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Regul Toxicol Pharmacol. 2017 Dec;91 Suppl 1:S27-S35. doi: 10.1016/j.yrtph.2017.10.007. Epub 2017 Oct 5.

Framework for the quality assurance of 'omics technologies considering GLP requirements.

Author information

1
BASF SE, Germany.
2
Metanomics GmbH, Germany.
3
U.S. Environmental Protection Agency, USA.
4
ExxonMobil Petroleum and Chemical B.V.B.A., Belgium.
5
Imperial College London, United Kingdom.
6
Department of Molecular Systems Biology, Helmholtz Centre for Environmental Research - UFZ, Germany.
7
Center for Environmental Toxicology, Agency for Prevention, Environment and Energy (Arpae), Emilia-Romagna, Italy.
8
European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC), Belgium.
9
Scientific Consultancy - Animal Welfare, Germany.
10
Norwegian Institute for Water Research (NIVA), Norway.
11
Department of Chemical and Product Safety, German Federal Institute of Risk Assessment (BfR), Germany.
12
Environmental Health Science and Research Bureau, Health Canada, Canada.
13
BASF SE, Germany. Electronic address: bennard.ravenzwaay@basf.com.

Abstract

'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.

KEYWORDS:

Data storage; Documentation; Good laboratory practice (GLP); Independent quality assurance; Quality assurance inspection; Raw data definition; Reproducibility; Software validation; Standard operating procedure

PMID:
28987912
DOI:
10.1016/j.yrtph.2017.10.007
[Indexed for MEDLINE]
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