a Graduate School of Pharmaceutical Sciences , Duquesne University , Pittsburgh , PA , USA.
b Chronic Pain Research Consortium , Duquesne University , Pittsburgh , PA , USA.
Pharm Dev Technol. 2019 Jul;24(6):700-710. doi: 10.1080/10837450.2019.1578372. Epub 2019 Mar 1.
Multiple linear regression (MLR) modeling as a novel methodological advancement for design, development, and optimization of perfluorocarbon nanoemulsions (PFC NEs) is presented. The goal of the presented work is to develop MLR methods applicable to design, development, and optimization of PFC NEs in broad range of biomedical uses. Depending on the intended use of PFC NEs as either therapeutics or diagnostics, NE composition differs in respect to specific applications (e.g. magnetic resonance imaging, drug delivery, etc). PFC NE composition can significantly impact on PFC NE droplet size which impacts the NE performance and quality. We demonstrated earlier that microfluidization combined with sonication produces stable emulsions with high level of reproducibility. The goal of the presented work was to establish correlation between droplet size and composition in complex PFC-in-oil-in-water NEs while manufacturing process parameters are kept constant. Under these conditions, we demonstrate that MLR model can predict droplet size based on formulation variables such as amount and type of PFC oil and hydrocarbon oil. To the best of our knowledge, this is the first report where PFC NE composition was directly related to its colloidal properties and MLR used to predict colloidal properties from composition variables.
提出了一种新的方法学进展，即多元线性回归（MLR）建模，用于设计、开发和优化全氟碳纳米乳液（PFC NEs）。本研究的目的是开发适用于广泛的生物医学用途的 PFC NEs 的设计、开发和优化的 MLR 方法。根据 PFC NEs 作为治疗剂或诊断剂的预期用途，NE 组成在特定应用方面有所不同（例如磁共振成像、药物输送等）。PFC NE 的组成会显著影响 PFC NE 液滴大小，从而影响 NE 的性能和质量。我们之前已经证明，微流化结合超声处理可产生具有高度重现性的稳定乳液。本研究的目的是在保持制造工艺参数不变的情况下，建立复杂的 PFC-油包水型 NE 中液滴大小与组成之间的相关性。在这些条件下，我们证明 MLR 模型可以根据配方变量（如 PFC 油和碳氢油的用量和类型）预测液滴大小。据我们所知，这是首次将 PFC NE 的组成与其胶体性质直接相关，并使用 MLR 从组成变量预测胶体性质的报告。
