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

Abstract 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. Prediction of output from input was tested with Partial Least Squares (PLS) and Artificial Neural Network (ANN) modeling. A reliable prediction ability was achieved for granulation water amount (± 2 kg in a 16 - 31 kg range), tableting speed (± 5000 tablets/h in a 23000 – 72500 tabl./h range) and disintegration time of cores (± 100 seconds; in a 250 - 900 sec. range). Offsets from the optimal process evolution and certain raw material properties were correlated with differences observed in the output variables. Improvement options were identified for 80% of the batches with high disintegration time. Hence, the trained models can be applied for systematic process improvement, enabling feed forward control.