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Evidence-Based Complementary and Alternative Medicine
Volume 2013 (2013), Article ID 256782, 11 pages
Research Article

Predicting the Drug Safety for Traditional Chinese Medicine through a Comparative Analysis of Withdrawn Drugs Using Pharmacological Network

1Shanghai Key Laboratory of New Drug Design, State Key Laboratory of Bioreactor Engineering, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
2Department of Phytochemistry, School of Pharmacy, Second Military Medical University, Shanghai 200433, China

Received 5 March 2013; Accepted 7 April 2013

Academic Editor: Aiping Lu

Copyright © 2013 Mengzhu Xue et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


As the major issue to limit the use of drugs, drug safety leads to the attrition or failure in clinical trials of drugs. Therefore, it would be more efficient to minimize therapeutic risks if it could be predicted before large-scale clinical trials. Here, we integrated a network topology analysis with cheminformatics measurements on drug information from the DrugBank database to detect the discrepancies between approved drugs and withdrawn drugs and give drug safety indications. Thus, 47 approved drugs were unfolded with higher similarity measurements to withdrawn ones by the same target and confirmed to be already withdrawn or discontinued in certain countries or regions in subsequent investigations. Accordingly, with the 2D chemical fingerprint similarity calculation as a medium, the method was applied to predict pharmacovigilance for natural products from an in-house traditional Chinese medicine (TCM) database. Among them, Silibinin was highlighted for the high similarity to the withdrawn drug Plicamycin although it was regarded as a promising drug candidate with a lower toxicity in existing reports. In summary, the network approach integrated with cheminformatics could provide drug safety indications effectively, especially for compounds with unknown targets or mechanisms like natural products. It would be helpful for drug safety surveillance in all phases of drug development.