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Previous research demonstrated that 12-O-tetradecanoylphorbol-13-acetate (TPA) treatment increased the number of skin papillomas in v-Ha-ras transgenic (Tg.AC) mice that had received sodium arsenite [(As(III)] in drinking water, indicating that this model is useful for studying the toxic effects of arsenic in vivo. Because the liver is a known target of arsenic, we examined the pathophysiologic and molecular effects of inorganic and organic arsenical exposure on Tg.AC mouse liver in this study. Tg.AC mice were provided drinking water containing As(III), sodium arsenate [As(V)], monomethylarsonic acid [(MMA(V)], and 1,000 ppm dimethylarsinic acid [DMA(V)] at dosages of 150, 200, 1,500, or 1,000 ppm as arsenic, respectively, for 17 weeks. Control mice received unaltered water. Four weeks after initiation of arsenic treatment, TPA at a dose of 1.25 mu g/200 mu L acetone was applied twice a week for 2 weeks to the shaved dorsal skin of all mice, including the controls not receiving arsenic. In some cases arsenic exposure reduced body weight gain and caused mortality (including moribundity). Arsenical exposure resulted in a dose-dependent accumulation of arsenic in the liver that was unexpectedly independent of chemical species and produced hepatic global DNA hypomethylation. cDNA microarray and reverse transcriptase-polymerase chain reaction analysis revealed that all arsenicals altered the expression of numerous genes associated with toxicity and cancer. However, organic arsenicals [MMA(V) and DMA(V)] induced a pattern of gene expression dissimilar to that of inorganic arsenicals. In summary, subchronic exposure of Tg.AC mice to inorganic or organic arsenicals resulted in toxic manifestations, hepatic arsenic accumulation, global DNA hypomethylation, and numerous gene expression changes. These effects may play a role in arsenic-induced hepatotoxicity and carcinogenesis and may be of particular toxicologic relevance.

The use of nanomaterials in medicine depends largely on nanotoxicological evaluation in order to ensure safe application on living organisms. Artificial intelligence (AI) and machine learning (MI) can be used to analyze and interpret large amounts of data in the field of toxicology, such as data from toxicological databases and high-content image-based screening data. Physiologically based pharmacokinetic (PBPK) models and nano-quantitative structure–activity relationship (QSAR) models can be used to predict the behavior and toxic effects of nanomaterials, respectively. PBPK and Nano-QSAR are prominent ML tool for harmful event analysis that is used to understand the mechanisms by which chemical compounds can cause toxic effects, while toxicogenomics is the study of the genetic basis of toxic responses in living organisms. Despite the potential of these methods, there are still many challenges and uncertainties that need to be addressed in the field. In this review, we provide an overview of artificial intelligence (AI) and machine learning (ML) techniques in nanomedicine and nanotoxicology to better understand the potential toxic effects of these materials at the nanoscale.

Plastics are widespread pollutants found in atmospheric, terrestrial and aquatic ecosystems due to their extensive usage and environmental persistence. Plastic additives, that are intentionally added to achieve specific functionality in plastics, leach into the environment upon plastic degradation and pose considerable risk to ecological and human health. Limited knowledge concerning the presence of plastic additives throughout plastic life cycle has hindered their effective regulation, thereby posing risks to product safety. In this study, we leveraged the adverse outcome pathway (AOP) framework to understand the mechanisms underlying plastic additives-induced toxicities. We first identified an exhaustive list of 6470 plastic additives from chemicals documented in plastics. Next, we leveraged heterogenous toxicogenomics and biological endpoints data from five exposome-relevant resources, and identified associations between 1287 plastic additives and 322 complete and high quality AOPs within AOP–Wiki. Based on these plastic additive–AOP associations, we constructed a stressor-centric AOP network, wherein the stressors are categorized into ten priority use sectors and AOPs are linked to 27 disease categories. We visualized the plastic additives–AOP network for each of the 1287 plastic additives and made them available in a dedicated website: https://cb.imsc.res.in/saopadditives/. Finally, we showed the utility of the constructed plastic additives–AOP network by identifying highly relevant AOPs associated with benzo[a]pyrene (B[a]P), bisphenol A (BPA), and bis(2-ethylhexyl) phthalate (DEHP) and thereafter, explored the associated toxicity pathways in humans and aquatic species. Overall, the constructed plastic additives–AOP network will assist regulatory risk assessment of plastic additives, thereby contributing towards a toxic-free circular economy for plastics.

Toxicity testing and regulation of advanced materials at the nanoscale, i.e. nanosafety, is challenged by the growing number of nanomaterials and their property variants requiring assessment for potential human health impacts. The existing animal-reliant toxicity testing tools are onerous in terms of time and resources and are less and less in line with the international effort to reduce animal experiments. Thus, there is a need for faster, cheaper, sensitive and effective animal alternatives that are supported by mechanistic evidence. More importantly, there is an urgency for developing alternative testing strategies that help justify the strategic prioritization of testing or targeting the most apparent adverse outcomes, selection of specific endpoints and assays and identifying nanomaterials of high concern. The Adverse Outcome Pathway (AOP) framework is a systematic process that uses the available mechanistic information concerning a toxicological response and describes causal or mechanistic linkages between a molecular initiating event, a series of intermediate key events and the adverse outcome. The AOP framework provides pragmatic insights to promote the development of alternative testing strategies. This review will detail a brief overview of the AOP framework and its application to nanotoxicology, tools for developing AOPs and the role of toxicogenomics, and summarize various AOPs of relevance to inhalation toxicity of nanomaterials that are currently under various stages of development. The review also presents a network of AOPs derived from connecting all AOPs, which shows that several adverse outcomes induced by nanomaterials originate from a molecular initiating event that describes the interaction of nanomaterials with lung cells and involve similar intermediate key events. Finally, using the example of an established AOP for lung fibrosis, the review will discuss various in vitro tests available for assessing lung fibrosis and how the information can be used to support a tiered testing strategy for lung fibrosis. The AOPs and AOP network enable deeper understanding of mechanisms involved in inhalation toxicity of nanomaterials and provide a strategy for the development of alternative test methods for hazard and risk assessment of nanomaterials.

The risk assessment of chemical carcinogens is one major task in toxicology. Even though exposure has been mitigated effectively during the last decades, low levels of carcinogenic substances in food and at the workplace are still present and often not completely avoidable. The distinction between genotoxic and non-genotoxic carcinogens has traditionally been regarded as particularly relevant for risk assessment, with the assumption of the existence of no-effect concentrations (threshold levels) in case of the latter group. In contrast, genotoxic carcinogens, their metabolic precursors and DNA reactive metabolites are considered to represent risk factors at all concentrations since even one or a few DNA lesions may in principle result in mutations and, thus, increase tumour risk. Within the current document, an updated risk evaluation for genotoxic carcinogens is proposed, based on mechanistic knowledge regarding the substance (group) under investigation, and taking into account recent improvements in analytical techniques used to quantify DNA lesions and mutations as well as “omics” approaches. Furthermore, wherever possible and appropriate, special attention is given to the integration of background levels of the same or comparable DNA lesions. Within part A, fundamental considerations highlight the terms hazard and risk with respect to DNA reactivity of genotoxic agents, as compared to non-genotoxic agents. Also, current methodologies used in genetic toxicology as well as in dosimetry of exposure are described. Special focus is given on the elucidation of modes of action (MOA) and on the relation between DNA damage and cancer risk. Part B addresses specific examples of genotoxic carcinogens, including those humans are exposed to exogenously and endogenously, such as formaldehyde, acetaldehyde and the corresponding alcohols as well as some alkylating agents, ethylene oxide, and acrylamide, but also examples resulting from exogenous sources like aflatoxin B1, allylalkoxybenzenes, 2-amino-3,8-dimethylimidazo[4,5-f] quinoxaline (MeIQx), benzo[a]pyrene and pyrrolizidine alkaloids. Additionally, special attention is given to some carcinogenic metal compounds, which are considered indirect genotoxins, by accelerating mutagenicity via interactions with the cellular response to DNA damage even at low exposure conditions. Part C finally encompasses conclusions and perspectives, suggesting a refined strategy for the assessment of the carcinogenic risk associated with an exposure to genotoxic compounds and addressing research needs.

A toxicogenomic approach was used for toxicity evaluation of arsenic in the aquatic environment, and differential gene expression was investigated from 24 h and 96 h water-only acute toxicity tests with the aquatic oligochaete, Tubifex tubifex (Annelida, Clitellata). Several toxicological endpoints (survival and autotomy) of the oligochaete and tissue residues were measured, and dose-response modelling of gene expression data was studied. A reference transcriptome of the aquatic oligochaete, T. tubifex, was reconstructed for the first time, and genes related to cell stress response (Hsc70, Hsp10, Hsp60, and Hsp83), energy metabolism (COX1), oxidative stress (Cat, GSR, and MnSOD), and the genes involved in the homeostasis of organisms (CaM, RpS13, and UBE2) were identified and characterised. The potential use of the genes identified for risk assessment in freshwater ecosystems as early biomarkers of arsenic toxicity is discussed.

The rational for the study was to review the literature on the toxicity and corresponding mechanisms associated with lead (Pb), mercury (Hg), cadmium (Cd), and arsenic (As), individually and as mixtures, in the environment. Heavy metals are ubiquitous and generally persist in the environment, enabling them to biomagnify in the food chain. Living systems most often interact with a cocktail of heavy metals in the environment. Heavy metal exposure to biological systems may lead to oxidation stress which may induce DNA damage, protein modification, lipid peroxidation, and others. In this review, the major mechanism associated with toxicities of individual metals was the generation of reactive oxygen species (ROS). Additionally, toxicities were expressed through depletion of glutathione and bonding to sulfhydryl groups of proteins. Interestingly, a metal like Pb becomes toxic to organisms through the depletion of antioxidants while Cd indirectly generates ROS by its ability to replace iron and copper. ROS generated through exposure to arsenic were associated with many modes of action, and heavy metal mixtures were found to have varied effects on organisms. Many models based on concentration addition (CA) and independent action (IA) have been introduced to help predict toxicities and mechanisms associated with metal mixtures. An integrated model which combines CA and IA was further proposed for evaluating toxicities of non-interactive mixtures. In cases where there are molecular interactions, the toxicogenomic approach was used to predict toxicities. The high-throughput toxicogenomics combines studies in genetics, genome-scale expression, cell and tissue expression, metabolite profiling, and bioinformatics.

Hazard assessment is the first step in evaluating the potential adverse effects of chemicals. Traditionally, toxicological assessment has focused on the exposure, overlooking the impact of the exposed system on the observed toxicity. However, systems toxicology emphasizes how system properties significantly contribute to the observed response. Hence, systems theory states that interactions store more information than individual elements, leading to the adoption of network based models to represent complex systems in many fields of life sciences. Here, they develop a network‐based approach to characterize toxicological responses in the context of a biological system, inferring biological system specific networks. They directly link molecular alterations to the adverse outcome pathway (AOP) framework, establishing direct connections between omics data and toxicologically relevant phenotypic events. They apply this framework to a dataset including 31 engineered nanomaterials with different physicochemical properties in two different in vitro and one in vivo models and demonstrate how the biological system is the driving force of the observed response. This work highlights the potential of network‐based methods to significantly improve their understanding of toxicological mechanisms from a systems biology perspective and provides relevant considerations and future data‐driven approaches for the hazard assessment of nanomaterials and other advanced materials. They present a novel framework for the assessment of the mechanisms of action of chemicals that relies on network models. This approach links toxicogenomics data to adverse outcome pathways, providing insights into biological responses to chemical exposures. This eases the interpretation of omics responses, providing a robust and generalizable strategy to link molecular evidence with toxicologically relevant phenotypes.

Drinking water is an important natural resource. For many people worldwide, especially in developing countries, access to safe drinking water is still a dream. An increasing number of human activities and industrialization have caused various physical, chemical, and biological pollutants to enter water bodies, affecting human health. Water pollutants contain a vast number of additives, such as perfluorinated chemicals, polybrominated diphenyl ethers, phthalate, nanomaterials, insecticides, microcystins, heavy metals, and pharmacologies. In this work, we aim to explore the potential relationship between water pollutants and human diseases. Here, we explored an integrative approach to identify genes, biological processes, molecular functions, and diseases linked to exposure to these water pollutants. These processes and functions affected by water pollutants are related to many diseases, including colonic neoplasms, breast neoplasms, hepatitis B, bladder cancer, and human cytomegalovirus infection. In addition, further analysis revealed the genes that play a key role in the human diseases induced by water pollutants. Therefore, conducting an integrative toxicogenomic analysis of water pollutants is more appropriate for evaluating the potential effects of water pollutants on human health.

Lanthanides are a series of critical elements widely used in multiple industries, such as optoelectronics and healthcare. Although initially considered to be of low toxicity, concerns have emerged during the last few decades over their impact on human health. The toxicological profile of these metals, however, has been incompletely characterized, with most studies to date solely focusing on one or two elements within the group. In the current study, we assessed potential toxicity mechanisms in the lanthanide series using a functional toxicogenomics approach in baker’s yeast, which shares many cellular pathways and functions with humans. We screened the homozygous deletion pool of 4,291 Saccharomyces cerevisiae strains with the lanthanides and identified both common and unique functional effects of these metals. Three very different trends were observed within the lanthanide series, where deletions of certain proteins on membranes and organelles had no effect on the cellular response to early lanthanides while inducing yeast sensitivity and resistance to middle and late lanthanides, respectively. Vesicle-mediated transport (primarily endocytosis) was highlighted by both gene ontology and pathway enrichment analyses as one of the main functions disturbed by the majority of the metals. Protein–protein network analysis indicated that yeast response to lanthanides relied on proteins that participate in regulatory paths used for calcium (and other biologically relevant cations), and lanthanide toxicity included disruption of biosynthetic pathways by enzyme inhibition. Last, multiple genes and proteins identified in the network analysis have human orthologs, suggesting that those may also be targeted by lanthanides in humans.