Will the future of pharmacovigilance be more automated?

References

• EMA. Good pharmacovigilance practices; 2018. [cited Mar 17, 2023]. Available from: https://www.ema.europa.eu/en/human-regulatory/post-authorisation/pharmacovigilance/good-pharmacovigilance-practices
   Google Scholar
• US FDA. Good pharmacovigilance practices and pharmacoepidemiologic assessment. [cited Mar 17, 2023]. Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/good-pharmacovigilance-practices-and-pharmacoepidemiologic-assessment
   Google Scholar
• Watson S, Härmark L. Desogestrel and panic attacks - a new suspected adverse drug reaction reported by patients and health care professionals on spontaneous reports. Br J Clin Pharmacol. 2018;84(8):1858–1859. doi: 10.1111/bcp.13617
   PubMed Web of Science ®Google Scholar
• Pariente A, Micallef J, Lahouegue A, et al. What place for intelligent automation and artificial intelligence to preserve and strengthen vigilance expertise in the face of increasing declarations? Therapie. 2023;78:131–143. doi: 10.1016/j.therap.2022.11.004
   PubMedGoogle Scholar
• Moride Y, Haramburu F, Requejo AA, et al. Under-reporting of adverse drug reactions in general practice. Br J Clin Pharmacol. 1997;43(2):177–181. doi: 10.1046/j.1365-2125.1997.05417.x
   PubMed Web of Science ®Google Scholar
• Khalili M, Mesgarpour B, Sharifi H, et al. Estimation of adverse drug reaction reporting in Iran: correction for underreporting. Pharmacoepidemiol Drug Saf. 2021;30:1101–1114. doi: 10.1002/pds.5235
   PubMed Web of Science ®Google Scholar
• Ito S, Narukawa M. Estimation of the under-reporting of suspected serious adverse drug reactions in Japan using an interrupted time series analysis. Ther Innov Regul Sci. 2022;56(2):358–365. doi: 10.1007/s43441-022-00379-z
   PubMed Web of Science ®Google Scholar
• Irving G, Neves AL, Dambha-Miller H, et al. International variations in primary care physician consultation time: a systematic review of 67 countries. BMJ Open. 2017;7(10):e017902. doi: 10.1136/bmjopen-2017-017902
   PubMed Web of Science ®Google Scholar
• Cocos A, Fiks AG, Masino AJ. Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts. J Am Med Inform Assoc JAMIA. 2017;24(4):813–821. doi: 10.1093/jamia/ocw180
   PubMed Web of Science ®Google Scholar
• Nikfarjam A, Sarker A, O’Connor K, et al. Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. J Am Med Inform Assoc JAMIA. 2015;22(3):671–681. doi: 10.1093/jamia/ocu041
   PubMed Web of Science ®Google Scholar
• Létinier L, Jouganous J, Benkebil M, et al. Artificial intelligence for unstructured healthcare data: application to coding of patient reporting of adverse drug reactions. Clin Pharmacol Ther. 2021;110:392–400. doi: 10.1002/cpt.2266
   PubMed Web of Science ®Google Scholar
• Combi C, Zorzi M, Pozzani G, et al. From narrative descriptions to MedDRA: automagically encoding adverse drug reactions. J Biomed Inform. 2018;84:184–199. doi: 10.1016/j.jbi.2018.07.001
   PubMed Web of Science ®Google Scholar
• Gil-Jardiné C, Chenais G, Pradeau C, et al. Trends in reasons for emergency calls during the COVID-19 crisis in the department of Gironde, France using artificial neural network for natural language classification. Scand J Trauma Resusc Emerg Med. 2021;29:55. doi: 10.1186/s13049-021-00862-w
   PubMed Web of Science ®Google Scholar
• Norén N, Orre R, Bate A, et al. Duplicate detection in adverse drug reaction surveillance. Data Min Knowl Discov. 2007;14(3):305–328. doi: 10.1007/s10618-006-0052-8
   Web of Science ®Google Scholar
• Routray R, Tetarenko N, Abu-Assal C, et al. Application of augmented intelligence for pharmacovigilance case seriousness determination. Drug Saf. 2020;43(1):57–66. doi: 10.1007/s40264-019-00869-4
   PubMed Web of Science ®Google Scholar
• EMA list of important medical events. React Wkly. 2016;1617(1):1–1. doi: 10.1007/s40278-016-20780-z
   Google Scholar
• Ly T, Pamer C, Dang O, et al. Evaluation of Natural Language Processing (NLP) systems to annotate drug product labeling with MedDRA terminology. J Biomed Inform. 2018;83:73–86. doi: 10.1016/j.jbi.2018.05.019
   PubMed Web of Science ®Google Scholar
• Shen Z, Spruit M. Automatic extraction of adverse drug reactions from summary of product characteristics. Appl Sci. 2021;11:2663. doi: 10.3390/app11062663
   Google Scholar
• International Energy Agency. Data centres and data transmission networks – analysis. [cited Apr 7, 2023]. Available from: https://www.iea.org/reports/data-centres-and-data-transmission-networks
   Google Scholar
• Ouzzani M, Hammady H, Fedorowicz Z, et al. Rayyan—a web and mobile app for systematic reviews. Syst Rev. 2016;5(1):210. doi: 10.1186/s13643-016-0384-4
   PubMedGoogle Scholar
• Habets PC, van IJzendoorn DG, Vinkers CH, et al. Development and validation of a machine-learning algorithm to predict the relevance of scientific articles within the field of teratology. Reprod Toxicol. 2022;113:150–154. doi: 10.1016/j.reprotox.2022.09.001
   PubMed Web of Science ®Google Scholar
• Assistant by scite. Assistant by scite – Your AI-powered research partner. [cited Apr 7, 2023]. Available from: https://scite.ai.
   Google Scholar
• Consensus - Evidence-based answers, faster. [cited Apr 7, 2023]. Available from: https://consensus.app/home/.
   Google Scholar
• Obsidian. [cited Apr 7, 2023]. Available from: https://obsidian.md/
   Google Scholar
• ResearchRabbit. [cited Apr 7, 2023]. Available from: https://www.researchrabbit.ai
   Google Scholar
• Cherkas Y, Ide J, van Stekelenborg J. Leveraging machine learning to facilitate individual case causality assessment of adverse drug reactions. Drug Saf. 2022;45(5):571–582. doi: 10.1007/s40264-022-01163-6
   PubMed Web of Science ®Google Scholar
• Bergvall T, Norén GN, Lindquist M. vigiGrade: a tool to identify well-documented individual case reports and highlight systematic data quality issues. Drug Saf. 2014;37(1):65–77. doi: 10.1007/s40264-013-0131-x
   PubMed Web of Science ®Google Scholar
• Galatti L, Giustini SE, Sessa A, et al. Neuropsychiatric reactions to drugs: an analysis of spontaneous reports from general practitioners in Italy. Pharmacol Res. 2005;51(3):211–216. doi: 10.1016/j.phrs.2004.08.003
   PubMed Web of Science ®Google Scholar
• Kamel Boulos MN, Zhang P. Digital twins: from personalised medicine to precision public health. J Pers Med. 2021;11:745. doi: 10.3390/jpm11080745
   PubMed Web of Science ®Google Scholar
• Thiong’o GM, Rutka JT. Digital twin technology: the future of predicting neurological complications of pediatric cancers and their treatment. Front Oncol. 2021;11:781499. doi: 10.3389/fonc.2021.781499
   PubMed Web of Science ®Google Scholar
• Gosselt HR, Bazelmans EA, Lieber T, et al. Development of a multivariate prediction model to identify individual case safety reports which require clinical review. Pharmacoepidemiol Drug Saf. 2022;31:1300–1307. doi: 10.1002/pds.5553
   PubMed Web of Science ®Google Scholar
• Muñoz MA, Dal Pan GJ, Wei Y-J, et al. Towards automating adverse event review: a prediction model for case report utility. Drug Saf. 2020;43:329–338. doi: 10.1007/s40264-019-00897-0
   PubMed Web of Science ®Google Scholar
• Kolodner JL. An introduction to case-based reasoning. Artif Intell Rev. 1992;6(1):3–34. doi: 10.1007/BF00155578
   Web of Science ®Google Scholar
• Kim HR, Sung M, Park JA, et al. Analyzing adverse drug reaction using statistical and machine learning methods: a systematic review. Medicine (Baltimore). 2022;101(25):e29387. doi: 10.1097/MD.0000000000029387
   PubMed Web of Science ®Google Scholar
• Pham M, Cheng F, Ramachandran K. A comparison study of algorithms to detect drug-adverse event associations: frequentist, bayesian, and machine-learning approaches. Drug Saf. 2019;42:743–750. doi: 10.1007/s40264-018-00792-0
   PubMed Web of Science ®Google Scholar
• Ryan PB, Schuemie MJ, Welebob E, et al. Defining a reference set to support methodological research in drug safety. Drug Saf. 2013;36(S1):S33–47. doi: 10.1007/s40264-013-0097-8
   PubMed Web of Science ®Google Scholar
• Bae J-H, Baek Y-H, Lee J-E, et al. Machine learning for detection of safety signals from spontaneous reporting system data: example of nivolumab and docetaxel. Front Pharmacol. 2020;11:602365. doi: 10.3389/fphar.2020.602365
   PubMed Web of Science ®Google Scholar
• Lee J-E, Kim JH, Bae J-H, et al. Detecting early safety signals of infliximab using machine learning algorithms in the Korea adverse event reporting system. Sci Rep. 2022;12(1):14869. doi: 10.1038/s41598-022-18522-z
   PubMed Web of Science ®Google Scholar
• Hauben M. Artificial intelligence in pharmacovigilance: do we need explainability? Pharmacoepidemiol Drug Saf. 2022;31:1311–1316. doi: 10.1002/pds.5501
   PubMed Web of Science ®Google Scholar
• Pinheiro LC, Kurz X. Artificial intelligence in pharmacovigilance: a regulatory perspective on explainability. Pharmacoepidemiol Drug Saf. 2022;31:1308–1310. doi: 10.1002/pds.5524
   PubMed Web of Science ®Google Scholar
• Drancé M, Boudin M, Mougin F, et al. Neuro-symbolic XAI for computational drug repurposing. Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021). 2023;220–225.
   Google Scholar
• Brown TB, Mann B, Ryder N, et al. Language models are few-shot learners. arXiv200514165v4. 2020;1:3–75 .
   Google Scholar
• Radford A, Narasimhan K, Salimans T, et al. Improving language understanding by generative pre-training. Available from: https://paperswithcode.com/paper/improving-language-understanding-by
   Google Scholar
• Haupt CE, Marks M. AI-Generated medical advice—GPT and beyond. JAMA. 2023;329(16):1349–1350. doi: 10.1001/jama.2023.5321
   PubMedGoogle Scholar
• Holzinger A, Keiblinger K, Holub P, et al. AI for life: trends in artificial intelligence for biotechnology. N Biotechnol. 2023;74:16–24. doi: 10.1016/j.nbt.2023.02.001
   PubMed Web of Science ®Google Scholar
• Lee P, Bubeck S, Petro J. Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine. N Engl J Med. 2023;388:1233–1239. doi: 10.1056/NEJMsr2214184
   PubMed Web of Science ®Google Scholar
• Mueller H, Mayrhofer MT, Van Veen EB, et al. The Ten commandments of ethical medical AI. IEEE Comput. 2021;54:119–123. doi: 10.1109/MC.2021.3074263
   Google Scholar
• Liang L, Hu J, Sun G, et al. Artificial intelligence-based pharmacovigilance in the setting of limited resources. Drug Saf. 2022;45:511–519. doi: 10.1007/s40264-022-01170-7
   PubMed Web of Science ®Google Scholar
• Deplanque D, de Retz S, Bellissant É. Participants of Giens XXXV round table “Regulatory and organizational.” How to attract doctors to careers in the fields of pharmaceutical medicine and medical devices? Therapie. 2020;75:93–100. doi: 10.1016/j.therap.2019.11.009
   PubMedGoogle Scholar
• US FDA. New drug therapy approvals; 2022. [cited Apr 7, 2023]. Available from: https://www.fda.gov/drugs/new-drugs-fda-cders-new-molecular-entities-and-new-therapeutic-biological-products/new-drug-therapy-approvals-2022
   Google Scholar