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Novel urinary protein biomarkers have recently been identified and qualified in rats for the early detection of renal injury in drug development studies. However, there seems to be no standardized normalization method for analyzing these urinary biomarkers, as some users normalize with urinary creatinine (uCr), urine volume (uVol), or leave biomarker un-normalized. More recently, urinary cystatin C is also emerging as a urinary biomarker normalizer, given some of its characteristics as a glomerular filtration marker. The purpose of this study was to identify an optimal drug-induced kidney injury biomarker normalization method that can be adopted more uniformly in the field. To this end, we compared the variability of uVol, urinary cystatin C, and Cr in healthy rats; we evaluated the sensitivity of the renal biomarkers to renal injury after normalization with uVol, uCr, and cystatin C in rats with cisplatin-induced renal injury. We showed that, over time, uCr was less variable than urinary cystatin C and uVol. When the renal biomarkers were normalized with the 3 normalizing end points, the biomarkers showed (1) least variability following normalization with Cr in healthy animals and (2) poor sensitivity when normalized with urinary cystatin C in animals with renal injury. Overall, the results suggested that uCr is better than urinary cystatin C and uVol for normalizing renal biomarkers in rats under controlled preclinical conditions. To our knowledge, this is the first report that compared the variability of uVol, cystatin C, and Cr in the context of renal biomarkers’ normalization.

A number of novel urinary biomarkers have been identified and partially qualified for use as markers for renal injury in rats. We use two novel multiplex assays to quantify biomarker concentration in multiple urine collections made prior to and following administration of cisplatin, a common nephrotoxicant, to rats. We investigate the correlation of the magnitude of biomarker changes with the severity of histopathological observations and explore the relationship of these to both dose and sex. The novel biomarkers evaluated are urinary albumin, alpha glutathione s-transferase (α-GST), glutathione S-transferase-yb1 (GSTYb1), lipocalin-2, kidney injury molecule-1 (KIM-1), osteopontin, and renal papillary antigen 1 (RPA-1) and plasma cystatin C, alongside the traditional biomarkers of plasma urea, creatinine, and urinary n-acetyl-beta-d-glucosaminidase (NAG), total protein, and glucose. We show for all time points, and for almost all doses, that male rats consistently had either more severely graded or a higher incidence of histologically observed lesions than females; that changes in urinary glucose, total urinary protein, NAG, and the novel urinary biomarkers albumin, osteopontin, and KIM-1 are clearly temporally associated; and that changes are related to the severity of injury. We also found that receiver operating characteristic curve analysis and area under the curve are significantly higher than urea or creatinine for all new biomarkers except aGST, GSTYb1, cystatin c, and total protein in both sexes.

A number of novel urinary biomarkers have been identified and partially qualified for use as markers for renal injury in rats. We used two multiplex assays for these novel biomarkers to quantify biomarker concentration in serial urine collections from rats of both sexes administered varying concentrations of cisplatin. From these data, we calculate inter-individual variation and reference ranges from predose animals and intra-individual variation and reference change values from undosed control animals. The biomarkers evaluated are albumin, α glutathione s-transferase, glutathione S-transferase-yb1, lipocalin-2, kidney injury molecule-1, osteopontin, and renal papillary antigen 1. For any creatinine-corrected novel biomarkers, we found intra-individual variation to be no greater than 44% and inter-individual variation to be no greater than 46%. Reference change values for most corrected analytes (except osteopontin) were 50–100%, indicating that a >100% increase in analyte concentration between serial samples would be unlikely to be associated with inherent analytical or biological variation.