Soft sensor for real-time estimation of tablet potency in continuous direct compression manufacturing operation.

One of the critical quality attributes of the solid oral dosage forms produced in continuous direct compression operations is the tablet potency. A novel soft sensor comprising of a combination of first principle-based and empirical models has been developed to enable real-time monitoring of blend and tablet potency, and concentrations of other excipients at various stream levels along the direct compression line. The soft sensor model has only three adjustable parameters, primarily associated with the equipment design and operation, so the model is product agnostic which is key to enable flexible manufacturing. The estimation accuracy of the soft sensor is demonstrated through a series of real time experiments which include steady state and dynamic transitions of potency during the runs, compared with offline analytically tested tablet cores. The results indicate that the proposed soft sensor can be utilized as a robust tool for real-time monitoring of potency, suggesting an extension of its utilization to higher levels of control. Two potential applications of the soft sensor are: 1. An element of a control strategy for product diversion; 2. A predictive model for advanced process control strategy to minimize the variability in tablet composition.

A hybrid NIR-soft sensor method for real time in-process control during continuous direct compression manufacturing operations.

Near Infrared (NIR) spectroscopy is commonly utilized for continuous manufacturing as Process Analytical Technology (PAT) tool. This paper focus on a continuous direct compression manufacturing process, in which an NIR PAT probe is integrated into the tablet press feed frame and into the tablet diversion control system to ensure continuous monitoring of the potency and homogeneity of the blend within the process line. The quantification of NIR spectra is achieved through Partial Least-Squares (PLS) modeling, calibrated with offline analyzed tablet cores at different potency levels. Because the NIR measurements are often sensitive to sample physical properties caused by raw materials or process conditions, etc., adopting a data-driven approach will require a large amount of representative data throughout the method lifecycle. During the early stages of process development, whenever new uncaptured source of variability in the model space are encountered, the chemometric predictions can deviate from the offline reference, requiring frequent model updates. These deviations can be reduced by integrating process and physico-chemical knowledge in the on-line potency estimation. This paper presents a novel hybrid method combining the online NIR PLS and a potency soft sensor estimation, enabling a robust potency prediction whilst minimizing maintenance downtimes and facilitating cross-site method transfer.