Digitalization of Quality Assurance in Pharmacovigilance: Emerging Trends and Implications for Regulatory Science

Abstract

The evolution of pharmacovigilance (PV) from a manual, paper-driven process to a technology-enabled system has transformed the way drug safety is monitored worldwide. Digitalization of quality assurance (QA) within PV processes has become critical in managing increasing data volumes, ensuring regulatory compliance, and enhancing real-time risk detection. This paper explores how digital technologies—such as robotic process automation (RPA), artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and cloud-based systems—are redefining QA in pharmacovigilance. It also examines the implications of these innovations for regulatory science, including the shift toward risk-based inspections, real-time dashboards, and data integrity assurance. While these advancements offer significant efficiency and accuracy benefits, they also pose challenges related to validation, data privacy, and workforce adaptation. The paper provides a roadmap for achieving digital maturity in PV QA and emphasizes the need for collaborative regulatory frameworks and standardized digital KPIs.

Keywords: Pharmacovigilance, Digital Quality Assurance, Artificial Intelligence, Regulatory Science, Robotic Process Automation, Signal Detection, Cloud Computing, Data Integrity, Machine Learning, GxP Compliance