Nov 19, 2024 | 2024, Cells, Journal publications
Abstract
The polarised expression of specific transporters in proximal tubular epithelial cells is important for the renal clearance of many endogenous and exogenous compounds. Thus, ideally, the in vitro tools utilised for predictions would have a similar expression of apical and basolateral xenobiotic transporters as in vivo. Here, we assessed the functionality of organic cation and anion transporters in proximal tubular-like cells (PTL) differentiated from human induced pluripotent stem cells (iPSC), primary human proximal tubular epithelial cells (PTEC), and telomerase-immortalised human renal proximal tubular epithelial cells (RPTEC/TERT1). Organic cation and anion transport were studied using the fluorescent substrates 4-(4-(dimethylamino)styryl)-N-methylpyridinium iodide (ASP) and 6-carboxyfluorescein (6-CF), respectively. The level and rate of intracellular ASP accumulation in PTL following basolateral application were slightly lower but within a 3-fold range compared to primary PTEC and RPTEC/TERT1 cells. The basolateral uptake of ASP and its subsequent apical efflux could be inhibited by basolateral exposure to quinidine in all models. Of the three models, only PTL showed a modest preferential basolateral-to-apical 6-CF transfer. These results show that organic cation transport could be demonstrated in all three models, but more research is needed to improve and optimise organic anion transporter expression and functionality.
Nov 19, 2024 | 2024, Altex, Journal publications
Abstract
Next generation risk assessment of chemicals revolves around the use of mechanistic information without animal experimentation. In this regard, toxicogenomics has proven to be a useful tool to elucidate the mechanisms underlying the adverse effects of xenobiotics. In the present study, two widely used human hepatocyte culture systems, namely primary human hepatocytes (PHH) and human hepatoma HepaRG cells, were exposed to liver toxicants known to induce liver cholestasis, steatosis, or necrosis. Benchmark concentration (BMC) response modelling was applied to transcriptomics gene co-expression networks (modules) to derive BMCs and to gain mechanistic insight into the hepatotoxic effects. BMCs derived by concentration-response modelling of gene co-expression modules recapitulated concentration-response modelling of individual genes. Although PHH and HepaRG cells showed overlap in the genes and modules deregulated by the liver toxicants, PHH demonstrated a higher responsiveness, based on the lower BMCs of co-regulated gene modules. Such BMCs can be used as transcriptomics points of departure (tPOD) for assessing module-associated cellular (stress) pathways/processes. This approach identified clear tPODs of around maximum systemic concentration (Cmax) levels for the tested drugs, while for cosmetics ingredients the BMCs were 10-100-fold higher than the estimated plasma concentrations. This approach could serve next generation risk assessment practice to identify early responsive modules at low BMCs that could be linked to key events in liver adverse outcome pathways. In turn, this can assist in delineating potential hazards of new test chemicals using in vitro systems and be used in a risk assessment where BMCs are paired with chemical exposure assessment.
Nov 19, 2024 | 2024, Journal publications, Molecular Informatics
Abstract
Drug development is a long and costly process, often limited by the toxicity and adverse drug reactions (ADRs) caused by drug candidates. Even on the market, some drugs can cause strong ADRs that can vary depending on an individual polymorphism. The development of Genome-wide association studies (GWAS) allowed the discovery of genetic variants of interest that may cause these effects. In this study, the objective was to investigate a deep learning approach to predict genetic variations potentially related to ADRs. We used single nucleotide polymorphisms (SNPs) information from dbSNP to create a network based on ADR-drug-target-mutations and extracted matrixes of interaction to build deep Neural Networks (DNN) models. Considering only information about mutations known to impact drug efficacy and drug safety from PharmGKB and drug adverse reactions based on the MedDRA System Organ Classes (SOCs), these DNN models reached a balanced accuracy of 0.61 in average. Including molecular fingerprints representing structural features of the drugs did not improve the performance of the models. To our knowledge, this is the first model that exploits DNN to predict ADR-drug-target-mutations. Although some improvements are suggested, these models can be of interest to analyze multiple compounds over all of the genes and polymorphisms information accessible and thus pave the way in precision medicine.
Jun 12, 2024 | 2024, Expert Opinion on Drug Metabolism & Toxicology, Journal publications
Abstract
Development of a new drug currently takes 10–12 years with costs of around 2 billion EUR. The two main reasons for failures comprise lack of efficacy and unforeseen toxicity. For the latter, a standard process pursued to minimize the risk is the so-called toxicological read across. Briefly, toxicologists query the available literature and databases for compounds, which are structurally similar to their development candidate in order to retrieve information on potential hazards. In addition, computational models might be applied which are either trained for a single protein, such as hERG or P-glycoprotein, or for a respective in vivo endpoint (cholestasis, steatosis, drug-induced liver injury (DILI), ….). In both cases, a proper ‘description’ of the compound of interest is key for the predictive ability of the models. In the following editorial, we will highlight a few general approaches for compound description with a focus on bioactivity-based characterization of compounds.
Jun 12, 2024 | 2024, Altex, Journal publications
Abstract
The webinar series and workshop titled Trust Your Gut: Establishing Confidence in Gastrointestinal Models – An Overview of the State of the Science and Contexts of Use was co-organized by NICEATM, NIEHS, FDA, EPA, CPSC, DoD, and the Johns Hopkins Center for Alternatives to Animal Testing (CAAT) and hosted at the National Institutes of Health in Bethesda, MD, USA on October 11-12, 2023. New approach methods (NAMs) for assessing issues of gastrointestinal tract (GIT)-related toxicity offer promise in addressing some of the limitations associated with animal-based assessments. GIT NAMs vary in complexity, from two-dimensional monolayer cell line-based systems to sophisticated 3-dimensional organoid systems derived from human primary cells. Despite advances in GIT NAMs, challenges remain in fully replicating the complex interactions and processes occurring within the human GIT. Presentations and discussions addressed regulatory needs, challenges, and innovations in incorporating NAMs into risk assessment frameworks; explored the state of the science in using NAMs for evaluating systemic toxicity, understanding absorption and pharmacokinetics, evaluating GIT toxicity, and assessing potential allergenicity; and discussed strengths, limitations, and data gaps of GIT NAMs as well as steps needed to establish confidence in these models for use in the regulatory setting.
Jun 12, 2024 | 2024, Environmental Health Pespectives, Journal publications
Abstract
Background: Understanding the variability across the human population with respect to toxicodynamic responses after exposure to chemicals, such as environmental toxicants or drugs, is essential to define safety factors for risk assessment to protect the entire population. Activation of cellular stress response pathways are early adverse outcome pathway (AOP) key events of chemical-induced toxicity and would elucidate the estimation of population variability of toxicodynamic responses.
Objectives: We aimed to map the variability in cellular stress response activation in a large panel of primary human hepatocyte (PHH) donors to aid in the quantification of toxicodynamic interindividual variability to derive safety uncertainty factors.
Methods: High-throughput transcriptomics of over 8,000 samples in total was performed covering a panel of 50 individual PHH donors upon 8 to 24 h exposure to broad concentration ranges of four different toxicological relevant stimuli: tunicamycin for the unfolded protein response (UPR), diethyl maleate for the oxidative stress response (OSR), cisplatin for the DNA damage response (DDR), and tumor necrosis factor alpha (TNFαTNF𝛼) for NF-κBNF-𝜅𝐵 signaling. Using a population mixed-effect framework, the distribution of benchmark concentrations (BMCs) and maximum fold change were modeled to evaluate the influence of PHH donor panel size on the correct estimation of interindividual variability for the various stimuli.
Results: Transcriptome mapping allowed the investigation of the interindividual variability in concentration-dependent stress response activation, where the average of BMCs had a maximum difference of 864-, 13-, 13-, and 259-fold between different PHHs for UPR, OSR, DDR, and NF-κBNF-𝜅𝐵 signaling-related genes, respectively. Population modeling revealed that small PHH panel sizes systematically underestimated the variance and gave low probabilities in estimating the correct human population variance. Estimated toxicodynamic variability factors of stress response activation in PHHs based on this dataset ranged between 1.6 and 6.3.
Discussion: Overall, by combining high-throughput transcriptomics and population modeling, improved understanding of interindividual variability in chemical-induced activation of toxicity relevant stress pathways across the human population using a large panel of plated cryopreserved PHHs was established, thereby contributing toward increasing the confidence of in vitro-based prediction of adverse responses, in particular hepatotoxicity. https://doi.org/10.1289/EHP11891.
