Automation Workflow for Automated Generation of Review Packets for Medical, Legal, Regulatory (MLR) Review

Manual assembly of review packets—pulling documents from Veeva Vault, SharePoint, or Documentum, finding previous comments, and cross-referencing study data—consumes 4-8 hours per reviewer per packet. A custom agentic workflow automates this retrieval and compilation, delivering a consolidated, hyperlinked packet to each reviewer's inbox. The savings convert administrative labor into focused review time, directly shortening each MLR cycle by days.

Manual packet assembly introduces risk of missing critical documents, outdated versions, or unresolved previous comments. The automated workflow uses orchestration agents (e.g., LangGraph) to enforce business rules: verifying document versions, appending relevant regulatory guidance snippets via RAG, and linking all referenced data. This ensures every reviewer operates from the same, correct information baseline, reducing the probability of approval delays due to oversight.

As portfolio complexity grows, manual packet generation becomes a bottleneck, limiting throughput. This custom workflow acts as a force multiplier. By automating the assembly and routing logic, a single regulatory operations specialist can manage packet generation for multiple concurrent submissions across different therapeutic areas. The architecture integrates with existing systems (Veeva PromoMats, SAP, CTMS) to pull data, making scalability a configuration exercise, not a hiring spree.

Beyond simply bundling PDFs, the workflow enriches packets with structured data. Agents extract key efficacy/safety endpoints from clinical databases, generate summary tables, and highlight changes from previous submissions. This transforms the packet from a document dump into an analytical briefing, enabling Medical and Legal reviewers to make higher-confidence decisions faster. The result is fewer review rounds and more robust, defensible approvals.

Every automated packet generation event is logged with a complete audit trail: source documents, retrieval timestamps, version IDs, and routing logic. This immutable log, stored alongside the final packet, provides instant defensibility during internal audits or regulatory inspections. The workflow turns a previously opaque, person-dependent process into a transparent, system-driven procedure that strengthens overall compliance posture.

For global products, packet requirements differ by region (FDA vs. EMA vs. PMDA). The workflow incorporates regional rule sets, automatically adjusting packet contents, required summaries, and formatting. This eliminates the manual rework needed for each jurisdiction, allowing parallel MLR reviews. The architecture uses configurable agents to apply regional templates, turning a sequential bottleneck into a parallel process that can cut months off global launch timelines.

This primary agent orchestrates the retrieval of all necessary documents for a given review item. It queries content management systems (e.g., Veeva Vault, Documentum), clinical data warehouses, and regulatory databases using a RAG-enhanced understanding of the review context. It resolves document versions, pulls previous comment histories, and fetches referenced guidelines, ensuring the packet is comprehensive and current. Exception handling routes missing or conflicting versions to a human administrator.

An assembly agent structures the retrieved artifacts into a logical, reviewer-specific packet. It generates an executive summary highlighting changes from previous versions, synthesizes key data points across documents, and creates a hyperlinked table of contents. For complex submissions, it can draft brief explanatory narratives connecting scientific data to proposed claims, providing crucial context that reviewers would otherwise have to deduce manually.

This control layer manages the workflow's governance. It enforces business rules for packet completeness and validation before release. Based on document type, therapeutic area, and change significance, it dynamically routes the assembled packet to the correct sequence of MLR reviewers in systems like Veeva PromoMats, manages review cycle deadlines, and escalates overdue items. It integrates with email and collaboration platforms for notifications.

A critical component for iterative reviews, this engine automatically integrates new comments from the current review cycle into the packet's living history. It performs line-level diffs between document versions, visually highlighting changes for reviewers. This eliminates the manual, error-prone task of comparing documents side-by-side and ensures all stakeholders are reviewing the latest, consolidated set of feedback.

Operating in parallel with assembly, this agent performs automated checks to ensure packet integrity and compliance. It validates that all required document types are present, checks for internal consistency (e.g., figure numbers match narratives), and verifies that referenced regulatory guidelines are correctly cited. Any violations are flagged in a pre-submission report, allowing for correction before the packet is finalized and locking the submission-ready version.

The workflow is built on an orchestration framework like LangGraph or Prefect, with agents developed as modular services. Key integrations include: CMS/RIM Systems (Veeva, Documentum) for source docs, Clinical Data Repositories for study data, Identity & Access Management for reviewer routing, and Collaboration Platforms (MS Teams, Slack) for notifications. Deployment is typically containerized (Docker/K8s) with observability (OpenTelemetry) for monitoring agent performance and packet assembly latency.

Owns the submission timeline and is measured on cycle time. They fund the build to eliminate the 2-3 day manual scramble before each review cycle, where admins manually pull documents from Veeva Vault, SharePoint, and legacy archives. Their success metric is reviewer readiness within hours, not days, directly compressing the overall MLR cycle.

Designs the agentic orchestration layer using LangGraph or Temporal to manage state across retrieval, assembly, and routing. Specifies integrations with CMS (Veeva, Documentum), RIM systems, and email/calendar APIs. Responsible for the exception-handling logic (e.g., missing document routing to a human queue) and the audit trail that logs every packet version and its constituent sources for compliance.

The workflow's primary consumers. They need a consolidated, hyperlinked PDF packet with all relevant source documents, previous comment histories, and reference guidelines—context they currently spend hours assembling themselves. The build must include a configurable packet template so each role (Medical vs. Legal) receives a view tailored to their review focus, improving review quality and reducing context-switching.

Controls access to the source systems (Veeva Vault, SharePoint, SAP Quality Management). They mandate security and compliance protocols for automated data retrieval, often requiring service accounts with scoped permissions. This role is critical for approving the integration architecture, ensuring the workflow does not violate data governance policies, and supporting the deployment into the validated IT environment.

The engineering team that executes the build. They develop the retrieval agents (using RAG over document repositories), the packet assembly engine (PDF generation with dynamic TOC), and the notification/routing layer. They manage the pilot, typically starting with one therapeutic area, and define the rollout plan to scale across the organization's entire portfolio, ensuring the system handles increased volume and complexity.

Ensures the automated workflow is 21 CFR Part 11 compliant and that its outputs are reliable for regulatory use. They define the requirement for a human-in-the-loop checkpoint before packet distribution and mandate rigorous testing of the retrieval logic to prove no documents are omitted or incorrectly versioned. Their sign-off is non-negotiable for production deployment.