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

Prediction of Endocrine-Disrupting Chemicals Related to Estrogen, Androgen, and Thyroid Hormone (EAT) Modalities Using Transcriptomics Data and Machine Learning

Endocrine-disrupting chemicals (EDCs) are chemicals that can interfere with homeostatic processes. They are a major concern for public health, and they can cause adverse long-term effects such as cancer, intellectual impairment, obesity, diabetes, and male infertility. The endocrine system is a complex machinery, with the estrogen (E), androgen (A), and thyroid hormone (T) modes of action being of major importance. In this context, the availability of in silico models for the rapid detection of hazardous chemicals is an effective contribution to toxicological assessments. We developed Qualitative Gene expression Activity Relationship (QGexAR) models to predict the propensities of chemically induced disruption of EAT modalities. We gathered gene expression profiles from the LINCS database tested on two cell lines, i.e., MCF7 (breast cancer) and A549 (adenocarcinomic human alveolar basal epithelial). We optimized our prediction protocol by testing different feature selection methods and classification algorithms, including CATBoost, XGBoost, Random Forest, SVM, Logistic regression, AutoKeras, TPOT, and deep learning models. For each EAT endpoint, the final prediction was made according to a consensus prediction as a function of the best model obtained for each cell line. With the available data, we were able to develop a predictive model for estrogen receptor and androgen receptor binding and thyroid hormone receptor antagonistic effects with a consensus balanced accuracy on a validation set ranging from 0.725 to 0.840. The importance of each predictive feature was further assessed to identify known genes and suggest new genes potentially involved in the mechanisms of action of EAT perturbation.

Screening of the Antagonistic Activity of Potential Bisphenol A Alternatives toward the Androgen Receptor Using Machine Learning and Molecular Dynamics Simulation

Over the past few decades, extensive research has indicated that exposure to bisphenol A (BPA) increases the health risks in humans. Toxicological studies have demonstrated that BPA can bind to the androgen receptor (AR), resulting in endocrine-disrupting effects. In recent investigations, many alternatives to BPA have been detected in various environmental media as major pollutants. However, related experimental evaluations of BPA alternatives have not been systematically implemented for the assessment of chemical safety and the effects of structural characteristics on the antagonistic activity of the AR. To promote the green development of BPA alternatives, high-throughput toxicological screening is fundamental for prioritizing chemical tests. Therefore, we proposed a hybrid deep learning architecture that combines molecular descriptors and molecular graphs to predict AR antagonistic activity. Compared to previous models, this hybrid architecture can extract substantial chemical information from various molecular representations to improve the model's generalization ability for BPA alternatives. Our predictions suggest that lignin-derivable bisguaiacols, as alternatives to BPA, are likely to be nonantagonist for AR compared to bisphenol analogues. Additionally, molecular dynamics (MD) simulations identified the dihydrotestosterone-bound pocket, rather than the surface, as the major binding site of bisphenol analogues. The conformational changes of key helix H12 from an agonistic to an antagonistic conformation can be evaluated qualitatively by accelerated MD simulations to explain the underlying mechanism. Overall, our computational study is helpful for toxicological screening of BPA alternatives and the design of environmentally friendly BPA alternatives.

Development and application of consensus in silico models for advancing high-throughput toxicological predictions

Computational toxicology models have been successfully implemented to prioritize and screen chemicals. There are numerous in silico (quantitative) structure-activity relationship ([Q]SAR) models for the prediction of a range of human-relevant toxicological endpoints, but for a given endpoint and chemical, not all predictions are identical due to differences in their training sets, algorithms, and methodology. This poses an issue for high-throughput screening of a large chemical inventory as it necessitates several models to cover diverse chemistries but will then generate data conflicts. To address this challenge, we developed a consensus modeling strategy to combine predictions obtained from different existing in silico (Q)SAR models into a single predictive value while also expanding chemical space coverage. This study developed consensus models for nine toxicological endpoints relating to estrogen receptor (ER) and androgen receptor (AR) interactions (i.e., binding, agonism, and antagonism) and genotoxicity (i.e., bacterial mutation, in vitro chromosomal aberration, and in vivo micronucleus). Consensus models were created by combining different (Q)SAR models using various weighting schemes. As a multi-objective optimization problem, there is no single best consensus model, and therefore, Pareto fronts were determined for each endpoint to identify the consensus models that optimize the multiple-criterion decisions simultaneously. Accordingly, this work presents sets of solutions for each endpoint that contain the optimal combination, regardless of the trade-off, with the results demonstrating that the consensus models improved both the predictive power and chemical space coverage. These solutions were further analyzed to find trends between the best consensus models and their components. Here, we demonstrate the development of a flexible and adaptable approach for in silico consensus modeling and its application across nine toxicological endpoints related to ER activity, AR activity, and genotoxicity. These consensus models are developed to be integrated into a larger multi-tier NAM-based framework to prioritize chemicals for further investigation and support the transition to a non-animal approach to risk assessment in Canada.

Binary Classification of the Endocrine Disrupting Chemicals by Artificial Neural Networks

We develop a machine learning framework that integrates high content/high throughput image analysis and artificial neural networks (ANNs) to model the separation between chemical compounds based on their estrogenic receptor activity. Natural and man-made chemicals have the potential to disrupt the endocrine system by interfering with hormone actions in people and wildlife. Although numerous studies have revealed new knowledge on the mechanism through which these compounds interfere with various hormone receptors, it is still a very challenging task to comprehensively evaluate the endocrine disrupting potential of all existing chemicals and their mixtures by pure in vitro or in vivo approaches. Machine learning offers a unique advantage in the rapid evaluation of chemical toxicity through learning the underlying patterns in the experimental biological activity data. Motivated by this, we train and test ANN classifiers for modeling the activity of estrogen receptor-α agonists and antagonists at the single-cell level by using high throughput/high content microscopy descriptors. Our framework preprocesses the experimental data by cleaning, scaling, and feature engineering where only the middle 50% of the values from each sample with detectable receptor-DNA binding is considered in the dataset. Principal component analysis is also used to minimize the effects of experimental noise in modeling where these projected features are used in classification model building. The results show that our ANN-based nonlinear data-driven framework classifies the benchmark agonist and antagonist chemicals with 98.41% accuracy.