(693e) The Development of a Predictive Tableting Platform in the Context of Continuous Direct Compression

The development of robust tableting processes in a timely manner is still challenging due to a lack of mechanistic process understanding, fundamental understanding of influence of raw material attributes, and limited usage of sophisticated process simulation tools. Experimentally determining the effects of involved process- and formulation parameters and thereafter optimization is labor-intensive, expensive and time-consuming. Therefore, raw material database management and process modeling using multivariate data analysis techniques and numerical simulations of the compaction process based on Finite Element Analysis (FEA) can provide a valuable and efficient alternative.
A set of 55 powders covering both excipients and active pharmaceutical ingredients (API) was characterized using over 20 techniques describing particle size and -shape, density, moisture content, powder flow, compressibility, aeration, surface area and triboelectric charging. Additionally, multiple mechanical properties, including plastic-, elastic, and brittle deformation, were determined based on in-die compaction tests. Principal Component Analysis (PCA) was then performed to elucidate correlations between the powders and their measured properties. Based on this analysis, formulation blends were selected containing multiple API’s and fillers covering a maximal area of the material variability space determined via the PCA. Disintegrant (Sodium Crosscarmellose; 5%), glidant (Colloidal Silicon Dioxide; 0,5%) and lubricant (Magnesium Stearate; 0,75%) were kept fixed in the selected blends. Formulation blend bulk properties were characterized with a minimum number of relevant tests, as derived from the PCA model of the raw materials. These blends were subsequently compacted using the press type simulation tool on a Huxley Bertram (HB) 1088-C compaction simulator to evaluate tablet quality attributes under different process conditions using the Design of Experiments (DoE) approach.
T-PLS models were developed to link raw material properties, blending ratio’s and process settings with the resulting tablet quality attributes. Based on these models it is possible to determine which formulation- and process parameters affect the direct compaction process and resulting tablet properties. By optimizing this model for new API’s, it can be used to predict an optimal formulation and finetune the process settings based on a minimum of relevant raw material characterization techniques and compaction tests. So far, this approach has only been tested on a compaction simulator, for further implementation scale-up experiments are required.
These results contribute to a better understanding of the impact of powder properties and process settings on a direct compaction process and final properties of the produced tablets. Applying this knowledge can reduce consumption of expensive API’s during product development phase. This predictive platform, which combines the use of PLS and FEA models, can be implemented for future development of formulations for direct compression and finding optimal process settings in a minimal amount of time. This platform can later be linked with additional unit operations such as feeding and granulation for implementation towards continuous manufacturing processes.