Automation Workflow for Generating Safety Update Reports and Periodic Safety Update Reports (PSURs)

The manual PSUR process is a high-cost, high-risk operational bottleneck. Teams spend weeks aggregating adverse event data from global safety databases like Argus or ARISg, performing cumulative analyses, and drafting narratives. A custom agentic workflow automates this repetitive assembly, cutting compilation time by 60-80% and eliminating consistency errors. The business case is clear: reduced labor cost, mitigated compliance risk from missed deadlines, and freed medical expertise for higher-value signal evaluation rather than administrative report-building.

Implementation integrates with core systems: the orchestrator triggers agents that query safety databases and clinical data warehouses, execute statistical programs, and draft narratives using RAG over previous reports. Crucially, the architecture mandates human review gates for medical sign-off on cumulative analyses and regulatory approval of the final narrative. Observability is built in, tracking data lineage from source to submission and logging all agent actions for audit. This creates a repeatable, defensible operating model that scales across multiple products and regions.

The workflow automates the aggregation of adverse event data from global safety databases (e.g., Argus, ArisG), performs cumulative analyses, and drafts required narratives. It eliminates weeks of manual data collation and narrative writing per reporting cycle, directly reducing labor costs and mitigating the risk of late submissions. The architecture is triggered by scheduled calendar events or data-volume thresholds, initiating a sequence of specialized agents for data retrieval, statistical analysis, and narrative generation, all governed by a central orchestrator.

Implementation integrates with enterprise systems like Veeva Vault for document management and uses frameworks like LangGraph for agent orchestration. Critical controls include medical review gates for all drafted narratives, automated validation against ICH E2C guidelines, and comprehensive audit logging to Splunk. The pipeline is deployed as containerized services, allowing phased rollout by product line or region. This architecture ensures timely, accurate PSURs while creating a defensible, repeatable operating model for global pharmacovigilance teams.

Phase 1 establishes the core orchestration engine and connects to the primary safety database (e.g., Argus, ArisG). This initial workflow automates the scheduled extraction and aggregation of adverse event data, performing basic cumulative analyses and generating a draft executive summary. The focus is on proving the data pipeline's reliability and creating a tangible time-saving for safety scientists, typically reducing the initial data collation effort from days to hours. This phase delivers a standalone, auditable output for review.

Phase 2 introduces specialized narrative agents and multi-authority formatting logic, integrating with document management systems like Veeva Vault. Phase 3 completes the loop with RIM system updates and secure portal transmission. Each phase includes defined approval gates, exception routing to a validated workflow management tool, and comprehensive observability dashboards. This incremental approach controls cost, validates each integration's ROI, and ensures the final system operates within a governed, production-ready architecture before full rollout.

The operational upside of automating PSUR generation comes from compressing a calendar-driven, multi-week manual process into a scheduled, agentic pipeline. Savings derive from eliminating data aggregation toil, reducing medical writer drafting time, and minimizing the risk of late submissions. However, this requires a control architecture that embeds medical, regulatory, and quality oversight directly into the workflow. Key systems include the global safety database (e.g., ARISg, Argus), clinical data warehouses, and document management platforms like Veeva Vault. The implementation must account for data quality exceptions, version control, and audit-ready logging of all automated actions.

Implementation follows a phased rollout, starting with a single product or region to validate data integrations and review protocols. The orchestrator, built on a framework like LangGraph, manages state and routes tasks between specialized agents for data retrieval, analysis, and drafting. Each phase incorporates stricter controls: Phase 1 might require full medical review of all drafts, while Phase 3 could allow auto-approval for unchanged narrative sections. Monitoring focuses on exception rates, review cycle times, and validation error counts pre-submission. This controlled approach de-risks the automation while progressively capturing the efficiency gains of a fully autonomous PSUR pipeline.

Manual PSUR compilation is a high-risk, low-margin administrative bottleneck, consuming weeks of pharmacovigilance effort to reconcile data from Argus, Oracle Argus Safety, Veeva Safety, and regional databases. A custom automation workflow eliminates this repetitive aggregation and narrative drafting, cutting report preparation from 4-6 weeks to days. The operational upside comes from redirecting expert resources to signal analysis and risk management, while ensuring submissions are never delayed by manual data wrangling, directly protecting market authorization.

Implementation requires an orchestrator like LangGraph to manage stateful agents for data pooling, statistical analysis, and narrative generation, integrated via APIs with safety systems and clinical warehouses. Critical controls include medical review gates for drafted narratives, validation against ICH E2C(R2) templates, and full audit trails for all data transformations. The architecture must handle data quality exceptions by routing discrepancies to a pharmacovigilance queue and support phased rollout, starting with a single product line before scaling to the full portfolio.