275977 PAT for Pharmaceutical Extrusion Monitoring and Supervisory Control

Monday, October 29, 2012: 2:10 PM
Allegheny II (Westin )
Stephan Sacher1, Patrick Wahl1, Daniel Markl1, Daniel Treffer2, Jose C. Menezes3, Gerold Koscher1, Eva Roblegg4 and Johannes G. Khinast1,2, (1)Research Center Pharmaceutical Engineering GmbH, Graz, Austria, (2)Institute for Process and Particle Engineering, Graz University of Technology, Graz, Austria, (3)Research Center for Pharmaceutical Engineering, Graz, Austria, (4)Department of Pharmaceutical Technology, University of Graz, Graz, Austria


Hot Melt Extrusion (HME) is increasingly attracting interest in pharmaceutical manufacturing, for two main reasons: (1) it allows to produce controlled release formulations by embedding API crystals in polymers, lipids or sugars (Kleinebudde, 2011) and (2) it significantly enhances the bioavailability of APIs with low solubility. This can be achieved with a solid solution, in which the API and the matrix are molecularly dispersed. Estimations predict that up to 90% of future new chemical entities may be classified as poorly soluble. Therefore, enhancing the bioavailability is a significant part for successful drug development. Further dosage forms like transdermal patches and implants among others can be produced by HME as well (Breitenbach, 2002).

HME also has the potential to implement continuous production in a straightforward way. One way is to use a hot-strand cutter as a downstream processing unit. The (hot) extrudate is cut directly at the die face, thereby forming nearly spherical pellets. If necessary, pellets with different APIs or release characteristics can be mixed and further processed by a capsule filling machine or a tablet press. This production line is in development at RCPE.

At present, many Process Analytical Technology (PAT) tools are available for establishing physicochemical product properties, such as the chemical composition. Multivariate data analysis (MVDA) combined with spectroscopic techniques, including near infrared (NIR) and Raman, allows quantitative and non-invasive process monitoring (Koller et al., 2011). Applying multivariate statistical process control (MSPC) reduces real-time process data streams to a convenient control chart. Clearly, process analysis calls for an IT infrastructure that can aggregate real-time process data from multiple unit operations, raw material data, PAT data and equipment status. SIPAT is a commercial PAT software solution that was created to meet the above requirements.

In this study the PAT concept consisted of: (1) In-line, real-time monitoring of extruder parameters and collection of NIR spectra of the extrudate close to the die; (2) Analysis of the spectrum with multivariate data analysis (MVDA) tools; (3) Integration and implementation of this PAT concept in SIPAT.

In this presentation we intend to show the setup of the HME system, Principal Component Analysis (PCA) to reduce the dimensionality of the spectra and a Partial Least Squares (PLS) model to predict the response of the extrusion process (the API content at the die) to step changes of the feed rates.


Experimental Methods


Paracetamol, donated by G.L. Pharma GmbH, Lannach, Austria, was used as an API (volume mean particle size 139.2 m). The matrix carrier system was calcium stearate (Werba-Chem GmbH, Vienna, Austria; mean particle size 16.62 m) (Roblegg et al., 2011). The paracetamol crystals were embedded in CaSt, resulting in a solid dispersion.

Extruder setup

Extrusions were performed on a co-rotating twin-screw extruder (ZSK 18, Coperion GmbH, Stuttgart, Germany) with 18mm screw diameter. The barrel temperatures and the screw setup was chosen in such way that the melt temperature at the die was 130C. Two twin-screw gravimetric feeders (KT20, K-Tron, Pitman, USA) were filled with a premix. Mixtures with a mass fraction of paracetamol from 0% to 60% can be obtained by adjusting the feed rates of the two feeders. In the experiments the throughput of the extruder was 0.6 kg/h. To monitor, control and store process parameters of the extruder, NIR spectra and results of calculations (e.g. API content prediction) the software SIPAT 3.1.1 (Siemens AG, Brussel, Belgium) was used.


Figure 1: Overview of monitored (green, output) and controlled (red, input) parameters of the extruder and the NIR spectrum, which was taken close to the die.


NIR spectroscopy


NIR spectra were collected in transflexion mode with a process spectrometer SentroPAT FO (Sentronic GmbH, Dresden, Germany). The spectrometer has a spectral range of 1100 nm - 2200 nm and 2 nm resolution. A fibre-optic Dynisco NIR probe was mounted close to the extrusion die, after the screws, for in-line monitoring of the melt. For each measurement 120 spectra were averaged, with an integration time of 0.014s per spectrum. However, significant modifications of the die were necessary to obtain spectra which reflect the current bulk composition of the melt.

Chemometric models


Around 100 spectra of the melt of each step of API content were collected to develop a PCA and a PLS model. Principal component analysis applied to the observed data may be used to classify the main events affecting the process. The PLS model enables the direct prediction of the paracetamol content. The chemometric model was built with the software Simca P+ 12.0 (Umetrics, Ume, Sweden). Simca-Q 12.0.1 (Umetrics, Ume, Sweden) is used as an external calculation engine in SIPAT, which has the advantage of directly integrate the models developed in Simca P+ in SIPAT.



Selecting a good NIR measurement position


Ensuring a good sample presentation to the Dynisco NIR probe is crucial for correct API predictions. The main reason for improper sample presentation is a too slow exchange of melt in the field of view of the NIR probe. First, if the probe is not perfectly in line with the barrel surface, melt will fill the dead volume once. The shear forces are not high enough to exchange the material. This is likely for measurements above the screws. A second possibility is a too large volume of melt directly before exiting the die. Here a funnel flow might build up, which causes slow melt exchange at the barrel walls, where the probe is situated. Although NIR penetrates a transparent melt, in the mentioned cases the measurement could be biased. Thus, a special probe set-up was designed and tested to represent the actual melt composition.

Monitoring the process


The feeding dynamics are the most important factor for content uniformity of the melt, especially for pharmaceutical powders, which are often not free flowing. Despite the mixing elements of the screw, the extruder has only a limited capability to compensate lower frequency feeding variations by its backmixing ability.

Monitoring the process in the reduced PC space is a straightforward way of implementing real-time process analysis. Adjusting the feed rates of the feeders yielded different API concentrations in the extrudate that appeared in the score plot as clusters of observations.

Figure 2: Different API concentrations (20%, 30%, 40% and 50%) result in the clustering of samples in the score plot (t1 versus t2).

Moreover, the prediction of the API content by the PLS model enables another way of monitoring the process in a convenient control chart.

Using Simca P+ models or models defined in MATLAB, the observations can be processed and visualized in SIPAT in real-time. The integration of the PCA and PLS models facilitates different ways to detect in real-time abnormalities in the process.

Figure 3: Predicted paracetamol content (blue). Variations induced by the feeders are noticeable. For the chosen screw configuration the time delay between feeder settings (magenta) and predicted API content at the extrusion die is 55s.


The presented hot melt extrusion system with an integrated PAT concept is capable of monitoring the critical process parameters. Special attention was paid to ensure a good NIR measurement position. However, the MVDA models that are developed in this process can be applied to only one formulation. In a typical campaign-driven manufacturing environment (which even continuous manufacturing of the future will be) formulations and/or products may change weekly or monthly. Automatic model development can be performed based on the setup including the HME process, SIPAT and Simca-Q. Using several models at the same time opens up the opportunity to validate different PCA or PLS models in real-time.


[1] Breitenbach J (2002) Melt extrusion: from process to drug delivery technology. Eur. J. Pharm. Biopharm. 54:107-117.

[2] Kleinebudde P (2011) Pharmaceutical Product Design: Tailored Dissolution of Drugs by Different Extrusion Techniques. Chem-Ing-Tech. 83:589-597.

[3] Roblegg E, Jger E, Hodzic A, Koscher G, Mohr S, Zimmer A and Khinast J (2011) Development of sustained-release lipophilic calcium stearate pellets via hot melt extrusion. Eur. J. Pharm. Biopharm. 79:635-645.

[4] Koller D M, Posch A, Hrl G, Voura C, Radl S, Urbanetz N, et al. (2011) Continuous quantitative monitoring of powder mixing dynamics by near-infrared spectroscopy. Powder Technology. 205: 87-96


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