Multiple linear regression applied to predicting droplet size of complex perfluorocarbon nanoemulsions for biomedical applications.
Multiple linear regression applied to predicting droplet size of complex perfluorocarbon nanoemulsions for biomedical applications.
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DOI：
10.1080/10837450.2019.1578372
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发表时间：
2019-07
影响因子：
3.4
通讯作者：
Janjic, Jelena M.
中科院分区：
文献类型：
--
作者：
Lambert, Eric;Janjic, Jelena M.
多元线性回归（MLR）建模作为一种新的方法学进步的设计，开发和优化的全氟化碳纳米乳液（PFC内斯）。本研究的目的是开发适用于生物医学领域PFC内斯设计、开发和优化的MLR方法。根据PFC内斯作为治疗剂或诊断剂的预期用途，纳米乳液组合物在具体应用（例如磁共振成像、药物递送等）方面不同。PFC NE组成可显著影响PFC NE液滴尺寸，这影响纳米乳液性能和质量。我们先前证明了微流化结合超声处理产生具有高水平再现性的稳定乳液。所提出的工作的目标是建立液滴尺寸和组成之间的相关性，在复杂的PFC-在-油-在-水纳米乳液，而制造工艺参数保持不变。在这些条件下，我们表明，MLR模型可以预测液滴尺寸的基础上，配方变量，如PFC油和烃油的量和类型。据我们所知，这是第一份报告，其中PFC NE组合物是直接相关的，其胶体性质和多元线性回归用于预测胶体性质的组成变量。
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, nanoemulsion 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 nanoemulsion 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 nanoemulsions 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 multiple linear regression used to predict colloidal properties from composition variables.
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