A Gene Expression Signature that Predicts the Future Onset of Drug-Induced Renal Tubular Toxicity

One application of genomics in drug safety assessment is the identification of biomarkers to predict compound toxicity before it is detected using traditional approaches, such as histopathology. However, many genomic approaches have failed to demonstrate superiority to traditional methods, have not been appropriately validated on external samples, or have been derived using small data sets, thus raising concerns of their general applicability. Using kidney gene expression profiles from male SD rats treated with 64 nephrotoxic or non-nephrotoxic compound treatments, a gene signature consisting of only 35 genes was derived to predict the future development of renal tubular degeneration weeks before it appears histologically following short-term test compound administration. By comparison, histopathology or clinical chemistry fails to predict the future development of tubular degeneration, thus demonstrating the enhanced sensitivity of gene expression relative to traditional approaches. In addition, the performance of the signature was validated on 21 independent compound treatments structurally distinct from the training set. The signature correctly predicted the ability of test compounds to induce tubular degeneration 76% of the time, far better than traditional approaches. This study demonstrates that genomic data can be more sensitive than traditional methods for the early prediction of compound-induced pathology in the kidney.

[1]  Antitumor effect, cardiotoxicity, and nephrotoxicity of doxorubicin in the IgM solid immunocytoma-bearing LOU/M/WSL rat. , 1984, Journal of the National Cancer Institute.

[2]  T. Monks,et al.  Identification of 2-bromohydroquinone as a metabolite of bromobenzene and o-bromophenol: implications for bromobenzene-induced nephrotoxicity. , 1984, The Journal of pharmacology and experimental therapeutics.

[3]  J. Vos,et al.  Time-course study on doxorubicin-induced nephropathy and cardiomyopathy in male and female LOU/M/Wsl rats: lack of evidence for a causal relationship. , 1986, Journal of the National Cancer Institute.

[4]  T. Tanaka,et al.  Effect of chronic renal failure on the level of albumin messenger RNA. , 1989, Metabolism: clinical and experimental.

[5]  T. Yokozawa,et al.  Decrease in the level of albumin mRNA with progression of renal failure in rats. , 1994, Nihon Jinzo Gakkai shi.

[6]  Urinary gamma-glutamyl transferase activity in rats with nonsteroidal anti-inflammatory drug-induced nephrotoxicity. , 1997, Archivum immunologiae et therapiae experimentalis.

[7]  E Zeiger,et al.  Identification of rodent carcinogens and noncarcinogens using genetic toxicity tests: premises, promises, and performance. , 1998, Regulatory toxicology and pharmacology : RTP.

[8]  R G Ulrich,et al.  Clustering of hepatotoxins based on mechanism of toxicity using gene expression profiles. , 2001, Toxicology and applied pharmacology.

[9]  Mark W. Craven,et al.  Identification of toxicologically predictive gene sets using cDNA microarrays. , 2001, Molecular pharmacology.

[10]  E. Hickey,et al.  Diclofenac induced in vivo nephrotoxicity may involve oxidative stress-mediated massive genomic DNA fragmentation and apoptotic cell death. , 2001, Free radical biology & medicine.

[11]  Lee Bennett,et al.  Gene expression analysis reveals chemical-specific profiles. , 2002, Toxicological sciences : an official journal of the Society of Toxicology.

[12]  S. Gullans,et al.  Monitoring changes in gene expression in renal ischemia-reperfusion in the rat. , 2002, Kidney international.

[13]  M. Gekle,et al.  Albumin induces NF-kappaB expression in human proximal tubule-derived cells (IHKE-1). , 2002, Cellular physiology and biochemistry : international journal of experimental cellular physiology, biochemistry, and pharmacology.

[14]  Kerry Blanchard,et al.  Methapyrilene Toxicity: Anchorage of Pathologic Observations to Gene Expression Alterations , 2002, Toxicologic pathology.

[15]  Stephen H. Friend,et al.  Toxicogenomics and drug discovery: will new technologies help us produce better drugs? , 2002, Nature Reviews Drug Discovery.

[16]  Richard Baumgartner,et al.  Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions , 2003, Bioinform..

[17]  N. Vardı,et al.  Carbon tetrachloride-induced nephrotoxicity and protective effect of betaine in Sprague-Dawley rats. , 2003, Urology.

[18]  D. Ransohoff Rules of evidence for cancer molecular-marker discovery and validation , 2004, Nature Reviews Cancer.

[19]  Timothy A Bertram,et al.  Identification of putative gene based markers of renal toxicity. , 2004, Environmental health perspectives.

[20]  William Pennie,et al.  Toxicogenomics in risk assessment: an overview of an HESI collaborative research program. , 2004, Environmental health perspectives.

[21]  M. Fielden,et al.  Development of a large-scale chemogenomics database to improve drug candidate selection and to understand mechanisms of chemical toxicity and action. , 2005, Journal of biotechnology.

[22]  Gert R. G. Lanckriet,et al.  Classification of a large microarray data set: algorithm comparison and analysis of drug signatures. , 2005, Genome research.