EHMN2026®T: A License-Aware AI-QSP Integration Framework Linking EHMN2026® with TRANSFAC®, TRANSPATH® and HumanPSD™ for Diagnostic-Metabolite Interpretation

Background/Objectives: Diagnostic metabolites measured in newborn screening, inherited metabolic disease, lysosomal storage disease, oncometabolite testing and routine clinical biochemistry are direct read-outs of human metabolic state. Their mechanistic interpretation requires linking measured metabolites to enzymes, pathways, regulatory context, disease knowledge and, increasingly, AI-assisted quantitative systems pharmacology (AI-QSP) workflows. We developed EHMN2026®T as a license-aware AI-QSP integration framework that connects the EHMN2026® metabolic backbone with licensed geneXplain knowledge resources while keeping ownership, licensing and redistribution constraints explicit. Methods: EHMN2026®T integrates the SBML-encoded EHMN2026® metabolic backbone with licensed TRANSFAC® 2025.2, TRANSPATH® 2025.2 and HumanPSD™ 2025.2 resources. TRANSFAC® position weight matrices were used for promoter-level analysis of EHMN metabolic genes. The resulting transcription factor (TF)–gene connections were mapped to EHMN genes, TRANSPATH® signalling/molecular-state entries and HumanPSD™ disease/drug context. The framework is positioned as a controlled component of the IQANOVA AI-QSP environment, but only aggregate statistics, non-proprietary EHMN-derived summaries and manuscript-level examples are reported publicly unless separate permission is obtained from the relevant rightsholders. Results: Promoter analysis of 1681 EHMN2026® metabolic genes using 1147 mapped TRANSFAC® matrices identified 291,387 ENSG-level TF–gene regulatory-potential connections involving 398 TFs and 1,107,264 predicted binding sites. The diagnostic panel contained 80 covered genes (63.5%), including complete coverage of oncometabolite enzymes and high coverage of organic acidaemia, steroidogenesis and fatty-acid oxidation categories. Mapping to TRANSPATH® expanded the EHMN genes into 144,529 molecular-state representations and 14,879 gene–pathway or gene–chain pairs. HumanPSD™ was used as a licensed translational context layer; EHMN-specific HumanPSD™ outputs are treated as license-controlled derived outputs and are therefore not redistributed as open detailed tables in this manuscript. Conclusions: EHMN2026®T provides a license-aware AI-QSP integration framework for tracing a diagnostic metabolite from a measured clinical value to candidate enzyme nodes, regulatory potential, signalling/molecular-state context and disease or therapeutic interpretation. PWM-derived TF–gene links are presented as regulatory hypotheses, not proof of active regulation. Public release should be limited to aggregate statistics and non-proprietary EHMN-derived components; detailed TRANSFAC®, TRANSPATH® and HumanPSD™-derived edges, mappings, annotations and SBML outputs remain subject to geneXplain ownership and licensing terms.