SITUATION
A global pharmaceutical company needed a faster and more consistent way to draft regulatory product labels.
Regulatory writing teams spent significant time manually compiling content from clinical study reports (CSRs), investigational brochures (IBs), and safety documents to prepare region-specific Structured Product Labeling (SPL) documents.
This manual process slowed down submission timelines and introduced variability across draft versions.
The client wanted to automate the first-draft creation of product labels across key regions (US FDA, EMA, CDSCO) while maintaining compliance and traceability.
CHALLENGE
The core challenge lay in the fragmentation and unstructured nature of source documents. Relevant data such as dosage, efficacy outcomes, adverse events, and contraindications was often buried within lengthy clinical files, tables, scanned PDFs, and medical narratives.
The client’s key bottlenecks included:
- Delays in identifying and reusing reusable content from past submissions
- Inconsistencies in medical language and regulatory formatting across product lines
- High resource dependency for repetitive drafting tasks
- Time-consuming internal review and legal validation cycles
An intelligent, compliant, and traceable GenAI solution was needed to scale labeling operations without compromising quality or regulatory standards.
OUR APPROACH
Document Ingestion and Structuring
Consainsights built a robust ingestion pipeline to process diverse regulatory documents, including CSRs, Investigator Brochures, safety reports, and CTD modules. The pipeline handled semi-structured and unstructured formats using advanced parsing, OCR for scanned content, and rule-based extraction to isolate relevant sections such as efficacy summaries, dosage protocols, and adverse events.
Domain-Specific Language Model Deployment
A transformer-based large language model was fine-tuned using thousands of historical labels, regional regulatory templates (FDA PLR, EMA SmPC), and pharmacovigilance standards (MedDRA, WHO-DD). This model was designed to understand biomedical context, enforce structural compliance, and generate human-like, regulator-ready label content for all major sections.
Smart Label Draft Generation
The model dynamically generated first-draft content for key label components—such as Indications and Usage, Dosage and Administration, Contraindications, Warnings, and Clinical Studies—by referencing validated trial data and source documents. Citations were auto-tagged to maintain traceability and simplify medical-legal reviews.
Human-in-the-Loop Validation
To ensure accuracy and compliance, Consainsights integrated a collaborative review interface. Regulatory writers and safety experts could inspect AI-generated text, make inline edits, and validate source references. Feedback from each cycle was captured to further train and improve the model’s accuracy and adaptability.
Compliance and Audit Readiness
The solution enforced regional labeling standards through embedded rule sets and automated structure validation. Each section of the generated label maintained source traceability, supporting audit-readiness and transparent documentation for internal QA and external regulatory inspections.
IMPACT
- 70% reduction in time to generate first-draft product labels
- 85% consistency rate between AI-generated drafts and approved final versions
- Full traceability enabled faster cross-functional reviews and regulatory audits
- Enabled writers to focus on high-value work such as interpretation and risk justification rather than formatting
BUSINESS IMPACT
- 70% Faster Label Drafting
- 85% Accuracy with Final Submissions
- 100% Document Traceability for Compliance
“Consainsights’ GenAI solution transformed our labeling workflow. It’s like going from hand-stitching to automated assembly. Our teams can now focus on strategy—not formatting.”
— Director, Global Regulatory Affairs