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bioRxiv · 10.1101/2025.11.12.688044

Using AI-MIDD for Mechanistic Erythropoietin Modeling: A Digital-Twin Framework for Optimizing ESA Therapies

Goryanin, I.·Goryanin, I. V.
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Abstract

Erythropoiesis-stimulating agents (ESAs) remain a mainstay for anemia therapy, yet variability in response and emerging resistance mechanisms limit their effectiveness. We developed a hybrid AI-MIDD (Model-Informed Drug Development) and Quantitative Systems Pharmacology (QSP) platform and applied it for integrating mechanistic signaling (EPO-EpoR-JAK2/STAT5-SOCS3/CIS) with metabolic (mTOR), iron-homeostatic (hepcidin), and HIF-mediated endogenous EPO feedback. The model was implemented in SBML (epo_qsp_combined_all.xml) and simulated over 12 weeks under various ESA, SUMO-blocker, miR-486 exosome, mTOR, and HIF-PHI perturbations. AI-assisted parameter scanning revealed distinct dose-sparing regimes: SUMO inhibition improved receptor recycling and reduced ESA requirement by [~]30%; exosomal miR-486 reduced SOCS3/CIS burden, restoring STAT5 sensitivity; HIF-PHI enhanced baseline EPO synthesis, while mTOR modulation stabilized reticulocyte oscillations. Multi-objective optimization identified triplet combinations achieving Hb targets with minimized pSTAT5 burden. The AI-MIDD-QSP integration provides a digital-twin for patient-specific ESA optimization and enables rational design of combination regimens and patentable therapeutic concepts. This framework generalizes to other hematopoietic and cytokine-signaling systems, advancing mechanism-based drug development. Manuscript HighlightsO_LIA hybrid AI-MIDD and QSP "digital-twin" of erythropoiesis was developed, integrating core EPO signaling with SUMO-recycling, exosomal miR-486, and mTOR metabolic pathways. C_LIO_LIThe model identifies and quantifies SUMO-pathway inhibition as a novel, dose-sparing mechanism, showing a [~]30% reduction in ESA requirement by increasing EpoR membrane recycling. C_LIO_LIThe model demonstrates how exosomal miR-486 delivery can restore signaling sensitivity in resistant states by reducing the SOCS3/CIS negative feedback burden by [~]40%. C_LIO_LIAI-driven multi-objective optimization identified a novel triplet combination (ESA + SUMO inhibition + miR-486) as the most effective regimen, achieving target hemoglobin with a 45% reduction in cumulative ESA and minimized pSTAT5 signaling burden. C_LI

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Goryanin, I., Goryanin, I. V.. 2025-11-13. Using AI-MIDD for Mechanistic Erythropoietin Modeling: A Digital-Twin Framework for Optimizing ESA Therapies. https://doi.org/10.1101/2025.11.12.688044

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