title: Drugs meeting the molecular basis of diabetic kidney disease: bridging from molecular mechanism to personalized medicine.
abstract: Diabetic kidney disease (DKD) is a complex, multifactorial disease and is associated with a high risk of renal and cardiovascular morbidity and mortality. Clinical practice guidelines for diabetes recommend essentially identical treatments for all patients without taking into account how the individual responds to the instituted therapy. Yet, individuals vary widely in how they respond to medications and therefore optimal therapy differs between individuals. Understanding the underlying molecular mechanisms of variability in drug response will help tailor optimal therapy. Polymorphisms in genes related to drug pharmacokinetics have been used to explore mechanisms of response variability in DKD, but with limited success. The complex interaction between genetic make-up and environmental factors on the abundance of proteins and metabolites renders pharmacogenomics alone insufficient to fully capture response variability. A complementary approach is to attribute drug response variability to individual variability in underlying molecular mechanisms involved in the progression of disease. The interplay of different processes (e.g. inflammation, fibrosis, angiogenesis, oxidative stress) appears to drive disease progression, but the individual contribution of each process varies. Drugs at the other hand address specific targets and thereby interfere in certain disease-associated processes. At this level, biomarkers may help to gain insight into which specific pathophysiological processes are involved in an individual followed by a rational assessment whether a specific drug's mode of action indeed targets the relevant process at hand. This article describes the conceptual background and data-driven workflow developed by the SysKid consortium aimed at improving characterization of the molecular mechanisms underlying DKD at the interference of the molecular impact of individual drugs in order to tailor optimal therapy to individual patients.
authors.affiliation: Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
authors.forename: Hiddo J
authors.initials: HJ
authors.lastname: Lambers Heerspink
authors.affiliation: Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria.
authors.forename: Rainer
authors.initials: R
authors.lastname: Oberbauer
authors.affiliation: Emergentec biodevelopment GmbH, Vienna, Austria.
authors.forename: Paul
authors.initials: P
authors.lastname: Perco
authors.affiliation: Emergentec biodevelopment GmbH, Vienna, Austria.
authors.forename: Andreas
authors.initials: A
authors.lastname: Heinzel
authors.affiliation: Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria.
authors.forename: Georg
authors.initials: G
authors.lastname: Heinze
authors.affiliation: Department of Internal Medicine IV, Medical University of Innsbruck, Innsbruck, Austria.
authors.forename: Gert
authors.initials: G
authors.lastname: Mayer
authors.affiliation: Emergentec biodevelopment GmbH, Vienna, Austria.
authors.forename: Bernd
authors.initials: B
authors.lastname: Mayer
mesh_terms: D015415:Biomarkers / Q000032:analysis*; D003928:Diabetic Nephropathies / Q000188:drug therapy / Q000235:genetics / Q000517:prevention & control*; D018450:Disease Progression; D004358:Drug Therapy / Q000379:methods*; D014644:Genetic Variation / Q000235:genetics*; D023281:Genomics; D006801:Humans; D010597:Pharmacogenetics; D057285:Precision Medicine
chemical_list: D015415:Biomarkers
keywords: drug; personalized medicine; prediction; systems biology; type 2 diabetes