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Site-Targeted Drug Delivery Systems: Polymeric Carriers

Site-Targeted Drug Delivery Systems: Polymeric Carriers

Polymeric carriers enhance targeted drug delivery by increasing efficacy while minimizing off-target effects. These carriers comprise a biodegradable polymeric backbone integrated with functional elements that enable targeting, improve physicochemical properties, and regulate drug release.Targeting MechanismsThe targeting ability of polymeric carriers is mediated by a homing device, which is a molecular recognition component designed to selectively bind to specific tissues or cells. Monoclonal...

Modified-Release Drug Delivery Systems: Site-Targeted

Modified-Release Drug Delivery Systems: Site-Targeted

Site-targeted drug delivery systems enhance therapeutic efficacy while minimizing systemic toxicity and treatment costs. Unlike conventional methods, these systems ensure precise drug delivery, improving bioavailability and reducing side effects. Targeted drug delivery is classified into three levels. First-order targeting directs drugs to the capillary beds of specific organs or tissues. Second-order targets specific cell types, such as tumor cells, using receptor-mediated interactions.

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相关实验视频

Updated: Jun 15, 2026

Nanosponge Tunability in Size and Crosslinking Density

Nanosponge Tunability in Size and Crosslinking Density

Published on: August 4, 2017

图纳-AI:一种混合内核机器,用于设计可调节的纳米粒子,用于药物输送.

Zilu Zhang¹, Yan Xiang¹, Joe Laforet¹

¹Department of Biomedical Engineering, Duke University, Durham, North Carolina 27708, United States.

|September 11, 2025

概括

人工智能 (AI) 通过同时优化材料和比率来加快纳米粒子配方. 这种由人工智能驱动的方法增强了药物输送开发,并提高了配方成功率.

关键词:

药物输送是药物输送的过程.实验室自动化实验室自动化机器学习是机器学习.纳米粒子是一种纳米粒子.可调制的组合组成.

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Last Updated: Jun 15, 2026

Nanosponge Tunability in Size and Crosslinking Density

Nanosponge Tunability in Size and Crosslinking Density

Published on: August 4, 2017

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Manufacture and Drug Delivery Applications of Silk Nanoparticles

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科学领域:

生物技术和生物纳米技术
材料科学材料科学材料科学
计算化学计算化学

背景情况:

目前的纳米粒子开发往往可以独立优化材料选择或组件比率.
同时优化材料选择和成分比率对于高效的纳米粒子配方至关重要.
现有的方法缺乏系统的方法来探索复杂的纳米粒子配方空间.

研究的目的:

开发一个集成的平台,结合自动化实验和机器学习,同时优化纳米粒子配方.
创建一个新的混合内核机器学习模型,以更好地预测配方结果.
证明该平台在制造具有挑战性的药物分子和优化现有配方方面的有效性.

主要方法:

集成自动化液体处理平台与机器学习算法.
创建一个包括1275个不同的纳米粒子配方的综合数据集.
开发一个定制的混合内核机器,将分子特征学习和组成推理结合起来.
使用支持向量机器 (SVM) 与新型内核进行预测和指导.

主要成果:

通过组合优化,成功的纳米粒子形成实现了42.9%的增加.
混合内核显著提高了基于内核的算法的预测性能,SVM显示出优异的结果.
成功配制了venetoclax,改善了对白血病细胞的体外疗效.
在特拉美提尼布配方中减少了75%的辅助剂使用,同时保持了疗效和药物动力学特性.

结论:

这项研究建立了一个可通用的AI驱动的框架,用于加速纳米粒子组成优化.
集成平台可以同时优化材料和比例,克服传统方法的局限性.
这种方法在推进基于纳米粒子的药物输送系统方面具有重大潜力.