (9.0%) had experimental data in at least one of the training sets of the four applied cancer models and in at least one of the four applied in vitro genotoxicity models, see Fig. 1, i.e. for these substances the prescreening result are based solely or partly on experimental data. Of the 8172 substances flagged for developmental toxicity 447 substances (5.5%) had experimental data in at least one of the training sets of the three applied models, i.e. the call is based solely or partly on experimental data, see Fig. 2. Of the 3695 substances flagged for germs cell mutagenicity 240 substances (6.5%) had experimental data in at least one of the training sets of the two applied models, and of the 7867 substances flagged for somatic cell genotoxicity 390 substances (5.0%) had experimental data in at least one of the training sets of the three applied models, i.e. these calls are based solely or partly on experimental data, see Fig. 3. Some counts of the overlap of substances with positive predictions for the different endpoints revealed that altogether there were 19,335, corresponding to 27.2%, having a positive call for either genotoxic carcinogenicity (C) and/or mutagenicity (M) and/or developmental toxicity (R). 776 substances, corresponding to 1.1%, were predicted positive for both C, M and R. 1711 substances,

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Fig. 4. Numbers and percentages of substances predicted positive for the selected effects out of the prediction set of 70,983 discrete organic REACH pre-registration substances.

corresponding to 2.4%, were predicted positive for both C and M, or in other words 37.1% of the substances predicted positive for C were also predicted positive for M and 14.8% of the substances predicted positive for M were also predicted positive for C. It is not surprising that there are differences between the substances predicted positive for C and M, as the models are different in terms of endpoints and applicability domains of the models. Overlap between the substances predicted positive in the model for human teratogenicity potential and the models for human in vitro oestrogen receptor activation and androgen receptor antagonism were also investigated. According to the literature, endocrine disrupters may lead to reproductive disorders in adults, tumour developments in adults and offspring, deterioration of genital organ development in offspring and developmental neurotoxicity in offspring. The mechanisms involved may be altered hormonal function through receptor recognition/binding, altered hormone biosynthesis, altered hormone storage and/or release, altered hormone transport and clearance, altered post-receptor activation and others [20]. The in vitro tests for androgen receptor antagonism and activation of the oestrogen receptor are very sensitive, but only cover part of the possible endocrine disruption mechanisms in reproduction. Of 5068 chemicals predicted positive in the model for human teratogenicity potential, positive predictions were found for 382, corresponding to 7.5% in the androgen receptor antagonism model and 158, corresponding to 3.1%, in the oestrogen receptor activation model. These results are similar to results found in [19]. As QSARs are models they are associated with uncertainty. The uncertainty of QSARs is caused predominantly by (a) the inherent variability of the training set data which are results from experimental tests used to establish the model; and (b) the uncertainty resulting from the fact that a model can only be a partial representation of reality. However, as a model averages the uncertainty over all training set chemicals, it is possible for an individual model estimate to be more accurate than an individual measurement [1]. The QSAR models applied in this project have according to cross-validation results sensitivities in the range of 30–84% and specificities in the range of 77–95% within the defined applicability domains of the models. Where external validations were available, i.e. for the AR antagonism model and the total suite of FDA cancer models, sensitivities were in the range of 57–59% and specificity was 98% (see supplementary information). With the exception of the model for Ames, which has both high sensitivity and high specificity, all the models applied had higher specificity than sensitivity.

Our purpose with the QSAR pre-screening was to generate CMRrelevant information for as many as possible of the pre-registered substances. When designing the prediction algorithm, both on single model level and when integrating predictions from multiple models, decisions about preferences for high sensitivity versus high specificity need to be made. In this project, rather than going for “high sensitivity” to “catch” as many as possible of the potential CMR substances among REACH PRS, the strategy applied was to aim for “high specificity” predictions so that when a positive call came out it would be more reliable, and therefore of higher quality in further processing. This means that there will be substances not identified since the sensitivities of the applied models are 30–84%. For these substances the situation is “status quo” and they can e.g. be directed to in vitro testing. A number of combinations of the available QSAR models were analyzed and discussed with the ChemScreen partners, and in the end the algorithms presented in Figs. 1–3 were applied for the present study. The applied algorithms were also applied in e.g. [21]. With the applied algorithms for prediction combinations, specifically for genotoxic carcinogens and mutagens where more models were required to give positive predictions, we attempted as mentioned above to reduce the number of false positives, as it should be less likely that complementary systems produce erroneous predictions for the same substances. The results presented in Fig. 4 may raise the question of what is the “true proportion” of substances which are carcinogenic, mutagenic and/or toxic to reproduction in industrial chemical substances inventories. To address the question of the proportions of mutagens among chemicals in commerce Zeiger et al. [22] created a database of 100 chemicals from a random selection of chemicals. The 100 chemicals were statistically representative of 65,725 U.S. substances covering cosmetic ingredients, pesticides and inert ingredients of pesticide formulations, drugs and drug formulation excipients, food additives and chemicals in commerce. These chemicals were tested for mutagenicity in Salmonella and 22% were found to be mutagenic. Furthermore, the mutagenicity of the 46 highest U.S. production organic chemicals was also compiled and 20% were found to be mutagenic. These values were claimed to provide a more accurate estimate of the proportions of mutagens among chemicals in commerce than can be derived from published mutagenicity databases, where for example Zeiger et al. referred to investigations showing proportions of 35% (522/1497) Salmonella mutagens in the U.S. National Toxicology Program genetic toxicity database, 39% (123/319) mutagens in the Gold carcinogenicity

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database, and 56% (603/1078) Salmonella mutagens in the U.S. EPA Gene-Tox database. These databases may be biased towards more positives, as chemicals are usually selected for testing based on a suspicion of activity. Also, results of tests showing no mutagenic activity may be under-reported in the scientific literature because of reluctance to publish negative results. In an in silico investigation of whether U.S. High Production Volume, HPV, chemicals are more or less toxic than “average” chemicals Cunningham et al. [23] used the CASE/MultiCASE programme to predict a sample of 200 chemicals from the U.S. Environmental Protection Agency’s HPV Chemical Challenge Program and a reference set of 10,000 chemicals. The reference set was, again, statistically representative of 65,725 U.S. substances covering cosmetic ingredients, pesticides and inert ingredients of pesticide formulations, drugs and drug formulation excipients, food additives and chemicals in commerce. The 200 chemicals were randomly selected and were (a) pure and unique substances; (b) organic; (c) nonpolymeric; and (d) not containing metals. Both sets were processed through a number of models, covering among other things in vivo genotoxicity, carcinogenicity and developmental toxicity. The numbers of substances predicted positive were listed. For all toxicity effects assessed except for the in vitro induction of sister chromatid exchanges, the proportion of chemicals predicted positive among the HPV sample was significantly smaller than the proportion of chemicals predicted positive in the reference set. Based on an algorithm for in vivo genotoxic substances the percentages of positives were 8.0% and 23.0% for the HPV and reference sets, respectively. For genotoxic carcinogens the percentages of positives were 4.5% and 16.0%, respectively. For developmental toxicity in humans the percentages were 3.0% and 16.4%. We note that smaller sets, here 200, should be used with caution, especially when compared to much bigger ones, as a small set may be more sensitive to the actual selection of chemicals. That being mentioned, the results from the Cunningham study showed that the chemicals in the highest tonnages, as it could be hoped, seems skewed towards fewer chemicals with CMR properties, although according to their QSAR screening also a significant part of the HPV chemicals did possess these properties. In a further attempt to spread light on the question of the “true proportion” of CMR substances within the EU chemical substances, counts from harmonized classifications published online in the ECHA Classification & Labelling Inventory Database [24] were performed. The counts were based on entries as returned by the online ECHA database, where for some Index Numbers there may be more than one entry in terms of EC and CAS numbers. Furthermore, a small number of chemicals, in terms of EC or CAS numbers, may fall under more than one Index Number. It is also important to note that some Index Number may cover substantially more than the CAS numbers specified, but no official exhaustive list exists of all REACH chemicals that fall under the individual so-called group entries. It should also be noted that some harmonized classifications may be due to expert judgement that does not adhere strictly to a specific animal test result. Also, for some entries not all effects have been assessed so lack of classification for some endpoints for a chemical which appear on the list of substances with harmonized classifications is not necessarily due to the chemical having been assessed not to have these hazardous properties. Likewise, if a substance used in the EU does not appear on the list of substances with harmonized classification, it may be because hazardous effects were not yet identified due to lack of experimental data. Counts were made of harmonized classifications for mutagenicity, carcinogenicity and reproductive toxicity. Please note that the carcinogenicity classifications cover both genotoxic and other cancer mechanisms, and classifications for reproductive toxicity cover sexual function, fertility and development.

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The total number of entries identified by us in this exercise in the ECHA database was 4522, herunder 3973 with a health hazard classification. Of these, 1215 had any of the three possible carcinogenicity classifications corresponding to 26.9% of the total number of entries in the ECHA database: 336 Carc. 1A, 681 Carc. 1B, and 198 Carc. 2. There were 620 entries corresponding to 13.7% of the total number of entries in the ECHA database having any germ cell mutagenicity classification: 0 Muta. 1A, 425 Muta. 1B, and 195 Muta. 2. There were 359 entries (7.9%) having any of the classifications for reproductive toxicity – 24 Repr. 1A, 199 Repr. 1B and 136 Repr. 2. In total, there were 1480 entries (32.7%) with a harmonized classification for C and/or M and/or R. The harmonized classifications may be biased towards more positives, again as chemicals are usually selected for testing based on a suspicion of activity and as results of tests showing no mutagenic activity may be under-reported in the scientific literature. However, there may also be bias in the opposite direction, as most experimental testing has been performed on HPV chemicals, and they may therefore be “overrepresented” in the ECHA harmonized classifications database in comparison with the full list of pre-registered substances. The HPV substances should hopefully, as also indicated in the Cunningham study summarized above, have a lower percentage of CMR substances than the average. The main results from Zeiger et al., Cunningham et al., the ECHA database with harmonized EU classifications and the ChemScreen pre-screening are integrated in Table 1. In relation to cancer, the ChemScreen pre-screening identified 6.5% potential genotoxic carcinogens out of 70,983 REACH substances. The Cunningham study predicted 4.5% of 200 U.S. high tonnage substances and 16.0% of 10,000 representatives of 65,725 U.S. substances as genotoxic carcinogens. In the ECHA database with EU harmonized classifications 26.9% of the 4522 entries had classifications for cancer. Of these results, the Cunningham study of 10,000 U.S. substances seems most comparable to the ChemScreen pre-screening, and in this comparison ChemScreen identified approximately a factor 2.5 fewer percents. In the Cunningham study, four cancer models and a model for Salmonella mutagenicity were used in combination to reach the call for genotoxic carcinogenicity. There can be many reasons for the difference in predicted percentages in the Cunningham and ChemScreen studies, but we note that they screen different chemical universes and use different models and decision algorithms. Furthermore, for the cancer part of the selection algorithms applied in the two studies, the RCA methodology used in the ChemScreen pre-screening requires two positive cancer predictions out of four models plus identified transgender or transspecies biophores, while the method used in the Cunningham study can in some cases reach a positive call based on a single model [25]. This may at least partly explain the higher percentage found in the Cunningham study. In relation to mutagenicity, the ChemScreen pre-screening identified 5.2% potential in vivo germ cell mutagens and in total 16.3% potential in vivo mutagens out of 70,983 REACH substances. The Zeiger study reported 22% experimental Salmonella mutagens out of 100 chemicals statistically representative of 65,725 U.S. substances and 20% experimental Salmonella mutagens out of the 46 highest U.S. production organic chemicals. The Cunningham study predicted 8.0% of 200 U.S. high tonnage substances and 23.0% of 10,000 representatives of 65,725 U.S. substances as in vivo genotoxic. In the ECHA database with EU harmonized classifications, 13.7% of the 4522 entries had classifications for germ cell mutagenicity. Of these results, the Cunningham study of 10,000 U.S. substances seems most comparable to the ChemScreen pre-screening, and in comparison with this the ChemScreen study identified a little lower percent. In the Cunningham study predictions from two models, one for in vivo micronucleus assay and one for Salmonella mutagenicity, were required to be positive to reach the call for in vivo

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Table 1 Percentages of substances experimentally tested, predicted positive by QSAR models or with harmonized EU classifications for carcinogenicity, mutagenicity or reproductive toxicity in studies reported in the literature, the ECHA classification database and the ChemScreen pre-screening. Study

Number of chemicals investigated

Zeiger et al. [22]

46 U.S. highest tonnage substances 100 representative of 65,275 U.S. substances

Cunningham et al. [23]

200 U.S. high production volume substances 10,000 presentative of 65,725 U.S. substances

4.5b 16.0b

8.0%c 23.0%c

3.0d 16.4d

EU harmonized classifications [24]

4522 entries

26.9e

13.7%f

7.9g

ChemScreen pre-screening

70,983 REACH PRS

a b c d e f g h i

Cancer (%)

Mutagenicity

Reproductive toxicity (%)

20%a 22%a

6.5b

5.2%h /16.5%c

11.5i

Salmonella experimental tests. Genotoxic carcinogenicity predictions. In vivo genotoxicity predictions. Developmental toxicity in humans predictions. Carc. 1A, Carc. 1B or Carc. 2 EU harmonized classifications. Muta. 1A, Muta. 1B or Muta. 2 EU harmonized classifications. Repr. 1A, Repr. 1B or Repr. 2 EU harmonized classifications. In vivo germ cell mutagenicity predictions. Developmental toxicity in humans or foetal lethality related genotoxicity predictions.

genotoxicity. In the ChemScreen pre-screening predictions from two out of five models for in vivo genotoxicity were required to be positive. All else being equal, these algorithms could give expectations that the ChemScreen pre-screening would give a higher predicted percentage of in vivo mutagens than the Cunningham study, but some reasons why the opposite is the case can be that the two studies screen different chemical universes and use different models with different sensitivities and applicability domains. In relation to reproductive toxicity, the ChemScreen prescreening identified 11.5% substances with potential developmental toxicity out of 70,983 REACH substances. The Cunningham study predicted 3.0% of 200 U.S. high tonnage substances and 16.4% of 10,000 representatives of 65,725 U.S. substances with human developmental toxicity. In the ECHA database with EU harmonized classifications, 7.9% of the 4522 entries had classifications for reproductive toxicity. Of these results, the Cunningham study of 10,000 U.S. substances again seems most comparable to the ChemScreen pre-screening, and in comparison with this the ChemScreen pre-screening identified a little lower percent. In the Cunningham study predictions from a model for human developmental toxicity were required to be positive. In the ChemScreen pre-screening, predictions from at least one out of three models, one for human developmental toxicity and two for in vivo germ cell mutagenicity, were required to be positive, and the human developmental toxicity model was required not to be negative to reach a final call for developmental toxicity. The model used in the ChemScreen prescreening for human developmental toxicity is in fact the same model as used in the Cunningham study, however in a newer version of the software. Again, all else being equal, these algorithms could give expectations that the ChemScreen pre-screening would give a higher predicted percentage of substances with developmental toxicity than the Cunningham study. Some reasons why this was not the case can be that the two studies screen different chemical universes and that the human developmental toxicity models are different versions, which may have different sensitivities and defined applicability domains. 4. Conclusion As a contribution to the EU ChemScreen project’s aim to make a rapid and cost-effective screening system with a special focus on reproductive toxicity testing under REACH, an in silico QSAR pre-screen to identify substances potentially positive for genotoxic carcinogenicity, mutagenicity and a limited number of developmental toxicity effects was performed. Predictions for in vitro human oestrogen receptor activation and androgen receptor

antagonism were furthermore included. If adequate, results from QSARs may be applied under REACH for example as supporting evidence in grouping and read-across approaches, to contribute to weight of evidence approaches, to guide design of experimental testing strategies, or even to fill data gaps. In all these approaches, results from QSAR models may be used in combinations with other results from e.g. in vitro tests and other available information. Substances which are concluded to be genotoxic carcinogens or germ cell mutagens and have appropriate risk management measures implemented need not be tested for reproductive toxicity under REACH. The results from the pre-screening indicate that a potentially high number of REACH pre-registered substances may possess CMR properties. These findings are similar to earlier QSAR and experimental studies of CMR-substances proportions of chemical inventories, and illustrate how QSAR predictions may be used to identify potential genotoxic carcinogens, mutagens and developmental toxicants by high-throughput virtual screening. Conflict of interest The authors declare that there are no conflicts of interest. Transparency document The Transparency document associated with this article can be found in the online version. Acknowledgements We would like to thank Prof. Anne Marie Vinggaard for proof reading of the article. This work was carried out with financial support from the Commission of the European Communities, the collaborative project ChemScreen (GA244236). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.reprotox. 2015.03.002. References [1] Guidance for the implementation of REACH – Guidance on information requirements and chemical safety assessment Chapter R.6: (Q)SARs and grouping of chemicals. European Chemicals Agency; 2008.

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QSAR screening of 70,983 REACH substances for genotoxic carcinogenicity, mutagenicity and developmental toxicity in the ChemScreen project.

The ChemScreen project aimed to develop a screening system for reproductive toxicity based on alternative methods. QSARs can, if adequate, contribute ...
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