Multi-Agent Based Automation of Submission Data Consistency Checking

Up to 80% of major regulatory questions stem from internal data inconsistencies missed during manual review. A multi-agent workflow automates the exhaustive checking of figures, statistical outputs, and narrative claims across thousands of document and data points, surfacing discrepancies for resolution weeks before submission. This directly reduces the risk of a major deficiency letter, which can delay approval by 6-18 months and incur millions in lost revenue and rework costs.

The final pre-submission quality check is a massive, parallelized human effort prone to fatigue and oversight. Orchestrating specialized validation agents (e.g., a Statistical Output Agent, a Table-to-Text Agent, a Source Data Trace Agent) in a framework like LangGraph allows for continuous, scheduled consistency runs. This turns a 3-4 week bottleneck of manual cross-referencing into an overnight automated process, freeing senior medical writers and statisticians for higher-value scientific review.

Manual checks leave no defensible, granular audit trail. A custom automation layer logs every agent interaction—data source queried, rule applied, discrepancy flagged, and resolution path. This creates an immutable validation record that satisfies ALCOA+ principles for data integrity, providing ready evidence for regulatory inspections and reducing pre-audit preparation panic. The workflow integrates directly with Veeva Vault or Documentum to version-control findings and link them to source documents.

By automating the tedious verification of table numbers, p-values, and patient counts, you reallocate your most expensive regulatory and clinical talent from administrative checking to strategic activities: interpreting trends, strengthening narratives, and engaging early with health authorities. This operational leverage turns the regulatory function from a cost center focused on error prevention into a strategic assurance team that actively de-risks the pipeline and accelerates market access.

Manual processes vary by team and region, creating inconsistency risk. A centrally orchestrated agentic workflow applies the same validation rules—whether checking an SDTM dataset against a CSR table or a SmPC claim against an ISE—across all simultaneous submissions (NDA, MAA, NDS). This creates a repeatable, scalable operating model for quality control, ensuring that a submission in Japan receives the same rigorous automated checks as one for the EMA, reducing global compliance risk.

Without automation, all discrepancies feel equally urgent. An intelligent workflow scores inconsistencies by severity (e.g., a mismatched primary endpoint statistic vs. a formatting error) and potential regulatory impact, using historical review data. It routes high-severity items to a prioritized queue in Jira or ServiceNow for immediate action, while logging minor issues for batch correction. This data-driven triage prevents teams from wasting cycles on low-risk items as the submission deadline looms.

This agent acts as the workflow's initial orchestrator, responsible for ingesting the submission plan and fetching all relevant source documents and datasets from fragmented repositories (e.g., Veeva Vault, clinical data warehouses, SAS servers). It normalizes file paths, extracts text and tabular data using OCR and parsers, and creates a unified manifest for downstream agents, ensuring no critical evidence is missed in the verification scope.

A rule-based agent that parses statistical analysis outputs (e.g., from SAS, R) to extract key values like p-values, hazard ratios, confidence intervals, and descriptive statistics. It validates the computational integrity of these outputs against predefined statistical plans and flags any calculations that deviate from expected logic or range boundaries before they propagate into narratives.

This LLM-powered agent performs semantic analysis on clinical narrative text (e.g., CSR results sections) to identify all stated numerical findings, trends, and conclusions. It then programmatically cross-references each claim against the validated statistical outputs and source data tables, flagging discrepancies in magnitude, direction, or statistical significance. It generates a discrepancy log with direct quotes and data citations.

Focused on visual evidence, this agent ingests submission-ready tables and figures (PDFs, images). It uses computer vision and layout analysis to extract data from charts and tabular formats, then reconciles the visualized data points with the underlying source datasets and the narratives describing them. It catches common errors like mislabeled axes, incorrect figure references in text, or data transposition mistakes.

The workflow's control plane. It ingests all flags and discrepancies from the checking agents, classifies them by severity (critical, major, minor) and functional area (statistics, medical, data management). It then automatically routes issues to the appropriate reviewer's queue in systems like Jira or Veeva Vault, attaching all relevant context, and escalates critical, unresolved items as deadlines approach.

A persistent logging system that records every agent action, data point checked, discrepancy found, and resolution status. This creates an immutable audit trail for regulatory inspection. A live dashboard synthesizes this data into a submission 'health score,' showing the percentage of cross-references completed, open issues, and overall consistency readiness, giving program managers real-time visibility into QA progress.