Comment from NEXUS CONCORDAT INC

See attached file(s)VIA ELECTRONIC SUBMISSION May 12, 2026 Dockets Management Staff (HFA-305) Food and Drug Administration 5630 Fishers Lane, Rm. 1061 Rockville, MD 20852 RE: Docket No. FDA-2024-D-4689 Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products; Draft Guidance for Industry and Other Interested Parties To Whom It May Concern, Nexus Concordat Inc. submits this comment in response to the FDA's draft guidance, Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products. This comment is filed under 21 CFR 10.115(g)(5), which permits comments on draft guidance documents at any time prior to finalization. I. Commenter Credentials & Standing The undersigned is the inventor of record for four U.S. provisional patent applications (Nos. 63/939,190; 63/962,385; 63/988,485; 64/034,536) directly relevant to the architectural questions raised in this draft guidance. My expertise is grounded in biochemistry, molecular biology, and clinical laboratory practice subject to CLIA standards, with concurrent direct-care experience as a Certified Nursing Assistant in long-term care. This is the third in a May 2026 series of substrate-grounded architectural comments. The first was filed on Docket FDA-2026-N-4390 (Document ID FDA-2026-N-4390-0015) and the second on Docket FDA-2025-D-6131 (Document ID FDA-2025-D-6131-0002). II. Executive Proposals Architectural Credentialing as a Distinct Regulatory Tier The draft guidance's seven-step credibility assessment framework anchors on ASME V&V40, the standard for verifying physics-based computational models. We propose its natural extension to AI: a class of substrate-grounded AI systems in which inference is deterministic by construction, reasoning trajectories are cryptographically attested under SHA-256 (FIPS PUB 180-4), post-deployment changes pass through a compiler-enforced verification gate, and computational primitives are biochemistry rather than arbitrary nonlinearities. For such substrates, credibility assessment can be partially or fully discharged at the architectural level rather than entirely through behavioral evidence. Compiler-Verified Synthetic Twins and Biochemistry-Closed Opcode Sets The terms synthetic twin, digital twin, virtual patient, and synthetic cohort are currently used with inconsistent architectural meaning in the drug-development literature, spanning categories from statistical surrogates to compiler-grounded substrate models. We propose that FDA define a regulatory category called the compiler-verified synthetic twin, built from a biochemistry-closed opcode set: a substrate-grounded, compiler-verified parameterization whose dynamics are computed exclusively from a defined set of biology-native opcodes (Hodgkin-Huxley kinetics, neurochemical signaling, pharmacokinetic ADME, receptor binding), with cryptographically attested mapping from physiological parameters to opcode invocations and bit-for-bit reproducible trajectories under identical inputs. III. Summary of Substantive Comment The attached full comment maps the proposal across the seven-step framework (Sections III.A through III.G), proposes a third architectural category for life-cycle maintenance under Section IV.B (substrates that update post-deployment only through compiler-enforced verification gates), and recommends engagement intake through ISTAND, MIDD, C3TI, or a successor program. IV. Conclusion I respectfully request that the FDA consider these proposals, particularly within the Step 4 (Model Description and Evaluation) and Section IV.B (Life Cycle Maintenance) portions of the framework, and recognize compiler-verified synthetic twins as a distinct architectural category in a final version of the guidance or in companion guidance. Sincerely, Marjorie Gayle McCubbins, BSc Founder and Chief Executive Officer Nexus Concordat Inc. Hamilton, Alabama