AI-Enabled Optimization of Early- Phase Clinical Trials Pilot Program; Request for Information

ZS Associates Inc., a management consulting and technology firm, supports the FDA's proposed pilot program to assess AI-enabled technologies in early-phase clinical trials. They argue that the pilot will improve efficiency, speed, and decision-making quality by helping sponsors and the FDA stress-test critical assumptions and mitigate risks of late-stage regulatory failures.

Tempus AI, a multimodal data and AI company, supports the FDA's proposed pilot program for AI-enabled optimization of early-phase clinical trials. They provide specific methodological recommendations for trial archetypes, therapeutic areas, and the inclusion of foundation models in the pilot's scope.

The Duke-Margolis Institute for Health Policy supports the FDA's pilot program to use AI for optimizing early-phase clinical trials, praising the agency's commitment to advancing evidence generation. They provide constructive feedback on prioritizing generalizable results, managing the compressed timeline, and balancing AI-driven speed with rigorous risk-benefit frameworks.

Syneos Health, a Contract Research Organization (CRO), expresses strong support for the AI-enabled clinical trials pilot program. They provide detailed recommendations on prioritizing oncology and rare disease trials, specific AI use cases like real-time safety monitoring, and methodologies for rigorous comparative evaluation.

Noetik, an AI-native oncology company, supports the FDA's pilot program for using AI to optimize early-phase clinical trials. They recommend prioritizing oncology trials, establishing a coordinated regulatory engagement track, and ensuring the pilot includes a significant share of slots for small and mid-size AI-native sponsors.

UCB, Inc., a global biopharmaceutical company, expresses strong support for the FDA's pilot program to optimize early-phase clinical trials using AI. They provide specific recommendations on trial design, evaluation metrics, and the importance of open-source tools and collaborative learning to improve patient outcomes and trial efficiency.

Cyntegrity, an RBQM platform provider, supports the FDA's initiative to explore AI in early-phase clinical trials and provides specific recommendations for the pilot's design. They advocate for using retrospective analyses to establish AI reliability, prioritizing safety monitoring as a primary use case, and utilizing side-by-side human-versus-AI comparisons to evaluate performance.

LatentRx Therapeutics Inc., an AI-driven drug repurposing company, supports the AI-Enabled Optimization of Early-Phase Clinical Trials Pilot Program and requests that it be expanded to include metabolic disease trials. The company argues that metabolic diseases offer significant opportunities for real-time efficacy signal transmission using digital, high-frequency data streams like CGM-derived PPG-AUC.

AyurDatta Solutions, a physician-led clinical safety and governance firm, supports the pilot program and recommends that it explicitly require and measure documented human adjudication of AI-assisted safety outputs. They argue that while AI can detect and score signals, qualified human judgment is necessary for regulatory accountability and suggest using a "decision-boundary" audit trail as a vendor-neutral metric for trustworthiness.

Domino Data Lab, a technology company, supports the FDA's pilot program for AI in early-phase clinical trials. They argue that the pilot should focus on high-consequence use cases like safety monitoring and dose selection, emphasizing that AI should be used to enhance human decision-making while maintaining rigorous standards for transparency, reproducibility, and human oversight.