ADReCS: an ontology database for aiding standardization and hierarchical classification of adverse drug reaction terms

被引:77
作者
Cai, Mei-Chun [1 ]
Xu, Quan [1 ]
Pan, Yan-Jing [1 ,2 ]
Pan, Wen [1 ]
Ji, Nan [3 ]
Li, Yin-Bo [1 ]
Jin, Hai-Jing [1 ]
Liu, Ke [1 ]
Ji, Zhi-Liang [1 ,2 ]
机构
[1] Xiamen Univ, Sch Life Sci, State Key Lab Stress Cell Biol, Xiamen 361102, Fujian, Peoples R China
[2] Xiamen Univ, Key Lab Chem Biol Fujian Prov, Xiamen 361005, Fujian, Peoples R China
[3] Xiamen Huli Ctr Dis Control & Prevent, Xiamen 361000, Fujian, Peoples R China
基金
中国国家自然科学基金;
关键词
LARGE-SCALE PREDICTION; INFORMATION; SYSTEMS;
D O I
10.1093/nar/gku1066
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Adverse drug reactions (ADRs) are noxious and unexpected effects during normal drug therapy. They have caused significant clinical burden and been responsible for a large portion of new drug development failure. Molecular understanding and in silico evaluation of drug (or candidate) safety in laboratory is thus so desired, and unfortunately has been largely hindered by misuse of ADR terms. The growing impact of bioinformatics and systems biology in toxicological research also requires a specialized ADR term system that works beyond a simple glossary. Adverse Drug Reaction Classification System (ADReCS; http://bioinf.xmu.edu.cn/ADReCS) is a comprehensive ADR ontology database that provides not only ADR standardization but also hierarchical classification of ADR terms. The ADR terms were pre-assigned with unique digital IDs and at the same time were well organized into a four-level ADR hierarchy tree for building an ADR-ADR relation. Currently, the database covers 6544 standard ADR terms and 34 796 synonyms. It also incorporates information of 1355 single active ingredient drugs and 134 022 drug-ADR pairs. In summary, ADReCS offers an opportunity for direct computation on ADR terms and also provides clues to mining common features underlying ADRs.
引用
收藏
页码:D907 / D913
页数:7
相关论文
共 19 条
[1]  
[Anonymous], 2002, SAFETY MED GUIDE DET
[2]   Phase II and Phase III attrition rates 2011-2012 [J].
Arrowsmith, John ;
Miller, Philip .
NATURE REVIEWS DRUG DISCOVERY, 2013, 12 (08) :568-568
[3]   Role of systems pharmacology in understanding drug adverse events [J].
Berger, Seth I. ;
Iyengar, Ravi .
WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE, 2011, 3 (02) :129-135
[4]   The Unified Medical Language System (UMLS): integrating biomedical terminology [J].
Bodenreider, O .
NUCLEIC ACIDS RESEARCH, 2004, 32 :D267-D270
[5]   Formalizing MedDRA to support semantic reasoning on adverse drug reaction terms [J].
Bousquet, Cedric ;
Sadou, Eric ;
Souvignet, Julien ;
Jaulent, Marie-Christine ;
Declerck, Gunnar .
JOURNAL OF BIOMEDICAL INFORMATICS, 2014, 49 :282-291
[6]   The Medical Dictionary for Regulatory Activities (MedDRA) [J].
Brown, EG ;
Wood, L ;
Wood, S .
DRUG SAFETY, 1999, 20 (02) :109-117
[7]   An assessment of the publicly disseminated evidence of safety used in decisions to withdraw medicinal products from the UK and US markets [J].
Clarke, A ;
Deeks, JJ ;
Shakir, SAW .
DRUG SAFETY, 2006, 29 (02) :175-181
[8]   When good drugs go bad [J].
Giacomini, Kathleen M. ;
Krauss, Ronald M. ;
Roden, Dan M. ;
Eichelbaum, Michel ;
Hayden, Michael R. .
NATURE, 2007, 446 (7139) :975-977
[9]   Data, information, knowledge and principle: back to metabolism in KEGG [J].
Kanehisa, Minoru ;
Goto, Susumu ;
Sato, Yoko ;
Kawashima, Masayuki ;
Furumichi, Miho ;
Tanabe, Mao .
NUCLEIC ACIDS RESEARCH, 2014, 42 (D1) :D199-D205
[10]  
Kavitha D, 2010, ASIAN J PHARM RES HE, V2, P124