The importance of powder characterization

Why is effective powder characterisation so important to the pharmaceutical industry?

Understanding and controlling powder behaviour can help to formulate clinically effective products, design efficient processes and, ultimately, manufacture a consistently high-quality product; this is particularly important in the pharmaceutical industry where the majority of APIs are delivered as powders — for inhalation, dissolution for intravenous use, or for ingestion as tablets or capsules. Effective powder characterisation has long been recognised by the pharma industry as providing core knowledge that informs powder understanding and control, and many powder measurement methods have been devised in response.

Tim Freeman

In today's industry, Quality by Design (QbD) and economic pressures have increased the emphasis on manufacturing efficiency and product quality. QbD suggests 'ground up' development of robust manufacturing processes that consistently deliver in-specification product without excessive reliance on QC, while economic pressures, arising from low yields, lengthy time to market and an active generics market, make it vital to ensure optimised production from the outset. Because the seeds of successful manufacture are sown during the earliest stages of a project, it is important to employ a more holistic approach to product and process development. Tools that deliver relevant, reproducible powder property data provide a common language and can help break down the traditional barriers between formulators, process designers and manufacturers.

Powder characterisation is also growing in importance because innovation in the industry is increasingly reliant on sophisticated technologies, which intensify powder handling challenges. Highly potent actives, incorporated in extremely small proportions pose one set of issues; newer delivery technologies, such as dry powder inhalers (DPIs), another. DPIs are a practical way of delivering systemic drugs, especially where molecules are too delicate to survive the gastrointestinal tract, but the powder engineering involved is demanding.

Why are powders difficult to characterise?

The extensive range of powder testing methods used in the pharmaceutical industry (and elsewhere) highlights both the importance and the difficulties of powder characterisation. Many simple techniques (angle of repose, flow through an orifice, Carr's Index) have some intuitive logic and provide insight, but reproducibility and process relevance are common problems. Understanding why is crucial for both measurement and processing success.

Many variables influence powder behaviour. Primary factors include particle size, shape, surface energy and roughness, but secondary or system variables will also have a profound impact; for example, the degree of aeration and the level of moisture. In many industrial systems, the only known variables may be particle size, particle shape and composition, with other relevant parameters remaining unquantified. Furthermore, the science of powders is not sufficiently advanced to predict performance from all contributing factors, making a mathematical approach to powder characterisation impractical for most industrial applications.

This indicates that reproducible measurement and, for analogous reasons, consistent processing, depends on keeping a significant number of influential variables constant. A sample extracted from a high-shear mixer and analysed immediately could exhibit flow properties that are very different from an otherwise identical sample given time to release air and consolidate under its own weight. Addressing this issue is essential for reproducible testing. High reproducibility is, in turn, the key to sensitive differentiation between different powders. 'Noisy' data makes it much harder to distinguish between a sample anomaly and an analytical artefact.

Certain modern powder testers improve reproducibility by conditioning the powder ahead of measurement, minimising the impact of packing history. Gentle agitation before analysis releases excess air or dissipates consolidation, leaving a homogeneous, loosely packed bed for baseline measurement. Adding automation ensures a welldefined analytical procedure and removes other potential sources of variability, such as the operator! Together, these strategies take the reproducibility of newer instruments far beyond levels achieved with many traditional tests, and, more importantly, modern powder testers add new test methodologies and techniques that extend our powder knowledge.

For instance, a test such as angle of repose is attractive because of its simplicity, but provides only a single number description of a powder, which is inadequate for modern needs. Measuring an array of powder properties is essential because powders have many characteristics. The parameters that best describe behaviour in a tabletting compression process, for example, are not those that most precisely reflect aerosolisation performance in a DPI, or during mixing. Consequently, instruments that incorporate a range of measurement techniques offer advantages. For manufacturing-related studies, the inclusion of dynamic test methods is especially beneficial.

How is dynamic powder characterisation different from other techniques and what insight does it provide?

Although relatively new, dynamic powder characterisation is appealing when dealing with the practicalities of powder handling. Dynamic measurements are made using a powder rheometer, which precisely records the force and torque acting on a blade as it rotates through a powder sample, quantifying flow energy. Automation, in combination with sample conditioning, ensures high reproducibility. Flow energy measurements are extremely differentiating, especially for process–related studies.

A unique feature of dynamic testing is its ability to characterise powders in consolidated, conditioned, aerated or even fluidised states, allowing direct study of a powder's response to air, which is frequently a defining characteristic. In tabletting, for example, any entrained air not rapidly released as the powder settles in the die will be compressed with the dose, with post-compression expansion of the air resulting in capping or lamination. With DPIs, however, response to air affects the intensity of the aerosolisation process that drives drug delivery. Here, research has shown a direct correlation between the dynamic term, aeration energy (the flow energy measured in a sample whilst air is passed though it), and fine particle dose, a measure of delivery.¹

In the examples above, aeration or deaeration is intentional, but often it occurs inadvertently. For example, during an unplanned plant stoppage, powder settles and deaeration begins. Some powders tolerate this, but others rapidly assume a solid-like state, complicating subsequent start-up. In another scenario, highshear blending will entrain air, potentially affecting the powder's behaviour in downstream processes.

While these points emphasise the need to quantify a powder's response to air, dynamic testing offers more than this alone. The robust flow energy measurement provides a firm baseline for exploring other variables. The propensity of a powder to segregate, cake or attrite can be assessed directly in simple backtoback dynamic studies. A similar approach quantifies the impact on flow properties of process variables such as flow rate, moisture and vibrational consolidation.

Dynamic testing is therefore highly complementary to traditional powder measurement techniques, such as shear and bulk testing. Used in combination, these methods generate a rich data set from which to identify the variables that best describe powder behaviour in any situation. Shear testing, for example, is highly appropriate for understanding powder behaviour in hoppers and acceptable for classifying the flow properties of cohesive powders. However, with more free-flowing materials, sensitivity becomes an issue as the measured shear forces are low and similar. Dynamic measurement maintains sensitivity across the cohesivity spectrum and, in many cases, provides more informative flow data.

How can powder characterisation techniques be used to improve formulation?

Consider the process of vacuum drum vial filling used to produce pre-metered powdered doses for in situ reconstitution for intravenous delivery (Figure 1). Powder is transferred from the hopper under vacuum into the port at position 1. The vacuum draws powder into the die and keeps it there as the drum rotates to bring the filled port directly above the vial. Then, the powder plug is ejected using positive pressure and the filled vial passes down the line for sealing.

Figure 1

Fundamental compatibility between material and processing equipment is the key to powder processing success, easing manufacture and supporting consistent product quality. During formulation, the powder properties are not fixed and can still be tailored for a specific unit operation. This demands knowing which powder properties are relevant to the process; if the proposition is to manufacture using a vacuum drum filling system, the question is "what powder attributes are advantageous?".

Smooth flow from the feed hopper is a prerequisite, so flow properties are important, but arguably the most important issue here is the powder's response to a vacuum. Is the formulation drawn into the port effectively? Will it stay in place as the drum rotates? If a relatively strong vacuum is needed to ensure success, then a correspondingly large positive pressure is needed to eject the dose, increasing the risk of powder spillage.

Figure 2

Characterisation data for two forms of lactose allow comparative predictions of their inprocess behaviour for this application (Figure 1 and Figure 2). These data allow comparative predictions of the in-process behaviour of the two forms of lactose, for this application. The spray dried lactose is more heavily influenced by the application of a vacuum than the milled alternative, with flow energy rising to a much higher level. The permeability data (Figure 3) suggest why. The spray dried lactose bed is relatively porous so an applied vacuum induces a uniform pressure drop across the full height of the sample. With the less porous milled lactose, the pressure drop is concentrated near the vacuum's source. In both, the pressure drop is similar, but has different effects on the powder bulk. The flow energy of the spray dried lactose increases more markedly because the vacuum acts on the entire bed. With the milled lactose only a portion of the bed is affected, making the rise in flow energy more modest.

Figure 3

The results suggest that the spray dried lactose has more appropriate properties in relation to response to vacuum for this application. With this excipient the vacuum will work more efficiently, during port filling and retaining the powder plug in place. Only a relatively low vacuum will be needed, with a concomitant reduction in positive pressure.

This approach suggests how formulators and process designers, together, can use powder characterisation data to engineer a better process. While this can be done without referencing other information, existing manufacturing experience adds an extra dimension. Those routinely operating a similar vial filling process may, for example, notice that processing formulation 1 is easy, while formulation 2 presents problems. By characterising these formulations, this experience can be converted into more useful knowledge, such as "formulations with a permeability of x and a flow energy of y, under an applied vacuum of 5 kPa, process well; those with differing values of x and y do not". This sets more specific guidelines for formulators targeting a similar process.

So far, the focus has been formulation, but what about the next steps: detailed process design and ongoing manufacture?

Once clinical development is complete, the designer is faced with the task of developing a robust manufacturing process, which may be difficult because the formulation is now fixed and may not be ideal from a manufacturing stance. Here, the exact same powder understanding permits intelligent process development. Returning to the vial filling process, if permeability and flow energy are suboptimal then it is clear where problems will arise, and decisions can be made at this relatively early stage to include additional operations, such as a granulation step. Adding shear data aids optimal design of the feed hopper and selection of the best material of construction. All these steps help to reduce postinstallation modifications.

Lastly, manufacturing success relies on the skills brought to bear during daytoday operation. Batch processing is the norm in the pharmaceutical industry and often relies on an individual's operating experience. Batchtobatch variability and compromised yields are relatively common, but rationalising experience helps develop the understanding essential for a knowledge-led approach. By understanding and quantifying why formulation 1 works well and formulation 2 is more difficult, more sensitive and relevant feed specifications can be set, and a poorly performing batch can be detected before entering the plant. This helps to avoid downtime resulting from, for example, a change in excipient supplier, or variability in an upstream unit.

In summary, powder characterisation data provide information to support every phase, from the knowledgeled product and process development that underpins manufacturing success over the longterm, to the intelligent daily operation that ensures consistent product quality with minimum waste.

Reference

1. T. Freeman and R. Price, Drug Delivery Technology (May 2009).

The author says...

• With the majority of APIs delivered as powders, the importance of effective powder characterisation has long been recognised by the pharma industry.

• Many variables influence powder behaviour; however, it is not possible to predict the performance from all contributing factors.

• Dynamic powder characterisation is appealing when it comes to powder handling because it can characterise powders in consolidated, conditioned, aerated and fluidised states.

• Used in combination, dynamic testing and traditional powder measurement techniques can generate a data set that helps identify the variables that best describe powder behaviour in any situation.