The document-intensive reality of drug development
Drug development is, at its core, a documentation enterprise. Every clinical trial generates a protocol, an investigator's brochure, informed consent forms, case report forms, statistical analysis plans, clinical study reports, and ultimately regulatory submission documents. A single New Drug Application (NDA) submitted to the FDA can contain over 100,000 pages.
The scientific and clinical work is essential and irreplaceable. But the documentation work — drafting, reviewing, cross-referencing, formatting, checking consistency — consumes a disproportionate share of the timeline. A clinical trial protocol amendment that changes eligibility criteria requires updates to the informed consent, the case report form, the randomisation plan, the statistical analysis plan, and potentially the investigator's brochure. Each update must be consistent across all documents.
This is where AI delivers value in drug development: not in making scientific decisions, but in accelerating the documentation, review, and processing workflows that surround those decisions. The scientists and clinicians focus on the science. AI handles the document processing at scale.
