Reviewing Pharmacovigilance Strategies Using Real-World Data for Drug Safety Monitoring and Management
DOI:
https://doi.org/10.32628/CSEIT25112853Keywords:
Pharmacovigilance, Artificial intelligence, Adverse drug reactions, Signal detection, Drug safety monitoringAbstract
Pharmacovigilance plays a critical role in ensuring drug safety and protecting public health by identifying, assessing, and mitigating adverse drug reactions (ADRs). The integration of real-world data (RWD) from sources such as electronic health records, patient registries, and social media has significantly enhanced pharmacovigilance strategies, addressing limitations of traditional methods. This paper reviews the framework of pharmacovigilance systems, emphasizing the integration of innovative tools like artificial intelligence (AI), machine learning (ML), and predictive analytics for improved signal detection and proactive risk management. Additionally, advancements in real-time monitoring systems and global collaborations have strengthened drug safety efforts, although challenges related to data quality, privacy, and resource disparities remain significant. Recommendations for improving pharmacovigilance strategies include standardizing data, enhancing stakeholder capacity, and addressing ethical concerns to fully harness the potential of RWD. Future research and policy directions focus on advancing technologies and fostering international cooperation to create a robust and equitable pharmacovigilance ecosystem capable of adapting to evolving healthcare needs.
Downloads
References
Abernethy, A., Adams, L., Barrett, M., Bechtel, C., Brennan, P., Butte, A., . . . Halamka, J. (2022). The promise of digital health: then, now, and the future. NAM perspectives, 2022.
Adelodun, M. O., & Anyanwu, E. C. Global Standards in Radiation Safety: A Comparative Analysis of Healthcare Regulations.
Bérard, A. (2021). Pharmacoepidemiology research-real-world evidence for decision making. In (Vol. 12, pp. 723427): Frontiers Media SA.
Chatzopoulou, S., Eriksson, N. L., & Eriksson, D. (2020). Improving risk assessment in the European food safety authority: lessons from the European Medicines Agency. Frontiers in Plant Science, 11, 349.
Cioeta, R., Cossu, A., Giovagnoni, E., Rigoni, M., & Muti, P. (2022). A new platform for post-marketing surveillance and real-world evidence data collection for substance-based medical devices. Frontiers in Drug Safety and Regulation, 2, 992359.
Dagenais, S., Russo, L., Madsen, A., Webster, J., & Becnel, L. (2022). Use of real‐world evidence to drive drug development strategy and inform clinical trial design. Clinical Pharmacology & Therapeutics, 111(1), 77-89.
Dharmani, C., Wang, E., Salas, M., McCabe, C., Diggs, A., Choi, Y., . . . Keedy, V. L. (2022). Turalio risk evaluation and mitigation strategy for treatment of tenosynovial giant cell tumor: framework and experience. Future Oncology, 18(13), 1595-1607.
Ehidiamen, A. J., & Oladapo, O. O. (2024a). Innovative approaches to risk management in clinical research: Balancing ethical standards, regulatory compliance, and intellectual property concerns. World Journal of Biology Pharmacy and Health Sciences, 20(1), 349–363. Retrieved from https://doi.org/10.30574/wjbphs.2024.20.1.0791.
Ehidiamen, A. J., & Oladapo, O. O. (2024b). The intersection of clinical trial management and patient advocacy: How research professionals can promote patient rights while upholding clinical excellence.
Ehidiamen, A. J., & Oladapo, O. O. (2024c). Optimizing contract negotiations in clinical research: Legal strategies for safeguarding sponsors, vendors, and institutions in complex trial environments. World Journal of Biology Pharmacy and Health Sciences, 20(1), 335-348. Retrieved from https://doi.org/10.30574/wjbphs.2024.20.1.0790
Ehidiamen, A. J., & Oladapo, O. O. (2024d). The role of electronic data capture systems in clinical trials: Streamlining data integrity and improving compliance with FDA and ICH/GCP guidelines.
Goel, A., Goel, A. K., & Kumar, A. (2023). The role of artificial neural network and machine learning in utilizing spatial information. Spatial Information Research, 31(3), 275-285.
Hodel, K. V. S., Fiuza, B. S. D., Conceição, R. S., Aleluia, A. C. M., Pitanga, T. N., Fonseca, L. M. d. S., . . . Machado, B. A. S. (2024). Pharmacovigilance in Vaccines: Importance, Main Aspects, Perspectives, and Challenges—A Narrative Review. Pharmaceuticals, 17(6), 807.
Johnson, O. B., Olamijuwon, J., Cadet, E., Osundare, O. S., & Ekpobimi, H. O. Optimizing Predictive Trade Models through Advanced Algorithm Development for Cost-Efficient Infrastructure.
Kataria, S., & Ravindran, V. (2022). Harnessing of real-world data and real-world evidence using digital tools: utility and potential models in rheumatology practice. Rheumatology, 61(2), 502-513.
Kelvin-Agwu, M., Adelodun, M. O., Igwama, G. T., & Anyanwu, E. C. (2024). The Impact of Regular Maintenance on the Longevity and Performance of Radiology Equipment.
Kelvin-Agwu, M. C., Adelodun, M. O., Igwama, G. T., & Anyanwu, E. C. (2024). Strategies For Optimizing The Management Of Medical Equipment In Large Healthcare Institutions. Strategies, 20(9), 162-170.
Liu, J., Barrett, J. S., Leonardi, E. T., Lee, L., Roychoudhury, S., Chen, Y., & Trifillis, P. (2022). Natural history and real‐world data in rare diseases: applications, limitations, and future perspectives. The Journal of Clinical Pharmacology, 62, S38-S55.
Lucas, S., Ailani, J., Smith, T. R., Abdrabboh, A., Xue, F., & Navetta, M. S. (2022). Pharmacovigilance: reporting requirements throughout a product’s lifecycle. Therapeutic advances in drug safety, 13, 20420986221125006.
Mbunge, E., Fashoto, S. G., Akinnuwesi, B. A., Metfula, A. S., Manyatsi, J. S., Sanni, S. A., . . . Mnisi, P. M. (2024). Machine Learning Approaches for Predicting Individual’s Financial Inclusion Status with Imbalanced Dataset. Paper presented at the Computer Science On-line Conference.
Mofid, S., Bolislis, W. R., & Kühler, T. C. (2022). Real-world data in the postapproval setting as applied by the EMA and the US FDA. Clinical Therapeutics, 44(2), 306-322.
Nwokike, J. (2023). Regulatory reliance and post-marketing surveillance systems for safe and accelerated introduction of new medical products in low-and middle-income countries. University of Plymouth,
Palatty, P. L., Sacheendran, D., & Jayachandran, M. (2024). Mitigating Challenges in Pharmacovigilance.
Phatak, A. A., Wieland, F.-G., Vempala, K., Volkmar, F., & Memmert, D. (2021). Artificial intelligence based body sensor network framework—narrative review: proposing an end-to-end framework using wearable sensors, real-time location systems and artificial intelligence/machine learning algorithms for data collection, data mining and knowledge discovery in sports and healthcare. Sports Medicine-Open, 7(1), 79.
Piccirillo, R. A., & Parish, J. (2022). Pharmacovigilance principles: the building blocks of benefit-risk assessments. Glob Clin Transl Res, 4(4), 1-7.
Sadaf, D. S., & Sameer, S. (2023). PHARMACOVIGILANCE AND ADVERSE EVENT DETECTION: A COMPREHENSIVE REVIEW OF ARTIFICIAL INTELLIGENCE APPLICATIONS. EUROPEAN JOURNAL OF MODERN MEDICINE AND PRACTICE, 3(12), 97-104.
Sarker, I. H. (2023). Machine learning for intelligent data analysis and automation in cybersecurity: current and future prospects. Annals of Data Science, 10(6), 1473-1498.
Schmidt, T., Valuck, T., Perkins, B., Riposo, J., Patel, P., Westrich, K., . . . McClellan, M. (2021). Improving patient-reported measures in oncology: a payer call to action. Journal of Managed Care & Specialty Pharmacy, 27(1), 118-126.
Segun-Falade, O. D., Osundare, O. S., Kedi, W. E., Okeleke, P. A., Ijomah, T. I., & Abdul-Azeez, O. Y. (2024). Utilizing machine learning algorithms to enhance predictive analytics in customer behavior studies.
Shittu, R. A., Ehidiamen, A. J., Ojo, O. O., & Christophe, S. J. (2024). The role of business intelligence tools in improving healthcare patient outcomes and operations.
Tapkir, N., Soni, F., Sahu, A. K., Jadav, T., Tekade, R. K., & Sengupta, P. (2021). A comprehensive review on assessment and key control strategies for impurities in drug development with a special emphasis on post-marketing surveillance. Journal of Pharmaceutical Innovation, 1-20.
Wang, S. V., Pinheiro, S., Hua, W., Arlett, P., Uyama, Y., Berlin, J. A., . . . Schneeweiss, S. (2021). STaRT-RWE: structured template for planning and reporting on the implementation of real world evidence studies. Bmj, 372.
Xu, Y., Zheng, X., Li, Y., Ye, X., Cheng, H., Wang, H., & Lyu, J. (2023). Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review. Journal of Evidence‐Based Medicine, 16(3), 342-375.
Yang, H. (2021). Using real-world evidence to transform drug development: opportunities and challenges. Real-World Evidence in Drug Development and Evaluation, 1-26.
Zisis, K., Pavi, E., Geitona, M., & Athanasakis, K. (2023). Real-world data: A systematic literature review on the barriers, challenges, and opportunities associated with their inclusion in the health technology assessment process. medRxiv, 2023.2010. 2018.23297151.
Zou, K. H., Salem, L. A., & Ray, A. (2022). Real-world evidence in a patient-centric digital era: CRC Press.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.