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Area of Science:

  • Pharmaceutical Sciences
  • Nanotechnology
  • Chemical Engineering

Background:

  • Pharmaceutical nanoformulations face significant regulatory and clinical translation hurdles.
  • Scaling up nano-sized drug carriers presents challenges in maintaining formulation integrity and performance.
  • Early-stage development requires robust methods to ensure successful scale-up.

Purpose of the Study:

  • To develop predictive mathematical models for microemulsion formulation, manufacturing, and scale-up using a quality by design approach.
  • To identify a design space where microemulsion colloidal properties depend solely on composition, facilitating scale-up.
  • To address key challenges in the early development of pharmaceutical nanoformulations.

Main Methods:

  • Employed a quality by design framework integrating risk management and design of experiments.
  • Utilized multiple linear regression (MLR) to model microemulsion colloidal properties (diameter, PDI, stability).
  • Applied logistic regression to predict quality control testing success probability.

Main Results:

  • Developed robust MLR models predicting microemulsion diameter, polydispersity index (PDI), and storage stability.
  • Logistic regression models accurately predicted the likelihood of passing quality control.
  • A stable microemulsion formulation was identified and successfully scaled up tenfold (to 1L) without compromising key characteristics.

Conclusions:

  • The quality by design approach successfully established predictive models for microemulsion development and scale-up.
  • Identified a design space enabling composition-dependent control of colloidal properties for easier scale-up.
  • Demonstrated successful tenfold scale-up of a stable microemulsion, validating the predictive models and approach.