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

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.

[1]  T. Wilson,et al.  Thromboxane synthase inhibition enhances furosemide-induced renal vasodilation. , 1993, Clinical and investigative medicine. Medecine clinique et experimentale.

[2]  C. A. Jankel,et al.  Epidemiology of Drug-Drug Interactions as a Cause of Hospital Admissions , 1993, Drug safety.

[3]  D. Bates,et al.  Systems analysis of adverse drug events. ADE Prevention Study Group. , 1995, JAMA.

[4]  N. Dickey,et al.  Systems analysis of adverse drug events. , 1996, JAMA.

[5]  D. Greenblatt,et al.  Sertraline does not inhibit cytochrome P450 3A-mediated drug metabolism in vivo. , 1997, Psychopharmacology bulletin.

[6]  S D Hall,et al.  Regioselective and stereoselective metabolism of ibuprofen by human cytochrome P450 2C. , 1997, Biochemical pharmacology.

[7]  J. Vane,et al.  Nonsteroid drug selectivities for cyclo-oxygenase-1 rather than cyclo-oxygenase-2 are associated with human gastrointestinal toxicity: a full in vitro analysis. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[8]  B. Horowitz,et al.  Cyproheptadine for serotonin syndrome in an accidental pediatric sertraline ingestion. , 1999, Pediatric emergency care.

[9]  K. Sayal,et al.  Psychotropic interactions with warfarin , 2000, Acta psychiatrica Scandinavica.

[10]  O. Corrigan,et al.  A risky business: the detection of adverse drug reactions in clinical trials and post-marketing exercises. , 2002, Social science & medicine.

[11]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[12]  D. Greenblatt,et al.  Interaction of Warfarin With Drugs, Natural Substances, and Foods , 2005, Journal of clinical pharmacology.

[13]  D. Havrda,et al.  Enhanced Antithrombotic Effect of Warfarin Associated with Low‐Dose Alcohol Consumption , 2005, Pharmacotherapy.

[14]  Jon Emery,et al.  CYP2C9 gene variants, drug dose, and bleeding risk in warfarin-treated patients: A HuGEnet™ systematic review and meta-analysis , 2005, Genetics in Medicine.

[15]  R. Obach,et al.  SERTRALINE IS METABOLIZED BY MULTIPLE CYTOCHROME P450 ENZYMES, MONOAMINE OXIDASES, AND GLUCURONYL TRANSFERASES IN HUMAN: AN IN VITRO STUDY , 2005, Drug Metabolism and Disposition.

[16]  Paul A Clemons,et al.  The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease , 2006, Science.

[17]  P. Neuvonen,et al.  Effects of the Antifungals Voriconazole and Fluconazole on the Pharmacokinetics of S-(+)- and R-(−)-Ibuprofen , 2006, Antimicrobial Agents and Chemotherapy.

[18]  A. Barabasi,et al.  Drug—target network , 2007, Nature Biotechnology.

[19]  T. Miyata,et al.  Warfarin dose and the pharmacogenomics of CYP2C9 and VKORC1 - rationale and perspectives. , 2007, Thrombosis research.

[20]  Sang Chung,et al.  New Era in Drug Interaction Evaluation: US Food and Drug Administration Update on CYP Enzymes, Transporters, and the Guidance Process , 2008, Journal of clinical pharmacology.

[21]  G. Guyatt,et al.  Physicians Evidence-Based Clinical Practice Development : American College of Chest Thrombolytic Therapy Guideline Methodology for Antithrombotic and , 2008 .

[22]  P. Bork,et al.  Drug Target Identification Using Side-Effect Similarity , 2008, Science.

[23]  S. Hennessy,et al.  Warfarin With Fluoroquinolones, Sulfonamides, or Azole Antifungals: Interactions and the Risk of Hospitalization for Gastrointestinal Bleeding , 2008, Clinical pharmacology and therapeutics.

[24]  Lang Li,et al.  Literature mining on pharmacokinetics numerical data: A feasibility study , 2009, J. Biomed. Informatics.

[25]  Ravi Iyengar,et al.  Network analyses in systems pharmacology , 2009, Bioinform..

[26]  Lang Li,et al.  DMD 28746 1 Physiologically-Based Pharmacokinetic Model of Mechanism-Based Inhibition of CYP 3 A by Clarithromycin , 2009 .

[27]  Michael J. Keiser,et al.  Predicting new molecular targets for known drugs , 2009, Nature.

[28]  Chitta Baral,et al.  Discovering drug–drug interactions: a text-mining and reasoning approach based on properties of drug metabolism , 2010, Bioinform..

[29]  H. Farber,et al.  Successful Bosentan and Nonnucleoside Reverse Transcriptase Inhibitor‐Based Therapy in a Patient with Acquired Immunodeficiency Syndrome and Pulmonary Arterial Hypertension , 2010, Pharmacotherapy.

[30]  Shiew-Mei Huang,et al.  Drug interactions evaluation: an integrated part of risk assessment of therapeutics. , 2010, Toxicology and applied pharmacology.

[31]  Sara K. Quinney,et al.  Physiologically Based Pharmacokinetic Model of Mechanism-Based Inhibition of CYP3A by Clarithromycin , 2010, Drug Metabolism and Disposition.

[32]  Paloma Martínez,et al.  A linguistic rule-based approach to extract drug-drug interactions from pharmacological documents , 2011, BMC Bioinformatics.

[33]  Carol Friedman,et al.  Drug-drug interaction through molecular structure similarity analysis , 2012, J. Am. Medical Informatics Assoc..

[34]  Russ B. Altman,et al.  Discovery and Explanation of Drug-Drug Interactions via Text Mining , 2011, Pacific Symposium on Biocomputing.

[35]  Xu Han,et al.  Literature Based Drug Interaction Prediction with Clinical Assessment Using Electronic Medical Records: Novel Myopathy Associated Drug Interactions , 2012, PLoS Comput. Biol..

[36]  Mark Crowther,et al.  Oral anticoagulant therapy: Antithrombotic Therapy and Prevention of Thrombosis, 9th ed: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines. , 2012, Chest.

[37]  L. Brewer,et al.  Drug interactions that matter , 2012 .

[38]  J. Krzysztoń-Russjan,et al.  Interactions between drugs and sulforaphane modulate the drug metabolism enzymatic system , 2012, Pharmacological reports : PR.

[39]  R. Altman,et al.  Data-Driven Prediction of Drug Effects and Interactions , 2012, Science Translational Medicine.

[40]  Adis Medical Writers.  Drug interactions result from a number of underlying pharmacokinetic and pharmacodynamic mechanisms , 2013 .

[41]  S. Vilar,et al.  Detection of Drug-Drug Interactions by Modeling Interaction Profile Fingerprints , 2013, PloS one.

[42]  R. Altman,et al.  Informatics confronts drug-drug interactions. , 2013, Trends in pharmacological sciences.

[43]  Xiaoqian Jiang,et al.  Text mining driven drug-drug interaction detection , 2013, 2013 IEEE International Conference on Bioinformatics and Biomedicine.