Comment from ActioPath Technologies, LLC

See attached file(s). This comment proposes that the pilot evaluate AI as reusable, persistent trial-operations infrastructure that sites and sponsors keep across studies, rather than as standalone tools rebuilt for each trial. This reframing addresses two linked accessibility gaps in early-phase research: community oncology practices and their patients lack access to early-phase trials, and emerging biotechnology sponsors lack access to scalable, compliant operational infrastructure. A single shared infrastructure can close both. The case is grounded in national data. Approximately 85% of U.S. cancer patients are treated in community settings, yet early-phase trials concentrate at a small number of academic centers. Participation is 4.1% at community programs versus 21.6% at NCI-designated comprehensive centers (Unger et al., 2024), and for 56% of patients no trial was available where they receive care (Unger et al., 2019). The binding constraint is reach, not patient willingness. Organized to the RFI's questions (A.1 through A.5 and B.1 through B.5), the comment proposes an 18-month oncology dose-finding pilot anchored at community practices, with an AI-assisted orchestration layer coordinating site activation, safety surveillance, and execution, and an evaluation framework centered on activation-time reduction, representative enrollment, and safety responsiveness. Its governance model holds that a named principal investigator or medical monitor owns every decision, with reproducible explainability, predefined stopping rules for the AI system itself, and a tamper-evident record for every AI-assisted action, consistent with 21 CFR Part 11, ICH E6(R3), and FDA's draft AI guidance.