Blum Jonathan, Brüll Markus, Hengstler Jan G, Dietrich Daniel R, Gruber Andreas J, Dipalo Michele, Kraushaar Udo, Mangas Iris, Terron Andrea, Fritsche Ellen, Marx-Stoelting Philip, Hardy Barry, Schepky Andreas, Escher Sylvia, Hartung Thomas, Landsiedel Robert, Odermatt Alex, Sachana Magdalini, Koch Katharina, Dönmez Arif, Masjosthusmann Stefan, Bothe Kathrin, Schildknecht Stefan, Beilmann Mario, Beltman Joost B, Fitzpatrick Suzanne, Mangerich Aswin, Rehm Markus, Tangianu Silvia, Zickgraf Franziska M, Kamp Hennicke, Burger Gerhard, van de Water Bob, Kleinstreuer Nicole, White Andrew, Leist Marcel
In vitro Toxicology and Biomedicine, University of Konstanz, Konstanz, Germany.
Leibniz Research Centre for Working Environment and Human Factors (IfADo), Technical University of Dortmund, Dortmund, Germany.
ALTEX. 2025;42(1):167-180. doi: 10.14573/altex.2412171.
Toxicological test methods generate raw data and provide instructions on how to use these to determine a final outcome such as a classification of test compounds as hits or non-hits. The data processing pipeline provided in the test method description is often highly complex. Usually, multiple layers of data, ranging from a machine-generated output to the final hit definition, are considered. Transition between each of these layers often requires several data processing steps. As changes in any of these processing steps can impact the final output of new approach methods (NAMs), the processing pipeline is an essential part of a NAM description and should be included in reporting templates such as the ToxTemp. The same raw data, processed in different ways, may result in different final outcomes that may affect the readiness status and regulatory acceptance of the NAM, as an altered output can affect robustness, performance, and relevance. Data management, processing, and interpretation are therefore important elements of a comprehensive NAM definition. We aim to give an overview of the most important data levels to be considered during the development and application of a NAM. In addition, we illustrate data processing and evaluation steps between these data levels. As NAMs are increasingly standard components of the spectrum of toxicological test methods used for risk assessment, awareness of the significance of data processing steps in NAMs is crucial for building trust, ensuring acceptance, and fostering the reproducibility of NAM outcomes.
毒理学测试方法生成原始数据，并提供关于如何利用这些数据来确定最终结果的指导，例如将测试化合物分类为命中或未命中。测试方法描述中提供的数据处理流程通常非常复杂。通常会考虑多层数据，从机器生成的输出到最终的命中定义。这些层之间的转换通常需要几个数据处理步骤。由于这些处理步骤中的任何一个发生变化都可能影响新方法（NAMs）的最终输出，因此处理流程是NAM描述的重要组成部分，应包含在诸如ToxTemp等报告模板中。以不同方式处理相同的原始数据可能会导致不同的最终结果，这可能会影响NAM的就绪状态和监管接受度，因为改变后的输出可能会影响稳健性、性能和相关性。因此，数据管理、处理和解释是全面定义NAM的重要要素。我们旨在概述在NAM的开发和应用过程中需要考虑的最重要的数据层次。此外，我们还说明了这些数据层次之间的数据处理和评估步骤。由于NAMs越来越成为用于风险评估的毒理学测试方法体系中的标准组成部分，了解NAMs中数据处理步骤的重要性对于建立信任、确保接受度以及促进NAM结果的可重复性至关重要。
