Ontology-based literature mining and class effect analysis of adverse drug reactions associated with neuropathy-inducing drugs

BackgroundAdverse drug reactions (ADRs), also called as drug adverse events (AEs), are reported in the FDA drug labels; however, it is a big challenge to properly retrieve and analyze the ADRs and their potential relationships from textual data. Previously, we identified and ontologically modeled over 240 drugs that can induce peripheral neuropathy through mining public drug-related databases and drug labels. However, the ADR mechanisms of these drugs are still unclear. In this study, we aimed to develop an ontology-based literature mining system to identify ADRs from drug labels and to elucidate potential mechanisms of the neuropathy-inducing drugs (NIDs).ResultsWe developed and applied an ontology-based SciMiner literature mining strategy to mine ADRs from the drug labels provided in the Text Analysis Conference (TAC) 2017, which included drug labels for 53 neuropathy-inducing drugs (NIDs). We identified an average of 243 ADRs per NID and constructed an ADR-ADR network, which consists of 29 ADR nodes and 149 edges, including only those ADR-ADR pairs found in at least 50% of NIDs. Comparison to the ADR-ADR network of non-NIDs revealed that the ADRs such as pruritus, pyrexia, thrombocytopenia, nervousness, asthenia, acute lymphocytic leukaemia were highly enriched in the NID network. Our ChEBI-based ontology analysis identified three benzimidazole NIDs (i.e., lansoprazole, omeprazole, and pantoprazole), which were associated with 43 ADRs. Based on ontology-based drug class effect definition, the benzimidazole drug group has a drug class effect on all of these 43 ADRs. Many of these 43 ADRs also exist in the enriched NID ADR network. Our Ontology of Adverse Events (OAE) classification further found that these 43 benzimidazole-related ADRs were distributed in many systems, primarily in behavioral and neurological, digestive, skin, and immune systems.ConclusionsOur study demonstrates that ontology-based literature mining and network analysis can efficiently identify and study specific group of drugs and their associated ADRs. Furthermore, our analysis of drug class effects identified 3 benzimidazole drugs sharing 43 ADRs, leading to new hypothesis generation and possible mechanism understanding of drug-induced peripheral neuropathy.

[1]  Bjoern Peters,et al.  VO: Vaccine Ontology , 2009 .

[2]  Ryan R Brinkman,et al.  OntoFox: web-based support for ontology reuse , 2010, BMC Research Notes.

[3]  Christoph Steinbeck,et al.  A database for chemical proteomics: ChEBI. , 2012, Methods in molecular biology.

[4]  Dragomir R. Radev,et al.  Literature-Based Discovery of IFN-γ and Vaccine-Mediated Gene Interaction Networks , 2010, Journal of biomedicine & biotechnology.

[5]  J. Gerring.  A case study , 2011, Technology and Society.

[6]  Cui Tao,et al.  OAE: The Ontology of Adverse Events , 2014, J. Biomed. Semant..

[7]  Christophe G. Lambert,et al.  Bridging Islands of Information to Establish an Integrated Knowledge Base of Drugs and Health Outcomes of Interest , 2014, Drug Safety.

[8]  Yongqun He,et al.  Ontology-based Brucella vaccine literature indexing and systematic analysis of gene-vaccine association network , 2011, BMC Immunology.

[9]  E. Brown,et al.  The Medical Dictionary for Regulatory Activities (MedDRA) , 1999, Drug safety.

[10]  Natalya F. Noy,et al.  Protégé: A Tool for Managing and Using Terminology in Radiology Applications , 2007, Journal of Digital Imaging.

[11]  Jian Yang,et al.  Towards Internet-Age Pharmacovigilance: Extracting Adverse Drug Reactions from User Posts in Health-Related Social Networks , 2010, BioNLP@ACL.

[12]  D. Lindberg,et al.  Unified Medical Language System , 2020, Definitions.

[13]  W. Loh,et al.  Integrated Systems Pharmacology Analysis of Clinical Drug-Induced Peripheral Neuropathy , 2014, CPT: pharmacometrics & systems pharmacology.

[14]  Yongqun He,et al.  Development and application of an interaction network ontology for literature mining of vaccine-associated gene-gene interactions , 2015, Journal of Biomedical Semantics.

[15]  K. Petersen,et al.  A summary of Food and Drug Administration‐reported adverse events and drug interactions occurring during therapy with omeprazole, lansoprazole and pantoprazole , 2003, Alimentary pharmacology & therapeutics.

[16]  Azadeh Nikfarjam,et al.  Pattern mining for extraction of mentions of Adverse Drug Reactions from user comments. , 2011, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[17]  Yongqun He,et al.  Identification of fever and vaccine-associated gene interaction networks using ontology-based literature mining , 2012, J. Biomed. Semant..

[18]  Giovanni Scardoni,et al.  Analyzing biological network parameters with CentiScaPe , 2009, Bioinform..

[19]  Dina Demner-Fushman,et al.  A dataset of 200 structured product labels annotated for adverse drug reactions , 2018, Scientific Data.

[20]  Abdul Mateen Rajput,et al.  Automatic detection of adverse events to predict drug label changes using text and data mining techniques , 2013, Pharmacoepidemiology and drug safety.

[21]  Liwei Wang,et al.  Ontology-based systematical representation and drug class effect analysis of package insert-reported adverse events associated with cardiovascular drugs used in China , 2017, Scientific Reports.

[22]  Jon D Duke,et al.  ADESSA: A Real-Time Decision Support Service for Delivery of Semantically Coded Adverse Drug Event Data. , 2010, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[23]  A. B. Thomson,et al.  Are the orally administered proton pump inhibitors equivalent? A comparison of lansoprazole, omeprazole, pantoprazole, and rabeprazole , 2000, Current gastroenterology reports.

[24]  Yen S. Low,et al.  Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art , 2014, Drug Safety.

[25]  Adam D. Schuyler,et al.  SciMiner: web-based literature mining tool for target identification and functional enrichment analysis , 2009, Bioinform..

[26]  J. Dent,et al.  Long-term omeprazole treatment in resistant gastroesophageal reflux disease: efficacy, safety, and influence on gastric mucosa. , 2000, Gastroenterology.

[27]  David S. Wishart,et al.  DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs , 2010, Nucleic Acids Res..

[28]  P. Bork,et al.  A side effect resource to capture phenotypic effects of drugs , 2010, Molecular systems biology.

[29]  Pernille Warrer,et al.  Using text-mining techniques in electronic patient records to identify ADRs from medicine use. , 2012, British Journal of Clinical Pharmacology.

[30]  Yongqun He,et al.  Ontology-based literature mining of E. coli vaccine-associated gene interaction networks , 2017, J. Biomed. Semant..

[31]  Rebecca Racz,et al.  Ontology-based collection, representation and analysis of drug-associated neuropathy adverse events , 2016, J. Biomed. Semant..

[32]  C. H. Camargo,et al.  Species distribution and susceptibility profile of Candida species in a Brazilian public tertiary hospital , 2010, BMC Research Notes.

[33]  Dragomir R. Radev,et al.  Mining of vaccine-associated IFN-γ gene interaction networks using the Vaccine Ontology , 2011, J. Biomed. Semant..

[34]  P. Corey,et al.  Incidence of Adverse Drug Reactions in Hospitalized Patients , 2012 .

[35]  Chinwe O. Ikpo,et al.  Functionalized Benzimidazole Scaffolds: Privileged Heterocycle for Drug Design in Therapeutic Medicine , 2016, Archiv der Pharmazie.

[36]  Prakash M. Nadkarni,et al.  Determining correspondences between high-frequency MedDRA concepts and SNOMED: a case study , 2010, BMC Medical Informatics Decis. Mak..