A multivariate formulation and process development platform for direct compression.

Area of Science:

• Pharmaceutical Sciences
• Chemical Engineering
• Materials Science

Background:

• Developing robust tableting processes is hindered by a lack of mechanistic understanding regarding raw material properties and process parameters' impact on tablet quality.
• Traditional experimental optimization of process and formulation parameters is time-consuming, expensive, and labor-intensive.

Purpose of the Study:

• To develop a more efficient and optimized direct compression (DC) tableting process development strategy.
• To identify key material attributes and in-process mechanical properties influencing tablet processability and quality.
• To establish a predictive model correlating tablet quality with process settings and material characteristics.

Main Methods:

• Extensive characterization of 55 raw materials (100+ descriptors) and 26 formulation blends (31 descriptors).
• Compaction simulation of blends under multiple process conditions using a design of experiments (DoE) approach.
• Development of a T-shaped partial least squares (T-PLS) model linking tablet quality attributes to process settings, raw material properties, and blend ratios.

Main Results:

• A comprehensive database of material properties and blend characteristics was established.
• A T-PLS model was successfully developed, demonstrating the correlation between input variables and tablet quality.
• The model provides a predictive platform for preliminary formulation and process setting selection.

Conclusions:

• The study enhances understanding of how raw material properties and process settings affect DC tableting.
• The developed model offers a platform for more efficient and optimized development of robust tableting processes.
• This approach facilitates faster optimization of direct compression formulations and processes for new APIs.