AI-Powered eCTD Assembly & Gap Analysis
What you'll learn
- 1Understand the eCTD structure (Modules 1-5) and where AI adds the most value
- 2Use AI to perform automated cross-reference checks across submission sections
- 3Build prompts that identify inconsistencies between clinical study reports and summary documents
- 4Apply AI-driven gap analysis to catch missing elements before submission
# AI-Powered eCTD Assembly & Gap Analysis
Regulatory submissions are the gateway to market for every pharmaceutical product. The electronic Common Technical Document (eCTD) format structures these submissions into five modules, and a single New Drug Application (NDA) or Marketing Authorization Application (MAA) can span over 100,000 pages.
The eCTD Structure and AI Opportunities
Module 1 — Administrative and prescribing information (region-specific) Module 2 — Summaries (Quality Overall Summary, Nonclinical Overview, Clinical Overview, Clinical Summary) Module 3 — Quality (CMC data, manufacturing, controls, stability) Module 4 — Nonclinical study reports Module 5 — Clinical study reports
AI adds the most value at the intersection points — where information in one module must align perfectly with information in another. A patient count cited in the Module 2.7.3 Summary of Clinical Efficacy must match the CSR in Module 5.3.5. A specification in Module 3.2.S must be consistent with what is described in Module 2.3.
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What you'll learn:
- Understand the eCTD structure (Modules 1-5) and where AI adds the most value
- Use AI to perform automated cross-reference checks across submission sections
- Build prompts that identify inconsistencies between clinical study reports and summary documents