Chemical Research in Toxicology最新文献

MK-3207 was withdrawn from development because of drug-inducedliver injury. Metabolic studies identified bioactivation to a protein-reactive3,5-difluorophenylglyoxal (DFPG) intermediate. Although phenylglyoxalmetabolites readily form protein adducts, their immunogenic potentialhas been poorly understood. We compared the immunogenicity of threeDFPG isomers (3,5-, 2,4-, and 3,4-DFPG) using T-cell clones generatedfrom treated healthy donor PBMCs. Reactive clones were detected againstisomers with 3,5-DFPG inducing the strongest responses. Activationrequired antigen processing, and no cross-reactivity with MK-3207was observed. These findings demonstrate the immunogenicity of 3,5-DFPGand show that structural differences among phenylglyoxal isomers influenceT cell activation.

Early warning systems (EWSs) are currently being developed by various authorities aiming at identifying potentially hazardous chemicals before they become a threat to the environment and human health. In this context, patents provide an excellent data source for exploring novel chemistry or the use of chemicals in materials and products. However, analysis of patents is challenging, including unraveling molecular structures presented as graphics depicting various elements, functional groups, and molecular bonds. Our study aims to improve EWS using automated artificial intelligence-based molecular structure recognition methods for encoding these for further hazard analysis. Current structure extraction tools are primarily trained on chemical structures collected from publicly available data sets, and the application of these tools to patent-specific chemical data has received little attention. This paper presents a field study utilizing the three tools Decimer, Molscribe, and Mathpix and assesses their performance in recognizing chemical structures in patents. Two data sets were compiled and curated including (1) diverse organic chemicals and (2) per- and polyfluoroalkyl substances (PFAS). It was revealed that these tools perform well on simpler molecular structures, whereas they struggle with more complex structural features, including repetitive units, cross-bonding, and Markush structures. Furthermore, it was discovered that these tools are extremely sensitive to image artifacts such as noise from lines and dots or distortions. Overcoming these challenges will be critical before implementation in automated EWS and thereby enable screening of patents for rapid and effective identification of potentially hazardous emerging chemicals.

Bacteria encounter acid stress under a variety of circumstances. Acid stress induces DNA damage and genomic instability, most directly via acid-catalyzed depurination reactions that generate apurinic (abasic, AP) sites on the deoxyribose phosphate backbone. DNA damage responses are important in bacterial resistance to acids. A recent report provided evidence that a DNA repair glycosylase, AlkX, which is capable of initiating the repair of interstrand DNA cross-links (ICLs), contributes to acid resistance by the pulmonary pathogen Acinetobacter baumannii (Kunkle et al. Proc. Nat. Acad. Sci. USA, 2024, 121, e2402422121). This suggested the possibility that AP-derived ICLs might contribute to the acid stress in bacteria. This idea is predicated on earlier work showing that AP sites can generate ICLs via reactions of the ring-opened AP aldehyde with the exocyclic amino groups of nucleobases on the opposing strand of duplex DNA (Price, N. E. J. Am. Chem. Soc. 2014, 136, 3483). However, it was not clear from previous work whether AP-derived ICLs could be generated under conditions of acid stress. The results reported here provide evidence for ICL formation under conditions of acid stress via a sequential process involving acid-catalyzed depurination followed by cross-linking of the resulting AP site with an adenine residue on the opposing strand of duplex DNA. This supports the possibility that AP-derived interstrand cross-links could contribute to the effects of acid stress in bacteria, and proteins involved in the repair of these lesions could be involved in resistance to acid stress.

Adverse drug reactions (ADRs) are a major concern for public health and patient safety and a burden in drug discovery, with drug-induced liver injury (DILI) as a prototypical case. Next to computational models such as deep learning, knowledge graph models can help capture biological systems and pathways underlying toxic effects. We present a hierarchical, context-aware knowledge graph model to detect ADRs, with DILI as a case study. The model builds on a commercial knowledge graph integrating over 200 life-science databases and millions of publications and patents. It combines translation- and path-based methods, where two-hop paths link drugs and ADRs via an intermediate concept (e.g., genes, proteins, pathways). These paths are combined in path bundles as the graph’s basic units. Evaluation used the FDA DILIrank data set of DILI-positive and DILI-negative drugs, mapped onto the graph. Performance was evaluated with classification metrics, including the area under the receiver operating characteristic curve (AUC) and Matthews correlation coefficient (MCC). The output probabilities of the path bundles generated by base classifiers formed the input for multilevel statistical modeling. Multilevel analysis was used to predict DILI risk of the drugs while accounting for the dependencies of the path bundles on the ADR contexts and on the different relations in the graph. The four-level model performed best (AUC = 0.850; MCC = 0.639). Average prediction probabilities showed good separation (0.641 for DILI-positive drugs and 0.402 for DILI-negative drugs). Additional results included the correct classification of 29 of the 37 wrongly classified drugs by a QSAR study using molecular descriptors. The added value of a cascaded QSAR and knowledge graph approach was shown on a 130-compound subset of the DeepDILI test set, where the combined model rescued additional DILI-positive drugs missed by DeepDILI alone at the expense of a limited number of false positives. The study model and data are available through https://github.com/mi-erasmusmc/multilevel_dili.

Adverse outcome pathways (AOPs) are biological mechanism-based models that connect chemical exposures to adverse outcomes (AOs) through molecular initiating events (MIEs) and key events (KEs). However, there is a notable gap in the available data on AOPs, such as chemicals associated with known AOPs and the associations between biological molecules and events. To address this challenge, we developed AOP Network Box─a multilayered network integrating chemical–protein interaction (CPI), protein–protein interaction (PPI), Gene Ontology (GO), and AOP network data. By leveraging this comprehensive integration, the AOP Network Box identifies AOPs associated with chemicals and facilitates the discovery of previously unknown relationships among chemicals, biological molecules, and events. This unified framework enables the discovery of previously unknown relationships among chemicals, biological molecules, and events, providing deeper insights into the mechanistic basis of chemical adverse outcomes. The web-based platform is freely available at https://aop-network-box.kaist.ac.kr.

Reliable quantification of uncertainty is critical for the interpretation and regulatory use of the QSAR models. Applicability domain (AD) assessment was introduced precisely for this purpose─the original OECD guidance defines AD in terms of prediction reliability─yet in practice AD metrics output heuristic similarity scores without statistically guaranteed confidence estimates. We present conformal prediction as a calibration layer that retrofits any QSAR models into a confidence predictor, producing prediction intervals for regression and prediction sets for classification at a user-specified nominal confidence level (e.g., 90%), with statistically guaranteed coverage, without retraining, using only model predictions and a calibration set. The guarantee holds under the exchangeability assumption─that calibration and test compounds are drawn from the same input space─and follows as a mathematical consequence of the rank-based calibration procedure. When the assumption is violated, coverage may fall below the nominal level─signaled by widening intervals and shrinking singleton rates. The framework uses auxiliary models trained on molecular fingerprints as nonconformity scores, a role that most existing AD indices can equally fulfill; a novel ordinal distance strategy extends the approach to hard-label classifiers by generating pseudoproabilities compatible with standard conformal methods. Applied to over 100 VEGA QSAR models spanning physicochemical properties, toxicity, and environmental endpoints, the framework consistently achieves nominal coverage across all models and endpoint types. Conformal efficiency metrics─prediction interval width for regression and singleton rate for classification─correlate strongly with AD indices, demonstrating that CP formalizes and quantifies what AD heuristics approximate: the relationship between structural novelty and prediction reliability, successfully transforming heuristic chemical similarity into statistically valid prediction intervals or label sets. Large-scale application to the EPA CompTox chemical inventory demonstrates practical deployment at a regulatory scale. An open-source pipeline facilitates application to any QSAR/QSPR platform, enabling an improved transparency and reliability assessment.

The gas phase of tobacco smoke has been implicated as a major cause of cancer and chronic obstructive pulmonary disease. Furan, a component of the gas phase, is highly toxic and carcinogenic in rodents; however, its toxicity in humans has not been well investigated. Furan’s toxicity results from cytochrome P450 2E1-catalyzed oxidation to cis-2-butene-1,4-dial (BDA). The structure of furan metabolites demonstrates that BDA reacts with glutathione (GSH) to form a reactive GSH conjugate, 2-(S-glutathionyl)succinaldehyde (GSH-BDA), which reacts with protein lysine groups as well as other cellular nucleophiles. In this report, GSH-BDA-glycerolphosphorylethanolamine (GSH-BDA-GPE), GSH-BDA-ethanolamine, and GSH-BDA-glutamic acid are identified as human hepatocellular metabolites of furan. To determine if the downstream mercapturic acid metabolites are present in human urine, stable isotopically labeled forms of the expected metabolites were used as internal standards in an established liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay. This approach indicated that N-acetyl-S-(1-(2-hydroxyethyl)-1H-pyrrol-3-yl)cysteine sulfoxide (NAC-BDA-ethanolamine sulfoxide) was detected in human urine. A sensitive LC-MS/MS assay for this metabolite was developed and applied to urine samples from a variety of smoke-exposed individuals as well as nonsmokers. Its levels were 2.5–5 times higher in people who smoke than in nonsmokers. They were not elevated in exclusive e-cigarette or cannabis users. Two case-control studies were performed to determine whether NAC-BDA-lysine metabolites or NAC-BDA-ethanolamine sulfoxide were associated with smoking-related diseases. In the first study, there was no association between any of the furan metabolites and liver cancer. In the second study, there was a suggestive association between NAC-BDA-ethanolamine sulfoxide and the risk of smoking-related respiratory disease symptoms. However, the overall trend was not significant. Therefore, there was no clear link between this furan metabolite and the risk of smoking-related respiratory disease symptoms.

Paracetamol (PARA) is a widely used antipyretic and analgesic drug; however, overdose is a major cause of hepatotoxicity. Although N-acetylcysteine (NAC) is the standard antidote, its limitations necessitate the investigation of alternative therapeutic approaches. Artemisinin (ART), a natural compound with antioxidant and anti-inflammatory properties, was evaluated for its hepatoprotective potential in a paracetamol-induced acute liver injury model. Biochemical liver function parameters (ALT, AST), oxidative stress markers (SOD, GSH, MDA), molecular markers (TNF-α, IL-1β, iNOS, NF-κB, CYP2E1 mRNA expression), and histopathological changes were assessed using spectrophotometric assays, quantitative real-time PCR (qPCR), and histological staining methods. PARA administration significantly increased ALT and AST levels compared to the CONTROL group (p < 0.001). Among the treatment groups, PARA + ART 14 significantly reduced these enzyme levels compared to the PARA group (p < 0.001). ART treatment also increased antioxidant parameters (SOD, GSH) and decreased MDA levels compared to the PARA group (p < 0.001), with effects comparable to PARA + NAC. Inflammation- and oxidative stress-related gene expressions were significantly elevated in the PARA group versus CONTROL (p < 0.001), whereas PARA + ART 14 significantly downregulated all markers (p < 0.001). Histopathological findings supported the biochemical and molecular results, showing partial improvement in PARA + ART 7 and more pronounced recovery in PARA + ART 14, while no significant improvement was observed in PARA + ART 35. These findings suggest that ART may exert dose-dependent hepatoprotective effects against paracetamol-induced liver injury by modulating oxidative stress and inflammatory responses. Further studies are needed to determine the optimal therapeutic dose and confirm its clinical potential.

Oxidative DNA lesions such as 8-oxo-7,8-dihydroguanine (8-oxoG) and abasic (AP) sites contribute to mutagenesis and genetic instability. H-DNA, a naturally occurring intramolecular triplex structure, promotes genetic instability and is susceptible to oxidative damage. Here, we examined how oxidative lesions distribute within an H-DNA-forming sequence. Although oxidative stress increased oxidized purines overall, no template-dependent differences were detected at a distal B-DNA locus. In contrast, AP sites and FPG-sensitive lesions were enriched within the H-DNA region relative to the B-DNA control, even under basal conditions. These findings indicate that H-DNA topology enhances local susceptibility to oxidative damage and shapes the spatial distribution of oxidative DNA lesions that can contribute to mutagenesis.

Polycyclic aromatic hydrocarbons (PAHs) are common environmental pollutants generated from the incomplete combustion of organic materials and represent an important source of occupational and environmental exposure. Aside from their carcinogenic properties, PAHs are known to exert immunomodulatory and proinflammatory effects. Immune activation is closely associated with alterations in pteridine metabolism and activation of the kynurenine pathway; however, evidence linking PAH exposure to these immune-inflammatory pathways in humans remains limited. This study aimed to investigate the systemic biological effects of occupational exposure to PAHs from asphalt fumes in road construction workers, with a particular focus on alterations in pteridine metabolism and activation of the kynurenine pathway, using a combined panel of exposure and mechanistic biomarkers. Routine clinical parameters remained within normal ranges while urinary 1-hydroxypyrene, a well-established biomarker of internal PAH exposure, was significantly elevated, confirming substantial PAH exposure. Key findings revealed profound alterations in two critical metabolic pathways: (i) Pteridine metabolism was shifted, with increased neopterin and decreased biopterin levels, indicating activation of cell-mediated immunity and reduced cofactor availability; (ii) the kynurenine pathway was concurrently activated, as reflected by elevated kynurenine, reduced tryptophan, and increased estimated indoleamine 2,3-dioxygenase activity. These findings indicate that low-level but chronic PAH exposure induces sustained Th1-type immune activation and metabolic disturbances in the absence of overt clinical pathology, representing a state of subclinical biological adaptation.