Automated Microscopy - Morphologi Applications and Case Studies

Morphologi G3 automated microscopy has applications across all industry sectors. At any point in your manufacturing process from early research and development, through process analysis, manufacturing trouble-shooting and root-cause analysis to final product quality control, this instrument provides you with an unprecedented level of product and process understanding.

Many industries require the ability to accurately characterize particulate materials. Sometimes the correlation between particle size or shape and a product property or behavior is well-known and understood. More often, this link is not well understood, due mainly to the fact that, up until now analytical tools capable of identifying subtle relative differences between samples have not been available. Some example applications are:

Example 1: Pharmaceutical applications and the PAT initiative

The pharmaceutical industry is different from many other manufacturing industries in a number of ways. These include:

• a product with a high added value
• a long product development time
• a high level of regulation by outside agencies
• a heavy emphasis on quality assurance and control within the production process
• a product which can literally mean the difference between life and death

For all these reasons it is essential that the manufacturing process is well understood, highly controlled and as efficient as possible.

The FDA’s PAT (Process Analytical Technologies) initiative provides a regulatory framework and a cultural impetus for the pharmaceutical industry to focus on improving process understanding and evaluate new analytical techniques. The initiative encourages the pharmaceutical industry to identify which parameters are critical to quality and identify which technologies are most appropriate to measuring these parameters

Differences in the physical properties of both excipient and active components of pharmaceuticals can cause final formulation variability. Even subtle differences in particle size or shape can significantly affect product performance measures such as bioavailability, flowability, stability, blending and tabletting efficiency etc.

The sources of differences can be both the raw materials used and the subsequent manufacturing steps. Differences can occur in batches from different raw-materials suppliers even though specifications are identical. As robust shape data has historically not been available many raw-material specifications have not been narrowly defined enough to ensure sufficient similarity between different batches.

Many manufacturing processing steps such as crystallization, drying, milling, blending, filtering can all introduce variability into the product and have to be precisely controlled. Traditional sizing methods are often not sufficient to establish the causal links between manufacturing process variables and final product performance. The extra sensitivity and resolution available in the Morphologi G3 instrument provides users with the ability to identify, measure and monitor those process variables which are critical to product quality.

Example 2: Sensitivity to fines

Ensemble particle sizing methods usually provide data on what is known as a ‘volume-basis’. This means that the contribution each particle makes is proportional to its volume – large particles dominate the distribution and small particles are effectively hidden because their volume is so much smaller than the larger ones.

Image analysis provides data on what is known as a ‘number-basis’. This means that the contribution each particle makes to the distribution is the same - a very small particle has exactly the same weighting as a very large particle.

The presence of fines may or may not be important. If they are deemed to be unimportant then the volume response of ensemble systems will be faster and more convenient.

However for diagnostic or trouble-shooting purposes the presences of fines could be very important in order to fully understand the manufacturing process and the extra-sensitivity to fines of image analysis may be required.

Example 3: Sensitivity to shape

A batch of pharmaceutical excipient was found to continuously fail at the tabletting stage of the manufacturing process. This was very expensive because the tabletting process is at the very end of the manufacturing process where all the value has been locked into the product.

The user wanted some way of identifying the failed batch much earlier – ideally as a raw material. Tradition microscopy or ensemble sizing methods could not distinguish between the four batches.

Automated image analysis was used to evaluate the average convexity of the four batches. Convexity is a measure of surface roughness or ‘spikeyness’ of the particle surface. The failed batch was found to consistently exhibit a lower average convexity than the other three good batches.

Example 4: Foreign particle identification

Thanks to the fact that data is generated on a number basis and its sensitivity to particle shape, image analysis is an ideal technology for detecting the presence of very small numbers of foreign particles.

Using single parameters or combinations of parameters foreign particles can be detected and quantified. For example - mean intensity can be used to identify darker, higher-contrast particles which are different from the main sample.

Displaying a distribution of mean intensity shows clearly 2 nodes – a “transparent particle” node which contains all the main sample particles and a “dark particle” mode which identifies the darker foreign particles.