Multiple Linear Regression Applied to Predicting Droplet Size of Complex Perfluorocarbon Nanoemulsions for Biomedical Applications.

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.