By Tim Freeman, Managing Director, Freeman Technology
Whether as raw materials, intermediates or final products, powders are integral to a diverse range of industrial processes, contributing to some 80% of all manufactured goods. However, despite their ubiquity they continue to present challenges during product development, manufacturing, and in quality assurance. Powders are often labelled as ‘bad’, when it would be more accurate to say we simply don’t understand how they are behaving. Powders are neither intrinsically good nor bad but they can be suited to different situations.
Effective powder processing underpins almost all pharmaceutical manufacture. The majority of active ingredients are delivered in powder form, as a tablet or suspension, or via a dry powder inhaler for example. Even those that aren’t delivered as a powder, are commonly handled as a powder at some point during production. A good understanding of powder behavior is therefore essential to implement a knowledge-based approach to development, as advocated by the FDA’s Quality by Design (QbD) initiative.
Powder behavior is often described as erratic, primarily when the interactions between plant and material result in processing problems such as segregation, blockage of a line or flooding from a hopper. Powder formulators and processors need to understand the causes of such behavior in order to ensure that the properties of the material are compatible with the conditions in the process and the demands of the application . Reliably measuring properties that comprehensively characterize powders, in ways that relate to their processing performance, is increasingly important.
Processing and characterizing powders are both complicated by the fact that powder properties are influenced by so many different variables. Primary parameters such as particle size and shape, porosity, surface roughness and the potential for electrostatic charge are all influential, but system variables and environmental conditions can also have a profound effect. Air and moisture content are especially important.
This sensitivity explains why making a seemingly unimportant operational change can easily have a major impact. For example, the ease with which material discharges from a hopper may be influenced by how the hopper is initially filled, and the running level maintained.
The filling method can affect the amount of air entrained within the powder, while the height of the material bed in the hopper will determine the compacting forces acting on the exiting material; powders that are compressible will be more susceptible to changes in behaviour as a consequence of powder bed height.
This complexity makes powders challenging to process and difficult to reliably quantify their properties. Consider the example of powder flowability – one of the most important properties for pharmaceutical manufacturers. An intuitive analysis would suggest that particle size and shape, degree of aeration/compaction and moisture content will all influence flowability. Reproducibly quantifying flowability will therefore demand, at a minimum, consistency in terms of all of these variables. Sample preparation and procedural consistency are also extremely important.
Within the context of QbD, researchers need to have sufficient understanding of the factors that influence powder behavior to ensure that product quality goals will be consistently met by employing a defined process. Suitable tools are needed to develop this knowledge. The complexities of powder behavior cannot be adequately captured by using a single parameter, so multi-variate characterization provides a much better approach. The most valuable powder characterization tools now allow researchers to investigate the bulk and shear properties of powders as well as the dynamic flow properties of consolidated, conditioned, aerated and even fluidized powders. The resulting data extends understanding well beyond the levels achieved using conventional methods such as angle of repose, Hausner Ratio and Carr’s Index, allowing developers to more accurately predict process performance . Using this knowledge to ensure an optimal powder-plant combination builds ...
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