Protocol To Data

protocol-to-data turns a clinical trial protocol into a validated, analyzable synthetic SDTM dataset in one Claude-driven agentic loop. Drop in a protocol (PDF/HTML/text) and Claude reads the prose into a typed study design — arms, visit schedule, endpoints mapped to the right SDTM domains — then deterministic Python generates SDTM-shaped synthetic CSVs, validates them (schema, referential + temporal integrity, physiologic ranges, planned-domain coverage), and on any failure Claude repairs its own design and regenerates. A second Claude agent then injects controlled data-quality defects and hunts them down, scored against ground truth. What I found: the loop generalizes from a toy cardiology sample to a real 179-page oncology protocol (Amgen AMG 510 vs docetaxel) with no code changes — extracting cycle-based visits and self-repairing 8 unproducible oncology domains in one pass. The generated data is biologically responsive: it models docetaxel-induced myelosuppression, and the Copilot's "average NEUT per arm" chart shows the docetaxel arm's neutrophils sitting well below the sotorasib arm — real pharmacology, not random distributions. The detector even flags a grade-4 neutrophil count on its own. Why it matters: clinical data managers and biostatisticians spend days-to-weeks hand-building SDTM test data before an EDC (Medidata/Veeva) is stood up. This collapses that to minutes and produces Databricks-ready, reproducible, PHI-free data they can stress-test analysis and edit-check pipelines against before first patient in — turning sequential setup into parallel work. It ships with a two-tab web UI (pipeline + a memory-safe DuckDB Data Copilot with charts), a Registry Cross-Check against ClinicalTrials.gov, a clean HTTP/MCP API, and a live cloud deployment.