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Computational Drug Repurposing Based on a Recommendation System and Drug–Drug Functional Pathway Similarity
Authors:Mengting Shao  Leiming Jiang  Zhigang Meng  Jianzhen Xu
Institution:1.Computational Systems Biology Laboratory, Department of Bioinformatics, Shantou University Medical College (SUMC), Shantou 515041, China; (M.S.); (L.J.);2.Department of Computer Science, College of Computer Engineering and Applied Mathematics, Changsha University, Changsha 410005, China
Abstract:Drug repurposing identifies new clinical indications for existing drugs. It can be used to overcome common problems associated with cancers, such as heterogeneity and resistance to established therapies, by rapidly adapting known drugs for new treatment. In this study, we utilized a recommendation system learning model to prioritize candidate cancer drugs. We designed a drug–drug pathway functional similarity by integrating multiple genetic and epigenetic alterations such as gene expression, copy number variation (CNV), and DNA methylation. When compared with other similarities, such as SMILES chemical structures and drug targets based on the protein–protein interaction network, our approach provided better interpretable models capturing drug response mechanisms. Furthermore, our approach can achieve comparable accuracy when evaluated with other learning models based on large public datasets (CCLE and GDSC). A case study about the Erlotinib and OSI-906 (Linsitinib) indicated that they have a synergistic effect to reduce the growth rate of tumors, which is an alternative targeted therapy option for patients. Taken together, our computational method characterized drug response from the viewpoint of a multi-omics pathway and systematically predicted candidate cancer drugs with similar therapeutic effects.
Keywords:drug functional similarity  drug repurposing  pathway activities  recommender system
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