Mining and visualizing high-order directional drug interaction effects using the FAERS database

Abstract

Background 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. Availability and implementation http://lishenlab.com/d3i/explorer.html

The International Conference on Intelligent Biology and Medicine 2019: computational methods for drug interactions

“…However, the existing approaches become inefficient from both computational and illustrative perspectives when considering more than three drugs in interactions. Yao et al [2] develop an efficient approach to estimate the directional effects of high-order DDIs through frequent itemset mining. They also introduce 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.…”

A pharmacovigilance study of pharmacokinetic drug interactions using a translational informatics discovery approach

“…Most of these methods were initially used to detect only single-drug effects but were later expanded to include two-and even higher-order drug combinations. [20][21][22] However, evaluation of two drug combinations using these SRS data sources often do not take into account the This article is protected by copyright. All rights reserved.…”

“…Most of these methods were initially used to detect only single-drug effects but were later expanded to include two-and even higher-order drug combinations. [20][21][22] However, evaluations of two drug combinations using these SRS data sources often do not take into account the relationship between the two drugs. 23 Thus, to address this shortcoming, we focused on known pharmacokinetic (PK) mechanisms to determine a relationship between drug pairs in our study.…”

Novel method for the prediction of drug-drug Interaction based on Gene Expression profiles

“…In drug discovery, analyses of drug-drug interactions (DDIs) are aimed at the prevention or reduction of possible reactions caused by therapeutic drug combinations [12][13][14][15][16][17][18]. Several machine-learning approaches have been proposed to accurately predict interactions between drugs.…”

“…This non-linearity often prevents the selection of effective drugs, since it is difficult to determine if expression levels of individual genes are up- or downregulated by particular drug treatments. In drug discovery, analyses of drug-drug interactions (DDIs) are aimed at the prevention or reduction of possible reactions caused by therapeutic drug combinations (Celebi, Uyar, Yasar, Gumus, Dikenelli and Dumontier, 2019; Yao, Tsang, Sun, Quinney, Zhang, Ning, Li and Shen, 2020; Shi, Shang, Gao, Zhang and Yiu, 2018; Poleksic and Xie, 2019; Zhang, Sun, Wang and Zhang, 2019; Langness and Everson, 2016; Masoudi-Sobhanzadeh, Omidi, Amanlou and Masoudi-Nejad, 2019). However, the above-mentioned methods are not capable of predicting unknown interactions if data for known DDIs are not available.…”