Title: The development of a database for metabolomics - looking back on ten years of experience

Authors: Bennard Van Ravenzwaay; Hennicke Kamp; Gina Alejandra Montoya-Parra; Volker Strauss; Eric Fabian; Werner Mellert; Gerhard Krennrich; Tilmann Walk; Erik Peter; Ralf Looser; Michael Herold

Addresses: Department Experimental Toxicology and Ecology, BASF SE, 67056 Ludwigshafen, Germany ' Department Experimental Toxicology and Ecology, BASF SE, 67056 Ludwigshafen, Germany ' Department Experimental Toxicology and Ecology, BASF SE, 67056 Ludwigshafen, Germany ' Department Experimental Toxicology and Ecology, BASF SE, 67056 Ludwigshafen, Germany ' Department Experimental Toxicology and Ecology, BASF SE, 67056 Ludwigshafen, Germany ' Department Experimental Toxicology and Ecology, BASF SE, 67056 Ludwigshafen, Germany ' Department Material Physics and Analytics, Scientific Computing, BASF SE, 67056 Ludwigshafen, Germany ' Metanomics GmbH, Tegeler Weg 33, 10589 Berlin, Germany ' Metanomics GmbH, Tegeler Weg 33, 10589 Berlin, Germany ' Metanomics GmbH, Tegeler Weg 33, 10589 Berlin, Germany ' Metanomics GmbH, Tegeler Weg 33, 10589 Berlin, Germany

Abstract: Metabolome profiles of ca. 750 compounds obtained from blood samples from 28 day rat studies (OECD 407) were combined with toxicity profiles in one database over ten years to predict toxicity of new compounds. We provide detailed descriptions of procedures and recommendations for 'omics data-bases. Control of variability (biological, sampling/storage and technical measurement) is essential. At the start of large scale projects control variability should be extensively investigated. Reference (positive control) substances should be used to evaluate and obtain a good signal/noise ratio. Procedures should be documented in standard operating procedures and followed meticulously. Exact repeats of positive controls should be regularly performed, to assess variability of positive responses. Control data should be regularly checked for shifts and analysed to obtain information concerning normality. If possible, data should be analysed by multiple procedures and conclusions should be drawn based on a joint assessment, not unlike peer review processes in histopathology.

Keywords: metabolomics database; chemical MOA; mode of action; statistics; quality assurance; toxicity prediction; metabolite patterns; pairwise comparison; biochemical pathways; normality; variability control; signal to noise ratio; SNR; joint assessment.

DOI: 10.1504/IJBT.2015.074801

International Journal of Biotechnology, 2015 Vol.14 No.1, pp.47 - 68

Received: 24 Oct 2014
Accepted: 20 Aug 2015
Published online: 19 Feb 2016 *

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