1. Cross-study and cross-omics comparisons of three nephrotoxic compounds reveal mechanistic insights and new candidate biomarkers.
- Author
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Matheis KA, Com E, Gautier JC, Guerreiro N, Brandenburg A, Gmuender H, Sposny A, Hewitt P, Amberg A, Boernsen O, Riefke B, Hoffmann D, Mally A, Kalkuhl A, Suter L, Dieterle F, and Staedtler F
- Subjects
- Animals, Biomarkers analysis, Dose-Response Relationship, Drug, Kidney Tubules, Proximal pathology, Kidney Tubules, Proximal physiology, Male, Rats, Rats, Wistar, Cyclosporins toxicity, Gene Expression Profiling methods, Genetic Markers genetics, Gentamicins toxicity, Kidney Tubules, Proximal drug effects, Proteomics methods
- Abstract
The European InnoMed-PredTox project was a collaborative effort between 15 pharmaceutical companies, 2 small and mid-sized enterprises, and 3 universities with the goal of delivering deeper insights into the molecular mechanisms of kidney and liver toxicity and to identify mechanism-linked diagnostic or prognostic safety biomarker candidates by combining conventional toxicological parameters with "omics" data. Mechanistic toxicity studies with 16 different compounds, 2 dose levels, and 3 time points were performed in male Crl: WI(Han) rats. Three of the 16 investigated compounds, BI-3 (FP007SE), Gentamicin (FP009SF), and IMM125 (FP013NO), induced kidney proximal tubule damage (PTD). In addition to histopathology and clinical chemistry, transcriptomics microarray and proteomics 2D-DIGE analysis were performed. Data from the three PTD studies were combined for a cross-study and cross-omics meta-analysis of the target organ. The mechanistic interpretation of kidney PTD-associated deregulated transcripts revealed, in addition to previously described kidney damage transcript biomarkers such as KIM-1, CLU and TIMP-1, a number of additional deregulated pathways congruent with histopathology observations on a single animal basis, including a specific effect on the complement system. The identification of new, more specific biomarker candidates for PTD was most successful when transcriptomics data were used. Combining transcriptomics data with proteomics data added extra value., (Copyright © 2010 Elsevier Inc. All rights reserved.)
- Published
- 2011
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