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Biography

Vladimir Tolstikov, Ph.D., is Scientist at Eli Lilly and Company. He received B.S. and M.S in Organic Chemistry in 1977 from M.V. Lomonosov Institute of Fine Chemical Technology, in Moscow, Russia. He received Ph.D. in Organic Chemistry in 1983 from the Institute of Chemical Means for Plant Protection, Moscow, Russia. He is a pioneer in HILIC separations development applied to Metabolomics. During his career he was working in leading research institutions in USA, Germany, Hungary and Russian Federation. He has joined Eli Lilly and Company in 2012. He is author, contributor and participant of 45 conferences, 5 book chapters, and 54 articles.

Abstract

Eli Lilly and Company’s metabolomics platform, comprised of state-of-the-art instrumentation technologies, is capable of information delivery on hundreds of small molecules showing statistically significant biochemical alterations. Current prognostic biomarkers of renal function manifest significance only after substantial disease progression. Early diagnosis could enable improved therapeutic treatments and patient segregation. We hypothesized that changes in the plasma and urine metabolome with CKD disease progression that were independent of metabolic changes resulting from type II diabetes could provide the basis for CKD diagnostic and prognostic biomarkers and assist in understanding the chronic renal injury process linked to diabetic nephropathy. Donor matched urine and serum clinical samples were obtained, extracted and analyzed using in house metabolomics platform. The first study was powered with 39 healthy, type II diabetic CKD (stages 3-5), and non-diabetic CKD (stages 3-5) patients. The second study was powered with 71 healthy, diabetic, diabetic CKD, and non-diabetic CKD patients. Metabolic pathway analysis using Ingenuity Systems allowed integration across metabolomics, proteomics and transcriptomics data obtained from analysis and literature. Pathway and network analysis allowed pinpointing distinct metabolic pathways in CKD patients that offer potential as novel biomarkers supporting patient segmentation. Described studies demonstrate the potential of metabolomics and omics data integration as promising tools for patient diagnosis and tailoring, and for identification of disease mechanisms that may be tractable to pharmaceutical intervention.

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