Extraction of pharmacokinetic evidence of drug-drug interactions from the literature

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 F10.93, MCC0.74, iAUC0.99) and sentences (F10.76, MCC0.65, iAUC0.83). 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.National Institutes of Health, National Library of Medicine Program, grant 01LM011945-01 "BLR: Evidence-based Drug-Interaction Discovery: In-Vivo, In-Vitro and Clinical," a grant from the Indiana University Collaborative Research Program 2013, "Drug-Drug Interaction Prediction from Large-scale Mining of Literature and Patient Records," as well as a grant from the joint program between the Fundação Luso-Americana para o Desenvolvimento (Portugal) and National Science Foundation (USA), 2012-2014, "Network Mining For Gene Regulation And Biochemical Signaling.

Potential Determinants of Prescribers' Drug-Drug Interaction Knowledge

10.1016/j.sapharm.2007.10.004Research in Social and Administrative Pharmacy44355-36

Clinical risk management in Dutch community pharmacies: the case of drug-drug interactions.

Item does not contain fulltextBACKGROUND: The prevention of drug-drug interactions requires a systematic approach for which the concept of clinical risk management can be used. The objective of our study was to measure the frequency, nature and management of drug-drug interaction alerts as these occur in daily practice of Dutch community pharmacies. METHODS: In total, 63 Dutch pharmacies collected all drug-drug interaction alerts during 153 research days (on average 2.4 days/pharmacy), as well as variables related to these alerts, such as involved medicines, first time or recurrent drug-drug interaction, same or different prescribers, patient data (age, sex) and information about the management of drug-drug interactions by the pharmacy. The latter was discriminated into internal procedures only and external action, such as communication with the patient, the prescriber or the anticoagulation clinic and prescription modification. All drug-drug interactions were classified into categories of clinical relevance (A-F) and available evidence (0-4). RESULTS: A total of 43,129 prescription-only medicines were dispensed during the study period. On average, 16.8 interaction alerts per day per pharmacy were collected. Approximately 6% of all prescriptions generated a drug-drug interaction alert. Of all alerts (n = 2572), 31.1% occurred for the first time and with 21% two different prescribers were involved. The 20 most frequently occurring drug-drug interaction alerts accounted for approximately 76% of all alerts. Cardiovascular drugs, NSAIDs, oral contraceptives and antibacterials were most frequently involved. External action was taken in response to 27.3% of the alerts, meaning either a modification of one of the concerned prescriptions (n = 65; 9.3%), communication with the prescriber or anticoagulation clinic (n = 90; 12.8%) or communication with the patient or a relative (n = 547; 77.9%). Where there was no external action (n = 1860; 72.3%), pharmacists concluded in about two-thirds of cases that the drug-drug interaction had been managed in the past. Other reasons not to intervene externally were for instance: incorrect alert; acceptable drug-drug interaction; or outcome of the interaction considered irrelevant. Adjusted for several variables, a first alert was found to be a main determinant for external action. After stratifying for first alerts no other significant determinants were found. CONCLUSIONS: A high frequency of drug-drug interaction alerts was found. Most concerned recurrent alerts, which were the main reason not to act externally. Concerning the assessment phase in the risk-management process, drug-drug interactions with no or low evidence/relevance should be reconsidered. Concerning the management of drug-drug interactions in pharmacies, the opportunity to actively suppress alerts for a certain period of time should be studied in more detail. There are indicators that the management of patient-orientated advice could be improved and a greater degree of consistency developed for the management of first and recurrent interaction alerts