AI for Life Science Regulatory Document Intelligence

Regulatory submissions in life sciences are among the most complex document packages in any industry. A single New Drug Application (NDA) can exceed 100,000 pages spread across dozens of modules: clinical study reports, Chemistry, Manufacturing, and Controls (CMC) documentation, nonclinical summaries, pharmacovigilance data, and labeling. Every data point in a submission must be internally consistent and traceable to its source. Inconsistencies between modules, whether a mismatched batch number in CMC or a conflicting adverse event count between a clinical study report and the integrated summary of safety, can trigger regulatory queries, review delays, or rejection.

The challenge is not extracting data from individual documents. It is reasoning across the full dossier to verify that data is consistent, complete, and defensible.

The Problem

Submission preparation is manual cross-referencing at scale

Regulatory affairs teams preparing IND (Investigational New Drug), NDA, BLA (Biologics License Application), or MAA (Marketing Authorisation Application) filings spend weeks verifying consistency across modules. A typical submission follows the Common Technical Document (CTD) format, organized into five modules:

Module 1: Administrative and prescribing information
Module 2: Summaries (quality, nonclinical, clinical)
Module 3: Quality (CMC) data
Module 4: Nonclinical study reports
Module 5: Clinical study reports

Each module is authored by different teams, often at different times, using different source data snapshots. Summary tables in Module 2 must match the detailed data in Modules 3, 4, and 5. Labeling claims must align with clinical evidence. Manufacturing specifications must be consistent between the drug substance and drug product sections of Module 3.

Verifying this consistency manually means reviewers hold multiple documents open simultaneously, scanning for numeric mismatches, terminology drift, and data that was updated in one module but not carried through to another.

Existing tools process documents individually

Current document extraction tools operate at the single-document level. They can parse a clinical study report or extract a table from a CMC batch record. They cannot verify that the stability data cited in Module 2.3 (Quality Overall Summary) matches the detailed stability studies in Module 3.2.S.7, or that the adverse event frequencies in the integrated summary of safety are consistent with the individual study reports in Module 5.

RAG-based approaches retrieve relevant passages using semantic search, but top-K retrieval silently drops content that does not match the query embedding closely enough. For regulatory work, where a single omitted data point can trigger a deficiency letter, this introduces unacceptable risk. See why RAG fails for risk-grade decisions for a detailed analysis.

Post-submission queries are costly

Regulatory agencies issue queries (Information Requests, Refuse to File letters, Complete Response Letters) when they find inconsistencies, missing data, or unsupported claims. Each query cycle adds months to the review timeline. The cost of a delayed approval, measured in lost market exclusivity, can reach millions per day for a major product. Early detection of internal inconsistencies before filing reduces this risk substantially.

How Parsewise Addresses It

Parsewise operates at the document-package level, not the single-document level. Instead of extracting data from individual files, it ingests the entire submission dossier and reasons across all modules simultaneously using the Parsewise Data Engine.

Cross-module consistency verification

Parsewise’s cross-document reasoning links entities across the full CTD structure. It detects contradictions between modules: a batch number in the drug substance section that does not match the drug product section, an efficacy endpoint reported differently in the clinical study report versus the integrated summary of efficacy, or a stability specification in Module 3 that conflicts with the shelf-life claim in labeling. Every flagged inconsistency includes exact source citations from both documents, with page and paragraph references, so reviewers can verify and resolve directly.

Exhaustive processing with no retrieval gaps

The Parsewise Data Engine processes over 25,000 pages per run, reading every page rather than relying on retrieval-based sampling. For regulatory submissions where completeness is a requirement, not a preference, this eliminates false negatives from retrieval gaps. The system handles 20,000+ requests per minute and can run autonomously for over 5 hours, processing the full scope of a large submission without manual intervention.

Structured extraction with configurable agents

Regulatory teams configure extraction agents with topics, dimensions, and natural-language instructions tailored to their submission type. An agent for CMC review might extract batch records, stability data, specifications, and analytical methods across all Module 3 subsections, then reconcile values against the quality overall summary. Agents are reusable across submissions, versioned, and can be refined over time as regulatory requirements evolve. They can be created conversationally through Navi or programmatically via the API.

Pharmacovigilance and safety data reconciliation

Safety data in regulatory submissions spans multiple documents: individual clinical study reports, the integrated summary of safety, periodic safety update reports (PSURs), and labeling. Parsewise links adverse event terms, frequencies, and severity classifications across these documents, flagging cases where reported incidence rates differ or where a safety signal in a study report is not reflected in the summary tables.

Multi-language regulatory filings

Regulatory submissions for global filings often include documents in multiple languages: English for FDA submissions, local languages for national competent authorities in the EU, Japanese for PMDA. Parsewise supports over 70 languages and can extract data in one language and produce structured outputs in another, supporting the multi-language document packages common in international regulatory programs.

Example Inputs and Outputs

Inputs

Complete CTD submissions (Modules 1 through 5) in PDF, Word, and Excel
Clinical study reports (CSRs) with tables, figures, and appendices
CMC batch records, analytical certificates, and stability reports
Nonclinical study reports and summaries
Labeling drafts, prescribing information, and SmPC documents
Pharmacovigilance reports (PSURs, DSURs, aggregate safety data)
Regulatory correspondence and agency queries

Outputs

Cross-module consistency reports: Structured tables showing data points that differ between modules, with exact source citations from each document
CMC data extraction: Batch numbers, specifications, test results, and stability data standardized into comparable formats across drug substance and drug product sections
Safety data reconciliation: Adverse event frequencies cross-checked between individual study reports and integrated summaries, with discrepancies flagged and sourced
Submission readiness checklists: Structured identification of missing sections, incomplete cross-references, and unresolved internal inconsistencies
Structured JSON output: Schema-based extraction for integration with regulatory information management systems (RIMS) and electronic submission platforms

Security and Compliance for Regulated Life Sciences

Life science regulatory data requires rigorous security controls. Parsewise is SOC 2 Type II and GDPR compliant, encrypts data with TLS 1.2+ in transit and AES-256 at rest, and does not train on customer data. Enterprise customers can deploy in their own VPC or on-premises, with regional data residency options for EU, US, and other regions. Full audit trails and versioning are maintained across all projects and extractions. See the Trust Center overview for complete details.

Ready to see Parsewise in action? Request a demo or contact sales to discuss your use case.