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 ß-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.

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.

Comparison of modes of action between fish, cell and mitochondrial toxicity based on toxicity correlation, excess toxicity and QSAR for class-based compounds.

Mitochondria are significant targets in cells for many environmental chemicals. Mitochondrial damage and dysfunction can lead to apoptosis and death of fish. The objectives of this study were to compare the modes of action (MOAs) between fish, cell and mitochondrial toxicity. To achieve the goal, toxicity correlation, excess toxicity and quantitative structure-activity relationship (QSAR) were investigated between these three toxicity endpoints for a wide range of compounds. Results showed that fish toxicity is well correlated to cytotoxicity, but overall fish toxicity is relatively greater than the cytotoxicity. On the other hand, fish or cell toxicity is poorly related to mitochondrial toxicity, suggesting some compounds share same toxic mechanism but some not. The excess toxicity calculated from toxicity ratio (TR) shows that specifically-acting compounds in cytotoxicity, such as insecticides, fungicides, herbicides, dyes and medications used to treat cancer, depression, heart failure and blood pressure, are active compounds in mitochondrial toxicity. However, the less inert compounds identified in fish and cell toxicity exhibit greatly mitochondrial toxicity. QSAR models reveal that fish or cell toxicity is closely related to the chemical hydrophobicity, ionization, energy of lowest unoccupied molecular orbital, hydrogen bonding potential and stability. These descriptors reflect chemical bio-uptake, reactivity and interaction with target receptors. On the other hand, binomial model reveals that mitochondrial toxicity is closely related to the chemical hydrophobicity and polarizability/dipolarity, indicating bio-uptake and Van der Waals interaction play key roles in mitochondrial toxicity. Theoretical equations have been used to explain the toxicity correlation, excess toxicity and QSAR for fish, cell and mitochondrial toxicity. Above results suggest that cytotoxicity can serve as a surrogate for fish toxicity and be used in the safety evaluation of organic pollutants in aqueous environment, but not mitochondrial toxicity, although some compounds share same modes of action between fish or cell toxicity and mitochondrial toxicity.

Prediction of organic compounds adsorbed by polyethylene and chlorinated polyethylene microplastics in freshwater using QSAR.

Microplastics (MPs), a growing class of emerging pollutants in the environment, have attracted widespread attention due to their adsorption properties. Recent research on MPs has mainly concentrated on seawater, and little work has been conducted on freshwater. Investigating and predicting the adsorption behavior of organic pollutants by MPs are necessary in freshwater. In this study, the adsorption behavior of 13 organic chemicals by polyethylene (PE) and chlorinated polyethylene (CPE) MPs was determined under freshwater conditions. Results shows the majority of the organic chemicals exhibit no distinctive differences in their adsorption on two MPs. However, the adsorption of polycyclic aromatic hydrocarbons and chlorobenzene on CPE is obviously stronger than that on PE, and the result is a counter for two pesticides. Quantitative structure activity relationship (QSAR) analysis was performed for the prediction of adsorption capacity. A QSAR model with acceptable performance (R2 = 0.8586) was built to predict the adsorptive affinity (expressed as logKd) of organic compounds on the PE MPs via multivariable linear regression (MLR) on forty-nine determined and collected data. The octanol/water partition coefficient (logKow) and excess molar refractive index (E) play dominant roles in the model. A QSAR model with satisfactory performance (R2 = 0.9302) was also established for logKd values from CPE MPs in freshwater by using 13 adsorption data determined. The logKow and most negative charge on Cl atom (Q-max,cl) play decisive roles in the adsorption. The findings can provide a scientific basis for the risk assessment of waters contaminated by MPs and organic pollutants.