• SWETHA RANI AITHA Department of Pharmacology, Osmania Medical College, Koti, Hyderabad, Telangana, India.
  • SRAVANI MARPAKA Adverse Drug Reaction Monitoring Centre-Osmania Medical College, Koti, Pharmacovigilance Program of India, Indian Pharmacopeia Commission, Hyderabad, India.
  • CHAKRADHAR T Department of Pharmacology, Osmania Medical College, Koti, Hyderabad, Telangana, India.
  • BHUVANESHWARI E Department of Pharmacology, Gandhi Medical College, Musheerabad, Secunderabad, Telangana, India.
  • SWARUPA RANI KASUKURTHI Department of Pharmacology, Osmania Medical College, Koti, Hyderabad, Telangana, India.



Big data, Computerized medical information, Health-care sectors, Analysis, Pharmacovigilance


Big data analysis has enhanced its demand nowadays in various sectors of health-care including pharmacovigilance. The exact definition of big data is not known to many people though it is routinely used by them. Big data refer to immense and voluminous computerized medical information which are obtained from electronic health records, administrative data, registries related to disease, drug monitoring, etc. This data are usually collected from doctors and pharmacists in a health-care facility. Analysis of big data in pharmacovigilance is useful for early raising of safety alerts, line listing them for signal detection of drugs and vaccines, and also for their validation. The present paper is intended to discuss big data analytics in pharmacovigilance focusing on global prospect and domestic country-India.


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Harpaz R, Dumochel W, Shah NH. Big data and adverse drug reaction detection. Clin Pharmacol Ther 2016; 99:268-70.

Walker AM, Zhou X, Ananthakrishnan AN, Weiss LS, Shen R, Sobel RE, et al. Computer-assisted expert case definition in electronic health records. Int J Med Inform 2016; 86:62-70.

Trifiro G, Patadia V, Sturkenboom M. Post-marketing safety surveillance: Where does signal detection using electronic healthcare records fit into the big picture? Drug Saf 2013; 36:183-97.

Patadia VK, Coloma P, Schuemie MJ, Herings R, Gini R, Mazzaglia G, et al, EU-ADR Consortium. Using real-world healthcare data for pharmacovigilance signal detection-the experience of the EU-ADR project. Expert Rev Clin Pharmacol 2015; 8:95-102.

Uppsala Monitoring Centre. What is VigiBase? Available from: http:// [Last accessed on 2021 Jun 06].

Bate A, Reynolds RF, Caubel P. The hope, hype, and reality of big data for pharmacovigilance. Ther Adv Drug Saf 2018; 9:5-11.

Ventola CL. Big data and Pharmacovigilance: Data mining for adverse drug events and interactions. P T 2018; 43:340-51.

Sarker A, Gonzalez G. Portable automatic text classification for adverse drug reaction detection through multi-corpus training. J Biomed Inform 2015; 53:196-207.

De Rosa M, Fenza G, Gallo A, Gallo M, Loia V. Pharmacovigilance in the era of social media: Discovering adverse drug events cross-relating Twitter and PubMed. Future Gen Comput Syst 2021; 114:394-402.

Bate A, Pariente A, Hauben M, Begaud B. Quantitative signal detection and analysis in pharmacovigilance. Manns Pharm 2014;26:159-86.

Iyer SV, Harpaz R, LePendu P, Bauer-Mehren A, Shah NH. Mining clinical text for signals of adverse drug-drug interactions. J Am Med Inform Assoc 2014; 21:353-62.

Jose Rossello. Available from: big-data-approaches-in-pharmacovigilance. [Last accessed on 2021 Jun 13].

Duggirala HJ, Tonning JM, Smith E, Bright RA, Baker JD, Ball R, et al. Use of data mining at the food and drug administration. J Am Med Inform Assoc 2016; 23:428-34.

AsPEN collaborators; Andersen M, Bergman U, Choi NK, Gerhard T,Huang C, Jalbert J, et al. The Asian pharmacoepidemiology network (AsPEN): Promoting multi-national collaboration for pharmacoepidemiologic research in Asia. Pharmacoepidemiol Drug Saf 2013; 22:700-4.

Suissa S, Henry D, Caetano P, Dormuth CR, Ernst P, Hemmelgarn B, et al. CNODES: The Canadian network for observational drug effect studies. Open Med 2012; 6:e134-40.

Hripcsak G, Duke JD, Shah NH, Reich CG, Huser V, Schuemie MJ, et al. Observational health data sciences and informatics (OHDSI): Opportunities for observational researchers. Stud Health Technol Inform 2015; 216:574-8.

Wang SV, Schneeweiss S. On behalf of the joint ISPE-ISPOR special task force on real-world evidence in health care decision making: Reporting to improve reproducibility and facilitate validity assessment for health care database studies V1.0. Pharmacoepidemiology Drug Saf 2017; 26:1570.

Berger ML, Sox H, Willke R. Good practices for real-world data studies of treatment and/or comparative effectiveness: Recommendations from the Joint ISPOR-ISPE special task force on real-world evidence in health care decisionmaking. Pharmacoepidemiol Drug Saf 2017; 26:1033-9.

Bate A. Guidance to reinforce the credibility of health care database studies and ensure their appropriate impact. Pharmacoepidemiol Drug Saf 2017; 26:1013-7.

Topol EJ. High-performance medicine: The convergence of human and artificial intelligence. Nat Med 2019; 25:44-56.

Food and Drug Administration. Artificial Intelligence and Machine Learning in Software as a Medical Device. United States: Food and Drug Administration; 2019. Available from: media/122535/download. [Last accessed on 2021 Jun 01].

Chen Y, Argentinis E, Weber G. IBM Watson: How cognitive computing can be applied to big data challenges in life sciences research. Clin Ther 2016; 38:688-701.

Simon Fraser University. Big Data Glossary. Available from: https:// [Last accessed on 2021 May 26].

Global Health Network. Global Pharmacovigilance. Glossary of Drug Safety Terms. Available from: https://www.globalpharmacovigilance. [Last accessed on 2021 Jun 05].

Big Data Made Simple. Big Data A to Z: A Glossary of Big Data Terminology; 2021. Available from: http://www.bigdata-madesimple. com/big-data-a-to-zz-a-glossary-of-big-data-terminology. [Last accessed on 2021 Jun 16].

Klungel OH, Kurz X, de Groot MC, Schlienger RG, Tcherny- Lessenot S, Grimaldi L, et al. Multi-centre, multi-database studies with common protocols: Lessons learned from the IMI PROTECT project. Pharmacoepidemiol Drug Saf 2016; 25:156-65.

Big Data Approaches in Pharmacovigilance: Using Big Real-world Data to Create Decision-relevant Evidence by Amith Govil; 2019. Available from: [Last accessed on 2021 Jun 06].

Heads of Medicines Agencies (HMA)/European Medicines Agency (EMA) Joint Task Force Meeting on Big Data: Identifying Solutions for Big Data Challenges; 2018. Available from: en/events/heads-medicines-agencies-hma-european-medicines-agency-ema-joint-big-data-task-force-meeting. [Last accessed on 2021 Jun 03].

Abernethy DR, Woodcock J, Lesko LJ. Pharmacological mechanism-based drug safety assessment and prediction. Clin Pharmacol Ther 2011;89:793-7.

USFDA-CBER Biologicals Effectiveness and Safety (BEST) System; 2020. Available from: safety-availability-biologics/cber-biologics-effectiveness-and-safety-best-system. [Last accessed on 2021 Jun 03].

Yang CC, Jiang L, Yang H, Tang X. Detecting Signals of Adverse Drug Reactions from Health Consumer Contributed Content in Social Media. Beijing, China: Presentation at 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining; 2012. p. 33-40.

Arlett P, Kurz X, Fitt H. Increasing scientific standards, independence and transparency in post-authorization studies: The role of the European network of centres for pharmacoepidemiology and pharmacovigilance. Pharmacoepidemiol Drug Saf 2012; 21:690-6.

White RW, Harpaz R, Shah NH, DuMouchel W, Horvitz E. Toward enhanced pharmacovigilance using patient-generated data on the internet. Clin Pharmacol Ther 2014; 96:239-46.

Olsen M, Petronis KR, Froslev T, Mo J, Stephansson O, Granath F, et al. Maternal use of varenicline and risk of congenital malformations. Pharmacoepidemiol Drug Saf 2015; 24:244.

Purcell B, Marie R, Griffin, Haynes K, Lin ND, McMahill-Walraven CN, et al. Safety of Trumenba vaccine among pregnant women in the United States: Planning and design of a large-scale multi-site observational study. Pharmacoepidemiol Drug Saf 2017; 26:410.

Reade S, Spencer K, Sergeant JC, Sperrin M, Schultz DM, Ainsworth J, et al. Cloudy with a chance of pain: Engagement and subsequent attrition of daily data entry in a smartphone pilot study tracking weather, disease severity, and physical activity in patients with rheumatoid arthritis. JMIR Mhealth Uhealth 2017; 5:e37.

Piwek L, Ellis DA, Andrews S, Joinson A. The rise of consumer health wearables: Promises and barriers. PLoS Med 2016; 13:e1001953.

How is Pharmacovigilance Analytics Helping the Fight Against COVID-19, Shanawaz Sheriff; 2020. Available from: https://www. [Last accessed on 10 Jun 2021].

Joppi R, Bertele V, Garattini S. Orphan drugs, orphan diseases. The first decade of orphan drug legislation in the EU. Eur J Clin Pharmacol 2013; 69:1009-24.

Brill J. Federal Trade Commission. Protecting Consumer Privacy in an Era of Rapid Change: A Discussion of the FTC’s Privacy Report. Available from: [Last available on 2021 Jun 10].

European Parliament. Council of the European Union E. U. Directive, 95/46/EC of the European Parliament and of the Council of 24 October, 1995 on the Protection of Individuals Concerning the Processing of Personal Data and on the Free Movement of Such Data. Vol. 23. Brussels, Belgium: European Parliament; 1995. p. 348.

Sloane R, Osanlou O, Lewis D, Bollegala D, Maskell S, Pirmohamed M, et al. Social media and pharmacovigilance: A review of the opportunities and challenges. Br J Clin Pharmacol 2015; 80:910-20.

McKee R. Ethical issues in using social media for health and health care research. Health Policy 2013; 110:6.

Korda M. 21st Century Cures Act: Back to the Future; 2021 Available from: [Last accessed on 2021 Jun 10].



How to Cite

RANI AITHA, S., S. MARPAKA, C. T, B. E, and S. RANI KASUKURTHI. “BIG DATA ANALYTICS IN PHARMACOVIGILANCE - A GLOBAL TREND”. Asian Journal of Pharmaceutical and Clinical Research, vol. 14, no. 10, Oct. 2021, pp. 19-24, doi:10.22159/ajpcr.2021.v14i10.42765.



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