Korean Journal of Clinical Pharmacy (한국임상약학회지)

Korean College Of Clinical Pharmacy (한국임상약학회)
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Trends in Artificial Intelligence Applications in Clinical Trials: An analysis of ClinicalTrials.gov

임상시험에서 인공지능의 활용에 대한 분석 및 고찰: ClinicalTrials.gov 분석

  • Jeong Min Go (School of Pharmacy, Jeonbuk National University) ;
  • Ji Yeon Lee (School of Pharmacy, Jeonbuk National University) ;
  • Yun-Kyoung Song (College of Pharmacy, The Catholic University of Korea) ;
  • Jae Hyun Kim (School of Pharmacy, Jeonbuk National University)
  • Received : 2024.05.24
  • Accepted : 2024.06.12
  • Published : 2024.06.30
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Abstract

Background: Increasing numbers of studies and research about artificial intelligence (AI) and machine learning (ML) have led to their application in clinical trials. The purpose of this study is to analyze computer-based new technologies (AI/ML) applied on clinical trials registered on ClinicalTrials.gov to elucidate current usage of these technologies. Methods: As of March 1st, 2023, protocols listed on ClinicalTrials.gov that claimed to use AI/ML and included at least one of the following interventions-Drug, Biological, Dietary Supplement, or Combination Product-were selected. The selected protocols were classified according to their context of use: 1) drug discovery; 2) toxicity prediction; 3) enrichment; 4) risk stratification/management; 5) dose selection/optimization; 6) adherence; 7) synthetic control; 8) endpoint assessment; 9) postmarketing surveillance; and 10) drug selection. Results: The applications of AI/ML were explored in 131 clinical trial protocols. The areas where AI/ML was most frequently utilized in clinical trials included endpoint assessment (n=80), followed by dose selection/optimization (n=15), risk stratification/management (n=13), drug discovery (n=4), adherence (n=4), drug selection (n=1) and enrichment (n=1). Conclusion: The most frequent application of AI/ML in clinical trials is in the fields of endpoint assessment, where the utilization is primarily focuses on the diagnosis of disease by imaging or video analyses. The number of clinical trials using artificial intelligence will increase as the technology continues to develop rapidly, making it necessary for regulatory associates to establish proper regulations for these clinical trials.

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Acknowledgement

본 연구는 2023년 식품의약품안전처의 지원을 받아 수행되었으며(23212한임평226) 이에 감사드립니다.

References

  1. Food and Drug Administration. Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. 2024. Available from: https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development. Accessed April 28, 2024. 
  2. ElZarrad MK. Regulation in the Age of Exponential Innovations Artificial Intelligence. In: 18th Annual Scientific Meeting of the International Society for CNS Clinical Trials and Methodology, Washington DC, USA, Feb 25, 2022. 
  3. Liu Q, Huang R, Hsieh J, et al. Landscape Analysis of the Application of Artificial Intelligence and Machine Learning in Regulatory Submissions for Drug Development From 2016 to 2021. Clin Pharmacol Ther. 2023;113(4):771-4.  https://doi.org/10.1002/cpt.2668
  4. Food and Drug Administration. Using Artificial Intelligence and Machine Learning in the Development of Drug and Biological Products. 2023. Available from: https://www.fda.gov/media/167973/download. Accessed April 28, 2024. 
  5. European Medicines Agency. EMA's Regulatory Science Strategy to 2025. 2023. Available from: https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/ema-regulatory-science-2025-strategic-reflection_en.pdf. Accessed April 28, 2024. 
  6. Ministry of Food and Drug Safety. Guidance on Clinical Trials Design of Artificial Intelligence(AI)-based Medical Devices. 2022. Available from: https://www.mfds.go.kr/brd/m_1060/down.do?brd_id=data0011&seq=15041&data_tp=A&file_seq=1. Accessed April 28, 2024. 
  7. ICONplc. Improving Pharma R&D Efficiency: The Case for a Holistic Approach to Transforming Clinical Trials. 2018. Available from: https://www.iconplc.com/insights/transforming-trials/improving-pharma-research-and-development-efficiency. Accessed April 28, 2024. 
  8. Forbes. World's Top 20 AI Drug Development Companies. 2018. Available from: https://www.forbes.com/sites/yiannismouratidis/2018/12/16/the-rising-star-companies-in-ai-drug-development/. Accessed April 28. 2024. 
  9. ICONplc. Digital Disruption in Biopharma: How digital transformation can reverse declining ROI in R&D. 2019. Available from: https://www.iconplc.com/insights/digital-disruption/digital-disruption-in-biopharma. Accessed April 28, 2024. 
  10. Askin S, Burkhalter D, Calado G, El Dakrouni S. Artificial Intelligence Applied to clinical trials: opportunities and challenges. Health Technol (Berl). 2023;13(2):203-13.  https://doi.org/10.1007/s12553-023-00738-2
  11. Liu X, Rivera SC, Moher D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension. BMJ. 2020;370:m3164. 
  12. Wang A, Xiu X, Liu S, Qian Q, Wu S. Characteristics of Artificial Intelligence Clinical Trials in the Field of Healthcare: A Cross-Sectional Study on ClinicalTrials.gov. Int J Environ Res Public Health. 2022;19(20). 
  13. Zhang B, Zhang L, Chen Q, Jin Z, Liu S, Zhang S. Harnessing artificial intelligence to improve clinical trial design. Commun Med (Lond). 2023;3(1):191. 
  14. Food and Drug Administration. Emergency Use Authorization (EUA) for Kineret (Anakinra). 2022. Available from: https://www.fda.gov/media/163546/download?attachment. Accessed April 28. 2024. 
  15. Liu R, Rizzo S, Whipple S, et al. Evaluating eligibility criteria of oncology trials using real-world data and AI. Nature. 2021;592(7855):629-33.  https://doi.org/10.1038/s41586-021-03430-5
  16. Nagendran M, Chen Y, Lovejoy CA, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ. 2020;368:m689. 
  17. Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med. 2018;24(11):1716-20.  https://doi.org/10.1038/s41591-018-0213-5
  18. Le GN, Bones J, Coyne M, et al. Current and future biomarkers for risk-stratification and treatment personalisation in multiple myeloma. Mol Omics. 2019;15(1):7-20.  https://doi.org/10.1039/C8MO00193F
  19. Lee YT, Fujiwara N, Yang JD, Hoshida Y. Risk stratification and early detection biomarkers for precision HCC screening. Hepatology. 2023;78(1):319-62.  https://doi.org/10.1002/hep.32779
  20. Zelniker T, Uhlmann L, Spaich S, et al. Novel biomarkers for risk stratification in pulmonary arterial hypertension. ERJ Open Res. 2015;1(2). 
  21. Shaban-Nejad A, Michalowski M, Buckeridge DL. Health intelligence: how artificial intelligence transforms population and personalized health. NPJ Digit Med. 2018;1:53.