Graduate School of Pharmaceutical Sciences, Duquesne University, Pittsburgh, PA 15228, USA.
Chronic Pain Research Consortium, Duquesne University, Pittsburgh, PA 15228, USA.
Molecules. 2019 May 30;24(11):2066. doi: 10.3390/molecules24112066.
The development of pharmaceutical nanoformulations has accelerated over the past decade. However, the nano-sized drug carriers continue to meet substantial regulatory and clinical translation challenges. In order to address some of these key challenges in early development, we adopted a quality by design approach to develop robust predictive mathematical models for microemulsion formulation, manufacturing, and scale-up. The presented approach combined risk management, design of experiments, multiple linear regression (MLR), and logistic regression to identify a design space in which microemulsion colloidal properties were dependent solely upon microemulsion composition, thus facilitating scale-up operations. Developed MLR models predicted microemulsion diameter, polydispersity index (PDI), and diameter change over 30 days storage, while logistic regression models predicted the probability of a microemulsion passing quality control testing. A stable microemulsion formulation was identified and successfully scaled up tenfold to 1L without impacting droplet diameter, PDI, or stability.
在过去的十年中，药物纳米制剂的发展迅速。然而，纳米药物载体仍然面临着重大的监管和临床转化挑战。为了解决早期开发中的一些关键挑战，我们采用了质量源于设计的方法，为微乳液制剂、制造和放大开发了强大的预测性数学模型。所提出的方法结合了风险管理、实验设计、多元线性回归（MLR）和逻辑回归，以确定一个设计空间，其中微乳液的胶体性质仅取决于微乳液的组成，从而便于放大操作。开发的 MLR 模型预测了微乳液的直径、多分散指数（PDI）和储存 30 天后的直径变化，而逻辑回归模型预测了微乳液通过质量控制测试的概率。确定了一种稳定的微乳液配方，并成功地放大了十倍至 1L，而不会影响液滴直径、PDI 或稳定性。
