BASF SE, Germany.
Metanomics GmbH, Germany.
Regul Toxicol Pharmacol. 2017 Dec;91 Suppl 1(Suppl 1):S27-S35. doi: 10.1016/j.yrtph.2017.10.007. Epub 2017 Oct 5.
'Omics technologies are gaining importance to support regulatory toxicity studies. Prerequisites for performing 'omics studies considering GLP principles were discussed at the European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) Workshop Applying 'omics technologies in Chemical Risk Assessment. A GLP environment comprises a standard operating procedure system, proper pre-planning and documentation, and inspections of independent quality assurance staff. To prevent uncontrolled data changes, the raw data obtained in the respective 'omics data recording systems have to be specifically defined. Further requirements include transparent and reproducible data processing steps, and safe data storage and archiving procedures. The software for data recording and processing should be validated, and data changes should be traceable or disabled. GLP-compliant quality assurance of 'omics technologies appears feasible for many GLP requirements. However, challenges include (i) defining, storing, and archiving the raw data; (ii) transparent descriptions of data processing steps; (iii) software validation; and (iv) ensuring complete reproducibility of final results with respect to raw data. Nevertheless, 'omics studies can be supported by quality measures (e.g., GLP principles) to ensure quality control, reproducibility and traceability of experiments. This enables regulators to use 'omics data in a fit-for-purpose context, which enhances their applicability for risk assessment.
“组学”技术对于支持监管毒理学研究变得越来越重要。在欧洲生态毒理学和化学品毒理学中心（ECETOC）举办的“将‘组学’技术应用于化学品风险评估”研讨会上，讨论了在符合 GLP 原则的前提下进行“组学”研究的前提条件。GLP 环境包括标准操作规程体系、适当的预先规划和文件记录，以及独立质量保证人员的检查。为了防止数据不受控制地发生变化，必须明确规定在各自的“组学”数据记录系统中获得的原始数据。进一步的要求包括透明且可重复的数据处理步骤，以及安全的数据存储和存档程序。用于数据记录和处理的软件应经过验证，并且数据更改应具有可追溯性或被禁用。对于许多 GLP 要求而言，“组学”技术符合 GLP 标准的质量保证似乎是可行的。然而，挑战包括：(i) 定义、存储和存档原始数据；(ii) 对数据处理步骤进行透明描述；(iii) 软件验证；以及 (iv) 确保最终结果相对于原始数据具有完全的可重现性。尽管如此，仍可以通过质量措施（例如 GLP 原则）来支持“组学”研究，以确保实验的质量控制、重现性和可追溯性。这使监管机构能够在适当的背景下使用“组学”数据，从而增强其在风险评估中的适用性。
