Heterogeneous Transfer Learning from Batch to Continuous Direct Compression Tablet Manufacturing

There is growing interest in shifting from batch manufacturing to continuous manufacturing in the pharmaceutical industry. Constructing statistical models that accurately predict critical quality attributes (CQAs) from operating conditions with minimal experiments in the new process is required to identify the optimal operating conditions and monitor the process. This study aims to demonstrate that heterogeneous transfer learning (TL) using data from the batch direct compression (BDC) process can enhance the prediction performance of CQAs in the continuous direct compression (CDC) process. We conducted 26 BDC experiments and 19 CDC experiments. Predictive models of tablet hardness were then built using partial least squares regression, Gaussian process regression, and random forest regression. We employed frustratingly easy heterogeneous domain adaptation (FEHDA) to the two experimental datasets, treating BDC as the source domain and CDC as the target domain. We found that FEHDA achieved lower RMSE and higher R 2 than those by models trained using only the CDC dataset. RFR attained the best predictive performance with an average RMSE improvement of 9.36 N. Notably, FEHDA improved the prediction performance in the region where no samples were obtained from the BDC process. These results support the effectiveness of heterogeneous TL for the shift from BDC to CDC.