Highly Precise Anomaly Detection Using Multivariate Statistical Process Control with Appropriate Scaling of Input Variables in Pharmaceutical Continuous Manufacturing

Abstract

Multivariate statistical process control (MSPC) has attracted considerable attention as a monitoring method for pharmaceutical continuous manufacturing. However, there are few examples of its application in pharmaceutical manufacturing, and previous studies have shown high false-positive rates. One of the reasons is the use of inappropriate scaling factors. In pharmaceutical processes, the number of experiments for MSPC modeling tends to be small because the active pharmaceutical ingredients are expensive. Subsequently, the standard deviation, a common scaling factor for some variables, becomes too small, and the model may become sensitive to small variations. In this study, we have proposed methods for determining the appropriate scaling factors. These methods were applied to granulation and drying processes in pharmaceutical continuous manufacturing. The MSPC model can detect changes in the process parameters and raw materials used during continuous wet granulation and fluidized bed drying using the proposed scaling method.