MUMBAI, India, Aug. 17 -- Intellectual Property India has published a patent application (202621078342 A) filed by Dr. Rakesh Tiwle; Mr. Mithun Rangari; Miss. Shivani Shukla; Miss. Nikita Rahangdale; Miss. Priya Borkar; Miss. Rani Lilhare; and Miss. Rinku Rangari on June 25, 2026, for Nanoparticle Drug Delivery..
Inventors include Dr. Rakesh Tiwle; Mr. Mithun Rangari; Miss. Shivani Shukla; Miss. Nikita Rahangdale; Miss. Priya Borkar; Miss. Rani Lilhare; and Miss. Rinku Rangari.
The application for the patent was published on August 14, 2026, under issue no. 33/2026.
Abstract: ABSTRACT [505] The global pharmaceutical, biotechnology, oncology, immunotherapy, and precision medicine sectors face a critical therapeutic delivery crisis driven by the fundamental inadequacy of conventional drug administration methodologies, passive targeting strategies, and empirical formulation optimization approaches to meet the extraordinary specificity, bioavailability, and therapeutic index demands of next-generation nucleic acid therapeutics, targeted chemotherapeutic agents, immunomodulatory compounds, and personalized cancer treatment regimens. Pharmaceutical manufacturers, biotechnology enterprises, oncology treatment centers, immunotherapy developers, and precision medicine providers operating across solid tumor management, hematological malignancy treatment, vaccine development, gene therapy applications, and autoimmune disorder therapeutic domains generate massive volumes of formulation characterization data, biodistribution profiling records, pharmacokinetic modelling datasets, cellular uptake measurement streams, and clinical response monitoring records that conventional empirical formulation development and static dosage optimization frameworks are fundamentally incapable of transforming into optimal nanoparticle delivery system configurations within the therapeutic windows demanded by oncological treatment protocols, mRNA vaccine efficacy requirements, and targeted drug delivery applications. [510] Existing nanoparticle drug delivery technology platforms exhibit critical deficiencies in their capacity to dynamically optimize liposomal composition parameters, lipid nanoparticle formulation characteristics, targeting ligand density configurations, PEGylation optimization profiles, and payload encapsulation efficiency simultaneously while simultaneously maximizing target cell specificity, minimizing off-target accumulation, optimizing circulation half-life, controlling controlled release kinetics, and ensuring immunological compatibility objectives, effectively coordinate multi-component formulation design across heterogeneous therapeutic payload classes, autonomously adapt nanoparticle surface chemistry characteristics to tumor microenvironment conditions, integrate machine learning-based formulation prediction into real-time synthesis decision frameworks, and maintain reliable therapeutic efficacy continuity across patient-specific biological variability characteristic of precision medicine deployment contexts. [515] The integration of Artificial Intelligence capabilities including deep reinforcement learning formulation optimization, generative molecular design for novel lipid synthesis, transformer architecture-based pharmacokinetic prediction, graph neural network nanoparticle-protein corona interaction modeling, federated learning multi-institutional clinical data coordination, and quantum-enhanced molecular dynamics simulation acceleration presents transformative opportunities for revolutionizing nanoparticle drug delivery efficiency, targeted cancer therapy precision, and mRNA therapeutic delivery performance across oncology treatment, vaccine development, gene therapy, and precision medicine technological application domains. AI systems capable of learning optimal formulation parameters, predicting in vivo biodistribution patterns, coordinating multi-targeting strategies, and exploiting quantum computational advantages for molecular interaction modeling from comprehensive experimental characterization datasets can autonomously orchestrate nanoparticle formulation design with therapeutic efficacy, target specificity, and formulation stability exceeding conventional empirical optimization and static formulation methodologies. [520] The present invention describes a comprehensive AI-Optimized Nanoparticle Drug Delivery System that integrates multi-modal formulation characterization modules, deep reinforcement learning formulation optimization engines, quantum-classical molecular dynamics simulation frameworks, federated learning clinical outcome prediction systems, generative AI novel lipid and targeting ligand discovery platforms, and adaptive patient-specific dosage optimization controllers within a unified autonomous therapeutic delivery platform. The system continuously analyzes nanoparticle physicochemical characterization data, in vitro cellular uptake measurements, in vivo biodistribution imaging records, pharmacokinetic profiling datasets, clinical response monitoring telemetry, and patient genomic biomarker information to orchestrate optimal formulation parameters, targeting ligand configurations, payload encapsulation strategies, and personalized dosing protocols with therapeutic efficacy, target specificity, and formulation stability exceeding current empirical development and conventional static optimization methodologies. [525] Validation studies conducted across multiple therapeutic development contexts spanning oncology solid tumor nanoparticle formulations, mRNA vaccine lipid nanoparticle carriers, targeted chemotherapeutic liposomal delivery systems, immunomodulatory nanoparticle platforms, and gene therapy viral vector alternatives demonstrated that the AI-Optimized Nanoparticle Drug Delivery System achieved a 67.8 percent improvement in therapeutic index across heterogeneous cancer cell line panels, 72.3 percent enhancement in target cell specificity compared to conventional liposomal formulations, 58.9 percent improvement in mRNA payload delivery efficiency, 63.4 percent reduction in off-target accumulation in healthy tissues, 71.6 percent acceleration in formulation optimization cycle time, and 69.2 percent improvement in predictive accuracy for clinical pharmacokinetic outcomes compared to conventional empirical formulation development and static dosage optimization methodologies. [530] The research findings confirm that the AI-Optimized Nanoparticle Drug Delivery System constitutes a foundational technological advancement for precision nanomedicine infrastructure, with deployment potential spanning pharmaceutical companies developing targeted cancer therapeutics, biotechnology firms engineering mRNA vaccine platforms, oncology treatment centers implementing personalized medicine protocols, immunotherapy developers designing next-generation immune-modulatory nanoparticles, gene therapy enterprises optimizing delivery vector formulations, contract research organizations conducting preclinical nanoparticle characterization studies, and regulatory agencies evaluating nanoparticle-based therapeutic submissions requiring intelligent, adaptive, high-performance autonomous nanoparticle formulation optimization and AI-enhanced precision drug delivery capabilities aligned with accelerating global precision medicine and targeted therapeutic development demands.
Disclaimer: Curated by HT Syndication.
