PDF(Pubmed)
Abstract：
UNASSIGNED: In the last few decades, nose-to-brain delivery has been investigated as an alternative route to deliver molecules to the Central Nervous System (CNS), bypassing the Blood-Brain Barrier. The use of nanotechnological carriers to promote drug transfer via this route has been widely explored. The exact mechanisms of transport remain unclear because different pathways (systemic or axonal) may be involved. Despite the large number of studies in this field, various aspects still need to be addressed. For example, what physicochemical properties should a suitable carrier possess in order to achieve this goal? To determine the correlation between carrier features (eg, particle size and surface charge) and drug targeting efficiency percentage (DTE%) and direct transport percentage (DTP%), correlation studies were performed using machine learning.
UNASSIGNED: Detailed analysis of the literature from 2010 to 2021 was performed on Pubmed in order to build \"NANOSE\" database. Regression analyses have been applied to exploit machine-learning technology.
UNASSIGNED: A total of 64 research articles were considered for building the NANOSE database (102 formulations). Particle-based formulations were characterized by an average size between 150-200 nm and presented a negative zeta potential (ZP) from -10 to -25 mV. The most general-purpose model for the regression of DTP/DTE values is represented by Decision Tree regression, followed by K-Nearest Neighbors Regressor (KNeighbor regression).
UNASSIGNED: A literature review revealed that nose-to-brain delivery has been widely investigated in neurodegenerative diseases. Correlation studies between the physicochemical properties of nanosystems (mean size and ZP) and DTE/DTP parameters suggest that ZP may be more significant than particle size for DTP/DTE predictability.
UNASSIGNED: Detailed analysis of the literature from 2010 to 2021 was performed on Pubmed in order to build \"NANOSE\" database. Regression analyses have been applied to exploit machine-learning technology.
UNASSIGNED: A total of 64 research articles were considered for building the NANOSE database (102 formulations). Particle-based formulations were characterized by an average size between 150-200 nm and presented a negative zeta potential (ZP) from -10 to -25 mV. The most general-purpose model for the regression of DTP/DTE values is represented by Decision Tree regression, followed by K-Nearest Neighbors Regressor (KNeighbor regression).
UNASSIGNED: A literature review revealed that nose-to-brain delivery has been widely investigated in neurodegenerative diseases. Correlation studies between the physicochemical properties of nanosystems (mean size and ZP) and DTE/DTP parameters suggest that ZP may be more significant than particle size for DTP/DTE predictability.
摘要：
■在过去的几十年里,已经研究了鼻-脑递送作为将分子递送到中枢神经系统（CNS）的替代途径，绕过血脑屏障.已经广泛探索了使用纳米技术载体通过该途径促进药物转移。由于可能涉及不同的途径（系统或轴突），因此确切的运输机制尚不清楚。尽管在这一领域有大量的研究，各个方面仍然需要解决。例如,为了实现这一目标，合适的载体应该具有什么物理化学性质？为了确定载体特征之间的相关性(例如，粒径和表面电荷）和药物靶向效率百分比（DTE％）和直接转运百分比（DTP％），使用机器学习进行相关研究。
■在Pubmed上对2010年至2021年的文献进行了详细分析，以建立“NANOSE”数据库。回归分析已应用于利用机器学习技术。
■总共64篇研究文章被考虑用于构建NANOSE数据库(102种配方)。基于颗粒的制剂的特征在于平均尺寸在150-200nm之间并且呈现-10至-25mV的负ζ电位(ZP)。DTP/DTE值回归的最通用模型由决策树回归表示，其次是K-最近邻回归(Kneighbor回归)。
■文献综述显示，鼻-脑传递在神经退行性疾病中得到了广泛的研究。纳米系统的物理化学性质（平均尺寸和ZP）与DTE/DTP参数之间的相关性研究表明，对于DTP/DTE可预测性，ZP可能比粒径更重要。
■在Pubmed上对2010年至2021年的文献进行了详细分析，以建立“NANOSE”数据库。回归分析已应用于利用机器学习技术。
■总共64篇研究文章被考虑用于构建NANOSE数据库(102种配方)。基于颗粒的制剂的特征在于平均尺寸在150-200nm之间并且呈现-10至-25mV的负ζ电位(ZP)。DTP/DTE值回归的最通用模型由决策树回归表示，其次是K-最近邻回归(Kneighbor回归)。
■文献综述显示，鼻-脑传递在神经退行性疾病中得到了广泛的研究。纳米系统的物理化学性质（平均尺寸和ZP）与DTE/DTP参数之间的相关性研究表明，对于DTP/DTE可预测性，ZP可能比粒径更重要。
