Abstract
Drug-drug interaction (DDI) is becoming a serious issue in clinical pharmacy as the use of multiple medications is more common. The PubMed database is one of the biggest literature resources for DDI studies. It contains over 150,000 journal articles related to DDI and is still expanding at a rapid pace. The extraction of DDI-related information, including compounds and proteins from PubMed, is an essential step for DDI research. In this paper, we introduce a tool, CuDDI (compute unified device architecture-based DDI searching), for identification of DDI-related terms (including compounds and proteins) from PubMed. There are three modules in this application, including the automatic retrieval of substances from PubMed, the identification of DDI-related terms, and the display of relationship of DDI-related terms. For DDI term identification, a speedup of 30–105 times was observed for the compute unified device architecture (CUDA)-based version compared with the implementation with a CPU-based Python version. CuDDI can be used to discover DDI-related terms and relationships of these terms, which has the potential to help clinicians and pharmacists better understand the mechanism of DDIs. CuDDI is available at: https://github.com/chengusf/CuDDI.
Highlights
A drug-drug interaction (DDI) occurs when the pharmacologic effect of a given drug is altered by the action of another drug, leading to unpredictable clinical effects [1]
To identify Drug-drug interaction (DDI) information from PubMed literature, we have developed a random sampling-based statistical algorithm using substances [8,9]
We introduce a compute unified device architecture (CUDA)-based application, CuDDI
Summary
A drug-drug interaction (DDI) occurs when the pharmacologic effect of a given drug is altered by the action of another drug, leading to unpredictable clinical effects [1]. DDIs may make the drug less effective, delay drug absorption, or cause unexpected harmful side effects [2]. In 2007, DDIs caused approximately 0.054% of emergency room visits, 0.57% of hospital admissions, and 0.12% of rehospitalizations in the United States [3]. Polypharmacy, the concurrent use of multiple medications, is an important factor for increasing the risk of DDIs [3]. Detecting DDIs is of great interest to the pharmaceutical industry, drug regulatory agencies, healthcare professionals and patients [4]. DDIs are frequently reported in clinical and scientific journals [5,6,7]. The PubMed, developed by Molecules 2019, 24, 1081; doi:10.3390/molecules24061081 www.mdpi.com/journal/molecules
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