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Adrenergic receptors (ARs) are preferentially expressed by innate lymphocytes such as natural killer (NK) cells. Here, we study the effect of epinephrine-mediated stimulation of the β2-adrenergic receptor (β2AR) on the function of human NK cells. Epinephrine stimulation inhibited early NK cell signaling events and blocked the function of the integrin LFA-1. This reduced the adhesion of NK cells to ICAM-1, explaining how NK cells are mobilized into the peripheral blood upon epinephrine release during acute stress or exercise. Additionally, epinephrine stimulation transiently reduced NK cell degranulation, serial killing, and cytokine production and affected metabolic changes upon NK cell activation via the cAMP-protein kinase A (PKA) pathway. Repeated exposure to β2AR agonists resulted in the desensitization of the β2AR via a PKA feedback loop-initiated G-protein switch. Therefore, acute epinephrine stimulation of chronically β2AR stimulated NK cells no longer resulted in inhibited signaling and reduced LFA-1 activity. Sustained stimulation by long-acting β2-agonists (LABA) not only inhibited NK cell functions but also resulted in desensitization of the β2AR. However, peripheral NK cells from LABA-treated asthma patients still reacted unchanged to epinephrine stimulation, demonstrating that local LABA administration does not result in detectable systemic effects on NK cells., (© 2024 The Author(s). European Journal of Immunology published by Wiley‐VCH GmbH.)

Background: Immune checkpoint inhibition (ICI) currently is the most effective treatment to induce durable responses in metastatic melanoma. The aims of this study are the characterization of patients with early, late and non-response to ICI and analysis of survival outcomes in a real-world patient cohort., Methods: Patients who received PD-1-based immunotherapy for non-resectable stage-IV melanoma in any therapy line were selected from the prospective multicenter real-world DeCOG study ADOREG-TRIM (NCT05750511). Patients showing complete (CR) or partial (PR) response already during the first 3 months of treatment (Early Responders, EarlyR) were compared to patients showing CR/PR at a later time (Late Responders, LateR), a stable disease (SD) and to patients showing progressive disease (Non-Responders, NonR)., Results: Of 522 patients, 8.2 % were EarlyR (n = 43), 19.0 % were LateR (n = 99), 37.0 % had a SD (n = 193) and 35.8 % were NonR (n = 187). EarlyR, LateR and SD patients had comparable baseline characteristics. Multivariate logbinomial regression analyses adjusted for age and sex revealed positive tumor PD-L1 (RR=1.99, 95 %-CI=1.14-3.46, p = 0.015), and normal serum CRP (RR=1.59, 95 %-CI=0.93-2.70, p = 0.036) as independently associated with the achievement of an early response compared to NonR. The median progression-free and overall survival was 46.0 months (95 % CI 19.1; NR) and 47.8 months (95 %-CI 36.9; NR) for EarlyR, NR (95 %-CI NR; NR) for LateR, 8.1 months (7.0; 10.4) and 35.4 months (29.2; NR) for SD, and 2.0 months (95 %-CI 1.9; 2.1) and 6.1 months (95 %-CI 4.6; 8.8) for NonR patients., Conclusion: Less than 10 % of metastatic melanoma patients achieved an early response during the first 3 months of PD-1-based immunotherapy. Early responders were not superior to late responders in terms of response durability and survival., Competing Interests: Declaration of Competing Interest GCL received travel support from Sun Pharma, Pierre Fabre, research funding from Novartis. RH is employee of Helios Kliniken GmbH. PT has received honoraria from Bristol-Myers Squibb, Novartis, Merck Sharp & Dohme, Pierre Fabre, CureVac, Merck Serono, Sanofi, Roche, Kyowa Kirin and Biofrontera; and travel support from Bristol-Myers Squibb and Pierre Fabre. JU received honoraria (speaker honoraria or honoraria as a consultant) and travel support from Bristol-Myers Squibb, Kyowa Kirin, Merck Sharp & Dohme, Novartis, Pfizer, Pierre Fabre, Roche, Sanofi/Regeneron, Sunpharma outside the submitted work. CP received honoraria (speaker honoraria or honoraria as a consultant) and travel support from Novartis, BMS, MSD, Merck Serono, MSD, Celgene, AbbVie, Sunpharma, Pierre Fabre, UCB, Nutricia Milupa, Janssen and LEO, outside the submitted work. JU is on the advisory board or has received honoraria and travel support from Amgen, Bristol Myers Squibb, GSK, Immunocore, LeoPharma, Merck Sharp and Dohme, Novartis, Pierre Fabre, Roche, Sanofi outside the submitted work. AK served as a speaker and/or consultant and/or advisory board for MSD, AbbVie, Boehringer Ingelheim, Janssen, and Sanofi. PM declares research support from Bristol Myers Squibb, Merck Sharp & Dohme and Novartis; speakers and advisory board honoraria from Bristol Myers Squibb, Beiersdorf, Merck Sharp & Dohme, Pierre Fabre, Sun Pharma, Immunocore, Sanofi and Novartis, and travel support from Bristol Myers Squibb, Merck Sharp & Dohme, Sanofi, Sun Pharma, and Pierre Fabre, outside the submitted work. RG received honoraria as speaker from BMS, MSD, Novartis, Amgen, Merck Serono, Almirall Hermal, SUN, Sanofi/Regeneron, Pierre-Fabre, as advisory board member from BMS, Novartis, Almirall Hermal, MSD, Amgen, SUN, Sanofi/Regeneron, Pierre-Fabre, 4SC, MerckSerono, Pfizer, Immunocore, Delcath, for meeting support from SUN, Pierre-Fabre, Boehringer Ingelheim and for research projects (to institution) from Amgen, Merck-Serono, SUN Pharma, Sanofi/Regeneron, Kyowa-Kirin, Almirall-Hermal. FM has received travel support or/and speaker’s fees or/and advisor’s honoraria by Novartis, Roche, BMS, MSD, Pierre Fabre, Sanofi and Immunocore and research funding from Novartis and Roche. MW received grants from Bristol-Myers Squibb and Merck Sharp & Dohme, consulting fees from Merck Sharp & Dohme, Immunocore and Novartis, lecture honoraria from Bristol-Myers Squibb and Merck Sharp & Dohme and Pierre-Fabre, and advisory board honoraria from Merck Sharp & Dohme. AP reports grants from Bristol Myers Squibb (BMS) and Merck Sharp & Dohme (MSD) outside the submitted work. JCB is receiving speaker’s bureau honoraria from Amgen, Pfizer, Recordati and Sanofi, and is a paid consultant/advisory board member/DSMB member for Almirall, Boehringer Ingelheim, InProTher, ICON, MerckSerono, Pfizer, 4SC, and Sanofi/Regeneron. His group received research grants from Bristol-Myers Squibb, Merck Serono, HTG, IQVIA, and Alcedis. EL served as consultant and/or has received honoraria from Bristol-Myers Squibb, Merck Sharp & Dohme, Novartis, Pierre-Fabre, Sanofi, Sunpharma, Takeda and travel support from Bristol-Myers Squibb, Pierre- Fabre, Sunpharma and Novartis, outside the submitted work. LZ served as consultant and has received honoraria from BMS, MSD, Novartis, Pierre Fabre, Sanofi, and Sunpharma and travel support from MSD, BMS, Pierre Fabre, Sanofi, Sunpharma and Novartis, outside the submitted work. DS reports partial financial support from Bristol Myers Squibb for the conduct of this study and drug supply (nivolumab and ipilimumab) support; grants (or contracts) from Amgen, Array/Pfizer, Bristol-Myers Squibb, MSD, Novartis and Roche; consulting fees from 4SC, Amgen, Array Biopharma, AstraZeneca, Bristol-Myers Squibb, Daiichi Sankyo, Haystick, Immunocore, InFlarX, Innocent, LabCorp, Merck Serono, MSD, Nektar, NeraCare, Novartis, OncoSec, Pfizer, Philogen, Pierre Fabre, Replimune, Roche, Sandoz, Sanofi/Regeneron, Sun Pharma; honoraria from Bristol-Myers Squibb, MSD/Merck, Merck Serono, Novartis, Roche, Sanofi and Sun Pharma; support for attendings meetings or travel support from Bristol-Myers Squibb, MSD, Merck Serono, Novartis, Pierre Fabre and Sanofi; participation on drug safety monitoring or advisory boards for 4SC, Amgen, Array Biopharma, AstraZeneca, Bristol-Myers Squibb, Daiichi Sankyo, Immunocore, InFlarX, Merck Serono, MSD, Nektar, NeraCare, Novartis, OncoSec, Pfizer, Philogen, Pierre Fabre, Replimune, Roche, Sandoz, Sanofi/Regeneron and SunPharma; leadership roles for DeCOG, German Cancer Society, Hiege-Stiftung, Deutsche Hautkrebsstiftung, NVKH e.V. and EuMelaReg. SU declares research support from Bristol Myers Squibb and Merck Serono; speakers and advisory board honoraria from Bristol Myers Squibb, Merck Sharp & Dohme, Merck Serono, and Novartis; and meeting and travel support from Almirall, Bristol-Myers Squibb, IGEA Clinical Biophysics, Merck Sharp & Dohme, Novartis, Pierre Fabre, and Sun Pharma. All other authors declared no conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Latent Dirichlet allocation (LDA) is a popular method for analyzing large text corpora, but it suffers from instability due to its reliance on random initialization. This results in different outcomes for replicated runs, hindering reproducibility. To address this, we introduce LDAPrototype, a new approach for selecting the most representative LDA run from multiple replications on the same dataset. LDAPrototype enhances the reliability of LDA conclusions by ensuring greater similarity between replications compared to traditional LDA runs or models chosen based on perplexity or NPMI. A key feature of LDAPrototype is its use of a novel model similarity measure called S-CLOP (Similarity of multiple sets by Clustering with LOcal Pruning). It is based on topic similarities, for which we compare the usage of measures like the thresholded Jaccard coefficient, cosine similarity, Jensen-Shannon divergence, and rank-biased overlap. The effectiveness of LDAPrototype is demonstrated through its application to six real datasets, including newspaper articles and tweets. The results show improved reproducibility and reliability in topic modeling outcomes. LDAPrototype's approach is noteworthy for its practical applicability, comprehensibility, ease of implementation, and computational efficiency. Furthermore, the algorithm's concept can be generalized to other topic modeling procedures that characterize topics through word distributions, making it a versatile tool in text data analysis., Competing Interests: The authors declare there are no competing interests., (©2024 Rieger et al.)

Simulation is a crucial tool for the evaluation and comparison of statistical methods. How to design fair and neutral simulation studies is therefore of great interest for both researchers developing new methods and practitioners confronted with the choice of the most suitable method. The term simulation usually refers to parametric simulation, that is, computer experiments using artificial data made up of pseudo-random numbers. Plasmode simulation, that is, computer experiments using the combination of resampling feature data from a real-life dataset and generating the target variable with a known user-selected outcome-generating model, is an alternative that is often claimed to produce more realistic data. We compare parametric and Plasmode simulation for the example of estimating the mean squared error (MSE) of the least squares estimator (LSE) in linear regression. If the true underlying data-generating process (DGP) and the outcome-generating model (OGM) were known, parametric simulation would obviously be the best choice in terms of estimating the MSE well. However, in reality, both are usually unknown, so researchers have to make assumptions: in Plasmode simulation studies for the OGM, in parametric simulation for both DGP and OGM. Most likely, these assumptions do not exactly reflect the truth. Here, we aim to find out how assumptions deviating from the true DGP and the true OGM affect the performance of parametric and Plasmode simulations in the context of MSE estimation for the LSE and in which situations which simulation type is preferable. Our results suggest that the preferable simulation method depends on many factors, including the number of features, and on how and to what extent the assumptions of a parametric simulation differ from the true DGP. Also, the resampling strategy used for Plasmode influences the results. In particular, subsampling with a small sampling proportion can be recommended., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Stolte et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The alkaline comet assay is frequently used as in vivo follow-up test within different regulatory environments to characterize the DNA-damaging potential of different test items. The corresponding OECD Test guideline 489 highlights the importance of statistical analyses and historical control data (HCD) but does not provide detailed procedures. Therefore, the working group "Statistics" of the German-speaking Society for Environmental Mutation Research (GUM) collected HCD from five laboratories and >200 comet assay studies and performed several statistical analyses. Key results included that (I) observed large inter-laboratory effects argue against the use of absolute quality thresholds, (II) > 50% zero values on a slide are considered problematic, due to their influence on slide or animal summary statistics, (III) the type of summarizing measure for single-cell data (e.g., median, arithmetic and geometric mean) may lead to extreme differences in resulting animal tail intensities and study outcome in the HCD. These summarizing values increase the reliability of analysis results by better meeting statistical model assumptions, but at the cost of information loss. Furthermore, the relation between negative and positive control groups in the data set was always satisfactorily (or sufficiently) based on ratio, difference and quantile analyses., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background & Aims: Cholemic nephropathy (CN) is a severe complication of cholestatic liver diseases for which there is no specific treatment. We revisited its pathophysiology with the aim of identifying novel therapeutic strategies., Methods: Cholestasis was induced by bile duct ligation (BDL) in mice. Bile flux in kidneys and livers was visualized by intravital imaging, supported by MALDI mass spectrometry imaging and liquid chromatography-tandem mass spectrometry. The effect of AS0369, a systemically bioavailable apical sodium-dependent bile acid transporter (ASBT) inhibitor, was evaluated by intravital imaging, RNA-sequencing, histological, blood, and urine analyses. Translational relevance was assessed in kidney biopsies from patients with CN, mice with a humanized bile acid (BA) spectrum, and via analysis of serum BAs and KIM-1 (kidney injury molecule 1) in patients with liver disease and hyperbilirubinemia., Results: Proximal tubular epithelial cells (TECs) reabsorbed and enriched BAs, leading to oxidative stress and death of proximal TECs, casts in distal tubules and collecting ducts, peritubular capillary leakiness, and glomerular cysts. Renal ASBT inhibition by AS0369 blocked BA uptake into TECs and prevented kidney injury up to 6 weeks after BDL. Similar results were obtained in mice with humanized BA composition. In patients with advanced liver disease, serum BAs were the main determinant of KIM-1 levels. ASBT expression in TECs was preserved in biopsies from patients with CN, further highlighting the translational potential of targeting ASBT to treat CN., Conclusions: BA enrichment in proximal TECs followed by oxidative stress and cell death is a key early event in CN. Inhibiting renal ASBT and consequently BA enrichment in TECs prevents CN and systemically decreases BA concentrations., Impact and Implications: Cholemic nephropathy (CN) is a severe complication of cholestasis and an unmet clinical need. We demonstrate that CN is triggered by the renal accumulation of bile acids (BAs) that are considerably increased in the systemic blood. Specifically, the proximal tubular epithelial cells of the kidney take up BAs via the apical sodium-dependent bile acid transporter (ASBT). We developed a therapeutic compound that blocks ASBT in the kidneys, prevents BA overload in tubular epithelial cells, and almost completely abolished all disease hallmarks in a CN mouse model. Renal ASBT inhibition represents a potential therapeutic strategy for patients with CN., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

High throughput RNA sequencing experiments are widely conducted and analyzed to identify differentially expressed genes (DEGs). The statistical models calculated for this task are often not clear to practitioners, and analyses may not be optimally tailored to the research hypothesis. Often, interaction effects (IEs) are the mathematical equivalent of the biological research question but are not considered for different reasons. We fill this gap by explaining and presenting the potential benefit of IEs in the search for DEGs using RNA-Seq data of mice that receive different diets for different time periods. Using an IE model leads to a smaller, but likely more biologically informative set of DEGs compared to a common approach that avoids the calculation of IEs., (© 2023. The Author(s).)

In toxicological concentration-response studies, a frequent goal is the determination of an 'alert concentration', i.e. the lowest concentration where a notable change in the response in comparison to the control is observed. In high-throughput gene expression experiments, e.g. based on microarray or RNA-seq technology, concentration-response profiles can be measured for thousands of genes simultaneously. One approach for determining the alert concentration is given by fitting a parametric model to the data which allows interpolation between the tested concentrations. It is well known that the quality of a model fit improves with the number of measured data points. However, adding new replicates for existing concentrations or even several replicates for new concentrations is time-consuming and expensive. Here, we propose an empirical Bayes approach to information sharing across genes, where in essence a weighted mean of the individual estimate for one specific parameter of a fitted model and the mean of all estimates of the entire set of genes is calculated as a result. Results of a controlled plasmode simulation study show that for many genes a notable improvement in terms of the mean squared error (MSE) between estimate and true underlying value of the parameter can be observed. However, for some genes, the MSE increases, and this cannot be prevented by using a more sophisticated prior distribution in the Bayesian approach., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Kappenberg, Rahnenführer. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: An important problem in toxicology in the context of gene expression data is the simultaneous inference of a large number of concentration-response relationships. The quality of the inference substantially depends on the choice of design of the experiments, in particular, on the set of different concentrations, at which observations are taken for the different genes under consideration. As this set has to be the same for all genes, the efficient planning of such experiments is very challenging. We address this problem by determining efficient designs for the simultaneous inference of a large number of concentration-response models. For that purpose, we both construct a D-optimality criterion for simultaneous inference and a K-means procedure which clusters the support points of the locally D-optimal designs of the individual models., Results: We show that a planning of experiments that addresses the simultaneous inference of a large number of concentration-response relationships yields a substantially more accurate statistical analysis. In particular, we compare the performance of the constructed designs to the ones of other commonly used designs in terms of D-efficiencies and in terms of the quality of the resulting model fits using a real data example dealing with valproic acid. For the quality comparison we perform an extensive simulation study., Conclusions: The design maximizing the D-optimality criterion for simultaneous inference improves the inference of the different concentration-response relationships substantially. The design based on the K-means procedure also performs well, whereas a log-equidistant design, which was also included in the analysis, performs poorly in terms of the quality of the simultaneous inference. Based on our findings, the D-optimal design for simultaneous inference should be used for upcoming analyses dealing with high-dimensional gene expression data., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Parabens have been used for decades as preservatives in food, drugs and cosmetics. The majority however, were banned in 2009 and 2014 leaving only methyl-, ethyl-, propyl-, and butyl-derivates available for subsequent use. Methyl- and propylparaben have been extensively tested in vivo, with no resulting evidence for developmental and reproductive toxicity (DART). In contrast, ethylparaben has not yet been tested for DART in animal experiments, and it is currently debated if additional animal studies are warranted. In order to perform a comparison of the four currently approved parabens, we used a previously established in vitro test based on human induced pluripotent stem cells (iPSC) that are exposed to test substances during their differentiation to neuroectodermal cells. EC 50 values for cytotoxicity were 906 μM, 698 μM, 216 μM and 63 μM for methyl-, ethyl-, propyl- and butylparaben, respectively, demonstrating that cytotoxicity increases with increasing alkyl chain length. Genome-wide analysis demonstrated that FDR-adjusted significant gene expression changes occurred only at cytotoxic or close to cytotoxic concentrations, for example 1720 differentially expressed genes (DEG) at 1000 μM ethylparaben, 1 DEG at 316 μM, and no DEG at 100 μM or lower concentrations. The highest concentration of ethylparaben that did not induce any cytotoxicity nor DEG was 1670-fold above the highest concentration reported in biomonitoring studies (60 nM ethylparaben in cord blood). In conclusion, cytotoxicity and gene expression alterations of ethylparaben occurred at concentrations of approximately three orders of magnitude above human blood concentrations; moreover, the substance fitted well into a scenario where toxicity increases with the alkyl chain length, and gene expression changes only occur at cytotoxic or close to cytotoxic concentrations. Therefore, no evidence was obtained suggesting that in vivo DART with ethylparaben would lead to different results as the methyl- or propyl derivates., Competing Interests: Declaration of competing interest The parabens tested in this study were provided by Clariant Produkte (Deutschland) GmbH. Companies of the Clariant Group produce and market parabens, among other products. Susann Fayyaz and Reinhard Kreiling are employees of Clariant Produkte (Deutschland) GmbH., (Copyright © 2023. Published by Elsevier B.V.)

The analysis of dose-response, concentration-response, and time-response relationships is a central component of toxicological research. A major decision with respect to the statistical analysis is whether to consider only the actually measured concentrations or to assume an underlying (parametric) model that allows extrapolation. Recent research suggests the application of modelling approaches for various types of toxicological assays. However, there is a discrepancy between the state of the art in statistical methodological research and published analyses in the toxicological literature. The extent of this gap is quantified in this work using an extensive literature review that considered all dose-response analyses published in three major toxicological journals in 2021. The aspects of the review include biological considerations (type of assay and of exposure), statistical design considerations (number of measured conditions, design, and sample sizes), and statistical analysis considerations (display, analysis goal, statistical testing or modelling method, and alert concentration). Based on the results of this review and the critical assessment of three selected issues in the context of statistical research, concrete guidance for planning, execution, and analysis of dose-response studies from a statistical viewpoint is proposed., (© 2023. The Author(s).)

Animal studies for embryotoxicity evaluation of potential therapeutics and environmental factors are complex, costly, and time-consuming. Often, studies are not of human relevance because of species differences. In the present study, we recapitulated the process of cardiomyogenesis in human induced pluripotent stem cells (hiPSCs) by modulation of the Wnt signaling pathway to identify a key cardiomyogenesis gene signature that can be applied to identify compounds and/or stress factors compromising the cardiomyogenesis process. Among the 23 tested teratogens and 16 non-teratogens, we identified three retinoids including 13-cis-retinoic acid that completely block the process of cardiomyogenesis in hiPSCs. Moreover, we have identified an early gene signature consisting of 31 genes and associated biological processes that are severely affected by the retinoids. To predict the inhibitory potential of teratogens and non-teratogens in the process of cardiomyogenesis we established the "Developmental Cardiotoxicity Index" (CDI 31g ) that accurately differentiates teratogens and non-teratogens to do or do not affect the differentiation of hiPSCs to functional cardiomyocytes., (© 2023. Cell Death Differentiation Association (ADMC).)

Background & Aims: Nonalcoholic fatty liver disease (NAFLD) is a major health burden associated with the metabolic syndrome leading to liver fibrosis, cirrhosis and ultimately liver cancer. In humans, the PNPLA3 I148M polymorphism of the phospholipase patatin-like phospholipid domain containing protein 3 (PNPLA3) has a well-documented impact on metabolic liver disease. In this study, we used a mouse model mimicking the human PNPLA3 I148M polymorphism in a long-term high fat diet (HFD) experiment to better define its role for NAFLD progression., Methods: Male mice bearing wild-type Pnpla3 (Pnpla3 WT ), or the human polymorphism PNPLA3 I148M (Pnpla3 148M/M ) were subjected to HFD feeding for 24 and 52 weeks. Further analysis concerning basic phenotype, inflammation, proliferation and cell death, fibrosis and microbiota were performed in each time point., Results: After 52 weeks HFD Pnpla3 148M/M animals had more liver fibrosis, enhanced numbers of inflammatory cells as well as increased Kupffer cell activity. Increased hepatocyte cell turnover and ductular proliferation were evident in HFD Pnpla3 148M/M livers. Microbiome diversity was decreased after HFD feeding, changes were influenced by HFD feeding (36%) and the PNPLA3 I148M genotype (12%). Pnpla3 148M/M mice had more faecal bile acids. RNA-sequencing of liver tissue defined an HFD-associated signature, and a Pnpla3 148M/M specific pattern, which suggests Kupffer cell and monocytes-derived macrophages as significant drivers of liver disease progression in Pnpla3 148M/M animals., Conclusion: With long-term HFD feeding, mice with the PNPLA3 I148M genotype show exacerbated NAFLD. This finding is linked to PNPLA3 I148M-specific changes in microbiota composition and liver gene expression showing a stronger inflammatory response leading to enhanced liver fibrosis progression., (© 2023 The Authors. Liver International published by John Wiley & Sons Ltd.)

Background: In high-dimensional data (HDD) settings, the number of variables associated with each observation is very large. Prominent examples of HDD in biomedical research include omics data with a large number of variables such as many measurements across the genome, proteome, or metabolome, as well as electronic health records data that have large numbers of variables recorded for each patient. The statistical analysis of such data requires knowledge and experience, sometimes of complex methods adapted to the respective research questions., Methods: Advances in statistical methodology and machine learning methods offer new opportunities for innovative analyses of HDD, but at the same time require a deeper understanding of some fundamental statistical concepts. Topic group TG9 "High-dimensional data" of the STRATOS (STRengthening Analytical Thinking for Observational Studies) initiative provides guidance for the analysis of observational studies, addressing particular statistical challenges and opportunities for the analysis of studies involving HDD. In this overview, we discuss key aspects of HDD analysis to provide a gentle introduction for non-statisticians and for classically trained statisticians with little experience specific to HDD., Results: The paper is organized with respect to subtopics that are most relevant for the analysis of HDD, in particular initial data analysis, exploratory data analysis, multiple testing, and prediction. For each subtopic, main analytical goals in HDD settings are outlined. For each of these goals, basic explanations for some commonly used analysis methods are provided. Situations are identified where traditional statistical methods cannot, or should not, be used in the HDD setting, or where adequate analytic tools are still lacking. Many key references are provided., Conclusions: This review aims to provide a solid statistical foundation for researchers, including statisticians and non-statisticians, who are new to research with HDD or simply want to better evaluate and understand the results of HDD analyses., (© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

We examined differences in HER2 expression between primary tumors and distant metastases, particularly within the HER2-negative primary breast cancer cohort (HER2-low and HER2-zero). The retrospective study included 191 consecutive paired samples of primary breast cancer and distant metastases diagnosed between 1995 and 2019. HER2-negative samples were divided into HER2-zero (immunohistochemistry [IHC] score 0) and HER2-low (IHC score 1+ or 2+/in situ hybridization [ISH]-negative). The main objective was to analyze the discordance rate between matched primary and metastatic samples, focusing on the site of distant metastasis, molecular subtype, and de novo metastatic breast cancer. The relationship was determined by cross-tabulation and calculation of Cohen's Kappa coefficient. The final study cohort included 148 paired samples. The largest proportion in the HER2-negative cohort was HER2-low [primary tumor 61.4% ( n = 78), metastatic samples 73.5% ( n = 86)]. The discordance rate between the HER2 status of primary tumors and corresponding distant metastases was 49.6% ( n = 63) (Kappa -0.003, 95%CI -0.15-0.15). Development of a HER2-low phenotype occurred most frequently ( n = 52, 40.9%), mostly with a switch from HER2-zero to HER2-low ( n = 34, 26.8%). Relevant HER2 discordance rates were observed between different metastatic sites and molecular subtypes. Primary metastatic breast cancer had a significantly lower HER2 discordance rate than secondary metastatic breast cancer [30.2% (Kappa 0.48, 95%CI 0.27-0.69) versus 50.5% (Kappa 0.14, 95% CI -0.03-0.32)]. This highlights the importance of evaluating potentially therapy-relevant discordance rates between a primary tumor and corresponding distant metastases.

A range of regularization approaches have been proposed in the data sciences to overcome overfitting, to exploit sparsity or to improve prediction. Using a broad definition of regularization, namely controlling model complexity by adding information in order to solve ill-posed problems or to prevent overfitting, we review a range of approaches within this framework including penalization, early stopping, ensembling and model averaging. Aspects of their practical implementation are discussed including available R-packages and examples are provided. To assess the extent to which these approaches are used in medicine, we conducted a review of three general medical journals. It revealed that regularization approaches are rarely applied in practical clinical applications, with the exception of random effects models. Hence, we suggest a more frequent use of regularization approaches in medical research. In situations where also other approaches work well, the only downside of the regularization approaches is increased complexity in the conduct of the analyses which can pose challenges in terms of computational resources and expertise on the side of the data analyst. In our view, both can and should be overcome by investments in appropriate computing facilities and educational resources.

The experience of adversity in childhood has been associated with poor health outcomes in adulthood. In search of the biological mechanisms underlying these effects, research so far focused on alterations of DNA methylation or shifts in transcriptomic profiles. The level of protein, however, has been largely neglected. We utilized mass spectrometry to investigate the proteome of CD14 + monocytes in healthy adults reporting childhood adversity and a control group before and after psychosocial stress exposure. Particular proteins involved in (i) immune processes, such as neutrophil-related proteins, (ii) protein metabolism, or (iii) proteins related to mitochondrial biology, such as those involved in energy production processes, were upregulated in participants reporting exposure to adversity in childhood. This functional triad was further corroborated by protein interaction- and co-expression analyses, was independent of stress exposure, i.e. observed at both pre- and post-stress time points, and became evident especially in females. In line with the mitochondrial allostatic load model, our findings provide evidence for the long-term effects of childhood adversity on mitochondrial biology., (© 2023. The Author(s).)

Background: Intrinsic or acquired resistance to HER2-targeted therapy is often a problem when small molecule tyrosine kinase inhibitors or antibodies are used to treat patients with HER2 positive breast cancer. Therefore, the identification of new targets and therapies for this patient group is warranted. Activated choline metabolism, characterized by elevated levels of choline-containing compounds, has been previously reported in breast cancer. The glycerophosphodiesterase EDI3 (GPCPD1), which hydrolyses glycerophosphocholine to choline and glycerol-3-phosphate, directly influences choline and phospholipid metabolism, and has been linked to cancer-relevant phenotypes in vitro. While the importance of choline metabolism has been addressed in breast cancer, the role of EDI3 in this cancer type has not been explored., Methods: EDI3 mRNA and protein expression in human breast cancer tissue were investigated using publicly-available Affymetrix gene expression microarray datasets (n = 540) and with immunohistochemistry on a tissue microarray (n = 265), respectively. A panel of breast cancer cell lines of different molecular subtypes were used to investigate expression and activity of EDI3 in vitro. To determine whether EDI3 expression is regulated by HER2 signalling, the effect of pharmacological inhibition and siRNA silencing of HER2, as well as the influence of inhibiting key components of signalling cascades downstream of HER2 were studied. Finally, the influence of silencing and pharmacologically inhibiting EDI3 on viability was investigated in vitro and on tumour growth in vivo., Results: In the present study, we show that EDI3 expression is highest in ER-HER2 + human breast tumours, and both expression and activity were also highest in ER-HER2 + breast cancer cell lines. Silencing HER2 using siRNA, as well as inhibiting HER2 signalling with lapatinib decreased EDI3 expression. Pathways downstream of PI3K/Akt/mTOR and GSK3β, and transcription factors, including HIF1α, CREB and STAT3 were identified as relevant in regulating EDI3 expression. Silencing EDI3 preferentially decreased cell viability in the ER-HER2 + cells. Furthermore, silencing or pharmacologically inhibiting EDI3 using dipyridamole in ER-HER2 + cells resistant to HER2-targeted therapy decreased cell viability in vitro and tumour growth in vivo., Conclusions: Our results indicate that EDI3 may be a potential novel therapeutic target in patients with HER2-targeted therapy-resistant ER-HER2 + breast cancer that should be further explored., (© 2023. The Author(s).)

Proteasome inhibition is associated with parkinsonian pathology in vivo and degeneration of dopaminergic neurons in vitro. We explored here the metabolome (386 metabolites) and transcriptome (3257 transcripts) regulations of human LUHMES neurons, following exposure to MG-132 [100 nM]. This proteasome inhibitor killed cells within 24 h but did not reduce viability for 12 h. Overall, 206 metabolites were changed in live neurons. The early (3 h) metabolome changes suggested a compromised energy metabolism. For instance, AMP, NADH and lactate were up-regulated, while glycolytic and citric acid cycle intermediates were down-regulated. At later time points, glutathione-related metabolites were up-regulated, most likely by an early oxidative stress response and activation of NRF2/ATF4 target genes. The transcriptome pattern confirmed proteostatic stress (fast up-regulation of proteasome subunits) and also suggested the progressive activation of additional stress response pathways. The early ones (e.g., HIF-1, NF-kB, HSF-1) can be considered a cytoprotective cellular counter-regulation, which maintained cell viability. For instance, a very strong up-regulation of AIFM2 (=FSP1) may have prevented fast ferroptotic death. For most of the initial period, a definite life-death decision was not taken, as neurons could be rescued for at least 10 h after the start of proteasome inhibition. Late responses involved p53 activation and catabolic processes such as a loss of pyrimidine synthesis intermediates. We interpret this as a phase of co-occurrence of protective and maladaptive cellular changes. Altogether, this combined metabolomics-transcriptomics analysis informs on responses triggered in neurons by proteasome dysfunction that may be targeted by novel therapeutic intervention in Parkinson's disease.

Background & Aims: Pluripotent stem cell (PSC)-derived hepatocyte-like cells (HLC) have enormous potential as a replacement for primary hepatocytes in drug screening, toxicology and cell replacement therapy, but their genome-wide expression patterns differ strongly from primary human hepatocytes (PHH)., Methods: We differentiated human induced pluripotent stem cells (hiPSC) via definitive endoderm to HLC and characterized the cells by single-cell and bulk RNA-seq, with complementary epigenetic analyses. We then compared HLC to PHH and publicly available data on human fetal hepatocytes (FH) ex vivo; we performed bioinformatics-guided interventions to improve HLC differentiation via lentiviral transduction of the nuclear receptor FXR and agonist exposure., Results: Single-cell RNA-seq revealed that transcriptomes of individual HLC display a hybrid state, where hepatocyte-associated genes are expressed in concert with genes that are not expressed in PHH - mostly intestinal genes - within the same cell. Bulk-level overrepresentation analysis, as well as regulon analysis at the single-cell level, identified sets of regulatory factors discriminating HLC, FH, and PHH, hinting at a central role for the nuclear receptor FXR in the functional maturation of HLC. Combined FXR expression plus agonist exposure enhanced the expression of hepatocyte-associated genes and increased the ability of bile canalicular secretion as well as lipid droplet formation, thereby increasing HLCs' similarity to PHH. The undesired non-liver gene expression was reproducibly decreased, although only by a moderate degree., Conclusion: In contrast to physiological hepatocyte precursor cells and mature hepatocytes, HLC co-express liver and hybrid genes in the same cell. Targeted modification of the FXR gene regulatory network improves their differentiation by suppressing intestinal traits whilst inducing hepatocyte features., Lay Summary: Generation of human hepatocytes from stem cells represents an active research field but its success is hampered by the fact that the stem cell-derived 'hepatocytes' still show major differences to hepatocytes obtained from a liver. Here, we identified an important reason for the difference, specifically that the stem cell-derived 'hepatocyte' represents a hybrid cell with features of hepatocytes and intestinal cells. We show that a specific protein (FXR) suppresses intestinal and induces liver features, thus bringing the stem cell-derived cells closer to hepatocytes derived from human livers., Competing Interests: Conflict of interest Patricio Godoy is also affiliated with F. Hoffmann-La Roche Ltd (Roche Innovation Center Basel, Basel, Switzerland), Barbara Küppers-Munther is affiliated with Takara Bio Europe AB (former Cellartis AB) (Arvid Wallgrens Backe 20, 41346 Gothenburg, Sweden). Please refer to the accompanying ICMJE disclosure forms for further details., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Human-relevant tests to predict developmental toxicity are urgently needed. A currently intensively studied approach makes use of differentiating human stem cells to measure chemically-induced deviations of the normal developmental program, as in a recent study based on cardiac differentiation (UKK2). Here, we (i) tested the performance of an assay modeling neuroepithelial differentiation (UKN1), and (ii) explored the benefit of combining assays (UKN1 and UKK2) that model different germ layers. Substance-induced cytotoxicity and genome-wide expression profiles of 23 teratogens and 16 non-teratogens at human-relevant concentrations were generated and used for statistical classification, resulting in accuracies of the UKN1 assay of 87-90%. A comparison to the UKK2 assay (accuracies of 90-92%) showed, in general, a high congruence in compound classification that may be explained by the fact that there was a high overlap of signaling pathways. Finally, the combination of both assays improved the prediction compared to each test alone, and reached accuracies of 92-95%. Although some compounds were misclassified by the individual tests, we conclude that UKN1 and UKK2 can be used for a reliable detection of teratogens in vitro, and that a combined analysis of tests that differentiate hiPSCs into different germ layers and cell types can even further improve the prediction of developmental toxicants.

In bottom-up proteomics, proteins are enzymatically digested into peptides before measurement with mass spectrometry. The relationship between proteins and their corresponding peptides can be represented by bipartite graphs. We conduct a comprehensive analysis of bipartite graphs using quantified peptides from measured data sets as well as theoretical peptides from an in silico digestion of the corresponding complete taxonomic protein sequence databases. The aim of this study is to characterize and structure the different types of graphs that occur and to compare them between data sets. We observed a large influence of the accepted minimum peptide length during in silico digestion. When changing from theoretical peptides to measured ones, the graph structures are subject to two opposite effects. On the one hand, the graphs based on measured peptides are on average smaller and less complex compared to graphs using theoretical peptides. On the other hand, the proportion of protein nodes without unique peptides, which are a complicated case for protein inference and quantification, is considerably larger for measured data. Additionally, the proportion of graphs containing at least one protein node without unique peptides rises when going from database to quantitative level. The fraction of shared peptides and proteins without unique peptides as well as the complexity and size of the graphs highly depends on the data set and organism. Large differences between the structures of bipartite peptide-protein graphs have been observed between database and quantitative level as well as between analyzed species. In the analyzed measured data sets, the proportion of protein nodes without unique peptides ranged from 6.4% to 55.0%. This highlights the need for novel methods that can quantify proteins without unique peptides. The knowledge about the structure of the bipartite peptide-protein graphs gained in this study will be useful for the development of such algorithms., Competing Interests: The authors have declared that no competing interests exist.

The accumulation of lipid droplets in hepatocytes is a key feature of drug-induced liver injury (DILI) and can be induced by a subset of hepatotoxic compounds. In the present study, we optimized and evaluated an in vitro technique based on the fluorescent dye Nile Red, further named Nile Red assay to quantify lipid droplets induced by the exposure to chemicals. The Nile Red assay and a cytotoxicity test (CTB assay) were then performed on cells exposed concentration-dependently to 60 different compounds. Of these, 31 were known to induce hepatotoxicity in humans, and 13 were reported to also cause steatosis. In order to compare in vivo relevant blood concentrations, pharmacokinetic models were established for all compounds to simulate the maximal blood concentrations (C max ) at therapeutic doses. The results showed that several hepatotoxic compounds induced an increase in lipid droplets at sub-cytotoxic concentrations. To compare how well (1) the cytotoxicity test alone, (2) the Nile Red assay alone, and (3) the combination of the cytotoxicity test and the Nile Red assay (based on the lower EC 10 of both assays) allow the differentiation between hepatotoxic and non-hepatotoxic compounds, a previously established performance metric, the Toxicity Separation Index (TSI) was calculated. In addition, the Toxicity Estimation Index (TEI) was calculated to determine how well blood concentrations that cause an increased DILI risk can be estimated for hepatotoxic compounds. Our findings indicate that the combination of both assays improved the TSI and TEI compared to each assay alone. In conclusion, the study demonstrates that inclusion of the Nile Red assay into in vitro test batteries may improve the prediction of DILI compounds., (Copyright © 2022. Published by Elsevier B.V.)

Background: Recently, novel antibody--drug conjugates (ADCs) showed clinical activity in a subset of advanced human epidermal growth factor receptor 2 (HER2)-negative patients. We investigated the prognostic significance of HER2-low and HER2-zero tumours., Patients and Methods: The retrospective cohort study included 410 consecutive node-negative breast cancer patients without adjuvant systemic therapy treated between 1985 and 2000 (median follow-up: 16.73 [IQR 8.58-23.45] years). 351 (85.6%) were HER-2 negative and subdivided into HER2-zero (immunohistochemistry [IHC] score 0) and HER2-low (IHC score 1+ or 2+/in situ hybridisation [ISH]-negative). HER2 gene expression was available in 170 (48.4%) patients. Differences in HER2 status for immunohistochemistry, gene expression and clinico-pathologic parameters were assessed using Fisher's exact test, Pearson's correlation and Mann-Whitney test. Prognosis was investigated using the Kaplan-Meier method and Cox regression analyses., Results: Of the 351 HER2-negative patients, 198 (56.4%) had HER2-low tumours and 153 (43.6%) were HER2-zero. Significant differences between HER2-zero and HER2-low tumours were found in histologic grading (P = 0.001), Ki-67 (P = 0.013) and HER2 gene expression (P = 0.002). HER2-low patients had significantly longer disease-free survival (DFS) (15-year rate: 67.5% [95% CI 61.0-74.7] vs. 47.3% [95% CI 39.9-56.1], P < 0.001) and overall survival (OS) (15-year rate: 75.4% [95% CI 69.4-81.9] vs. 66.8% [95% CI 59.5-74.9], P = 0.009). The OS difference was observed in hormone receptor (HR)-positive (P = 0.039) but not HR-negative (P = 0.086) tumours. The results of multivariable analyses confirmed the independent prognostic significance of HER2 status (DFS: HR, 0.546; 95% CI, 0.402-0.743; P < 0.001; OS: HR, 0.653; 95% CI, 0.458-0.932; P = 0.019)., Conclusion: HER2-low patients had a better survival than HER2-zero patients., Competing Interests: Conflict of interest statement The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: K. Almstedt received speaker honoraria from Roche Pharma AG, Pfizer Pharma GmbH and AstraZeneca. A.-S. Heimes received speaker honoraria from Pfizer Pharma GmbH and honoraria from Medupdate GmbH. M. J. Battista received honoraria and expenses from Astra Zeneca, Clovis Oncology, GSK, MSD, Pharma Mar, Roche and Tesaro Bio Germany GmbH. He is consultant to Eisai, GSK, MSD, Pharma Mar, Roche Pharma AG and Tesaro Bio Germany GmbH. He received funded research from Astra Zeneca, Clovis Oncology, MSD and Novartis. S. Krajnak received speaker honoraria from Roche Pharma AG and Novartis Pharma GmbH Germany, research funding from Novartis Pharma GmbH Germany and travel reimbursement from PharmaMar and Novartis Pharma GmbH Germany. M. Gehrmann is an employee of Bayer AG A. Hasenburg received honoraria from AstraZeneca, Celegen, MedConcept Gm, Med update GmbH, Medicultus, Pfizer, Promedicis GmbH, Pierre Fabre, Softconsult, Roche Pharma AG, Streamedup!GmbH and Tesaro Bio Germany GmbH. She is a member of the advisory board of PharmaMar, Promedicis GmbH, Pierre Fabre Pharma GmbH, Roche Pharma AG and Tesaro Bio Germany GmbH. She received research funding from Celgene. M. Schmidt reports personal fees from AstraZeneca, BioNTech, Daiichi Sankyo, Eisai, Lilly, MSD, Novartis, Pantarhei Bioscience, Pfizer, Roche, and SeaGen outside the submitted work. Institutional research funding from AstraZeneca, BioNTech, Eisai, Genentech, German Breast Group, Novartis, Palleos, Pantarhei Bioscience, Pierre Fabre, and SeaGen. In addition, Marcus Schmidt has a patent for EP 2390370 B1 issued and a patent for EP 2951317 B1 issued. All other authors (A. Lebrecht, K. Stewen, J.-G. Hengstler, F. Kappenberg, J. Rahnenführer, H-A. Lehr, W. Brenner, W. Weikel) declare that they have no conflict of interest., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background & Aims: Acetaminophen (APAP) overdose remains a frequent cause of acute liver failure, which is generally accompanied by increased levels of serum bile acids (BAs). However, the pathophysiological role of BAs remains elusive. Herein, we investigated the role of BAs in APAP-induced hepatotoxicity., Methods: We performed intravital imaging to investigate BA transport in mice, quantified endogenous BA concentrations in the serum of mice and patients with APAP overdose, analyzed liver tissue and bile by mass spectrometry and MALDI-mass spectrometry imaging, assessed the integrity of the blood-bile barrier and the role of oxidative stress by immunostaining of tight junction proteins and intravital imaging of fluorescent markers, identified the intracellular cytotoxic concentrations of BAs, and performed interventions to block BA uptake from blood into hepatocytes., Results: Prior to the onset of cell death, APAP overdose causes massive oxidative stress in the pericentral lobular zone, which coincided with a breach of the blood-bile barrier. Consequently, BAs leak from the bile canaliculi into the sinusoidal blood, which is then followed by their uptake into hepatocytes via the basolateral membrane, their secretion into canaliculi and repeated cycling. This, what we termed 'futile cycling' of BAs, led to increased intracellular BA concentrations that were high enough to cause hepatocyte death. Importantly, however, the interruption of BA re-uptake by pharmacological NTCP blockage using Myrcludex B and Oatp knockout strongly reduced APAP-induced hepatotoxicity., Conclusions: APAP overdose induces a breach of the blood-bile barrier which leads to futile BA cycling that causes hepatocyte death. Prevention of BA cycling may represent a therapeutic option after APAP intoxication., Lay Summary: Only one drug, N-acetylcysteine, is approved for the treatment of acetaminophen overdose and it is only effective when given within ∼8 hours after ingestion. We identified a mechanism by which acetaminophen overdose causes an increase in bile acid concentrations (to above toxic thresholds) in hepatocytes. Blocking this mechanism prevented acetaminophen-induced hepatotoxicity in mice and evidence from patients suggests that this therapy may be effective for longer periods after ingestion compared to N-acetylcysteine., Competing Interests: Conflict of interest S.U. is holder and inventor on patents protecting Myrcludex B (Hepcludex/bulevirtide). All other authors declare that they have no conflict of interest. Please refer to the accompanying ICMJE disclosure forms for further details., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Aims/hypothesis: It was shown that maternal endothelial nitric oxide synthase (eNOS) deficiency causes fatty liver disease and numerically lower fasting glucose in female wild-type offspring, suggesting that parental genetic variants may influence the offspring's phenotype via epigenetic modifications in the offspring despite the absence of a primary genetic defect. The aim of the current study was to analyse whether paternal eNOS deficiency may cause the same phenotype as seen with maternal eNOS deficiency., Methods: Heterozygous (+/-) male eNOS (Nos3) knockout mice or wild-type male mice were bred with female wild-type mice. The phenotype of wild-type offspring of heterozygous male eNOS knockout mice was compared with offspring from wild-type parents., Results: Global sperm DNA methylation decreased and sperm microRNA pattern altered substantially. Fasting glucose and liver glycogen storage were increased when analysing wild-type male and female offspring of +/- eNOS fathers. Wild-type male but not female offspring of +/- eNOS fathers had increased fasting insulin and increased insulin after glucose load. Analysing candidate genes for liver fat and carbohydrate metabolism revealed that the expression of genes encoding glucocorticoid receptor (Gr; also known as Nr3c1) and peroxisome proliferator-activated receptor gamma coactivator 1-alpha (Pgc1a; also known as Ppargc1a) was increased while DNA methylation of Gr exon 1A and Pgc1a promoter was decreased in the liver of male wild-type offspring of +/- eNOS fathers. The endocrine pancreas in wild-type offspring was not affected., Conclusions/interpretation: Our study suggests that paternal genetic defects such as eNOS deficiency may alter the epigenome of the sperm without transmission of the paternal genetic defect itself. In later life wild-type male offspring of +/- eNOS fathers developed increased fasting insulin and increased insulin after glucose load. These effects are associated with increased Gr and Pgc1a gene expression due to altered methylation of these genes., (© 2022. The Author(s).)

We propose to use Bayesian optimization (BO) to improve the efficiency of the design selection process in clinical trials. BO is a method to optimize expensive black-box functions, by using a regression as a surrogate to guide the search. In clinical trials, planning test procedures and sample sizes is a crucial task. A common goal is to maximize the test power, given a set of treatments, corresponding effect sizes, and a total number of samples. From a wide range of possible designs, we aim to select the best one in a short time to allow quick decisions. The standard approach to simulate the power for each single design can become too time consuming. When the number of possible designs becomes very large, either large computational resources are required or an exhaustive exploration of all possible designs takes too long. Here, we propose to use BO to quickly find a clinical trial design with high power from a large number of candidate designs. We demonstrate the effectiveness of our approach by optimizing the power of adaptive seamless designs for different sets of treatment effect sizes. Comparing BO with an exhaustive evaluation of all candidate designs shows that BO finds competitive designs in a fraction of the time., (© 2022 The Authors. Biometrical Journal published by Wiley-VCH GmbH.)

Bile acids (BA) are known to influence the susceptibility of hepatocytes to chemicals. We investigated the cytotoxicity of 18 compounds with known hepatotoxicity status and pharmacokinetics in cultivated primary human hepatocytes with and without the addition of a BA mix to the cell culture medium. This BA mix consisted of physiological ratios of the most abundant human BA at a cholestatic sum concentration of 0.5 mM, which corresponds to 50% of the EC 10 (cytotoxicity) of the mix. The BA mix decreased the EC 10 of 7 compounds by a factor greater than 1.5, but also increased the EC 10 of 5 compounds. The compounds with increased susceptibility include the known hepatotoxicants and BSEP/MRP2 inhibitors rifampicin, ketoconazole, atorvastatin, and cyclosporin A. However, the cytotoxicity of some non-hepatotoxic compounds was also enhanced, among them glucose, which is not known to be an inhibitor of canalicular bile acid export. A recently established technique to quantify how well hepatotoxic and non-hepatotoxic compounds are separated by an in vitro test indicated that the addition of the BA mix did not improve separation. In conclusion, the addition of BA to cultivated hepatocytes leads to a complex situation with increased and decreased susceptibilities depending on the specific compound., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Despite the progress made in developmental toxicology, there is a great need for in vitro tests that identify developmental toxicants in relation to human oral doses and blood concentrations. In the present study, we established the hiPSC-based UKK2 in vitro test and analyzed genome-wide expression profiles of 23 known teratogens and 16 non-teratogens. Compounds were analyzed at the maximal plasma concentration ( C max ) and at 20-fold C max for a 24 h incubation period in three independent experiments. Based on the 1000 probe sets with the highest variance and including information on cytotoxicity, penalized logistic regression with leave-one-out cross-validation was used to classify the compounds as test-positive or test-negative, reaching an area under the curve (AUC), accuracy, sensitivity, and specificity of 0.96, 0.92, 0.96, and 0.88, respectively. Omitting the cytotoxicity information reduced the test performance to an AUC of 0.94, an accuracy of 0.79, and a sensitivity of 0.74. A second method, which used the number of significantly deregulated probe sets to classify the compounds, resulted in a specificity of 1; however, the AUC (0.90), accuracy (0.90), and sensitivity (0.83) were inferior compared to those of the logistic regression-based procedure. Finally, no increased performance was achieved when the high test concentrations (20-fold C max ) were used, in comparison to testing within the realistic clinical range (1-fold C max ). In conclusion, although further optimization is required, for example, by including additional readouts and cell systems that model different developmental processes, the UKK2-test in its present form can support the early discovery-phase detection of human developmental toxicants.

Feature selection is crucial for the analysis of high-dimensional data, but benchmark studies for data with a survival outcome are rare. We compare 14 filter methods for feature selection based on 11 high-dimensional gene expression survival data sets. The aim is to provide guidance on the choice of filter methods for other researchers and practitioners. We analyze the accuracy of predictive models that employ the features selected by the filter methods. Also, we consider the run time, the number of selected features for fitting models with high predictive accuracy as well as the feature selection stability. We conclude that the simple variance filter outperforms all other considered filter methods. This filter selects the features with the largest variance and does not take into account the survival outcome. Also, we identify the correlation-adjusted regression scores filter as a more elaborate alternative that allows fitting models with similar predictive accuracy. Additionally, we investigate the filter methods based on feature rankings, finding groups of similar filters., (© The Author(s) 2021. Published by Oxford University Press.)

An in vitro/in silico method that determines the risk of human drug induced liver injury in relation to oral doses and blood concentrations of drugs was recently introduced. This method utilizes information on the maximal blood concentration (C max ) for a specific dose of a test compound, which can be estimated using physiologically-based pharmacokinetic modelling, and a cytotoxicity test in cultured human hepatocytes. In the present study, we analyzed if the addition of an assay that measures the inhibition of bile acid export carriers, like BSEP and/or MRP2, to the existing method improves the differentiation of hepatotoxic and non-hepatotoxic compounds. Therefore, an export assay for 5-chloromethylfluorescein diacetate (CMFDA) was established. We tested 36 compounds in a concentration-dependent manner for which the risk of hepatotoxicity for specific oral doses and the capacity to inhibit hepatocyte export carriers are known. Compared to the CTB cytotoxicity test, substantially lower EC 10 values were obtained using the CMFDA assay for several known BSEP and/or MRP2 inhibitors. To quantify if the addition of the CMFDA assay to our test system improves the overall separation of hepatotoxic from non-hepatotoxic compounds, the toxicity separation index (TSI) was calculated. We obtained a better TSI using the lower alert concentration from either the CMFDA or the CTB test (TSI: 0.886) compared to considering the CTB test alone (TSI: 0.775). In conclusion, the data show that integration of the CMFDA assay with an in vitro test battery improves the differentiation of hepatotoxic and non-hepatotoxic compounds in a set of compounds that includes bile acid export carrier inhibitors., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)