Harnessing Semi-Supervised Machine Learning to Automatically Predict Bioactivities of Per- and Polyfluoroalkyl Substances (PFASs)

Toxicity and speciation of inorganic arsenics and their adverse effects on in vivo endpoints and oxidative stress in the marine medaka Oryzias melastigma

Here, we investigate the effects of acute and chronic exposure to arsenate (AsV) and arsenite (AsIII) in the marine medaka Oryzias melastigma. In vivo effects, biotransformation, and oxidative stress were studied in marine medaka exposed to the two inorganic arsenics for 4 or 28 days. An investigation of embryonic development revealed no effect on in vivo parameters, but the hatching rate increased in the group exposed to AsIII. Exposure to AsIII also caused the greatest accumulation of arsenic in medaka. For acute exposure, the ratio of AsV to AsIII was higher than that of chronic exposure, indicating that bioaccumulation of inorganic arsenic can induce oxidative stress. The largest increase in oxidative stress was observed following acute exposure to AsIII, but no significant degree of oxidative stress was induced by chronic exposure. During acute exposure to AsV, the increase in the enzymatic activity of glutathione-S-transferase (GST) was twice as high compared with exposure to AsIII, suggesting that GST plays an important role in the initial detoxification process. In addition, an RNA-seq-based ingenuity pathway analysis revealed that acute exposure to AsIII may be related to cell-cycle progression. A network analysis using differentially expressed genes also revealed a potential link between the generation of inflammatory cytokines and oxidative stress due to arsenic exposure.

Emerging contaminants: A One Health perspective

Environmental pollution is escalating due to rapid global development that often prioritizes human needs over planetary health. Despite global efforts to mitigate legacy pollutants, the continuous introduction of new substances remains a major threat to both people and the planet. In response, global initiatives are focusing on risk assessment and regulation of emerging contaminants, as demonstrated by the ongoing efforts to establish the UN's Intergovernmental Science-Policy Panel on Chemicals, Waste, and Pollution Prevention. This review identifies the sources and impacts of emerging contaminants on planetary health, emphasizing the importance of adopting a One Health approach. Strategies for monitoring and addressing these pollutants are discussed, underscoring the need for robust and socially equitable environmental policies at both regional and international levels. Urgent actions are needed to transition toward sustainable pollution management practices to safeguard our planet for future generations.

Building species trait-specific nano-QSARs: Model stacking, navigating model uncertainties and limitations, and the effect of dataset size

A strong need exists for broadly applicable nano-QSARs, capable of predicting toxicological outcomes towards untested species and nanomaterials, under different environmental conditions. Existing nano-QSARs are generally limited to only a few species but the inclusion of species characteristics into models can aid in making them applicable to multiple species, even when toxicity data is not available for biological species. Species traits were used to create classification- and regression machine learning models to predict acute toxicity towards aquatic species for metallic nanomaterials. Afterwards, the individual classification- and regression models were stacked into a meta-model to improve performance. Additionally, the uncertainty and limitations of the models were assessed in detail (beyond the OECD principles) and it was investigated whether models would benefit from the addition of more data. Results showed a significant improvement in model performance following model stacking. Investigation of model uncertainties and limitations highlighted the discrepancy between the applicability domain and accuracy of predictions. Data points outside of the assessed chemical space did not have higher likelihoods of generating inadequate predictions or vice versa. It is therefore concluded that the applicability domain does not give complete insight into the uncertainty of predictions and instead the generation of prediction intervals can help in this regard. Furthermore, results indicated that an increase of the dataset size did not improve model performance. This implies that larger dataset sizes may not necessarily improve model performance while in turn also meaning that large datasets are not necessarily required for prediction of acute toxicity with nano-QSARs.

Can the concentration of environmentally persistent free radicals describe its toxicity to Caenorhabditis elegans? Evidence provided by neurotoxicity and oxidative stress

Environmentally persistent free radicals (EPFRs) are emerging pollutants stabilized on or inside particles. Although the toxicity of EPFR-containing particles has been confirmed, the conclusions are always ambiguous because of the presence of various compositions. A clear dose-response relationship was always challenged by the fact that the concentrations of these coexisted components simultaneously changed with EPFR concentrations. Without these solid dose-response pieces of evidence, we could not confidently conclude the toxicity of EPFRs and the description of potential EPFR risks. In this study, we established a particle system with a fixed catechol concentration but different reaction times to obtain particles with different EPFR concentrations. Caenorhabditis elegans (C. elegans) in response to different EPFR concentrations was systematically investigated at multiple biological levels, including behavior observations and biochemical and transcriptome analyses. Our results showed that exposure to EPFRs disrupted the development and locomotion of C. elegans. EPFRs cause concentration-dependent neurotoxicity and oxidative damage to C. elegans, which could be attributed to reactive oxygen species (ROS) promoted by EPFRs. Furthermore, the expression of key genes related to neurons was downregulated, whereas antioxidative genes were upregulated. Overall, our results confirmed the toxicity from EPFRs and EPFR concentration as a rational parameter to describe the extent of toxicity.

An artificial intelligence platform for automated PFAS subgroup classification: A discovery tool for PFAS screening

Since structural analyses and toxicity assessments have not been able to keep up with the discovery of unknown per- and polyfluoroalkyl substances (PFAS), there is an urgent need for effective categorization and grouping of PFAS. In this study, we presented PFAS-Atlas, an artificial intelligence-based platform containing a rule-based automatic classification system and a machine learning-based grouping model. Compared with previously developed classification software, the platform's classification system follows the latest Organization for Economic Co-operation and Development (OECD) definition of PFAS and reduces the number of uncategorized PFAS. In addition, the platform incorporates deep unsupervised learning models to visualize the chemical space of PFAS by clustering similar structures and linking related classes. Through real-world use cases, we demonstrate that PFAS-Atlas can rapidly screen for relationships between chemical structure and persistence, bioaccumulation, or toxicity data for PFAS. The platform can also guide the planning of the PFAS testing strategy by showing which PFAS classes urgently require further attention. Ultimately, the release of PFAS-Atlas will benefit both the PFAS research and regulation communities.

Prediction of the Interactions of a Large Number of Per- and Poly-Fluoroalkyl Substances with Ten Nuclear Receptors

Per- and poly-fluoroalkyl substances (PFASs) are persistent, toxic chemicals that pose significant hazards to human health and the environment. Screening large numbers of chemicals for their ability to act as endocrine disruptors by modulating the activity of nuclear receptors (NRs) is challenging because of the time and cost of in vitro and in vivo experiments. For this reason, we need computational approaches to screen these chemicals and quickly prioritize them for further testing. Here, we utilized molecular modeling and machine-learning predictions to identify potential interactions between 4545 PFASs with ten different NRs. The results show that some PFASs can bind strongly to several receptors. Further, PFASs that bind to different receptors can have very different structures spread throughout the chemical space. Biological validation of these in silico findings should be a high priority.

Environmental impact of PFAS: Filling data gaps using theoretical quantum chemistry and QSPR modeling

Per- and polyfluorinated alkyl substances (PFAS), known for their widespread environmental presence and slow degradation, pose significant concerns. Of the approximately 10,000 known PFAS, only a few have undergone comprehensive testing, resulting in limited experimental data. In this study, we employed a combination of physics-based methods and data-driven models to address gaps in PFAS bioaccumulation potential. Using the COnductor-like Screening MOdel for Realistic Solvents (COSMO-RS) method, we predicted n-octanol/water partition coefficients (logKOW), crucial for PFAS bioaccumulation. Our developed Quantitative Structure-Property Relationship (QSPR) model exhibited high accuracy (R2 = 0.95, RMSEC = 0.75) and strong predictive ability (Q2LOO = 0.93, RMSECV = 0.83). Leveraging the extensive NORMAN, we predicted logKOW for over 4,000 compounds, identifying 244 outliers out of 4519. Further categorizing the database into eight Organisation for Economic Co-operation and Development (OECD) categories, we confirmed fluorine atoms role in enhanced bioaccumulation. Utilizing predicted logKOW, water solubility logSW, and vapor pressure logVP values, we calculated additional physicochemical properties that are responsible for the transport and dispersion of PFAS in the environment. Parameters such as Henry's Law (kH), air-water partition coefficient (KAW), octanol-air coefficient (KOA), and soil adsorption coefficient (KOC) exhibited favorable correlations with literature data (R2 > 0.66). Our study successfully filled data gaps, contributing to the understanding of ubiquitous PFAS in the environment and estimating missing physicochemical data for these compounds.

PFAS: exploration of neurotoxicity and environmental impact

Per- and polyfluoroalkyl substances (PFAS) are widespread contaminants stemming from various industrial and consumer products, posing a grave threat to both human health and ecosystems. PFAS contamination arises from multiple sources, including industrial effluents, packaging, and product manufacturing, accumulating in plants and impacting the food chain. Elevated PFAS levels in water bodies pose significant risks to human consumption. This review focuses on PFAS-induced neurological effects, highlighting disrupted dopamine signalling and structural neuron changes in humans. Animal studies reveal apoptosis and hippocampus dysfunction, resulting in memory loss and spatial learning issues. The review introduces the BKMR model, a machine learning technique, to decipher intricate PFAS-neurotoxicity relationships. Epidemiological data underscores the vulnerability of young brains to PFAS exposure, necessitating further research. Stricter regulations, industry monitoring, and responsible waste management are crucial steps to reduce PFAS exposure.

Beyond Conventional Density Functional Theory: Advanced Quantum Dynamical Methods for Understanding Degradation of Per- and Polyfluoroalkyl Substances

Computational chemistry methods, such as density functional theory (DFT), have now become more common in environmental research, particularly for simulating the degradation of per- and polyfluoroalkyl substances (PFAS). However, the vast majority of PFAS computational studies have focused on conventional DFT approaches that only probe static, time-independent properties of PFAS near stationary points on the potential energy surface. To demonstrate the rich mechanistic information that can be obtained from time-dependent quantum dynamics calculations, we highlight recent studies using these advanced techniques for probing PFAS systems. We briefly discuss recent applications ranging from ab initio molecular dynamics to DFT-based metadynamics and real-time time-dependent DFT for probing PFAS degradation in various reactive environments. These quantum dynamical approaches provide critical mechanistic information that cannot be gleaned from conventional DFT calculations. We conclude with a perspective of promising research directions and recommend that these advanced quantum dynamics simulations be more widely used by the environmental research community to directly probe PFAS degradation dynamics and other environmental processes.

Global classification models for predicting acute toxicity of chemicals towards Daphnia magna

Molecular descriptors reflecting structural information on hydrophobicity, reactivity, polarizability, hydrogen bond and charged groups, were used to predict the toxicity (pLC50) of chemicals towards Daphnia magna with global quantitative structure-activity/toxicity relationship (QSAR/QSTR) models. A sufficiently large dataset including 1517 chemical toxicity to Daphnia magna was divided into a training set (758 pLC50) and a test set (759 pLC50). By applying random forest algorithm, two classification models, Class Model A and Class Model B were developed, having prediction accuracy, sensitivity and specificity above 85% for Class 1 (with pLC50 ≤ 4.48) and Class 2 (with pLC50 > 4.48). The Class Model A was based on nine molecular descriptors and RF parameters of nodesize = 1, ntree = 80 and mtry = 2, and yielded accuracy of 92.3% (training set), 85.6% (test set) and 88.9% (total data set). Class Model B was based on ten descriptors and parameters, nodesize = 1, ntree = 90 and mtry = 2, produced accuracy of 88.3% (training set), 86.8% (test set) and 87.5% (total data set). The two classification models were satisfactory compared with other classification model reported in the literature, although classification models in this work dealt with more samples. Thus, the two classification models with a larger applicability domain provided efficient tools for assessing chemical aquatic toxicity towards Daphnia magna.

Digital Transformation in Toxicology: Improving Communication and Efficiency in Risk Assessment

Toxicology is undergoing a digital revolution, with mobile apps, sensors, artificial intelligence (AI), and machine learning enabling better record-keeping, data analysis, and risk assessment. Additionally, computational toxicology and digital risk assessment have led to more accurate predictions of chemical hazards, reducing the burden of laboratory studies. Blockchain technology is emerging as a promising approach to increase transparency, particularly in the management and processing of genomic data related with food safety. Robotics, smart agriculture, and smart food and feedstock offer new opportunities for collecting, analyzing, and evaluating data, while wearable devices can predict toxicity and monitor health-related issues. The review article focuses on the potential of digital technologies to improve risk assessment and public health in the field of toxicology. By examining key topics such as blockchain technology, smoking toxicology, wearable sensors, and food security, this article provides an overview of how digitalization is influencing toxicology. As well as highlighting future directions for research, this article demonstrates how emerging technologies can enhance risk assessment communication and efficiency. The integration of digital technologies has revolutionized toxicology and has great potential for improving risk assessment and promoting public health.