Multiscale fusion network drives the repurposing of anticancer drugs
Zhaoman Wan, Nan Jiang, Mingming Su, Xinlei Zhang, Yang Cao, Aiping Wu, Peng Zhang, Taijiao Jiang · Clinical and Translational Medicine · 2024
Dear Editor,Drug repurposing is at the forefront of a transformative shift in computational methods driving new applications of approved or investigational drugs.1,2 With the development of network pharmacology, repositioning algorithms for drug effects or drug targets are constantly expanding, but integrating multidimensional data to achieve precise repurposing is still a challenge.[3][4][5][6][7][8][9] We focus on drug attribute characteristics and propose a scalable systematic paradigm.Using the Genomics of Drug Sensitivity in Cancer (GDSC) database for anti-tumor drugs, a integrated drug similarity network (iDSN) derived from different drug similarity networks (DSNs) based on chemical structure and drug target sequence data is constructed to infer potential drug pathways from drug properties and realise drug repurposing.Initially, we processed drug profile data by vectorizing it (Figure 1A).Based on chemical and pharmacological properties, we constructed two separate DSNs: chem-DSN and pharm-DSN.These were then merged into an iDSN using a nonlinear fusion algorithm called Similarity Network Fusion (SNF) (Figure 1B).To validate the iDSN's potential in therapeutic similarity, we utilized a spectral clustering model with seven gold-standard annotations from PubChem (Figure 1C).Downstream analysis was delineated across three dimensions for drug repurposing (Figure 1D): (1) identifying similar components within classes, amalgamating pharmacological mechanisms with pathway annotation; (2) establishing associations between drug network clusters and distinct biological pathways; (3) prioritizing higher-ranked drug pairs for drug repositioning.Employing spectral clustering, iDSN exhibited a more distinct clustering structure compared to the chem-DSN and a more evenly distributed structure than the pharm-DSN (Figures 2A and S1).With the advantage of framework transparency, pharmacological properties contribute more than chemical properties through the quantita-