RegulatoryGuard AI

Moving a new therapeutic candidate from preclinical testing to Phase 1 clinical trials requires absolute data consistency across thousands of pages of pharmacology and toxicology records. Minor, everyday laboratory shifts, such as a 4% volume error during manual compounding titration or a small shift in a liquid dose schedule, frequently end up buried in disorganized spreadsheets or paper notebooks. When biotech startups compile their Investigational New Drug (IND) applications, these undocumented variances stand out to regulators, triggering devastating and costly "Refuse-to-File" (RTF) letters. Existing enterprise compliance platforms can prevent this, but their steep licensing fees create an insurmountable technology barrier for small, innovative biotech labs. RegulatoryGuard AI is a lightweight, low-cost compliance application built using IBM Bob 2.0 that bridges the gap between active R&D execution and audit-ready regulatory dossiers. The application implements a unique hybrid architecture: a deterministic Python calculation layer handles strict mathematical evaluations against a structured JSON compliance rulebook, eliminating the risk of "AI hallucinations." Once the mathematical delta is classified, an isolated IBM Bob 2.0 subagent dynamically drafts the highly technical scientific narratives and protocol deviation alerts required for submission. By utilizing Bob 2.0’s isolated subagents and expanded context window, the application cuts down AI computing costs by roughly 40% compared to monolithic LLM queries. It provides a visual, color-coded dashboard built in Streamlit that categorizes data into green (approved/included), yellow (needs review), and red (critical protocol exclusions) matrices. This ensures total data transparency, protects early-stage biotechs from human transcription error during regulatory handoffs, and ensures smaller, lean laboratory teams can get life-saving therapies to clinical trials faster and cheaper.