Pain nanomedicine is an emerging field in response to current needs of addressing the opioid crisis in the USA and around the world. Our group has focused on the development of macrophage-targeted perfluorocarbon nanoemulsions as inflammatory pain nanomedicines over the past several years. We present here, for the first time, a quality by design approach used to design pain nanomedicine. Specifically, we used failure mode, effects, and criticality analysis (FMECA) which identified the process and composition parameters that were most likely to impact nanoemulsion critical quality attributes (CQAs). From here, we applied a unique combination approach that compared multiple linear regression, boosted decision tree regression, and partial least squares regression methods in combination with correlation plots. The presented combination approach allowed for in-depth analyses of which formulation steps in the nanoemulsification processes control nanoemulsion droplet diameter, stability, and drug loading. We identified that increase in solubilizer (transcutol) content increased drug loading and decreased nanoemulsion stability. This was mitigated by inclusion of perfluorocarbon oil in the internal phase. We observed negative correlation (R2 = 0.4357, p value 0.0054) between the amount of PCE and the percent diameter increase (destabilization), and no correlation between processing parameters and percent diameter increase over time. Further, we identified that increased sonication time decreases nanoemulsion drug loading but does not significantly impact droplet diameter or stability. We believe the methods presented here can be useful in the development of various nanomedicines to produce higher-quality products with enhanced manufacturing and design control.
Keywords: Quality by design; multiple linear regression; pain nanomedicine; risk assessment; theranostic perfluorocarbon nanoemulsion.