Ai-driven Personalized Leukemia Therapy Integrating CRISPR Nanoparticle Delivery and Car-t Immunotherapy: A Cfd-based Analysis Using Ansys Fluent

Ai-driven Personalized Leukemia Therapy Integrating CRISPR Nanoparticle Delivery and Car-t Immunotherapy: A Cfd-based Analysis Using Ansys Fluent | 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 Ai-driven Personalized Leukemia Therapy Integrating CRISPR Nanoparticle Delivery and Car-t Immunotherapy: A Cfd-based Analysis Using Ansys Fluent Joon Shakya, Pramod Dhungana, Manish Giri, Bishesh Shahi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9611183/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Leukemia is an enigmatic and heterogeneous hematology malignancy and the traditional treatment therapy e.g. chemotherapy and radiotherapy has been largely constrained by the systemic toxicity, drug resistance as well as relapses. These limitations demand the development of more specific and reactive interventions of treatment. The paper under consideration proposes a future treatment paradigm of leukemia that will be a combination of artificial intelligence, CRISPR-based gene editing, logic-gated CAR-T immunotherapy, and nanoparticle-based drug delivery. It has personalized genomic and transcriptomic data of the patients to produce a digital twin which is trained on AI and may be utilized to forecast and streamline the treatment regimen. ANSYS Fluent was used to test and optimize the effectiveness of nanoparticles delivery by simulating delivery of nanoparticles through computational fluid dynamics (CFD). Tracing the transport of lipid nanoparticles in a bifurcated vascular geometry under physiologically relevant conditions was done through the Discrete Phase Model (DPM). The most important parameters of targeting efficiency and delivery performance were velocity distribution, pressure gradient and particle trackings. As it has been noted, the distribution of nanoparticles is highly reliant on the hemodynamic conditions of which by the velocity of flow and the vessel geometry are highly important in the retention and distribution of the nanoparticles. The ideal flow conditions improve the precision of targeting as well as minimizes the off-target accumulation thus, improving the efficacy of the treatment. Precision-engineered drug delivery can be developed based on the platform of CFD-based simulation and the AI-driven decision-making. This piece of work will contribute towards the creation of personalized medicine since it explains a multi-disciplinary approach that will bridge the fields of biological therapy and optimization of engineering. The suggested framework provides a scalable and flexible approach to treating leukemia, and the possibility of enhancing clinical outcomes and decreasing the chances of relapse, which will eventually shift to making leukemia biologically unsustainable. Leukemia CRISPR Gene Editing CAR-T Cell Therapy Digital Twin Modeling Nanoparticle Drug Delivery ANSYS Fluent Computational Fluid Dynamics (CFD) Precision Medicine Full Text Additional Declarations The authors declare no competing interests. Figures are available in the Supplementary Files section Supplementary Files Figures.pdf Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9611183","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634335243,"identity":"633a5256-a9e8-4e38-9317-3882f8b9c364","order_by":0,"name":"Joon 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