[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118888-en":3,"doc-seo-118888-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118888,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","An industry perspective on the use of machine learning in drug and vaccine safety","Growing interest in machine learning across the pharmacovigilance lifecycle is examined through an industry-focused research scope. The study reviewed 393 papers from 2000–2021, attributing contributions to industry, academia, or regulatory authorities, and identified 33 industry papers mapped to six PV functions. Results show RWD and social media account for 63% of papers, while signal detection and data ingestion contribute 18%, with disease-specific studies and literature review comprising 12% and 6%. Trends, opportunities, and barriers are discussed, concluding that progress has been uneven but patient-safety benefits are anticipated.","TYPE Perspective  \nPUBLISHED 01 February 2023 DOI 10.3389/fdsfr.2023.1110498  \nOPEN ACCESS  \nEDITED BY  \nTaxiarchis Botsis,  \nJohns Hopkins University, United States  \nREVIEWED BY  \nJuan M. Banda,  \nGeorgia State University, United States Jenna Reps,  \nJanssen Pharmaceuticals, Inc., United States  \n*CORRESPONDENCE  \nAndrew Bate,  \n [andrew.x.bate@gsk.com](andrew.x.bate@gsk.com)  \nSPECIALTY SECTION  \nThis article was submitted to Advanced Methods in Pharmacovigilance and  \nPharmacoepidemiology, a section of the journal  \nFrontiers in Drug Safety and Regulation  \nRECEIVED 28 November 2022  \nACCEPTED 18 January 2023  \nPUBLISHED 01 February 2023  \nCITATION  \nPainter JL, Kassekert R and Bate A (2023),  \nAn industry perspective on the use of machine learning in drug and vaccine safety.  \nFront. Drug. Saf. Regul. 3:1110498 .  \ndoi: 10.3389/fdsfr.2023.1110498  \nCOPYRIGHT  \n© 2023 Painter, Kassekert and Bate. This isan open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nAn industry perspective on the use of machine learning in drug and vaccine safety  \nJeffery L. Painter 1, Raymond Kassekert 2 and Andrew Bate 3,4*  \n1GlaxoSmithKline, Global Safety, Durham, NC, United States, 2GlaxoSmithKline, Global Safety, Upper Providence, PA, United States, 3GlaxoSmithKline, Global Safety, Brentford, Middlesex, United Kingdom, 4 London School of Hygiene and Tropical Medicine, London, United Kingdom  \nIn recent years there has been growing interest in the use of machine learning across the pharmacovigilance lifecycle to enhance safety monitoring of drugs and vaccines. Here we describe the scope of industry-based research into the use of machine learning for safety purposes. We conducted an examination of the findings from a previously published systematic review; 393 papers sourced from a literature search from 2000–2021 were analyzed and attributed to either industry, academia, or regulatory authorities. Overall, 33 papers verified to be industry contributions were then assigned to one of six categories representing the most frequent PV functions (data ingestion, disease-specific studies, literature review, real world data, signal detection, and social media) . RWD and social media comprised 63%(21/33) of the papers, signal detection and data ingestion comprised 18%(6/33) of the papers, while disease-specific studies and literature reviews represented 12%(4/33) and 6%(2/33) of the papers, respectively. Herein we describe the trends and opportunities observed in industry application of machine learning in pharmacovigilance, along with discussing the potential barriers. We conclude that although progress to date has been uneven, industry is very interested in applying machine learning to the pharmacovigilance lifecycle, which it is hoped may ultimately enhance patient safety.  \nKEYWORDS  \npharmacovigilance, machine learning-ML, drug safety, vaccines safety, artificial intelligence  \n1 Introduction  \nThe vast increase in the volume of safety reporting over the last years was only exacerbated during the global COVID-19 pandemic. The introduction of new vaccines and medicines in response to the pandemic resulted in more than 1.8 million new safety reports. Enabling the safety community to cope with the onslaught of data has led to increased interest in automating pharmacovigilance (PV) activities within the pharmaceutical industry and prompted safety organizations to advance their technological capabilities (Rudolph et al., 2022) .  \nEven before the pandemic, automation and its potential benefits for PV activities were well recognized (Kassekert et al., 2022) . Rules based autom","cbCaiqQYvC7fLD3R","https://ap.wps.com/l/cbCaiqQYvC7fLD3R","pdf",1573823,1,10,"English","en",105,"# Introduction\n## Pharmacovigilance data growth and COVID-19 impact\n## Automation and robotic process automation (RPA)\n# Industry research scope for machine learning in PV\n## Review of 2000–2021 literature and study attribution\n## Six pharmacovigilance functions and paper categorization\n## Observed trends, opportunities, and barriers\n# Conclusion","[{\"question\":\"What is the document’s main focus regarding machine learning in safety?\",\"answer\":\"It focuses on how machine learning is being used across the pharmacovigilance lifecycle to enhance safety monitoring for drugs and vaccines, emphasizing industry-based research scope.\"},{\"question\":\"How were papers selected and categorized in the review?\",\"answer\":\"A literature search covering 2000–2021 yielded 393 papers, which were attributed to industry, academia, or regulatory authorities, and the 33 industry contributions were mapped to six PV functions.\"},{\"question\":\"Which pharmacovigilance functions were most represented among industry papers?\",\"answer\":\"RWD and social media formed 63% of the industry papers, while signal detection and data ingestion accounted for 18%; disease-specific studies and literature reviews made up 12% and 6%, respectively.\"}]","An industry perspective on the use of machine learning in drug and vaccine safety | PDF",1785720791,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"an-industry-perspective-on-the-use-of-machine-learning-in-drug-and-vaccine-safety","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/an-industry-perspective-on-the-use-of-machine-learning-in-drug-and-vaccine-safety/118888/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the document’s main focus regarding machine learning in safety?","Question",{"text":75,"@type":76},"It focuses on how machine learning is being used across the pharmacovigilance lifecycle to enhance safety monitoring for drugs and vaccines, emphasizing industry-based research scope.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were papers selected and categorized in the review?",{"text":80,"@type":76},"A literature search covering 2000–2021 yielded 393 papers, which were attributed to industry, academia, or regulatory authorities, and the 33 industry contributions were mapped to six PV functions.",{"name":82,"@type":73,"acceptedAnswer":83},"Which pharmacovigilance functions were most represented among industry papers?",{"text":84,"@type":76},"RWD and social media formed 63% of the industry papers, while signal detection and data ingestion accounted for 18%; 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