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      Targeting drugs to tumours using cell membrane-coated nanoparticles

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          Shedding light on the cell biology of extracellular vesicles

          Extracellular vesicles are a heterogeneous group of cell-derived membranous structures comprising exosomes and microvesicles, which originate from the endosomal system or which are shed from the plasma membrane, respectively. They are present in biological fluids and are involved in multiple physiological and pathological processes. Extracellular vesicles are now considered as an additional mechanism for intercellular communication, allowing cells to exchange proteins, lipids and genetic material. Knowledge of the cellular processes that govern extracellular vesicle biology is essential to shed light on the physiological and pathological functions of these vesicles as well as on clinical applications involving their use and/or analysis. However, in this expanding field, much remains unknown regarding the origin, biogenesis, secretion, targeting and fate of these vesicles.
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            Cancer immunotherapy using checkpoint blockade

            The release of negative regulators of immune activation (immune checkpoints) that limit antitumor responses has resulted in unprecedented rates of long-lasting tumor responses in patients with a variety of cancers. This can be achieved by antibodies blocking the cytotoxic T lymphocyte antigen-4 (CTLA-4) or the programmed death-1 (PD-1) pathway, either alone or in combination. The main premise for inducing an immune response is the pre-existence of antitumor T cells that were limited by specific immune checkpoints. Most patients who have tumor responses maintain long lasting disease control, yet one third of patients relapse. Mechanisms of acquired resistance are currently poorly understood, but evidence points to alterations that converge on the antigen presentation and interferon gamma signaling pathways. New generation combinatorial therapies may overcome resistance mechanisms to immune checkpoint therapy.
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              Engineering precision nanoparticles for drug delivery

              In recent years, the development of nanoparticles has expanded into a broad range of clinical applications. Nanoparticles have been developed to overcome the limitations of free therapeutics and navigate biological barriers — systemic, microenvironmental and cellular — that are heterogeneous across patient populations and diseases. Overcoming this patient heterogeneity has also been accomplished through precision therapeutics, in which personalized interventions have enhanced therapeutic efficacy. However, nanoparticle development continues to focus on optimizing delivery platforms with a one-size-fits-all solution. As lipid-based, polymeric and inorganic nanoparticles are engineered in increasingly specified ways, they can begin to be optimized for drug delivery in a more personalized manner, entering the era of precision medicine. In this Review, we discuss advanced nanoparticle designs utilized in both non-personalized and precision applications that could be applied to improve precision therapies. We focus on advances in nanoparticle design that overcome heterogeneous barriers to delivery, arguing that intelligent nanoparticle design can improve efficacy in general delivery applications while enabling tailored designs for precision applications, thereby ultimately improving patient outcome overall.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Nature Reviews Clinical Oncology
                Nat Rev Clin Oncol
                Springer Science and Business Media LLC
                1759-4774
                1759-4782
                January 2023
                October 28 2022
                January 2023
                : 20
                : 1
                : 33-48
                Article
                10.1038/s41571-022-00699-x
                36307534
                50bc6c5e-6504-4739-b7ca-41dbddf8dd05
                © 2023

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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