Chemical Research in Toxicology

BACKGROUND Botanicals are widely consumed as herbal remedies and plant-derived products, but their complex and variable chemical composition presents major challenges for safety assessment. Although hepatotoxicity has been reported for several botanicals, the constituent compounds responsible and the underlying mechanisms often remain poorly characterised. OBJECTIVES We aimed to determine whether Cell Painting could identify candidate constituent drivers of botanical bioactivity and generate hypotheses about potential mechanisms of action in primary human hepatocytes. METHODS We profiled 13 botanical extracts and 85 known constituents across multiple concentrations using Cell Painting alongside assays of metabolic activity and membrane integrity. To interpret morphological phenotypes, we trained machine-learning models using a Cell Painting reference atlas of >1,000 chemicals linked to ToxCast/Tox21 molecular and cellular bioactivity annotations. RESULTS Cell Painting detected bioactivity in 11/13 botanical extracts and 46/85 constituents, compared with 9/13 extracts and 40/85 constituents in the cell-health assays, and generally detected activity at lower concentrations. For 9/13 extracts, at least one constituent was active at a lower concentration than the corresponding botanical extract, supporting the contribution of a subset of potent constituents to mixture-level responses. Aristolochia and goldenseal were the most potent extracts across Cell Painting and cell-health assays and contained highly active constituents. Reference-atlas models generated specific mechanistic hypotheses, including cytotoxicity-, endocrine-, and genotoxicity-associated responses for several botanicals and constituents. CONCLUSION These results support Cell Painting combined with machine learning as a sensitive approach for characterising cellular responses to botanicals, prioritising candidate constituent drivers of hepatotoxicity-associated phenotypes, and generating testable hypotheses about potential mechanisms of action.

Experimental assessment of developmental neurotoxicity (DNT) is costly, time-consuming, and difficult to scale across large chemical inventories. New approach methodologies (NAMs), including computational approaches based on machine learning, deep learning, and large language models (LLMs), offer new ways to screen and prioritize chemicals. However, these emerging computational approaches have largely focused on predicting toxicity endpoints, with less attention to the mechanisms underlying those predictions. For DNT assessment, understanding this mechanistic basis is equally important. In this study, we integrated LLM inference with the adverse outcome pathway (AOP) framework to connect DNT predictions with plausible mechanisms. We constructed an AOP-informed benchmark linking ChEMBL target-activity measurements for 5,861 compounds to 14 AOPs representing five neurodevelopmental outcomes and evaluated ten LLMs for the prediction of molecular initiating events (MIEs), key events (KEs), key event relationships (KERs), and adverse outcomes (AOs), as well as the generation of mechanistic explanations. Explanation quality was evaluated using both a naive LLM judge and a rubric-based LLM judge. Across the zero-shot models, the highest observed macro-F1 scores were 0.818 for MIEs and 0.463 for AOs, but only 0.056 for KEs and 0.034 for KERs, identifying downstream pathway reconstruction as the principal challenge. Few-shot prompting substantially improved performance, with Gemini 3.8 Flash under four-shot prompting achieving macro-F1 scores of 0.956 for AOs, 0.813 for MIEs, 0.492 for KEs, and 0.457 for KERs. Both LLM-judge configurations indicated improved mechanistic explanation quality with few-shot examples, although strong outcome prediction did not consistently ensure recovery of the recorded pathway. Our results demonstrate the potential of AOP-informed LLMs to support DNT assessment by linking predicted neurodevelopmental outcomes to plausible biological mechanisms and enabling predictions to be evaluated at both the outcome and pathway levels.

Benzyl paraben (BzP) and butyl paraben (BuP) are used as preservatives and antimicrobial agents in cosmetics, pharmaceuticals, and foods. BzP and BuP have been detected in urine, placental tissue, and ovarian follicular fluid and are understudied with regards to their endocrine activity, raising concerns on their impact to the reproductive system. We tested the hypothesis that BzP and BuP are toxic to the ovary and behave similarly to other structurally similar endocrine disrupting chemicals, such as phthalates and terephthalates, by altering hormone levels and impacting cell growth and gene expression in vitro. Untreated mouse antral follicles were exposed to vehicle, 0.1, 1, 10, or 100 g/mL BzP, BuP, or the BuP analog monobutyl terephthalate (MBuTP) for 96 hours. The highest dose of BzP and BuP were cytotoxic to follicles, suggesting that medium chain length hydroxybenzoates are more cytotoxic to the ovary than corresponding terephthalic acids. BzP significantly increased levels of estradiol and testosterone compared to control. BuP decreased follicle growth and disrupted expression of cell cycle regulators, similar to phthalates. The peroxisome proliferator-activated receptor gamma (PPARg)-transcriptionally controlled gene Fabp4 was significantly upregulated by both BzP and BuP, whereas MBuTP upregulated all four PPAR target genes and decreased follicle growth. Overall, BzP, BuP, and MBuTP disrupted hormone levels, follicle growth, and cell cycle progression, suggesting similarities in toxicity to phthalates and potential endocrine disruption. Further study should be done on the reproductive toxicity and endocrine disrupting effects of chemicals similar in structure to phthalates to reduce exposure to endocrine disruptors and inform design of safer replacements.

Current health risk assessments from thirdhand exposure to methamphetamine (meth) to occupants of contaminated residences when no cooking or use is still occurring assume that inhalation exposure is insignificant. After Wright et al. (2021) reported substantial meth concentrations in a contaminated residence, the present study was conducted to further test the hypothesis that concentrations were high enough to contribute to total occupant dose. Air sampling was conducted in contaminated residences that had been previously sampled for surface contamination. Samples were collected on personal sampling pumps in fixed locations on treated glass fiber filters is cassettes and analyzed by gas or liquid chromatography with mass spectroscopy. Sixty-nine samples were collected from eighteen residences for the study. Meth concentrations ranged from 0.02 to 15 ug/m3. Concentrations in supply air vents were significantly higher than in return vents or in ambient air. Residences with no HVAC systems (baseboard heat) had airborne concentrations similar to residences with HVAC systems turned off, and lower than when HVAC was on. Whether a residence was occupied or not did not show differences. All data from residences with HVAC had a significant correlation with surface concentrations (R2 = 0.61) but some factors that affected results were not determined in the study. In the context of the risk assessment and regulatory standard established by California and adopted in several other states, the inhalation exposure would approximately double total occupant dose for infants, and this population would exceed the California reference dose by approximately 100%. Further studies should be conducted to refine these estimates and determine the factors that influence airborne concentrations.

Objective To systematically evaluate the predictability of drug chemical structures against the complete set of MedDRA Preferred Terms (PTs), construct a comprehensive structure adverse reaction landscape, validate the utility of molecular fingerprint representations in pan-screening tasks, and establish quantitative tools for drug safety pre-screening. Methods Drug adverse event association data were compiled from SIDER 4.1 and its derivative compilation Nsider, encompassing 2,005 approved drugs, of which 1,120 small-molecule drugs with successfully retrieved chemical structures were retained for analysis. For each Preferred Term (PT) meeting the minimum sample-size threshold ([&ge;]25 positive and [&ge;]30 negative drugs; n = 768), XGBoost classifiers were trained using MACCS 166-bit fingerprints, with model performance evaluated via 5-fold stratified cross-validation. A PT was designated as high-confidence if it achieved a validation AUC [&ge;] 0.75, a cross-validation AUC [&ge;] 0.70, and passed Benjamini Hochberg false discovery rate (FDR) correction (q < 0.05). The entire analytical pipeline was replicated using Morgan ECFP4 2048-bit fingerprints to assess the robustness of fingerprint selection. The robustness of significance screening was independently validated by permutation testing (500 random label permutations), and model generalizability was evaluated through Bemis-Murcko scaffold-based stratified split validation. SHAP (SHapley Additive exPlanations) analysis was employed to identify critical structural features, and mechanism-based case studies further demonstrated concordance between model-identified features and established pharmacophores. Results Among the 768 modelable PTs, 71 (9.2%) met the high-confidence significance criteria (FDR q < 0.05), predominantly comprising nervous system disorders (10 PTs) and psychiatric disorders (9 PTs). The toxicities exhibiting the strongest structural predictability included hyperprolactinaemia (AUC = 0.968), dermatitis acneiform (AUC = 0.966), tardive dyskinesia (AUC = 0.952), and breast tenderness (AUC = 0.979). Under a unified XGBoost hyperparameter configuration, the low-dimensional MACCS fingerprint (166-bit) demonstrated near-equivalent performance to the high-dimensional Morgan fingerprint (2048-bit) in pan-screening tasks ({Delta}AUC = -0.012, Cohen's d = 0.16), and achieved comparable performance at a substantially lower dimensionality across 18 of 22 organ systems. SHAP analysis identified key structural fragments corresponding to established pharmacophores (six-membered rings, tertiary amine cores, heteroatomic bonds, and bridged heteroatomic chains), and mechanism-based case studies further demonstrated that the model captured structural patterns concordant with known pharmacophores of D2 antagonists, EGFR inhibitors, and related agents. Bemis-Murcko scaffold-based stratified split validation revealed a median AUC decrease of merely 0.042, with 66% of PTs maintaining an AUC [&ge;] 0.70 under scaffold-split conditions. Immune-mediated and multifactorial endpoints remained structurally opaque under the current data conditions. Conclusion This study constructed a comprehensive structure-adverse reaction predictability landscape encompassing 768 MedDRA PTs; seventy-one PTs achieved high-confidence criteria. The MACCS 166-bit fingerprint achieved near-equivalent performance to the Morgan 2048-bit fingerprint at a markedly lower dimensionality, and its chemically explicit features enable direct translation of model outputs into structural alerts, providing a computational tool for prospective screening of potential adverse reaction profiles for any novel compound. A tiered assessment strategy is proposed: machine learning ensemble models for high-confidence screening, substructure alert rules for high-specificity exclusion, and pharmacovigilance signal detection for complementary coverage of structurally unpredictable endpoints. Bemis-Murcko scaffold-based stratified split validation further demonstrated that the median generalization performance decrease was merely 0.042 AUC, with 66% of PTs maintaining an AUC [&ge;] 0.70 under scaffold-split conditions, corroborating the existence of a genuine chemical basis underlying structure-adverse reaction mappings.

The delivery of small-molecule therapeutics across cellular membranes remains a fundamental challenge in drug development, particularly for compounds whose physicochemical properties limit intracellular bioavailability. Inspired by the low toxicity, biodegradability, and synthetic accessibility of triethanolamine-derived lipids, we investigated a series of C18 aminotriester lipids as excipients for the codelivery of anticancer therapeutics. Four structurally related triester lipids bearing saturated, cis-monounsaturated, trans-monounsaturated, and di-unsaturated C18 tails were synthesized through straightforward esterification reactions with triethanolamine. These lipids were evaluated for their ability to enhance the antiproliferative activity of several anticancer agents, including paclitaxel, doxorubicin, amonafide, homoharringtonine, camptothecin, and SN-38. Across multiple drug classes, the aminotriester lipids exhibited minimal intrinsic cytotoxicity and generally produced limited enhancement of drug potency. However, the fully saturated tris-stearate aminotriester lipid selectively enhanced the antiproliferative activity of amonafide in HCT-116 colorectal cancer cells, particularly at lower concentrations. In contrast, unsaturated lipid analogues displayed negligible enhancement effects, and the activity of the tris-stearate lipid was substantially attenuated in A549 lung adenocarcinoma cells, weakly observed in HT-29 colorectal cancer cells, and absent in CT26 murine colorectal cancer cells. Lactate dehydrogenase release assays indicated that these effects were not associated with membrane lysis or nonspecific cytotoxicity. Further, fluorescence microscopy time course experiments suggest improved membrane trafficking of amonafide when codelivered stoichiometrically with fully saturated C18 aminotriester lipids. Collectively, these findings demonstrate that saturated triethanolamine-based aminotriester lipids may function as selective and biocompatible codelivery excipients for certain small-molecule therapeutics. The observed dependence on both lipid saturation state and cellular context suggests that membrane organization and lipid packing may play important roles in mediating these effects. These results establish a foundation for further mechanistic investigations into aminotriester lipid-assisted small-molecule delivery systems.

Biodiversity is globally threatened by chemical pollution, yet toxicity data remain unavailable for millions of species and tens of thousands of chemicals, severely limiting our ability to assess ecological impacts. Here we present TRIDENT-2, a multimodal artificial intelligence model for predicting chemical toxicity across evolutionarily diverse eukaryotic species. Trained on 560,780 toxicity assays spanning 82,775 chemicals, 6,793 species, and multiple exposure scenarios, TRIDENT-2 accurately predicts toxicity across Eukaryota with an average median absolute error ranging from 1.76 to 3.80. By jointly learning from chemical, biological, and experimental information, it remains accurate across broad chemical and taxonomic distances, allowing for toxicity assessment for species and chemicals beyond the current experimental evidence. Our findings demonstrate that artificial intelligence can help overcome longstanding data limitations in ecotoxicology, paving the way for improved decision-making and reducing chemical impacts on biodiversity and ecosystems.

Computational safety evaluation of phytochemical libraries commonly relies on models trained on experimentally tested compounds and applied to much larger and chemically diverse collections. An important but often overlooked question is whether predicted compounds are adequately represented within the experimental chemical space underlying these models. Here, we examined this question for the Indian Medicinal Plants, Phytochemistry and Therapeutics (IMPPAT) database using experimentally characterized datasets for three anti-target endpoints: hERG blockade, cytochrome P450 (CYP) inhibition, and pregnane X receptor (PXR) induction. After structure standardization, 14,956 unique IMPPAT compounds were compared with 8,877 hERG, 4,905 CYP, and 4,138 PXR reference compounds using consensus nearest-neighbor similarity based on Morgan and atom-pair fingerprints. At a Tanimoto similarity threshold of 0.80, only 0.92%, 0.30%, and 0.46% of IMPPAT compounds had close measured analogs in the hERG, CYP, and PXR datasets, respectively. Relative to the internal nearest-neighbor distributions of the reference datasets, a typical IMPPAT compound lay near the 1st percentile, indicating substantial chemical-space separation. Compounds with close experimental analogs also showed distinct molecular characteristics, with descriptor-based logistic regression yielding cross-validated AUC values of 0.77-0.80. Plant-level enrichment was markedly reduced after excluding widely distributed metabolites and was further restricted when the nearest measured analog was required to have pIC50 or pEC50 [&ge;] 5. The remaining signals were concentrated in a limited number of taxa, including Rauvolfia, Sinopodophyllum/Dysosma, Cinchona, and Catharanthus roseus. Overall, most IMPPAT phytochemicals lack close structural representation among currently measured anti-target compounds. Computational safety predictions for these molecules should therefore be interpreted as extrapolations and accompanied by explicit measures of structural support. Absence of a close experimental analog should be regarded as a gap in available evidence rather than evidence of safety.

Human metabolism converts inorganic arsenic to the pentavalent methylated species MMA(V) and DMA(V), the forms most people excrete, and the forms long read as the end of a detoxification pathway. Whether a transporter sets how much of these metabolites reaches the genome has not been tested in a mammalian cell. We expressed human AQP3, AQP7, AQP9 or AQP10 in HEK293T and MRC5-SV40 cells and measured gamma-H2AX by flow cytometry across dose series of As(V), MMA(V) and DMA(V), pairing every aquaporin with a GFP-Tubulin control and an untransfected mock acquired in the same replicate. As(V) was inactive in HEK293T cells and only weakly active in MRC5-SV40 cells to 20 micromolar, and both methylated species damaged DNA only in the millimolar range, DMA(V) being the more potent of the two in both cell lines. Against that weak baseline, AQP9 and AQP10 raised DMA(V)-induced gamma-H2AX in HEK293T cells by roughly 17 percentage points over the matched control, more than doubling the damage the same exposure produced in control cells, whereas AQP3 and AQP7 changed it not at all. AQP9 alone remained active with MMA(V). The ranking held in MRC5-SV40 fibroblasts at one-sixth the size, and within single wells the damage rose with the amount of AQP9 a cell carried while the control was flat. Aquaglyceroporins therefore discriminate among arsenic species, and AQP9 and AQP10 turn a weakly genotoxic metabolite into a substantially more genotoxic one.

Biological effectiveness in ion therapy varies with radiation quality and biological context, yet cancer cell response data acquired under well-characterized physical conditions remain limited. We investigated clonogenic survival and relative biological effectiveness (RBE) in PANC-1 and BxPC-3 pancreatic ductal adenocarcinoma (PDAC) cells irradiated at four positions along pristine 180.1 MeV proton and 352.4 MeV/u carbon ion Bragg curves, with 4 cm spread-out Bragg peak (SOBP) irradiation included as a clinically relevant comparison. Cell plane positioning and dose were verified in a water-tank system using an Advanced Markus ionization chamber, and Monte Carlo simulations using TOPAS provided dose-averaged linear energy transfer (LETD) and microdosimetric quantities. RBE was calculated relative to 200 kVp X-rays using the linear-quadratic survival model. Across the sampled pristine beam positions, spanning LETD values of approximately 1-9 keV/m for protons and 12-163 keV/m for carbon ions, RBE increased with LETD. Proton RBE10 exceeded the conventional clinical value of 1.1 at distal positions, whereas carbon ion RBE10 increased from 2.3 to 5.2 in PANC-1 and from 1.3 to 3.7 in BxPC-3, with larger differences between cell lines observed near the Bragg peak. SOBP measurements showed different biological responses across field conditions despite comparable LETD. These physically characterized survival data provide PDAC-relevant benchmarks for evaluating whether RBE models reproduce biological responses across pristine and SOBP irradiation conditions, supporting the future integration of radiation-quality-dependent effects into ion planning.

This study evaluated the Delayed Cmax Drug Delivery System (DCMax), an erosion-based controlled- release formulation designed to delay peak plasma concentration (Cmax) and sustain drug release. The tablet matrix contained hydroxypropyl methylcellulose, hydroxypropyl methylcellulose acetate succinate, and polyethylene oxide. In-vitro dissolution and erosion studies confirmed the delayed release profile. In-vivo performance was assessed in six healthy volunteers by measuring salivary caffeine concentrations after administration of immediate-release caffeine tablets and DCMax tablets. DCMax induced a delayed Cmax of 5 hours compared with 1 hour for the immediate-release formulation, while peak concentrations were lower compared to immediate release. These findings demonstrate that erosion-based drug delivery can achieve delayed release, with potential application in chronotherapy for endocrine disorders.

Environmental exposure involves pollutant mixtures. Building on evidence that individual bisphenols alter zebrafish larval physiology and transcriptomic profiles at ng/L concentrations, we assessed the effects of specific mixtures of BPS, BPF, and BPAF without (M1) or with (M2) BPA. Experimentally, M1 and M2 induced 4.16% and 6.54% mortality in zebrafish larvae, despite the concentration addition model predicting no toxicity. Both mixtures reduced swimming-locomotor activities. These behaviors were accompanied by organ-level functional defects, as in vivo optical mapping revealed bradycardia and increased high-frequency (30-80 Hz) brain oscillations for both mixtures, while M2 additionally altered low-frequency (0.2-4 Hz) brain activity. The neurophysiological changes were accompanied by neurovascular remodeling, with in vivo two-photon microscopy revealing decreased vascular length and branching. Finally, transcriptomic analysis revealed common M1- and M2-induced alterations linked to pathways of vascular function, neuronal communication, and metabolism. M2 altered additional pathways of cardiac and neuronal signaling, consistent with the reported organ-physiology effects. We discuss the environmental relevance of mixture exposures and assess whether BPA intensifies mixture-induced responses.

Drug-induced liver injury (DILI) remains a major challenge in drug development because existing in vitro models poorly predict clinical hepatotoxicity. We coupled patient-derived human liver organoids (HLOs) with high-content imaging and automated machine learning (ML) to detect hepatotoxicity-associated phenotypes by morphological profiling. HLOs from six DILI patients and one healthy donor (n = 7) retained donor-specific baseline phenotypes in hepatocyte maturity, metabolism, and morphology. ML models trained per line on MAD-robustized, well-level-aggregated median features achieved compound-level AUROCs of 0.88 (95% CI 0.86-0.90) to 0.91 (95% CI 0.89-0.93) and balanced accuracies of 0.81-0.84 in distinguishing 54 hepatotoxicants from 20 non-hepatotoxic controls. Averaging predictions across all seven lines at an optimized threshold gave 94.4% sensitivity (95% CI 84.6-98.8%) and 85.0% specificity (95% CI 62.1-96.8%), substantially outperforming HLO cytotoxicity assays and cancer cell line profiling. Lines were moderately-to-highly concordant (Pearson r = 0.66-0.85), and benchmark concentration modeling revealed reproducible inter-line differences in phenotypic sensitivity (median 4.0-fold, several compounds exceeding 20-fold). Exposure anchoring of benchmark concentrations to human-equivalent plasma concentrations, using in vitro mass-balance dosimetry and curated plasma free fractions, placed organoid points of departure within 100-fold of clinical Cmax for 60 of 66 compounds (23-fold residual offset, 95% CI 12-45), and placed two of the three misclassified negative controls an order of magnitude above clinical Cmax. Sulindac injury signatures were significantly enhanced by TNF co-treatment, demonstrating capacity to detect pro-inflammatory-mediated DILI. This platform preserves inter-line variability while enabling reproducible, scalable multi-line consensus hazard classification; because compounds were tested over a common nominal concentration range, the readout defines hepatocellular hazard, which must be combined with exposure as an independent input to estimate clinical DILI risk.

BACKGROUND: Pesticides are extensively used in agriculture, raising concerns about their potential impact on human health through dietary and environmental exposure. OBJECTIVES: This study evaluated the in vitro cytotoxicity of ten commonly used pesticides and their mixtures (lambda-cyhalothrin, cypermethrin, deltamethrin, tebuconazole, glyphosate, acetamiprid, cyprodinil, piperonyl butoxide, fluopyram, and imazalil) on human intestinal Caco-2 cells. METHODS: Cytotoxicity was assessed using the MTT assay, as a measure of metabolic activity, and the trypan blue exclusion test, as an indicator of cell membrane integrity. FINDINGS: Results showed that high concentrations (100 mg/L) of all pesticides significantly reduced cell viability and vitality. Notably, glyphosate and tebuconazole exhibited significant toxicity even at lower concentrations, respectively 0.1 mg/L and 10 mg/L. Combination treatments (Top 3 and Top 8 pesticide mixtures) retained the cytotoxic effects observed for individual compounds, showing additive (non-synergistic) effects. CONCLUSIONS: Overall, these findings indicate that certain pesticides-based herbicides can exert cytotoxic effects on intestinal cells even at relatively low concentrations and highlight the importance of using the component-based approach in mixture risk assessment for humans. This study was performed as part of the EU SPRINT (Sustainable Plant Protection Transition: A Global Health Approach) project.

The pharmacokinetics concentration-time profile encodes ADME dynamics. Its optimization in drug discovery curbs late-stage attrition, yet forecasting human pharmacokinetics from chemical structure remains difficult. Machine learning models ignore continuous ADME dynamics, whereas physiologically based pharmacokinetics (PBPK) models demand unavailable parameters and impose rigid, misspecified compartmental structures. We present PK-MUSE (PharmacoKinetic hybrid Mechanistic Universal System Equation), augmenting a two-compartment model with bounded, state- and time-dependent neural corrections to elimination and intercompartmental distribution. Molecular structure parameterizes compound-specific behavior, while learned corrections refine dynamic equations. Across scaffold and temporal out-of-distribution evaluations, PK-MUSE achieved the lowest mean exposure-endpoint MdFE, reducing MdFE by 7.2%, 45.2%, and 26.2% versus state-of-the-art machine learning (HML- RF), deep learning compartmental (DeepCt), and PBPK (PK-Sim) models. Interrogating the corrections localized misspecification to distribution rather than elimination. By extending mechanistic simulators with data-driven constrained neural dynamics that preserve interpretability and generalize to unseen chemotypes, PK-MUSE advances digital twins for drug discovery.

Dehydroascorbic acid (DHA), the oxidized form of vitamin C, exhibits specific toxicity against KRAS-mutant cancer cells; however, the early metabolic reprogramming mechanisms leading to cell death remain incompletely understood. In this study, we reveal the non-linear, dose-dependent response of the intracellular metabolome during the ultra-early phase of 1-hour DHA exposure. Under 1 mM DHA exposure, an early significant accumulation of the primary catabolite erythrulose (p = 0.030) and moderate consumption of glutamate (p = 0.0086) were observed, while core TCA cycle homeostasis and cell viability were preserved. In contrast, under 10 mM DHA exposure, an acute and profound alteration of the metabolic profile was observed. Along with the significant accumulation of glycerate, indicative of a glycolytic blockade, and the terminal peroxidation product oxalate, core TCA cycle intermediates such as -ketoglutarate and malate showed rapid depletion (Log2FC < -2.0, p < 10-7). Furthermore, coupled with this acute energy depletion, a statistically significant accumulation of free methionine (Log2FC = 1.14, p = 0.011), which was below the limit of detection in the control, was identified alongside profound depletions of glutamate (p < 0.001) and cysteine (p = 0.0027). These results strongly suggest that the initial response to high-dose DHA is not merely a linear accumulation of oxidative stress, but rather an "abrupt metabolic switch" characterized by rapid TCA cycle dysfunction followed by the early signature of a methionine trap.

Lead (Pb) exposure is strongly associated with cardiovascular diseases, but the effects of developmental Pb exposure on the electrophysiological properties of human cardiomyocytes have not yet been investigated. Using human induced pluripotent stem cell (iPSC)-derived cardiomyocytes, we investigated the effects of human-relevant Pb exposure during ventricular cardiomyocyte differentiation on action potential properties and calcium dynamics in differentiated myocytes. We exposed 2 iPSC lines, from 1 male and 1 female donor, to Pb (0.5 or 5 micromolar) or control during only the first 8 days of differentiation, prior to functional maturation. Differentiation was carried out using an established protocol involving temporal modulation of Wnt signaling, followed by purification and maturation of contractile myocytes. We measured action potential properties and calcium dynamics using fluorescent dyes and optical mapping. We hypothesized that Pb exposure would adversely affect cardiomyocyte function, with effects persisting after cessation of exposure. In keeping with this hypothesis, we found significant Pb-induced changes in calcium (Ca) transients and action potential dynamics that were concentration and cell line specific and persisted well after exposure had ended. In the female cell line, beat frequency significantly increased with Pb exposure for both action potentials and Ca transients, while in the male cell line, beat frequency significantly decreased with Pb exposure for action potentials and increased for Ca transients. For voltage measurements, we observed significantly increased arrythmias with Pb exposure in both cell lines. Significant changes in repolarization and recovery time (for voltage and Ca, respectively) were present with Pb exposure in both cell lines. Gene expression of Ca transporters/channels and K channels were not affected, suggesting that persistent effects of Pb exposure on cardiomyocyte electrophysiology may occur through post-translational mechanisms. Overall, this study paves the way for future work to examine how exposure to Pb impacts long-term cardiac function through developmental programming.

Chemobrain, characterized by cognitive dysfunction, has emerged as a significant health concern following cancer treatment. Doxorubicin, an FDA-approved chemotherapeutic agent, is widely used to treat various cancers. However, its adverse effects raise concerns regarding toxicity in non-cancerous cell types. Microglia, the resident immune cells of the brain, play a central role in maintaining neural homeostasis. Despite this, the mechanisms by which microglia become dysfunctional following doxorubicin exposure remain poorly understood. Understanding the transcriptomic alterations induced by doxorubicin in microglia could provide critical insights into mechanisms underlying its neurotoxic effects. In this study, the transcriptomic response of human microglia to doxorubicin using mRNA sequencing combined with bioinformatic analysis was investigated. Exposure to doxorubicin significantly altered microglial morphology, promoting a shift toward a process-bearing phenotype in human HMC3 cells. This phenotypic change was accompanied by elevated levels of DNA damage and was associated with the induction of cellular senescence. At the gene regulation level, the Illumina-based transcriptomic analysis provided 25,834 gene expression profiling in which 5,669 were differentially expressed. The future analyses revealed that 3,431 (upregulated) and 2,238 (downregulated) were differentially expressed under the treatment of doxorubicin. Genes associated with the p53 signaling pathway and inflammatory responses were upregulated, whereas genes involved in translation, DNA replication, and cell-cycle progression were downregulated. This transcriptional dysregulation was accompanied by differential expression of key transcription factors. This study provides transcriptomic evidence elucidating the genotoxic response to doxorubicin in microglial cells, offering mechanistic insights into chemobrain and highlighting key molecular pathways as potential targets for therapeutic intervention.

Cardiovascular diseases (CVD) are the leading cause of morbidity and mortality worldwide. This clinical burden is predominantly attributable to atherothrombotic events resulting from platelet hyperactivation, a process integral to the pathophysiology of atherosclerosis. Cilostazol (CLZ) is a clinically established phosphodiesterase-IIIA inhibitor with antiplatelet and vasodilatory activity. However, its extremely low aqueous solubility results in dissolution-limited and variable oral absorption, while dose-dependent adverse effects and cardiovascular safety restrictions may constrain its clinical use. Lipid-polymer hybrid nanoparticles (LPHNPs) represent a highly promising drug delivery system to address these biopharmaceutical limitations. However, their interactions with blood components remain poorly understood, with surface charge among the most controversial physicochemical parameters implicated in platelet toxicity. Here, we applied an integrated ex vivo and in silico screening strategy to design LPHNPs based on poly(lactic acid) and poly(ethylene glycol)-modified lipids displaying positive (amine), nominally neutral (methoxy), and negative (carboxyl) surface charges to systematically identify safe boundaries at the nano-human platelet interface and evaluate their suitability as CLZ carriers. Positively charged nanoparticles precipitated immediately during self-assembly due to uncontrolled ionic pairing and were therefore structurally unviable. Neutral nanoparticles maintained a stable size (~113 nm hydrodynamic diameter) but unexpectedly induced spontaneous platelet aggregation, limiting their applicability. In contrast, negatively charged nanoparticles exhibited exceptional colloidal stability (hydrodynamic size of ~120 nm and zeta potential of -48 mV), achieved significant inhibition of ADP-induced platelet aggregation, and adhered to the platelet surface without inducing activation or membrane disruption. Steered molecular dynamics simulations, performed under identical non-equilibrium pulling conditions, showed the highest interfacial resistance to CLZ extraction for the negatively charged model, qualitatively consistent with the sustained CLZ release profile observed in vitro. These findings highlight the importance of precise surface charge tuning in the development of safe cardiovascular nanomedicines based on LPHNPs, emphasizing the need for comprehensive platelet compatibility testing before further biological evaluation.

The classical estrogen receptor (ER) is a steroid hormone receptor known for its role in both health and disease. ER, like its counterpart ER{beta}, acts as a receptor for its endogenous ligand 17{beta}-estradiol (E2) and is capable of binding a diverse set of natural and synthetic small molecules. Several screening campaigns have identified various ER ligands, based on the oxabicyclo[3.3.1]-nonene scaffold, that act as ER agonists. One such campaign extensively characterized an oxabicyclic ER agonist, termed AB-1. Herein, we report the synthesis of a series of analogs based on the scaffold of AB-1 (herein termed 1), focusing on the enhancement of their binding to ER and functional potencies in ER mediated assays. We identify a new substituted compound 9 that achieves subnanomolar potencies for ER binding (IC50= 0.45 nM) and ER dependent transcription (EC50= 0.37 nM) and proliferation (EC50= 0.03 nM), with potencies approaching those of E2. The high potency of 9 provides a promising scaffold for the potential development of new ER-targeted therapeutics.