Saxagliptin for Disorders of Consciousness: AI, Retrospective Observations, and Preclinical Data

Saxagliptin for Disorders of Consciousness: AI, Retrospective Observations, and Preclinical Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Saxagliptin for Disorders of Consciousness: AI, Retrospective Observations, and Preclinical Data Daniel Toker, Jeffrey Chiang, Paul Vespa, Caroline Schnakers, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4810276/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Feb, 2025 Read the published version in Neurocritical Care → Version 1 posted 5 You are reading this latest preprint version Abstract Despite advancements in the neuroscience of consciousness, no new effective medications for disorders of consciousness (DOC), such as coma or vegetative state, have been discovered in over a decade. Repurposing existing FDA-approved drugs for DOC is crucial for improving clinical management and patient outcomes. To identify potential new treatments among existing FDA-approved drugs, we used a deep learning-based drug screening model to predict the efficacy of drugs as awakening agents based on their three-dimensional molecular structure. A retrospective cohort study from April 2012 to April 2024 tested the model’s predictions, focusing on changes in Glasgow Coma Scale (GCS) scores in 3,879 coma patients. Our deep learning drug screens identified saxagliptin, a dipeptidyl peptidase-4 inhibitor, as a promising awakening drug for both acute and prolonged DOC. The retrospective clinical analysis showed that saxagliptin was associated with the highest recovery rate from acute coma among diabetes medications. Incretin-based therapies, including dipeptidyl peptidase-4 inhibitors and glucagon-like peptide-1 analogues, were linked to higher coma recovery rates compared to non-incretin-based diabetes medications, after matching patients by age, gender, initial GCS score, coma etiology, and glycemic status (95% confidence interval of 0.63–12.02%, p = 0.0013). Preclinical studies identified several pathways through which saxagliptin may aid awakening from DOC: restoring neurotransmission, reducing brain inflammation and oxidative damage, clearing hyperphosphorylated tau and amyloid-beta, normalizing thalamocortical glucose metabolism, increasing neural plasticity, and mitigating excitotoxic brain damage. Our findings suggest saxagliptin as a potential novel therapeutic agent for DOC. Further prospective clinical trials are needed to confirm its efficacy and safety in DOC. coma persistent vegetative state stroke traumatic brain injuries Full Text Supplementary Files saxagliptinsupplementalNeurocritCare.pdf Cite Share Download PDF Status: Published Journal Publication published 04 Feb, 2025 Read the published version in Neurocritical Care → Version 1 posted Reviewers agreed at journal 01 Aug, 2024 Reviewers invited by journal 01 Aug, 2024 Editor invited by journal 01 Aug, 2024 Editor assigned by journal 31 Jul, 2024 First submitted to journal 30 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4810276","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":334902342,"identity":"37a9106d-784d-422c-a5d7-e2a9bdbf71a7","order_by":0,"name":"Daniel Toker","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYDCCAyBUwMAgwcDA+AAkwAcieAhqMTAAaWE2AAmwEaOFAaqFTYIoLXzHzx488MHgj5xke++zyi8Mh+XZ2A8wPnjbhluL5Jm8hIMzDAyMpXmOm92WYThs2MaTwGw4F48WgwM5Bod5DAwS50mksd2WYDicwMaQwCbNi0/L+TcGh/+AtMg/YysGa+F/wP4br5YbQFuA3k+cLcHGxvgBpEUigY0ZnxbJG28MDvYYGBtL9qQxSzMYpBu2STxslpxzDrcWvvM5xh9+VMjJSRw/xvjxR4W1PD9/8sEPb8pwa0EBzDzgyGRsIFI9SO0P4tWOglEwCkbBCAIAfAlMqzn5sJcAAAAASUVORK5CYII=","orcid":"","institution":"University of California Los Angeles","correspondingAuthor":true,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Toker","suffix":""},{"id":334902343,"identity":"f1ffd8bf-d9a6-420e-ae09-f754dcdb3bee","order_by":1,"name":"Jeffrey Chiang","email":"","orcid":"","institution":"University of California Los Angeles","correspondingAuthor":false,"prefix":"","firstName":"Jeffrey","middleName":"","lastName":"Chiang","suffix":""},{"id":334902344,"identity":"d03dd43b-2e4b-44c5-acbe-e30e6cd56ba5","order_by":2,"name":"Paul Vespa","email":"","orcid":"","institution":"University of California Los Angeles","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Vespa","suffix":""},{"id":334902345,"identity":"67313c1d-6c2b-4508-8f89-e8ed63db7f03","order_by":3,"name":"Caroline Schnakers","email":"","orcid":"","institution":"Casa Colina Hospital: Casa Colina Hospital and Centers for Healthcare","correspondingAuthor":false,"prefix":"","firstName":"Caroline","middleName":"","lastName":"Schnakers","suffix":""},{"id":334902346,"identity":"dd772a98-0535-4fbc-8f18-b415a52bc808","order_by":4,"name":"Martin M. 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Repurposing existing FDA-approved drugs for DOC is crucial for improving clinical management and patient outcomes.\u003c/p\u003e \u003cp\u003eTo identify potential new treatments among existing FDA-approved drugs, we used a deep learning-based drug screening model to predict the efficacy of drugs as awakening agents based on their three-dimensional molecular structure. A retrospective cohort study from April 2012 to April 2024 tested the model\u0026rsquo;s predictions, focusing on changes in Glasgow Coma Scale (GCS) scores in 3,879 coma patients.\u003c/p\u003e \u003cp\u003eOur deep learning drug screens identified saxagliptin, a dipeptidyl peptidase-4 inhibitor, as a promising awakening drug for both acute and prolonged DOC. The retrospective clinical analysis showed that saxagliptin was associated with the highest recovery rate from acute coma among diabetes medications. 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