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The pioneering work on liposomes in the 1960s and subsequent research in controlled drug release systems significantly advances the development of nanocarriers (NCs) for drug delivery. This field is evolved to include a diverse array of nanocarriers such as liposomes, polymeric nanoparticles, dendrimers, and more, each tailored to specific therapeutic applications. Despite significant achievements, the clinical translation of nanocarriers is limited, primarily due to the low efficiency of drug delivery and an incomplete understanding of nanocarrier interactions with biological systems. Addressing these challenges requires interdisciplinary collaboration and a deep understanding of the nano‐bio interface. To enhance nanocarrier design, scientists employ both physics‐based and data‐driven models. Physics‐based models provide detailed insights into chemical reactions and interactions at atomic and molecular scales, while data‐driven models leverage machine learning to analyze large datasets and uncover hidden mechanisms. The integration of these models presents challenges such as harmonizing different modeling approaches and ensuring model validation and generalization across biological systems. However, this integration is crucial for developing effective and targeted nanocarrier systems. By integrating these approaches with enhanced data infrastructure, explainable AI, computational advances, and machine learning potentials, researchers can develop innovative nanomedicine solutions, ultimately improving therapeutic outcomes.
The pioneering work on liposomes in the 1960s and subsequent research in controlled drug release systems significantly advances the development of nanocarriers (NCs) for drug delivery. This field is evolved to include a diverse array of nanocarriers such as liposomes, polymeric nanoparticles, dendrimers, and more, each tailored to specific therapeutic applications. Despite significant achievements, the clinical translation of nanocarriers is limited, primarily due to the low efficiency of drug delivery and an incomplete understanding of nanocarrier interactions with biological systems. Addressing these challenges requires interdisciplinary collaboration and a deep understanding of the nano‐bio interface. To enhance nanocarrier design, scientists employ both physics‐based and data‐driven models. Physics‐based models provide detailed insights into chemical reactions and interactions at atomic and molecular scales, while data‐driven models leverage machine learning to analyze large datasets and uncover hidden mechanisms. The integration of these models presents challenges such as harmonizing different modeling approaches and ensuring model validation and generalization across biological systems. However, this integration is crucial for developing effective and targeted nanocarrier systems. By integrating these approaches with enhanced data infrastructure, explainable AI, computational advances, and machine learning potentials, researchers can develop innovative nanomedicine solutions, ultimately improving therapeutic outcomes.
BackgroundTherapeutic tumor vaccines have emerged as a compelling avenue for treating patients afflicted with advanced prostate cancer (PCa), particularly those experiencing biochemical relapse or ineligible for surgical intervention. This study serves to consolidate recent research findings on therapeutic vaccines targeting prostate tumors while delineating prevalent challenges within vaccine research and development.MethodsWe searched electronic databases, including PubMed, Web of Science, Embase, and Scopus, up to August 31, 2024, using keywords such as 'vaccine', 'prostate cancer', 'immunotherapy', and others. We reviewed studies on various therapeutic vaccines, including dendritic cell‐based, antigen, nucleic acid, and tumor cell vaccines.ResultsStudies consistently showed that therapeutic vaccines, notably DC vaccines, had favorable safety profiles with few adverse effects. These vaccines, with varied antigenic formulations, demonstrated strong clinical outcomes, as indicated by metrics such as PSA response rates (9.5%‐58%), extended PSA doubling times (52.9%–89.7%), overall survival durations (17.7–33.8 months), two‐year mortality rates (0%–12.5%), biochemical relapse rates (42%–73%), and antigen‐specific immune responses (33.3%–71.4% in responsive groups).ConclusionWhile clinical data for tumor vaccines have illuminated robust evidence of tumoricidal activity, the processes of their formulation and deployment are riddled with complexities. Combining vaccines with other therapies may enhance outcomes, and future research should focus on early interventions and deciphering the immune system's role in oncogenesis.
Biomedical polymers play a key role in preventing, diagnosing, and treating diseases, showcasing a wide range of applications. Their unique advantages, such as rich source, good biocompatibility, and excellent modifiability, make them ideal biomaterials for drug delivery, biomedical imaging, and tissue engineering. However, conventional biomedical polymers suffer from poor degradation in vivo, increasing the risks of bioaccumulation and potential toxicity. To address these issues, degradable biomedical polymers can serve as an alternative strategy in biomedicine. Degradable biomedical polymers can efficiently relieve bioaccumulation in vivo and effectively reduce patient burden in disease management. This review comprehensively introduces the classification and properties of biomedical polymers and the recent research progress of degradable biomedical polymers in various diseases. Through an in-depth analysis of their classification, properties, and applications, we aim to provide strong guidance for promoting basic research and clinical translation of degradable biomedical polymers.
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