Supervisory control system for monitoring a pharmaceutical hot melt extrusion process

Abstract

Continuous pharmaceutical manufacturing processes are of increased industrial interest and require uni- and multivariate Process Analytical Technology (PAT) data from different unit operations to be aligned and explored within the Quality by Design (QbD) context. Real-time pharmaceutical process verification is accomplished by monitoring univariate (temperature, pressure, etc.) and multivariate (spectra, images, etc.) process parameters and quality attributes, to provide an accurate state estimation of the process, required for advanced control strategies. This paper describes the development and use of such tools for a continuous hot melt extrusion (HME) process, monitored with generic sensors and a near-infrared (NIR) spectrometer in real-time, using SIPAT (Siemens platform to collect, display, and extract process information) and additional components developed as needed. The IT architecture of such a monitoring procedure based on uni- and multivariate sensor systems and their integration in SIPAT is shown. SIPAT aligned spectra from the extrudate (in the die section) with univariate measurements (screw speed, barrel temperatures, material pressure, etc.). A multivariate supervisory quality control strategy was developed for the process to monitor the hot melt extrusion process on the basis of principal component analysis (PCA) of the NIR spectra. Monitoring the first principal component and the time-aligned reference feed rate enables the determination of the residence time in real-time.

Fig. 1

Schematic illustration of a continuous plant, including the extruder and various downstream processes

Fig. 2

Screw configuration showing the arrangement of conveying and kneading elements

Fig. 3

Schematic illustration of the extruder and the input parameters, i.e., controlled parameters (screw speed, barrel temperatures, and feed rate) are highlighted in red and the output parameters (screw speed, feed rate, torque, barrel temperatures, material pressure, material temperature, and the spectrum) are shown in green

Fig. 4

Network architecture of the hot melt extrusion process. The SIPAT components are highlighted

Fig. 5

The feedback connection via SIPAT allows the SIPAT user to manipulate input parameters

Fig. 6

Manipulation of the reference feed rate and observation of the spectrum that was analyzed in real-time using SIMCA-Q. The input and output data were analyzed off-line, i.e., in MATLAB, where the input sequences and the data from SIMCA-Q were time-aligned

Fig. 7

The hardware components (spectrometer, extruder, and feeder), SIPAT, Umetrics, and MATLAB interact to distribute predefined samples and collect measurements time-aligned. After the experiment, the entire data (input data, measurements and calculated data) is accessible time-aligned

Fig. 8

NIR spectra collected while changing the reference feed rates of feeder 1 and 2. The total throughput was constant at 0.6 kg/h

Fig. 9

a Different API concentrations (20%, 30%, 40%, and 50%) result in the clustering of samples in the score plot (t1 versus t2). b The trajectory from the stable state 1 (20% API concentration) to the stable state 2 (30% API concentration) was monitored in real-time via SIPAT. The mean settling time from one process condition to the other stable state was approximately 100 s

Fig. 10

The reference feed rate of feeder 1 a and the score values of the PC 1 b can be used to obtain the residence time in real-time in SIPAT