By integrating LQPPV and lifecycle management with the application of artificial intelligence, preparing for future compliance after approval.

Align local pharmacovigilance and regulatory change JAY GANDECHA, Senior Director, Regulatory AffairsANA PEDRO JESUÍNO, Director, Marketed Product Safety Table of contents Introduction1The global-scale compliance challenges sponsors face2A synergistic operating model3Adaptive resourcing4Applied AI5An effective AI governance approach6Support models with strategic partnerships6A unified system without reorganization7One operating model with integrated PV and regulatory functions7Conclusion7About IQVIA8About the authors9 Introduction All approved drugs follow the same product lifecycle: discovery and development;clinical trials; evaluation and authorization; and finally, post-approval and safetymonitoring. Though a drug’s approval is a major achievement, it is only the of the International Organization for Standardization —Identification of Medicinal Products (ISO IDMP) via thesubstance, product, organization, and referential data(SPOR) program, and the move toward electronic common The post-approval environment is where globalgovernance, legal obligations, volume variations,and market-specific expectations converge, making Regulators now expect high-quality, traceable,data-driven submissions with fewer inconsistencies The global-scale compliancechallenges sponsors face Country-specific hurdles include translation nuances,health authority communication preferences, andexpectations for local qualified persons for PV (LQPPVs).These micro-requirements compound to slow down Manufacturing, distributing, and monitoring an approvedtherapeutic at a global scale is inherently complex. Theburden is further complicated by managing complianceand consistency across a variety of jurisdictions. Local The volume of post-approval work keeps increasingand includes variations, renewals, labeling updates,manufacturing changes, and risk management actions,which are meant to run in parallel. While these activities Sponsor teams must coordinate across PV; regulatoryaffairs; chemistry, manufacturing and controls (CMC);quality assurance (QA); and local affiliate teams tomaintain alignment on evidence and rationale. It often The volume of post-approval workkeeps increasing and includesvariations, renewals, labeling A synergistic operating model This structure delivers several additional The post-approval stage requires work from both safetyand regulatory teams. For example, a safety signal canaffect labeling, and the labeling change will triggera variation that requires coordinated submissions.When safety and regulatory functions are disconnected, •Affiliates receive an aligned package •Fragmentation from isolated functions •Inspection readiness improves. •Parallel truths are replaced with a sharedevidence base that improves quality and •Timelines are shortened. Perhaps most importantly, the synergistic operatingmodel builds inspection confidence via shared decisionlogs, consistent wording frameworks, and clearaccountability. When regulators audit sponsors, they willhear the same story across PV, regulatory, and affiliates. A synergistic operating model bringsPV and regulatory functions underone umbrella: a single governance Adaptive resourcing regulatory and safety cycles, enabling pre-trainedsurge resources to be quickly deployed, while operating Unfortunately, post-approval work does not followa smooth, predictable curve. Pressure builds quicklyduring renewal waves, label cascades, safety-drivenupdates, inspection-readiness activities, and geographicexpansion. Though cyclical demand spikes can beplanned for, they are often too intense and uneven As priorities shift, this resourcing model adapts toaccommodate different scenarios without triggeringfull recruitment, onboarding, and offboarding cycles. As priorities shift, this resourcingmodel adapts to accommodatedifferent scenarios without triggering Adaptive resourcing provides sponsors with access tospecialist capabilities that may be inefficient or difficultto hire on a full-time basis, including labeling strategists, Applied AI accountability. The goal is to deploy AI in a way thataccelerates workflows without creating regulatory AI-supported PV and regulatory workflows are no longerthe future — they have arrived. Though technology willnot replace experts or perform unsupervised work,AI will execute repetitive work and structured tasksthat consume much of an expert’s time. AI is effective At IQVIA, our team has developed technology-agnosticmodels that embed AI directly into existing safety orRIM systems. To ensure successful application, we beginby working with sponsors to apply AI to a single high-volume, high-value workflow. From there, we establish For AI integration to succeed, guardrails are essential:primarily human-in-the-loop configurations thatapply risk-based validation consistent with regulatoryexpectations. With AI applications, regulators are Applied AI adapts based on real performance. As reviewersinteract with the system, they can