Improvement in the screening performance of potential aryl hydrocarbon receptor ligands by using supervised machine learning

Long-term exposure to aryl hydrocarbon receptor agonist neburon induces reproductive toxicity in male zebrafish (Danio rerio)

Neburon is a phenylurea herbicide that is widely used worldwide, but its toxicity is poorly studied. In our previous study, we found that neburon has strong aryl hydrocarbon receptor (AhR) agonist activity, but whether it causes reproductive toxicity is not clear. In the present study, zebrafish were conducted as a model organism to evaluate whether environmental concentrations of neburon (0.1, 1 and 10 µg/L) induce reproductive disorder in males. After exposure to neburon for 150 days from embryo to adult, that the average spawning egg number in high concentration group was 106.40, which was significantly lower than 193.00 in control group. This result was mainly due to the abnormal male reproductive behavior caused by abnormal transcription of genes associated with reproductive behavior in the brain, such as secretogranin-2a. The proportions of spermatozoa in the medium and high concentration groups were 82.40% and 83.84%, respectively, which were significantly lower than 89.45% in control group. This result was mainly caused by hormonal disturbances and an increased proportion of apoptotic cells. The hormonal disruption was due to the significant changes in the transcription levels of key genes in the hypothalamus-pituitary-gonadal axis following neburon treatment. Neburon treatment also significantly activated the AhR signaling pathway, causing oxidative stress damage and eventually leading to a significant increase in apoptosis in the exposed group. Together, these data filled the currently more vacant profile of neburon toxicity and might provide information to assess the ecotoxicity of neburon on male reproduction at environmentally relevant concentrations.

Prediction of Potential Risk for Flupyradifurone and Its Transformation Products to Hydrobionts

Flupyradifurone (FPF) is considered the latest generation of neonicotinoid insecticides. Here, we investigated the toxicity and ecological risk of FPF and its aerobic transformation products (TPs) to aquatic species using the method of prediction. We found that FPF exhibited moderate or high toxicity to some aquatic species. The 5% hazardous concentration of FPF was 3.84 μg/L for aquatic organisms. We obtained 91 aerobic TPs for FPF, and almost half of FPF TPs exhibited toxicity to fish or Daphnia. Eleven of the TPs of FPF exhibited a high or moderate risk to aquatic ecosystems. All FPF TPs with high and moderate risks contained a 6-chloropyridine ring structure, indicating that the derivant of a pyridine ring exhibits potential risks to aquatic ecosystems. Our results provide insight into the potential risk of FPF to aquatic ecosystems and could be used to help set criteria to control pollution caused by FPF.

Application of machine learning in the study of development, behavior, nerve, and genotoxicity of zebrafish

Machine learning (ML) as a novel model-based approach has been used in studying aquatic toxicology in the environmental field. Zebrafish, as an ideal model organism in aquatic toxicology research, has been widely used to study the toxic effects of various pollutants. However, toxicity testing on organisms may cause significant harm, consume considerable time and resources, and raise ethical concerns. Therefore, ML is used in related research to reduce animal experiments and assist researchers in conducting toxicological research. Although ML techniques have matured in various fields, research on ML-based aquatic toxicology is still in its infancy due to the lack of comprehensive large-scale toxicity databases for environmental pollutants and model organisms. Therefore, to better understand the recent research progress of ML in studying the development, behavior, nerve, and genotoxicity of zebrafish, this review mainly focuses on using ML modeling to assess and predict the toxic effects of zebrafish exposure to different toxic chemicals. Meanwhile, the opportunities and challenges faced by ML in the field of toxicology were analyzed. Finally, suggestions and perspectives were proposed for the toxicity studies of ML on zebrafish in future applications.

Data driven toxicity assessment of organic chemicals against Gammarus species using QSAR approach

Nowadays, organic chemicals play an essential role in almost all walks of life and have become indispensable to modern society. However, the continually synthesized chemicals and the numerous potential adverse endpoints against living organisms increasingly promote the regulators regarding the computational approach as a crucial supplement and an alternative to the traditional animal tests in chemical risk assessment. In this present research, we evaluated the ecotoxicity of chemicals against four typical Gammarus species, which constituted a critical element in detritus cycle and also the recommended species for water monitoring. We first screened the molecular descriptors based on the Genetic Algorithm and then developed the Quantitative Structure-Activity Relationship models using the Multiple Linear Regression method. The statistical results from various validation metrics suggested that the obtained models were internally robust and externally predictive. The application domain analysis based on the leverage approach and standardized residual method demonstrated the broad application range of each model. The interpretation of molecular descriptors in each model suggested that the chemicals with higher polarity and hydrophilicity tend to be less toxic, whereas the lipophilic moieties would enhance the chemical toxicity. Meanwhile, the other selected descriptors, such as Chi-cluster, heterocyclic, and distance matrix descriptors, manifested that the chemical toxicity was also affected by molecular branching, connectivity, electrotopological state, and other various properties. In summary, the present work proposed well-performed QSAR models and clarified the possible toxic mechanism of chemicals against Gammarus species. The obtained models could help predict the toxicity data and conduct a preliminary risk assessment, thus guiding the subsequent animal tests and reducing the assessment cost.

Toxicity and ecological risk assessment for two AhR agonistic pesticides mepanipyrim and cyprodinil and their metabolites

Mepanipyrim and cyprodinil are widely used to control and/or prevent fungal diseases in fruit culture. They are frequently detected in the aquatic environment and some food commodities. Different from TCDD, mepanipyrim and cyprodinil are more easily metabolised in the environments. However, the risk of their metabolites to the ecological environment is unclear and needs to be further confirmed. In this study, we investigated the temporal pattern of mepanipyrim- and cyprodinil-induced CYP1A and AhR2 expression and EROD enzyme activity at different time frames during zebrafish embryonic and larval development. Then, we assessed the ecological risk of mepanipyrim, cyprodinil, and their metabolites to aquatic organisms. Our results showed that mepanipyrim and cyprodinil exposure could increase the expression level of cyp1a and ahr2 genes and EROD activity by a dynamic pattern in different developmental stages of zebrafish. Besides, their several metabolites showed strong AhR agonistic activity. Importantly, these metabolites could cause potential ecological risks to aquatic organisms and should be paid more attention to. Our results would provide an important reference value for environmental pollution control and the use management of mepanipyrim and cyprodinil.

Ensemble-based, high-throughput virtual screening of potential inhibitor targeting putative farnesol dehydrogenase of Metisa plana (Lepidoptera: Psychidae)

Metisa plana (Lepidoptera: Psychidae) bagworm is a leaf-eater caterpillar ubiquitously found as a damaging pest in oil palm plantations, specifically in Malaysia. Various strategies have been implemented, including the usage of chemical insecticides. However, the main challenges include the development of insecticide resistance and its detrimental effects on the environment and non-target organisms. Therefore, a biorational insecticide is introduced by targeting the juvenile hormone (JH) biosynthetic pathway, which is mainly present in the insect and vital for the insect's growth, diapause, metamorphosis, and adult reproduction. This study aimed to investigate the potential inhibitor for the rate-limiting enzyme involved in the JH pathway known as farnesol dehydrogenase. A 255 amino acids sequence encoded for the putative M. plana farnesol dehydrogenase (MpFolDH) open reading frame had been identified and isolated. The three-dimensional structure of MpFolDH was predicted to have seven β- sheets with α-helices at both sides, showing typical characteristics for classical short-chain dehydrogenase and associated with oxidoreductase activity. Then, the ensemble-based virtual screening was conducted based on the ZINC20 database, in which 43 768 compounds that fulfilled pesticide-likeness criteria were screened by site-specific molecular docking. After a short molecular dynamics simulation (5 ns) was conducted towards 102 compounds, only the top 10 compounds based on their most favourable binding energy were selected for a more extended simulation (100 ns). Based on the protein-ligand stability, protein compactness, residues rigidity, binding interaction, binding energy throughout the 100 ns simulation, and physicochemical analysis, ZINC000408743205 was selected as a potential inhibitor for this enzyme. Amino acids decomposition analysis indicates Ile18, Ala95, Val198 and Val202 were the critical contributor residues for MpFolDH-inhibitors(s) complex.

Machine learning study: from the toxicity studies to tetrahydrocannabinol effects on Parkinson's disease

Aim: Investigating molecules having toxicity and chemical similarity to find hit molecules. Methods: The machine learning (ML) model was developed to predict the arylhydrocarbon receptor (AHR) activity of anti-Parkinson's and US FDA-approved drugs. The ML algorithm was a support vector machine, and the dataset was Tox21. Results: The ML model predicted apomorphine in anti-Parkinson's drugs and 73 molecules in FDA-approved drugs as active. The authors were curious if there is any molecule like apomorphine in these 73 molecules. A fingerprint similarity analysis of these molecules was conducted and found tetrahydrocannabinol (THC). Molecular docking studies of THC for dopamine receptor 1 (affinity = -8.2 kcal/mol) were performed. Conclusion: THC may affect dopamine receptors directly and could be useful for Parkinson's disease.

Mepanipyrim induces cardiotoxicity of zebrafish (Danio rerio) larvae via promoting AhR-regulated COX expression pathway

The wide use of pesticides has seriously threatened human health and the survival of beneficial organisms. The fungicide mepanipyrim is widely used in viticulture practices. Studies of mepanipyrim-induced toxicity in organisms are still scarce, especially studies on cardiotoxicity. In this study, we aimed to investigate mepanipyrim-induced cardiotoxicity in zebrafish (Danio rerio) larvae. We found that mepanipyrim could induce cardiotoxicity by altering the heart rate and cardiomyocyte diameter of larvae. Meanwhile, RNA sequencing and RT-qPCR data indicated that mepanipyrim exposure could dramatically alter the mRNA expression of calcium signaling pathway-, cardiac muscle contraction-, and oxidative respiratory chain-related genes. Interestingly, by the CALUX cell bioassay, we found that most cytochrome c oxidase (COX) family genes exhibited potential AhR-regulated activity, suggesting that mepanipyrim induced cardiotoxicity via a novel AhR-regulated manner in larvae. Additionally, the AhR antagonist CH223191 could effectively prevent mepanipyrim-induced cardiotoxicity in zebrafish larvae. In conclusion, the AhR agonist mepanipyrim could induce cardiotoxicity in a novel unreported AhR-regulated manner, which could specifically affect the expression of COX family genes involved in the mitochondrial oxidative respiratory chain. Our data will help explain the toxic effects of mepanipyrim on organisms and provide new insight into the AhR agonistic activity pesticide-induced cardiotoxicity.

Computer-aided toxicity prediction and potential risk assessment of two novel neonicotinoids, paichongding and cycloxaprid, to hydrobionts

Paichongding (IPP) and cycloxaprid (CYC) have been effectively used as the alternative products of imidacloprid (IMI) against IMI-resistant insects and exhibit a great market potential. However, risk assessment of IPP and CYC for non-target organisms, especially ecological risk assessment for non-target aquatic organisms, is still lacking. Here, we predicted the toxicity and potential risks of IPP, CYC, and their transformation products (TPs) to hydrobionts. The results indicated that IPP and CYC could generate 428 and 113 TPs, respectively, via aerobic microbial transformation. Nearly half of the IPP TPs and nearly 41 % of the CYC TPs exhibited high or moderate toxicity to Daphnia or fish. Moreover, we found that IPP, CYC, and 80 TPs of them posed potential risks to aquatic ecosystems. Almost all harmful TPs contained a 6-chloropyridine ring structure, suggesting that this structure may be associated with the strong toxicity of these TPs to aquatic organisms, and these TPs (IPP-TP2 or CYC-TP2, IPP-TP197 or CYC-TP71, IPP-TP198 or CYC-TP72, and IPP-TP212 or CYC-TP80) may appear in aquatic environments as final products. The risks posed by these TPs to aquatic ecosystems require more attention. This study provides insights into the toxicity and ecological risks of IPP and CYC.

Mepanipyrim induces visual developmental toxicity and vision-guided behavioral alteration in zebrafish larvae

Mepanipyrim, an anilinopyrimidine fungicide, has been extensively used to prevent fungal diseases in fruit culture. Currently, research on mepanipyrim-induced toxicity in organisms is still very scarce, especially visual developmental toxicity. Here, zebrafish larvae were employed to investigate mepanipyrim-induced visual developmental toxicity. Intense light and monochromatic light stimuli-evoked escape experiments were used to investigate vision-guided behaviors. Meanwhile, transcriptomic sequencing and real-time quantitative PCR assays were applied to assess the potential mechanisms of mepanipyrim-induced visual developmental toxicity and vision-guided behavioral alteration. Our results showed that mepanipyrim exposure could induce retinal impairment and vision-guided behavioral alteration in larval zebrafish. In addition, the grk1b gene of the phototransduction signaling pathway was found to be a potential aryl hydrocarbon receptor (AhR)-regulated gene. Mepanipyrim-induced visual developmental toxicity was potentially related to the AhR signaling pathway. Furthermore, mepanipyrim-induced behavioral alteration was guided by the visual function, and the effects of mepanipyrim on long and middle wavelength light-sensitive opsins may be the main cause of vision-guided behavioral alteration. Our results provide insights into understanding the relationship between visual development and vision-guided behaviors induced by mepanipyrim exposure.

Predicting and assessing the toxicity and ecological risk of seven widely used neonicotinoid insecticides and their aerobic transformation products to aquatic organisms

Neonicotinoid insecticides (NIs) are widely used worldwide, accounting for 25 % of the global insecticide market, and are easily transported into surrounding aquatic ecological environments after application. At present, >80 % of surface water is contaminated by NIs globally. Some transformation products (TPs) of NIs can exhibit greater toxicity to aquatic organism than their parent products. However, few studies have evaluated the toxicity and ecological risk of the TPs of NIs. In this study, we aimed to assess the toxicity and ecological risk of seven widely used NIs and their aerobic TPs to aquatic organisms using a prediction method. We found that partial aerobic TPs of NIs have greater toxicity to aquatic organisms than their parent products, and some of them could severely damage aquatic ecosystems. Meanwhile, acetamiprid, thiacloprid, and several other TPs of NIs with a chlorinated ring structure showed strong bioconcentration abilities, which could potentially harm aquatic organisms through the food chain. Moreover, the widespread use of NIs has certain aquatic ecological risks, which should be controlled and limited. This study comprehensively evaluated the ecological risk of seven widely used NIs and their aerobic TPs to aquatic organisms for the first time. Our results could provide an important reference for assessment of the aquatic environmental risk posed by NIs and pollution control.

Aryl hydrocarbon receptor agonist diuron and its metabolites cause reproductive disorders in male marine medaka (Oryzias melastigma)

Diuron, a widely used phenylurea herbicide, has been frequently detected in marine organism and seawater all over the world. But the understanding of potential damage of diuron on reproduction in marine fish is currently not enough. Herein, marine medaka (Oryzias melastigma) were continuously exposed to 0, 5, 50, 500, and 5000 ng/L diuron from embryo (0 dpf) to adult (180 dpf) stage. The results suggested that diuron had an adverse influence on male reproduction for marine medaka, including decreased gonado somatic index, histological changes of testes, decreased mobility of sperm, and reduced fecundity through disrupting the balance of sex hormone and genes expression related to hypothalamus-pituitary-gonadal-liver (HPGL) axis. The reduced fecundity was reflected in abnormal sexual behaviors, further inhibited growth and development of F1 embryo and larvae. Moreover, the proportion of diuron metabolites (DCPMU and DCPU) was increased in fish, but the proportion of diuron was decreased with the increasing of exposure concentration. Diuron, DCPMU, and DCPU was identified as aryl hydrocarbon receptor agonist (AhR) agonist using in silico and in vivo models. DCPMU and DCPU induced the gene expression of AhR signaling and metabolizing enzymes (such as cyp1a1) in the livers. A great deal of major metabolites affected various organs related to HPGL axis of male marine medaka and led to serious reproductive disorders. Consequently, it reveals that long-term exposure to environmentally relevant concentrations of diuron and even AhR agonist pesticides pose a potential ecological risk for marine fish.

Promotion of Bladder Cancer Cell Metastasis by 2-Mercaptobenzothiazole via Its Activation of Aryl Hydrocarbon Receptor Transcription: Molecular Dynamics Simulations, Cell-Based Assays, and Machine Learning-Driven Prediction

2-Mercaptobenzothiazole (MBT) is an industrial chemical widely used for rubber products, corrosion inhibitors, and polymer materials with multiple environmental and exposure pathways. A growing body of evidence suggests its potential bladder cancer (BC) risk as a public health concern; however, the molecular mechanism remains poorly understood. Herein, we demonstrate the activation of the aryl hydrocarbon receptor (AhR) by MBT and reveal key events in carcinogenesis associated with BC. MBT alters conformational changes of AhR ligand binding domain (LBD) as revealed by 500 ns molecular dynamics simulations and activates AhR transcription with upregulation of AhR-target genes CYP1A1 and CYP1B1 to approximately 1.5-fold. MBT upregulates the expression of MMP1, the cancer cell metastasis biomarker, to 3.2-fold and promotes BC cell invasion through an AhR-mediated manner. MBT is further revealed to induce differentially expressed genes (DEGs) most enriched in cancer pathways by transcriptome profiling. The exposure of MBT at environmentally relevant concentrations induces BC risk via AhR signaling disruption, transcriptome aberration, and malignant cell metastasis. A machine learning-based model with an AUC value of 0.881 is constructed to successfully predict 31 MBT analogues. Overall, we provide molecular insight into the BC risk of MBT and develop an effective tool for rapid screening of AhR agonists.

Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications

Recently, research on the development of artificial intelligence (AI)-based computational toxicology models that predict toxicity without the use of animal testing has emerged because of the rapid development of computer technology. Various computational toxicology techniques that predict toxicity based on the structure of chemical substances are gaining attention, including the quantitative structure-activity relationship. To understand the recent development of these models, we analyzed the databases, molecular descriptors, fingerprints, and algorithms considered in recent studies. Based on a selection of 96 papers published since 2014, we found that AI models have been developed to predict approximately 30 different toxicity end points using more than 20 toxicity databases. For model development, molecular access system and extended-connectivity fingerprints are the most commonly used molecular descriptors. The most used algorithm among the machine learning techniques is the random forest, while the most used algorithm among the deep learning techniques is a deep neural network. The use of AI technology in the development of toxicity prediction models is a new concept that will aid in achieving a scientific accord and meet regulatory applications. The comprehensive overview provided in this study will provide a useful guide for the further development and application of toxicity prediction models.

Artificial intelligence in virtual screening: models versus experiments

A typical drug discovery project involves identifying active compounds with significant binding potential for selected disease-specific targets. Experimental high-throughput screening (HTS) is a traditional approach to drug discovery, but is expensive and time-consuming when dealing with huge chemical libraries with billions of compounds. The search space can be narrowed down with the use of reliable computational screening approaches. In this review, we focus on various machine-learning (ML) and deep-learning (DL)-based scoring functions developed for solving classification and ranking problems in drug discovery. We highlight studies in which ML and DL models were successfully deployed to identify lead compounds for which the experimental validations are available from bioassay studies.