Automation Workflow for Preparing and Formatting Data for PSURs and PBRERs

The operational bottleneck is the manual labor of querying disparate safety databases (e.g., Oracle Argus, Veeva Safety), reconciling line listings, and reformatting summary tabulations into rigid regulatory templates. This repetitive work ties up medical writers and PV associates for weeks per report, introducing formatting errors and numerical inconsistencies that risk regulatory queries. Automating this flow directly targets labor cost, cycle time compression, and data integrity—key drivers for compliance and operational efficiency.

Implementation requires a multi-agent architecture orchestrated by a state manager like LangGraph. Specialized agents handle autonomous query execution against legacy PV systems, data transformation into ICH E2C-compliant formats, and population of template documents. Critical controls include automated quality checks for numerical consistency, a human review gate for medical sign-off, and full audit trails linking source data to final outputs. This creates a repeatable, scalable workflow that eliminates copy-paste drudgery while preserving the traceability required for audit.

This workflow automates the high-labor, error-prone process of pulling data from safety databases (e.g., Oracle Argus, Veeva Safety), transforming it into line listings and summary tabulations, and formatting it to precise regulatory templates. The operational upside is a 70-90% reduction in manual preparation time, compressing a multi-week activity into days while improving numerical consistency and auditability. The architecture is triggered by the PSUR due-date calendar, launching a series of specialized, governed agents.

Implementation uses an orchestrator like LangGraph to manage state across query, transformation, and templating agents. Each agent integrates with specific systems: query agents use direct DB connectors or APIs, transformation agents apply business logic for MedDRA groupings, and templating agents populate Word or PDF via engines like DocuGenerate. Mandatory quality gates validate data completeness against the defined review period and check for numerical drift between source and output. The final draft is routed to a document management system, with a full audit trail linking each data point to its source query for defensible traceability.

Phase 1 focuses on the highest-ROI task: automating the extraction and tabulation of line listings from the safety database (e.g., Oracle Argus, Veeva Safety). We deploy a dedicated data agent with read-only API access to execute predefined queries for the review period, pulling patient demographics, drug exposure, and adverse event data. This agent outputs structured JSON, which is validated against source record counts to ensure completeness before any transformation, delivering immediate time savings for safety scientists.

Phase 2 introduces the template engine and formatting logic, building on the validated data pipeline. We implement a Jinja2 or similar engine to populate predefined PSUR section templates (e.g., cumulative summary tabulations, reference safety information). A separate formatting agent applies regulatory styling and pagination, generating draft PDFs. This phase includes a mandatory human review checkpoint in a staging environment, allowing medical writers to verify layouts before the system touches production documents, ensuring control.

This workflow automates the extraction, tabulation, and formatting of cumulative safety data from systems like Oracle Argus or Veeva Safety into compliant PSUR layouts. It eliminates weeks of manual, error-prone work but introduces new risks around data traceability and numerical consistency. The architecture must embed quality gates, approval checkpoints, and a full audit trail to satisfy internal QA and health authority inspectors, turning a high-volume administrative task into a controlled, repeatable process.

Implementation follows a phased rollout, starting with a single product's line listings to validate transformation logic and reconciliation controls before scaling. Each phase incorporates explicit exception routing for mismatched totals or uncoded terms, ensuring no silent failures. The final architecture integrates with document management systems like Veeva Vault for version control and includes monitoring dashboards for data lineage, providing operational confidence and a defensible path to full automation.

The operational bottleneck is the manual, error-prone process of querying safety databases, reconciling data across sources, and reformatting line listings and summary tabulations into rigid regulatory templates. This workflow automates that end-to-end, compressing a multi-week task into days. The savings come from eliminating copy-paste labor, reducing QC rework, and accelerating submission readiness, directly impacting compliance cycle times and freeing medical writers for higher-value analysis.

Implementation requires integrating with the safety database (e.g., Oracle Argus, Veeva Safety) and clinical warehouses via secure APIs. The orchestrator, built on LangGraph, manages state across data pull agents, a template engine using tools like DocuGenerate or Python-docx, and a validation agent running business rules for numerical consistency and MedDRA code alignment. Critical controls include a mandatory human review gate before finalization, full audit trails of all data transformations, and exception routing for discrepancies that require investigator intervention.