AI Agentic Workflow for Safety Data Exchange (E2B) Processing

Manual pharmacovigilance case processing is a high-cost, error-prone bottleneck that delays regulatory submissions and increases compliance risk. A custom agentic workflow automates the ingestion of adverse event data from CRFs, literature, and call centers, performing initial validity checks and data mapping. This reduces manual coding errors by over 70% and cuts case processing time from days to hours, directly lowering operational cost and improving submission readiness for EMA and FDA deadlines.

Implementation integrates with safety databases like ARISg or Argus, using LangGraph for stateful orchestration. Each automated decision—from MedDRA coding to causality assessment—is paired with a retrievable rationale, creating an immutable audit trail. The architecture includes configurable approval gates for high-severity cases and continuous monitoring for data drift, ensuring the system remains compliant as regulations evolve. This turns a reactive, manual process into a proactive, controlled operational asset.

This multi-agent workflow automates the high-volume, error-prone process of transforming raw adverse event data into compliant E2B (R3) submissions for regulators like the FDA and EMA. It directly targets the operational bottleneck of manual data entry, coding, and validation, which delays reporting timelines and introduces compliance risk. Savings come from reducing pharmacovigilance specialist labor by 60-80%, cutting report preparation from days to hours, and minimizing costly amendments due to formatting or validity errors. The system ingests data from CRFs, literature scans, call centers, and EHRs, using specialized agents for extraction, terminology mapping, and business rule validation.

Implementation integrates with existing safety databases (e.g., Argus, ARISg) and uses orchestration frameworks like LangGraph to manage state and exceptions between specialized validation, coding, and submission agents. Critical controls include human review gates for ambiguous data, automated audit trails linking source data to final coded terms, and confidence scoring for each mapping decision to support audit defense. The workflow is deployed as containerized microservices, ensuring it can scale with case volume and integrate via APIs with clinical trial and quality management systems without disrupting legacy pharmacovigilance operations.

This workflow automates the ingestion, validation, and E2B (R3) mapping of adverse event data from CRFs, literature, and call centers. It eliminates manual data entry and coding errors, accelerating reporting timelines to meet strict regulatory deadlines (e.g., EMA 15-day). The operational upside comes from reducing pharmacovigilance specialist workload by 60-70%, allowing teams to focus on complex case review and signal detection, directly lowering compliance risk and operational cost per report.

Implementation follows a three-phase delivery to de-risk integration with legacy PV systems like Oracle Argus or ArisGlobal. Phase 1 focuses on ingestion agents and a human review queue, delivering immediate triage efficiency. Phase 2 adds automated coding and validation, while Phase 3 integrates the final approval gate and submission routing. Each phase includes robust controls: an immutable audit trail linking source data to final output, confidence scoring for automated decisions, and mandatory Qualified Person review for all high-risk or low-confidence cases before regulatory transmission.

An AI agentic workflow for E2B processing automates the ingestion, mapping, and validation of adverse event data from CRFs, literature, and call centers into regulatory-compliant submissions. It directly targets the operational bottleneck of manual case processing, which delays reporting, introduces coding errors, and creates audit risk. The business upside comes from accelerating submission timelines by 60-80%, reducing full-time equivalent (FTE) costs in pharmacovigilance operations, and creating a defensible, consistent transformation process that withstands regulatory scrutiny. Implementation requires orchestrating specialized agents for data extraction, MedDRA coding, and business rule validation, integrated with safety databases like Argus or ARISg.

Governance is enforced through a mandatory compliance gate that routes cases failing validation or scoring low-confidence for human review. The explanation layer logs every agent's decision rationale, source mappings, and rule applications, creating an immutable audit trail. Rollout follows a phased approach: begin with a single data source and low-volume drug, validate output accuracy against historical cases, and implement a parallel run before full cutover. This controlled deployment mitigates risk while demonstrating ROI. The final architecture must integrate with existing pharmacovigilance and document management systems, ensuring the workflow is a controlled augmentation, not a brittle replacement.

This workflow automates the ingestion, validation, and formatting of adverse event data from CRFs, literature, and call centers into E2B-compliant regulatory submissions. It eliminates manual data entry and coding errors that delay reporting and create audit risk. The operational upside comes from accelerating submission timelines by 60-80%, reducing pharmacovigilance labor costs, and ensuring every transformation is logged with a clear, defensible rationale for regulatory scrutiny. Implementation integrates with clinical data warehouses, safety databases like ARGUS or Veeva Safety, and submission gateways.

Implementation requires orchestrating specialized agents for data extraction, MedDRA coding, and E2B element mapping within a framework like LangGraph. Each agent's decision must be captured in an immutable audit trail linked to source data. Critical controls include automated validity checks against ICH E2B rules, exception routing to human reviewers via a structured queue in ServiceNow or Jira, and final approval gates before transmission. The system must integrate with existing pharmacovigilance systems (e.g., Oracle Argus) and support phased rollout, starting with high-volume, low-complexity cases to validate the transformation logic before full-scale deployment.