Dataset Information

A novel algorithm for analyzing drug-drug interactions from MEDLINE literature.

ABSTRACT: Drug-drug interaction (DDI) is becoming a serious clinical safety issue as the use of multiple medications becomes more common. Searching the MEDLINE database for journal articles related to DDI produces over 330,000 results. It is impossible to read and summarize these references manually. As the volume of biomedical reference in the MEDLINE database continues to expand at a rapid pace, automatic identification of DDIs from literature is becoming increasingly important. In this article, we present a random-sampling-based statistical algorithm to identify possible DDIs and the underlying mechanism from the substances field of MEDLINE records. The substances terms are essentially carriers of compound (including protein) information in a MEDLINE record. Four case studies on warfarin, ibuprofen, furosemide and sertraline implied that our method was able to rank possible DDIs with high accuracy (90.0% for warfarin, 83.3% for ibuprofen, 70.0% for furosemide and 100% for sertraline in the top 10% of a list of compounds ranked by p-value). A social network analysis of substance terms was also performed to construct networks between proteins and drug pairs to elucidate how the two drugs could interact.

SUBMITTER: Lu Y

PROVIDER: S-EPMC4661569 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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A novel algorithm for analyzing drug-drug interactions from MEDLINE literature.

Lu Yin Y Shen Dan D Pietsch Maxwell M Nagar Chetan C Fadli Zayd Z Huang Hong H Tu Yi-Cheng YC Cheng Feng F

Scientific reports 20151127

Drug-drug interaction (DDI) is becoming a serious clinical safety issue as the use of multiple medications becomes more common. Searching the MEDLINE database for journal articles related to DDI produces over 330,000 results. It is impossible to read and summarize these references manually. As the volume of biomedical reference in the MEDLINE database continues to expand at a rapid pace, automatic identification of DDIs from literature is becoming increasingly important. In this article, we pres ...[more]

PMID: 26612138

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Project description:ObjectiveThe research sought to evaluate seven drug information resources, specifically designed for analyzing drug interactions for scope, completeness, and ease of use, and determine the consistency of content among the seven resources.MethodsA cross-sectional study was conducted where 100 drug-drug and drug-dietary supplement interactions were analyzed using 7 drug information resources: Lexicomp Interactions module, Micromedex Drug Interactions, Clinical Pharmacology Drug Interaction Report, Facts & Comparisons eAnswers, Stockley's Drug Interactions (10th edition), Drug Interactions Analysis and Management (2014), and Drug Interaction Facts (2015). The interaction sample was developed based on published resources and peer input. Two independent reviewers gathered data for each interaction from each of the 7 resources using a common form.ResultsEighty-two drug-drug and 18 drug-dietary supplement interactions were analyzed. Scope scores were higher for Lexicomp Interactions (97.0%), Clinical Pharmacology Drug Interaction Report (97.0%), and Micromedex Drug Interactions (93.0%) compared to all other resources (p<0.05 for each comparison). Overall completeness scores were higher for Micromedex Drug Interactions (median 5, interquartile range [IQR] 4 to 5) compared to all other resources (p<0.01 for each comparison) and were higher for Lexicomp Interactions (median 4, IQR 4 to 5), Facts & Comparisons eAnswers (median 4, IQR 4 to 5), and Drug Interaction Facts (4, IQR 4 to 5) compared to all other resources, except Micromedex (p<0.05 for each comparison). Ease of use, in terms of time to locate information and time to gather information, was similar among resources. Consistency score was higher for Micromedex (69.9%) compared to all other resources (p<0.05 for each comparison).ConclusionsClinical Pharmacology Drug Interaction Report, Lexicomp Interactions, and Micromedex Drug Interactions scored highest in scope. Micromedex Drug Interactions and Lexicomp Interactions scored highest in completeness. Consistency scores were overall low, but Micromedex Drug Interactions was the highest.

Project description:Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases. Though DDI is investigated in domains ranging in scale from intracellular biochemistry to human populations, literature mining has not been used to extract specific types of experimental evidence, which are reported differently for distinct experimental goals. We focus on pharmacokinetic evidence for DDI, essential for identifying causal mechanisms of putative interactions and as input for further pharmacological and pharmacoepidemiology investigations. We used manually curated corpora of PubMed abstracts and annotated sentences to evaluate the efficacy of literature mining on two tasks: first, identifying PubMed abstracts containing pharmacokinetic evidence of DDIs; second, extracting sentences containing such evidence from abstracts. We implemented a text mining pipeline and evaluated it using several linear classifiers and a variety of feature transforms. The most important textual features in the abstract and sentence classification tasks were analyzed. We also investigated the performance benefits of using features derived from PubMed metadata fields, various publicly available named entity recognizers, and pharmacokinetic dictionaries. Several classifiers performed very well in distinguishing relevant and irrelevant abstracts (reaching F1?0.93, MCC?0.74, iAUC?0.99) and sentences (F1?0.76, MCC?0.65, iAUC?0.83). We found that word bigram features were important for achieving optimal classifier performance and that features derived from Medical Subject Headings (MeSH) terms significantly improved abstract classification. We also found that some drug-related named entity recognition tools and dictionaries led to slight but significant improvements, especially in classification of evidence sentences. Based on our thorough analysis of classifiers and feature transforms and the high classification performance achieved, we demonstrate that literature mining can aid DDI discovery by supporting automatic extraction of specific types of experimental evidence.

Dataset Information

A novel algorithm for analyzing drug-drug interactions from MEDLINE literature.

Publications

A novel algorithm for analyzing drug-drug interactions from MEDLINE literature.

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