Big Data-Enabled Repurposing of Clopidogrel for Focal Segmental Glomerulosclerosis Using Network Biology

Abstract

Focal segmental glomerulosclerosis (FSGS) is a rare kidney disease characterized by glomerular lesions and damage to podocytes, terminally differentiated renal cells, that are part of the filtration apparatus in glomeruli. FSGS is often associated with nephrotic syndrome and may lead to end-stage renal failure. FSGS pathophysiology is complex with a number of molecular mechanisms being involved in its development and progression. We used a network-based drug repositioning approach to computationally screen for novel treatment options for FSGS in a systematic way. In brief, we first generated a network-based molecular pathobiology model for FSGS following previously established workflows. FSGS-associated molecular features (i.e., genes and proteins) were mapped onto a human protein–protein dependency network, and network clustering algorithms were used to identify dysregulated FSGS molecular mechanisms and biological processes. Key affected enriched mechanisms included glomerular filtration, cell adhesion and extracellular matrix derangements, inflammation, apoptosis, calcineurin signaling, renin–angiotensin–aldosterone system, and platelet activation as well as dysregulation of fibrinolysis. The network-based FSGS pathobiology model was subsequently used to computationally screen against a library of drug mechanism of action molecular models. The platelet inhibitor clopidogrel was identified as one of the top compounds significantly interfering with FSGS pathophysiology based on in silico graph alignment analysis. Due to its potential to beneficially interfere with key dysregulated molecular FSGS processes, its positive in vivo data in ameliorating renal sclerosis, and its favorable safety profile, clopidogrel appears as an attractive candidate for subsequent evaluation in clinical trials.