(13b) Development of an Inline Measurement Tool for Particle Size and –Shape Analysis during the Granulation Process

Authors: 
Niesing, M., TU Dortmund
Thommes, M., TU Dortmund University
Weis, D., Technische Universität Kaiserslautern
Antonyuk, S., Technische Universität Kaiserslautern

Development
of an Inline Measurement Tool for Particle Size and –Shape Analysis during the
Granulation Process

Maria
Niesing, Technical University Dortmund

Dominik
Weis, Technical University Kaiserslautern

Sergiy Antonyuk, Technical University Kaiserslautern

Markus
Thommes, Technical University Dortmund

Inline
measurement tools provide an efficient way of monitoring industrial processes.
Generated data during the process can prove to be valuable for process
optimization, and processing times can be shortened by implementing
endpoint-detection. Especially for pharmaceutical applications, close process
surveillance is necessary to ensure quality by design. Therefore, several
process analytical tools for particle size measurement of granules have been
developed. However, the quality of a granule is not only determined by the
particle size, but also by the particle shape. With most commercially available
tools for inline particle size measurement, no information about the particle
shape is gained[1].

A common
technique to gain uniform, spherical granules for pharmaceutical use is
extrusion/spheronisation. Here, extrudate strands are
generated by wet extrusion. The wet extrudates are then rounded in a spheronizer, a machine which consists of a cylindrical
jacket and a rotating plate at the bottom. Through breakage, attrition and
agglomeration, the long extrudate strands are formed into spherical
particles[2]. Previous works show that so far, no commercially available
process analytical tool can provide inline particle size and shape measurement
of moving particles in a spheronizer. The aim of this
study is the development of a method for the inline particle size and shape
determination for the spheronization process. The
measurement is facilitated by the round shape of the particles and the narrow
size- and shape distribution[3].

An optical
method was preferred to get information about the particle shape. A script for
image analysis was written in Python 2.7, using the libraries Pillow 3.2.0, Scipy 0.17.1, Numpy 1.11.1, Scikit-image 0.12.3 and Mahotas
1.4.1. The equivalent diameter was used for size determination of each
particle. For the shape description, the maximum feret
diameter, its perpendicular feret diameter and the
minimum feret diameter was calculated. The aspect
ratio, calculated by dividing the maximum feret
diameter by the minimum feret diameter, was used to
describe the sphericity. The calculation of the aspect ratio by the maximum feret diameter and its perpendicular feret
diameter is also possible.

To validate
the correct function of the script, granules of different shapes and sizes were
generated by extrusion/spheronization of a powder
mixture (20% MCC, 80% Lactose) with different amounts of water, which served as
granulation fluid. Images of the dried particles where taken under a microscope
on dark background. These images were analyzed with the python script and with
the open source software ImageJ.

The inline
measurement was achieved by placing a High-Speed-Camera (HighSpeedStar
3G, LaVision, Göttingen,
Germany) in front of the glass cylinder of a spheronizer(Ø
120 mm). The friction plate was rotating with a peripheral speed of 2 m/s. To
ensure that no particle was contained in consecutive images, the Camera took 60
images per second, each with an exposure time of 1/4000 s., LED lights were
placed beside, over and under the spheronizer,
illuminating the particles from a sharp angle and enhancing particle-background
contrast Granules from the same batches that were measured under the microscope
were now measured inline in the spheronizer.

Comparison
of the results from the python script and ImageJ showed that the equivalent
diameter, the maximum feret diameter and the minimum feret diameters did not differ between the two methods.
ImageJ is by default not able to detect the feret
diameter that lies orthogonal to the maximum diameter. The python script was
able to analyze a picture of 2592 x 1944 pixels in less than six seconds on a
standard office computer, while the semiautomatic analysis with ImageJ took
about two minutes per image.

The equivalent diameter measured
inline was systematically smaller than the equivalent diameter that was
obtained by the offline measurement. This was expected, since the inline
measurement technique tends to ignore particle areas that are shadowed by other
particles. Therefore, the outer edges of a particle are often cut off. The
amount of deviation between the inline and offline measurement depends on the
particle size and shape. Since the minimum feret
diameter is affected stronger than the maximum feret
diameter, the calculated aspect ratio is too high. However, the inline
measurement still gives valuable information about trends in the change of
particle size and shape.

The results
show that inline measurement of particle size and shape during extrusion/spheronization is possible. However, improvement concerning
the measurement of the particle shape is needed.

1.           A.F.T. Silva, A. Burggraeve,
Q. Denon, P.V.d. Meeren, N. Sandler, T.V.D. Kerkhof,
M. Hellings, C. Vervaet,
J.P. Remone, J.A. Lopes, and T.D. Beer, Particle
sizing measurements in pharmaceutical applications:  Comparison of in-process methods versus
off-line methods. European Journal of Pharmaceutics and Biopharmaceutics, 2013.
85(3): p. 12.

2.           M.D. Köster,
Spheronization Process - Particle Kinematics and
Pellet Formation Mechanisms, in Institut für pharmazeutische Technologie und Biopharmazie.
2012, HHU Düsseldorf. p. 108.

3.           L. Hellen and J. Yliruusi,
Process variables of instant granulator and spheroniser:
III. Shape and shape distributions of pellets International Journal of
Pharmaceutics, 1993. 96.