Supervisory control system for monitoring a pharmaceutical hot melt extrusion process.

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.

In-line solid state prediction during pharmaceutical hot-melt extrusion in a 12 mm twin screw extruder using Raman spectroscopy.

The aim of this research was to use Raman spectroscopy for the in-line monitoring of the solid state of materials during pharmaceutical hot-melt extrusion in the die head of a 12 mm (development scale) twin-screw extruder during formulation development. A full factorial (mixed) design was generated to determine the influence of variations in concentration of Celecoxib (CEL) in Eudragit® E PO, three different screw configurations and varying barrel temperature profiles on the solid state, 'melt temperature' and die pressure of continuously produced extrudates in real-time. Off-line XRD and DSC analysis were used to evaluate the suitability of Raman spectroscopy for solid state predictions. First, principal component analysis (PCA) was performed on all in-line collected Raman spectra from the experimental design. The resulting PC 1 versus PC 2 scores plot showed clustering according to solid state of the extrudates, and two classes, one class where crystalline CEL is still present and a second class where no crystalline CEL was detected, were found. Then, a soft independent modelling of class analogy (SIMCA) model was developed, by modelling these two classes separately by disjoint PCA models. These two separate PCA models were then used for the classification of new produced extrudates and allowed distinction between glassy solid solutions of CEL and crystalline dispersions of CEL. All extrudates were classified similarly by Raman spectroscopy, XRD and DSC measurements, with exception of the extrudates with a 30% CEL concentration extruded at 130 °C. The Raman spectra of these experiments showed bands which were sharper than the amorphous spectra, but broader than the crystalline spectra, indicating the presence of CEL that has dissolved into the matrix and CEL in its crystalline state. XRD and DSC measurements did not detect this. Modifications in the screw configuration did not affect the solid state and did not have an effect on the solid state prediction of new produced extrudates. Secondly, the influence of variations in die pressure on the Raman spectra was examined. The applied drug concentration, processing temperature and feeder performance influence the die pressure, which is reflected in the Raman spectra as a change in spectral intensity. When applying PCA on the raw spectra from the experimental design, the first principal component describes the influence of die pressure on the spectra, which was seen as a decrease in Raman intensity of the whole spectrum when the pressure in the sample increased. Clustering according to processing temperature was found, although the temperature in the die remained constant, indicating that a difference in viscosity, resulting in a changed die pressure, was detected. When the feeder was stopped, the score values of the first principal component almost simultaneously decreased, and only stabilized once the die pressure became stable. Since Raman spectra collected in the extrusion die are influenced by changes in die pressure, disturbances upstream of the extrusion process can be observed and identified in the Raman measurements.

Hybrid modeling for quality by design and PAT-benefits and challenges of applications in biopharmaceutical industry.

This report highlights the drivers, challenges, and enablers of the hybrid modeling applications in biopharmaceutical industry. It is a summary of an expert panel discussion of European academics and industrialists with relevant scientific and engineering backgrounds. Hybrid modeling is viewed in its broader sense, namely as the integration of different knowledge sources in form of parametric and nonparametric models into a hybrid semi-parametric model, for instance the integration of fundamental and data-driven models. A brief description of the current state-of-the-art and industrial uptake of the methodology is provided. The report concludes with a number of recommendations to facilitate further developments and a wider industrial application of this modeling approach. These recommendations are limited to further exploiting the benefits of this methodology within process analytical technology (PAT) applications in biopharmaceutical industry.

Modeling individual cell lag time distributions for Listeria monocytogenes.

The food industry faces two paradoxical demands: on the one hand, foods need to be microbiologically safe for consumption and on the other hand, consumers want fresh, minimally processed foods. To meet these demands, more insight into the mechanisms of microbial growth is needed, which includes, among others, the microbial lag phase. This is the time needed by bacterial cells to adapt to a new environment (for example, after food product contamination) before starting an exponential growth regime. Since food products are often contaminated with low amounts of pathogenic microorganisms, it is important to know the distribution of these individual cell lag times to make accurate predictions concerning food safety. More precisely, cells with the shortest lag times (i.e., appearing in the left tail of the distribution) are largely decisive for the outgrowth of the population. In this study, an integrated modeling approach is proposed and applied to an existing data set of individual cell lag time measurements of Listeria monocytogenes. In a first step, a logistic modeling approach is applied to predict the fraction of zero-lag cells (which start growing immediately) as a function of temperature, pH, and water activity. For the nonzero-lag cells, the mean and variance of the lag time distribution are modeled with a hyperbolic-type model structure. This mean and variance allow identification of the parameters of a two-parameter Weibull distribution, representing the nonzero-lag cell lag time distribution. The integration of the developed models allows prediction of a global distribution of individual cell lag times for any combination of environmental conditions in the interpolation domain of the original temperature, pH, and water activity settings. The global fitting quality of the model is quantified using several measures indicating that the model gives accurate predictions, erring slightly on the fail-safe side when predicting the shortest lag times.