How Experts Search for Potential Drug-Drug Interactions Using Popular Available Data Sources

“Life moves pretty fast.”

Ferris Bueller made that pronouncement popular back in 1986. But today, in the world of R&D-intensive sciences, data is what’s moving quickly. It is abundant, ever increasing, essential, and constantly updating all around us. Identifying information in a timely fashion is a critical need, and there are many curated resources out there. How are people using them, how could they be improved, and what can we learn from their similarities and differences? In this series, we’ll look at some popular databases and strategies for how to use them effectively.

Challenges in PDDI

Identifying and preventing potential drug-drug interactions (PDDI) is an important goal to maximize patient benefits and minimize potential harm. There are drug information experts who seek out and evaluate evidence of PDDI. Among the challenges for this effort: there is no single repository, nor a defined, validated, standard search strategy. A recent study in the Journal of Medical Internet Research set out to identify common methods for conducting PDDI literature searches.

The authors began by identifying 70 drug information experts, including compendia editors, pharmacists, physicians, researchers, and regulatory scientists. Study participants were sent a survey of with questions covering six areas:

Work setting, experience and expertise
Developing and conducting literature searches
Resources used in the search
Keywords specific to PDDI information needs
Study types included or excluded
Search terms

Of those who responded, 60% had over 10 years of experience and just over half worked for clinical solutions or knowledge-base vendors. Participants were mostly English- speaking and based in the US, a limitation noted by the authors.

Survey Results

Over 90% of respondents did not utilize assistance from a librarian when developing or conducting searches.
All participants used PubMed for scientific literature; the next most commonly used search engine was Google Scholar. Google Scholar, utilizing a different algorithm across a broader domain, would likely locate ‘gray literature’ not found in Medline. The authors discuss a combination of PubMed and Google Scholar allowing for more comprehensive retrieval.
There were few reports of using EMBASE, which might be influenced by the study demographics (EMBASE is known for expanded international coverage, supplementing Medline).
Common additional search strategies were based on citations, both found within a paper’s bibliography (80%), or newer works citing the identified paper (45%). “Find Similar” or “Find Related” features were also heavily used (75%).
Only 20% of participants reported searching by author name as a strategy.
Product labels, accessed over the web, are highly valued, sought by 90% of participants. Open-access databases Drugs@FDA (85%) and DailyMed (65%) were also used frequently. Subscription databases, including Lexicomp and Micromedex, were listed by participants less than 50% of the time, as were a variety of compendia including Facts and Comparisons and Top 100 Drug Interactions
Across all PDDI searching, the generic drug name was the most common keyword. The highest frequency of search terms occurred in searches for identifying if a PDDI exists, and for assessing the mechanism. The fewest keywords were used in queries assessing health outcomes. It is important to note that there is no standard nomenclature for the role of a drug in an PDDI interaction. There were several custom lists and much variation in the keyword groups and terms provided; clearly there is opportunity here for model and ontology development.
Trials were the most common study types included in searches, followed by case reports and systematic reviews. It is interesting that ClinicalTrials.gov was not called out as a resource by the participants. Most respondents included meeting abstracts or conference proceedings in their results. Less frequently mentioned “Other study types” included dose-effect relationship, pharmacokinetic, and meeting abstracts. Most commonly excluded study types were animal and in-vitro studies.

Using Machine Learning and Data Mining Tools for PDDI

Many groups are now looking at PDDI using machine learning and data mining tools referencing titles, abstracts, MeSH headings, and even full text of articles and reports. None of the participants surveyed mentioned using any of these specialized tools in their search activities, and there were open-ended questions in the survey to allow for any response. There are also pre-defined filters, often created in conjunction with librarians and clinical experts, that few (20%) of the respondents mentioned.

Ideally, PDDI searching should be transparent and repeatable. Further studies, including a larger survey base, would help ascertain if these results apply more broadly to the field. Automated tools, standard search strategies and ontologies, and a compendium of sources could assist in bringing a higher level of recall with precision to this critical task.

Through this series, we have looked at a range of information needs. The goals and strategies of those involved in clinical trials, potential drug-drug interaction research, clinical care, or systematic reviews vary widely. There are multiple resources, both public and private, designed to provide intelligent access to the most timely and relevant data. Life is moving fast. Biomedical sciences are committed to identifying and refining tools and best practices to advance our knowledge, in the 21^st century and beyond.

Interested in learning about other data sources and how they’re used? Check out:

Did you know? RightFind® Navigate unifies data sources within an open integration ecosystem to maximize the value of your organization’s digital information assets and enables knowledge workers with contextualized discovery to relevant information. Learn more here.