PAT-based batch statistical process control of a manufacturing process for a pharmaceutical ointment

In this study, a process analytical technology (PAT)-based batch statistical process control (BSPC) model was developed for the laboratory-scale manufacturing process of a commercially available pharmaceutical ointment. The multivariate BSPC model was developed based on the in-line measured viscosity (viscometer), product temperature (viscometer), particle size distribution (PSD) (focused beam reflectance measurement (FBRM)) and active pharmaceutical ingredient (API) concentration (Raman spectroscopy) of four reference batches using a partial least squares (PLS) approach. From this in-line collected data, the characteristic trajectory of the batch process under normal operating conditions was acquired. To assess the capability of the process analyzers and BSPC model to detect deviations from the expected batch trajectory, two test batches with induced process and formulation disturbances were monitored in-line. The elevated process temperature in test batch 1 resulted in a deviating viscosity, product temperature and number of small particles (<100 μm). After correcting the process temperature, the viscosity and product temperature were within the control interval, while the particle size was smaller compared to the reference batches. For test batch 2, API was added at three different time points, whereas the same amount of API was added in one step during manufacturing of the reference batches. The induced disturbance was reflected in the in-line measured viscosity, PSD and API concentration. The combination of process analyzers and multivariate batch modelling enabled early fault detection and real-time process adjustments, thereby preventing batch loss or reprocessing. In addition, the feasibility of the investigated process analyzers to measure certain quality attributes in-line during manufacturing of an ointment was demonstrated.