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Framework for Identifying Chemicals with Structural Features Associated with the Potential to Act as Developmental or Reproductive Toxicants Shengde Wu,* Joan Fisher, Jorge Naciff, Michael Laufersweiler, Cathy Lester, George Daston, and Karen Blackburn Central Product Safety Department, The Procter & Gamble Company, 8700 Mason Montgomery Road, Mason, Ohio 45040, United States S Supporting Information *

ABSTRACT: Developmental and reproductive toxicity (DART) end points are important hazard end points that need to be addressed in the risk assessment of chemicals to determine whether or not they are the critical effects in the overall risk assessment. These hazard end points are difficult to predict using current in silico tools because of the diversity of mechanisms of action that elicit DART effects and the potential for narrow windows of vulnerability. DART end points have been projected to consume the majority of animals used for compliance with REACH; thus, additional nonanimal predictive tools are urgently needed. This article presents an empirically based decision tree for determining whether or not a chemical has receptor-binding properties and structural features that are consistent with chemical structures known to have toxicity for DART end points. The decision tree is based on a detailed review of 716 chemicals (664 positive, 16 negative, and 36 with insufficient data) that have DART end-point data and are grouped into defined receptor binding and chemical domains. When tested against a group of chemicals not included in the training set, the decision tree is shown to identify a high percentage of chemicals with known DART effects. It is proposed that this decision tree could be used both as a component of a screening system to identify chemicals of potential concern and as a component of weight-of-evidence decisions based on structure−activity relationships (SAR) to fill data gaps without generating additional test data. In addition, the chemical groupings generated could be used as a starting point for the development of hypotheses for in vitro testing to elucidate mode of action and ultimately in the development of refined SAR principles for DART that incorporate mode of action (adverse outcome pathways).

1. INTRODUCTION

these end points is time-consuming and animal-intensive. DART studies are projected to consume a larger number of animals and resources than other types of toxicity tests required under REACH (registration, evaluation, authorization, and restriction of chemicals). There are many uncertainties in the projection of numbers of animals that will be necessary to meet the requirements of REACH, but there appears to be agreement that the numbers will be large. For example, it is conservatively estimated that 54 million vertebrate animals will be required and that 90% of the animals used will be for

The use of structure−activity relationships (SAR) for “read across” to fill data gaps has become an integral part of many toxicological assessment efforts, including the category approach for high-production-volume (HPV) chemicals and the Joint FAO/WHO Expert Committee on Food Additives (JECFA) monograph reviews. In addition, the Research Institute for Fragrance Materials (RIFM) has recently published reviews of several groups of chemicals.1a,b Less progress has been made in the use of QSAR/structural alert tools for complex end points. The use of in silico approaches is particularly appealing for developmental and reproductive toxicity (DART) end points because empirical testing for © XXXX American Chemical Society

Received: June 21, 2013

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dx.doi.org/10.1021/tx400226u | Chem. Res. Toxicol. XXXX, XXX, XXX−XXX

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developmental- or reproductive-toxicity testing.2 Furthermore, in the EU, all animal testing for cosmetic products and ingredients has been eliminated.3 Clearly, both the development of nonanimal methods to support risk assessments for DART end points as well as tools to facilitate better use of existing data will have significant benefit for reducing animal use while continuing to ensure the safe use of chemicals. DART end points are considered to be difficult to predict using in silico tools. Although a variety of modes of action have been established for DART chemicals, the total number of possible adverse outcome pathways is unclear, and an understanding of how chemical structures across the universe of tested chemicals map to mode(s) of action has yet to be established. Cronin and Worth4 summarize the difficulties as follows: a perceived lack of data for modeling, the lack of knowledge of the mode and mechanisms of action, and the fact that reproductive toxicity is a composite effect comprising a number of end points, some with specific (and in certain cases, well-defined) mechanisms. As demonstrated in our previous publications, we contend that DART effects can be adequately predicted using SAR/read across when there are adequate analogues; however, additional tools will be needed to supplement SAR/read across when adequate analogues with data are not available. A survey of current in silico tools for DART end points was conducted, and these tools are described below. Maślankiewicz et al.5 examined a set of chemical substances classified for reprotoxicity within the scope of Directive 67/548/EC based on whether or not these chemicals triggered DEREK (Lhasa Ltd.) alerts and/or were associated with categories for TSCA (Toxic Substances Control Act) new chemicals that had documented developmental or reproductive toxicity effects. Maślankiewicz et al.5 concluded that DEREK did not recognize 90% of the substances classified for ‘impaired fertility’ and 79% of the substances classified for ‘harm to the unborn child’. The TSCA chemical category list missed 77 and 82% of the cases, respectively. However, it needs to be pointed out that the TSCA chemical category list was not necessarily designed to facilitate the identification of chemicals with DART toxicity. Using these categories to inform decisions concerning the potential for DART hazard would require both the determination that the chemical with a data gap fell within one of these categories and a determination that the other chemicals within the category for which DART data were available were indeed suitable analogues for read across for this end point. An important contribution of this work is the collection of data drawn from a chemical list that is not pharmaceutically focused and that can be used to examine further tools for the prediction of DART end points. Several recent papers have proposed in silico tools to identify DART end points. Jensen et al.6 used MultiCase (MultiCase, Inc.) to develop QSARs based on several different sets of input data that were intended to represent different portions of the spectrum of possible DART end points: teratogenicity based on the FDA Teratogen Information System (FDA TERIS) database, mutagenicity based on two heritable mutation assays and hormone receptor interactions (estrogen receptor binding and activation or androgen receptor antagonism). Each QSAR was validated against the prediction of responses in each of these individual data sets but not against a list of chemicals with a comprehensive assessment of each chemical’s DART effects. Novič and Vračko7 reviewed QSARs for DART end points. They focused on the CAESAR (computer assisted evaluation of

industrial chemical substances according to regulations) model, discussed below, and QSARs for estrogen binding and endocrine disruption. The CAESAR model is described in Cassano et al.8 This QSAR relied on input data described in Arena et al.9 Data from the FDA TERIS database were used that were subsequently reviewed and coded to reflect binary decisions on developmental toxicity. One shortcoming of the classification system with respect to the development of a QSAR is that chemicals in FDA category C (positive animal studies with no human data or no developmental toxicity data at all) were considered to be developmental toxicants. Including chemicals with no data as part of the list of chemicals with binary classifications as active developmental toxicants is concerning and could compromise the assessment of the predictivity of the model because it is not known if chemicals with no data would be active if tested. There are advantages and disadvantages to the various choices of how to categorize the data. In this work, we have been influenced by the scenarios we generally see with nondrug chemicals where there is reliance on animal data and no opportunity to link those data with outcomes in humans. Conservatively, in our risk assessments, we have considered a positive response in an animal model to be suggestive of a possible hazard for humans unless there is clear and compelling mode of action data to counter this. On the basis of this perspective, all positive responses in animal models have been considered to be positives in this work because the focus of the DART tree is on screening. In-depth assessments of a particular chemical where there are additional data on mode of action and species differences could result in a conclusion that the animal response is not relevant to humans. Hewitt et al.10 presented an integrated approach to developing weight-of-evidence decisions for DART toxicity. They utilized the CAESAR QSAR model, DEREK alerts, and OECD (Organizations for Economic Co-operation and Development) QSAR toolbox predictions for estrogen receptor binding and a model for placental transfer as well as a chemical category approach using ToxMatch software to form categories. The ToxMatch category approach is described in an earlier publication, which presents a good discussion of its potential use in creating read across categories.11 Given the complexity of the end point, an overall weight-of-evidence approach that combines multiple lines of evidence is appealing. The authors note that the data set is based entirely on drugs, and the performance of chemicals from other domains (industrial chemicals, personal care product ingredients) is unknown. Recently, we published a systematic approach for identifying and evaluating analogues for read across assessments based upon chemical and biochemical principles.12 The purpose of that process was to provide toxicology experts with a framework for SAR assessments that would drive transparency concerning the assumptions and reasoning involved in each assessment and to facilitate consistency in making determinations concerning read across. Subsequently, we have updated this process by incorporating screening principles for estrogen receptor interactions adapted from the U.S. EPA13 as well as with additional flags (decision tree concept) for DART described in our previous manuscript. This framework was tested using a series of blinded case studies and performed well for the end points examined (repeat dose toxicity, developmental toxicity, reproductive toxicity, genetic toxicity, and carcinogenicity).14 The current article presents a DART decision tree for screening potential DART chemicals that was developed on the B



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From the sources described above, we have included data primarily from studies that show one or more in vivo positive testing results in a mammalian species, although for some group members, when mammalian in vivo data were not available, we relied on a weightof-evidence approach (including a variety of data types such as in vitro or nonmammalian). The references used to support each group member are detailed in the Supporting Information. Because the goal of this work is to provide a flag for the potential for reproductive or developmental toxicity of any given chemical, we have focused primarily on chemicals with positive results, although examples of chemicals that fall within a given subcategory definition that have been tested and are negative are included when we encountered these in our searches. We have also included some subcategories that do not appear to be selective developmental/reproductive toxicants, where effects are seen only at doses toxic to the parental animal. In these cases, we have clearly flagged the chemicals because the DART effects would not be anticipated to be the critical effect for the risk assessment. The consideration of relative potency within each group would need to be assessed on a case by case basis based on the identified structural analogues with data within the context of a specific read across assessment. All chemicals were analyzed by their core structural features (e.g., acyclic alkyl chain, cyclic/heterocyclic, and aromatic/heteroaromatic rings, etc.) and key functional groups (e.g., halogenated hydrocarbons, esters, aldehydes, acids, amides, alcohols, amines, urea, etc.) as well as common, important structural fragments within the molecules. The mode of action and the interaction with receptors, especially the nuclear receptors, were also considered. These are the key features that we used to determine chemical similarity and to form the chemical categories and subcategories here described. On the basis of these 716 chemicals, a screening level decision tree for DART flags was developed. The decision tree was developed in an iterative fashion where a medicinal chemist first grouped an initial set of chemicals based solely on chemical features, and toxicologists then examined the data sets for each group to verify that the chemicals were correctly identified as reproductive or developmental toxicants to assess if there were potential group members apparent within the toxicology literature that were not included and to assess the groups for consistency of biological effects. These effects are differentiated in the Supporting Information (developmental toxicants below maternal toxic doses, developmental toxicants only at maternally toxic doses, female reproductive toxicants, male reproductive toxicants, and teratogens). In instances where distinct patterns of effects were seen within a group (as enumerated in the previous sentence), the medicinal chemist reassessed the group to determine if there were clear differentiating features that would suggest that a group could be split. During this process, mode-of-action information was added to the discussion where available. If a structure is represented in the decision tree, then it has direct DART data that show effects unless it is clearly noted in the text that the effect is inferred from a known/ suspected mode of action. The association between a putative mode of action (e.g., receptor interaction) and a DART hazard is suggested for structures covered by the decision tree when there are group members that have both pharmacological activity data and DART data and is meant to be used at a screening level or as a starting point for more detailed investigations. As a result of the development process for the decision tree, this article describes a decision tree that is a framework that primarily uses a chemical classification system but that also considers mode of action, when known, to connect these broad categories and subcategories together. Furthermore, the classification of subcategories of chemicals in the decision tree also considers metabolites that affect toxicological outcomes when known. Many examples illustrate the importance of considering metabolic fate. For example, we include the corresponding small alkyl esters and alcohols in the α-alkyl-substituted acids. These chemicals may show the same DART effects as their corresponding acids because of metabolic convergence among the esters, alcohols, and acids. As discussed earlier, to cast a broad net of potential DART toxicants, there are chemicals that would be flagged by the decision tree that have been demonstrated not to be DART toxicants, and we have not necessarily

basis of the combination of known modes of action (MOA) and associated structural features as well as an empirical association of structural fragments within molecules of DART active chemicals when MOA information was lacking. It also briefly describes the rules defining the boundaries for each category/subcategory of chemicals in the decision tree and the results of testing the performance of the decision tree. The decision tree is not intended to be used as a stand-alone tool, and by design it is intended to broadly capture chemicals with features that are similar to chemicals with precedent for DART effects. This means that many chemicals that in fact do not have DART effects will be flagged when using the decision tree, and additional steps will need to be taken to assess whether or not a chemical with a flag should be considered to be a potential reproductive or developmental toxicant. Given that the decision tree is based primarily on chemicals that show activity, with structurally related chemicals that lack activity being under represented because of the lack of data, boundaries between active and inactive chemicals are poorly defined. However, as a first step, we propose that the decision tree can be used as part of the weight of evidence in an integrated SAR read across assessment. For example, if there is uncertainty about the strength of the analogue data for read across for DART end points, an association with a broader group of structures in the decision tree that show positive effects (but may not be closely related enough to the subject chemical to be considered suitable analogues) could tip the assessment toward deciding not to read across from analogues that do not show DART effects. Alternatively, uncertainty in the strength of the analogue data could trigger the use of the data from a worst-case analogue within the broader class identified by the decision tree. Perhaps more importantly, the decision tree is intended to be used as a starting point to select groups of chemicals to explore mode-of-action hypotheses. We envision future refinements to the decision tree based on enhanced mode-of-action insight that will facilitate improved discrimination of active versus inactive chemicals and in the future will allow expansion of the chemical domain for which predictions can be made with confidence.

2. METHODS A screening level decision tree for DART flags was developed on the basis of an in-depth evaluation of a database of 716 chemicals with DART data. This database was compiled from a number of sources. An initial list of ∼260 chemicals with DART data was originally developed as part of an evaluation of threshold of toxicologic concern (TTC) for DART.15 This data set, compiled specifically for TTC, appropriately excluded chemicals that are excluded from the TTC approach taken by Kroes et al.16 and also did not include chemicals where a NOAEL for DART could not be reliably identified (important for TTC but not for a hazard-screening exercise). Sources of additional chemical-specific data included chemicals from Maślankiewicz et al.5 and Schardein17 as well as those identified by additional searching of the primary literature. For pharmaceutical chemicals, we relied heavily on the ReproRisk (Thomson Reuters Micromedex) Database because much of the toxicology information on pharmaceuticals is not available in the primary literature. Given the number of publications already available that specifically target the identification of estrogenic chemicals, we have not explicitly addressed these lists or functional groups in detail. However, consideration of the potential for interaction with the estrogen receptor is clearly one important component of the screening level assessment for potential DART effects. The analyses developed by the U.S. EPA13 and Fang et al.18 were found to be particularly helpful. C



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eliminated chemicals based on divergent metabolism. For example, 2methoxypropan-1-ol and 1-methoxypropan-2-ol are isomers that are flagged as DART toxicants according to the decision tree. However, mechanistic studies indicate that the DART toxicants for glycol ether derivatives are not the parent compounds but are the metabolites, alkoxy carboxylic acids. Given what we know about the toxicity of the glycol ethers, we would predict that the 1-methoxypropan-2-ol has much less potential for DART compared with the 2-methoxypropan-1ol because it has no potential to form an alkoxy carboxylic acid. However, we have not eliminated the parent chemicals that diverge metabolically from the decision tree. It is anticipated that this relationship would be explored further when evaluating the overall weight of evidence related to the assessment of the DART potential of a specific chemical. The selection of subcategories is presently biased by the availability of the frequency of occurrence of similar analogues or homologues that exhibit DART effects. The definition of core structural features, especially the cutoff values (for example, chain length and certain aryl substituents) are set broadly as a default when data are not available to define the limits clearly. This becomes more apparent as the structures of the chemicals become more complicated (i.e., containing multiple reactive moieties). In addition, there are situations where more complex chemicals fit into more than one subcategory. For example, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) derivatives belong to subcategories of multihalogenated oxidibenzenes as well as aryl hydrocarbon receptor (AhR) binders. The benzodiazepine and thalidomide derivatives can be classified as either aromatic compounds with amide and urea moieties or as aryl ring-fused heterocycles. In these cases, we grouped the chemicals on the basis of known receptor binding affinities first and then on the basis of the structural features that appeared to be most important for biological activity (e.g., fused ring system prior to the functional group). Clearly, when chemicals with data gaps are run through the decision tree, they may be classified differently by different users, depending on the priority/order given to different substituents. It would be appropriate for chemicals with multiple possible paths through the decision tree to be run through the multiple scenarios to understand if there may be more than one cluster of potentially relevant analogues. If they have the potential for DART, then they will still end up being categorized as chemicals with DART precedent, and the relative importance of the various functional groups should become apparent in later assessment steps when an overall weight of evidence assessment is done. Chemicals with known DART effects with insufficient related structures ( organic chemicals > noncyclic chemicals > receptor binders > fused ring systems > functional group. Each step in the decision tree leads either to another step or to a final conclusion. The decision tree can be generalized as a set of “yes/no” rules for guidance in flagging known precedent/no known precedent for in vivo DART effects and does not presume to be predictive. In the final weight-of-evidence assessment, the impact of chemical structure association with chemicals with known DART effects would need to be carefully considered in the context of the overall data set for the chemical of interest and appropriately rated analogue chemicals. The 25 categories along with a representative core structure from each subcategory are shown in Table 1 (see the Supporting Information for specific chemicals and detail references). Importantly, the chemisty domains covered by the decision tree are driven by what was discovered using tested chemicals with known DART effects, there was no attempt to broadly define the chemical space a priori (e.g., on the basis of the diversity of industrially produced chemicals). The DART tree does not presume to be a QSAR with a defined chemical domain but represents the sampled chemical space where we found both DART test data and a sufficient number of tested group members (4 or more). When

3. RESULTS AND DISCUSSION 3.1. Category of Chemicals and the Expert System Decision Tree. The chemicals were categorized by identifying core structural fragments within molecules that are associated with DART effects. The core structural fragments are based either on hypotheses related to the mechanisms of action of a chemical class or on observed empirical relationships. After analysis of their biological activity and structural features, we have categorized the 716 DART chemicals into 25 categories that include five (involving 10 receptors) receptor-binding categories and 20 chemical-structural-related categories. The expert system decision tree was then developed on the basis of these major categories and subcategories that reflect, to the extent possible, all of the positive chemicals evaluated. In some instances, orphan chemicals were found for which we were unable to locate a sufficient number of chemicals (≥4) with related structures that had been tested for DART effects to form a category. These chemicals are noted in the text as miscellaneous chemicals that were not grouped. The selection of subcategories is based on criteria described in the Methods H



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Figure 1. Overall process of the decision tree for screening developmental/reproductive toxicity.

the molecule also be run through the decision tree to determine if there are structurally related chemicals with precedent for DART effects. The impact of the metallic substituent on potential DART effects would need to be assessed on a case-bycase basis. In step II, the organic compounds are separated into cyclic or noncyclic compounds. At this stage, the cyclic chemicals are further categorized mainly on the basis of their potential interacting properties with more than 10 different biological targets, such as the ER (estrogen receptor), AR (androgen receptor), RAR (retinoic acid receptor), AhR (aryl hydrocarbon receptor), nAChRs (nicotinic acetylcholine receptors), AChE (acetylcholinesterase), ion channels, ACE/ ARA (angiotensin-converting enzyme/angiotensin II receptor type I antagonists), α/β-adrenergic receptors, tubulin, and opioid receptors. In step III, other cyclic compounds, which do not have structures related to chemicals known to interact with the receptors mentioned above, are further separated into aromatic/heteroaromatic cyclic compounds and nonaromatic cyclic compounds. In steps IV and V, the noncyclic aliphatic chemicals are further separated into substituted hydrocarbons and (≤C9) carboxylic acids with derivatives (including esters and amides as well as ureas, thioureas, carbamates, etc.). The substituted positions on the hydrocarbons are generally at the terminal carbon. Separate consideration is given to carboxylic

using the decision tree, it is very important to keep in mind that if the core structural features of the chemical or its major metabolites are not represented in the decision tree, then this should not be interpreted as putting the chemical in a final conclusion bin of having no DART structural precedent. Rather, it means that no/insufficient chemicals with the structural features of the subject chemical have been evaluated and further investigation is required. Furthermore, it also should signal that this chemical and any structurally related chemicals with DART data could be used for expanding/ updating the decision tree. 3.2. Overall Process of the Decision Tree. Figure 1 outlines the decision tree and the overall process for screening DART. Generally, the decision tree is organized by four different types of color-coded boxes. Teal boxes represent the major classification or sorting steps, and blue boxes represent categories that are numbered by Arabic numerals with subcategory descriptions and definitions. The orange and green boxes display the final conclusions. The process of applying the decision tree is described as follows. In step I, the chemicals are separated into organic compounds and metal, metallic/metallo-organic chemicals (including metallocomplexes), organophosphorus, or organosiloxane derivatives. For the organo-metallics, it is suggested that the organic portion of I



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Figure 2. Chemicals used to illustrate the process of going through the decision tree.

Figure 3. Flow diagram illustration of six examples going through the decision tree.

3.3. Examples of Applying the Decision Tree. In applying the decision tree, it is important to note that although there is detailed structural information for the chemicals used to derive each group, the structures shown are intended to be used as guidelines to demonstrate the types of chemicals associated with DART activity. They are not intended to be rigid rules to which a chemical being assessed must completely conform. We used six chemicals (Figure 2) to demonstrate the procedure of the decision tree (detailed descriptions of the groups/subgroups in the DART decision tree are available in the Supporting Information). Figure 3 illustrates the flow diagram of these six examples on the decision tree. The final conclusion for each individual chemical is reached by following the definition of each step. The pathway to the final conclusion for each chemical is indicated by the chemical number. Chemical 1. Ethylene glycol methyl ether (CAS no. 109-804) was identified as a chemical with features in common with

acids and acid derivatives with chain lengths of nine carbons or less because these compounds have been extensively studied and DART activity related to structural features and metabolism is well-defined. In step VI, acid precursors and derivatives are divided into α,β-unsaturated aldehydes, esters, amides, and α-substituted acids/acid derivatives such as α-alkyl, α-alkoxy, α-halogenated acids, and their corresponding precursors. Steps I−VI are “sorting” steps. After these steps, we then further divide the sorted chemicals into 25 different categories and eventually into around 129 subcategories according to alert core structural features. An additional note is that chemicals that have sufficient resemblance to physiologically important molecules can potentially disrupt normal physiologically processes and should be closely scrutinized. We have noted some of these either as miscellaneous chemicals or, when sufficient group members are available, as categories with the mode of action mentioned in the discussion. J



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Figure 4. Scope of structural features of steroid nucleus derived estrogen receptor (ER) and androgen receptor (AR) binders.

ring); to IV, no (>C9 hydrocarbon); to V, no (>C9 carboxylic acid); to 19, no (not an alkyl carbamodi-thioic acids or an alkyl sulfonate); to 20, no (does not belong to this group of miscellaneous compounds); to no known precedent for DART. 3.4. Examples of Category Information (See the Supporting Information for Category Information for the Entire Decision Tree). The collected reference information for each category member’s DART effects is summarized in the Supporting Information. In addition, each category and subcategory has an extensive discussion that summarizes what is known about the structural features associated with DART activity along with a summary of the mode-of-action data for category members (when known). Because of the length of this summary material, two examples are excerpted here to illustrate to the reader the information contained in the Supporting Information. Example 1: Category 2, Estrogen Receptor (ER) and Androgen Receptor (AR) Binding Compounds. 2a. Steroid Nucleus-Derived ER and AR Binders. The fused tetracyclic (A, B, C, and D rings) steroid nucleus (2a) in Figure 4 provides the carbon framework for different groups of mammalian hormones exemplified by estrogens, corticosteroids, mineralcorticoids, progestagens, and androgens. Each has its own receptor, but there is a great deal of overlapping binding of these receptors by various ligands. As examples, there is an overlap between glucocorticoid and mineralcorticoid receptor binders as well as cross-reactivity between chemicals that bind the progesterone receptor and chemicals that bind to the testosterone receptor. There is more limited cross-reactivity between androgen/progesterone ligands and the glucocorticoid receptors.26 For the purposes of this document, it is not practical to separate clearly the expected relative specificity of chemicals for the glucocorticoid versus mineralcorticoid and androgen versus progesterone receptors. Although the decision tree rules do not clearly separate these classes, we have separated them for the purposes of discussion by the receptor target that they are primarily associated with in the literature. 2a-1. Estradiol-Like Compounds. 17-β-Estradiol (E2 CAS no. 50-28-2) and its analogues (Figure 4, 2a-1) are prototypical

known DART toxicants via the following steps of the decision tree. Step I, organic compound; to II, no (does not contain a ring); to IV, yes (contains terminal methoxy groups and 95%. Although we anticipated that in theory our decision tree would detect the chemicals used to develop it, given that going through the decision tree is a manual process, this high percentage of concordance shows that the rules can be interpreted with confidence (the decision tree was tested by three chemists, two of whom were not involved in writing the rules). These encouraging results clearly confirm the usefulness of following the decision tree steps as part of a weight-ofevidence approach for screening and evaluating chemicals for DART potential. An example of how use of the decision tree can augment an assessment is briefly described. We had a historical SAR assessment on bromoethane (CAS no. 74-96-4). This assessment identified one analogue (CAS no. 106-94-5) that had reproductive data that indicated toxicity but no developmental

4. APPLICATION OF THE DECISION TREE TO IDENTIFY CHEMICALS STRUCTURALLY RELATED TO DART TOXICANTS 4.1. Assessment of the Decision Tree as an Initial Screening Tool to Identify DART Toxicants. The following four sets of chemicals were assembled for evaluating the performance of this decision-tree approach for the identification of chemicals structurally related to DART toxicants: our test set (set 1) (comprised of negative chemicals from the CAESAR tool plus Annex VI positive chemicals supplemented with additional positive chemicals from a critical review of veterinary drugs),20 CAESAR (set 2), RIVM chemical set5 (set 3), and our DART chemical set used to create the decision tree (set 4). These four sets were used to assess the performance of the decision tree as an initial screening tool and as a component of SAR assessments for the DART end points. It has to be stressed that the purpose of this exercise was to evaluate and improve the performance of a structure-based approach and with emphasis on whether or not the rules could be unambiguously applied, to determine if chemicals with core structural features represented in the tree could be correctly identified as being related to chemicals with structural features associated with DART toxicity, and to assess if major core structural features known to be associated with DART effects that were represented in these test sets had been omitted from the design of the decision tree and could be added. This exercise should not be considered analogous to the kind of validation exercise that would be done for a QSAR analysis because of the following. (1) The decision tree is not intended to be used as a stand-alone prediction tool. (2) The decision tree was built around positive chemicals and designed to identify broadly structures associated with chemicals that have shown effects; insufficient tested chemicals were available for most groups to clearly define structural boundaries associated with the transition from a positive to a negative response. (3) We did not have access to an independent set of chemicals to use for testing that had a balanced number of positive and negative chemicals. Set 1 contains 179 chemicals for developmental and reproductive toxicity (106 positive and 73 negative (from the CAESAR data set). The overall performance of our decision tree in identifying chemicals with structures associated with DART toxicity is summarized in Figure 12. We first applied our initial decision tree to set 1, and it showed 63% sensitivity P



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Figure 13. Benzimidazole core structure formation from netobimin.

piperazinyl)-1,2-benzisothiazole hydrochloride, CAS no. 8769188-1 etc.) are excluded from the decision tree because the data needed to develop the category were too limited. The fourth limitation is that the decision tree does not discrimate between developmental or reproductive effects; however, the type of effect is clearly noted in the Supporting Information so that the type of effect associated with a group can be evaluated in the context of a specific assessment or for further research. As noted in the introduction, the decision tree is designed to form part of an overall weight of evidence in the risk assessment process. Chemicals that are run through the decision tree could fall into groups with known DART precedent or groups with no known DART precedent, or they could have core structural features that fall outside of the chemical domains covered by the DART decision tree (part of the no known DART precedent result). The result of no known DART precedent does not demonstrate the absence of DART end-point effects. Thus, every single chemical should be further evaluated by careful examination of the effects of structurally related chemicals (both those cited in the decision tree as well as analogues not in the decision tree) along with a detailed review of metabolism and any other relevant data contributing to a final assessment (i.e., core substructures with potential DART alerts, etc.). These evaluations are particularly important and necessary for those chemicals having core structural features not covered by the DART tree and therefore not evaluable using the decision tree. An illustration of a chemical with structural features outside of the decision tree is the broad spectrum anthelmintic prodrug of a benzimidazolic compound, netobimin (CAS no. 88255-01-0), which has demonstrated DART effects in animals.123,124 The structural features of this chemical are completely outside of the core structural coverage of the decision tree. Therefore, one would conclude that this chemical cannot be evaluated using the decision tree. However, several important metabolites of netobimin contain a benzimidazole core structure125 (Figure 13), which is covered in category 6 of the decision tree. Thus, the final assessment for this chemical would be that its metabolites are associated with structures with known DART activity. Overall, the results obtained in this study support the use of the decision tree approach as a valuable tool for DART prediction or screening and set the stage for further advancements in the understanding of these complex and multifaceted end points. In addition, the automation of this decision tree may be performed with any software capable of generating a database of structures enumerated from those described in the Supporting Information. The software must then be able to perform substructure similarity against this database. Recently, we have automated this decision tree using Pipeline Pilot (Accelrys), and preliminary results from our DART data set look promising, as more than 98% of DART

toxicity data. Using the decision tree, bromoethane would be placed in category 23b where there is a wealth of data that support that related chemicals have the potential for reproductive toxicity and also some evidence for developmental toxicity. Bringing these additional data into our SAR assessment to see the broader picture of chemical structure with related DART effects would have supplemented our assessment that was based on our strict analogue criteria and provided additional weight to consider that reproductive effects and possibly developmental effects were likely to be seen should this chemical be tested. 4.2. Advantages and Limitations of the Decision Tree. We contend that a major advantage of our DART decision tree is its transparency because each step can be visualized with the delineation of groups of chemicals and MOAs (where available) to form the basis for developing testable mode-of-action hypotheses for groups of structurally related chemicals. Additional advantages of the decision tree, for future development, include enhancement of the discriminating power of the decision tree by iterative addition of positive and negative chemicals within each subcategory with subsequent rule refinement. The third advantage, perhaps more importantly, is that the decision tree format easily allows for adding structural groups and expanding the chemical coverage of the decision tree if sufficient related structures with DART data are available. Because the decision tree was developed on the basis of core structural fragments within molecules of active DART chemicals and MOA, it is expected that chemically similar core structures should elicit similar DART responses. However, we cite numerous examples in which not all members of a subcategory exhibit comparable toxicity. The major limitation of the decision tree is that the boundary (or cutoff values) may not be very clear, mostly because of the inclusion of few negative chemicals. Another limitation is that when a subcategory of chemicals shows weak DART activity, the substituents on the core structure may significantly impact the DART effects. This would also increase the difficulty and uncertainty in identifying the DART potential by core structure. For example, the chemical dioxide benzothiazine carboxamide derivative (CAS no. 944461-99-8) not only contains a core structural feature of aromatic sulfonamide that was covered in category 10 of the decision tree but also contains another functional group not covered in the decision tree. Thus, the application of the decision tree is intentionally defined broadly when there is uncertainty in the relationship of structure to activity, with the intent to capture as many positive chemicals as possible. The third limitation is that because the development of the categories was based on the availability of enough similar chemicals with DART data, some known DART active chemicals (e.g., Diclazuri, CAS no. 101831-37-2; 3-(1Q



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toxicants (629/635 without metallic compounds) have been identified. Furthermore, we have combined our DART decision tree with the CAESAR model, representing two very different approaches, to form a battery of tools for identifying DART potential with very encouraging results for the four chemical sets mentioned above.

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Funding

This study was part of internal research program of the Procter & Gamble Company. No third party funding was involved.

5. CONCLUSIONS Developmental and reproductive toxicity occurs through many different mechanisms and involves a number of different target sites, making it very difficult to predict this end point by a single computational model. This article presents an empirically based decision tree for determining whether or not a chemical has structural features that are consistent with chemical structures known to have toxicity for DART end points. The decision tree is based on a detailed review of 716 chemicals that have DART end-point data, grouped into defined core chemical structures. The data set reveals information of the MOA and important structural fragments of a variety of subcategories of chemicals for DART toxicity. The knowledge derived from this article enhances our understanding of the relationship between DART toxicity and MOA/important structural fragments of chemicals, which in turn should be important for rapid screening of DART toxicity potential. It is proposed that this decision tree could be used both for screening to prioritize chemicals for testing, on the basis of an increased level of concern if they fall into structural groups with known effects, and/or as a component of weight-of-evidence decisions on the basis of SAR read across to fill data gaps without generating additional test data. It is expected that it will continue to be necessary to evaluate the potential toxicity of new chemicals using the decision tree as one piece of a weight-of-evidence assessment. Furthermore, the chemical groups in the decision tree also can be used as a starting point for the development of hypotheses for in vitro testing to elucidate mode of action and for the ultimate development of refined SAR principles for DART that incorporate mode of action. We specifically plan to evaluate large numbers of structures using the decision tree once it is automated, and in the process we expect to discover additional group members that will help to refine boundaries and define additional chemical classes that can increase the chemical coverage of the decision tree. We also intend to use groupings from the decision tree to explore how to integrate additional nontraditional data (e.g., high-throughput, genomics, ToxCast, and high-content data, etc.) into DART assessments. We plan to use defined structural groups from the decision tree both to explore further mode of action (when not already clearly elucidated) and to explore how to integrate additional types of nontraditional toxicology data into the DART risk assessment process. A major stumbling block to the development of these alternative approaches is finding appropriate groups of chemicals to work with that have associated traditional data. However, we believe that the groundwork established in this article in terms of relating DART effects to distinct chemical groups will be invaluable for using read across predictions within multiple chemical categories in DART assessments.

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Article

Notes

The opinions expressed are solely those of the authors and do not reflect an official policy of the company. The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We gratefully acknowledge Drs. Tom Federle and Rob Roggeband for their valuable scientific comments and suggestions. We also thank Dr. Nick Fendinger and the CREG group for support.

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ABBREVIATIONS DART, developmental and reproductive toxicity; REACH, registration, evaluation, authorization and restriction of chemicals; SAR, structure−activity relationships; HPV, high production volume; JECFA, Chemicals and the Joint FAO/ WHO Expert Committee on Food Additives; RIVM, National Institute for Public Health and Environment; RIFM, Research Institute for Fragrance Materials; TSCA, Toxic Substances Control Act; DEREK, deductive, estimation of risk from existing knowledge; CAESAR, computer assisted evaluation of industrial chemical substances according to regulations; FDA TERIS, FDA Teratogen Information System; OECD, Organization for Economic Co-operation and Development; U.S. EPA, U.S. Environmental Protection Agency; MOA, modes of action; TTC, threshold of toxicologic concern; VEGA, virtual models for property evaluation of chemicals within a global architecture; ER, estrogen receptor; AR, androgen receptor; RAR, retinoic acid receptor; AhR, aryl hydrocarbon receptor; nAChRs, nicotinic acetylcholine receptors; AChE, acetylcholinesterase; ACE/ARA, angiotensin-converting enzyme/angiotensin II receptor type I antagonists; BPA, BPM bisphenol A and M; DES, diethylstilbesterol; DDT, dichlorodiphenyltrichloroethane; TCDD, tetrachlorodibenzodioxin; PCDDs, polychlorinated dibenzodioxins; PCDFs, polychlorinated dibenzofurans; HAHs, halogenated aromatic hydrocarbons; PCBs, polychlorinated biphenyl; PBBs, polybrominated biphenyl; PAHs, polycyclic aromatic hydrocarbons; PGE1, PGE2, PGF2α, PGH2, PGD2, prostaglandin E1, E2, F2alpha, H2, D2; DEREKfW (Df W), DEREK for Windows

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ASSOCIATED CONTENT

* Supporting Information S

Category/subcategory information, rules, and decription for the entire tree; and DART data, code, and chemical structures used to generate the decision tree. This material is available free of charge via the Internet at http://pubs.acs.org. R



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