Adjustment for day-to-day variability in the estimation of effective concentrations for the assessment of mixture toxicity - Franziska Kappenberg; Tim Brecklinghaus; Leonie Schürmeyer; Wiebke Albrecht; Kirsten Schorning; Jan G. Hengstler; Jörg Rahnenführer (Archives of Toxicology, 2025)
Humans are exposed to a multitude of substances at the same time; thus, toxicity assessment for mixtures of several substances and not only for the individual substances is critical. An important challenge in testing the mixture effects of many substances in vitro is the variability between individual experiments, also named ‘day-to-day variability’. Two steps, each consisting of independent experiments, are needed when mixtures are tested. In the first experimental step, for each substance, the concentration–response relationship is usually measured with 6–10 concentrations for at least 3 experiments, and EC 20 values, i.e., the concentration where a fitted curve intersects with a viability value of 80%, are determined. These values are used as reference values and in the second experimental step, the mixture of several substances is based on these individual EC 20 values. Since the mixture experiments are new, independent experiments, typically conducted on different days, day-to-day variability can lead to deviations in the cytotoxicity at the reference EC 20 concentration, i.e., to higher or lower observed viabilities. In this work, a procedure is proposed, how a single concentration per substance tested in the same experiment as the mixture can be used to reliably adjust for day-to-day variability. In the here-established procedure, a specific additive model denoted as ‘budget approach’ is introduced as a reference model to explore potential positive or negative interaction effects between substances.

Design of optimal concentrations for in vitro cytotoxicity experiments - Leonie Schürmeyer; Chen Peng; Wiebke Albrecht; Tim Brecklinghaus; Pauline Baur; Jan G. Hengstler; Kirsten Schorning (Archives of Toxicology, 2025)
Concentration-dependent cytotoxicity experiments are frequently used in toxicology. Although it has been reported that an adequate choice of concentrations improves the quality of the statistical inference substantially, a recent literature review of three major toxicological journals has shown that the corresponding methods are rarely used in toxicological practice. In this study the performance of different sets of concentrations, also called designs, are analyzed, while the overall goal is to promote the advantages of optimal design procedures and to present a user-friendly guideline for planning new cytotoxicity concentration-response experiments. We compare the frequently used log-equidistant design to a Bayesian design, which is constructed by methods of optimum design theory. Using both a dense data set of concentration-cytotoxicity data of valproic acid (VPA) and regular assay data of 104 substances, the performance of the different designs is analyzed in two scenarios, where detailed previous knowledge on VPA is available or not. The results show that it is critical to apply a specific design strategy to determine optimal concentrations for cytotoxicity testing. In particular, the Bayesian design technique with and without incorporating pre-existing knowledge of a specific test substance resulted in a more precise statistical inference than the other used designs. Finally, we present a guideline for upcoming experiments and an accessible user-friendly Shiny app (see http://shiny.statistik.tu-dortmund.de:8080/app/occe ).

Guidance for statistical design and analysis of toxicological dose–response experiments, based on a comprehensive literature review - Franziska Kappenberg; Julia C. Duda; Leonie Schürmeyer; Onur Gül; Tim Brecklinghaus; Jan G. Hengstler; Kirsten Schorning; Jörg Rahnenführer (Archives of Toxicology, 2023)
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.

Comparing in vitro human liver models to in vivo human liver using RNA-Seq - Rajinder Gupta; Yannick Schrooders; Duncan Hauser; Marcel van Herwijnen; Wiebke Albrecht; Bas ter Braak; Tim Brecklinghaus; Jose V. Castell; Leroy Elenschneider; Sylvia Escher; Patrick Guye; Jan G. Hengstler; et al. (Archives of Toxicology, 2021)
The liver plays an important role in xenobiotic metabolism and represents a primary target for toxic substances. Many different in vitro cell models have been developed in the past decades. In this study, we used RNA-sequencing (RNA-Seq) to analyze the following human in vitro liver cell models in comparison to human liver tissue: cancer-derived cell lines (HepG2, HepaRG 3D), induced pluripotent stem cell-derived hepatocyte-like cells (iPSC-HLCs), cancerous human liver-derived assays (hPCLiS, human precision cut liver slices), non-cancerous human liver-derived assays (PHH, primary human hepatocytes) and 3D liver microtissues. First, using CellNet, we analyzed whether these liver in vitro cell models were indeed classified as liver, based on their baseline expression profile and gene regulatory networks (GRN). More comprehensive analyses using non-differentially expressed genes (non-DEGs) and differential transcript usage (DTU) were applied to assess the coverage for important liver pathways. Through different analyses, we noticed that 3D liver microtissues exhibited a high similarity with in vivo liver, in terms of CellNet (C/T score: 0.98), non-DEGs (10,363) and pathway coverage (highest for 19 out of 20 liver specific pathways shown) at the beginning of the incubation period (0 h) followed by a decrease during long-term incubation for 168 and 336 h. PHH also showed a high degree of similarity with human liver tissue and allowed stable conditions for a short-term cultivation period of 24 h. Using the same metrics, HepG2 cells illustrated the lowest similarity (C/T: 0.51, non-DEGs: 5623, and pathways coverage: least for 7 out of 20) with human liver tissue. The HepG2 are widely used in hepatotoxicity studies, however, due to their lower similarity, they should be used with caution. HepaRG models, iPSC-HLCs, and hPCLiS ranged clearly behind microtissues and PHH but showed higher similarity to human liver tissue than HepG2 cells. In conclusion, this study offers a resource of RNA-Seq data of several biological replicates of human liver cell models in vitro compared to human liver tissue.

Handling deviating control values in concentration-response curves - Franziska Kappenberg; Tim Brecklinghaus; Wiebke Albrecht; Jonathan Blum; Carola van der Wurp; Marcel Leist; Jan G. Hengstler; Jörg Rahnenführer (Archives of Toxicology, 2020)
In cell biology, pharmacology and toxicology dose-response and concentration-response curves are frequently fitted to data with statistical methods. Such fits are used to derive quantitative measures (e.g. EC $$_{20}$$ 20 values) describing the relationship between the concentration of a compound or the strength of an intervention applied to cells and its effect on viability or function of these cells. Often, a reference, called negative control (or solvent control), is used to normalize the data. The negative control data sometimes deviate from the values measured for low (ineffective) test compound concentrations. In such cases, normalization of the data with respect to control values leads to biased estimates of the parameters of the concentration-response curve. Low quality estimates of effective concentrations can be the consequence. In a literature study, we found that this problem occurs in a large percentage of toxicological publications. We propose different strategies to tackle the problem, including complete omission of the controls. Data from a controlled simulation study indicate the best-suited problem solution for different data structure scenarios. This was further exemplified by a real concentration-response study. We provide the following recommendations how to handle deviating controls: (1) The log-logistic 4pLL model is a good default option. (2) When there are at least two concentrations in the no-effect range, low variances of the replicate measurements, and deviating controls, control values should be omitted before fitting the model. (3) When data are missing in the no-effect range, the Brain-Cousens model sometimes leads to better results than the default model.

Roadmap for the development of alternative test methods - Tim Brecklinghaus (Archives of Toxicology, 2020)

Role of autophagy in drug induced liver injury - Tim Brecklinghaus (Archives of Toxicology, 2020)

The EU-ToxRisk method documentation, data processing and chemical testing pipeline for the regulatory use of new approach methods - Alice Krebs; Barbara M. A. van Vugt-Lussenburg; Tanja Waldmann; Wiebke Albrecht; Jan Boei; Bas ter Braak; Maja Brajnik; Thomas Braunbeck; Tim Brecklinghaus; Francois Busquet; Andras Dinnyes; Joh Dokler; et al. (Archives of Toxicology, 2020)
Hazard assessment, based on new approach methods (NAM), requires the use of batteries of assays, where individual tests may be contributed by different laboratories. A unified strategy for such collaborative testing is presented. It details all procedures required to allow test information to be usable for integrated hazard assessment, strategic project decisions and/or for regulatory purposes. The EU-ToxRisk project developed a strategy to provide regulatorily valid data, and exemplified this using a panel of > 20 assays (with > 50 individual endpoints), each exposed to 19 well-known test compounds (e.g. rotenone, colchicine, mercury, paracetamol, rifampicine, paraquat, taxol). Examples of strategy implementation are provided for all aspects required to ensure data validity: (i) documentation of test methods in a publicly accessible database; (ii) deposition of standard operating procedures (SOP) at the European Union DB-ALM repository; (iii) test readiness scoring accoding to defined criteria; (iv) disclosure of the pipeline for data processing; (v) link of uncertainty measures and metadata to the data; (vi) definition of test chemicals, their handling and their behavior in test media; (vii) specification of the test purpose and overall evaluation plans. Moreover, data generation was exemplified by providing results from 25 reporter assays. A complete evaluation of the entire test battery will be described elsewhere. A major learning from the retrospective analysis of this large testing project was the need for thorough definitions of the above strategy aspects, ideally in form of a study pre-registration, to allow adequate interpretation of the data and to ensure overall scientific/toxicological validity.

Highlight report: mechanisms of nephrotoxicity and available in vitro systems - Tim Brecklinghaus (Archives of Toxicology, 2020)

Prediction of human drug-induced liver injury (DILI) in relation to oral doses and blood concentrations - Wiebke Albrecht; Franziska Kappenberg; Tim Brecklinghaus; Regina Stoeber; Rosemarie Marchan; Mian Zhang; Kristina Ebbert; Hendrik Kirschner; Marianna Grinberg; Marcel Leist; Wolfgang Moritz; Cristina Cadenas; et al. (Archives of Toxicology, 2019)

Relevance of the incubation period in cytotoxicity testing with primary human hepatocytes - Xiaolong Gu; Wiebke Albrecht; Karolina Edlund; Franziska Kappenberg; Jörg Rahnenführer; Marcel Leist; Wolfgang Moritz; Patricio Godoy; Cristina Cadenas; Rosemarie Marchan; Tim Brecklinghaus; Laia Tolosa Pardo; et al. (Archives of Toxicology, 2018)