搜索结果：Chemical research in toxicology

Current methods for determining the neurotoxic potential of (agro)chemicals are not comprehensive enough, as is suggested by the increased incidence of Parkinson's disease (PD) among people exposed to certain pesticides. Mechanism-based in silico screening can address this shortcoming by predicting molecular initiating event activation, a precursor of the adverse outcome. However, a limited amount of protein-binding data has been collected on pesticides, meaning that for screening, an approach is required that is well suited for extrapolation to a wide variety of chemicals. Here, the group I metabotropic glutamate receptors (mGluRs) were taken as a case study because of their role in chemical-induced PD. Compounds with known activity for these receptors were docked into the allosteric binding site, interaction fingerprints (IFPs) were computed, and used to train classification models. Afterward, model enrichment was evaluated, feature importance was derived, and the applicability domain was analyzed. Both IFP-based mGluR models demonstrated good enrichment with the area under the Receiver Operating Characteristic Curve (ROC AUC) being 0.78 and 0.66. The interactions that were most important for model predictions were hydrogen bond formation with Asn760 for mGluR1 and aromatic interactions with Trp785 for mGluR5. The applicability domain varied depending on the training set but was consistently larger for IFPs than for Morgan fingerprints, a fragment-based descriptor. A virtual screen using the final models identified 132 potential mGluR binders, of which one, bifenthrin, had been previously found to bind in in vitro experiments. To promote the implementation of the screening technique presented here, a platform was created where users can make predictions using our models. All in all, these results highlight the potential of combining IFPs with machine learning and contribute to the shift toward mechanism-based in silico toxicology.

MDMB-5'Br-PINACA is a recently identified brominated synthetic cannabinoid that was detected in herbal materials seized in Brazil in 2025, raising concerns regarding further potential intoxication cases. In this sense, the evaluation of physicochemical properties and metabolic fate may improve its analytical detectability. Therefore, an integrated in silico and in vitro approach was employed to investigate the physicochemical properties and phase I metabolism of MDMB-5'Br-PINACA. Physicochemical parameters and predicted metabolic pathways were first evaluated using BioTransformer 3.0 and XenoSite, providing complementary insights into likely sites of metabolism. In vitro metabolism was subsequently assessed using pooled human liver microsomes associated with liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) analysis. MS2-based molecular networking (MN) was applied as an exploratory and confirmatory strategy to guide metabolite annotation by clustering structurally related features and prioritizing candidates linked to MDMB-5'Br-PINACA. A total of twenty-seven metabolites were level 2 annotated, encompassing aliphatic and aromatic hydroxylation, sequential alcohol oxidation to ketone, aldehyde, and carboxylic acid derivatives, ester hydrolysis, intramolecular lactone formation, and N-dealkylation with loss of the pentyl side chain. Hydroxylations of the pentyl chain and tert-butyl moiety and secondary oxidative reactions emerged as the predominant pathways under the experimental conditions, in agreement with in silico predictions. However, lactone formation was exclusively revealed by in vitro experiments, demonstrating limitations of current in silico prediction approaches. The integration of computational prediction, LC-HRMS, and MN substantially enhanced metabolite coverage and confidence of structural assignment. These findings provide a detailed metabolic map of MDMB-5'Br-PINACA and underscore the value of combining in silico and in vitro approaches to improve metabolite identification, supporting forensic and clinical investigations of intoxication involving this synthetic cannabinoid.

Metal additive manufacturing (AM) relies on alloy feedstock powders that may come into contact with the workers' skin during handling, yet skin-relevant data on metal release and biological reactivity remain limited. Here, we assessed the cutaneous bioactivity of the fine particle fraction of four gas-atomized Fe-based AM powders (316L stainless steel, Fe-powder A, and tooling steels B and C). Powders were sieved to <10 &#x3bc;m and characterized by scanning electron microscopy and X-ray photoelectron spectroscopy before and after incubation in artificial sweat (ASW). Metal biodissolution was quantified in ASW and keratinocyte culture medium using atomic absorption spectrophotometry. Cellular responses were evaluated in HaCaT keratinocytes using Cell Painting-based phenomics and multiplex cytokine/chemokine profiling and in an ex vivo full-thickness human skin explant model, including superficial barrier disruption, IL-8/CXCL8 quantification, and histological assessment. ASW exposure induced marked shifts in the outermost surface composition across powders, indicating sweat-driven surface transformation. Biodissolution was low and medium-dependent, with Fe dominating the release in ASW, and with an overall metal release remaining limited in cell culture medium. In HaCaT cells, MCP-1/CCL2, IL-6, and IL-8/CXCL8 were quantifiable but showed no significant changes following powder exposure. Cell Painting revealed subtle, shared phenotypic signatures, primarily involving mitochondrial-associated features, without evidence of broad cellular stress. In the ex vivo skin model, AM powders did not increase IL-8/CXCL8 secretion, the particles remained localized to the skin surface without detectable penetration, and coexposure with Staphylococcus epidermidis did not enhance bacterial colonization or induce inflammation. To the best of our knowledge, this is the first study that applies a human skin explant model to evaluate dermal responses to metal AM powders. Overall, the tested AM powders showed low short-term cutaneous reactivity under skin-relevant conditions, providing human-relevant evidence to inform occupational risk assessment in AM environments.

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.

Quantitative Structure-Activity Relationship (QSAR) models are increasingly discussed in the broader context of artificial intelligence (AI). Indeed, they formally meet certain regulatory definitions of AI as data-driven inference systems. However, in the context of chemical safety assessment, QSARs represent a distinct, domain-specific class of models shaped by decades of dialogue between computational toxicology and regulatory science. This work summarizes roundtable discussions from the 21st International workshop on QSAR in Environmental and Health Sciences (QSAR2025) held in Milan in June 2025, addressing structural similarity and local performance assessment, model selection, integration of multiple predictions, and challenges posed by black-box models that demand careful consideration of the balance between predictive performance and explainability. These discussions highlight the experience of integrating QSAR approaches into regulatory frameworks, supported by internationally harmonized OECD principles, standardized reporting formats (QMRF, QPRF, QRRF), and the OECD QSAR Assessment Framework (QAF). The QAF provides a structured basis for evaluating the reliability and regulatory relevance of QSAR predictions through transparent documentation, consideration of applicability domain, and expert-driven interpretation. The discussion is then broadened to examine how modern AI, particularly Large Language Models, may support toxicological risk assessment beyond QSAR modeling itself. Building on both the conference insights and this extended analysis, this work reflects on how principles established through decades of QSAR development and regulatory integration (including considerations on data quality, applicability domain, uncertainty, and expert judgment) may inform the governance of emerging AI applications in chemical safety assessment.

As the most frequently occurring cancer worldwide, lung cancer is notoriously diagnosed at advanced stages, resulting in high mortality rates. The primary factor underlying this persistent global health burden remains tobacco consumption. Nicotine, a key component of cigarette smoke, is one of the major contributing factors to the development of lung cancer, but the molecular mechanisms remain incompletely elucidated. &#x3b1;7-nAChR, &#x3b2;2-AR, and HGF/c-Met are known to contribute to lung cancer development, respectively. However, the specific signaling cascade through which they interact in nicotine-induced malignancy is unclear. In this study, using CCK-8 assays across a range of concentrations (0-5 &#x3bc;M), we determined that 1 &#x3bc;M nicotine treatment for 48 h optimally enhances A549 cell proliferation, establishing this condition for subsequent experiments. This study uncovers nicotine's role in driving malignant behaviors including proliferation, migration, and invasion in nonsmall cell lung cancer A549 cells. Notably, nicotine potently stimulated migration and invasion, accompanied by upregulation of Cyclin D1 and MMP-2, and downregulation of BAX, BAD, and Caspase-3. Mechanistically, nicotine induced synergistic engagement of &#x3b1;7-nAChR and &#x3b2;2-AR, leading to activation of the HGF/c-Met/PI3K/AKT axis and enhancing the secretion of both HGF and MMP-2. Importantly, we reveal a previously unrecognized bidirectional regulatory loop between &#x3b1;7-nAChR and &#x3b2;2-AR that functionally converges on the HGF/c-Met axis, which acts as a critical signaling hub to drive the downstream PI3K/AKT pathway and facilitate tumor progression. Our findings provide the first evidence of a coordinated &#x3b1;7-nAChR/&#x3b2;2-AR interface regulating HGF/c-Met signaling that orchestrates both intracellular signaling and extracellular secretory programs in nicotine-promoted lung cancer progression. This offers innovative insights for identifying potential antitumor therapeutic targets and presents novel perspectives for the prevention and clinical management of lung cancer, particularly in smokers with nonsmall cell lung cancer.

Environmental chemical exposure has emerged as an important modulator of cancer progression and therapeutic outcomes. Di-2-ethylhexyl phthalate (DEHP), a ubiquitous environmental plasticizer, has been widely recognized for its endocrine-disrupting effects; however, its impact on intracellular signaling reprogramming and cancer drug responsiveness remains incompletely understood. In this investigation, we aimed to elucidate the toxicological mechanisms by which DEHP alters cellular phenotypes and therapeutic sensitivity in hepatocellular carcinoma (HCC) cells. We demonstrate that DEHP exposure does not primarily promote cancer cell proliferation but instead induces epithelial-mesenchymal transition (EMT), leading to enhanced migratory and invasive capacities and reduced responsiveness to the tyrosine kinase inhibitor lenvatinib. Notably, integrative bioinformatic analyses combined with functional validation identified sperm-associated antigen 4 (SPAG4) as a key DEHP-responsive regulator mediating toxicant-induced cellular reprogramming. Mechanistic studies revealed that DEHP-induced upregulation of SPAG4 activates the MAPK/ERK signaling pathway, thereby driving EMT and attenuating lenvatinib responsiveness. Genetic silencing of SPAG4 or pharmacological inhibition of MAPK/ERK signaling effectively reversed DEHP-induced EMT and drug resistance. In conclusion, we highlight DEHP as a signaling-disrupting environmental toxicant that reprograms cancer cell states and modulates therapeutic responses through a SPAG4-dependent MAPK/ERK pathway. These findings provide mechanistic insight into how environmental chemical exposure can reshape intracellular signaling networks and influence cancer treatment outcomes, underscoring a previously underappreciated aspect of chemical toxicology.

The Tox21 10K chemical library, an in vitro toxicology toolbox consisting of more than 8900 unique chemical entities including environmental chemicals and drugs, has undergone analytical quality control (QC) testing after storage at room temperature for 0 and 4 months (T0 and T4). Each chemical was previously assigned a QC grade based on purity, identity, and concentration. In parallel, the Tox21 10K library has been tested across approximately 90 in vitro assays in a quantitative high-throughput screening (qHTS) format, generating >120 M data points to date. These data were used to analyze the correlation between chemical quality and bioassay activity, as well as chemical structure. The chemical characteristics of poor-quality and unstable compounds were explored to identify structural features that should be avoided. In addition, one of the high-throughput assays measuring the induction of p53 activity by small molecules was used to test the Tox21 10K compound library at T0 and T4 due to its robust performance and reproducibility. Approximately 2% of compounds in the library showed a significant change in activity in the p53 assay between T0 and T4 (active to inactive or vice versa), which also correlated with chemical stability. Here, machine learning models were constructed using bioassay data or chemical structures to predict poor-quality (low QC grades at T0) and unstable (grade drop from T0 to T4) chemicals. Chemical structure was found to be highly predictive (0.75) of chemical quality and stability, whereas bioassay data was less predictive (0.66) but still showed better than random performance. Taken together, these findings provide valuable guidance for interpreting the Tox21 assay results and informing best practices for future chemical selection and handling.

Rare earth elements (REEs) are critical to modern industries but pose growing health risks due to increasing environmental release, and neodymium nitrate (Nd(NO3)3), a representative REE compound, lacks comprehensive toxicological data. To address this, we developed an integrated computational toxicology framework combining artificial intelligence-enhanced in vitro to in vivo extrapolation (AIVIVE), physiologically based pharmacokinetic (PBPK) modeling, quantitative in vitro to in vivo extrapolation (QIVIVE), and high-throughput toxicokinetic (HTTK) validation for mechanism-based risk assessment. A closed-loop "model-informed experimental design" was employed, where AIVIVE, using conditional generative adversarial networks (cGAN) predicted toxicity pathways from multiomics data, experimental determination of key toxicokinetic parameters (plasma protein binding and partition coefficients) calibrated HTTK predictions, and a PBPK-QIVIVE framework incorporating nonlinear features extrapolated in vitro EC50 to human equivalent doses (HED), with Monte Carlo simulation and Sobol sensitivity analysis quantifying uncertainty. Results showed AIVIVE predicted transcriptomic responses with high fidelity (cosine similarity = 0.9986) and identified p53, apoptosis, and ferroptosis pathways with >85% accuracy. Experimental calibration revealed significant nonlinearity: plasma unbound fraction (fu) exhibited a U-shaped concentration dependence (0.556 at 1 &#x3bc;g/mL &#x2192; 0.176 at 10 &#x3bc;g/mL &#x2192; 0.965 at 100 &#x3bc;g/mL), while cellular partition coefficients (K) displayed an inverted U-shape (0.048-0.079). HTTK substantially underestimated fu (&#x223c;15-fold) and partition coefficients (2.4-5.5-fold). The integrated framework predicted a median HED of 0.032 mg/kg/day (95% CI: 0.012-0.098), with an 18% probability of exceeding the high-risk threshold (0.1 mg/kg/day). Sensitivity analysis identified fu (65%), K (22%), and EC50 (11%) as the dominant uncertainty sources. Probabilistic integration with exposure data indicated a high safety margin for the general population but concerns for mining area residents (100% probability of margin of exposure <100). This framework addresses the challenges of evaluating metals with nonlinear kinetics, reduces reliance on animal testing, and supports regulatory decisions, proposing an occupational exposure limit of 0.05 mg/m3 for neodymium nitrate.

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&#x2500;the original OECD guidance defines AD in terms of prediction reliability&#x2500;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&#x2500;that calibration and test compounds are drawn from the same input space&#x2500;and follows as a mathematical consequence of the rank-based calibration procedure. When the assumption is violated, coverage may fall below the nominal level&#x2500;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&#x2500;prediction interval width for regression and singleton rate for classification&#x2500;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.

Arsenic trioxide (ATO) is both a life-saving therapy for acute promyelocytic leukemia and a systemic toxicant whose hepatic effects remain incompletely defined. This study examined how a single clinically relevant ATO dose (8 mg/kg, i.p.) acutely remodels hepatic xenobiotic-metabolizing enzymes, arsenic transporters, and pro-inflammatory mediators in male and female C57Bl/6 mice. Mice were treated with ATO or saline and livers were collected at 6 and 24 h for integrated mRNA and protein profiling of major Cytochrome P450 (CYP) families, aquaglyceroporins (Aqp3/7/9), ATP-binding cassette (Abcb1, Abcc1-6) transporters, and cytokines (Tnf-&#x3b1;, Il-1&#x3b2;, Il-6). ATO induced highly sex-, time-, and isoform-specific reprogramming. Females exhibited a wider and earlier decline in several female-predominant CYP2, CYP3, and CYP4 isoforms, including a more pronounced reduction in hepatic CYP3A, CYP4A, and CYP4F protein abundance. In contrast, males showed mainly transcriptional induction of specific genes (Cyp1a1, Cyp2a, Cyp3a13, and Cyp4a), accompanied by comparatively modest decreases in overall CYP protein levels. Aqp and Abc transporters were differentially modulated, with males displaying early, relatively monotonic upregulation of Abcb1/Abcc efflux systems, while females exhibited higher basal Abc expression but more complex, biphasic regulation of both influx (Aqp7/9) and efflux pathways. These transcriptional changes paralleled a transient inflammatory response, including early Tnf-&#x3b1; induction and female-specific Il-6 elevation. Collectively, these findings highlight sex-dependent modulation of hepatic ATO handling and drug metabolizing capacity, with important implications for risk assessment and individualized ATO containing regimens.

Perfluorodecanoic acid (PFDA) ingestion is associated with liver, immune, developmental, and reproductive effects. Nevertheless, the molecular mechanisms underlying PFDA toxicity remain poorly understood. For example, despite its established presence in human milk, cow milk, and infant formula, its molecular interactions with constituent proteins and their consequences require further study. Here, we report the outcomes associated with the interaction between PFDA and &#x3b1;-lactalbumin (ALAC), a calcium-binding whey protein essential for nutrition and lactose production. Absorbance and Trp fluorescence data reveal PFDA-dependent changes consistent with interactions that perturb the native disposition of the optically active chromophores. Deconvolution of the amide I region of PFDA: protein IR spectra suggest dose-dependent distortions of PFDA in helical and sheet topologies. Ca2+-binding kinetics suggest that the "forever" chemical compromised metal-ion binding to the protein in a dose-dependent manner, reflecting impaired metal-dependent structural stabilization from the molten-globule-like apo-state to the native and biologically active holo-state. Molecular dynamics simulations identified two preferential PFDA binding regions enriched in hydrophobic and positively charged residues and showed that local rearrangements in ALAC's unstructured N-terminus coils can generate tightly bound PFDA states with favorable interaction energies. Combined, these results reveal a coherent molecular mechanism in which PFDA anchors to the ALAC surface, disrupts secondary structure organization, and weakens Ca2+ binding. Considering ALAC's role in early infant nutrition and human health, these findings provide a mechanistic insight into how PFAS exposure may compromise protein function in the postnatal environment.

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.

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-&#x3ba;B signaling pathway in both cell lines. Critically, FFAR1 inhibition largely abolished the suppressive effects of 20-HETE on EMT and NF-&#x3ba;B 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-&#x3ba;B 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.

Perfluoroalkyl substances (PFAS) are pollutants with relevant accumulation in humans, and the enterohepatic circulation of PFAS secreted in bile sustains their persistence. A significant increase in fecal excretion has been experimentally assessed with the use of oral adsorbents with negligible gut absorption. Here, we evaluated in vitro the use of activated charcoal (AC) for human consumption, as sorption material for a panel of PFAS, such as, perfluoro-butanoic acid (PFBA), perfluoro-butanesulfonic acid (PFBS), perfluoro-hexanoic acid (PFHxA), perfluoro-hexanesulfonic acid (PFHxS), perfluoro-octanoic acid (PFOA), and perfluoro-octanesulfonic acid (PFOS), in an experimental simulated bile juice (SBJ). The aim was to obtain preliminary data for possible clinical applications to reduce PFAS blood levels in humans. PFAS concentrations in experimental samples were quantified by liquid chromatography-mass spectrometry. In kinetic tests, equimolar solutions of single PFAS in SBJ were incubated with AC at 37 &#xb0;C up to 120 min, and the time-dependent reduction of PFAS concentration was monitored. In thermodynamic tests, PFAS solutions in SBJ were incubated at increasing concentrations with AC for 24 h at 37 &#xb0;C and the concentrations at equilibrium evaluated. Results were finally fitted with available models in order to characterize the PFAS interaction with AC. All PFAS showed more than 80% sorption on activated charcoal from simulated bile juice within 120 min. This suggests rapid and nearly complete removal. Modeling analysis indicated that the pseudo-first-order kinetic model best described short-chain PFAS, while PFOS and PFOA fitted better with the Elovich model. Thermodynamic analysis showed a general fitting with the Freundlich model, presumptive of a heterogeneous binding model. PFOS binding was concentration-dependent and was better described by the Sips model. These data are suggestive of a potential noninvasive intervention strategy to increase fecal PFAS excretion through the dietary use of AC, in order to mitigate health issues associated with PFAS exposure.

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.

Furan, a potent toxicant and liver carcinogen, is present in tobacco smoke and may contribute to smoking-related adverse health effects. Furan requires metabolic activation to its reactive metabolite, cis-2-butene-1,4-dial (BDA), to exert its toxic effect. Chemical characterization of hepatocellular and urinary metabolites indicates that the reaction of BDA with glutathione (GSH) generates a reactive GSH conjugate, 2-(S-glutathionyl)succinaldehyde (GSH-BDA), that targets protein lysine residues and polyamines to form GSH-BDA-amine cross-links. In this report, five additional amine targets of BDA following its reaction with GSH are identified as GSH-BDA-amine cross-links in furan-exposed rodent hepatocytes. They are ethanolamine, glutamic acid, citrulline, glycerolphosphorylethanolamine (GPE), and taurine. Furthermore, the corresponding mercapturic acid metabolites and their sulfoxides were identified in urine from furan-treated rats and mice, indicating that these amines are targets in vivo. Of particular interest is the modification of GPE, which is an important component of lipids. Since other reactive dialdehydes target GPE and the result of its alkylation is linked to toxicity, lipid alkylation by furan metabolites may play an important role in the toxicity associated with furan exposure. This study generates important new possible biomarkers of effect that can be used in human epidemiological studies to assess furan's human health effects.

The worldwide consumption of traditional herbal preparations has risen markedly in recent years, raising increasing toxicological concern regarding drug-drug interactions. A major mechanistic basis for these interactions is the ability of specific phytochemicals to inhibit cytochrome P450 enzymes, thereby altering xenobiotic metabolism at the molecular and enzymatic levels. Anwuligan (ANL), a bioactive compound isolated from nutmeg and Schisandra chinensis, has attracted considerable pharmacological interest, yet its inhibitory potential toward P450 enzymes remains poorly defined. In this study, we systematically investigated the effects of ANL on CYP2C19 and evaluated its pharmacokinetic interaction with amitriptyline. Enzyme kinetics revealed that ANL is a mechanism-based inhibitor of CYP2C19, and NAC trapping assays indicated that an o-quinone intermediate generated during its metabolism is the key species responsible for irreversible enzyme inactivation. In vivo pharmacokinetic studies in rats showed that ANL pretreatment increased amitriptyline exposure, with Cmax and AUC0-&#x221e; increased by 1.4-fold and &#x223c;30%, respectively, and CLz/F reduced by 24%. In contrast, nortriptyline exposure decreased, with reductions in Cmax (39%) and AUC (29-34%). These results demonstrate CYP2C19 inhibition by ANL and its potential to cause clinically relevant drug-drug interactions.

Per- and polyfluoroalkyl substances (PFAS) are environmentally persistent chemicals that require an improved understanding of the toxicity mechanisms and the development of predictive models for risk assessment. One observed effect of PFAS exposure is a decrease in thyroxine (T4) levels in vivo resulting from the direct displacement of T4 from a carrier protein, transthyretin (TTR), in a proposed adverse outcome pathway (AOP). In this study, the mechanism of thyroxine (T4) displacement from human and rat TTRs was investigated by using structural approaches (i.e., docking and molecular dynamics) and quantitative structure-activity relationship (QSAR) models. A QSAR model was developed using the largest available binding data set and a two-tier approach that allowed inclusion of all data. Docking models that utilized a pharmacophore approach showed nearly perfect overlap with independently sourced crystal structures for perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS). Molecular dynamics simulations demonstrated similar PFAS binding modes in rat and human TTR, enabling interspecies toxicity comparisons. All models predicted moderate to strong binding of the novel PFAS 4,8-Dioxa-3H-perfluorononanoic acid (ADONA) and hexafluoropropylene oxide dimer acid (GenX) to TTR, consistent with the limited toxicity and binding data for these chemicals. Predicted PFAS binding energies for rat TTR correlated well with the in vivo PFAS-associated decreases in T4 levels, supporting the AOP. The development of reliable predictive toxicity models for PFAS requires extensive validation, maximal use of available experimental data, and careful consideration of toxicokinetic differences in interchemical comparisons.