Abstract
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.
Highlights
The publication rate in drug delivery using nanoformulations has dramatically increased in the past decade, reaching 24,665 publications by 2017 [1]
Microemulsions underwent rigorous evaluation to simulate the stresses experienced upon sterilization, evaluation in in vitro pharmacological studies, transportation, and storage
We demonstrated that logistic regression could be used to predict the probability that a microemulsion formulation would meet one or more critical quality attributes (CQAs) specifications
Summary
The publication rate in drug delivery using nanoformulations has dramatically increased in the past decade, reaching 24,665 publications by 2017 [1]. Several explanations have been proposed to justify the lack of clinical translation of nanoformulations for drug delivery, including a lack of translatability from animal models to humans, an overemphasis on the advantages of nanoformulations, confirmation bias, and the development of increasingly complex nanoformulations [1]. These problems are all relevant and need to be addressed in order for nanoformulations to reach the market. Doing so may allow challenges to be identified and corrected early on, resulting in higher quality nanoformulations during early developmental stages
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