MUMBAI, India, May 1 -- Intellectual Property India has published a patent application (202641050247 A) filed by Dr. Latha Kiran Krishna Rajendran, Bangalore, Karnataka, on April 20, for 'ai-optimized car-t cell engineering platforms for personalized immunotherapy.'
Inventor(s) include Dr. Latha Kiran Krishna Rajendran.
The application for the patent was published on May 1, under issue no. 18/2026.
According to the abstract released by the Intellectual Property India: "Chimeric Antigen Receptor T-cell (CAR-T) therapy represents one of the most transformative breakthroughs in oncological medicine, yet its clinical implementation remains constrained by profound challenges in antigen target selection, CAR construct engineering, T-cell manufacturing variability, and the prediction of patient-specific therapeutic responses. Current CAR-T engineering workflows rely heavily on empirical trial-and-error methodologies that fail to account for the complex immunological heterogeneity characterizing individual patient tumor microenvironments, HLA haplotype variations, T-cell exhaustion profiles, and tumor antigen expression landscapes that collectively determine treatment efficacy and toxicity outcomes. [510] Existing CAR-T development platforms exhibit critical deficiencies in their capacity to systematically evaluate antigen target combinations, predict cytokine release syndrome severity prior to infusion, optimize co-stimulatory domain configurations for durable T-cell persistence, and model tumor immune evasion trajectories that undermine long-term therapeutic durability. These limitations contribute to unacceptably high rates of treatment failure, life-threatening adverse events, and prohibitive manufacturing costs that restrict CAR-T accessibility to a narrow patient population with late-stage malignancies. [515] The integration of Artificial Intelligence capabilities including deep learning protein structure prediction, reinforcement learning-guided construct optimization, single-cell transcriptomic analysis pipelines, and generative AI-driven neoantigen discovery presents transformative opportunities for establishing truly personalized CAR-T engineering workflows. AI systems capable of modeling the tumor-immune interface at single-cell resolution can identify optimal antigen target combinations, predict patient-specific toxicity risks, design novel CAR architectures with enhanced therapeutic windows, and optimize manufacturing protocols to maximize T-cell product quality and clinical efficacy. [520] The present invention describes a comprehensive AI-Optimized CAR-T Cell Engineering Platform that integrates multi-omic patient data analysis modules, deep learning antigen target prioritization engines, generative AI CAR construct design systems, reinforcement learning manufacturing optimization controllers, and patient-specific toxicity prediction models within a unified personalized immunotherapy engineering framework. The system continuously analyzes patient genomic, transcriptomic, and immunological data to identify optimal therapeutic targets, design superior CAR architectures, and predict clinical outcomes with unprecedented precision. [525] Validation studies conducted across multiple academic medical centers and CAR-T manufacturing facilities demonstrated that the AI-Optimized CAR-T Engineering Platform achieved a 52.3 percent improvement in complete remission rates, 67.8 percent reduction in Grade 3-4 cytokine release syndrome incidence, 44.1 percent acceleration in manufacturing cycle times, and 73.6 percent improvement in T-cell product functional quality scores compared to conventional empirical CAR-T engineering and manufacturing methodologies. [530] These research findings confirm that the AI-Optimized CAR-T Cell Engineering Platform constitutes a foundational technological advancement for personalized cancer immunotherapy, with deployment potential spanning academic oncology centers, pharmaceutical CAR-T manufacturing facilities, clinical research institutions, and healthcare systems requiring systematic, data-driven approaches to individualized cellular immunotherapy development and optimization."
Disclaimer: Curated by HT Syndication.
