Identification of active and inactive agonists/antagonists of estrogen receptor based on Tox21 10K compound library: Binomial analysis and structure alert

Identification of estrogen receptor agonists among hydroxylated polychlorinated biphenyls using classification-based quantitative structure-activity relationship models

Identification of estrogen receptor (ER) agonists among environmental toxicants is essential for assessing the potential impact of toxicants on human health. Using 2D autocorrelation descriptors as predictor variables, two binary logistic regression models were developed to identify active ER agonists among hydroxylated polychlorinated biphenyls (OH-PCBs). The classifications made by the two models on the training set compounds resulted in accuracy, sensitivity and specificity of 95.9 %, 93.9 % and 97.6 % for ERα dataset and 91.9 %, 90.9 % and 92.7 % for ERβ dataset. The areas under the ROC curves, constructed with the training set data, were found to be 0.985 and 0.987 for the two models. Predictions made by models I and II correctly classified 84.0 % and 88.0 % of the test set compounds and 89.8 % and 85.8% of the cross-validation set compounds respectively. The two classification-based QSAR models proposed in this paper are considered robust and reliable for rapid identification of ERα and ERβ agonists among OH-PCB congeners.

Idarubicin combats abiraterone and enzalutamide resistance in prostate cells via targeting XPA protein

Although second-generation therapies like abiraterone (ABI) and enzalutamide (ENZ) benefit patients with castration-resistant prostate cancer (CRPC), drug resistance frequently occurs, eventually resulting in therapy failure. In this study, we used two libraries, FDA-approved drug library and CRISP/Cas9 knockout (GeCKO) library to screen for drugs that overcome treatment resistance and to identify the potential drug-resistant genes involved in treatment resistance. Our screening results showed that the DNA-damaging agent idarubicin (IDA) overcame abiraterone and enzalutamide resistance in prostate cancer cells. IDA treatment inhibited the DNA repair protein XPA expression in a transcription-independent manner. Consistently, XPA knockout sensitized prostate cancer cells to abiraterone and enzalutamide treatment. In conclusion, IDA combats abiraterone and enzalutamide resistance by reducing XPA protein level in prostate cancer.

Application of machine learning to predict the inhibitory activity of organic chemicals on thyroid stimulating hormone receptor

With the promotion of carbon neutrality, it is also important to synchronously promote the assessment and sustainable management of chemicals so as to protect public health. Humans and animals are possibly exposed to endocrine disruptors that have inhibitory effects on thyroid stimulating hormone receptor (TSHR). As such, it is important to identify chemicals that inhibit TSHR and to develop models to predict their inhibitory activity. In this study, 5952 compounds derived from a cyclic adenosine monophosphate (cAMP) analysis, a key signaling pathway in thyrocytes, were used to establish a binary classification model comparing methods that included random forest (RF), extreme gradient boosting (XGB), and logistic regression (LR). The prediction model based on RF showed the highest identification accuracy for revealing chemicals that may inhibit TSHR. For the RF model, recall was calculated at 0.89, balance accuracy was 0.85, and its receiver operating characteristic (ROC) curve-area under (AUC) was 0.92, indicating that the model had very high predictive capacity. The lowest CDocker energy (CE) and CDocker interaction energy (CIE) for chemicals and TSHR were determined and were subsequently introduced into the predictive model as descriptors. A regression model, extreme gradient boosting-Regression (XGBR), was successfully established yielding an R² = 0.65 to predict inhibitory activity for active compounds. Parameters that included dissociation characteristics, molecular structure, and binding energy were all key factors in the predictive model. We demonstrate that QSAR models are useful approaches, not only for identifying chemicals that inhibit TSHR, but for predicting inhibitory activity of active compounds.

Orthogonal assay for validation of Tox21 PPARγ data and applicability to in silico prediction model

High-throughput screening data from the Tox21 database is used for prioritizing hazardous chemicals and building in silico-based toxicity prediction models. One of the Tox21 dataset, peroxisome proliferator-activated receptor-gamma (PPARγ), a nuclear receptor superfamily, identified various endpoints in HEK293 cells. PPARγ mediates various toxic effects when its receptors are activated or inhibited by ligands such as thiazolidinedione and GW9662. In this study, an orthogonal assay was constructed to verify the effectiveness of the Tox21 PPARγ data, and the effect of highly reliable data on in silico model construction was investigated. The orthogonal assay was a reporter gene assay based on the PPARγ ligand binding domain in CV-1 cells. Only 39% of agonists and 55% of antagonists had similar responses in CV-1 and HEK293 cells. Thus, the effectiveness of Tox21 data on PPARγ may vary depending on the cell line. However, in silico PLS-DA analysis with only high-reliability data (i.e., the same response in both cell lines), yielded more accurate prediction of the activity of potential chemical ligands, despite the small number of samples. Thus, obtaining reliable chemical screening data for PPARγ through orthogonal analysis, even for only limited chemicals, supports the construction of highly predictive in silico models with improved screening efficiency.

Identification of active or inactive agonists of tumor suppressor protein based on Tox21 library

Exposure of cells to xenobiotic human-made products can lead to genotoxicity and cause DNA damage. It is an urgent need to quickly identify the chemicals that cause DNA damage, and their toxicity should be predicted. In this study, recursive partitioning (RP), binary logistic regression, and one machine learning approach, namely, random forest (RF) classifier, were used to predict the active and inactive compounds of a total 5036 data based on the assay conducted by a β-lactamase reporter gene under control of the p53 response element (p53RE) from Tox21 library. Results show that the binary logistic regression model with a threshold of 0.5 has a high accuracy rate (83%) to distinguish active and inactive compounds. The RF classifier method has satisfactory results, with an accuracy rate (84.38%) approximately higher than that of binary logistic regression. The models established can identify compounds that induce DNA damage and activate p53, and provide a scientific basis for the risk assessment of organic chemicals in the environment.

Prediction Models for Agonists and Antagonists of Molecular Initiation Events for Toxicity Pathways Using an Improved Deep-Learning-Based Quantitative Structure-Activity Relationship System

In silico approaches have been studied intensively to assess the toxicological risk of various chemical compounds as alternatives to traditional in vivo animal tests. Among these approaches, quantitative structure-activity relationship (QSAR) analysis has the advantages that it is able to construct models to predict the biological properties of chemicals based on structural information. Previously, we reported a deep learning (DL) algorithm-based QSAR approach called DeepSnap-DL for high-performance prediction modeling of the agonist and antagonist activity of key molecules in molecular initiating events in toxicological pathways using optimized hyperparameters. In the present study, to achieve high throughput in the DeepSnap-DL system-which consists of the preparation of three-dimensional molecular structures of chemical compounds, the generation of snapshot images from the three-dimensional chemical structures, DL, and statistical calculations-we propose an improved DeepSnap-DL approach. Using this improved system, we constructed 59 prediction models for the agonist and antagonist activity of key molecules in the Tox21 10K library. The results indicate that modeling of the agonist and antagonist activity with high prediction performance and high throughput can be achieved by optimizing suitable parameters in the improved DeepSnap-DL system.