Abstract
Symmetric dimethylarginine (SDMA) is an excretory renal function biomarker shown to correlate well with glomerular filtration rate in dogs, cats, humans, and rats. The objectives of this study were to determine utility of serum SDMA as a renal biomarker in a rat model of gentamicin-induced renal injury and to provide validation of a commercially available SDMA immunoassay for rat serum. Rats were randomly assigned to one of three dose levels of gentamicin (20, 50, or 100 mg/kg) or a vehicle control group and dosed once daily by subcutaneous injection for either four or ten days. Serum and urine renal biomarker evaluation, including serum SDMA, hematologic and serum biochemical analysis, urinalysis, and histologic examination of kidney, were performed. Before biologic validation, analytic validation of the SDMA immunoassay for rat serum was performed, including assessment of assay accuracy, precision, analytical sensitivity, linearity, analyte stability, and interference testing. Among markers of excretory renal function, SDMA and serum creatinine increased earliest and at the lowest gentamicin concentrations and were significantly increased in both the 50- and 100- mg/kg dose levels in the four- and ten-dose treatment groups compared with controls. Time- and dose-dependent increases were noted for all urinary biomarkers investigated in this study, with microalbumin being most responsive and osteopontin least responsive for detection of gentamicin-induced injury across dose levels and schedules investigated. The SDMA immunoassay met all set quality requirements assessed in analytical validation. This study is the first to investigate performance of serum SDMA compared with other excretory renal function markers in a rat gentamicin acute toxicity model. In this study, serum SDMA was an earlier biomarker for detection of gentamicin-induced toxicity than serum cystatin C, BUN, and creatinine clearance. The SDMA immunoassay provides a reliable commercially available assay for future renal investigations in rat models.