Chemical Research in Toxicology最新文献

In recent years, the rapid increase in the production and environmental release of synthetic organic chemicals has raised serious concerns about their potential adverse effects on human health and the environment. Repeated exposure to such substances can lead to significant toxicological effects, underscoring the importance of early and reliable hazard assessment. However, experimental determination of repeated-dose toxicity (RDT) is costly, time-consuming, and constrained by ethical considerations. In this study, we developed various classification-based predictive models to evaluate the subchronic RDT potential of chemicals after oral exposure. We compiled data from eChemPortal and J-CHECK databases. The data set contains two study-derived effect levels: NOAEL (no observed adverse effect level) and LOAEL (lowest observed adverse effect level), for which separate models have been developed. A key strength of this data set is that all studies followed standardized OECD test guidelines (407, 408, and 422) and were conducted under good laboratory practice (GLP) conditions, ensuring regulatory relevance and high data reliability. Multiple machine learning algorithms were systematically evaluated, and the best models were selected using a multicriteria analysis based on the sum of ranking differences (SRD) technique. The final selected models achieved accuracies on the training sets ranging from 0.665 to 0.902, while the test sets showed accuracies ranging from 0.642 to 0.682. We also conducted a substructure analysis to identify the key substructures involved in the toxicity. This analysis revealed eight structural motifs, with chlorine- and amine-group-containing aromatic systems being particularly significant. The final developed models were experimentally validated using chemical substances provided by Global Product Compliance (GPC) Europe AB. Additionally, the models were applied to the Pesticides Properties DataBase (PPDB) to screen for pesticides with potential toxicity upon repeated exposure. To facilitate accessibility and regulatory application, the final models have been implemented in both a Python-based tool and a web application. Scientific contribution: this study presents predictive models as alternatives to traditional animal testing for assessing the subchronic oral repeated-dose toxicity (RDT) of chemicals. Our models demonstrate strong statistical performance, indicating their suitability for further application, as supported by experimental validation. These models could be used for preliminary hazard screening or weight-of-evidence evaluations. An additional advantage is that these models were developed using data that were tested in accordance with internationally harmonized test protocols, thereby enhancing their acceptance for regulatory decision-making.

We revealed in this study that prolonged aristolochic acid I (AA-I) exposure leads to an increase in oxidative stress level, and decreases in mitochondrial DNA (mtDNA) copy numbers and ATP levels in the heart, kidneys, and liver of exposed mice. The most significant decreases in ATP levels were observed in the heart and kidneys, both of which are high-energy-consuming organs. Additionally, high levels of AA-DNA adducts were detected in the mtDNA isolated from the kidneys. These combined observations of AA-induced mitochondrial dysfunction in key energy-consuming organs may help explain previous observations of rapidly progressive renal failure and the later onset of milder hypertension in patients with aristolochic acid nephropathy.

Cardiotoxicity remains a critical concern in drug development, often leading to late-stage attrition of promising compounds. While traditional assessments focus on Kv11.1 channel inhibition, the Comprehensive in Vitro Proarrhythmic Assay (CiPA) initiative emphasizes the importance of evaluating additional cardiac ion channels, notably Cav1.2 and Nav1.5. In this study, we address the limitations of existing machine learning (ML) models, which typically rely on Kv11.1-specific data, by developing a deep learning (DL) framework that integrates inhibition data across all three key ion channels. A large and diverse data set (Cardio-Tox) was curated by combining experimental data from the PubChem, CUPID, and CToxPred2 repositories, totaling 34,124 molecules for Kv11.1, 1564 for Cav1.2, and 3217 for Nav1.5. Using this data set, trained GNN models are capable of individual channel prediction. The developed CardiotoxPred method, which includes the Kv, Cav, and Nav models, achieved an average prediction accuracy of 86.7% on a test data set. In addition to robust predictive performance, GNNExplainer offers interpretable visualizations by highlighting atom- and bond-level contributions via colors. These insights support cardiac molecular severity estimation, optimization, and safety profiling. All the models are freely accessible via GitHub in a user-friendly Docker container, providing a practical tool for early-stage cardiotoxicity risk assessment in drug discovery pipelines.

Post-translational modifications (PTMs) include the addition of functional chemical groups, representing a crucial biochemical process that occurs after or during the synthesis of proteins, considerably impacting protein function, stability, localization, and interaction. In this ToxWatch, we review reactive metabolite PTMs as presented at the Fall 2025 American Chemical Society Meeting as part of the symposium entitled: Reactive Metabolite Post-Translational Modifications and Their Analysis.

Exposure to chemicals is significant for adults but may have an even greater negative impact on children, who undergo rapid developmental changes and heightened physiological plasticity. To better understand how broad environmental factors influence chemical exposures during childhood, we analyzed 438 urine samples from 187 children participating in the Baby Connectome Project using liquid chromatography-mass spectrometry (LC-MS) to characterize the early life exposome. We integrated the mass spectrometry data with demographic information to identify chemical features associated with average household income. We identified 85 compounds whose levels were significantly associated with household income. The most common exposure sources for these compounds included food, plants, endogenous production, animals, cosmetics, and household products. Our results demonstrate the effectiveness of high-resolution mass spectrometry for profiling the early life exposome and for examining its relationships with demographic factors, as illustrated here by household income. These findings underscore the value of high-resolution exposomics in characterizing the human exposome and revealing its connections to broad environmental influences.

Polyhalogenated carbazoles (PHCZs) are emerging environmental contaminants in aquatic ecosystems. Despite their reported endocrine-disrupting potential, the adverse effects of PHCZs on aquatic organisms and their underlying mechanisms remain poorly understood. In this study, we employed 2,7-dibromocarbazole (2,7-DBCZ) as a representative PHCZ to evaluate its detrimental effects on zebrafish and reveal the underlying mechanisms. Exposure to 2,7-DBCZ led to inhibited locomotor activity in zebrafish larvae. Mechanistically, 2,7-DBCZ significantly elevated embryonic estrogen (E₂) and triiodothyronine (T₃) levels, disrupting the normal transcription of E₂-related genes. Moreover, 2,7-DBCZ disrupted energy expenditure homeostasis by affecting energy metabolism-related metabolites, particularly carbohydrates (glucose 1-phosphate and fructose 6-phosphate) and amino acids (glutamine and serine). These findings suggest that the suppression of locomotor behavior in zebrafish larvae induced by 2,7-DBCZ could be attributed to its endocrine-disrupting potential, which triggers disturbances in energy metabolism. The data provide a comprehensive understanding of the ecological risks posed by PHCZs, supporting regulatory decisions regarding these emerging contaminants.

The gut microbiome is increasingly recognized as a key contributor to chemical toxicity. Endocrine-disrupting chemicals (EDCs) such as bisphenol A (BPA), tris(1,3-dichloro-2-propyl) phosphate (TDCPP), and perfluorooctanoic acid (PFOA) are widespread environmental contaminants with the potential to affect host health. To characterize microbiota-specific response to these compounds, we employed an ex vivo fecal fermentation model using samples from healthy adult donors. Fecal slurries were exposed to BPA, TDCPP and PFOA (75 μM) for up to 24 h under anaerobic conditions. Targeted LC-MS/MS quantified parent compounds over time, while untargeted metabolomics profiled microbial metabolic alterations at 4 and 24 h. TDCPP levels decreased similarly in fecal and abiotic controls, suggesting a nonmicrobial loss (e.g., instability or adsorption), whereas PFOA levels remained stable across donors. Untargeted metabolomics revealed compound- and time-dependent perturbations, with PFOA eliciting the strongest metabolic shifts. A curated set of 124 annotated metabolites indicated disruptions in bile acid transformation short-chain fatty acid production, nucleotide turnover, redox balance, and phytochemical catabolism. Several altered metabolites have been previously linked to immunomodulatory processes, suggesting potential implications for host-microbiota interactions. Overall, this study demonstrates the utility of ex vivo fermentation systems for assessing microbiota-mediated metabolic responses to xenobiotics and highlights the relevance of incorporating microbiome-related end points into chemical risk assessment.

The interaction of nanoplastics with trophoblasts can potentially disrupt fetal development. The additives associated with nanoplastics, their surface chemistry, and protein corona may lead to oxidative stress and affect the cellular functions. Detailed studies revealing a clear mechanism using an advanced placental model is essential to assess developmental toxicity and guide risk assessment.

The increasing use of flavored electronic cigarettes (e-cigarettes) raises concerns about their potential impact on the emission of harmful chemicals, particularly carbonyl compounds. This study systematically examines the effects of eight representative flavoring chemicals from four major classes including esters (ethyl acetate, ethyl butyrate), alcohols (menthol, ethyl maltol), aromatic aldehydes (benzaldehyde, vanillin), and terpenes (limonene, linalool) under varying power outputs (50 and 90 W), base liquid composition [propylene glycol (PG)/vegetable glycerin (VG) ratios (80:20, 50:50, and 20:80)], and flavor concentrations (1 and 5 mg/mL). Across all conditions, flavored e-liquids tend to produce carbonyl emissions that are higher than those of unflavored controls. Terpene-based flavors showed the strongest effects, with formaldehyde emissions being up to 2-fold higher and acrolein emissions up to 8-fold higher, frequently exceeding short-term exposure limits. Aromatic aldehydes and alcohols also increased emissions, though to a lesser extent, while esters showed smaller or inconsistent effects. The influence of flavors was further modulated by their concentration, PG/VG ratio, and device power, with higher concentration, VG content, and power amplifying emissions. These results highlight the complex interactions among e-liquid composition, flavor class, and vaping conditions, demonstrating that certain flavorings substantially elevate toxicant emissions. These findings underscore the importance of considering flavor composition, device power, and base material in evaluating the potential health risks associated with e-cigarette use.

Anaplastic lymphoma kinase (ALK) inhibitors have shown remarkable efficacy in targeted therapy for non-small cell lung cancer (NSCLC). However, it is worth noting that they may cause clinical drug-drug interactions (DDIs) by inhibiting the activities of drug-metabolizing enzymes, thereby leading to unnecessary side effects. This study sought primarily to characterize the inhibition effects of four well-known ALK inhibitors, crizotinib, ceritinib, alectinib, and brigatinib, on human UDP-glucuronosyltransferases (UGTs) in vitro and to assess their potential DDI risks in vivo. Initial studies demonstrated that all tested ALK inhibitors exhibited a varying extent of inhibition profile on UGTs at starting test concentrations (100 or 60 μM). Subsequent kinetic experiments indicated that alectinib was a selective potent inhibitor for UGT1A4 in a competitive manner with a K_i,u value of 0.07 μM. Ceritinib exhibited a potent noncompetitive inhibition against UGT1A1 and UGT1A7 with K_i,u values of 1.28 and 3.08 μM, respectively, and a competitive inhibition against UGT2B7 with K_i,u of 1.16 μM. Crizotinib and brigatinib demonstrated potent inhibition against UGT2B7 and UGT1A8, respectively. In vitro-in vivo extrapolation (IVIVE) suggested that all four ALK inhibitors have potential for DDIs due to their inhibitory effects on UGTs. Among them, the DDI risks of alectinib and brigatinib were much higher than the FDA standard, which may have an impact on the safety of clinical drug use.