Multi-Agent Based Automation of Institutional Review Board (IRB) Protocol Pre-review

This agent acts as the workflow's intake layer, ingesting submission packages from portals like IRBNet or Cayuse. It uses specialized document parsers for PDFs, Word documents, and spreadsheets to extract structured data (PI info, study design, consent forms) and chunk unstructured text for downstream analysis. It validates file completeness against a submission checklist and routes incomplete packages back to the investigator with a detailed gap report.

A dedicated agent trained on FDA 21 CFR 50, Common Rule, and institutional templates. It performs a line-by-line review of informed consent documents, flagging missing required elements (e.g., confidentiality, compensation for injury), identifying complex or inconsistent language, and checking risk descriptions against the protocol's study procedures. It generates specific, actionable feedback with citations to regulatory guidance.

This agent cross-references all protocol sections (background, objectives, design, statistical plan) for internal consistency. It uses retrieval-augmented generation (RAG) against a knowledge base of institutional SOPs and ICH-GCP guidelines to validate alignment between endpoints, sample size justifications, and inclusion/exclusion criteria. It identifies methodological gaps that commonly trigger IRB stipulations.

The agent maps the protocol against a dynamic rules engine containing institutional policies (e.g., radiation safety, biosafety), local law, and sponsor-specific requirements. It checks for necessary ancillary committee approvals (IBC, Radiation Safety) and flags studies requiring external IRB reliance or single IRB (sIRB) setup. This ensures the submission is routed correctly from the start.

The orchestrator agent collates findings from all specialist agents, de-duplicates issues, and prioritizes them by severity (critical, major, minor). It generates a comprehensive, investigator-facing pre-review report with clear recommendations, linked source text, and direct citations. The report is formatted for the IRB office's workflow system (e.g., integrated with Salesforce Service Cloud or ServiceNow) and triggers automated notification to the PI.

The core orchestration layer, built on a framework like LangGraph, manages state, handoffs, and exception routing between all agents. Every agent decision, piece of feedback, and data point is logged to an immutable audit trail with model reasoning traces. The system includes configurable approval gates where an IRB analyst can review, modify, or override automated feedback before it is sent, ensuring human oversight and final accountability.

Primary Impact: Shifts role from manual checklist reviewers to exception managers and process overseers. The system automates the initial completeness and consistency review of all submissions, flagging only the ~30% with deficiencies or complex issues for human attention.

Operational Upside: Enables a single coordinator to handle 2-3x the protocol volume, reducing administrative backlog and cutting initial review cycle time from days to hours. The system provides a standardized, defensible audit trail of all automated checks, simplifying responses to investigator queries and regulatory audits.

Primary Impact: Receives immediate, actionable feedback on submission drafts, reducing frustrating cycles of rejection and resubmission. The agentic system provides specific citations to protocol gaps, inconsistent risk descriptions, or non-compliant consent language before formal submission.

Operational Upside: Dramatically improves 'first-pass' submission quality, accelerating the path to full IRB review and study activation. Investigators gain a transparent, educational tool that clarifies regulatory expectations, leading to higher-quality protocols over time and fewer costly post-approval amendments.

Primary Impact: Gains a proactive, embedded control layer that enforces institutional policy and regulatory frameworks (e.g., 45 CFR 46, ICH GCP) at the point of protocol creation. The system's explainability layer documents every automated check against a specific rule or guideline.

Operational Upside: Transforms compliance from a reactive audit function to a proactive, scalable safeguard. Provides continuous assurance data and trend analysis on common submission pitfalls, enabling targeted training and policy updates. Creates a immutable, queryable log of all pre-review decisions for regulatory defense.

Implementation Core: A custom orchestration layer (e.g., LangGraph) coordinates specialized agents, each with a discrete, auditable function:

1. Ingestion & Parse Agent: Extracts text, tables, and metadata from PDF/Word submissions into a structured JSON schema.
2. Completeness Agent: Validates against a dynamic checklist (required sections, signatures, appendices).
3. Consistency Agent: Cross-references data across sections (e.g., participant numbers in protocol vs. consent).
4. Rule & Policy Agent: Applies a retrievable knowledge base of institutional SOPs and regulatory clauses using RAG.
5. Feedback Synthesis Agent: Generates a unified, prioritized deficiency report with clear citations and suggestions.

Integration Points: Connects to IRB management software (e.g., IRBNet, Click), document repositories, and identity systems via APIs. Human-in-the-loop queues are managed in a dedicated dashboard for IRB staff.

Build Phases:

1. Discovery & Rule Mapping: Codify institutional policies, common deficiencies, and approval criteria into a machine-executable rule set.
2. Pilot & Calibration: Deploy against historical submissions to tune agent accuracy, establish confidence thresholds, and design exception routing.
3. Integration & Rollout: Connect to live IRB submission queue with phased adoption (e.g., specific study types first).
4. Monitoring & Evolution: Implement dashboards for system performance, agent accuracy, and trend analysis of flagged issues.

Critical Controls: Requires configurable approval gates for any automated 'rejection' recommendation. All agent reasoning must be stored with the submission record. Regular reviews of the rule base by the IRB chair and compliance are mandatory to ensure alignment with evolving guidance.