Effects-based chemical category approach for prioritization of low affinity estrogenic chemicals

Regulatory agencies are charged with addressing the endocrine disrupting potential of large numbers of chemicals for which there is often little or no data on which to make decisions. Prioritizing the chemicals of greatest concern for further screening for potential hazard to humans and wildlife is an initial step in the process. This paper presents the collection of in vitro data using assays optimized to detect low affinity estrogen receptor (ER) binding chemicals and the use of that data to build effects-based chemical categories following QSAR approaches and principles pioneered by Gilman Veith and colleagues for application to environmental regulatory challenges. Effects-based chemical categories were built using these QSAR principles focused on the types of chemicals in the specific regulatory domain of concern, i.e. non-steroidal industrial chemicals, and based upon a mechanistic hypothesis of how these non-steroidal chemicals of seemingly dissimilar structure to 17ß-estradiol (E2) could interact with the ER via two distinct binding types. Chemicals were also tested to solubility thereby minimizing false negatives and providing confidence in determination of chemicals as inactive. The high-quality data collected in this manner were used to build an ER expert system for chemical prioritization described in a companion article in this journal.     

A reactivity pattern for discrimination of ER agonism and antagonism based on 3-D molecular attributes

Various models have been developed to predict the relative binding affinity (RBA) of chemicals to estrogen receptors (ER). These models can be used to prioritize chemicals for further tiered biological testing to assess the potential for endocrine disruption. One shortcoming of models predicting RBA has been the inability to distinguish potential receptor antagonism from agonism, and hence in vivo response. It has been suggested that steroid receptor antagonists are less compact than agonists; thus, ER binding of antagonists may prohibit proper alignment of receptor protein helices preventing subsequent transactivation. The current study tests the theory of chemical bulk as a defining parameter of antagonism by employing a 3-D structural approach for development of reactivity patterns for ER antagonists and agonists. Using a dataset of 23 potent ER ligands (16 agonists, 7 antagonists), molecular parameters previously found to be associated with ER binding affinity, namely global (E(HOMO)) and local (donor delocalizabilities and charges) electron donating ability of electronegative sites and steric distances between those sites, were found insufficient to discriminate ER antagonists from agonists. However, parameters related to molecular bulk, including solvent accessible surface and negatively charged Van der Waal's surface, provided reactivity patterns that were 100% successful in discriminating antagonists from agonists in the limited data set tested. The model also shows potential to discriminate pure antagonists from partial agonist/antagonist structures. Using this exploratory model it is possible to predict additional chemicals for their ability to bind but inactivate the ER, providing a further tool for hypothesis testing to elucidate chemical structural characteristics associated with estrogenicity and anti-estrogenicity.     

A reactivity pattern for discrimination of ER agonism and antagonism based on 3-D molecular attributes

Various models have been developed to predict the relative binding affinity (RBA) of chemicals to estrogen receptors (ER). These models can be used to prioritize chemicals for further tiered biological testing to assess the potential for endocrine disruption. One shortcoming of models predicting RBA has been the inability to distinguish potential receptor antagonism from agonism, and hence in vivo response. It has been suggested that steroid receptor antagonists are less compact than agonists; thus, ER binding of antagonists may prohibit proper alignment of receptor protein helices preventing subsequent transactivation. The current study tests the theory of chemical bulk as a defining parameter of antagonism by employing a 3-D structural approach for development of reactivity patterns for ER antagonists and agonists. Using a dataset of 23 potent ER ligands (16 agonists, 7 antagonists), molecular parameters previously found to be associated with ER binding affinity, namely global ( E HOMO ) and local (donor delocalizabilities and charges) electron donating ability of electronegative sites and steric distances between those sites, were found insufficient to discriminate ER antagonists from agonists. However, parameters related to molecular bulk, including solvent accessible surface and negatively charged Van der Waal's surface, provided reactivity patterns that were 100% successful in discriminating antagonists from agonists in the limited data set tested. The model also shows potential to discriminate pure antagonists from partial agonist/antagonist structures. Using this exploratory model it is possible to predict additional chemicals for their ability to bind but inactivate the ER, providing a further tool for hypothesis testing to elucidate chemical structural characteristics associated with estrogenicity and anti-estrogenicity.     

QSAR models using a large diverse set of estrogens

Endocrine disruptors (EDs) have a variety of adverse effects in humans and animals. About 58,000 chemicals, most having little safety data, must be tested in a group of tiered assays. As assays will take years, it is important to develop rapid methods to help in priority setting. For application to large data sets, we have developed an integrated system that contains sequential four phases to predict the ability of chemicals to bind to the estrogen receptor (ER), a prevalent mechanism for estrogenic EDs. Here we report the results of evaluating two types of QSAR models for inclusion in phase III to quantitatively predict chemical binding to the ER. Our data set for the relative binding affinities (RBAs) to the ER consists of 130 chemicals covering a wide range of structural diversity and a 6 orders of magnitude spread of RBAs. CoMFA and HQSAR models were constructed and compared for performance. The CoMFA model had a r2 = 0.91 and a q2LOO = 0.66. HQSAR showed reduced performance compared to CoMFA with r2 = 0.76 and q2LOO = 0.59. A number of parameters were examined to improve the CoMFA model. Of these, a phenol indicator increased the q2LOO to 0.71. When up to 50% of the chemicals were left out in the leave-N-out cross-validation, the q2 remained significant. Finally, the models were tested by using two test sets; the q2pred for these were 0.71 and 0.62, a significant result which demonstrates the utility of the CoMFA model for predicting the RBAs of chemicals not included in the training set. If used in conjunction with phases I and II, which reduced the size of the data set dramatically by eliminating most inactive chemicals, the current CoMFA model (phase III) can be used to predict the RBA of chemicals with sufficient accuracy and to provide quantitative information for priority setting.     

An integrated "4-phase" approach for setting endocrine disruption screening priorities--phase I and II predictions of estrogen receptor binding affinity

Recent legislation mandates the US Environmental Protection Agency (EPA) to develop a screening and testing program for potential endocrine disrupting chemicals (EDCs), of which xenoestrogens figure prominently. Under the legislation, a large number of chemicals will undergo various in vitro and in vivo assays for their potential estrogenicity, as well as other hormonal activities. There is a crucial need for priority setting before this strategy can be effectively implemented. Here we report an integrated computational approach to priority setting using estrogen receptor (ER) binding as an example. This approach rationally integrates different predictive computational models into a "Four-Phase" scheme so that it can effectively identify potential estrogenic EDCs based on their predicted ER relative binding affinity (RBA). The system has been validated using an in-house ER binding assay dataset for 232 chemicals that was designed to have both broad structural diversity and a wide range of binding affinities. When applied to 58,000 chemicals identified by Walker et al. as candidates for endocrine disruption screening, some 9100 chemicals were predicted to bind to ER. Of these, only 3600 were expected to bind to ER at RBA values up to 100,000-fold less than that of 17 g -estradiol. The method ruled out 83% of the chemicals as non-binders with a very low rate of false negatives. We believe that the same integrated scheme will be equally applicable to endpoints of other endocrine disrupting mechanisms, e.g. androgen receptor binding.     

An integrated "4-phase" approach for setting endocrine disruption screening priorities--phase I and II predictions of estrogen receptor binding affinity

Recent legislation mandates the US Environmental Protection Agency (EPA) to develop a screening and testing program for potential endocrine disrupting chemicals (EDCs), of which xenoestrogens figure prominently. Under the legislation, a large number of chemicals will undergo various in vitro and in vivo assays for their potential estrogenicity, as well as other hormonal activities. There is a crucial need for priority setting before this strategy can be effectively implemented. Here we report an integrated computational approach to priority setting using estrogen receptor (ER) binding as an example. This approach rationally integrates different predictive computational models into a "Four-Phase" scheme so that it can effectively identify potential estrogenic EDCs based on their predicted ER relative binding affinity (RBA). The system has been validated using an in-house ER binding assay dataset for 232 chemicals that was designed to have both broad structural diversity and a wide range of binding affinities. When applied to 58,000 chemicals identified by Walker et al. as candidates for endocrine disruption screening, some 9100 chemicals were predicted to bind to ER. Of these, only 3600 were expected to bind to ER at RBA values up to 100,000-fold less than that of 17beta-estradiol. The method ruled out 83% of the chemicals as non-binders with a very low rate of false negatives. We believe that the same integrated scheme will be equally applicable to endpoints of other endocrine disrupting mechanisms, e.g. androgen receptor binding.     

Experimental verification of structural alerts for the protein binding of sulfur-containing compounds

As often noted by Dr. Gilman Veith, a major barrier to advancing any model is defining its applicability domain. Sulfur-containing industrial organic chemicals can be grouped into several chemical classes including mercaptans (RSH), sulfides (RSR’), disulfides (RSSR’), sulfoxides (RS(=O)R’), sulfones (RS(=O)(=O)R’), sulfonates (ROS(=O)(=O)R’) and sulfates (ROS(=O)(=O)OR’). In silico expert systems that predict protein binding reactions from 2D structure sub-divide these chemical classes into a variety of chemical reactive mechanisms and reactions which have toxic consequences. Using the protein binding profilers in version 3.1 of the OECD QSAR Toolbox, a series of sulfur-containing chemicals were profiled for protein binding potential. From these results it was hypothesized which sulfur-containing chemicals would be reactive or non-reactive in an in chemico glutathione assay and whether if reactive they would exhibit toxicity in excess of baseline in the Tetrahymena pyriformis population growth impairment assay. Subsequently, these hypotheses were tested experimentally. The in chemico data show that the in silico profiler predictions were generally correct for all chemical categories, where testing was possible. Mercaptans could not be assessed for GSH reactivity because they react directly with the chromophore 5,5’-dithiobis-(2-nitrobenzoic acid). With some exceptions, the major being disulfides, the in vitro toxicity data supported the in chemico findings.     

Androgen receptor binding affinity of pesticide "active" formulation ingredients. QSAR evaluation by COREPA method

The COREPA approach for identifying the COmmon REactivity PAttern of biologically similar chemicals was employed to upgrade the recently derived affinity pattern for high androgen receptor (AR) binding affinity. The training set consisted of 28 steroidal and nonsteroidal ligands whose AR binding affinity was determined in competitive binding assays (in terms of pKi). The interatomic distances between nucleophilic sites and their charges providing distinct and non-overlapping integral patterns for active and inactive chemicals were assumed that it was related with the endpoint, which was under study. These stereoelectronic characteristics were used to predict pKi values of pesticide "active" formulation ingredients in an attempt to identify chemicals with potential AR binding affinity.     

Androgen receptor binding affinity of pesticide "active" formulation ingredients. QSAR evaluation by COREPA method

The COREPA approach for identifying the COmmon REactivity PAttern of biologically similar chemicals was employed to upgrade the recently derived affinity pattern for high androgen receptor (AR) binding affinity. The training set consisted of 28 steroidal and nonsteroidal ligands whose AR binding affinity was determined in competitive binding assays (in terms of p K i ). The interatomic distances between nucleophilic sites and their charges providing distinct and non-overlapping integral patterns for active and inactive chemicals were assumed that it was related with the endpoint, which was under study. These stereoelectronic characteristics were used to predict p K i values of pesticide "active" formulation ingredients in an attempt to identify chemicals with potential AR binding affinity.     

Identification of the estrogen receptor Cd-binding sites by chemical modification

The widely reported interactions of the estrogen receptor (ER) with endocrine disrupting chemicals (EDCs) present in the environment gave raise to public concern and led to a number of screening and testing initiatives on the international level. Recent studies indicated that certain heavy metals, including cadmium, can mimic the effects of the endogenous estrogen receptor agonist 17beta-estradiol, and lead to estrogen receptor activation. Previous studies of the chimeric proteins, which incorporate the ligand-binding domain of the human ER, identified Cys 381, Cys 447, Glu 523, His 524 and Asp 538 as possible sites of interactions with cadmium. In the present study we utilized the rainbow trout ER ligand-binding domain fused to glutathione-S-transferase, and used Cd-shielding against various types of chemical modification of the fusion protein to study non-covalent interactions between the ER and Cd. The distribution of exposed and shielded residues allowed to identify amino acid residues involved in the interaction. Our data indicated preferential protection of Cys groups by cadmium, suggesting their involvement in the interaction. This supports data found in the literature on the strong binding affinity of the thiol group towards metals. However, not all Cys in the fusion protein sequence were protected against chemical modification, illustrating the importance of their chemical environment. In general, the location of rtER-LBD Cys residues implicated in Cd interactions did not confirm assignments made by alanine-scanning mutagenesis for the hER, probably due to differences in experimental setup and fusion proteins used. The involvement of other functional groups such as carboxylic acids in the Cd interactions, though not confirmed, can not be completely ruled out due to the general limitations of the chemical modification approach discussed in detail. Suggestions for an improved experimental setup were made.     

Experimental verification of structural alerts for the protein binding of cyclic compounds acting as Michael acceptors

This study outlines how a combination of and in vitro data can be used to define the applicability domain of selected structural alerts within the protein binding profilers of the Organisation for Economic Co-operation (OECD) Quantitative Structure–Activity Relationship (QSAR) Toolbox. Thirty chemicals containing a cyclic moiety were profiled for reactivity using the OECD and Optimised Approach based on Structural Indices Set (OASIS) protein binding profilers. The profiling results identified 22 of the chemicals as being reactive towards proteins. Analysis of the experimentally data showed 19 of these chemicals to be reactive. Subsequent analysis allowed refinements to be suggested to improve the applicability domain of the structural alerts investigated. The accurate definition of the applicability domain for structural alerts within in silico profilers is important due to their use in chemical category in predictive and regulatory toxicology.     

Homology model of the rainbow trout estrogen receptor (rtERalpha) and docking of endocrine disrupting chemicals (EDCs)

A model for rainbow trout (Oncorhynchus mykiss) estrogen receptor (rtERa) was built by homology with the human estrogen receptor (hERa). A high level of sequence conservation between the two receptors was found with 64% and 80% of identity and similarity, respectively. Selected endocrine disrupting chemicals were docked into the ligand binding domain (LBD) of rtERa and the corresponding free binding energies Delta(DeltaG(bind)) values were calculated. A Quantitative Structure-Activity Relationship (QSAR) model between the relative binding affinity data and the Delta(DeltaG(bind)) values was derived in order to predict which further organic pollutants are likely to bind to rtERa.     

The importance of local chemical structure for chemical metabolism by human uridine 5'-diphosphate-glucuronosyltransferase

The uridine 5'-diphosphate- (UDP-)glucuronosyltransferase (UGT) family of enzymes catalyzes the conjugation of chemicals containing a suitable nucleophilic atom with glucuronic acid. Despite the importance of glucuronidation as an elimination and detoxification mechanism for drugs, environmental chemicals, and endogenous compounds, the structural features of substrates that confer isoform selectivity are poorly understood. The relationship between the local molecular structure of nucleophilic atoms of chemicals and the ability of UGT isoforms to glucuronidate the nucleophilic atoms was investigated here. The proximity of an aromatic ring to the nucleophilic atom was highly associated with a greater likelihood of glucuronidation by most UGT isoforms. Similarly, most UGT isoforms were found to have a statistically significant preference for oxygen over nitrogen as the nucleophilic atom. The converse was established only for UGT1A4. Na?ve Bayes models were trained to predict the site of glucuronidation for eight UGT isoforms on the basis of the partial charge and Fukui function of the nucleophilic atom and whether an aromatic ring was attached to the nucleophilic atom. On average, the cross-validated sensitivity and specificity of the models were approximately 75-80%. For all but UGT2B7, the area under the receiver operating characteristics curve of the model was greater than 0.8, indicating strong predictive ability. A chemical diversity analysis of the currently available data indicates bias toward chemicals containing phenolic groups, and it is likely that the availability of chemical data sets with greater diversity will facilitate further insights into the structural features of substrates that confer enzyme selectivity.