Template for the description of cell-based toxicological test methods to allow evaluation and regulatory use of the data

ALTEX 36(4), 2019 682 Received September 27, 2019; © The Authors, 2019. ALTEX 36(4), 682-699. doi:10.14573/altex.1909271 Correspondence: Marcel Leist, PhD In vitro Toxicology and Biomedicine, Dept inaugurated by the Doerenkamp-Zbinden foundation at the University of Konstanz, Universitaetsstr. 10, 78464 Konstanz, Germany ([email protected]) Template for the Description of Cell-Based Toxicological Test Methods to Allow Evaluation and Regulatory Use of the Data Alice Krebs1,2, Tanja Waldmann1, Martin F. Wilks3, Barbara M. A. van Vugt-Lussenburg4, Bart van der Burg4, Andrea Terron5, Thomas Steger-Hartmann6, Joelle Ruegg7, Costanza Rovida8, Emma Pedersen9, Giorgia Pallocca1,8, Mirjam Luijten10, Sofia B. Leite11, Stefan Kustermann12, Hennicke Kamp14, Julia Hoeng14, Philip Hewitt15, Matthias Herzler16, Jan G. Hengstler17, Tuula Heinonen18, Thomas Hartung8,19, Barry Hardy20, Florian Gantner21, Ellen Fritsche22, Kristina Fant9, Janine Ezendam10, Thomas Exner20, Torsten Dunkern23, Daniel R. Dietrich24, Sandra Coecke11, Francois Busquet8,25, Albert Braeuning26, Olesja Bondarenko27, Susanne H. Bennekou28, Mario Beilmann29 and Marcel Leist1,2,8 1 In vitro Toxicology and Biomedicine, Dept inaugurated by the Doerenkamp-Zbinden Foundation, University of Konstanz, Konstanz, Germany; 2 Konstanz Research School Chemical Biology (KoRS-CB), University of Konstanz, Konstanz, Germany; 3 Swiss Centre for Applied Human Toxicology, University of Basel, Basel, Switzerland; 4 BioDetection Systems BV, Amsterdam, The Netherlands; 5 European Food Safety Authority, Parma, Italy; 6 Investigational Toxicology, Drug Discovery, Pharmaceuticals, Bayer AG, Wuppertal, Germany; 7 Department of Organismal Biology, Uppsala University, Uppsala, Sweden; 8 CAAT-Europe, University of Konstanz, Konstanz, Germany; 9 RISE Research Institutes of Sweden, Göteborg, Sweden; 10 Centre for Health Protection, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands; 11 European Commission, Joint Research Centre (JRC), Ispra, Italy; 12 F. Hoffmann – La Roche, Pharma Research and Early Development, Pharmaceutical Sciences – Roche Innovation Center, Basel, Switzerland; 13 Experimental Toxicology and Ecology, BASF SE, Ludwigshafen, Germany; 14 Philip Morris International R&D, Neuchâtel, Switzerland; 15 Non Clinical Safety, Merck KGaA, Darmstadt, Germany; 16 German Federal Institute for Risk Assessment, Dept. Chemical Safety, Berlin, Germany; 17 Leibniz Research Centre for Working Environment and Human Factors (IfADo), Technical University of Dortmund, Dortmund, Germany; 18 FICAM, Faculty of Medicine and Life Sciences, Tampere University, Tampere, Finland; 19 Johns Hopkins University, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA; 20 Edelweiss Connect GmbH, Technology Park Basel, Basel, Switzerland; 21 Translational Medicine & Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany; 22 IUF – Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany; 23 Grünenthal GmbH, Aachen, Germany; 24 Human and Environmental Toxicology, University of Konstanz, Konstanz, Germany; 25 ALTERTOX SPRL, Ixelles, Bruxelles, Belgium; 26 German Federal Institute for Risk Assessment, Dept. Food Safety, Berlin, Germany; 27 Laboratory of Environmental Toxicology, National Institute of Chemical Physics and Biophysics, Tallinn, Estonia; 28 The National Food Institute, Technical University of Denmark, Kgs. Lyngby, Denmark; 29 Boehringer Ingelheim Pharma GmbH & Co. KG, Nonclinical Drug Safety, Biberach, Germany Abstract Only few cell-based test methods are described by Organisation for Economic Co-operation and Development (OECD) test guidelines or other regulatory references (e.g., the European Pharmacopoeia). The majority of toxicity tests still falls into the category of non-guideline methods. Data from these tests may nevertheless be used to support regulatory decisions or to guide strategies to assess compounds (e.g., drugs, agrochemicals) during research and development if they fulfill basic requirements concerning their relevance, reproducibility and predictivity. Only a method description of sufficient clarity and detail allows interpretation and use of the data. To guide regulators faced with increasing amounts of data from non-guideline studies, the OECD formulated Guidance Document 211 (GD211) on method documentation This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is appropriately cited. Disclaimer: The opinions expressed in this document strictly represent those of the authors and do not (necessarily) represent official views of the institutions they are affiliated with. Krebs et al. ALTEX 36(4), 2019 683 continuous malformation index. Any combination of these independent elements results in a different overall test method. 2 Compliance issues with OECD Guidance Document 211 (GD211) The key literature on how to describe non-guideline methods is OECD Guidance Document 211 (GD211) (OECD, 2017). In line with reports by many others (Freedman et al., 2015; Vogt et al., 2016; Hair et al., 2019), our own survey of the scientific literature has indicated that method descriptions still show an enormous heterogeneity in quality, detail and scope. Moreover, the clarity of the information, as well as the format that is used to provide the information, varies widely. This makes the extraction of necessary information as well as the use and interpretation of data produced using the method difficult. We further found that the items required to be addressed in GD211 are understood and interpreted in different ways by the users, or sometimes not understood at all. Part of this might be attributable to the fact that GD211 looks at non-guideline methods from a regulatory perspective, i.e., it explicitly provides a format for reporting non-guideline methodology so that it can be used by regulatory toxicologists for the safety assessment of chemicals. It asks for information that is of specific value for regulators (e.g., information on validation, predictivity, standardization, applicability domain, etc.), but does not provide detailed background information on why regulators need such information and what they use it for. Experience has shown that the target audience, which includes method developers in academia or fundamental research, is not necessarily familiar with the underlying regulatory background. Another reason for apparent non-compliance is that GD211 is a brief and highly condensed document. It often covers several distinct features of tests in a single question. Test developers may not consider all the different aspects of such a complex question without more specific guidance, and thus some issues may be missed entirely. From the information recipient’s point-of-view, non-complicance is not the only problem. Also, finding and retrieving the information within a report prepared in a not fully-standardized format can be a difficult task. For instance, it may be a time-consuming task to find out whether some information is absent or whether it is only placed or mentioned in another context than usual. 1 Main test elements Test, assay, test system, test method… All these terms are found in the literature and in discussions, but they need some definition to allow for specification of their background and requirements. “Test” is the shortest term and thus a good place to start: a toxicological “test” is a procedure to determine, in a quantifiable manner (with respect to damage and dose/concentration), whether a substance may harm/incapacitate an organism, a cell, or an essential component thereof. The terms “assay” and “test method” are used interchangeably with “test”. A test has various elements that are independent of one another to a large degree (Schmidt et al., 2017). The five main elements are: the test purpose (see Section 4), the test system, the exposure scheme, the endpoint, and the prediction model. Thus, the “test system” is one element of the “test method” and must not be confused with it. As this often causes confusion among non-specialists, it deserves some further explanation. The in vivo test method for acute toxicity assessment is a good example to explain the problem. The overall test method is defined by a test guideline, e.g., OECD TG 423 (acute oral toxicity) (OECD, 2002). The test system is, e.g., mouse, fasted for > 3 h prior to dosing; the exposure scheme involves single dosing by gavage and continued observation for 14 days; the endpoint is the number/percentage of dead animals; and the prediction model converts the test data into toxicity classes defined by the United Nation’s Globally Harmonized System (GHS), e.g., category 2 (comprising compounds with an LD50 in the range of 5-50 mg/kg bodyweight). Clearly, the mouse is the test system and not the test method. Each element of the test method may be modified independently, e.g., changing from oral to dermal exposure; using time to death as endpoint; employing a binary prediction model (toxic/non-toxic) and using rats instead of mice. Any such change results in a new, different test method that may no longer be in accordance with the guideline method. The same principles apply to cell-based assays, i.e., in vitro methods or new approach methods (NAM). For example, the test system may be neurons in 2D culture or neural organoids or liver cells; the exposure scheme may be 24 h exposure of cells cultured in standard medium plus test chemical or 72 h exposure in a special medium, with or without re-addition of test chemical every 24 h; the prediction model may be binary (toxic/non-toxic) or it may use a composite measure of several endpoints to define a for the purpose of safety assessment. As GD211 is targeted mainly at regulators, it leaves scientists less familiar with regulation uncertain as to what level of detail is required and how individual questions should be answered. Moreover, little attention was given to the description of the test system (i.e., cell culture) and the steps leading to it being established in the guidance. To address these issues, an annotated toxicity test method template (ToxTemp) was developed (i) to fulfill all requirements of GD211, (ii) to guide the user concerning the types of answers and detail of information required, (iii) to include acceptance criteria for test elements, and (iv) to define the cells sufficiently and transparently. The fully annotated ToxTemp is provided here, together with reference to a database containing exemplary descriptions of more than 20 cell-based tests. Abbreviations AC, acceptance criteria; GD, guidance document; OECD, Organisation for Economic Co-operation and Development; SOP, standard operating procedure; TG, test guideline; ToxTemp, toxicological test methods template Krebs et al. ALTEX 36(4), 2019 684 veloped for regulatory purposes. In this domain, the element “test purpose” plays a special role: Beyond the primary test purpose (e.g., determination of cytotoxicity), the results of a test may also be used for a secondary, regulatory purpose (regulation), and even a tertiary purpose (e.g., modelling a potential hazard in the population). Consider, e.g., in vitro tests used to predict Globally Harmonized System (GHS) classifications (e.g., moderate or strong skin sensitizer or eye irritant) (secondary purpose), which in turn are used to model the potential chemical hazard to which workers or the general population may be exposed (tertiary purpose). Test developers might not be familiar with the limitations and requirements demanded of their test when it is to be used for secondary (or tertiary) purposes, and this may in the end lead to misinterpretation of results obtained from a test method used for purposes that it was not originally designed for. The problem is further complicated by the fact that an apparently simple regulatory statement (e.g., “The substance is a skin sensitizer.”) in reality represents the outcome of a highly complex decision system based on a regulatory framework that has evolved over several decades, with paradigms and implicit assumptions that are often not obvious to the outsider. To avoid such problems, communication between developers and regulatory recipients needs to be as transparent, comprehensive and precise as possible. Description of a test method, especially of the test purpose, in a way that allows such communication is essential to achieve this. 5 Distinctions between a test method description and the overall testing process Reproducible toxicological research necessitates the comprehensive description of the testing process. Good information on this can be found in the series of guidance on Good Laboratory Practice (GLP) (e.g., OECD, 2005) or in the OECD Guidance Document on Good In Vitro Methods Practice (GIVIMP) (OECD, 2018). Several helpful tools are available, e.g., from the Science in Risk Assessment and Policy (SciRAP 3 ) web resource, the DB-ALM methods summary (adapted from GD211), the EURLECVAM test submission template (used for structuring information for test validation), or the ALTEX BenchMarks series (Kisitu et al., 2019; Krebs et al., 2018). Several earlier EU-funded projects devoted considerable resources to harmonize test method descriptions (Kinsner-Ovaskainen et al., 2009; Rovida et al., 2014), and similar activities are taking place in the USA (Flood et al., 2017). The overall reporting of in vitro experiments (data and methods) has been addressed by an NC3Rs initiative (RIVER) (Prior et al., 2019), large stakeholder workshops organized by CAAT-Europe (Hartung et al., 2019), and on the regulatory level (OECD harmonized templates for data reporting (OHT); OHT 201Intermediate effects4 is especially relevant). 3 Annotations and guiding questions for a test method template The problems that test developers face when trying to comply with the questions raised in GD211 may best be illustrated using an analogy from an entirely different field. Let us assume that a police recruit is asked in a questionnaire to “indicate body measures”. Some would answer by giving only their height and weight. Others may include their shoe size and special measures for jackets and trousers. Few would give their head circumference (required for the uniform hat) and/or their glove size. Possibly even fewer would think to provide information on their sight, i.e., data essential for ordering sunglasses (e.g., whether they are myopic, distance between the eyes, etc.). In the course of the European research project EU-ToxRisk (Daneshian et al., 2016), compliance of project case study results with GD211 became an important issue. While trying to implement high-quality assay documentation within the project, we realized that hardly any of the senior scientists and none of the junior faculty fully understood the requirements of GD211. How are situations as exemplified above (police recruit or EU-ToxRisk examples) best avoided? How can it be ensured that all required information (i) is reported, (ii) is presented in a structured way so that the recipients can verify its completeness, and (iii) can be found easily? Two major strategies towards this goal are (a) to sub-divide complex questions into sets of more simple questions, each dealing with a single, defined issue, and (b) to explain the questions by adding additional notes, comments and guiding questions. These measures help to structure the answer and ensure that all relevant aspects are considered. Such guidance is provided by the test method template (ToxTemp) shown in a compact version as Box 1 and provided as a printable version including additional notes and examples in the supplementary file 1 . Tab. 1 gives a synoptic overview of all items/ questions of GD211 and of the respective counterparts in the ToxTemp (see also the supplementary file 1 for the detailed comparison). Moreover, an online methods database, based on the ToxTemp, is under construction 2 . Using Tab. 1 should enable anyone accustomed to the structure of GD211 to retrieve all relevant test method information from the database. 4 Understanding the test purpose Any test (toxicological or not) is developed to probe a test hypothesis (e.g., whether a substance is toxic or not). The test design will always reflect that purpose and test parameters will ideally be optimized in order to achieve maximum certainty about whether the hypothesis should be accepted or rejected. It is a basic scientific principle that test results should – within limits – only be used for the purpose they were designed for. This is not trivial for in vitro or new approach methods (NAM) de1 doi:10.14573/altex.1909271s 2 https://eu-toxrisk.douglasconnect.com/public/ 3 http://scirap.org/Page/Index/aa44f63a-ce5d-4f26-bac3-346c27b34eb0/reporting-checklist 4 http://www.oecd.org/ehs/templates/harmonised-templates-intermediate-effects.htm Krebs et al. ALTEX 36(4), 2019 685 Box. 1: Documentation of a test method and its readiness status, guided by a test method questionnaire A print version of the complete test method questionnaire including notes and examples can be found in the supplementary file1. 1. Overview 1.1 Descriptive full-text title Provide a descriptive title using normal language without technical terms or acronyms. 1.2 Abstract Please describe in no more than 200 words the following: Which toxicological target (organ, tissue, physiological/biochemical function, etc.) is modelled? (8.1) Which test system and readout(s) are used? (4.1; 5.2) Which biological process(es) (e.g. neurite outgrowth, differentiation) and/or toxicological events (e.g. oxidative stress, cell death) are modelled/reflected by your test method? (8.1) To which (human) adverse outcome(s) is your test method related or could be related? (8.1; 9.2; 9.3) Which hazard(s) do(es) your test method (potentially) predict? (8.1; 8.6) Does the test method capture an endpoint of current regulatory studies? (9.5) If the method has undergone some form of validation/evaluation, give its status. (9.4) 2. General information 2.1 Name of test method Provide the original/published name, as well as the potential tradename. 2.2 Version number and date of deposition Provide the original deposition date of first version and date of current version. 2.3 Summary of introduced changes in comparison to previous version(s) This only applies to updated versions. If this is the original version, state “original version”. 2.4 Assigned data base name Normal text names often do not uniquely define the method. Therefore, each method should be assigned a clearly and uniquely defined data base name. These are some example data base names generated in the EU-ToxRisk project: UKN1a_DART_NPC_Diff_6D_02 UKN1b_DART_NPC_Diff_4D_01 UKN2a_DART_NC_Migr_24h_04 The name is assembled (in more generic terms) from the following elements: Axa_B_C_D_E Axa: mandatory part of the identifier allowing unambiguous identification A: Abbreviation/acronym of the partner depositing the assay x: Consecutive number (referring to the partner’s assay number) a: Sub-specifier (for variants, i.e. very similar assays but e.g. different readout or medium); not mandatory, but ‘Axa’ must be specific (i.e. clearly identifying) for each assay variant. B: Indication of the main intended use (max. 5 letters), e.g. DART, Neuro, Liver, Lung, Renal, Redox, Stress... C: Specifier of test system, e.g. cell type such as NPC (neural precursor cells), NC (neural crest), Hep (liver cells), REN (kidney cells), PUL (lung cells) (max. 4 letters) D: Identification of test endpoint, e.g. Diff_6D = Differentiation for 6 days; exp_24 h = exposure for 24 hours; RNA_6h = transcriptome after 6 hours (use max. 15 signs altogether; if desired in 2-3 blocks), name (and acronym) of the project partner home organisation. E: version number. 2.5 Name and acronym of the test depositor Include affiliation. 2.6 Name and email of contact person Provide the details of the principal contact person. 2.7 Name of further persons involved For example, the principal investigator (PI) of the lab, the person who conducted the experiments, etc. 2.8Referencetoadditionalfilesofrelevance Supply number of supporting files. Describe supporting files (e.g. metadata files, instrument settings, calculation template, raw data file, etc.). 3. Description of general features of the test system source 3.1 Supply of source cells Describe briefly whether the cells are from a commercial supplier, continuously generated by cell culture, or obtained by isolation from human/animal tissue (or other). Krebs et al. ALTEX 36(4), 2019 686 3.2 Overview of cell source component(s) Give a brief overview of your biological source system, i.e. the source or starting cells that you use. Which cell type(s) are used or obtained (e.g. monoculture/co-culture, differentiation state, 2D/3D, etc.)? If relevant, give human donor specifications (e.g. sex, age, pool of 10 donors, from healthy tissue, etc.). 3.3Characterizationanddefinitionofsourcecells List quantitative and semi-quantitative features that define your cell source/starting cell population. For test methods that are based on differentiation, describe your initial cells, e.g. iPSC, proliferating SH-SY5Y; the differentiated cells are described in section 4. Define cell identity, e.g. by STR signature (where available), karyotype information, sex (where available and relevant), ATCC number, passage number, source (supplier), sub-line (where relevant), source of primary material, purity of the cells, etc. Describe defining biological features you have measured or that are FIRMLY established (use simple listing, limit to max. 0.5 pages), e.g. the cells express specific marker genes, have specific surface antigens, lack certain markers, have or lack a relevant metabolic or transporting capacity, have a doubling time of x hours, etc. Transgenic cell lines have particular requirements concerning the characterization of the genetic manipulation (type of transgene, type of vector, integration/deletion site(s), stability, etc.). Organoids and microphysiological systems (MPS) may need some special/additional considerations as detailed in Pamies et al. (2018) and Marx et al. (2016), e.g. ratio of cell types used, percent of normal cells in tumor spheroids created from resected tissue; derivation of cells for re-aggregating brain cultures. 3.4 Acceptance criteria for source cell population Describe the acceptance criteria (AC) for your initial cells (i.e. the quality criteria for your proliferating cell line, tissue for isolation, organism, etc.). Which specifications do you consider to describe the material, which quality control criteria have to be fulfilled (e.g. pathogen-free)? Which functional parameters (e.g. certain biological responses to reference substances) are important? For iPSC maintenance: How do you control pluripotency? How stable are your cells over several passages? Which passage(s) are valid? For primary cells: Show stability and identity of supply; demonstrate stability of function (e.g. xenobiotic metabolism). Quantitative definitions for AC should be given based on this defining information. Exclusion criteria (features to be absent) are also important. As in 3.3., special/additional requirements apply to genetically-modified cells and microphysiological systems. 3.5 Variability and troubleshooting of source cells Name known causes of variability of the initial cells/source cells. Indicate critical consumables or batch effects (e.g. relevance of the plate format and supplier, batch effects of fetal calf serum (FCS) or serum replacement, critical additives like type of trypsin, apo-transferrin vs. holo-transferrin, etc.). Indicate critical handling steps and influencing factors (e.g. special care needed in pipetting, steps that need to be performed quickly, cell density, washing procedures, etc.). As in 3.3., special/additional requirements apply to genetically-modified cells and microphysiological systems, e.g. dependence on matrix chemistry and geometry, dependence on microfluidics system, consideration of surface cells vs core cells, etc. Give recommendations to increase/ensure reproducibility and performance. 3.6Differentiationtowardsthefinaltestsystem Describe the principles of the selected differentiation protocol, including a scheme and graphical overview, indicating all phases, media, substrates, manipulation steps (medium change/re-plating, medium additives, etc.). Special/additional requirements apply to microphysiological systems and organoids: e.g. cell printing, self-aggregation/self-organisation process, interaction with the matrix, geometrical characterization (size/shape), etc. 3.7 Reference/link to maintenance culture protocol Provide the SOP of the general maintenance procedure as a database link. This should also include the following information: How are the cells maintained outside the experiment (basic cell propagation)? How pure is the cell population (average, e.g. 95% of iPSC cells Oct4-positive)? What are the quality control measures and acceptance criteria for each cell batch? Which number(s) passage(s) can be used in the test? Is Good Cell Culture Practice (GCCP) and/or Good In Vitro Method Practice (GIVIMP) followed? How long can same cell batches be used? How are frozen stocks and cell banks prepared? For primary cells: how are they obtained in general and what are they characterized for (and what are inclusion and exclusion criteria). 4. Definitionofthetestsystemasusedinthemethod 4.1 Principles of the culture protocol Describe the test system as it is used in the test. If the generation of the test system involves differentiation steps or complex technical manipulation (e.g. formation of microtissues), this is described in 3.6. Give details on the general features/principles of the culture protocol (collagen embedding, 3D structuring, addition of mitotic inhibitors, addition of particular hormones/growth factors, etc.) of the cells that are used for the test. What is the percentage of contaminating cells; in co-cultures what is the percentage of each subpopulation? Krebs et al. ALTEX 36(4), 2019 687 Are there subpopulations that are generally more sensitive to cytotoxicity than others, and could this influence viability measures? Is it known whether specific chemicals/chemical classes show differential cytotoxicity for the cell sub-populations used? 4.2 Acceptance criteria for assessing the test system at its start What are the endpoint(s) that you use to control that your culture(s) is/are as expected at the start of toxicity testing (e.g. gene expression, staining, morphology, responses to reference chemicals, etc.)? Describe the acceptance criteria for your test system, i.e. the quality criteria for your cells/tissues/organoids: Which endpoints do you consider to describe the cells or other source material, which parameters are important? Describe the (analytical) methods that you use to evaluate your culture (PCR, ATP measurement) and to measure the acceptance criteria (AC). Which values (e.g. degree of differentiation or cell density) need to be reached/should not be reached? Historical controls: How does your test system perform with regard to the acceptance criteria, e.g. when differentiation is performed 10 times, what is the average and variation of the values for the acceptance criteria parameters)? Indicate actions if the AC are not met. Examples: cell are > 90% viable, or > 98% of cells express marker x (e.g. AP-2), or > 80% of the cells attach, etc. 4.3 Acceptance criteria for the test system at the end of compound exposure Describe the acceptance criteria for your test system, i.e. the quality criteria for your cells/tissues/organoids: Which endpoints do you consider to describe the cells or other source material, which parameters are important? Which values (e.g. degree of differentiation or cell density) need to be reached/should not be reached? Historical controls: How does your test system perform with regard to the acceptance criteria, e.g. when differentiation is performed 10 times, what is the average and variation of the values for the acceptance criteria parameters)? Indicate actions if the AC are not met. Examples: Usual neurite length is 50 ±15 µm; experiments with average neurite length below 25 µm in the negative controls (NC) are discarded. Usual nestin induction is 200 ±40 fold, experiments with inductions below 80-fold for NC are discarded. 4.4 Variability of the test system and troubleshooting Give known causes of variability for final test system state. Indicate critical consumables or batch effects (e.g. plate format and supplier, batch effects of FCS or serum replacement, additives). Indicate critical handling steps, and/or influencing factors identified (e.g. special care needed in pipetting, steps that need to be performed quickly, cell density). Indicate positive and negative controls and their expected values, and accepted deviation within and between the test repeats. Give recommendations to increase/ensure reproducibility and performance. 4.5 Metabolic capacity of the test system What is known about endogenous metabolic capacity (CYP system (phase I); relevant conjugation reactions (phase II))? What is known about other pathways relevant to xenobiotic metabolism? What specific information is there on transporter activity? 4.6 Omics characterization of the test system Are there transcriptomics data or other omics data available that describe the test system (characterization of cells without compounds)? Briefly list and describe such data. Indicate the type of data available (e.g. RNASeq or proteomics data). Refer to data file, data base or publication. 4.7Featuresofthetestsystemthatreflectthein vivo tissue Give information on where the test system differs from the mimicked human tissue and which gaps of analogy need to be considered. 4.8 Commercial and intellectual property rights aspects of cells Are there elements of the test system that are protected by patents or any other means? 4.9 Reference/link to the culture protocol Fill only if section 3 has not been answered. Provide the SOP for the general maintenance procedure as a database link. This should also include the following information: How are the cells maintained outside the experiment (basic cell propagation)? How pure is the cell population (average, e.g. 95% of iPSC cells Oct4-positive)? What are the quality control measures and acceptance criteria for each cell batch? Which number(s) passage(s) can be used in the test? Is Good Cell Culture Practice (GCCP) and/or Good In Vitro Method Practice (GIVIMP) followed? How long can same cell batches be used? How are freezing stocks and cell banks prepared? For primary cells: How are they obtained in general and what are they characterized for (and what are inclusion and exclusion criteria). 5. Test method exposure scheme and endpoints 5.1 Exposure scheme for toxicity testing Provide an exposure scheme (graphically, show timelines, addition of medium supplements and compounds, sampling, etc.), within the context of the overall cell culture scheme (e.g. freshly re-plated cells or confluent cells at start, certain coatings, etc.). Krebs et al. ALTEX 36(4), 2019 688 Include medium changes, cell re-plating, whether compounds are re-added in cases of medium change, critical medium supplements, etc. 5.2 Endpoint(s) of the test method Define the specific endpoint(s) of the test system that you use for toxicity testing (e.g. cytotoxicity, cell migration, etc.). Indicate whether cytotoxicity is the primary endpoint. What are secondary/further endpoints? Also describe here potential reference/normalization endpoints (e.g. cytotoxicity, protein content, housekeeping gene expression) that are used for normalization of the primary endpoint. 5.3 Overview of analytical method(s) to assess test endpoint(s) Define and describe the principle(s) of the analytical methods used. Provide here a general overview of the method’s key steps (e.g. cells are fixed or not, homogenized sample or not, etc.), sufficient for reviewers/regulators to understand what was done, but not in all detail for direct repetition. If you have two or more endpoints (e.g. viability and neurite outgrowth), do you measure both in the same well, under same conditions in parallel, or independently of each other? For imaging endpoints: Explain in general how quantification algorithm or how semi-quantitative estimates are obtained and how many cells are imaged (roughly). 5.4 Technical details (of e.g. endpoint measurements) Provide information on machine settings, analytical standards, data processing and normalization procedures. For imaging endpoints: provide detailed algorithm. This information should also be covered in an SOP, preferably in DB-ALM format (see link in 6.6). 5.5Endpoint-specificcontrols/mechanisticcontrolcompounds(MCC) MCC are chemicals/manipulations that show biologically plausible changes of the endpoint. List such controls (up to 10), indicate why you consider them as MCC, and describe expected data on such controls. Highlight the compounds to be used for testing day-to-day test performance, i.e. for setting acceptance criteria (AC). If available, indicate MCC that each increase or decrease the activity of the relevant pathway. Do pathway inhibitions or activations correlate with the test method response? 5.6 Positive controls What chemicals/manipulations are used as positive controls? Describe the expected data on such controls (signal and its uncertainty)? How good are in vivo reference data on the positive controls? Are in vivo relevant threshold concentrations known? 5.7Negativeandunspecificcontrols What chemicals/manipulations are used as negative controls? Describe the expected data on such controls (signal and its uncertainty)? (Such data define the background noise of the test method) What is the rationale for the concentration setting of negative controls? Do you use unspecific controls? If yes, indicate the compounds and the respective rationale for their use and the concentration selection. 5.8 Features relevant for cytotoxicity testing Does the test system have a particular apoptosis sensitivity or resistance? Is cytotoxicity hard to capture for minor cellular subpopulations? In multicellular systems, which cell population is the most sensitive? Are specific markers known for each cell population? Are there issues with distinguishing slowed proliferation from cell death? For repeated/prolonged dosing: Is early death and compensatory growth considered? For very short-term endpoints (e.g. electrophysiology measured 30 min after toxicant exposure): Is a delayed measure of cytotoxicity provided? 5.9 Acceptance criteria for the test method Which rule do you apply to test whether a test run is within the normal performance frame? How do you document this decision? Indicate actions if the AC are not met. 5.10 Throughput estimate Indicate “real data points per month” (not per week/per quarter, etc.): count three working weeks per month. Each concentration is a data point. Necessary controls that are required for calibration and for acceptability criteria are NOT counted as data points. All technical replicates of one condition are counted as one single data point (see notes for explanation) Indicate possibility/extent of repeated measures (over time) from same dish. Explain your estimate. 6. Handling details of the test method 6.1 Preparation/addition of test compounds Give an overview of the range of volumes, particular lab ware/instruments for dispensing, temperature/lighting considerations, particular media/buffers for dilution, decision rules for the solvent, tests of solubility as stocks and in culture medium, etc. How are compound stocks prepared (fold concentration, verification, storage, etc.)? Krebs et al. ALTEX 36(4), 2019 689 How are dilutions prepared? What solvent is used? Is filtering used to obtain sterility? How does the final addition to the test system take place? Give details of addition of test compounds to test systems (e.g. in which compartment of compartmentalized cultures, in which volume, before after or during medium change, etc.). 6.2 Day-to-day documentation of test execution How are day-to-day procedures documented (type of ‘lab book’ organisation, templates)? Define lab-specific procedures used for each practical experiment on how to calculate test compound concentrations (and to document this). How are plate maps defined and reported? Detailed information should also be covered in an SOP, preferably in DB-ALM format (see link in 6.6). 6.3 Practical phase of test compound exposure How is the time plan of pipetting established, followed, and documented? How is adherence to plate maps during pipetting documented? What are the routine procedures to document intermediate steps with potential errors, mistakes and uncertainties? How are errors documented (e.g. pipetting twice in one well)? How are the plate wells used sequentially – following which pattern? Detailed information should also be included in an SOP, preferably in DB-ALM format (see link in 6.6). 6.4 Concentration settings How is the concentration range of test compounds defined (e.g. only single concentrations, always 1:10 serial dilutions or variable dilution factors, ten different concentrations, etc.)? Is there a rule for defining starting dilutions? For functional endpoints that may not provide full concentration-response, how is the test concentration defined? E.g. EC10 of viability data are usually tested for gene expression endpoints. Detailed information should also be included in an SOP, preferably in DB-ALM format (see link in 6.6). 6.5 Uncertainties and troubleshooting What types of compounds are problematic, e.g. interference with analytical endpoint, low solubility, precipitation of medium components, etc.? What experimental variables are hard to control (e.g. because they are fluorescent)? What are critical handling steps during the execution of the assay? Robustness issues, e.g. known variations of test performance due to operator training, season, use of certain consumable or unknown causes, etc. Describe known pitfalls (or potential operator mistakes). 6.6 Detailed protocol (SOP) Ideally the SOP follows the DB-ALM or a comparable format: https://ecvam-dbalm.jrc.ec.europa.eu/home/contribute Refer to additional file(s) (containing information covered in sections 3 and 4), containing all details and explanations. Has the SOP been deposited in an accessible data base? Has the SOP been reviewed externally and if yes, how? 6.7 Special instrumentation Does the method require specialized instrumentation that is not found in standard laboratories? Is there a need for custom-made instrumentation or material? Is there a need for equipment that is not commercially available (anymore)? 6.8 Possible variations Describe possible variations, modifications and extensions of the test method: a) other endpoints, b) other analytical methods for same endpoint, c) other exposure schemes (e.g. repeated exposure, prolonged exposure, etc.), d) experimental variations (e.g. use of a specific medium, presence of an inhibitor or substrate that affects test outcome, etc.) 6.9 Cross-reference to related test methods Indicate the names (and database names) of related tests and give a short description (including a brief comment on differences to the present method). If the test method has been used for high throughput transcriptomics or deep sequencing as alternative endpoint, this should be indicated. 7. Data management 7.1 Raw data format What is the data format? Raw data: give general explanation. Upload an exemplary file of raw data (e.g. Excel file as exported out of plate reader). Provide an example of processed data at a level suitable for general display and comparison of conditions and across experiments and methods. Krebs et al. ALTEX 36(4), 2019 690 If the file format is not proprietary or binary, include a template. This will help other users to provide their data in a similar way to the general data infrastructure. Example as used in EU-ToxRisk: Excel sheet with columns specifying line number, assay name, date of experiment, identifier for reference to partner lab book, compound, concentration (in: -log[M]), line number of corresponding control, number of replicates, endpoints, data of endpoint(s), etc. 7.2 Outliers How are outliers defined and handled? How are they documented? Provide the general frequency of outliers. 7.3 Raw data processing to summary data How are raw data processed to obtain summary data (e.g. EC50, BMC15, ratios, PoD, etc.) in your lab? Describe all processing steps from background correction (e.g. measurement of medium control) to normalization steps (e.g. if you relate treated samples to untreated controls). 7.4Curvefitting How are data normally handled to obtain the overall test result (e.g. concentration response fitting using model X, determination of EC50 by method Y, use of EC50 as final data)? How do you model your concentration response curve (e.g. LL.4 parameter fit) and which software do you use (e.g. GraphPad Prism, R, etc.)? Do you usually calculate an uncertainty measure of your summary data (e.g. a 95% confidence interval for the BMC or a BMCL), and with which software? Can you give uncertainty for non-cytotoxicity or no-effect? How do you handle non-monotonic curve shapes or other curve features that are hard to describe with the usual mathematical fit model? 7.5 Internal data storage How and how long are raw and other related data stored? What backup procedures are used (how frequently)? How are data versions identified? 7.6 Metadata How are metadata documented and stored (lab book, Excel files, left in machine, etc.)? How are they linked to raw data? What metadata are stored/should be stored? 7.7Metadatafileformat Give example of the metadata file (if available). If metadata or data format (see 7.1) are pre-defined in the project, state here “as pre-defined in project xxx” (e.g. EU-ToxRisk). 8. Prediction model and toxicological application 8.1Scientificprinciple,testpurposeandrelevance What is the scientific rationale to link test method data to a relevant in vivo adverse outcome? Which toxicological target (organ, tissue, physiological/biochemical function, etc.) is modelled? Which biological process(es) (e.g. neurite outgrowth, differentiation) are modelled/reflected by your test method? Which toxicological events (e.g. oxidative stress, cell death) are modelled/reflected by your test method? To which (human) adverse outcome(s) is your test method related? Which hazard(s) do(es) your test method (potentially) predict? 8.2 Prediction model Provide the statistics of your benchmark response (threshold and variance): (i) For dichotomized data, provide your prediction model. When do you consider the result as toxic or not toxic? (ii) For pseudo-dichotomized outcomes (two classes with borderline class in between): define borderline range. (iii) For multi-class or continuous outcomes: provide definitions and rationale. What is the rationale for your threshold? This can be on a mathematical (e.g. 3-fold standard deviation) or a biological basis (e.g. below 80% viability). Is there a toxicological rationale for the threshold settings and definitions of your prediction model? What are the limitations of your prediction model? What is a ‘hit’ if the test is used in screening mode (= hit definition, if different from above)? 8.3 Prediction model setup How was the prediction model set up (using which test set of chemicals to train the model; using probing with what kind of classifiers/ statistical approaches)? Has the prediction model been tested (what was the test set of chemicals)? List chemicals or give n, if n > 50. Is the process documented (publication)? Does the prediction model (PM) apply to changes to both sides of controls (up/down)? If the PM is one-sided (e.g. toxicants leading Krebs et al. ALTEX 36(4), 2019 697 genomic instability and gene expression profiling in different HeLa clones. Sci Rep 5, 15377. doi:10.1038/srep15377 Freedman, L. P., Cockburn, I. M. and Simcoe, T. S. (2015). The economics of reproducibility in preclinical research. PLoS Biol 13, e1002165. doi:10.1371/journal.pbio.1002165 Fritsche, E., Crofton, K. M., Hernandez, A. F. et al. (2017). OECD/ EFSA workshop on developmental neurotoxicity (DNT): The use of non-animal test methods for regulatory purposes. ALTEX 34, 311-315. doi:10.14573/altex.1701171 Gantner, F., Leist, M., Kusters, S. et al. (1996). T cell stimulus-induced crosstalk between lymphocytes and liver macrophages results in augmented cytokine release. Exp Cell Res 229, 137146. doi:10.1006/excr.1996.0351 Gerhardt, E., Kugler, S., Leist, M. et al. (2001). Cascade of caspase activation in potassium-deprived cerebellar granule neurons: Targets for treatment with peptide and protein inhibitors of apoptosis. Mol Cell Neurosci 17, 717-731. doi:10.1006/ mcne.2001.0962 Gignac, S. M., Steube, K., Schleithoff, L. et al. (1993). Multiparameter approach in the identification of cross-contaminated leukemia cell lines. Leuk Lymphoma 10, 359-368. doi:10.3109/10428199309148561 Gutbier, S., May, P., Berthelot, S. et al. (2018). Major changes of cell function and toxicant sensitivity in cultured cells undergoing mild, quasi-natural genetic drift. Arch Toxicol 92, 34873503. doi:10.1007/s00204-018-2326-5 Hair, K., Macleod, M. R., Sena, E. S. et al. (2019). A randomised controlled trial of an intervention to improve compliance with the ARRIVE guidelines (IICARus). Res Integr Peer Rev 4, 12. doi:10.1186/s41073-019-0069-3 Hansson, O., Castilho, R. F., Kaminski Schierle, G. S. et al. (2000). Additive effects of caspase inhibitor and lazaroid on the survival of transplanted rat and human embryonic dopamine neurons. Exp Neurol 164, 102-111. doi:10.1006/exnr.2000.7406 Hartung, T., Bremer, S., Casati, S. et al. (2004). A modular approach to the ecvam principles on test validity. Altern Lab Anim 32, 467-472. doi:10.1177/026119290403200503 Hartung, T. (2007). Food for thought ... On validation. ALTEX 24, 67-80. doi:10.14573/altex.2007.2.67 Hartung, T. and Leist, M. (2008). Food for thought ... On the evolution of toxicology and the phasing out of animal testing. ALTEX 25, 91-102. doi:10.14573/altex.2008.2.91 Hartung, T., De Vries, R., Hoffmann, S. et al. (2019). Toward good in vitro reporting standards. ALTEX 36, 3-17. doi:10.14573/ altex.1812191 Hirt, U. A., Gantner, F. and Leist, M. (2000). Phagocytosis of nonapoptotic cells dying by caspase-independent mechanisms. J Immunol 164, 6520-6529. doi:10.4049/jimmunol. 164.12.6520 Hoffmann, S. and Hartung, T. (2006). Designing validation studies more efficiently according to the modular approach: Retrospective analysis of the Episkin test for skin corrosion. Altern Lab Anim 34, 177-191. doi:10.1177/026119290603400209 Horbach, S. and Halffman, W. (2017). The ghosts of HeLa: How cell line misidentification contaminates the scientific literature. PLoS One 12, e0186281. doi:10.1371/journal.pone.0186281 testing strategies. The report and recommendations of ECVAM workshop 58. Altern Lab Anim 34, 603-620. doi:10. 1177/026119290603400604 Balmer, N. V., Weng, M. K., Zimmer, B. et al. (2012). Epigenetic changes and disturbed neural development in a human embryonic stem cell-based model relating to the fetal valproate syndrome. Hum Mol Genet 21, 4104-4114. doi:10.1093/hmg/ dds239 Begley, C. G. and Ellis, L. M. (2012). Drug development: Raise standards for preclinical cancer research. Nature 483, 531-533. doi:10.1038/483531a Ben-David, U., Siranosian, B., Ha, G. et al. (2018). Genetic and transcriptional evolution alters cancer cell line drug response. Nature 560, 325-330. doi:10.1038/s41586-018-0409-3 Blaauboer, B. J., Boekelheide, K., Clewell, H. J. et al. (2012). The use of biomarkers of toxicity for integrating in vitro hazard estimates into risk assessment for humans. ALTEX 29, 411-425. doi:10.14573/altex.2012.4.411 Brigelius-Flohe, R., Lotzer, K., Maurer, S. et al. (1995). Utilization of selenium from different chemical entities for selenoprotein biosynthesis by mammalian cell lines. Biofactors 5, 125-131. Cao, M. D., Balasubramanian, S. and Boden, M. (2015). Sequencing technologies and tools for short tandem repeat variation detection. Brief Bioinform 16, 193-204. doi:10.1093/bib/bbu001 Coecke, S., Balls, M., Bowe, G. et al. (2005). Guidance on good cell culture practice. A report of the second ECVAM task force on good cell culture practice. Altern Lab Anim 33, 261-287. doi:10.1177/026119290503300313 Coecke, S., Bowe, G., Milcamps, A. et al. (2014). Considerations in the development of in vitro toxicity testing methods intended for regulatory use. In A. Bal-Price and P. Jennings (eds.), In Vitro Toxicology Systems. New York, NY: Springer New York. doi:10.1007/978-1-4939-0521-8_25 Coecke, S., Bernasconi, C., Bowe, G. et al. (2016). Practical aspects of designing and conducting validation studies involving multi-study trials. Adv Exp Med Biol 856, 133-163. doi:10.1007/978-3-319-33826-2_5 Daneshian, M., Kamp, H., Hengstler, J. et al. (2016). Highlight report: Launch of a large integrated European in vitro toxicology project: EU-ToxRisk. Arch Toxicol 90, 1021-1024. doi:10. 1007/s00204-016-1698-7 Delp, J., Funke, M., Rudolf, F. et al. (2019). Development of a neurotoxicity assay that is tuned to detect mitochondrial toxicants. Arch Toxicol 93, 1585-1608. doi:10.1007/s00204-01902473-y Drexler, H. G., Dirks, W. G., Matsuo, Y. et al. (2003). False leukemia-lymphoma cell lines: An update on over 500 cell lines. Leukemia 17, 416-426. doi:10.1038/sj.leu.2402799 Falsig, J., Porzgen, P., Lund, S. et al. (2006). The inflammatory transcriptome of reactive murine astrocytes and implications for their innate immune function. J Neurochem 96, 893-907. doi:10.1111/j.1471-4159.2005.03622.x Flood, S., Houck, K. and Grulke, C. (2017). Development of a Context-Rich Database of ToxCast Assay Annotations. doi:10. 23645/epacomptox.5178610.v1 Frattini, A., Fabbri, M., Valli, R. et al. (2015). High variability of Krebs et al. ALTEX 36(4), 2019 698 Marx, U., Andersson, T. B., Bahinski, A. et al. (2016). Biology-inspired microphysiological system approaches to solve the prediction dilemma of substance testing. ALTEX 33, 272-321. doi:10.14573/altex.1603161 Masters, J. (2002). Re: False cell lines. Exp Cell Res 272, 216. doi:10.1006/excr.2001.5439 Matsuda, K. (2017). PCR-based detection methods for single-nucleotide polymorphism or mutation: Real-time PCR and its substantial contribution toward technological refinement. Adv Clin Chem 80, 45-72. doi:10.1016/bs.acc.2016.11.002 Nardone, R. M. (2007). Eradication of cross-contaminated cell lines: A call for action. Cell Biol Toxicol 23, 367-372. doi:10.1007/s10565-007-9019-9 Nardone, R. M. (2008). Curbing rampant cross-contamination and misidentification of cell lines. Biotechniques 45, 221-227. doi:10.2144/000112925 OECD (2002). Test No. 423: Acute Oral Toxicity – Acute Toxic Class Method. OECD Guidelines for the Testing of Chemicals, Section 4. OECD Publishing, Paris. doi:10. 1787/9789264071001-en OECD (2005). Good Laboratory Practice (GLP): OECD Principles and Guidance for Compliance Monitoring. OECD Publishing, Paris. doi:10.1787/9789264012837-en OECD (2017). Guidance Document for Describing Non-Guideline In Vitro Test Methods. Series on Testing and Assessment No. 211. OECD Publishing, Paris. doi:10.1787/9789264274730-en OECD (2018). Guidance Document on Good In Vitro Method Practices (GIVIMP). OECD Publishing, Paris. doi:10. 1787/9789264304796-1-en Pamies, D., Bal-Price, A., Simeonov, A. et al. (2017). Good cell culture practice for stem cells and stem-cell-derived models. ALTEX 34, 95-132. doi:10.14573/altex.1607121 Pamies, D., Bal-Price, A., Chesne, C. et al. (2018). Advanced good cell culture practice for human primary, stem cell-derived and organoid models as well as microphysiological systems. ALTEX 35, 353-378. doi:10.14573/altex.1710081 Prinz, F., Schlange, T. and Asadullah, K. (2011). Believe it or not: How much can we rely on published data on potential drug targets? Nat Rev Drug Discov 10, 712. doi:10.1038/nrd3439-c1 Prior, H., Casey, W., Kimber, I. et al. (2019). Reflections on the progress towards non-animal methods for acute toxicity testing of chemicals. Regul Toxicol Pharmacol 102, 30-33. doi:10.1016/j.yrtph.2018.12.008 Ramaiahgari, S. C., den Braver, M. W., Herpers, B. et al. (2014). A 3D in vitro model of differentiated HepG2 cell spheroids with improved liver-like properties for repeated dose high-throughput toxicity studies. Arch Toxicol 88, 1083-1095. doi:10.1007/ s00204-014-1215-9 Rovida, C., Vivier, M., Garthoff, B. et al. (2014). ESNATS conference – The use of human embryonic stem cells for novel toxicity testing approaches. Altern Lab Anim 42, 97-113. doi:10.1177/026119291404200203 Schildknecht, S., Pape, R., Muller, N. et al. (2011). Neuroprotection by minocycline caused by direct and specific scavenging of peroxynitrite. J Biol Chem 286, 4991-5002. doi:10.1074/jbc. m110.169565 Ingre-Khans, E., Agerstrand, M., Beronius, A. et al. (2019). Toxicity studies used in registration, evaluation, authorisation and restriction of chemicals (REACH): How accurately are they reported? Integr Environ Assess Manag 15, 458-469. doi:10.1002/ieam.4123 Ioannidis, J. P. (2012). Why science is not necessarily self-correcting. Perspect Psychol Sci 7, 645-654. doi:10. 1177/1745691612464056 Kinsner-Ovaskainen, A., Rzepka, R., Rudowski, R. et al. (2009). Acutoxbase, an innovative database for in vitro acute toxicity studies. Toxicol In Vitro 23, 476-485. doi:10.1016/j. tiv.2008.12.019 Kisitu, J., Hougaard Bennekou, S. and Leist, M. (2019). Chemical concentrations in cell culture compartments (C5) – Concentration definitions. ALTEX 36, 154-160. doi:10.14573/ altex.1901031 Kleensang, A., Vantangoli, M. M., Odwin-DaCosta, S. et al. (2016). Genetic variability in a frozen batch of MCF-7 cells invisible in routine authentication affecting cell function. Sci Rep 6, 28994. doi:10.1038/srep28994 Krebs, A., Nyffeler, J., Rahnenfuhrer, J. et al. (2018). Normalization of data for viability and relative cell function curves. ALTEX 35, 268-271. doi:10.14573/1803231 Kruglikov, R. I., Getsova, V. M. and Uniial, M. (1976). (Effect of an excess of serotonin in the brain on consolidation of temporary connections). Zh Vyssh Nerv Deiat Im I P Pavlova 26, 1208-1213. Latta, M., Kunstle, G., Leist, M. et al. (2000). Metabolic depletion of ATP by fructose inversely controls CD95and tumor necrosis factor receptor 1-mediated hepatic apoptosis. J Exp Med 191, 1975-1985. doi:10.1084/jem.191.11.1975 Leist, M., Maurer, S., Schultz, M. et al. (1999). Cytoprotection against lipid hydroperoxides correlates with increased glutathione peroxidase activities, but not selenium uptake from different selenocompounds. Biol Trace Elem Res 68, 159-174. doi:10.1007/bf02784404 Leist, M., Efremova, L. and Karreman, C. (2010). Food for thought ... Considerations and guidelines for basic test method descriptions in toxicology. ALTEX 27, 309-317. doi:10.14573/ altex.2010.4.309 Leist, M., Hasiwa, N., Daneshian, M. et al. (2012). Validation and quality control of replacement alternatives – Current status and future challenges. Toxicol Res 1, 8-22. doi:10.1039/c2tx20011b Leist, M., Hasiwa, N., Rovida, C. et al. (2014). Consensus report on the future of animal-free systemic toxicity testing. ALTEX 31, 341-356. doi:10.14573/altex.1406091 Leist, M. and Hengstler, J. G. (2018). Essential components of methods papers. ALTEX 35, 429-432. doi:10.14573/altex. 1807031 Liu, Y., Mi, Y., Mueller, T. et al. (2019). Multi-omic measurements of heterogeneity in HeLa cells across laboratories. Nat Biotechnol 37, 314-322. doi:10.1038/s41587-019-0037-y Lorge, E., Moore, M. M., Clements, J. et al. (2016). Standardized cell sources and recommendations for good cell culture practices in genotoxicity testing. Mutat Res 809, 1-15. doi:10.1016/j. mrgentox.2016.08.001 Krebs et al. ALTEX 36(4), 2019 699 ed waves of gene expression during neuronal differentiation of embryonic stem cells as basis for novel approaches to developmental neurotoxicity testing. Cell Death Differ 18, 383-395. doi:10.1038/cdd.2010.109 Zimmer, B., Lee, G., Balmer, N. V. et al. (2012). Evaluation of developmental toxicants and signaling pathways in a functional test based on the migration of human neural crest cells. Environ Health Perspect 120, 1116-1122. doi:10.1289/ehp.1104489 Conflict of interest The authors declare that they have no conflict of interest. Acknowledgements This work was supported by the Doerenkamp-Zbinden foundation, the Land-BW (INVITE), the BMBF (e:ToP program, SysBioTop), grants by EFSA and DK-EPA, Estonian Research Council grant PUT1015 as well as the projects from the European Union’s Horizon 2020 research and innovation programme EU-ToxRisk (grant agreement No 681002), ENDpoiNTs (grant agreement No 825759), and the project CERST (Center for Alternatives to Animal Testing) of the Ministry for Culture and Science of the State of North-Rhine Westphalia, Germany [233-1.08.03.03-121972]. We are grateful to many contributors not listed as authors. Especially a whole team of experts from EURL-ECVAM gave important advice. Schildknecht, S., Karreman, C., Poltl, D. et al. (2013). Generation of genetically-modified human differentiated cells for toxicological tests and the study of neurodegenerative diseases. ALTEX 30, 427-444. doi:10.14573/altex.2013.4.427 Schmidt, B. Z., Lehmann, M., Gutbier, S. et al. (2017). In vitro acute and developmental neurotoxicity screening: An overview of cellular platforms and high-throughput technical possibilities. Arch Toxicol 91, 1-33. doi:10.1007/s00204-0161805-9 Stacey, G. N., Bolton, B. J. and Doyle, A. (1992). DNA fingerprinting transforms the art of cell authentication. Nature 357, 261-262. doi:10.1038/357261a0 Stacey, G. N. (2000). Cell contamination leads to inaccurate data: We must take action now. Nature 403, 356. doi:10. 1038/35000394 Stiegler, N. V., Krug, A. K., Matt, F. et al. (2011). Assessment of chemical-induced impairment of human neurite outgrowth by multiparametric live cell imaging in high-density cultures. Toxicol Sci 121, 73-87. doi:10.1093/toxsci/kfr034 Vogt, L., Reichlin, T. S., Nathues, C. et al. (2016). Authorization of animal experiments is based on confidence rather than evidence of scientific rigor. PLoS Biol 14, e2000598. doi:10.1371/ journal.pbio.2000598 Zhang, C., Cerveira, E., Romanovitch, M. et al. (2017). Arraybased comparative genomic hybridization (aCGH). Methods Mol Biol 1541, 167-179. doi:10.1007/978-1-4939-6703-2_15 Zimmer, B., Kuegler, P. B., Baudis, B. et al. (2011). Coordinat-