Comment from Intellicite Labs

Please find attached an additional technical paper that expands upon concepts discussed in my previous submission to FDA Docket FDA-2026-N-4699. Rather than presenting new policy recommendations, this roadmap outlines a broader long-term research agenda describing planned technical papers spanning AI-enabled regulatory science, pharmaceutical development, precision medicine, diagnostics, and continuous learning across the product lifecycle. I hope this document provides additional context regarding the broader computational framework and future research directions underlying the concepts proposed for the Expedited IND Pilot Program. I appreciate the FDA's consideration of these comments and the opportunity to contribute to the ongoing discussion surrounding modernization of regulatory science. This paper is also available at: Chu, M. (2026). Future Directions in AI-Enabled Regulatory Science and Pharmaceutical Development: A Research Roadmap and Preview of Planned Technical Papers. Zenodo. https://doi.org/10.5281/zenodo.21407303 This technical paper roadmap is in response to FDA Docket FDA-2026-N-4699* and other recent papers and expands upon recent work in AI-enabled regulatory science, pharmaceutical development, and computational. The document outlines a modular research program spanning drug discovery, preclinical development, regulatory science, clinical development, diagnostics, precision medicine, and continuous learning systems. Planned technical papers include knowledge graph–enabled drug discovery, multidomain similarity assessment, platform technologies, AI-assisted regulatory decision support, Qualified Research Institution (QRI) implementation, explainable AI, precision clinical trial optimization, combination therapies, digital therapeutics, point-of-care diagnostics, environmental exposure monitoring, real-world evidence, and integrated AI-enabled pharmaceutical development. Collectively these research modules describe a long-term computational ecosystem intended to support evidence generation across the pharmaceutical development lifecycle while maintaining rigorous scientific evaluation. Individual technical papers, retrospective analyses, software demonstrations, and proof-of-concept implementations will be released as the underlying research progresses. *Chu MB. AI-Enabled Progressive Derisking and Multi-Domain Similarity Assessment Frameworks for Expedited Investigational New Drug Development: A Technical Proposal for the FDA Expedited Investigational New Drug Pilot Program https://doi.org/10.5281/zenodo.21369632 Planned Technical Papers Drug Discovery & Computational Biology 1.Knowledge Graphs and Mechanistic Drug Development 2.Platform Technologies in Drug Development 3.AI-Enabled Companion Diagnostics and Patient Stratification Preclinical Development & Regulatory Science 4.Multi-Domain Similarity Assessment (MDSA) (Published) 5.Retrospective Validation of Multi-Domain Similarity Assessment 6.Population-Informed Evidence Generation 7.Progressive Pharmaceutical Development Analytics 8.AI-Assisted Regulatory Decision Support 9.Regulatory Implementation and Qualified Research Institution (QRI) Integration 10.Explainability and Uncertainty Communication for AI-Assisted Regulatory Science Clinical Development & Precision Medicine 11.Multi-Mechanism Guidance and Personalization Engine (MMGPE) (Published) 12.Retrospective Validation of MMGPE 13.Combination Therapy Development Framework 14.Digital Therapeutics and Drug Combination Products 15.Economic and Efficiency Modeling for AI-Enabled Drug Development Post-Marketing, Diagnostics & Continuous Learning 16.Precision Wellness and Environmental Exposure Monitoring 17.Point-of-Care and At-Home Diagnostic Platforms 18.Real-World Evidence Feedback Loop and Continuous Learning Systems 19.Global Regulatory Harmonization for AI-Enabled Drug Development Long-Term Platform Vision 20.Toward an Integrated AI Drug Development Platform Melinda B. Chu, M.D. M.B.A. CEO & Founder Intellicite Labs