Characterization of the mechanism of drug-drug interactions from PubMed using MeSH terms

Identifying drug-drug interaction (DDI) is an important topic for the development of safe pharmaceutical drugs and for the optimization of multidrug regimens for complex diseases such as cancer and HIV. There have been about 150,000 publications on DDIs in PubMed, which is a great resource for DDI studies. In this paper, we introduced an automatic computational method for the systematic analysis of the mechanism of DDIs using MeSH (Medical Subject Headings) terms from PubMed literature. MeSH term is a controlled vocabulary thesaurus developed by the National Library of Medicine for indexing and annotating articles. Our method can effectively identify DDI-relevant MeSH terms such as drugs, proteins and phenomena with high accuracy. The connections among these MeSH terms were investigated by using co-occurrence heatmaps and social network analysis. Our approach can be used to visualize relationships of DDI terms, which has the potential to help users better understand DDIs. As the volume of PubMed records increases, our method for automatic analysis of DDIs from the PubMed database will become more accurate.

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

[2]  B. Stricker,et al.  Hospitalisations and emergency department visits due to drug–drug interactions: a literature review , 2007, Pharmacoepidemiology and drug safety.

[3]  Susumu Goto,et al.  Network-Based Analysis and Characterization of Adverse Drug-Drug Interactions , 2011, J. Chem. Inf. Model..

[4]  J. M. Hutzler,et al.  Drug–Drug Interactions: Designing Development Programs and Appropriate Product Labeling , 2011 .

[5]  K. Brøsen,et al.  Rifampicin treatment greatly increases the apparent oral clearance of quinidine. , 1999, Pharmacology & toxicology.

[6]  L. Dubuske.  The Role of P-Glycoprotein and Organic Anion-Transporting Polypeptides in Drug Interactions , 2005, Drug safety.

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

[8]  M. Slovak,et al.  Cyclosporine inhibition of P-glycoprotein in chronic myeloid leukemia blast phase. , 2002, Blood.

[9]  R. Sharan,et al.  INDI: a computational framework for inferring drug interactions and their associated recommendations , 2012, Molecular systems biology.

[10]  W. L. Nelson,et al.  ROLE OF ITRACONAZOLE METABOLITES IN CYP3A4 INHIBITION , 2004, Drug Metabolism and Disposition.

[11]  Ping Zhang,et al.  DDI-CPI, a server that predicts drug–drug interactions through implementing the chemical–protein interactome , 2014, Nucleic Acids Res..

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

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

[14]  V. Prachayasittikul,et al.  Cytochrome P450 enzyme mediated herbal drug interactions (Part 2) , 2014, EXCLI journal.

[15]  T. Peeters,et al.  Theophylline and its metabolites produce a stimulating cholinergic effect on the small intestine and a nonadrenergic noncholinergic relaxing effect on the colon: a comparative study in the rabbit intestine. , 2007, Journal of veterinary pharmacology and therapeutics.

[16]  L J Lesko,et al.  Drug Interaction Studies: Study Design, Data Analysis, and Implications for Dosing and Labeling , 2007, Clinical pharmacology and therapeutics.

[17]  Andrew Bate,et al.  Drug-drug interactions - a preventable patient safety issue? , 2008, British journal of clinical pharmacology.

[18]  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..

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

[20]  Obach Rs.  Drug-drug interactions: an important negative attribute in drugs. , 2003 .

[21]  Yang Dai,et al.  In vitro metabolism of cyclosporine A by human kidney CYP3A5. , 2004, Biochemical pharmacology.

[22]  Ronald N. Kostoff,et al.  Information content in Medline record fields , 2004, Int. J. Medical Informatics.

[23]  David S. Wishart,et al.  DrugBank 4.0: shedding new light on drug metabolism , 2013, Nucleic Acids Res..

[24]  Hongkang Mei,et al.  Systematic Prediction of Pharmacodynamic Drug-Drug Interactions through Protein-Protein-Interaction Network , 2013, PLoS Comput. Biol..

[25]  Russ B. Altman,et al.  A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports , 2012, J. Am. Medical Informatics Assoc..

[26]  S. Carruthers,et al.  Quinidine-rifampin interaction. , 1981, The New England journal of medicine.

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

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

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

[30]  Jiezhong Chen,et al.  Roles of rifampicin in drug-drug interactions: underlying molecular mechanisms involving the nuclear pregnane X receptor. , 2006 .

[31]  Yi-Cheng Tu,et al.  A novel algorithm for analyzing drug-drug interactions from MEDLINE literature , 2015, Scientific Reports.

[32]  Jerome J. Schentag,et al.  Effects of the Concomitant Administration of Tamsulosin (0.8 mg/Day) on the Pharmacokinetic and Safety Profile of Theophylline (5 mg/kg): A Placebo-Controlled Evaluation , 2002, The Journal of international medical research.

[33]  Bharat T Thakrar,et al.  Detecting signals of drug-drug interactions in a spontaneous reports database. , 2007, British journal of clinical pharmacology.

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