Published September 26, 2026 | Version v1
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Quad-Platform Computational Architecture targeting Alzheimer's Neuroinflammation: Integrating Physical Biomodulation, Predictive Cheminformatics, Biological Foundation Models, and In Silico Bioenergetic Telemetries

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Neuroinflammation driven by microglial dysfunction stands as a primary hallmark in the pathogenesis of Alzheimer's disease (AD). However, therapeutic breakthroughs remain bottlenecked by rigid blood-brain barrier (BBB) kinetics, inefficient intracellular vector programming, and poor small-molecule bioavailability. To address these systemic hurdles, this paper introduces a modular, quad-platform computational architecture designed to disrupt the neuroinflammatory cascade across distinct scales. Tier 1 establishes a standardized data infrastructure to optimize non-invasive physical therapies, calibrating Low-Intensity Pulsed Ultrasound (LIPUS) parameters for transient, safe BBB permeabilization. Tier 2 deploys deep learning-based cheminformatics pipelines to virtual-screen a dual-axis combination therapy: a Structural Axis targeting GSK inhibitors to maintain endothelial integrity, and an Immunological Axis utilizing STING inhibitors to arrest chronic innate immune activation. Tier 3 leverages Evo 2, a long-context genomic foundation model, to program single-nucleotide resolution intracellular genetic instructions capable of restoring homeostatic microglial phenotypes in the long term. Finally, Tier 4 introduces an in silico verification scheme driven by a non-linear double-Lorentzian signal processing pipeline; this module acts as a non-invasive telemetric sensor, deconvolving overlapping low-field Phosphorus Magnetic Resonance Spectroscopy (³¹P-MRS) peaks to quantitatively verify that downstream GSK/STING inhibition successfully rescues the cerebral NAD+/NADH matrix and ATP-synthetic machinery from neuroinflammatory decay. In an integrated pipeline, Tier 1’s biophysical parameters reshape Tier 2's chemical screening constraints—rescuing molecules previously discarded due to strict BBB limitations—while Tier 3's genomic vectors drive the biological recovery validated in real-time by Tier 4's closed-loop bioenergetic index. Ultimately, this modular framework bridges biomedical data engineering, predictive drug discovery, and synthetic biology, pioneering a multi-modal paradigm to halt neurodegeneration.

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2026-09-26

References

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Innate immune activation in the CNS. [32] Paul, B. D., Snyder, S. H., & Bohr, V. A. (2021) Signaling by cGAS-STING in Neurodegeneration, Neuroinflammation, and Aging. Trends in Neurosciences, 44(2), 83–96. DOI: https://doi.org/10.1016/j.tins.2020.10.001 Link: https://www.cell.com/trends/neurosciences/fulltext/S0166-2236(20)30240-8 [33] Bennett, M. L., Bennett, F. C., Liddelow, S. A., et al. (2016) New Tools for Studying Microglia in the Mouse and Human CNS. Proceedings of the National Academy of Sciences (PNAS), 113(12), E1738–E1746. DOI: https://doi.org/10.1073/pnas.1525528113 Link: https://www.pnas.org/doi/10.1073/pnas.1525528113 Relevance: Establishes TMEM119 as a highly specific microglial marker. [34] Satoh, J., Kino, Y., Asahina, N., et al. (2016) TMEM119 Marks a Subset of Microglia in the Human Brain. DOI: https://doi.org/10.1007/s00401-016-1528-z Link: https://link.springer.com/article/10.1007/s00401-016-1528-z Relevance: Human brain microglial specificity. Useful justification for TMEM119 promoter selection.RNA Interference (shRNA Therapeutic Component) [35] Fire, A., Xu, S., Montgomery, M. K., Kostas, S. A., Driver, S. E., & Mello, C. C. (1998) Potent and Specific Genetic Interference by Double-Stranded RNA in Caenorhabditis elegans. Nature, 391(6669), 806–811. DOI: https://doi.org/10.1038/35888 Link: https://www.nature.com/articles/35888 Relevance:Foundational paper describing RNA interference. [36] Davidson, B. L., & McCray, P. B. Jr. (2011) Current Prospects for RNA Interference-Based Therapies. Nature Reviews Genetics, 12(5), 329–340. DOI: https://doi.org/10.1038/nrg2968 Link: https://www.nature.com/articles/nrg2968 Relevance: Clinical translation of shRNA/siRNA therapeutics. [37] Rao, D. D., Vorhies, J. S., Senzer, N., & Nemunaitis, J. (2009) siRNA vs. shRNA: Similarities and Differences. Advanced Drug Delivery Reviews, 61(9), 746–759. DOI: https://doi.org/10.1016/j.addr.2009.04.004 Link: https://www.sciencedirect.com/science/article/pii/S0169409X0900077X Relevance: Design and implementation of shRNA gene silencing systems. Foundation Models for Genomics [38] Nguyen, E., Poli, M., Durrant, M. G., et al. (2024) Evo: DNA Foundation Modeling from Molecules to Genomes. Arc Institute, Stanford University. arXiv: https://arxiv.org/abs/2408.08210 GitHub: https://github.com/evo-design/evo Relevance: Foundation model used throughout Tier 3. Long-context genomic generation. Sequence likelihood and generative DNA design. Dalla-Torre, H., Gonzalez, L., Mendoza-Revilla, J., et al. (2024) The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics. Nature Methods. DOI: https://doi.org/10.1038/s41592-024-02523-z Link: https://www.nature.com/articles/s41592-024-02523-z Relevance: Large language models applied to genomic sequence understanding. [39] Zvyagin, M., Brace, A., Hippe, K., et al. (2025) GenSLMs: Genome-Scale Language Models Reveal SARS-CoV-2 Evolutionary Dynamics. Nature Machine Intelligence. Link: https://www.nature.com/articles/s42256-024-00856-7 Relevance: Demonstrates large-scale genomic language modeling. AI-Based Protein Design [40] Watson, J. L., Juergens, D., Bennett, N. R., et al. (2023) De Novo Design of Protein Structure and Function with RFdiffusion. Nature, 620(7976), 1089–1100. DOI: https://doi.org/10.1038/s41586-023-06415-8 Link: https://www.nature.com/articles/s41586-023-06415-8 Relevance: State-of-the-art AI protein generation. Strong support if the project proposes de novo STING-binding mini-proteins. [41] Madani, A., McCann, B., Naik, N., et al. (2023) Large Language Models Generate Functional Protein Sequences Across Diverse Families. Nature Biotechnology, 41(8), 1099–1106. DOI: https://doi.org/10.1038/s41587-023-01763-2 Link: https://www.nature.com/articles/s41587-023-01763-2 Relevance: Demonstrates protein generation using language models. Supports AI-driven therapeutic peptide design. TIER 4 BIOENERGETIC TELEMETRY VIA ³¹P MRS [42] 1. Quantitative Measurement of Redox State in Human Brain by 31P MRS at 7T Ren J., Malloy C.R., Sherry A.D. Quantitative Measurement of Redox State in Human Brain by 31P MRS at 7T with Spectral Simplification and Inclusion of Multiple Nucleotide Sugar Components in Data Analysis. Journal: Magnetic Resonance in Medicine (2020) DOI: https://doi.org/10.1002/mrm.28306 PMC: https://pmc.ncbi.nlm.nih.gov/articles/PMC7396304/ Relevance to Tier 4 Direct measurement of NAD+ and NADH in vivo. Spectral deconvolution of overlapping phosphorus resonances. Brain NAD+/NADH redox ratio quantification. [43] 2. Development of a 31P Magnetic Resonance Spectroscopy Technique to Quantify NADH and NAD+ at 3T Mevenkamp J., Bruls Y.M.H., Mancilla R., et al. Development of a 31P Magnetic Resonance Spectroscopy Technique to Quantify NADH and NAD+ at 3 T. Journal: Nature Communications (2024) DOI: https://doi.org/10.1038/s41467-024-53292-4 Nature Link: https://www.nature.com/articles/s41467-024-53292-4 Relevance Demonstrates NAD+ and NADH detection on a clinical 3T MRI scanner. Directly supports your low-field 3T architecture. [44] 3. Intracellular Redox State Revealed by 31P MR Spectroscopy Measurement of NAD+ and NADH Contents In Vivo Lu M., Zhu X.H., Zhang Y., Chen W. ISMRM Proceedings. Link: https://cds.ismrm.org/protected/13MProceedings/PDFfiles/4027.PDF Relevance One of the earliest demonstrations that NAD+ and NADH can be separately modeled from 31P spectra. Uses Lorentzian simulations and fitting approaches. [45] 4. Systematic Review of 31P-Magnetic Resonance Spectroscopy Studies of Brain High-Energy Phosphates and Membrane Phospholipids in Aging and Alzheimer's Disease Jett S., Boneu C., Zarate C., et al. Journal: Frontiers in Aging Neuroscience (2023) DOI: https://doi.org/10.3389/fnagi.2023.1183228 Full Text: https://www.frontiersin.org/articles/10.3389/fnagi.2023.1183228/full Relevance Landmark review on:ATP, Pi, phosphocreatine mitochondrial dysfunction Alzheimer's disease bioenergetics. [46] 5. Altered Brain High-Energy Phosphate Metabolism in Mild Alzheimer's Disease: A 3-Dimensional 31P MR Spectroscopic Imaging Study Rijpma A., van der Graaf M., Meulenbroek O., et al. Journal: NeuroImage: Clinical (2018) DOI: https://doi.org/10.1016/j.nicl.2018.01.031 PMC: https://pmc.ncbi.nlm.nih.gov/articles/PMC5987799/ Relevance Demonstrates altered phosphate metabolism in AD patients. Supports ATP/Pi changes as measurable biomarkers of disease progression. [47] NAD+, CD38 and Neuroinflammation 6. CD38 in Neurodegeneration and Neuroinflammation Guerreiro S., Privat A.L., Bressac L., Toulorge D. Journal: Cells (2020) DOI: https://doi.org/10.3390/cells9020471 Link: https://www.mdpi.com/2073-4409/9/2/471 Relevance CD38-mediated NAD+ degradation. Neuroinflammation and aging. Alzheimer's disease association [48] 7. NAD+ Metabolism Drives Astrocyte Proinflammatory Reprogramming in Central Nervous System Autoimmunity Meyer T., Shimon D., Youssef S., et al. Journal: PNAS (2022) DOI: https://doi.org/10.1073/pnas.2211310119 Link: https://www.pnas.org/doi/10.1073/pnas.2211310119 Relevance CD38 upregulation in reactive astrocytes. NAD+ depletion and inflammatory programming. Strong support for the CD38 component of Tier 4 [49] STING and Neuroinflammation 8. cGAS-STING Drives Ageing-Related Inflammation and Neurodegeneration Nature (2023) Link: https://www.nature.com/articles/s41586-023-06373-1 Relevance STING signaling as a driver of chronic inflammation and neurodegeneration. 9. STING-Mediated Neuroinflammation: A Therapeutic Target in Neurodegenerative Diseases [50] Zhang H., He Z., Yin C., et al. Journal: Frontiers in Aging Neuroscience (2025) DOI: https://doi.org/10.3389/fnagi.2025.1659216 Link: https://www.frontiersin.org/articles/10.3389/fnagi.2025.1659216/full Relevance Comprehensive review of STING inhibitors. Neurodegenerative disease applications. Endothelial NAD+ Depletion Drives Vascular Senescence and Neuroinflammation via mtDNA-cGAS/STING-CD38 Signaling in Alzheimer's Disease [51] Luo Q.H., Li F., Yang L., et al. Journal: Alzheimer's & Dementia (2026) DOI: https://doi.org/10.1002/alz.71423 PMC: https://pmc.ncbi.nlm.nih.gov/articles/PMC13109647/ Relevance Probably the paper closest to your proposed mechanism. Directly links: NAD+ depletion, STING activation, CD38 upregulation, neuroinflammation in AD GSK3β – PGC-1α – Mitochondria 11. GSK3β Regulates Brain Energy Metabolism [52] Martin S.A., Souder D.C., Miller K.N., et al. Journal: Cell Reports (2018) DOI: https://doi.org/10.1016/j.celrep.2018.04.045 PMC: https://pmc.ncbi.nlm.nih.gov/articles/PMC6082412/ Relevance GSK3β inhibition increases mitochondrial respiration. Stabilizes PGC-1α. Modulates NAD(P)H metabolism. The Protease Omi Regulates Mitochondrial Biogenesis Through the GSK3β/PGC-1α Pathway [53] Xu R., Hu Q., Ma Q., et al. Journal: Cell Death & Disease (2014) DOI: https://doi.org/10.1038/cddis.2014.328 PMC: https://pmc.ncbi.nlm.nih.gov/articles/PMC4454303/ Relevance Direct mechanistic evidence linking GSK3β inhibition to mitochondrial biogenesis. 12.The Key Roles of GSK-3β in Regulating Mitochondrial Activity [54] Yang K., Chen Z., Gao J., et al. Journal: Cellular Physiology and Biochemistry (2017) DOI: https://doi.org/10.1159/000485580 Link: https://karger.com/cpb/article/44/4/1445/153089 Relevance Review of GSK3β effects on mitochondrial bioenergetics, ATP production and neuronal survival.