Streamlining tablet lubrication design via model-based design of experiments

Area of Science:

• Pharmaceutical Sciences
• Chemical Engineering
• Materials Science

Background:

• Powder lubrication is critical in direct compression tablet manufacturing to ensure manufacturability and quality.
• Existing semi-empirical models, like the Kushner and Moore model, require extensive experimental data for parameter identification.
• This extensive experimentation leads to high consumption of Active Pharmaceutical Ingredient (API) and significant time investment.

Purpose of the Study:

• To introduce a novel model-based design of experiments (MBDoE) approach for optimizing powder lubrication selection.
• To minimize the number of powder blends needed for model calibration and parameter estimation.
• To compare the efficacy of sequential and parallel MBDoE configurations.

Main Methods:

• Development and application of a model-based design of experiments (MBDoE) approach.
• Calibration of lubrication models using a reduced number of experimental blends.
• Comparison of sequential and parallel MBDoE strategies.
• Experimental validation using placebo blends with varying lubrication sensitivity.

Main Results:

• The proposed MBDoE methodology significantly reduces experimental effort by 60-70% compared to standard practices.
• Statistically sound parameter estimates and model predictions were achieved with minimized experimental runs.
• The reduction in experimental effort was consistent across different formulations and MBDoE configurations (sequential vs. parallel).

Conclusions:

• The novel MBDoE approach offers a more efficient and resource-sparing method for selecting optimal powder lubricants in direct compression.
• This methodology streamlines the tablet manufacturing process by reducing API consumption and experimental time.
• MBDoE provides a robust framework for model calibration and prediction in pharmaceutical powder formulation.