Chem. Res. Toxicol. [JOURNAL]
Sun
200 papers
ACSS2 Inhibition Alleviates Cisplatin-Induced Acute Kidney Injury: Insights from Targeted Metabolomics.
Chen W, Zhao X, Zheng J … +5 more , Zheng Z, Qin F, Zhang Z, Xu F, Zhang P
Chem Res Toxicol · 2026 Jul · PMID 42400592 · Publisher ↗
Cisplatin (DDP) is a widely used chemotherapeutic agent, but its clinical application is limited by dose-dependent nephrotoxicity. Although metabolic dysregulation is a hallmark of DDP-induced acute kidney injury (AKI),...
Cisplatin (DDP) is a widely used chemotherapeutic agent, but its clinical application is limited by dose-dependent nephrotoxicity. Although metabolic dysregulation is a hallmark of DDP-induced acute kidney injury (AKI), the specific changes in fatty acid oxidation (FAO)-associated metabolic programs and the enzymes linking metabolic disturbances to cell death remain incompletely defined. In this study, targeted metabolomic profiling of the kidney revealed a marked blockade of FAO, evidenced by the accumulation of fatty acids and a decrease in downstream acylcarnitines. Among FAO-related enzymes, acyl-CoA synthetase short-chain family member 2 (ACSS2) emerged as the most significantly downregulated enzyme, which was further confirmed in an ischemia/reperfusion AKI model. ACSS2 overexpression in HK-2 cells aggravated DDP-induced inflammation and apoptosis, whereas ACSS2 knockdown significantly reduced cytotoxicity and pro-inflammatory cytokine production. Furthermore, pharmacological inhibition of ACSS2 in vivo alleviated DDP-induced AKI characterized by reduced oxidative stress and improved renal function. Together, these findings indicate that targeting ACSS2 may represent a promising therapeutic strategy to mitigate DDP-induced renal injury.
AmesNet: A Task-Conditioned Deep Learning Model with Enhanced Sensitivity and Generalization in Ames Mutagenicity Prediction.
Umansky TJ, Woods VA, Russell SM … +1 more , Haders DJ
Chem Res Toxicol · 2026 Jun · PMID 42371678 · Publisher ↗
Regulatory agencies require comprehensive genotoxicity assessments for novel small-molecule therapeutics prior to human trials. Developers often delay these studies until a candidate is nearing regulatory submission beca...
Regulatory agencies require comprehensive genotoxicity assessments for novel small-molecule therapeutics prior to human trials. Developers often delay these studies until a candidate is nearing regulatory submission because they are expensive and secondary to bioactivity. This timing creates a bottleneck where late-stage failures can jeopardize >$10 million in capital and multiple years of developmental progress per candidate. The Ames assay detects a molecule's mutagenic potential. Regulators now explicitly support the use of in silico Ames mutagenicity models through enabling legislation, dedicated FDA AI toxicology programs, internationally harmonized guidelines, and benchmark challenges. However, current Ames models suffer from a sensitivity drop-off when they evaluate molecules outside their training domain. The out-of-domain (OOD) test set from the Second Ames/QSAR International Challenge Project yielded a participant average sensitivity of 0.46. Sensitivity is the most important metric in Ames prediction because false negatives allow mutagenic compounds to advance undetected and trigger the most costly late-stage failures. Attempts to fix this sensitivity drop-off often reduce overall model performance, which can be represented by balanced accuracy (BA). For example, DeepAmes reports high levels of sensitivity (0.87) by sacrificing its BA (0.52). We introduce AmesNet, a task-conditioned Ames model that achieves class-leading performance in novel chemical spaces. AmesNet utilizes a dual branch architecture containing a molecular encoder and a dedicated channel to condition Ames assay context such as metabolic activation and bacterial strain type. In comparative benchmarks, AmesNet reached a sensitivity of 0.72 (95% CI: 0.68-0.76) and a simultaneous BA of 0.81 (95% CI: 0.78-0.83) on the OOD test data. This performance improves discrimination without the sensitivity trade-off limiting existing approaches. Structural analysis demonstrates that AmesNet recovers difficult-to-detect mutagenic compounds missed by existing models. This framework provides a high-confidence filtering mechanism that enables drug developers to turn a costly late-stage safety bottleneck into an earlier-stage mutagenicity triage.
DNA Structure-Dependent Enrichment of Oxidative Lesions.
Zewail-Foote M, Klattenhoff AW, Vasquez KM
Chem Res Toxicol · 2026 Jun · PMID 42366563 · Publisher ↗
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 ins...
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.
Characterizing the Reactive Metabolites of Colony-Stimulating Factor 1 Receptor Inhibitor PLX5622 in Liver Microsomes and Mice.
Zhou S, Lee T, Ji X … +2 more , Mackenzie KR, Li F
Chem Res Toxicol · 2026 Jun · PMID 42363912 · Publisher ↗
Colony-stimulating factor 1 receptor (CSF1R) is a receptor tyrosine kinase involved in cell growth and differentiation, particularly in macrophages and microglia. CSF1R inhibitors are under investigation for various dise...
Colony-stimulating factor 1 receptor (CSF1R) is a receptor tyrosine kinase involved in cell growth and differentiation, particularly in macrophages and microglia. CSF1R inhibitors are under investigation for various diseases, including cancer, autoimmune/inflammatory diseases, and neurodegenerative disorders. PLX5622 is a highly specific, brain-penetrant, and orally bioavailable CSF1R inhibitor that is being evaluated in a clinical trial for rheumatoid arthritis and considered as an attractive candidate for the treatment of Alzheimer's disease (AD). Drug metabolism significantly influences both the efficacy and safety of therapeutic agents. In particular, bioactivation leading to the formation of reactive metabolites is often implicated in adverse drug effects. In this study, we investigated the metabolism and potential bioactivation of PLX5622 in mouse and human liver microsomes (MLM/HLM) and mice using LC-MS-based metabolomic approaches. Reduced glutathione (GSH) and methoxyamine (NHOMe) were used to capture reactive intermediates. In total, 12 PLX5622-GSH adducts and five NHOMe adducts were identified in both HLM and MLM, along with 22 nontrapped metabolites generated from demethylation, hydroxylation, and carbon-carbon cleavage reactions. PLX5622-GSH-related adducts in mice were also assessed and 8 GSH adducts were detected in mouse liver, confirming the occurrence of bioactivation in vivo. Using recombinant human cytochrome P450 (CYP) enzymes and selective chemical inhibitors in liver microsomes, CYP3A was determined to be the primary enzyme responsible for the metabolic activation of PLX5622. These insights into the metabolic pathways of PLX5622 are valuable for further study of its safety and potential drug interactions of CYP3A. Future studies using human primary hepatocytes or physiologically human-relevant models such as liver-on-a-chip systems are warranted to confirm clinical relevance and better predict in vivo outcomes.
Quantitation of E-Cigarette Aerosol Mass in Liquid Impinger Solution Using the C of E-Liquids: Application for Metal Analyses.
Salazar MR, Nguyen TB, Poulin BA
Chem Res Toxicol · 2026 Jun · PMID 42331398 · Publisher ↗
Liquid impingers, i.e., bubbling gaseous or aerosol samples through a liquid-based collection solution, are often used in electronic cigarette (e-cigarette) research to capture aerosols for metal analysis. However, a key...
Liquid impingers, i.e., bubbling gaseous or aerosol samples through a liquid-based collection solution, are often used in electronic cigarette (e-cigarette) research to capture aerosols for metal analysis. However, a key limitation is the accurate quantitation of the mass of aerosol collected in the impinger solution for metal data normalization, which is necessary for comparison with prevaped e-liquid metal concentrations and for comparison across studies. To address this gap, we developed a novel approach to quantitate collected aerosol mass in a simple liquid impinger by leveraging the carbon-13 (C) of aerosolized e-liquids, which enabled the parallel determination of aerosol mass and metal concentration by inductively coupled plasma mass spectrometry (ICP-MS). The method demonstrated low limits of detection and quantitation, excellent solution stability (≤4 months), and high accuracy and precision across spiked quality control samples, diverse e-liquid formulations, and multiple aerosol collection techniques (84.4-101% recovery with ≤4.6% relative percent deviation). Compared with an aerosol condensate collection method, the liquid impinger was equally effective at collecting metals, with metal concentrations on a mass-per-puff (ng/puff) basis within ∼7% for chromium (Cr), nickel (Ni), iron (Fe), copper (Cu), zinc (Zn), antimony (Sb), and lead (Pb). Aerosol metal concentrations on a mass-per-mass basis (ng/g) quantified using the C-derived aerosol mass from the liquid impinger were strongly correlated with (R ≥ 0.97) but consistently greater than those from the condensate collection approach, due to differences in aerosol collection efficiency between methods. The liquid impinger enabled parallel aerosol mass and metal quantitation, offering a new approach for future e-cigarette aerosol studies, with potential application for investigations into future liquid impinger collection optimizations, e-liquid-to-aerosol metal transfer, and redox-sensitive speciation (e.g., Fe, Cu, Cr, Sb).
Beyond Heuristics: A Model-Agnostic Framework for Uncertainty Quantification in QSAR via Adaptive Conformal Prediction.
Jeliazkova N, Kochev N, Iliev L … +1 more , Jeliazkov V
Chem Res Toxicol · 2026 Jun · PMID 42324899 · Publisher ↗
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 d...
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.
20-Hydroxyeicosatetraenoic Acid Ameliorates Nickel Nanoparticle-Induced Epithelial-Mesenchymal Transition by Modulating the FFAR1/NF-kB Pathway.
Yuan J, Yang H, Zhang S … +5 more , Yan Z, Li J, Mo Y, Zhang Q, Huang Z
Chem Res Toxicol · 2026 Jun · PMID 42315976 · Publisher ↗
Nickel nanoparticles (Nano-Ni) are widely utilized in industrial and biomedical applications due to their unique physicochemical properties. However, their expanded usage increases risks of occupational and environmental...
Nickel nanoparticles (Nano-Ni) are widely utilized in industrial and biomedical applications due to their unique physicochemical properties. However, their expanded usage increases risks of occupational and environmental exposure. In this study, we established a mouse exposure model via single intratracheal instillation of Nano-Ni and analyzed the perturbation characteristics of lung tissue metabolic profiles using untargeted metabolomics. Subsequently, the biological function of the key metabolite 20-hydroxyeicosatetraenoic acid (20-HETE) was explored in Nano-Ni-exposed lung epithelial cells to elucidate the underlying mechanisms of metabolic alterations in Nano-Ni-induced pulmonary fibrosis. Our results showed that exposure to Nano-Ni induced marked alveolar architecture destruction, interstitial thickening, and upregulated expression of fibrotic markers in mouse lung tissues. Metabolomics identified arachidonic acid metabolism as the most disrupted pathway, with 20-HETE exhibiting the most pronounced downregulation. In both BEAS-2B and A549 cell lines, exogenous 20-HETE supplementation significantly attenuated Nano-Ni-induced epithelial-mesenchymal transition (EMT). Furthermore, Nano-Ni exposure reduced mRNA and protein levels of free fatty acid receptor 1 (FFAR1) both in vivo and in vitro. Pretreatment with the FFAR1 agonist GW9508 mitigated Nano-Ni-induced EMT and the activation of NF-κB signaling pathway in both cell lines. Critically, FFAR1 inhibition largely abolished the suppressive effects of 20-HETE on EMT and NF-κB signaling. Altogether, our study suggests that 20-HETE may affect the EMT process in lung epithelial cells at least in part through regulating the FFAR1/NF-κB pathway, thereby potentially contributing to the process of Nano-Ni-induced lung fibrosis. These findings point to a possible role of specific metabolites in Nano-Ni-induced pulmonary fibrosis and may provide novel mechanistic insights into the inhalation toxicity of nanomaterials.
AOP Network Box: An Integrative Framework for Bridging Adverse Outcome Pathways and Biological Networks.
Na M, Kim D
Chem Res Toxicol · 2026 Jun · PMID 42301195 · Publisher ↗
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...
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.
Multiparametric and Pathomimetic Platform Mimicking Human Hepatic Steatosis, Lipotoxicity, and Associated Metabolic Stress.
Sharma J, Dey P
Chem Res Toxicol · 2026 Jun · PMID 42296247 · Publisher ↗
Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by hepatocellular lipid accumulation and lipotoxicity. However, there are currently limited well-characterized models that mimic these ea...
Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by hepatocellular lipid accumulation and lipotoxicity. However, there are currently limited well-characterized models that mimic these early pathogenic mechanisms in terms of molecular and metabolomic features. Here, we developed a customized fatty acid conjugate that facilitates efficient lipid accumulation in the cultured hepatocytes without loss of viability. This conjugate reflects the prominent fatty acids found in the serum of patients with chronic metabolic syndrome, MASLD. This conjugate consistently induces the formation of steatotic bodies in HepG2 cells within a short exposure. The model was validated using a comprehensive analytical framework that measured inflammatory markers (e.g., iNOS, TNF-α, IL-6, IL-1β, and MCP-1), alongside intracellular triglyceride and cholesterol accumulation, intracellular oxidative stress, and lipid peroxidation. The lipid conjugate significantly alters hepatocellular metabolism, as observed by intracellular untargeted metabolomic profiling. The mRNA expression of hepatocellular lipogenic genes (e.g., CD26, SERBP, FASN, SCD5, DAGT1/2) was elevated, indicating impaired lipid homeostasis under lipotoxic conditions. Pathway analysis revealed enrichment of metabolic pathways related to steroid and bile acid biosynthesis, as well as glycerolipid metabolism, consistent with compensatory lipid-metabolism responses. These coordinated metabolic changes confirm that the early MASLD-like metabolic phenotype is recapitulated in this model. Collectively, these results confirm this fatty acid conjugate as a physiologically relevant model for MASLD, suitable for therapeutic screening and mechanistic research.
Predicting Aquatic Species Sensitivity Distributions Using Machine Learning in a Regulatory Setting.
Minnema J, Viljanen M, Rorije E … +2 more , Peijnenburg WJGM, Wassenaar PNH
Chem Res Toxicol · 2026 Jun · PMID 42288993 · Publisher ↗
The assessment of chemical risks is increasingly challenged by insufficient toxicological data available for the large number of marketed substances. Recent advancements in artificial intelligence (AI) and machine learni...
The assessment of chemical risks is increasingly challenged by insufficient toxicological data available for the large number of marketed substances. Recent advancements in artificial intelligence (AI) and machine learning (ML) offer promising avenues to bridge these data gaps through model-based predictions. However, the integration of these methodologies into risk assessment practices remains limited, largely due to issues surrounding (regulatory) trust in the model outcomes. We developed an ML model to predict the ecotoxicity of chemicals across a broad range of aquatic species. These predictions are utilized to construct Species Sensitivity Distributions (SSDs), which constitute a key tool in environmental risk assessment. Trust of risk assessors in the SSDs is essential for use in chemical risk assessment, especially for modeled SSDs. Hence, besides quantitative performance evaluation, we also performed an extensive qualitative validation encompassing aspects such as interpretability and transparency. However, to truly advance the development of ML models for risk assessment, it is crucial to foster interdisciplinary collaboration to enhance the applicability of these technologies within regulatory frameworks. Therefore, this study emphasizes the importance of regulatory acceptance when developing new ML models for SSD predictions. We highlight future opportunities for ML in SSD prediction, while also addressing the challenges of qualitative model validation. As such, this work aims to stimulate the discussion on advancing in silico methodologies beyond the current state of the art and bridging the gap between the technological efforts made in the field of AI and the regulatory needs of chemical risk assessors.
Investigation of the Effects of Artemisinin on Acetaminophen-Induced Acute Liver Toxicity.
Kurt P, Ugan RA, Aydin A … +2 more , Aydemir Celep N, Cadirci E
Chem Res Toxicol · 2026 Jun · PMID 42267926 · Publisher ↗
Paracetamol (PARA) is a widely used antipyretic and analgesic drug; however, overdose is a major cause of hepatotoxicity. Although -acetylcysteine (NAC) is the standard antidote, its limitations necessitate the investiga...
Paracetamol (PARA) is a widely used antipyretic and analgesic drug; however, overdose is a major cause of hepatotoxicity. Although -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 ( < 0.001). Among the treatment groups, PARA + ART 14 significantly reduced these enzyme levels compared to the PARA group ( < 0.001). ART treatment also increased antioxidant parameters (SOD, GSH) and decreased MDA levels compared to the PARA group ( < 0.001), with effects comparable to PARA + NAC. Inflammation- and oxidative stress-related gene expressions were significantly elevated in the PARA group versus CONTROL ( < 0.001), whereas PARA + ART 14 significantly downregulated all markers ( < 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.
Uncovering the Hidden Risks: How PLA and PLGA Microplastics Disrupt Gut Microbiota and Metabolic Health.
Gao B, Chen L, Xu W … +5 more , Liu G, Wei M, Shen W, Tu P, Shan J
Chem Res Toxicol · 2026 Jun · PMID 42262316 · Publisher ↗
Biodegradable plastics are often promoted as an eco-sustainable alternative to conventional polymers. However, their potential to degrade into microplastics still poses significant health risks. Commonly used materials s...
Biodegradable plastics are often promoted as an eco-sustainable alternative to conventional polymers. However, their potential to degrade into microplastics still poses significant health risks. Commonly used materials such as polylactic acid (PLA) and poly(lactic--glycolic acid) (PLGA) have been widely adopted across various industries. While the toxicity of PLA microplastics has been studied extensively, the biological effects of PLGA microplastics remain largely unknown. Through metagenomic sequencing and untargeted metabolomic profiling, we evaluated the impacts of both PLA and PLGA microplastics on gut bacteria, fungi, virulence factors, microbial metabolic pathways, and metabolites in feces, serum, and liver tissue in this study. Our results demonstrate that both types of biodegradable microplastics disrupt gut microbiota and host metabolic homeostasis. PLA exposure provoked more pronounced changes in gut bacteria, fungi, virulence factors, and fecal and hepatic metabolites. In contrast, microbial metabolic pathways and serum metabolites were more strongly affected by PLGA. Several altered features were common to both microplastics, including enrichment of hepatic metabolic pathways related to valine, leucine, and isoleucine biosynthesis; one-carbon pool by folate; glycine, serine, and threonine metabolism; pantothenate and CoA biosynthesis; taurine and hypotaurine metabolism; and cysteine and methionine metabolism. Other disturbances were material-specific, such as UMP biosynthesis pathways, which were altered exclusively by PLA, while palmitate biosynthesis and unsaturated fatty acid biosynthesis were affected only by PLGA. These findings advance our understanding of the distinct and shared health risks posed by different biodegradable microplastics, providing a clearer basis for assessing their long-term safety.
pH Tunes the DNA Repair Efficiency and Strand Preference of the AlkB Family Enzymes.
Howarth SD, Mylie QJ, Boateng-Boakye E … +9 more , Falkowski V, Oliver Mella A, Fermin R, Peng Z, Bush X, Chen YT, Ma J, Cho B, Li D
Chem Res Toxicol · 2026 Jun · PMID 42257680 · Publisher ↗
AlkB-family Fe(II)/2-oxoglutarate-dependent dioxygenases repair alkylated nucleic acid lesions through oxidative dealkylation and play important roles in genome maintenance. 1-Methyl-2'-deoxyadenosine (1mA) and 3-methyl-...
AlkB-family Fe(II)/2-oxoglutarate-dependent dioxygenases repair alkylated nucleic acid lesions through oxidative dealkylation and play important roles in genome maintenance. 1-Methyl-2'-deoxyadenosine (1mA) and 3-methyl-2'-deoxycytidine (3mC) are well-established substrates of AlkB, ALKBH2, and ALKBH3. Although these enzymes have been extensively studied, the influence of proton concentration (pH) on their catalytic behavior and strand preference remains poorly defined. Here, we systematically examined how pH modulates the activity of the prototypical bacterial AlkB and the human homologues ALKBH2 and ALKBH3 using defined DNA substrates in both single-stranded (ssDNA) and double-stranded (dsDNA) contexts containing 1mA and 3mC lesions. Across a broad pH range, all three enzymes mainly exhibit bell-shaped activity profiles with distinct optima. The prevailing view in the field is that AlkB preferentially repairs these lesions in ssDNA, ALKBH2 favors dsDNA, and ALKBH3 prefers ssDNA. However, our results demonstrate that pH influences the catalytic efficiency and strand utilization in a substrate- and enzyme-dependent manner. AlkB maintains a consistent ssDNA preference for 3mC but exhibits variable strand preference for 1mA at different pH values. ALKBH2 retains a strong dsDNA preference for 1mA across all conditions but shows a clear pH-dependent strand switch for 3mC, favoring ssDNA under acidic conditions and preferring dsDNA at neutral to alkaline pH conditions. In contrast, ALKBH3 consistently favors ssDNA for 3mC but exhibits pH-dependent strand preference for 1mA. Our results show that the reported strand preferences largely hold at pH 7.0-8.0 but are not complete, as strand utilization and pH optima vary by enzyme and substrate. The observations demonstrate that proton availability strongly influences AlkB-family catalysis and is an important factor in how these enzymes process damaged DNA. These findings may also aid the optimization of AlkB-based protein engineering and sequencing technologies.
Generation of Interstrand DNA Cross-Links under Conditions of Acid Stress.
Mumu MF, Heidari M, Mahbub MS … +1 more , Gates KS
Chem Res Toxicol · 2026 Jun · PMID 42246514 · Publisher ↗
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 o...
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 (Kunkle et al. Nat. Acad. Sci. USA, , 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. 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.
Hemin Recapitulates the Labile Iron Pool in the Cellular Fenton Reaction with DNA.
Fleming AM, Burrows CJ
Chem Res Toxicol · 2026 Jun · PMID 42233480 · Publisher ↗
The iron-Fenton reaction in biology is influenced by diverse cellular molecules that coordinate with redox-active ferrous ion. Contemporary research proposed that hydroxyl radical or a ferryl species (HO/Fe=O) are the pr...
The iron-Fenton reaction in biology is influenced by diverse cellular molecules that coordinate with redox-active ferrous ion. Contemporary research proposed that hydroxyl radical or a ferryl species (HO/Fe=O) are the primary oxidants; however, this was challenged by the observation that physiological bicarbonate redirects the reaction to form carbonate radical anion (CO). Questions remained regarding the roles of O concentration, ascorbate, and iron speciation in CO formation. Accordingly, in cellulo studies were conducted under physiological O (∼25 μM) with ascorbate replenishment to monitor bicarbonate-dependent telomeric DNA damage. Under these conditions,. physiological bicarbonate (25 mM) yielded 2'-deoxyguanosine-specific oxidation consistent with CO formation at a ratio exceeding 80:1 relative to HO/Fe=O. In parallel in vitro experiments using a plasmid nicking assay, the cellular low molecular weight (LMW) ultrafiltrate was used as the source of iron and its endogenous coordination partners; under physiological O, bicarbonate, and 500 nM HO (the concentration required to produce detectable signal), the DNA damage profile was consistent with exclusive CO formation, mirroring the cell culture result. A panel of iron complexes approximating the intracellular labile iron pool (5 μM) was examined: hexaaquo-ferrous ion, ferrous citrate, ferrous α-ketoglutarate, ferrous pyrophosphate, ferrous glutathione, and hemin. Of these, only hemin reproduced the bicarbonate-dependent CO damage profile observed in cells with 100 nM HO and 25 mM bicarbonate present. This finding was corroborated using a defined biomimetic metabolome in which hemin, ferrous ion, or their combination was tested; hemin consistently supported CO as the dominant oxidant. Roles for the Udenfriend reaction (Fe(II), O, and reductant) and superoxide dismutase were also studied. Collectively, these results identify heme iron as a likely candidate to drive CO formation via the bicarbonate iron-Fenton reaction to damage dG in DNA during endogenous oxidative stress.
Classification of Thyroid Peroxidase (TPO) Inhibitors Using Transfer Learning with SMILES Embeddings.
Piir G, Sild S, Spilioti E … +4 more , Nikolopoulou D, Katsanou E, Langezaal I, Maran U
Chem Res Toxicol · 2026 Jun · PMID 42227560 · Full text
Thyroid hormones (THs) regulate many processes in mammals and, therefore, affect every organ in the body. Thyroid peroxidase (TPO) is an essential enzyme for the successful biosynthesis of THs. Although TPO inhibition is...
Thyroid hormones (THs) regulate many processes in mammals and, therefore, affect every organ in the body. Thyroid peroxidase (TPO) is an essential enzyme for the successful biosynthesis of THs. Although TPO inhibition is a well-documented molecular initiating event (MIE) in thyroid hormone system disruption adverse outcome pathways (AOPs), experimental methods and computational models to assess TPO activity are lacking. Efficient computational new approach methodologies (NAMs) are a viable solution for identifying TPO inhibitors from a large pool of agrochemicals. The aim of this study was to investigate the suitability of SMILES embeddings generated using a specialized language model (SLM) based on a pretrained deep neural network (DNN) for applying a transfer learning approach in the development of quantitative structure-activity relationships for classifying TPO inhibitors. Traditional theoretical molecular descriptors were used for comparison. Two different molecular descriptor sets resulted in Random Forest (RF) models that performed similarly on the training and test sets, while the sensitivity for the external validation set was substantially different between the two models (0.788 vs 0.490). Comparison of the predictions with the TPO inhibition data of the chemicals assessed by EFSA and EU-NETVAL laboratories showed good agreement. At the same time, analysis of experimental data from other sources showed some conflicting estimates. This suggests that further and more precise studies are needed for some compounds. This study advances in silico methodologies by implementing transfer learning for QSAR modeling from text representations (e.g., SMILES) using the pretrained Bidirectional Encoder Representations from Transformers (BERT) architecture. While traditional QSAR approach relies on molecular descriptors, this evaluation shows that model-generated SMILES embeddings can expand the applicability domain, indicating a more robust representation of structural information compared to traditional molecular descriptors.
Prediction of Ligand Binding to Transthyretin Using Machine Learning Algorithms and Low-Dimensional Molecular Descriptors: A Tox24 Challenge Study.
Stefaniak F
Chem Res Toxicol · 2026 Jun · PMID 42225255 · Full text
This study presents a comprehensive machine learning approach for predicting the percent displacement of ANSA from human transthyretin (TTR) at fixed assay conditions as defined in the Tox24 Challenge. TTR is a critical...
This study presents a comprehensive machine learning approach for predicting the percent displacement of ANSA from human transthyretin (TTR) at fixed assay conditions as defined in the Tox24 Challenge. TTR is a critical serum transport protein for thyroid hormones, and its disruption by environmental chemicals can lead to endocrine system dysregulation with serious developmental and metabolic consequences. However, the scarcity of large, chemically diverse data sets and the lack of standardized experimental protocols have limited the computational prediction of TTR binding, restricting the development of robust predictive models applicable to broad chemical spaces. The described pipeline uses computationally efficient, low-dimensional (0D-2D) molecular descriptors and fingerprints. This eliminates the need for costly 3D conformational analysis. Following the systematic benchmarking of individual machine learning (ML) methods, hyperparameter optimization was performed using Optuna for two gradient boosting algorithms: CatBoost and XGBoost. The feature importance analysis revealed complementary learning strategies between these algorithms. The proposed consensus model achieved an RMSE of 21.60 on the blind test set, ranking 15th among 79 participating teams. Chemical space analysis using PCA and t-SNE confirmed that, except for two outliers, the test compounds fell within the distribution for the training set. Postchallenge analyses evaluated the effect of the cross-validation strategy (random vs cluster-based split) and descriptor dimensionality (2D-only, 3D-only, or mixed) on model performance. To facilitate broader adoption, a freely accessible web server was developed, enabling rapid toxicity prediction across multiple Tox21 and Tox24 end points without requiring computational expertise (https://toxpred.genesilico.pl/). This work demonstrates that low-dimensional molecular descriptors combined with optimized consensus ML methods can achieve competitive predictive performance, making high-throughput toxicity screening practical for drug discovery, environmental risk assessment, and regulatory decision-making.
Ultrasensitive and Specific Detection of 8-Oxoguanine DNA Glycosylase via Locus-Specific Rolling Circle Amplification.
Huang ZQ, Dong JH, Zhang RH … +4 more , Zhao LL, Liu Y, Zhou YL, Zhang XX
Chem Res Toxicol · 2026 Jun · PMID 42224486 · Publisher ↗
8-Oxoguanine DNA glycosylase 1 (OGG1) is a key enzyme for maintaining genomic integrity, as it specifically recognizes and excises 8-oxoguanine (OG), a major oxidative DNA lesion, to initiate base excision repair. Dysreg...
8-Oxoguanine DNA glycosylase 1 (OGG1) is a key enzyme for maintaining genomic integrity, as it specifically recognizes and excises 8-oxoguanine (OG), a major oxidative DNA lesion, to initiate base excision repair. Dysregulation of OGG1 activity is closely associated with genomic instability, apoptosis, and tumorigenesis. However, conventional methods for detecting OGG1 activity often lack the sensitivity required for trace-level analysis, limiting their application in early diagnostics and mechanistic studies. In this study, we report an innovative assay termed OG-specific rolling circle amplification (OG-RCA), which for the first time integrates OGG1-mediated OG excision with RCA and subsequent G-triplex formation for signal readout. This novel approach establishes a unique conversion strategy that translates enzymatic activity into quantifiable amplification signals, significantly enhancing detection sensitivity and specificity. After systematic optimization of key reaction conditions, including dsOG substrate concentration, enzyme amounts, and DNA probe concentrations, the OG-RCA assay achieved an exceptionally low limit of detection (LOD) of 3 × 10 mg/mL (equivalent to ∼1.6 × 10 U/mL) for OGG1 activity, surpassing existing methods. The assay also exhibited high specificity, showing minimal cross-reactivity with other DNA repair glycosylases. Moreover, spike-recovery experiments using HeLa cell protein extracts and 293T cell lysates confirmed its robustness in complex biological samples. The OG-RCA method not only provides a powerful tool for the ultrasensitive detection of DNA base damage biomarkers but also offers a novel platform for investigating DNA damage and repair mechanisms. It holds significant promise for applications involving limited biological samples and mechanistic studies of DNA repair.
Silver-Form-Driven Nanotoxicity Reveals W. Koch as a Potential Bioindicator for Aquatic Systems.
Cruz MS, Petri M, Terraciano L … +3 more , Lagorio MG, González GA, Diz V
Chem Res Toxicol · 2026 Jun · PMID 42214062 · Publisher ↗
Silver nanoparticles (AgNPs) and silver sulfide nanoparticles (AgSNPs) are increasingly being used in diverse industries due to their unique physicochemical properties. However, their release into aquatic environments ra...
Silver nanoparticles (AgNPs) and silver sulfide nanoparticles (AgSNPs) are increasingly being used in diverse industries due to their unique physicochemical properties. However, their release into aquatic environments raises concerns regarding their potential ecological impact, making them an excellent model system for evaluating potential biomonitors. In this study, we evaluated and compared the phytotoxic effects of AgNPs and AgSNPs suspensions on the free-floating macrophyte W. Koch, a species with broad distribution from Mexico to South America and promising potential as a bioindicator. The designed protocol involved both types of nanoparticles, which were synthesized and characterized via UV-vis spectrophotometry and other techniques. In addition, morphometric parameters (frond number, frond abscission, and chlorosis), as well as enzymatic activity of ascorbate oxidase (AO) and chlorophyll-a variable fluorescence, were assessed after exposure to environmentally relevant concentrations. Results indicated that AgNPs induced significantly higher toxic effects than AgSNPs, with marked frond abscission, chlorosis, AO activity reduction, and damage to the photosynthetic apparatus. These findings indicate that, as expected, the chemical form of silver nanoparticles plays a key role in phytotoxicity and that exhibits adequate sensitivity to be proposed as a bioindicator candidate in aquatic ecosystems, thereby expanding the global portfolio of bioindicator species and highlighting the ecological value of native flora for sustainable water quality monitoring.
Democratizing Artificial Intelligence in Toxicology: Real-World Applications and Automated Computational Workflows.
Mansouri K, Moreira-Filho JT, S Tieghi R … +1 more , Kleinstreuer N
Chem Res Toxicol · 2026 Jun · PMID 42212723 · Full text
Artificial intelligence (AI) and machine learning are transforming toxicological research and chemical safety assessment. Although user-friendly computational toxicology platforms are increasingly available, integrating,...
Artificial intelligence (AI) and machine learning are transforming toxicological research and chemical safety assessment. Although user-friendly computational toxicology platforms are increasingly available, integrating, customizing, and deploying AI methods within end-to-end workflows still often requires programming expertise. This barrier increases the time to adoption of new methods and slows regulatory uptake. To address this limitation, we survey recent initiatives democratizing computational toxicology through no-code/low-code pipelines, automated workflows, and open-source tools. We emphasize solutions for four computational needs: (i) data extraction and access, (ii) data mining and curation, (iii) data analysis and visualization, and (iv) modeling and prediction. These initiatives transform complex computational methods into guided and web-accessible applications that enable toxicologists, regulators, and researchers to leverage AI without coding expertise. The broad applicability of computational methods will be essential for supporting and scaling federal initiatives that advance human-relevant alternatives to animal testing. We also offer practical considerations for domain-specific tool development, including large language model-based information extraction, chemical structure standardization, interactive chemical grouping, and the development of validated machine learning models, as used in the Modeling and Visualization (MoVIZ) pipeline. The authors map the future of computational toxicology and cheminformatics, one that does not require scientists to become programmers but rather makes sophisticated AI tools more broadly accessible, transparent, and guided through thoughtful interface design, transparent workflows, and open science initiatives.
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