Using Analytical Techniques to Examine Tablet Sticking

Published on: 
Pharmaceutical Technology, Pharmaceutical Technology-12-02-2013, Volume 37, Issue 12

Analytical tests, correlated with statistical techniques, are used to predict material behavior.

The Tableting Science Anti-Stick Research (TSAR) program conducted by tablet-compression tooling manufacturer I Holland in collaboration with the University of Nottingham in the UK aims to investigate the common tablet-manufacturing problem of “sticking” (i.e., build-up of granule on the punch-tip) that can cause tablet-press downtime and reduced output.

The causes of sticking are complex. Some of the underlying factors include the following:

  • Surface chemistry. Elements within the formulation are naturally attracted to elements within the tablet punch material through Van der Waals forces. These low forces are measured in nanonewtons (nN), but in bulk behavior, these forces can cause sticking.

  • Moisture content. Capillary action, which can be linked to high moisture content, creates the potential for sticking in both direct compression or wet granulation. However, if the granules are very dry, static electricity can form and cause sticking.

  • Morphology. The surface roughness of the contact affects sticking.

  • Deformation mechanics.Under compression, the granule is either elastic or plastic, and these characteristics influence sticking.

The TSAR project is developing a predictive tool that will use input parameters (e.g., surface chemistry, temperature, humidity, granule size, and whether the granule is elastic or plastic) to predict the sticking propensity of a given formulation. The tool can be used to optimize a punch or die-coating solution for sticky formulations.
Several advanced analytical techniques have been used to understand the underlying causes of sticking. These techniques include optical surface profilometry, scanning electron microscopy (SEM), x-ray photo electron spectroscopy (XPS), atomic force microscopy (AFM), and time-of-flight secondary-ion mass spectrometry (TOF-SIMS).

Atomic force microscopy
AFM works by scanning a probe with a sharp tip over the sample surface to build up a map of height and topography. The AFM uses an optical lever to monitor the motion of the tip mounted at the end of a reflective cantilever using a laser that is focused on the back of the cantilever. When scanning the surface sample, the tip undergoes upward and downward movements with the changes in the surface topography leading to changes in the deflection of an optical laser. The tip can be maintained at a constant force that allows real-time determination of height information or at a constant height to determine the deflection force above the sample. The images generated from the detector are three-dimensional topographical maps of the surface that are constructed by plotting the local sample height versus horizontal probe tip position (see Figure 1).

TSAR researchers prepared adhesion maps for each coating at different humidities to measure the different adhesive forces on a tablet-punch face. The variance of these forces must be understood to be able to account for the difference caused by surface topography differences.

AFM force-measurements mode. In addition to these topographic measurements, the AFM can provide much more information. Because the AFM relies on the forces between the tip and the sample, it can be used to measure the long-range attractive or repulsive forces between the probe tip and the sample surface at a single point or across a surface, thus elucidating local chemical and mechanical properties, such as adhesion and elasticity, and even molecular bond rupture strength. The force is not measured directly, but calculated by measuring the deflection of the lever and knowing the stiffness (spring-constant) of the cantilever following Hook’s law:

F = -kz

where F is the force, k is the spring constant (the stiffness of the lever), and z is the deflection distance of the cantilever.

A force-versus-distance curve, such as shown in Figure 2, typically shows the deflection of the free end of the AFM cantilever as the fixed end of the cantilever is brought vertically towards and then away from the sample surface. Experimentally, this is done by applying a triangle-wave voltage pattern to the electrodes for the z-axis scanner. This causes the scanner to expand and then contract in the vertical direction, generating relative motion between the cantilever and sample. The deflection of the free end of the cantilever is measured and plotted at many points as the z-axis scanner extends the cantilever towards the surface and then retracts it again.

Colloid probe AFM. The force-measuring principle of colloid-probe AFM is identical to that of a standard AFM, except particles are attached to AFM cantilevers and challenged to different surfaces. The drug-product particle is mounted onto the cantilever using a very small amount of glue. Once attached to the cantilever, the particle is brought into contact with the substrate (surface) and then retracted to determine the force of adhesion. This technique allows the quantification of the drug product adhesion to different surfaces in different environments.

Time-of-flight secondary-ion mass spectrometry

TOF-SIMS is a sensitive surface analysis technique that provides information about material composition, molecular structure, and chemical bonding. TOF-SIMS allows a mass spectrum of a material’s surface to be obtained to a depth of approximately 1 to 2 nm. TOF-SIMS can also be used to “image” surfaces, thereby showing the distribution of selected ions on a sample surface. It is possible to image areas ranging in size from a few micrometers to tens of millimeters by rastering the primary ion beam over the selected surface. Due to the simultaneous collection of all the mass fragments, the resulting images have a full mass spectrum at each pixel allowing the retrospective visualization of any ion in the spectrum and its position on a surface.

TOF-SIMS has the ability to obtain a depth profile and characterize a three-dimensional distribution of material components. A focused ion beam is rastered over a defined rectangular area on the surface of the sample to be analyzed (see

Figure 3

). While the sputter ion beam is sputtering a crater, the analysis ion beam is progressively analyzing the center of the crater bottom. The intensities of the characteristic ions of each layer can be reported as a function of the depth, resulting in a three-dimensional cube representing the sputtered/analyzed materials. The mass of every secondary ion reaching the detector is collected and stored along with the x, y, and z spatial coordinates, thus forming a three-dimensional stack of mass spectra suitable for retrospective visualization of selected ions. TOF-SIMS allows the visualization of the chemical distribution of selected ions in three-dimensional volumes of the analyzed materials.

Other techniques

Raman spectroscopy can be used for chemical identification, characterization of molecular structures, and identifying the effects of bonding, environment, and stress on a sample. XPS provides quantitative compositional information (e.g., amounts of elements and bonding environments) from the top 10 atomic layers of a sample surface. SEM employs a beam of highly energetic electrons to examine objects on a sub-micron scale and yields information about surface topography, morphology, composition, and crystallograpy. Laser profilometry is used to determine a surface’s profile to quantify its roughness.

The TSAR project uses these techniques to look at particles that are stuck to a punch tip and analyze exactly what they are chemically, thus working out what those interactions are.

Principal component analysis (PCA)

PCA, a multivariate statistical technique, aims to generate correlations between XPS, Raman spectroscopy, and TOF-SIMS data to show quantitative chemical information for individual systems that might be involved in the adhesion problem of the powder with different coatings. PCA can be viewed as a method for reducing a large data matrix (i.e., a series of samples with varying secondary ion peaks) to a few key combinations of variables that describe the most significant trends in the data and identify a correlation between the different data obtained. It is a way of identifying patterns in data and expressing the data in such a way as to highlight their similarities and differences. Because patterns in data can be hard to find in data of high dimension, PCA is a powerful tool for analyzing data. PCA is used to understand the key interactions that are causing a formulation to stick to the punch tip faces and to develop the TSAR predictive tool.