title: Improving drug safety: From adverse drug reaction knowledge discovery to clinical implementation.
abstract: Adverse drug reactions (ADRs) are a major public health concern, causing over 100,000 fatalities in the United States every year with an annual cost of $136 billion. Early detection and accurate prediction of ADRs is thus vital for drug development and patient safety. Multiple scientific disciplines, namely pharmacology, pharmacovigilance, and pharmacoinformatics, have been addressing the ADR problem from different perspectives. With the same goal of improving drug safety, this article summarizes and links the research efforts in the multiple disciplines into a single framework from comprehensive understanding of the interactions between drugs and biological system and the identification of genetic and phenotypic predispositions of patients susceptible to higher ADR risks and finally to the current state of implementation of medication-related decision support systems. We start by describing available computational resources for building drug-target interaction networks with biological annotations, which provides a fundamental knowledge for ADR prediction. Databases are classified by functions to help users in selection. Post-marketing surveillance is then introduced where data-driven approach can not only enhance the prediction accuracy of ADRs but also enables the discovery of genetic and phenotypic risk factors of ADRs. Understanding genetic risk factors for ADR requires well organized patient genetics information and analysis by pharmacogenomic approaches. Finally, current state of clinical decision support systems is presented and described how clinicians can be assisted with the integrated knowledgebase to minimize the risk of ADR. This review ends with a discussion of existing challenges in each of disciplines with potential solutions and future directions.
authors.affiliation: Big Data Decision Institute, The First Affiliated Hospital, International Immunology Center, The Biomedical Translational Research Institute, Jinan University, Guangzhou, Guangdong, China.
authors.forename: Yuxiang
authors.initials: Y
authors.lastname: Tan
authors.affiliation: Big Data Decision Institute, The First Affiliated Hospital, International Immunology Center, The Biomedical Translational Research Institute, Jinan University, Guangzhou, Guangdong, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Hu
authors.affiliation: Big Data Decision Institute, The First Affiliated Hospital, International Immunology Center, The Biomedical Translational Research Institute, Jinan University, Guangzhou, Guangdong, China.
authors.forename: Xiaoxiao
authors.initials: X
authors.lastname: Liu
authors.affiliation: Big Data Decision Institute, The First Affiliated Hospital, International Immunology Center, The Biomedical Translational Research Institute, Jinan University, Guangzhou, Guangdong, China.
authors.forename: Zhinan
authors.initials: Z
authors.lastname: Yin
authors.affiliation: Department of Computer Science, Wayne State University, Detroit, USA.
authors.forename: Xue-Wen
authors.initials: XW
authors.lastname: Chen
authors.affiliation: Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, USA. Electronic address: meiliu@kumc.edu.
authors.forename: Mei
authors.initials: M
authors.lastname: Liu
mesh_terms: D054316:Biomarkers, Pharmacological; D016208:Databases, Factual; D064420:Drug-Related Side Effects and Adverse Reactions / Q000453:epidemiology*; D006801:Humans; D008490:Medical Informatics; D010597:Pharmacogenetics; D014481:United States
chemical_list: D054316:Biomarkers, Pharmacological
keywords: Adverse drug reaction; Drug safety; Drug-target interaction; Pharmacogenomic