Exploiting heterogeneous publicly available data sources for drug safety surveillance: computational framework and case studies

ABSTRACT Objective: Driven by the need of pharmacovigilance centres and companies to routinely collect and review all available data about adverse drug reactions (ADRs) and adverse events of interest, we introduce and validate a computational framework exploiting dominant as well as emerging publicly available data sources for drug safety surveillance. Methods: Our approach relies on appropriate query formulation for data acquisition and subsequent filtering, transformation and joint visualization of the obtained data. We acquired data from the FDA Adverse Event Reporting System (FAERS), PubMed and Twitter. In order to assess the validity and the robustness of the approach, we elaborated on two important case studies, namely, clozapine-induced cardiomyopathy/myocarditis versus haloperidol-induced cardiomyopathy/myocarditis, and apixaban-induced cerebral hemorrhage. Results: The analysis of the obtained data provided interesting insights (identification of potential patient and health-care professional experiences regarding ADRs in Twitter, information/arguments against an ADR existence across all sources), while illustrating the benefits (complementing data from multiple sources to strengthen/confirm evidence) and the underlying challenges (selecting search terms, data presentation) of exploiting heterogeneous information sources, thereby advocating the need for the proposed framework. Conclusions: This work contributes in establishing a continuous learning system for drug safety surveillance by exploiting heterogeneous publicly available data sources via appropriate support tools.

[1]  Graciela Gonzalez-Hernandez,et al.  Pharmacovigilance on Twitter? Mining Tweets for Adverse Drug Reactions , 2014, AMIA.

[2]  P Ryan,et al.  Novel Data‐Mining Methodologies for Adverse Drug Event Discovery and Analysis , 2012, Clinical pharmacology and therapeutics.

[3]  Siddhartha R. Dalal,et al.  Using information mining of the medical literature to improve drug safety , 2011, J. Am. Medical Informatics Assoc..

[4]  Olivier Bodenreider,et al.  Leveraging MEDLINE indexing for pharmacovigilance - Inherent limitations and mitigation strategies , 2015, J. Biomed. Informatics.

[5]  Fan Yu,et al.  Towards large-scale twitter mining for drug-related adverse events , 2012, SHB '12.

[6]  Vassilis Koutkias,et al.  A Multiagent System for Integrated Detection of Pharmacovigilance Signals , 2016, Journal of Medical Systems.

[7]  Elizabeth Murphy,et al.  The importance of direct patient reporting of suspected adverse drug reactions: a patient perspective. , 2011, British journal of clinical pharmacology.

[8]  Marie Lindquist,et al.  Social Media and Networks in Pharmacovigilance , 2011, Drug safety.

[9]  [Atypical antipsychotic drugs]. , 2000, Nederlands tijdschrift voor geneeskunde.

[10]  M. Schuemie,et al.  Useful Interplay Between Spontaneous ADR Reports and Electronic Healthcare Records in Signal Detection , 2015, Drug Safety.

[11]  Nihan Kesim Cicekli,et al.  Postmarketing Safety Study Tool: A Web Based, Dynamic, and Interoperable System for Postmarketing Drug Surveillance Studies , 2015, BioMed research international.

[12]  Thomas Joseph,et al.  A pipeline to extract drug-adverse event pairs from multiple data sources , 2014, BMC Medical Informatics and Decision Making.

[13]  Markus Krötzsch,et al.  Wikidata , 2014, Commun. ACM.

[14]  Rachel E. Ginn,et al.  Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter , 2016, Drug Safety.

[15]  D. Celermajer,et al.  Myocarditis and cardiomyopathy associated with clozapine , 1999, The Lancet.

[16]  Hua Xu,et al.  Identifying Plausible Adverse Drug Reactions Using Knowledge Extracted from the Literature , 2014, AMIA.

[17]  Altug Akay,et al.  A Novel Data-Mining Approach Leveraging Social Media to Monitor Consumer Opinion of Sitagliptin , 2015, IEEE Journal of Biomedical and Health Informatics.

[18]  R. Wanchoo.  Adverse renal effects of novel molecular oncologic targeted therapies: A review of the FDA Adverse Event Reporting System (FAERS) , 2016 .

[19]  L. Hazell,et al.  Under-Reporting of Adverse Drug Reactions , 2006, Drug safety.

[20]  Danushka Bollegala,et al.  Social media and pharmacovigilance: A review of the opportunities and challenges. , 2015, British journal of clinical pharmacology.

[21]  Canary Wharf,et al.  Guidelines on good pharmacovigilance practices (GVP) , 2012 .

[22]  Kenneth H. Lai,et al.  Clinicians’ Reports in Electronic Health Records Versus Patients’ Concerns in Social Media: A Pilot Study of Adverse Drug Reactions of Aspirin and Atorvastatin , 2016, Drug Safety.

[23]  Jan A. Kors,et al.  Evaluating Social Media Networks in Medicines Safety Surveillance: Two Case Studies , 2015, Drug Safety.

[24]  Keyuan Jiang,et al.  Mining Twitter Data for Potential Drug Effects , 2013, ADMA.

[25]  Martijn J Schuemie,et al.  Using real-world healthcare data for pharmacovigilance signal detection – the experience of the EU-ADR project , 2015, Expert review of clinical pharmacology.

[26]  Rajesh Ghosh,et al.  Aims and approaches of Web-RADR: a consortium ensuring reliable ADR reporting via mobile devices and new insights from social media , 2015, Expert opinion on drug safety.

[27]  Marie-Christine Jaulent,et al.  Computational Approaches for Pharmacovigilance Signal Detection: Toward Integrated and Semantically-Enriched Frameworks , 2015, Drug Safety.

[28]  Jun Zhu,et al.  Apixaban in patients with atrial fibrillation. , 2011, The New England journal of medicine.

[29]  Ryen W. White,et al.  Toward Enhanced Pharmacovigilance Using Patient-Generated Data on the Internet , 2014, Clinical pharmacology and therapeutics.

[30]  Qing Zeng-Treitler,et al.  Exploring and developing consumer health vocabularies. , 2006, Journal of the American Medical Informatics Association : JAMIA.

[31]  Richard Platt,et al.  The U.S. Food and Drug Administration's Mini‐Sentinel Program , 2012 .

[32]  Taha A. Kass-Hout,et al.  Digital Drug Safety Surveillance: Monitoring Pharmaceutical Products in Twitter , 2014, Drug Safety.

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