Research Papers:

Predicting new indications of compounds with a network pharmacology approach: Liuwei Dihuang Wan as a case study

Yin-Ying Wang, Hong Bai, Run-Zhi Zhang, Hong Yan, Kang Ning and Xing-Ming Zhao _

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Oncotarget. 2017; 8:93957-93968. https://doi.org/10.18632/oncotarget.21398

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Yin-Ying Wang1,2,3,*, Hong Bai4,*, Run-Zhi Zhang4, Hong Yan3, Kang Ning4 and Xing-Ming Zhao1

1Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai 200433, China

2Department of Computer Science and Technology, Tongji University, Shanghai 201804, China

3Department of Electronic Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong

4Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China

*These authors have contributed equally to this work

Correspondence to:

Xing-Ming Zhao, email: xmzhao@fudan.edu.cn

Keywords: network pharmacology; drug repurposing; TCMs; pathway profile; LDW

Received: February 13, 2017    Accepted: September 05, 2017    Published: September 30, 2017


With the ever increasing cost and time required for drug development, new strategies for drug development are highly demanded, whereas repurposing old drugs has attracted much attention in drug discovery. In this paper, we introduce a new network pharmacology approach, namely PINA, to predict potential novel indications of old drugs based on the molecular networks affected by drugs and associated with diseases. Benchmark results on FDA approved drugs have shown the superiority of PINA over traditional computational approaches in identifying new indications of old drugs. We further extend PINA to predict the novel indications of Traditional Chinese Medicines (TCMs) with Liuwei Dihuang Wan (LDW) as a case study. The predicted indications, including immune system disorders and tumor, are validated by expert knowledge and evidences from literature, demonstrating the effectiveness of our proposed computational approach.

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