Faculty of Pharmacy, University of Coimbra, 3000-548 Coimbra, Portugal.
Coimbra Chemistry Centre, Department of Chemistry, University of Coimbra, 3004-535 Coimbra, Portugal.
Biomolecules. 2022 Jan 27;12(2):223. doi: 10.3390/biom12020223.
A properly designed nanosystem aims to deliver an optimized concentration of the active pharmaceutical ingredient (API) at the site of action, resulting in a therapeutic response with reduced adverse effects. Due to the vast availability of lipids and surfactants, producing stable lipid dispersions is a double-edged sword: on the one hand, the versatility of composition allows for a refined design and tuning of properties; on the other hand, the complexity of the materials and their physical interactions often result in laborious and time-consuming pre-formulation studies. However, how can they be tailored, and which premises are required for a "right at first time" development? Here, a stepwise framework encompassing the sequential stages of nanoparticle production for disulfiram delivery is presented. Drug in lipid solubility analysis leads to the selection of the most suitable liquid lipids. As for the solid lipid, drug partitioning studies point out the lipids with increased capacity for solubilizing and entrapping disulfiram. The microscopical evaluation of the physical compatibility between liquid and solid lipids further indicates the most promising core compositions. The impact of the outer surfactant layer on the colloidal properties of the nanosystems is evaluated recurring to machine learning algorithms, in particular, hierarchical clustering, principal component analysis, and partial least squares regression. Overall, this work represents a comprehensive systematic approach to nanoparticle formulation studies that serves as a basis for selecting the most suitable excipients that comprise solid lipid nanoparticles and nanostructured lipid carriers.
一个设计合理的纳米系统旨在将活性药物成分 (API) 的优化浓度递送到作用部位，从而产生治疗反应，同时减少不良反应。由于脂质和表面活性剂的广泛可用性，生产稳定的脂质分散体是一把双刃剑：一方面，组成的多功能性允许对性质进行精细设计和调整；另一方面，材料的复杂性及其物理相互作用常常导致繁琐和耗时的制剂前研究。然而，它们如何进行定制，以及“一次就对”开发需要哪些前提条件？在这里，提出了一个逐步框架，包括递法明传递的纳米颗粒生产的顺序阶段。药物在脂质溶解度分析中导致选择最适合的液体脂质。至于固体脂质，药物分配研究指出了具有增加溶解和包埋递法明能力的脂质。液体和固体脂质之间物理相容性的显微镜评估进一步指出了最有前途的核心成分。借助机器学习算法，特别是层次聚类、主成分分析和偏最小二乘回归，评估外层表面活性剂层对纳米系统胶体性质的影响。总的来说，这项工作代表了一种全面的系统方法，用于纳米粒子配方研究，为选择包含固体脂质纳米粒和纳米结构脂质载体的最合适赋形剂奠定了基础。
