Artificial intelligence-driven patient monitoring for adverse event detection in clinical trials

Authors

  • Sai Bhargavi Vampana Department of Pharmacy Practice, Pulla Reddy Institute of Pharmacy, Hyderabad, Telangana, India
  • Emani Sai Sri Jayanthi Department of Pharmacy Practice, Pulla Reddy Institute of Pharmacy, Hyderabad, Telangana, India
  • D. Aashritha Mary Department of Pharmacy Practice, Pulla Reddy Institute of Pharmacy, Hyderabad, Telangana, India
  • Chakravarthi Sriniketh Department of Pharmacy Practice, Pulla Reddy Institute of Pharmacy, Hyderabad, Telangana, India

DOI:

https://doi.org/10.18203/2319-2003.ijbcp20241657

Keywords:

Artificial intelligence, Patient monitoring, Adverse event detection, Advanced algorithms, Real-time surveillance, Regulatory compliance, Healthcare innovation

Abstract

Artificial intelligence (AI) keeps an eye on people in clinical studies to find out when bad things happen. This is a big way that AI is changing healthcare. It goes into a lot of detail about how AI has changed this field and stresses how important it is to use complicated formulas, always keep an eye on things, and follow the rules. These days, we have tools like deep learning frameworks, controlled and unsupervised learning models, and others that help us find bad things faster and more accurately. Tracking in real time is possible with early warning systems and constant data analysis. It helps make sure the experiment is done right and puts the safety of the people being tested first. AI-driven tracking systems can only work in an honest and reliable way if they follow the rules set by regulatory bodies such as the FDA and the EMA. The fact that AI has the ability to change the way medical research is done today, with benefits like making it faster and more accurate, makes its problems even more important. The report comes to the conclusion that more research, better teamwork, and a wider use of AI technologies are needed to make it more reliable to find bad events in clinical studies over time.

References

Niazi SK. The coming of age of ai/ml in drug discovery, development, clinical testing, and manufacturing: The FDA perspectives. Drug Design, Development Therapy. 2023;17:2691-725.

Sadaf DS, Sameer S. Pharmacovigilance and adverse event detection: a comprehensive review of artificial intelligence applications. Eur J Modern Med Pract. 2023;3(12):97-104.

Egon K. Machine Learning in Drug Safety Monitoring: Enhancing Pharmacovigilance Efforts. Osfpreprints. 2023.

Hosagowdar S, Kinkar MG. Pharmacovigilance and adverse event reporting in clinical trials: best practices. Eur J Modern Med Pract. 2023;3(10):18-27.

Chen E, Prakash S, Janapa Reddi V, Kim D, Rajpurkar P. A framework for integrating artificial intelligence for clinical care with continuous therapeutic monitoring. Nature Biomed Eng. 2023;1-10.

Irshad N. The Future Is Data-driven: Revolutionizing Clinical Trials Through Informatics. ResearchGate. 2023.

Pun FW, Ozerov IV, Zhavoronkov A. AI-powered therapeutic target discovery. Trends Pharmacological Sci. 2023;44(9):561-72.

Rana MS, Shuford J. AI in Healthcare: Transforming Patient Care through Predictive Analytics and Decision Support Systems. J Artificial Intelligence General Sci. 2024;1(1):3006-4023:.

Iqbal J, Jaimes DCC, Makineni P, Subramani S, Hemaida S, Thugu TR, et al. Reimagining healthcare: unleashing the power of artificial intelligence in medicine. Cureus. 2023;15(9):e44658.

Ganesh GS, Kolusu AS, Prasad K, Samudrala PK, Nemmani KV. Advancing health care via artificial intelligence: From concept to clinic. Eur J Pharmacol. 2022;934:175320.

Mittal P, Goyal R, Kapoor R, Gautam RK. Artificial intelligence (AI) and machine learning in the treatment of various diseases. In Computational Approaches in Drug Discovery, Development and Systems Pharmacology. Academic Press. 2023;139-58.

Singh H, Nim DK, Randhawa AS, Ahluwalia S. Integrating clinical pharmacology and artificial intelligence: potential benefits, challenges, and role of clinical pharmacologists. Expert Rev Clin Pharmacol. 2024;17(4):381-91.

Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educat. 2023;23(1):689.

Durga K. Intelligent Support for Cardiovascular Diagnosis: The AI-CDSS Approach. In Using Traditional Design Methods to Enhance AI-Driven Decision Making. IGI Global. 2024;64-76.

Li R, Curtis K, Zaidi ST, Van C, Castelino R. A new paradigm in adverse drug reaction reporting: consolidating the evidence for an intervention to improve reporting. Expert Opinion on Drug Safety. 2022;21(9):1193-204.

Higgins D, Madai VI. From bit to bedside: a practical framework for artificial intelligence product development in healthcare. Advanced Intelligent Systems. 2020;2(10):2000052.

Yoon JH, Pinsky MR, Clermont G. Artificial intelligence in critical care medicine. Annual Update in Intensive Care and Emergency Medicine. 2022;353-67.

Weatherall J, Khan FM, Patel M, Dearden R, Shameer K, Dennis G, et al. Clinical trials, real-world evidence, and digital medicine. In The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry. Academic Press. 2021;191-215.

Chakraborty A, Venkatraman JV. Pharmacovigilance Through Phased Clinical Trials, Post-Marketing Surveillance and Ongoing Life Cycle Safety. In The Quintessence of Basic and Clinical Research and Scientific Publishing. Singapore: Springer Nature Singapore. 2023;427-42.

AI, T. P. U. G. The Journal of Multidisciplinary Research (TJMDR). J Multi Rese. 2023;3(3):9-16.

Tiwari PC, Pal R, Chaudhary MJ, Nath R. Artificial intelligence revolutionizing drug development: Exploring opportunities and challenges. Drug Development Res. 2023;84(8):1652-63.

Abirami MS. AI Clinical Decision Support System (AI-CDSS) for Cardiovascular Diseases. In 2023 International Conference on Computer Science and Emerging Technologies (CSET). 2023;1-7.

Pramanik S, Khang A. Cardiovascular Diseases: Artificial Intelligence Clinical Decision Support System. In AI-Driven Innovations in Digital Healthcare: Emerging Trends, Challenges, and Applications. IGI Global. 2024;274-87.

Singh S, Kumar R, Payra S, Singh SK. Artificial Intelligence and Machine Learning in Pharmacological Research: Bridging the Gap Between Data and Drug Discovery. Cureus. 2023;15(8):e44359.

Davies M, Duffield EA. Safety of checkpoint inhibitors for cancer treatment: strategies for patient monitoring and management of immune-mediated adverse events. Immuno Targets Therapy. 2017;6:51-71.

Al Kuwaiti A, Nazer K, Al-Reedy A, Al-Shehri S, Al-Muhanna A, Subbarayalu AV, et al. A Review of the Role of Artificial Intelligence in Healthcare. J Personalized Med. 2023;13(6):951.

Yang H. Regulatory Perspective on Big Data, AI, and Machining Learning. In Data Science, AI, and Machine Learning in Drug Development. Chapman and Hall/CRC. 2022;19-42.

Coutré SE, Barrientos JC, Brown JR, De Vos S, Furman RR, Keating MJ, et al. Management of adverse events associated with idelalisib treatment: expert panel opinion. Leukemia Lymphoma. 2015;56(10):2779-86.

Shu Y, He X, Liu Y, Wu P, Zhang Q. A real-world disproportionality analysis of olaparib: data mining of the public version of FDA adverse event reporting system. Clin Epidemiol. 2022;14:789-802.

Lazarevic B, Boezelijn G, Diep LM, Kvernrod K, Ogren O, Ramberg H, et al. Efficacy and safety of short-term genistein intervention in patients with localized prostate cancer prior to radical prostatectomy: a randomized, placebo-controlled, double-blind Phase 2 clinical trial. Nutrit Cancer. 2011;63(6):889-98.

Bradstreet S, Allan S, Gumley A. Adverse event monitoring in mHealth for psychosis interventions provides an important opportunity for learning. J Mental Health. 2019;28(5):461-6.

Tuccori M, Montagnani S, Capogrosso-Sansone A, Mantarro S, Antonioli L, Fornai M, et al. Adverse reactions to oncologic drugs: spontaneous reporting and signal detection. Expert Rev Clin Pharmacol. 2015;8(1):61-75.

Seyhan AA, Carini C. Are innovation and new technologies in precision medicine paving a new era in patients centric care? J Translational Med. 2019;17:1-28.

Scheinfeld N. Efalizumab: a review of events reported during clinical trials and side effects. Expert Opin Drug Saf. 2006;5(2):197-209.

Huang S, Guo Z, Wang M, She Y, Ye X, Zhai Q, et al. Ocular adverse events associated with BRAF and MEK inhibitor combination therapy: a pharmacovigilance disproportionality analysis of the FDA adverse event reporting system. Expert Opin Drug Saf. 2023;22(2):175-81.

Kulldorff M, Davis RL, Kolczak M, Lewis E, Lieu T, Platt R. A maximized sequential probability ratio test for drug and vaccine safety surveillance. Sequential Analysis. 2011;30(1):58-78.

Li C, Li Z, Sun Q, Xiang Y, Liu A. Severe cutaneous adverse reactions associated with immune checkpoint inhibitors therapy and anti-VEGF combination therapy: a real-world study of the FDA adverse event reporting system. Expert Opin Drug Saf. 2023;23(6):1-8.

MacConell L, Brown C, Gurney K, Han J. Safety and tolerability of exenatide twice daily in patients with type 2 diabetes: integrated analysis of 5594 patients from 19 placebo-controlled and comparator-controlled clinical trials. Diabetes, Metabolic Syndrome Obesity: Targets Therapy. 2012;5:29-41.

Kumari Y, Bai P, Waqar F, Asif AT, Irshad B, Raj S, et al. Advancements in the management of endocrine system disorders and arrhythmias: a comprehensive narrative review. Cureus. 2023;15(10):e46484.

Jang MG, Cha S, Kim S, Lee S, Lee KE, Shin KH. Application of tree-based machine learning classification methods to detect signals of fluoroquinolones using the Korea Adverse Event Reporting System (KAERS) database. Expert Opin Drug Saf. 2023;22(7):1-8.

Achalu DL, Mohammed FG, Teferi M. Magnitude and Impacts of Adverse Events of Injectable Containing Shorter Regimen in Programmatic Management of Multi-Drug Resistant Tuberculosis in Ethiopia: A Retrospective Cohort Study. Therapeutics Clin Risk Management. 2023;19:889-901.

Kempen JH, Daniel E, Gangaputra S, Dreger K, Jabs DA, Kaçmaz RO, et al. Methods for identifying long-term adverse effects of treatment in patients with eye diseases: the Systemic Immunosuppressive Therapy for Eye Diseases (SITE) Cohort Study. Ophthalmic Epidemiol. 2008;15(1):47-55.

Kujtan L, Kancha RK, Gustafson B, Douglass L, Ward CR, Buzard B, Subramanian J. Squamous cell carcinoma of the lung: improving the detection and management of immune-related adverse events. Expert Rev Anticancer Therapy. 2022;22(2):203-13.

Mytheen S, Varghese A, Joy J, Shaji A, Tom AA. Investigating the risk of deep vein thrombosis with JAK inhibitors: a disproportionality analysis using FDA adverse event reporting system database. Expert Opin Drug Saf. 2023;22(10):985-94.

Shah V. AI-Powered Drug Repurposing for Pandemic Preparedness and Response. Int J Computer Sci Technol. 2023;7(3):227-42.

Wu XP, Lu XK, Wang ZT, Huang L, Cai RW, Yu HM, et al. Post-Marketing Safety Concerns with Upadacitinib: A Disproportionality Analysis of the FDA Adverse Event Reporting system. Expert Opin Drug Saf. 2023;22(10):975-84.

Huber P, Flynn A, Sultan MB, Li H, Rill D, Ebede B, et al. A comprehensive safety profile of tafamidis in patients with transthyretin amyloid polyneuropathy. Amyloid. 2019;26(4):203-9.

Patil S, Shankar H. Transforming healthcare: harnessing the power of AI in the modern era. Int J Multidis Sci Arts. 2023;2(1):60-70.

Sun C, Yang X, Tang L, Chen J. A pharmacovigilance study on drug-induced liver injury associated with antibody-drug conjugates (ADCs) based on the food and drug administration adverse event reporting system. Expert Opin Drug Saf. 2023;1-12.

Chaurasia A. Algorithmic Precision Medicine: Harnessing Artificial Intelligence for Healthcare Optimization. Asian J Biotechnol Bioresource Technol. 2023;9(4):28-43.

Mazhar F, Battini V, Gringeri M, Pozzi M, Mosini G, Marran AMN, et al. The impact of anti-TNFα agents on weight-related changes: new insights from a real-world pharmacovigilance study using the FDA adverse event reporting system (FAERS) database. Expert Opin Biological Ther. 2021;21(9):1281-90.

Mazhar F, Krantz Å, Schalin L, Lysell J, Carrero JJ. Occurrence of adverse events associated with the initiation of methotrexate and biologics for the treatment of psoriasis in routine clinical practice. J Dermatological Treatment. 2023;34(1):2215354.

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Published

2024-06-25

How to Cite

Vampana, S. B., Jayanthi, E. S. S., Mary, D. A., & Sriniketh, C. (2024). Artificial intelligence-driven patient monitoring for adverse event detection in clinical trials. International Journal of Basic & Clinical Pharmacology, 13(4), 543–550. https://doi.org/10.18203/2319-2003.ijbcp20241657

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Section

Review Articles