Analysis of adverse drug reactions using drug and drug target interactions and graph-based methods.
OBJECTIVE
The purpose of this study was to integrate knowledge about drugs, drug targets, and topological methods. The goals were to build a system facilitating the study of adverse drug events, to make it easier to find possible explanations, and to group similar drug-drug interaction cases in the adverse drug reaction reports from the US Food and Drug Administration (FDA).


METHODS
We developed a system that analyses adverse drug reaction (ADR) cases reported by the FDA. The system contains four modules. First, we integrate drug and drug target databases that provide information related to adverse drug reactions. Second, we classify drug and drug targets according to anatomical therapeutic chemical classification (ATC) and drug target ontology (DTO). Third, we build drug target networks based on drug and drug target databases. Finally, we apply topological analysis to reveal drug interaction complexity for each ADR case reported by the FDA.


RESULTS
We picked 1952 ADR cases from the years 2005-2006. Our dataset consisted of 1952 cases, of which 1471 cases involved ADR targets, 845 cases involved absorption, distribution, metabolism, and excretion (ADME) targets, and 507 cases involved some drugs acting on the same targets, namely, common targets (CTs). We then investigated the cases involving ADR targets, ADME targets, and CTs using the ATC system and DTO. In the cases that led to death, the average number of common targets (NCTs) was 0.879 and the average of average clustering coefficient (ACC) was 0.067. In cases that did not lead to death, the average NCTs was 0.551, and the average of ACC was 0.039.


CONCLUSIONS
We implemented a system that can find possible explanations and cluster similar ADR cases reported by the FDA. We found that the average of ACC and the average NCTs in cases leading to death are higher than in cases not leading to death, suggesting that the interactions in cases leading to death are generally more complicated than in cases not leading to death. This indicates that our system can help not only in analysing ADRs in terms of drug-drug interactions but also by providing drug target assessments early in the drug discovery process.
Lin
Shih-Fang
SF
Institute of Information Systems and Applications, National Tsing Hua University, Hsinchu 300, Taiwan. linfion07@gmail.com
Xiao
Ke-Ting
KT
Huang
Yu-Ting
YT
Chiu
Chung-Cheng
CC
Soo
Von-Wun
VW
D016907:Adverse Drug Reaction Reporting Systems; D001185:Artificial Intelligence; D016000:Cluster Analysis; D057225:Data Mining; D019992:Databases as Topic; D004347:Drug Interactions; D004358:Drug Therapy / Q000401:mortality; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D010599:Pharmacokinetics; D013329:Structure-Activity Relationship; D049490:Systems Biology; D018511:Systems Integration; D014481:United States; D014486:United States Food and Drug Administration