Comment from Intellicite Labs

Please find attached a technical proposal in response to the Request for Information on the Expedited Investigational New Drug (IND) Pilot Program (Docket FDA-2026-N-4699). The attached document proposes a structured Multi-Domain Similarity Assessment framework to support progressive derisking during preclinical development and IND preparation. The framework is designed to complement the proposed Qualified Research Institution (QRI) model and rolling submission process by providing a scientifically grounded methodology for evaluating transferable evidence across multiple domains while maintaining full sponsor responsibility. We believe this approach aligns with FDA’s goals of accelerating safe first-in-human studies, reducing unnecessary duplication of nonclinical work, and modernizing regulatory science through computational and platform-based methods. Summary of Recommendations We recommend that the FDA consider the following: 1.) Development of structured guidance describing multidomain similarity assessment. 2.) Explicit documentation of transferable evidence and remaining uncertainty. 3.) Pilot implementation using platform technologies with high scientific transferability. 4.) Continued sponsor responsibility for all scientific justification and safety assessments. 5.) Collection of prospective metrics including time to IND clearance, reduction in animal studies, submission quality, and clinical hold rates. 6.) Exploration of Qualified Research Institutions (QRIs) or independent scientific organizations to assist with validation of computational similarity methodologies. Illustrative applications discussed in the proposal include: - GLP-1 receptor agonists (class similarity and formulation-specific differences) - PD-1 immune checkpoint inhibitors (shared biology with product-specific characteristics) - Oral vs. subcutaneous semaglutide - Intravenous vs. subcutaneous pembrolizumab - Oral vs. topical tacrolimus - Lipid nanoparticle (LNP) platform technologies - Structure–activity relationship (SAR)-based optimization of related small molecules A version of this paper has also been deposited on Zenodo for public reference: https://doi.org/10.5281/zenodo.21369632 We appreciate the opportunity to provide input and remain available for further discussion. Respectfully submitted, Melinda B. Chu, M.D., M.B.A. CEO & Founder, Intellicite Labs