Development, validation and integration of in silico models to identify androgen active chemicals

Recognizing high-priority disinfection byproducts based on experimental and predicted endocrine disrupting data: Virtual screening and in vitro study

So far, about 130 disinfection by-products (DBPs) and several DBPs-groups have had their potential endocrine-disrupting effects tested on some endocrine endpoints. However, it is still not clear which specific DBPs, DBPs-groups/subgroups may be the most toxic substances or groups/subgroups for any given endocrine endpoint. In this study, we attempt to address this issue. First, a list of relevant DBPs was updated, and 1187 DBPs belonging to 4 main-groups (aliphatic, aromatic, alicyclic, heterocyclic) and 84 subgroups were described. Then, the high-priority endocrine endpoints, DBPs-groups/subgroups, and specific DBPs were determined from 18 endpoints, 4 main-groups, 84 subgroups, and 1187 specific DBPs by a virtual-screening method. The results demonstrate that most of DBPs could not disturb the endocrine endpoints in question because the proportion of active compounds associated with the endocrine endpoints ranged from 0 (human thyroid receptor beta) to 32% (human transthyretin (hTTR)). All the endpoints with a proportion of active compounds greater than 10% belonged to the thyroid system, highlighting that the potential disrupting effects of DBPs on the thyroid system should be given more attention. The aromatic and alicyclic DBPs may have higher priority than that of aliphatic and heterocyclic DBPs by considering the activity rate and potential for disrupting effects. There were 2 (halophenols and estrogen DBPs), 12, and 24 subgroups that belonged to high, moderate, and low priority classes, respectively. For individual DBPs, there were 23 (2%), 193 (16%), and 971 (82%) DBPs belonging to the high, moderate, and low priority groups, respectively. Lastly, the hTTR binding affinity of 4 DBPs was determined by an in vitro assay and all the tested DBPs exhibited dose-dependent binding potency with hTTR, which was consistent with the predicted result. Thus, more efforts should be performed to reveal the potential endocrine disruption of those high research-priority main-groups, subgroups, and individual DBPs.

Screening androgen receptor agonists of fish species using machine learning and molecular model in NORMAN water-relevant list

Androgen receptor (AR) agonists have strong endocrine disrupting effects in fish. Most studies mainly investigate AR binding capacity using human AR in vitro. However, there is still few methods to rapidly predict AR agonists in aquatic organisms. This study aimed to screen AR agonists of fish species using machine learning and molecular models in water-relevant list from NORMAN, a network of reference laboratories for monitoring contaminants of emerging concern in the environment. In this study, machine learning approaches (e.g., Deep Forest (DF)), Random Forests and artificial neural networks) were applied to predict AR agonists. Zebrafish, fathead minnow, mosquitofish, medaka fish and grass carp are all important aquatic model organisms widely used to evaluate the toxicity of new pollutants, and the molecular models of ARs from these five fish species were constructed to further screen AR agonists using AlphaFold2. The DF method showed the best performances with 0.99 accuracy, 0.97 sensitivity and 1 precision. The Asn705, Gln711, Arg752, and Thr877 residues in human AR and the corresponding sites in ARs from the five fish species were responsible for agonist binding. Overall, 245 substances were predicted as suspect AR agonists in the five fish species, including, certain glucocorticoids, cholesterol metabolites, and cardiovascular drugs in the NORMAN list. Using machine learning and molecular modeling hybrid methods rapidly and accurately screened AR agonists in fish species, and helping evaluate their ecological risk in fish populations.

Phthalates and uterine disorders

Humans are ubiquitously exposed to environmental endocrine disrupting chemicals such as phthalates. Phthalates can migrate out of products and enter the human body through ingestion, inhalation, or dermal application, can have potential estrogenic/antiestrogenic and/or androgenic/antiandrogenic activity, and are involved in many diseases. As a female reproductive organ that is regulated by hormones such as estrogen, progesterone and androgen, the uterus can develop several disorders such as leiomyoma, endometriosis and abnormal bleeding. In this review, we summarize the hormone-like activities of phthalates, in vitro studies of endometrial cells exposed to phthalates, epigenetic modifications in the uterus induced by phthalate exposure, and associations between phthalate exposure and uterine disorders such as leiomyoma and endometriosis. Moreover, we also discuss the current research gaps in understanding the relationship between phthalate exposure and uterine disorders.

Prediction model for milk transfer of drugs by primarily evaluating the area under the curve using QSAR/QSPR

PURPOSE

Information on milk transferability of drugs is important for patients who wish to breastfeed. The purpose of this study is to develop a prediction model for milk-to-plasma drug concentration ratio based on area under the curve (M/P_AUC). The quantitative structure-activity/property relationship (QSAR/QSPR) approach was used to predict compounds involved in active transport during milk transfer.

METHODS

We collected M/P ratio data from literature, which were curated and divided into M/P_AUC ≥ 1 and M/P_AUC < 1. Using the ADMET Predictor® and ADMET Modeler™, we constructed two types of binary classification models: an artificial neural network (ANN) and a support vector machine (SVM).

RESULTS

M/P ratios of 403 compounds were collected, M/P_AUC data were obtained for 173 compounds, while 230 compounds only had M/P_non-AUC values reported. The models were constructed using 129 of the 173 compounds, excluding colostrum data. The sensitivity of the ANN model was 0.969 for the training set and 0.833 for the test set, while the sensitivity of the SVM model was 0.971 for the training set and 0.667 for the test set. The contribution of the charge-based descriptor was high in both models.

CONCLUSIONS

We built a M/P_AUC prediction model using QSAR/QSPR. These predictive models can play an auxiliary role in evaluating the milk transferability of drugs.

Fate of progesterone and norgestrel in anaerobic/anoxic/oxic (A/A/O) process: Insights from biotransformation and mass flow

Progesterone (P4) and norgestrel (NGT) are two steroid progestogens that can pose adverse effects on aquatic organisms at ng/L levels. Despite increasing concern on their occurrence and removal in wastewater, their fate in the wastewater treatment process has not been well documented. This study identified the transformation products (TPs) of P4 and NGT in anaerobic/anoxic/oxic (A/A/O) process. Potential functional genes involved in biotransformation of P4 and NGT were explored. The elimination or formation behavior of P4, NGT and convinced TPs along various units of A/A/O process was revealed through the mass flow. Results showed that 12 and 13 TPs were identified in the P4 and NGT groups respectively, wherein 10 identical TPs and C-19 structures transformation pathways were observed in both groups. Six genes were found that may be involved in dehydrogenation and isomerization reactions in the pathways. Mass flow indicated that P4 and NGT were mainly eliminated in anaerobic and anoxic units, while convinced TPs mainly formed in anaerobic and anoxic units and were then eliminated in aerobic unit. Further, the ecological risks of the effluent should not be ignored as residual compounds including P4 or NGT and their TPs in the effluent still posed adverse effects on zebrafish transcript levels.

Novel machine learning models to predict endocrine disruption activity for high-throughput chemical screening

An area of ongoing concern in toxicology and chemical risk assessment is endocrine disrupting chemicals (EDCs). However, thousands of legacy chemicals lack the toxicity testing required to assess their respective EDC potential, and this is where computational toxicology can play a crucial role. The US (United States) Environmental Protection Agency (EPA) has run two programs, the Collaborative Estrogen Receptor Activity Project (CERAPP) and the Collaborative Modeling Project for Receptor Activity (CoMPARA) which aim to predict estrogen and androgen activity, respectively. The US EPA solicited research groups from around the world to provide endocrine receptor activity Qualitative (or Quantitative) Structure Activity Relationship ([Q]SAR) models and then combined them to create consensus models for different toxicity endpoints. Random Forest (RF) models were developed to cover a broader range of substances with high predictive capabilities using large datasets from CERAPP and CoMPARA for estrogen and androgen activity, respectively. By utilizing simple descriptors from open-source software and large training datasets, RF models were created to expand the domain of applicability for predicting endocrine disrupting activity and help in the screening and prioritization of extensive chemical inventories. In addition, RFs were trained to conservatively predict the activity, meaning models are more likely to make false-positive predictions to minimize the number of False Negatives. This work presents twelve binary and multi-class RF models to predict binding, agonism, and antagonism for estrogen and androgen receptors. The RF models were found to have high predictive capabilities compared to other in silico modes, with some models reaching balanced accuracies of 93% while having coverage of 89%. These models are intended to be incorporated into evolving priority-setting workflows and integrated strategies to support the screening and selection of chemicals for further testing and assessment by identifying potential endocrine-disrupting substances.

Integration of in silico methods to determine endocrine-disrupting tobacco pollutants binding potency with steroidogenic genes: comprehensive QSAR modeling and ensemble docking strategies

A myriad of tobacco-associated chemicals may have possibilities to developmental/reproductive axis and endocrine-disruption impacts. Mostly they breach the biotransformation of cholesterol in mitochondria by interfering with steroidogenic pathway genes, prompting to adverse effects in steroid biosynthesis; however, studies are scanty. The quantitative structure-activity relationship (QSAR) modeling and comparative docking strategies were used to understand structural features of dataset compounds that influence developmental/reproductive toxicity and estrogen and androgen receptor-binding abilities, and to predict binding levels of toxicants with steroidogenic acute regulatory protein (StAR) and cholesterol side-chain cleavage enzyme (CYP11A1) active sites. Developed QSAR models presented good robustness and predictive ability that were determined from the applicability domain and, clustering and classification of chemicals by performing self-organizing maps. Accordingly, the exorbitant amount of polycyclic aromatic hydrocarbons (PAHs) and a limited number of other chemicals including N-nitrosamines and nicotine was represented as potential developmental/reproductive toxicants as well as estrogen and androgen receptor binders. From the docking analysis, hydrogen bonding, nonpolar, atomic π-stacking, and π-cation interactions were found between PAHs (bay and fjord structural pockets) and functional hotspot residues of StAR and CYP11A1, which strengthened the subtle structural changes at domains. These govern barrier effects to cholesterol binding and/or locking cholesterol to complicate its ejection from the Ω1 loop of StAR, and further mitigates steroid biosynthesis through cholesterol by CYP11A1; therefore, they are presumably considered as block-cluster mechanisms. These outcomes are significant to be hopeful to estimate developmental/reproductive toxicity and endocrine-disruption activities of other environmental pollutants, and could be useful for further assessment to discover binding mechanisms of PAHs with other steroidogenesis pathway genes.

Review of in silico studies dedicated to the nuclear receptor family: Therapeutic prospects and toxicological concerns

Being in the center of both therapeutic and toxicological concerns, NRs are widely studied for drug discovery application but also to unravel the potential toxicity of environmental compounds such as pesticides, cosmetics or additives. High throughput screening campaigns (HTS) are largely used to detect compounds able to interact with this protein family for both therapeutic and toxicological purposes. These methods lead to a large amount of data requiring the use of computational approaches for a robust and correct analysis and interpretation. The output data can be used to build predictive models to forecast the behavior of new chemicals based on their in vitro activities. This atrticle is a review of the studies published in the last decade and dedicated to NR ligands in silico prediction for both therapeutic and toxicological purposes. Over 100 articles concerning 14 NR subfamilies were carefully read and analyzed in order to retrieve the most commonly used computational methods to develop predictive models, to retrieve the databases deployed in the model building process and to pinpoint some of the limitations they faced.

In Silico Models for Skin Sensitization and Irritation

The assessment of skin irritation, and in particular of skin sensitization, has undergone an evolution process over the last years, pushing forward to new heights of quality and innovation. Public and commercial in silico tools have been developed for skin sensitization and irritation, introducing the possibility to simplify the evaluation process and the development of topical products within the dogma of the computational methods, representing the new doctrine in the field of risk assessment.The possibility of using in silico methods is particularly appealing and advantageous due to their high speed and low-cost results.The most widespread and popular topical products are represented by cosmetics. The European Regulation 1223/2009 on cosmetic products represents a paradigm shift for the safety assessment of cosmetics transitioning from a classical toxicological approach based on animal testing, towards a completely novel strategy, where the use of animals for toxicity testing is completely banned. In this context sustainable alternatives to animal testing need to be developed, especially for skin sensitization and irritation, two critical and fundamental endpoints for the assessment of cosmetics.The Quantitative Risk Assessment (QRA) methodology and the risk assessment using New Approach Methodologies (NAM) represent new frontiers to further improve the risk assessment process for these endpoints, in particular for skin sensitization.In this chapter we present an overview of the already existing models for skin sensitization and irritation. Some examples are presented here to illustrate tools and platforms used for the evaluation of chemicals.

Combined Naïve Bayesian, Chemical Fingerprints and Molecular Docking Classifiers to Model and Predict Androgen Receptor Binding Data for Environmentally- and Health-Sensitive Substances

Many chemicals that enter the environment, food chain, and the human body can disrupt androgen-dependent pathways and mimic hormones and therefore, may be responsible for multiple diseases from reproductive to tumor. Thus, modeling and predicting androgen receptor activity is an important area of research. The aim of the current study was to find a method or combination of methods to predict compounds that can bind to and/or disrupt the androgen receptor, and thereby guide decision making and further analysis. A stepwise procedure proceeded from analysis of protein structures from human, chimp, and rat, followed by docking and subsequent ligand, and statistics based techniques that improved classification gradually. The best methods used multivariate logistic regression of combinations of chimpanzee protein structural docking scores, extended connectivity fingerprints, and naïve Bayesians of known binders and non-binders. Combination or consensus methods included data from a variety of procedures to improve the final model accuracy.

High-throughput toxicogenomic screening of chemicals in the environment using metabolically competent hepatic cell cultures

The ToxCast in vitro screening program has provided concentration-response bioactivity data across more than a thousand assay endpoints for thousands of chemicals found in our environment and commerce. However, most ToxCast screening assays have evaluated individual biological targets in cancer cell lines lacking integrated physiological functionality (such as receptor signaling, metabolism). We evaluated differentiated HepaRGTM cells, a human liver-derived cell model understood to effectively model physiologically relevant hepatic signaling. Expression of 93 gene transcripts was measured by quantitative polymerase chain reaction using Fluidigm 96.96 dynamic arrays in response to 1060 chemicals tested in eight-point concentration-response. A Bayesian framework quantitatively modeled chemical-induced changes in gene expression via six transcription factors including: aryl hydrocarbon receptor, constitutive androstane receptor, pregnane X receptor, farnesoid X receptor, androgen receptor, and peroxisome proliferator-activated receptor alpha. For these chemicals the network model translates transcriptomic data into Bayesian inferences about molecular targets known to activate toxicological adverse outcome pathways. These data also provide new insights into the molecular signaling network of HepaRGTM cell cultures.

Binary and multi-class classification for androgen receptor agonists, antagonists and binders

Androgens and androgen receptor regulate a variety of biological effects in the human body. The impaired functioning of androgen receptor may have different adverse health effects from cancer to infertility. Therefore, it is important to determine whether new chemicals have any binding activity and act as androgen agonists or antagonists before commercial use. Due to the large number of chemicals that require experimental testing, the computational methods are a viable alternative. Therefore, the aim of the present study was to develop predictive QSAR models for classifying compounds according to their activity at the androgen receptor. A large data set of chemicals from the CoMPARA project was used for this purpose and random forest classification models have been developed for androgen binding, agonistic, and antagonistic activity. In addition, a unique effort has been made for multi-class approach that discriminates between inactive compounds, agonists and antagonists simultaneously. For the evaluation set, the classification models predicted agonists with 80% of accuracy and for the antagonists' and binders' the respective metrics were 72% and 78%. Combining agonists, antagonists and inactive compounds into a multi-class approach added complexity to the modelling task and resulted to 64% prediction accuracy for the evaluation set. Considering the size of the training data sets and their imbalance, the achieved evaluation accuracy is very good. The final classification models are available for exploring and predicting at QsarDB repository (https://doi.org/10.15152/QDB.236).

QSAR Models for Human Carcinogenicity: An Assessment Based on Oral and Inhalation Slope Factors

Carcinogenicity is a crucial endpoint for the safety assessment of chemicals and products. During the last few decades, the development of quantitative structure-activity relationship ((Q)SAR) models has gained importance for regulatory use, in combination with in vitro testing or expert-based reasoning. Several classification models can now predict both human and rat carcinogenicity, but there are few models to quantitatively assess carcinogenicity in humans. To our knowledge, slope factor (SF), a parameter describing carcinogenicity potential used especially for human risk assessment of contaminated sites, has never been modeled for both inhalation and oral exposures. In this study, we developed classification and regression models for inhalation and oral SFs using data from the Risk Assessment Information System (RAIS) and different machine learning approaches. The models performed well in classification, with accuracies for the external set of 0.76 and 0.74 for oral and inhalation exposure, respectively, and r2 values of 0.57 and 0.65 in the regression models for oral and inhalation SFs in external validation. These models might therefore support regulators in (de)prioritizing substances for regulatory action and in weighing evidence in the context of chemical safety assessments. Moreover, these models are implemented on the VEGA platform and are now freely downloadable online.

Comparison of Machine Learning Models for the Androgen Receptor

The androgen receptor (AR) is a target of interest for endocrine disruption research, as altered signaling can affect normal reproductive and neurological development for generations. In an effort to prioritize compounds with alternative methodologies, the U.S. Environmental Protection Agency (EPA) used in vitro data from 11 assays to construct models of AR agonist and antagonist signaling pathways. While these EPA ToxCast AR models require in vitro data to assign a bioactivity score, Bayesian machine learning methods can be used for prospective prediction from molecule structure alone. This approach was applied to multiple types of data corresponding to the EPA's AR signaling pathway with proprietary software, Assay Central. The training performance of all machine learning models, including six other algorithms, was evaluated by internal 5-fold cross-validation statistics. Bayesian machine learning models were also evaluated with external predictions of reference chemicals to compare prediction accuracies to published results from the EPA. The machine learning model group selected for further studies of endocrine disruption consisted of continuous AC50 data from the February 2019 release of ToxCast/Tox21. These efforts demonstrate how machine learning can be used to predict AR-mediated bioactivity and can also be applied to other targets of endocrine disruption.

Toward a Computational Ecotoxicity Assay

Thousands of anthropogenic chemicals are released into the environment each year, posing potential hazards to human and environmental health. Toxic chemicals may cause a variety of adverse health effects, triggering immediate symptoms or delayed effects over longer periods of time. It is thus crucial to develop methods that can rapidly screen and predict the toxicity of chemicals to limit the potential harmful impacts of chemical pollutants. Computational methods are being increasingly used in toxicity predictions. Here, the method of molecular docking is assessed for screening potential toxicity of a variety of xenobiotic compounds, including pesticides, pharmaceuticals, pollutants, and toxins derived from the chemical industry. The method predicts the binding energy of pollutants to a set of carefully selected receptors under the assumption that toxicity in many cases is related to interference with biochemical pathways. The strength of the applied method lies in its rapid generation of interaction maps between potential toxins and the targeted enzymes, which could quickly yield molecular-level information and insight into potential perturbation pathways, aiding in the prioritization of chemicals for further tests. Two scoring functions are compared: Autodock Vina and the machine-learning scoring function RF-Score-VS. The results are promising, although hampered by the accuracy of the scoring functions. The strengths and weaknesses of the docking protocol are discussed, as well as future directions for improving the accuracy for the purpose of toxicity predictions.

OECD approaches and considerations for regulatory evaluation of endocrine disruptors

Identifying the potential endocrine disruptor hazard of environmental chemicals is a regulatory mandate for many countries. However, due to the adaptive nature of the endocrine system, absence of a single method capable of identifying endocrine disruption, and the latency between exposure to endocrine disrupting chemical during sensitive life stages and the manifestation of adverse responses, satisfying the regulatory requirement needed to identify a chemical as an endocrine disruptor is a challenge. There are now a variety of validated regulatory tests that can be used in combination to provide evidence that a chemical affects the oestrogen, androgen, thyroid, and steroidogenic pathways of vertebrates, but most rely (at least to some extent) on animal testing and require considerable cost and time to produce the necessary data. Emerging research methods are able to evaluate other endocrine pathways, incorporate more sensitive endpoints, and combine multiple alternative methods to predict in vivo outcomes. Some research approaches may also bridge gaps that have been identified in current endocrine regulatory testing. For the near term, considering new endpoints in a regulatory context may require adding them to existing test methods in order to establish relationships between the traditional and the innovative. From the outset, endocrine testing has always required integration of multiple methods that provide data on different levels of biological organisation, thus, the area of endocrine disruption is particularly adaptable to adverse outcome pathway (AOP) frameworks and integrated test methods built around AOPs. Herein, we provide a review of the status of endocrine disruptors in the OECD context, examples where innovation from research is needed to improve or bridge gaps in endocrine testing, and suggestions for regulators and researchers to facilitate uptake of innovate methods for endocrine disruptor regulatory testing. The increase in several human complex human disorders that include an endocrine component and the alarming decrease in wildlife biodiversity are commanding directives to include the best, most informative, innovative approaches to accelerate the rate and throughput of chemical evaluation for endocrine disruption.

New in silico models to predict in vitro micronucleus induction as marker of genotoxicity

The evaluation of genotoxicity is a fundamental part of the safety assessment of chemicals due to the relevance of the potential health effects of genotoxicants. Among the testing methods available, the in vitro micronucleus assay with mammalian cells is one of the most used and required by regulations targeting several industrial sectors such as the cosmetic industry and food-related sectors. As an alternative to the testing methods, in recent years, lots in silico methods were developed to predict the genotoxicity of chemicals, including models for the Ames mutagenicity test, the in vitro chromosomal aberrations and the in vivo micronucleus assay. We developed several in silico models for the prediction of genotoxicity as reflected by the in vitro micronucleus assay. The resulting models include both statistical and knowledge-based models. The most promising model is the one based on fragments extracted with the SARpy platform. More than 100 structural alerts were extracted, including also fragments associated with the non-genotoxic activity. The model is characterized by high accuracy and the lowest false negative rate, making this tool suitable for chemical screening according to the regulators' needs. The SARpy model will be implemented on the VEGA platform (https://www.vegahub.eu) and will be freely available.

Consensus versus Individual QSARs in Classification: Comparison on a Large-Scale Case Study

Consensus strategies have been widely applied in many different scientific fields, based on the assumption that the fusion of several sources of information increases the outcome reliability. Despite the widespread application of consensus approaches, their advantages in quantitative structure–activity relationship (QSAR) modeling have not been thoroughly evaluated, mainly due to the lack of appropriate large-scale data sets. In this study, we evaluated the advantages and drawbacks of consensus approaches compared to single classification QSAR models. To this end, we used a data set of three properties (androgen receptor binding, agonism, and antagonism) for approximately 4000 molecules with predictions performed by more than 20 QSAR models, made available in a large-scale collaborative project. The individual QSAR models were compared with two consensus approaches, majority voting and the Bayes consensus with discrete probability distributions, in both protective and nonprotective forms. Consensus strategies proved to be more accurate and to better cover the analyzed chemical space than individual QSARs on average, thus motivating their widespread application for property prediction. Scripts and data to reproduce the results of this study are available for download.

Binding interactions of halo-benzoic acids, halo-benzenesulfonic acids and halo-phenylboronic acids with human transthyretin

The anionic form-dependent binding interaction of halo-phenolic substances with human transthyretin (hTTR) has been observed previously. This indicates that ionizable compounds should be the primary focus in screening potential hTTR disruptors. Here, the potential binding potency of halo-benzoic acids, halo-benzenesulfonic acids/sulfates and halo-phenylboronic acids with hTTR was determined and analyzed by competitive fluorescence displacement assay integrated with computational methods. The laboratorial results indicated that the three test groups of model compounds exhibited a distinct binding affinity to hTTR. All the tested halo-phenylboronic acids, some of the tested halo-benzoic acids and halo-benzenesulfonic acids/sulfates were shown to be inactive with hTTR. Other halo-benzoic acids and halo-benzenesulfonic acids/sulfates were moderate and/or weak hTTR binders. The binding affinity of halo-benzoic acids and halo-benzenesulfonic acids/sulfates with hTTR was similar. The low distribution ability of the model compounds from water to hTTR may be the reason why they exhibited the binding potency observed with hTTR. By introducing other highly hydrophobic compounds, we observed that the binding affinity between compounds and hTTR increased with increasing molecular hydrophobicity. Those results indicated that the highly hydrophobic halo-benzoic acids and halo-benzenesulfonic acids/sulfates may be high-priority hTTR disruptors. Finally, a binary classification model was constructed employing three predictive variables. The sensitivity (S_n), specificity (S_p), predictive accuracy (Q) values of the training set and validation set were >0.83, indicating that the model had good classification performance. Thus, the binary classification model developed here could be used to distinguish whether a given ionizable compound is a potential hTTR binder or not.

Value and limitation of structure-based profilers to characterize developmental and reproductive toxicity potential

The uncertainty regarding the safety of chemicals leaching from food packaging triggers attention. In silico models provide solutions for screening of these chemicals, since many are toxicologically uncharacterized. For hazard assessment, information on developmental and reproductive toxicity (DART) is needed. The possibility to apply in silico toxicology to identify and quantify DART alerts was investigated. Open-source models and profilers were applied to 195 packaging chemicals and analogues. An approach based on DART and estrogen receptor (ER) binding profilers and molecular docking was able to identify all except for one chemical with documented DART properties. Twenty percent of the chemicals in the database known to be negative in experimental studies were classified as positive. The scheme was then applied to 121 untested chemicals. Alerts were identified for sixteen of them, five being packaging substances, the others structural analogues. Read-across was then developed to translate alerts into quantitative toxicological values. They can be used to calculate margins of exposure (MoE), the size of which reflects safety concern. The application of this approach appears valuable for hazard characterization of toxicologically untested packaging migrants. It is an alternative to the use of default uncertainty factor (UF) applied to animal chronic toxicity value to handle absence of DART data in hazard characterization.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

BACKGROUND

Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling.

OBJECTIVES

In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).

METHODS

The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays.

RESULTS

The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set.

DISCUSSION

The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

BACKGROUND

Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling.

OBJECTIVES

In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).

METHODS

The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays.

RESULTS

The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set.

DISCUSSION

The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼ 875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.