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Framework for the quality assurance of ’omics technologies considering GLP requirements

机译:考虑GLP要求的组学技术质量保证框架

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摘要

‘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.
机译:‘Omics技术在支持法规毒性研究方面变得越来越重要。在欧洲化学生态毒理学和毒理学中心(ECETOC)研讨会上讨论了使用GLP原理进行'组学研究的先决条件,该研讨会将'组学技术应用于化学风险评估。一个GLP环境包括一个标准的操作程序系统,适当的预先计划和文档以及对独立质量保证人员的检查。为了防止数据不受控制地更改,必须明确定义在各个“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和可复制的数据处理步骤,以及安全的数据存储和归档过程。用于数据记录和处理的软件应经过验证,并且数据更改应可追溯或禁用。对于许多GLP要求,符合omics技术的GLP质量保证看来是可行的。但是,挑战包括(i)定义,存储和归档原始数据; (ii)对数据处理步骤的透明描述; (iii)软件验证; (iv)确保有关原始数据的最终结果具有完全的可重复性。不过,‘组学研究可以得到质量指标(例如GLP原则)的支持,以确保实验的质量控制,可重复性和可追溯性。这使监管机构可以在适合目的的情况下使用'组学数据,从而增强其在风险评估中的适用性。

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