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@article{204413,
        author = {Suman Bhattacharyya},
        title = {AI-Directed Multifunctional Nanoparticles for Blood-Brain Barrier Penetration and Targeted Treatment of Leptomeningeal Metastases: A Review and Future Research Framework},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {1732-1734},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204413},
        abstract = {Leptomeningeal metastasis (LM) represents one of the most challenging complications of advanced cancer due to the limited ability of therapeutic agents to cross the blood-brain barrier (BBB) and reach disseminated tumor cells within the cerebrospinal fluid. Recent advances in nanotechnology have demonstrated significant potential in improving drug delivery, therapeutic efficacy, and diagnostic precision for central nervous system malignancies. Simultaneously, artificial intelligence (AI) has emerged as a powerful tool for optimizing nanoparticle design and predicting biological interactions. This paper reviews current nanotechnology-based approaches for brain-targeted drug delivery and proposes a novel AI-directed multifunctional nanoparticle framework for LM treatment. The proposed framework integrates machine learning-assisted nanoparticle optimization, ligand-mediated BBB targeting, controlled drug release, and real-time therapeutic monitoring. The study highlights existing research gaps and outlines future directions toward personalized nanomedicine for neurological cancers.},
        keywords = {Nanotechnology, Artificial Intelligence, Blood-Brain Barrier, Drug Delivery, Leptomeningeal Metastasis, Nanoparticles, Precision Medicine},
        month = {June},
        }