Identifying Common Methods Used by Drug Interaction Experts for Finding Evidence About Potential Drug-Drug Interactions: Web-Based Survey

Abstract

Background Preventing drug interactions is an important goal to maximize patient benefit from medications. Summarizing potential drug-drug interactions (PDDIs) for clinical decision support is challenging, and there is no single repository for PDDI evidence. Additionally, inconsistencies across compendia and other sources have been well documented. Standard search strategies for complete and current evidence about PDDIs have not heretofore been developed or validated. Objective This study aimed to identify common methods for conducting PDDI literature searches used by experts who routinely evaluate such evidence. Methods We invited a convenience sample of 70 drug information experts, including compendia editors, knowledge-base vendors, and clinicians, via emails to complete a survey on identifying PDDI evidence. We created a Web-based survey that included questions regarding the (1) development and conduct of searches; (2) resources used, for example, databases, compendia, search engines, etc; (3) types of keywords used to search for the specific PDDI information; (4) study types included and excluded in searches; and (5) search terms used. Search strategy questions focused on 6 topics of the PDDI information—(1) that a PDDI exists; (2) seriousness; (3) clinical consequences; (4) management options; (5) mechanism; and (6) health outcomes. Results Twenty participants (response rate, 20/70, 29%) completed the survey. The majority (17/20, 85%) were drug information specialists, drug interaction researchers, compendia editors, or clinical pharmacists, with 60% (12/20) having >10 years’ experience. Over half (11/20, 55%) worked for clinical solutions vendors or knowledge-base vendors. Most participants developed (18/20, 90%) and conducted (19/20, 95%) search strategies without librarian assistance. PubMed (20/20, 100%) and Google Scholar (11/20, 55%) were most commonly searched for papers, followed by Google Web Search (7/20, 35%) and EMBASE (3/20, 15%). No respondents reported using Scopus. A variety of subscription and open-access databases were used, most commonly Lexicomp (9/20, 45%), Micromedex (8/20, 40%), Drugs@FDA (17/20, 85%), and DailyMed (13/20, 65%). Facts and Comparisons was the most commonly used compendia (8/20, 40%). Across the 6 attributes of interest, generic drug name was the most common keyword used. Respondents reported using more types of keywords when searching to identify the existence of PDDIs and determine their mechanism than when searching for the other 4 attributes (seriousness, consequences, management, and health outcomes). Regarding the types of evidence useful for evaluating a PDDI, clinical trials, case reports, and systematic reviews were considered relevant, while animal and in vitro data studies were not. Conclusions This study suggests that drug interaction experts use various keyword strategies and various database and Web resources depending on the...

Potential Drug interactions with Drugs used for Bipolar Disorder: A Comparison of 6 Drug Interaction Database Programs

“…Poor agreement between drug interaction database programs is well documented [58][59][60], including in studies of psychiatric and antiepileptic drugs that involve potential DDI rated major or severe [32,[61][62][63][64]. Potential DDI are challenging to define and detect [5][6][7]14], and both polypharmacy and biologics further increase the methodological complexity [65][66][67]. Experts disagree on search strategies, resources for seeking evidence, and processes to rank evidence and classify potential DDI [7,9].…”

“…Potential DDI are challenging to define and detect [5][6][7]14], and both polypharmacy and biologics further increase the methodological complexity [65][66][67]. Experts disagree on search strategies, resources for seeking evidence, and processes to rank evidence and classify potential DDI [7,9]. Drug interaction database programs use various information sources and have inconsistent criteria to define severity [5-9, 59, 68].…”

“…Drug interaction database programs are widely recognized as the primary tool to assist physicians in preventing DDI but also demonstrate the need to understand the limitations of automation [4]. There are no internationally recognized standards to define DDI risk [5,6], and database programs use different methods to search, identify and classify risk [7][8][9].…”

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“…Another limitation can be the use of a single training or evaluation dataset which may be biased towards particular types of interactions. For example, methods that rely on DDIs in the DrugBank database (Wishart et al, 2017) for training or testing may perform well on the types of interactions in that database but may not generalize to other datasets that include different types of interactions (Peters et al, 2015;Grizzle et al, 2019).…”

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