Follow on Bluesky
RISK-HUNT3R Logo

Nutrient environment improves drug metabolic activity in human iPSC-derived hepatocytes and HepG2

Nov 14, 2025 | 2025, Archives of Toxicology, Journal publications

Abstract

Induced pluripotent stem cells (iPSCs) have emerged as a transformative tool in regenerative medicine, in liver research. The perspective of a stable and functional source of hepatocytes has led to developing protocols for human iPSC-derived hepatocytes-like cells (HLCs). Yet, hepatic models remain one of most challenging systems to functionally reproduce with iPSCs, due to its resulting limited metabolic function. Using an adapted nutrient regimen, two human hepatocyte models were characterized: HLCs (derived from iPSCs) and metabolically active HepG2 (mHepG2, derived from the cell line HepG2), for their drug metabolism activity. In these cell systems, the transcriptome, proteome, and metabolome of 11 drug-relevant cytochrome P450 (CYP) isoenzymes were studied. A liquid chromatography–mass spectrometry (LC–MS)-based metabolomics approach, using model drugs as isoenzyme reporters, was applied, achieving a comprehensive overview of mHepG2 and HLCs drug metabolism. Drugs used in this study to characterize xenobiotic machinery were: bupropion (25 µM), phenacetin (30 µM), rosiglitazone (10 µM), diclofenac (75 µM), dextromethorphan (15 µM), chlorzoxazone (60 µM), midazolam (15 µM), benzydamine (15 µM), coumarin (250 µM) and 7-ethoxycoumarin (60 µM). Being HepG2 notorious for its limited metabolic capacity, our study raises mHepG2 as a highly performant cell model, with activity on 8 drug-metabolizing CYPs. Modulation by nutrient environment in improving metabolic function of in vitro models is here proven as a key determinant. Likewise, HLCs hold the widest CYP coverage at the transcript level and were able to cope with a wide variety of chemical insults, making them a promising model for personalized metabolic studies.

Testing Strategies for Metabolite-Mediated Neurotoxicity

Nov 14, 2025 | 2025, International Journal of Molecular Sciences, Journal publications

Abstract

Compounds, which rely on metabolism to exhibit toxicity, pose a challenge for next-generation risk assessment (NGRA). Since many of the currently available non-animal new approach methods (NAMs) lack metabolic activity, their use may lead to an underestimation of the true hazard to humans (false negative predictions). We explored here strategies to deal with metabolite-mediated toxicity in assays for developmental neurotoxicity. First, we present an overview of substances that may serve as potential positive controls for metabolite-related neurotoxicity. Then, we demonstrate, using the MitoMet (UKN4b) assay, which assesses the adverse effects of chemicals on neurites of human neurons, that some metabolites have a higher toxic potency than their parent compound. Next, we designed a strategy to integrate elements of xenobiotic metabolism into assays used for (developmental) neurotoxicity testing. In the first step of this approach, hepatic post-mitochondrial fractions (S9) were used to generate metabolite mixtures (“metabolisation module”). In the second step, these were applied to a NAM (exemplified by the UKN4b assay) to identify metabolite-mediated toxicity. We demonstrate the applicability and transferability of these approaches to other assays, by an exemplary study on the basis of the cMINC (UKN2) assay, another NAM of the developmental neurotoxicity in vitro battery. Based on the experience gained from these experiments, we discuss key issues to be addressed if this approach is to be used more broadly for NAM in the NGRA context.

Computational Prediction of Mutagenicity Through Comprehensive Cell Painting Analysis

Nov 14, 2025 | 2025, Journal publications, Mutagenesis

Abstract

The mutagenicity of chemical compounds is a key consideration in toxicology, drug development, and environmental safety. Traditional methods such as the Ames test, while reliable, are time-intensive and costly. With advances in imaging and machine learning (ML), high-content assays like cell painting offer new opportunities for predictive toxicology. Cell painting captures extensive morphological features of cells, which can correlate with chemical bioactivity. In this study, we leveraged cell painting data to develop ML models for predicting mutagenicity and compared their performance with structure-based models. We used two datasets: a Broad Institute dataset containing profiles of over 30 000 molecules and a U.S.-Environmental Protection Agency dataset with images of 1200 chemicals tested at multiple concentrations. By integrating these datasets, we aimed to improve the robustness of our models. Among three algorithms tested—Random Forest, Support Vector Machine, and Extreme Gradient Boosting—the third showed the best performance for both datasets. Notably, selecting the most relevant concentration per compound, the phenotypic altering concentration, significantly improved prediction accuracy. Our models outperformed traditional quantitative structure activity relationship (QSAR) tools such as the Virtual models for property Evaluation of chemicals within a Global Architecture (VEGA) and the CompTox Dashboard for the majority of compounds, demonstrating the utility of cell painting features. The cell painting-based models revealed morphological changes related to DNA and RNA perturbation, especially in mitochondria, endoplasmic reticulum and nuclei, aligning with mutagenicity mechanisms. Despite this, certain compounds remained challenging to predict due to inherent dataset limitations and inter-laboratory variability in cell painting technology. The findings highlight the potential of cell painting in mutagenicity prediction, offering a complementary perspective to chemical structure-based models. Future work could involve harmonizing cell painting methodologies across datasets and exploring deep learning techniques to enhance predictive accuracy. Ultimately, integrating cell painting data with QSAR descriptors in hybrid models may unlock novel insights into chemical mutagenicity.

Utilizing rat kidney gene co-expression networks to enhance safety assessment biomarker identification and human translation

Jul 21, 2025 | 2025, iScience, Journal publications

Abstract

Toxicogenomic data provide key insights into molecular mechanisms underlying drug-induced organ toxicities. To simplify transcriptomic data interpretation, we applied weighted gene co-expression network analysis (WGCNA) to rat kidney transcriptomics data from TG-GATEs (TG) and DrugMatrix (DM), covering time- and dose-response data for 180 compounds. A total of 347 gene modules were incorporated into the rat kidney TXG-MAPr web-tool, that interactively visualizes and quantifies module activity using eigengene scores (EGSs). Several modules annotated for cellular stress, injury, and inflammation were associated with renal pathologies and included established and candidate biomarker genes. Many rat kidney modules were preserved across transcriptome datasets, suggesting potential applicability to other kidney injury contexts. Cross-species preservation analysis using human kidney data further supported the translational potential of these rat-derived modules. The TXG-MAPr platform facilitates upload and analysis of gene expression data in the context of rat kidney co-expression networks, which could identify mechanisms and safety liabilities of chemical or drug exposures.

Towards a quantitative adverse outcome pathway for liver carcinogenesis: From proliferation to prediction

Jul 21, 2025 | 2025, Computational Toxicology, Journal publications

Abstract

Hazard assessment of non-genotoxic carcinogens could greatly benefit from next generation risk assessment approaches, driven by the multitude of mechanisms through which non-genotoxic carcinogens operate. One method for structuring new approach methodology-derived data is the adverse outcome pathway (AOP) concept. Currently, mostly qualitative AOPs are described, limiting their application for regulatory decision making. In contrast, quantitative AOPs use mathematical terms to describe the relationships between key events (KEs), allowing for the derivation of a Point of Departure (PoD). Here, we report quantification of the key event relationship (KER) between sustained hepatocyte proliferation and liver tumour formation, two KEs of AOP#220 relating to CYP2E1 activation leading to liver cancer. We use incidence of histopathological lesions indicative of proliferation, as well as BrdU labelling obtained from existing sub-chronic toxicity studies in rats, to quantify proliferation. For liver cancer, incidences of hepatocellular adenoma and carcinoma from 2-year rodent carcinogenicity studies were collected. Data for both KEs were combined to calibrate a response-response model, and Bayesian logistic regression analysis was applied to obtain predictions and credible intervals for carcinogenicity. Proliferative lesion incidence was observed to be a highly specific, yet insensitive predictor, and combining this with BrdU labelling yields more accurate predictions of carcinogenicity. Importantly, we demonstrate that for most of the chemicals tested, inclusion of BrdU labelling returns more precise predicted benchmark dose intervals for PoD derivation. To further explore this quantitative KER and its regulatory application, we propose to include and standardize BrdU labelling for sub-chronic toxicity studies performed for regulatory purposes.

Identifying human toxicodynamic variability: A systematic evidence map of the current knowledge

Jul 21, 2025 | 2025, Journal publications, Regulatory Toxicology and Pharmacology

Abstract

Current chemical risk assessment uses a default uncertainty factor (UF) of 3.16 for toxicodynamic (TD) variability in humans. The objective was to create a systematic evidence map (SEM) of the human variability in TD by identifying and organizing the available empirical data to assess if a further refinement of the default UF of 3.16 for TD can be achieved. PubMed and Web of Science™ were searched from 2004 to 2023. Studies were screened according to the eligibility criteria. Inclusion criteria included studies, where TD could be separated from toxicokinetics (TK) to exclude an impact of TK on TD variability. The literature search retrieved 2408 studies. Manual screening identified 23 in vitro studies assessing human TD variability quantitively, of which only seven in vitro studies provided quantitative estimates of a TD variability factor. No in vivo study met the inclusion criteria. Several studies found TD UF of 3.16 not covering human variability; others did. However, the data were heterogeneous, and variability in Points of Departure (PODs) and methods used to estimate TD variability complicated comparisons across studies. A standardized approach for TDVFs determination is identified. This SEM underscores the scarcity of data assessing human variability in TD, while omitting the influence of TK.