Drug Repurposing by Simulating Flow Through Protein–Protein Interaction Networks

Abstract

As drug development is extremely expensive, the identification of novel indications for in‐market drugs is financially attractive. Multiple algorithms are used to support such drug repurposing, but highly reliable methods combining simulation of intracellular networks and machine learning are currently not available. We developed an algorithm that simulates drug effects on the flow of information through protein–protein interaction networks, and used support vector machine to identify potentially effective drugs in our model disease, psoriasis. Using this method, we screened about 1,500 marketed and investigational substances, identified 51 drugs that were potentially effective, and selected three of them for experimental confirmation. All drugs inhibited tumor necrosis factor alpha‐induced nuclear factor kappa B activity in vitro, suggesting they might be effective for treating psoriasis in humans. Additionally, these drugs significantly inhibited imiquimod‐induced ear thickening and inflammation in the mouse model of the disease. All results suggest high prediction performance for the algorithm.

Topological network measures for drug repositioning

“…A network-based repositioning study for psoriasis was described by Manczinger et al . [ 91 ]. They used a support vector machine learning model on intracellular PPI network.…”

Immunpathogenesis and genetics of skin diseases

“…Mélyre ható bioinformatikai vizsgálataink a pikkelysömör kialakulásában részvevő különböző mechanizmusok vizsgálatával kezdődtek. Online elérhető adatbázisok adatait felhasználva sejten belüli hálózatokat modelleztünk, és sikerült több olyan fehérjét azonosítanunk, amelyek fontos szerepet játszanak a betegség kialakulásában, és általunk kifejlesztett algoritmus alkalmazásával potenciálisan hatékony gyógyszereket tudtunk azonosítani (29,30).…”