A combination of widely accepted and published algorithm used in microarray data analysis and computational meta-analysis of publicly available genomic data sourced from the Gene Expression Omnibus (GEO) were utilized to discover new indications for existing phase IIa ready drugs and biologics. This methodology was applied to data obtained from peripheral blood mononuclear cells and glomerulus from diabetic patients. Two genes, FcER1G and CCR1, were identified as having significantly increased expression and potentially playing a key mechanistic role in the development and progression of diabetic kidney disease (DKD). The clinical evolution of DKD is thought to occur through a series of steps beginning with increased glomerular filtration rate (GFR) followed by hyperfiltration injury, microalbuminuria and ultimately leading to overt nephropathy with macroalbuminuria and decline in GFR. The mechanism of disease involves a number contributing factors but clear mechanisms have not been clearly elucidated. We hypothesize these targets are candidates for therapy with existing preapproved therapies. To test this hypothesis we propose administering these new therapies to established diabetic kidney mouse models as well as breeding knockout mice for these genes. We plan to perform quantitative morphometric analysis, immunohistochemistry, quantitative polyl studies for pre-clinical validation.
We identified novel therapies for diabetic kidney disease using a computational methodology. These therapies have existing indication in separate diseases and have potential to be repurposed. We propose validation studies of these therapies in order to determine the utility of the computational methodology and advance proposed treatments to phase IIa clinical trials.