Discriminating modes of toxic action in mice using toxicity in BALB/c mouse fibroblast (3T3) cells

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.

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.

Discrimination of active and inactive substances in cytotoxicity based on Tox21 10K compound library: Structure alert and mode of action

In vitro cytotoxicity assay is an ideal alternative method for the in vivo toxicity in the risk assessment of pollutants in environment. However, modes of action (MOAs) of cytotoxicity have not been investigated for a wide range of compounds. In this paper, binomial and recursive partitioning analysis were carried out between the cytotoxicity and molecular descriptors for 8981 compounds. The results showed that cytotoxicity is strongly related to the chemical hydrophobicity and excess molar refraction, indicating the bio-uptake and chemical-receptor interaction through π and n electron pair play important roles in the cytotoxicity. The decision tree derived from recursive partitioning analysis revealed that the studied compounds could be divided into 25 groups and their structural characteristics could be used as structure alert to identify active and inactive compounds in cytotoxicity. The descriptors used in the decision tree revealed that chemical ionization and bioavailability could affect the cytotoxicity for ionizable and highly hydrophobic compounds. Comparison of MOAs based on Verhaar's classification scheme showed that many inert or less inert compounds were inactive substance, and many reactive or specifically-acting compounds were active substances in the cytotoxicity. In vitro toxicity assay instead of in vivo toxicity assay can be used in the environmental hazard and risk assessment of organic pollutants. The descriptors used in the binomial equation and decision tree reveal that chemical hydrophobicity, ionization and solubility play very important roles for identification of active and inactive compounds. The results obtained in this paper are valuable for understanding the modes of action in cytotoxicity and in vivo-in vitro toxicity relationship.

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

Endocrine disrupting chemicals can mimic, block, or interfere with hormones in organisms and subsequently affect their development and reproduction, which has raised significant public concern over the past several decades. To investigate (quantitative) structure-activity relationship, 8280 compounds were compiled from the Tox21 10K compound library. The results show that 50% activity concentrations of agonists are poorly related to that of antagonists because many compounds have considerably different activity concentrations between the agonists and antagonists. Analysis on the chemical classes based on mode of action (MOA) reveals that estrogen receptor (ER) is not the main target site in the acute toxicity to aquatic organisms. Binomial analysis of active and inactive ER agonists/antagonists reveals that ER activity of compounds is dominated by octanol/water partition coefficient and excess molar refraction. The binomial equation developed from the two descriptors can classify well active and inactive ER chemicals with an overall prediction accuracy of 73%. The classification equation developed from the molecular descriptors indicates that estrogens react with the receptor through hydrophobic and π-n electron interactions. At the same time, molecular ionization, polarity, and hydrogen bonding ability can also affect the chemical ER activity. A decision tree developed from chemical structures and their applications reveals that many hormones, proton pump inhibitors, PAHs, progestin, insecticides, fungicides, steroid and chemotherapy medications are active ER agonists/antagonists. On the other hand, many monocyclic/nonaromatic chain compounds and herbicides are inactive ER compounds. The decision tree and binomial equation developed here are valuable tools to predict active and inactive ER compounds.

Predicting oxidative stress induced by organic chemicals by using quantitative Structure-Activity relationship methods

Cellular exposure to xenobiotic human-made products will lead to oxidative stress that gives rise to DNA damage, as well as chemical or mechanical damage. Distinguishing the chemicals that will induce oxidative stress and predicting their toxicity is necessary. In the present study, 4270 compounds in the ARE-bla assay were investigated to predict active and inactive compounds by using simple algorithms, namely, recursive partitioning (RP) and binomial logistic regression, and to develop the quantitative structure-activity relationship (QSAR) models of chemicals that activate the ARE pathway to induce oxidative stress and exert toxic effects on cells. A decision tree based on scaffold-based fragments obtained through RP analysis showed the best identification accuracy. However, the overall identification accuracy of this model for active compounds was unsatisfactory due to limited fragments. Furthermore, a binomial logistic regression model was developed from 638 active compounds and 3632 inactive chemicals. The model with a cutoff of 0.15 could predict chemicals that were active or inactive with the prediction accuracy of 69.1%. Its area under the receiver operating characteristic (ROC) curve metric (AUROC) was 0.762, which indicated the acceptable predictive ability of this model. The parameters nBM (number of multiple bonds) and H% (percentage of H atom) played dominant roles in the prediction of the activity (inactive or active) of chemicals. A global QSAR model was developed to predict the toxicity of active chemicals. However, the model displayed an unsatisfactory result with R² = 0.316 and R²_ext = 0.090. Active chemicals were then classified on the basis of structure. A total of 79 compounds with carbon chains could be predicted with acceptable performance by using a QSAR model with six descriptors (R² = 0.722, R²_ext = 0.798, Q²_Loo = 0.654, Q²_Boot = 0.755, Q²_ext = 0.721). The simple models established here contribute to efforts on identification compounds inducing oxidative stress and provide the scientific basis for risk assessment to organisms in the environment.

Toxicity assessment of the herbicide acetochlor in the human liver carcinoma (HepG2) cell line

Acetochlor is a high-volume herbicide used on a global scale and toxicity assessments are needed to define its potential for adverse effects in wildlife and humans. This study was conducted to determine the effects of acetochlor on human liver carcinoma cells (HepG2), a cell model widely used to assess the potential for chemical hepatotoxicity. Experiments were conducted at concentrations ranging 0-800 μM acetochlor over a 12 to 48h period to quantify underlying mechanisms of toxicity. Our data indicate that acetochlor suppressed HepG2 cell proliferation in both a concentration- and time-dependent manner. Acetochlor induced reactive oxygen species (ROS) generation more than 700% with exposure to 400 μM acetochlor, and acetochlor decreased the activities and levels of anti-oxidant responses (superoxide dismutase, glutathione) following exposure to 100 μM, 200 μM and 400 μM acetochlor. Acetochlor also (1) induced HepG2 cell damage through apoptotic-signaling pathways; (2) enhanced intracellular free Ca²⁺ concentration (>400%); (3) decreased mitochondrial transmembrane potential (∼77%), and reduced ATP levels (∼65%) following exposure to 400 μM acetochlor compared to untreated cells. Notably, cell cycle progression was blocked at G0/G1 phase in HepG2 cells when treated for 24 h with 400 μM acetochlor. Taken together, acetochlor induced significant cytotoxicity toward HepG2 cells, and the underlying toxicity mechanisms appear to be related to ROS generation, mitochondrial dysfunction and disruption in the cell cycle regulation. These data contribute to toxicity assessments for acetochlor, a high-use herbicide, to quantify risk to wildlife and human health.

Theoretical consideration on the prediction of in vivo toxicity from in vitro toxicity: Effect of bio-uptake equilibrium, kinetics and mode of action

Although in vitro assay is an ideal alternative method for the in vivo toxicity prediction, different in vivo-in vitro correlations have been observed for the toxicity endpoints obtained from different levels of species. In this paper, theoretical in vivo-in vitro toxicity correlations have been developed for cytotoxicity versus human, mammalian and fish toxicity, respectively. These theoretical models were then used to investigate the correlations and the influencing factors between in vivo and in vitro toxicity. Bio-uptake equilibrium theory can well explain why there is a significant correlation between fish and cell toxicity (R² = 0.70); why human toxicity is very close to fish toxicity; and why hydrophobic compounds exhibit relatively greater toxicity than reactive or specifically-acting compounds to human and fish as compared to cells. The kinetic theory can well explain why there is a very poor relationship between mammal and cell toxicity (R² = 0.44). This paper reveals that polar and ionized compounds can more easily pass through cell membrane and have greater bioconcentration potential. Increasing of hydrophobicity and ionization can increase the cytotoxicity. Inclusion of descriptors representing hydrophobicity, ionization, acidity and absorption into the correlation equations can significantly improve the correlations of cytotoxicity with human and fish toxicity (R² > 0.8), but not with mammal toxicity (R² = 0.49). These descriptors reflect the differences of the toxicodynamics and toxicokinetics between cells and organisms.