Developing and Validating a Stability- Indicating Ultra Performance Liquid Chromatography Method for the Concurrent Measurement of Estradiol and Dydrogesterone in Bulk and Tablet Doses

Pharmacovigilance (PV) is the science of drug safety, now established as a separate discipline driven by modern technologies such as digital health tools, big data, artificial intelligence (AI), and machine learning (ML). Legacy PV methods relied on manual reporting and spontaneous submissions of adverse drug reactions (ADRs), but these were hindered by delays in submissions, signal detection, and data quality issues. Emerging technologies such as AI, digital health, and big data play a critical role in drug safety and risk mitigation. Digital health tools, including wearable monitors, patient engagement modules, and electronic health records, generate Real- World Data that help healthcare professionals track patient reactions to drugs over extended periods, providing insights into genomics, vitals, and ADRs. Big data allows PV practitioners to handle complex, heterogeneous datasets, including patient reviews, which are analyzed using natural language processing to extract insights from social media, reports, and clinical data. ML algorithms automate signal detection, predictive modeling, and casualty assessment, significantly improving the speed and accuracy of ADR identification. Technologies such as AI-driven platforms (e.g., World Health Organization VigiBase and Food and Drug Administration Sentinel initiative) demonstrate how large-scale, real-time data can enhance risk signal identification, while digital health devices assist in monitoring patient vitals for early risk detection. Despite these advancements, challenges persist, including ethical concerns, data inoperability, algorithm bias, and regulatory issues. This review underscores the need for global collaboration, standardized reporting methods, and robust regulatory guidelines to address these challenges. Emerging technologies like the internet of medical things pave the way for personalized and ML-driven predictive PV.