Yao, XiaohuiTsang, TiffanySun, QingQuinney, SaraZhang, PengyueNing, XiaLi, LangShen, Li2022-04-192022-04-192020Yao X, Tsang T, Sun Q, Quinney S, Zhang P, Ning X, Li L, Shen L. Mining and visualizing high-order directional drug interaction effects using the FAERS database. BMC Med Inform Decis Mak. 2020 Mar 18;20(Suppl 2):50. doi: 10.1186/s12911-020-1053-z. PMID: 32183790; PMCID: PMC7079342.https://hdl.handle.net/1805/28568Background: Adverse drug events (ADEs) often occur as a result of drug-drug interactions (DDIs). The use of data mining for detecting effects of drug combinations on ADE has attracted growing attention and interest, however, most studies focused on analyzing pairwise DDIs. Recent efforts have been made to explore the directional relationships among high-dimensional drug combinations and have shown effectiveness on prediction of ADE risk. However, the existing approaches become inefficient from both computational and illustrative perspectives when considering more than three drugs. Methods: We proposed an efficient approach to estimate the directional effects of high-order DDIs through frequent itemset mining, and further developed a novel visualization method to organize and present the high-order directional DDI effects involving more than three drugs in an interactive, concise and comprehensive manner. We demonstrated its performance by mining the directional DDIs associated with myopathy using a publicly available FAERS dataset. Results: Directional effects of DDIs involving up to seven drugs were reported. Our analysis confirmed previously reported myopathy associated DDIs including interactions between fusidic acid with simvastatin and atorvastatin. Furthermore, we uncovered a number of novel DDIs leading to increased risk for myopathy, such as the co-administration of zoledronate with different types of drugs including antibiotics (ciprofloxacin, levofloxacin) and analgesics (acetaminophen, fentanyl, gabapentin, oxycodone). Finally, we visualized directional DDI findings via the proposed tool, which allows one to interactively select any drug combination as the baseline and zoom in/out to obtain both detailed and overall picture of interested drugs. Conclusions: We developed a more efficient data mining strategy to identify high-order directional DDIs, and designed a scalable tool to visualize high-order DDI findings. The proposed method and tool have the potential to contribute to the drug interaction research and ultimately impact patient health care.en-USAttribution 4.0 InternationalHigh-order drug interactionDirectional effectFAERSAprioriSunburstArticle