Metabolomic Biomarkers for Drug-Induced Renal Damage
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Owner/Developer: Agilent Technologies
United States of America
25 June 2015
United States of America
Adverse drug reactions (ADRs) account for 7% of all hospitalizations and are the 5th most common cause of inpatient death. There have been nearly 50 drugs withdrawn from the market over the past 20 years due to severe ADRs. Three out of 4 ADRs may be predictable if we understand their pharmacological mechanisms. Drug-induced nephrotoxicity is an example of a serious ADR. It is a major problem accounting for a 20% attrition rate in drug development and the frequent ADRs in clinical practice. Unfortunately prediction of nephrotoxicity during preclinical drug development is difficult and no reliable animal models exist to predict the human situation. In clinical practice, renal function of patients is monitored by serum creatinine and blood urea nitrogen levels. However, the diagnostic and predictive value of these biomarkers is poor and marked changes in these markers occur only when renal damage is well along the way to irreversibility.
In the quest to identify specific biomarkers for nephrotoxicity and to better understand the mechanisms of drug-induced kidney damage, metabolomics has become an attractive approach to studying nephrotoxicity.
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The advantage of metabolomics is that biological fluids can be collected routinely during clinical trials or in clinical practice to track the magnitude of change and the time course of change in metabolomic-specific analytes. This enables a biomarker signature of early renal damage which can be integrated into a nephrotoxicity prediction model.
In our Center, we are launching a LC-MS metabolomics study to profile pre- and post-dose plasma, urine and kidney samples from cohorts of rats administered multiple toxic doses of cisplatin over a period of 30 days. Besides tracking biochemical, physiological and histological parameters, we will use both global and targeted metabolomic profiling to look for the greatest metabolic differences between control and cisplatin-treated rats. The results in rats will be compared with those obtained from metabolomic profiling of plasma and urine collected from seriously ill patients in whom renal toxic drug administration is unavoidable. We will also explore highly differentiated cell culture models of human and rat kidney proximal tubule to provide mechanistic and species-specific insight into drug-induced renal damage. Finally, we will employ a systems biology platform (MASER, Molecular Analysis of Safety Information, Molecular Health) to access and search the FDA adverse event database (FAERS) for drug: renal toxicity pairs. Using MASER, we will map the targets and pathways most likely responsible for renal damage. We will also explore the potential to mitigate the risk of nephrotoxicity by inhibiting renal transporters to decrease the secretion and exposure of the proximal tubule to the nephrotoxic drug. Taken together, we intend that an integrated approach to the problem of renal toxicity using metabolomics, in situ cell systems and computational systems biology approaches will address many of the challenges we have today with defining predictive biomarkers of drug-induced kidney damage.
EU & US Pharmacopoeia
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