Multivariate Data Analysis to Assess Process Evolution and Systematic Root Causes Investigation in Tablet Manufacturing at an Industrial Scale-A Case Study Focused on Improving Tablet Hardness.

: Only a few studies performed at industrial scale in non-simulated conditions have investigated the effect of input variability from the product's lifecycle on product quality. The purpose of this work was to identify the root causes for the low and variable hardness of core tablets prepared using high-shear wet granulation through batch statistical modeling and to verify the short- and long-term effectiveness of the improvement actions. : The novelty of this study is the use of multivariate methods for the complex assessment of a wide data set belonging to two proportional composition strengths, manufactured at an industrial scale, with different tablet shapes and sizes, with the aim of identifying inter-related active ingredient and process variables with the highest impact on hardness value and for defining optimal processing conditions leading to a robust product.

Multivariate feed forward process control and optimization of an industrial, granulation based tablet manufacturing line using historical data.

The purpose of this work was to understand the variability in disintegration time and tableting yield of high drug load (>60%) tablets prepared by batch-wise high shear wet granulation. The novelty of the study is the use of multivariate methods (Batch Evolution Models - BEMs and Batch Level Models - BLMs) to enhance process control, with a feed forward component, using prediction models built from a historical dataset acquired for 95 industrial scale batches. Time dependent process variables and significant influences on investigated parameters were identified.