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BioMed Research International
Volume 2013 (2013), Article ID 202497, 12 pages
http://dx.doi.org/10.1155/2013/202497
Research Article

Prediction of Clinically Relevant Safety Signals of Nephrotoxicity through Plasma Metabolite Profiling

1PharmPoint Consulting, Poolesville, MD 20837, USA
2BASF SE, Experimental Toxicology and Ecology, Z 470, D-67056 Ludwigshafen, Germany
3Metanomics GmbH, Tegeler Weg 33, 10589 Berlin, Germany
4Drug Safety Research Laboratories, Astellas Pharma Inc., Osaka, Japan
5AstraZeneca R&D, Innovative Medicines Personalised Healthcare & Biomarkers, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden
6Pfizer R&D, Compound Safety Prediction, Groton, CT 06340, USA
7Drug Safety Executive Council, Needham, MA 02494, USA
8Metanomics Health GmbH, Tegeler Weg 33, 10589 Berlin, Germany
9Analytical and Bioanalytical Development, Bristol-Myers Squibb, Princeton, NJ 08543, USA

Received 23 January 2013; Revised 23 April 2013; Accepted 25 April 2013

Academic Editor: Tzung-Hai Yen

Copyright © 2013 W. B. Mattes et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Addressing safety concerns such as drug-induced kidney injury (DIKI) early in the drug pharmaceutical development process ensures both patient safety and efficient clinical development. We describe a unique adjunct to standard safety assessment wherein the metabolite profile of treated animals is compared with the MetaMap Tox metabolomics database in order to predict the potential for a wide variety of adverse events, including DIKI. To examine this approach, a study of five compounds (phenytoin, cyclosporin A, doxorubicin, captopril, and lisinopril) was initiated by the Technology Evaluation Consortium under the auspices of the Drug Safety Executive Council (DSEC). The metabolite profiles for rats treated with these compounds matched established reference patterns in the MetaMap Tox metabolomics database indicative of each compound’s well-described clinical toxicities. For example, the DIKI associated with cyclosporine A and doxorubicin was correctly predicted by metabolite profiling, while no evidence for DIKI was found for phenytoin, consistent with its clinical picture. In some cases the clinical toxicity (hepatotoxicity), not generally seen in animal studies, was detected with MetaMap Tox. Thus metabolite profiling coupled with the MetaMap Tox metabolomics database offers a unique and powerful approach for augmenting safety assessment and avoiding clinical adverse events such as DIKI.