Jamie Clayton

The COVID-19 pandemic has drawn attention to pharmaceutical supply chains, notably increasing public awareness of how and where drugs and vaccines are made. While the United States and European Union (EU) retain the two top spots in terms of number of FDA-registered manufacturing plants, China and India, together, now account for 31% of these facilities. India alone supplies 40% of the world’s generic drugs but relies on China for 70% of the raw materials and APIs used to make them. This complex supply network has flexed rapidly and successfully to answer to the demands of the pandemic, with manufacturing plants switched to the production of hand sanitizer, manufacture of materials in short supply due to regional outbreaks, or, for the longer term, making respiratory drugs and vaccines. However, changes in supply chain or plant use are challenging for this heavily regulated industry, particularly given the criticality of product quality and safety.

Reliable specifications, for products and raw materials, provide a secure foundation for those navigating the global pharmaceutical supply network and are increasingly important as the industry regroups, drawing on lessons learned. Powders are particularly challenging in this regard, with conventional specifications often failing to successfully differentiate materials. This article explores reasons why specifications for products and raw materials fail, and experimental data illustrating how powder testing can be used to establish better, more robust specifications will be provided.

Pharmaceutical Technology’s October 2021 APIs, Excipients, and Manufacturing eBook

Building Robust Specifications Using Powder Testing

Pharmaceutical Technology’s APIs, Excipients, and Manufacturing 

Jamie Clayton, jamie.clayton@freemantech.co.uk, is operations director, Freeman Technology (a Micromeritics company), Gloucestershire, UK.

When referring to this article, please cite it as J. Clayton, “Building Robust Specifications Using Powder Testing,” 

Pharmaceutical Technology’s APIs, Excipients, and Manufacturing

Articles

Advanced powder testing is a useful tool to differentiate materials and optimize the supply chain.

The past few decades have brought radical change to the pharmaceutical and healthcare industries. Generic pharmaceuticals now account for 90% of prescriptions in the United States, and manufacturers in the US and Europe continue to outsource more of their strategic operations to service providers. As a result, global pharmaceutical supply chains, even for widely used treatments, have become increasingly complex. Managing these supply chains is challenging but crucial, particularly in light of ongoing drug shortages (1). Maintaining product consistency and manufacturing reÂ­lies on identifying multiple suppliers that can make active ingredients and excipients to required standards. Manufacturers must set a specification that defines those standards as a critical first step to ensuring and managing product quality. However, it is not unusual for an established speciÂ­fication to fail to capture an aspect of behavior that results in poor performance during manufacturing. This article examines how powder testing can be integrated with manufacturing and quality operations and used to optimize raw material selection and process optimization, sharing results of some inhouse projects at GlaxoSmithKline (GSK). 

	Any supplier of a pharmaceutical raw material, excipient, or active ingredient must ensure that materials meet product specifications, which establish a basis for acceptance or rejection. For powders, however, materials may meet a product specifications, and yet go on to perform differently in the manufacturing process. This usually occurs because the specification failed to capture some aspect of powder behavior that is relevant to process performance. 

	Pharmaceutical manufacturers could get around this issue by challenging suppliers to meet more demanding specifications. However, this would likely drive up both raw material and, by extension, finished pharmaceutical costs. A smarter approach would be to develop a better understandÂ­ing of material performance requirements, based on what is actually needed for the product, and then choose suppliers whose products best meet those needs. 

	Using powder characterization methods can provide better insight into material properties to help determine which of those properties are most critical to quality and which correlate most robustly with in-process behavior and manufacturing efficiency. 

	Techniques for powder testing vary considerably in terms of sophistication, ease-of-use, sensitivity, and process relÂ­evance. United States Pharmacopeia (USP) <1174> (2) specifies the following techniques: 

	·     Compressibility index/Hausner ratio 

	The first three techniques are simple methods that provide single number assessments of flow. Shear cell methods, develÂ­oped in the 1960s, represent the first attempt to bring a more scientific approach to powder characterization. These tests measure the force required to shear one consolidated powder plane relative to another. They were advanced to support a directly associated hopper design methodology and are widely used. Modern instrumentation has made the technique more accessible and reproducible via automated protocols and refined engineering design.

	Shear cell testing is useful for assessing powders behavior under the moderate-to-high stress conditions typically found in a hopper. It can be challenging to implement, however, and is not intuitive with respect to data use or interpretation. Furthermore, the method is less relevant to process applications where the powder is in a low stress or dynamic state, particularly when aerated or fluidized. As a result, many companies consider powÂ­der testing to be an expert task that must be handled by external consultants. This may be a pragmatic approach, but it can result in one-off solutions, rather than integrating powder testing into improving understanding of process behavior for raw material selection and process optimization and troubleshooting. 

	To facilitate an integrated approach, powder testing methods and instrumentation should: 

	·     Offer relatively high productivity and deliver a good inÂ­formational return on investment 

	·     Provide data that correlate directly with process performance 

	·     Measure with high sensitivity to provide effective differentiation. 

	Dynamic powder testing was developed specifically to meet industrial requirements for process relevant data.  Dynamic properties are determined by measuring the axial and rotaÂ­tional forces that act on an impeller as it is precisely rotated through a powder sample, which may be consolidated, modÂ­erately stressed, aerated, or fluidized depending on the proÂ­cess and conditions of interest (see 

). Instruments for dynamic testing also have the capability for shear and bulk property measurement and can therefore be used to generate a database of powder properties to determine the precise characÂ­teristics that define optimal performance in any given process. GSK Consumer HealthCare has used dynamic testing methods to characterize powders that are routinely used in the production of a range of over-the-counter (OTC) pharmaceuticals. Results were incorporated into a database of specifications for all the materials used, enabling the evaluÂ­ation of new suppliers and assessment of the consistency of established supplies. Data have also been used to establish correlations that define optimal processing parameters in processes such as tableting and roller compaction, and more generally in process optimization and troubleshooting.  

Choosing a raw material for roller compaction. 

	One study of dynamic powder testing methods focused on the correlation between roller compaction and compressibilÂ­ity. Roller compaction, which converts fine powder blends to stable, more freely flowing granules, is used routinely to produce OTC respiratory powders. In this case, previous in-house studies had identified compressibility as a critical parameter for roller compactor performance. CompressibilÂ­ity quantifies change in bulk density (or volume) as a function of consolidation pressure (see 

), with powders of higher compressibility requiring a greater compaction force to produce a stable ribbon in the compactor. Studies had been previously carried out to determine optimal processing parameters for feeds of varying compressibility. 

	One of the active ingredients used in this process typically has a compressibility of approximately 5%. Alternative supÂ­plies, all of which met market specifications, were identified and tested to assess their suitability for use in the process. One material was found to have a compressibility of 10% and was easily accommodated by changing processing paÂ­rameters based on correlations and existing understanding. Another material was found to have a compressibility of 25%, which was somewhat surprising given all supplies had met the same specification. 

	An attempt was made to process the high compressibility material to see whether granules could be produced. Given the need for a very high compaction force, it was hypothÂ­esized that any resulting granules would be very hard. It proved impossible, however, to produce any granules at all with the existing equipment, because sufficient compaction force could not be generated to produce a stable ribbon. 

	As this example shows, measuring the right powder property (in this case, compressibility) allows manufacturers to differentiate between materials that are available on the market, even if they have all been manufactured to an existing specification, to determine which ingredients will and won’t work well in any given process. 

	In the previous example, powder testing allowed manufacturers to incorporate process requirements into the raw materials selection process, so that they could easily reject material that would be most likely to cause a process failure. Powder testing can also be used to solve a more common problem: the gradual decline in process performance that can often be traced to use of suboptimal raw materials.

	Often optimal material characteristics are not well understood, so when powder testing is available and accessible to those with processing expertise, it can be used to optimize existing processes, as the next two examples show. In this case, powder testing was performed to troubleshoot a problem that occurred when product was discharged: at low levels, the active ingredient would separate from bulk materials, so, depending on its position in the hopper, the discharged product could contain more, or less, active ingredient than required.

	A certain amount of product was therefore discarded with each batch to safeguard product consistency. The powder was tested, and results were processed through the instrument’s integral hopper design software (3). Based on results, a new hopper was specified with a 55% larger volume and different design parameters, a different half angle and outlet size, as shown in 

	Video footage from within both hoppers verified a change in flow and the new design’s superiority in delivering a better flow regime. The original hopper design induced funnel flow, a regime in which there is a central column of moving powder and static material at the hopper walls.

	This flow pattern can result in rolling segregation and associated powder interactions with the hopper. The new design, in contrast, maintains a desirable ‘first-in,first-out’ flow pattern with no edge effect until the vessel is completely emptied. Though the new hopper is larger than the original, it empties faster and eliminates the product waste associated with the old design, saving 8% in material costs

	In this case, powder testing was carried out to define specifications for a legacy tableting process. The process had been running for many years, but, over time, throughput had been steadily eroding. Alternative raw material supplies were proving progressively more difficult to process, reducing tableting rates by almost 50% over a number of years. In-house studies identified both compressibility and flowability as key parameters for the tableting blends, allowing materials to be selected that could drive throughput back up to historical levels. In cases such as this, where throughput is increased by 50%, investment in inhouse testing equipment can return between 50 and 100 times its initial cost, delivering substantial return on investment.

	1. S. Hoffmann, “Drug Shortages Pose a Public Health Crisis in the US,” 

, www.theconversation.com/drug-shortages-pose-a-publichealth-crisis-in-the-us-98295
	2. USP, <1174> Powder Flow, USP-NF 28(2), March 2002,

, www.usp.org/sites/default/files/usp/document/harmonization/gen-chapter/g05_pf_30_6_2004.pdf.
	3. T. Freeman, “Modern Tools for Hopper Design,” 

, www.freemantech.co.uk/literature/white%20papers/Modern_tools_for_hopper_design-LAF205-E.pdf

As tests at GSK have shown, integrating powder testing into overall operations can optimize process understanding, and with it, raw material selection, equipment design, and process development.

Integrating Powder Characterization into Raw Material Selection and Process Optimization

More pharmaceutical companies are becoming interested in continuous processing, but they may face an “analytics gap,” because continuous will require a different approach to analytics. The author discusses issues and best approaches for solid dosage form pharmaceuticals.

’s 2016 Solid Dosage Drug Development and Manufacturing eBook.

The author discusses issues and best approaches for solid dosage form pharmaceuticals.

Filling the Analysis Gap in the Move to Continuous Processing

	Shear cell testing was developed in the 1960s specifically to deliver design parameters for hopper specification (1). Its introduction marked a major step forward in powder testing, and the associated hopper design methodologies were the first to bring a mathematical approach to powder handling equipment specification. They remain in widespread use, but issues with hopper performance are still commonplace, highlighting the limitations of relying solely on shear cell analysis to design and optimize this crucial unit operation. Dynamic powder testing and the measurement of bulk powder properties can complement shear cell testing, especially when shear cell testing fails to reveal the causes of poor hopper performance, such as erratic discharge and inconsistent flow.


	The design methodologies developed by Jenike for hopper specification center on identifying the flow/no flow condition that marks the boundary between acceptable and unacceptable hopper discharge (1). Similarly, shear cell analysis involves measurement of the force required to shear one pre-consolidated powder plane relative to another to induce the transition from stasis to flow. 

 shows three common configurations of shear cells. The differences in physical geometry and configuration of the apparatus will influence aspects such as accuracy, repeatability, and applicability, but essentially, all shear cells are intended to measure similar properties of the sample.

Shear cell data is thus most relevant when investigating the performance of powders at the onset of flow, under moderate to high stress. This situation would occur in a large hopper in which the powder is stored under its own considerable weight. However, shear cell testing has a number of limitations that are problematic in developing a broader understanding of powder behavior, including:

		Declining accuracy for non-cohesive powders.

		Less relevance for powders in a low stress, unconsolidated state

		Limited scope to investigate the impact of parameters such as the extent of aeration.

	These limitations mean that the behavior of many powders, in a variety of unit operations (including small hoppers) can be quite different from what might be predicted on the basis of shear cell data. If a powder is free-flowing or prone to aeration, for example, it may flood from a hopper, flowing far more easily than shear cell data might predict. On the other hand, if a powder is particularly sensitive to the impact of moisture, hopper discharge may cease far more readily than shear cell analysis would indicate, if the processing environment is poorly controlled.


	Dynamic powder testing was developed in the 1990s to meet the need to measure powder flowability under process-relevant conditions (2). It involves measurement of the torque and force acting on a helical blade at it rotates through a powder sample along a defined path, as shown in 

As the blade traverses through the powder, force and torque, with respect to height, are recorded. These two measurements are used to determine the energy gradient (i.e., work done per mm of travel) as shown in 

. Total flow energy is the area under the energy gradient curve.

	Well-defined methodologies are in place for a range of dynamic properties, including the following:

		Basic flowability energy (BFE) is measured as the blade descends and exerts a bulldozing action on the powder; it reflects how a powder will flow under forced conditions.

		Specific energy (SE) is measured using an upward traverse of the blade to impose a gentle lifting action; SE quantifies flow behavior in a low stress, unconfined environment.

	Other parameters characterize how powders behave in a consolidated, conditioned, aerated, or even fluidized state, all of which can be investigated directly using dynamic methods. Dynamic testing, therefore, has strengths that directly address the limitations of shear cell analysis.


	Dynamic testing can be used to measure flowability under low stress conditions to simulate the conditions in smaller hoppers. Furthermore, dynamic testing can be deployed to systematically investigate the impact on powder flow behavior of a wide range of parameters, including aeration and moisture, even for relatively free-flowing powders. Such information can be valuable in troubleshooting poor hopper performance.

	In addition, there are bulk powder properties that are not routinely measured but have been demonstrated to significantly influence hopper flow. Permeability, for example, is a measure of how readily a powder transmits air through its bed. Poor permeability is known to contribute to low and/or pulsatile flow (3).

	In summary, shear, bulk, and dynamic properties are all relevant for the design and operation of hoppers. While shear cell data remains useful for generating fundamental parameters for large-scale hopper design, dynamic testing and permeability measurements can be valuable for optimizing the design and performance of hoppers, particularly smaller vessels routinely used in the pharmaceutical industry. Using these techniques in tandem allows comprehensive hopper optimization.

		A.W. Jenike, “Storage and flow of solids

Bulletin of the Utah Engineering Experiment Station, 123, University of Utah (Salt Lake City, Utah, 1964; revised 1980).	

		G. Carlson and B. Hancock, “Development of a Material Sparing Method to Determine Powder Permeability to Air,” poster presented at AAPS (Atlanta, GA, 2008). 


	Jamie Clayton is operations director at Freeman Technology, 1 Miller Court, Severn Drive, Tewkesbury, Gloucestershire, GL20 8DN, tel: +44 (0) 1684 851551, 

. For further information, read Jamie’s article in 

Identifying Powder Properties that Define Process Performance

Dynamic powder testing and measurement of bulk powder properties can complement shear cell testing to identify the causes of poor hopper performance in solid-dosage drug manufacturing.

Best Practices for Optimizing Hopper Performance

	Improved, knowledge-led powder processing has a vital role to play in transforming manufacturing efficiency in the pharmaceutical industry. The successful commercialization of continuous manufacturing, the realization of real-time release, and the competitive development of generic drugs all rely on understanding how to optimize the handling and processing of powders. It is essential, therefore, to develop strategies and tools to supply insight into powder properties.

	Powders consist of three distinct phases: solids (particles), air, and often water. Interactions between these phases give rise to the unique, industrially valuable properties that powders exhibit, but at the same time make it impractical to predict powder behavior from the properties of constituent components. Multi-faceted powder characterization has proven to be a pragmatic alternative. This approach generates a database that can be used to identify the properties that correlate most strongly with process performance.

	Powder properties can be used, for example, to predict the volumetric flow rate through a screw feeder. Screw feeders are used routinely within the pharmaceutical industry to control the flow of material from one part of the process to another, typically from a feed hopper into a processing vessel. A poorly specified feeder-one that is unsuited to the powder it is handling-may deliver low or erratic powder feed rates, may block completely, or may even increase the risk of batch-to-batch contamination from powder accumulated on the walls of the feeder. The following case study shows how to identify powder properties that can be used to predict screw-feeder performance with a high degree of certainty.


	The properties of six different powders (calcium hydroxide, maltodextrin, milk protein, cellulose, calcium citrate, and lactose) were measured with an FT4 Powder Rheometer (Freeman Technology) using methods for measuring dynamic, bulk, and shear properties (1). 

 summarizes the results. The dynamic properties of basic flowability energy (BFE), specific energy (SE), aeration energy (AE), and flow rate index (FRI) directly quantify how easily the powder flows under different conditions, such as when unconfined, forced to flow, or aerated. Bulk properties such as compressibility, permeability, and bulk density deliver insight into the packing behavior of particles within the powder bed and the ease with which the bed retains and releases air. Shear properties include the parameters flow function and unconfined yield strength, which relate to the cohesivity of a powder stored under consolidation and are widely used to design powder hoppers. These data are an important resource for process optimization.


	Table I. A comprehensive database of powder properties is generated by multi-faceted powder characterization. .


	The second step in developing robust correlations for process optimization is to identify those metrics that define process performance. Factors that influence screw-feeder specification include installation constraints, process requirements, and material properties, but feed capacity is usually the primary design parameter. Being able to predict feed capacity for any given powder is therefore crucial. Design variables that can be manipulated to meet specification criteria include the size of the feeder (i.e., diameter and length) and the geometry, drive, and pitch of the auger. Feed rate may be controlled on the basis of weight (gravimetric) or volume (volumetric).

	Samples of all of the powders characterized in the first stage of the study were run through two different screw-feeder models. The first was a compact, full-flight single-screw feeder (DIWE-GLD-87 VR, tube No. 3, Gericke) used for high accuracy, dry solid feeding, pilot-scale studies, and applications requiring frequent material changeover. The second was a flat-bottom, double-screw feeder (DIWE-GZD, 12x13.5mm tube with a conical core, Gericke), a self-cleaning, twin-screw extruder used for low-capacity applications and handling poorly flowing materials.

 shows the volumetric flow rate delivered by each feeder, operated at an auger rotation speed equivalent to 80 Hz. Volumetric flow rate (in L/hr) was calculated from measurements of mass flow rate (in kg/hr) and poured density. Performance of the feeder varies significantly as a result of the differing properties of the materials.

			Table II: Volumetric flow rates of powders through a single-screw feeder (GLD) and a double-screw feeder (GZD) show that flow rate is dependent on equipment design and the properties of the material being handled.			


	Performing a multiple linear regression using both sets of measured data reveals correlations between individual powder properties and the volumetric flow rate from the feeder. In basic terms, this mathematical process quantifies the probability that any specific parameter is making a statistically significant contribution to a developing correlation. The smaller the p value, the more likely it is that an independent x variable (in this case a powder property) is influencing the dependent y parameter (volumetric flow rate through the feeder).

	For this study, a p value of 0.1 was taken as the upper limit for relevance, and parameters with p values higher than this were eliminated to derive a robust relationship. Data for five of the powders were included in the linear regression exercise, leaving the lactose data available for testing the predictive powers of the developed model.

	For the single-screw feeder, this analysis generated the following relationship, where FRI is flow rate index and SE is specific energy:

	Feed Rate = 49.54 FRI – 13.81 SE + 163.8

	For the double screw feeder, the observed relationship was as follows

	Both of these models are statistically robust, as indicated by the R

 values (0.9466 and 0.8383, respectively), which quantify the ‘goodness of fit’ between the model and the data. Furthermore, both are relatively simple in terms of the number of the powder properties that are found to be of significance. For the single-screw feeder, two dynamic properties (FRI and SE) are needed to robustly predict feeder performance, while for the double screw feeder, only AE is considered to be statistically significant.

	The parameter SE reflects the flow characteristics of a powder in an unconfined state, while FRI describes whether a powder’s resistance to flow increases or decreases as it is induced to flow at a higher or lower rate. Incidentally, all the powders assessed in this study generated an FRI above 1, indicating that they offer less resistance to flow as the rate of flow increases.

	AE is a parameter that directly quantifies how a powder flows when aerated. With a cohesive powder, aeration does little to reduce resistance to flow, but for a free-flowing powder, AE can become very low as the powder fluidizes. The materials tested in this study exhibit a relatively broad range of AE values, but a robust relationship between AE and volumetric flow rate for the double-screw feeder holds for all materials.

	In addition to the fact that the derived correlations are relatively simple, it is also interesting to note that both contain only dynamic powder properties, which indicates that dynamic powder properties are more relevant to behavior in this particular process than any of the measured shear and bulk properties. An analogous study employing more traditional testing techniques, such as tapped density methods (Carr’s Index/Hausner Ratio) or shear cell analysis, would have failed to produce a robust correlation for process optimization studies.


	To challenge the reliability of the derived models, the equations were used to predict the performance of lactose from its measured properties. 

 shows that the models predict feeder performance well, with revised R

 values showing little deviation from those of the initially developed correlations.

	These results demonstrate the value of the outlined strategy for producing robust correlations to support equipment selection, optimization, and troubleshooting. They also illustrate the importance of a multi-faceted approach to powder testing and the need to incorporate dynamic powder testing. In these examples, different powder properties proved most relevant for the prediction of performance in different feeders, but in both cases, dynamic powder properties were of most value.

:
	When referring to this article, please cite it as J. Clayton, "Identifying Powder Properties that Define Process Performance," 

Multi-faceted powder characterization, including measurement of dynamic properties, can be used to correlate powder properties and process performance to support equipment selection, optimization, and troubleshooting.

Bringing the benefits of continuous manufacture to tablet production is a significant goal within the pharmaceutical industry, and considerable investment is being made to ensure a successful outcome. One approach, which exploits existing processing strengths, is to use the same individual steps that are applied in batch manufacture but link and convert them into a continuous operation. This strategy relies on being able to closely control each of the processing steps and effectively integrate them with each other.

This article considers the potential benefits of continuous manufacturing and, focusing on the steps that constitute typical tablet production, reviews the challenges associated with implementing continuous processes as well as the associated analytical requirements. Experimental work on wet granulation is presented that demonstrates the use of dynamic powder characterization as a technique to support the development of continuous processes.

Shifts in the pharmaceutical manufacturing landscape


In response to increasing development costs, lengthening time to market and the increase in the production of generics, the past decade has seen significant investment in improving efficiency across the pharmaceutical lifecycle. The traditional manufacturing model is a clear target for these efforts. The adoption of smarter approaches to process development and operation, based on risk assessment, quality by design (QbD), and the effective use of process analytical technology (PAT) are driving substantial progress. As a result, new approaches such as continuous manufacture are gaining recognition.

Although widely employed in allied industries, such as the food and chemicals sectors, continuous processing may be viewed with caution by the pharmaceutical industry. Batch production dominates as it is well established and has some recognized benefits. Manufacturing discrete batches of product makes it relatively straightforward to isolate, trace, and resolve problems. In addition, stringent regulations have, arguably, tended to deter process development and the evolution of manufacturing practices.

However, even the best understood batch processes suffer certain inefficiencies, with batch-to-batch variability and the production of material outside of the defined specification being common in the pharmaceutical industry. These inefficiencies, which result in re-work and high levels of waste, are not conducive with the pharmaceutical industry’s objectives of increased efficiency. Continuous manufacture represents a possible solution. 

Compared to batch production, continuous processes are generally more easily managed and controlled, and provide benefits in the form of reduced labor requirements, lower capital investment and simplified scale-up (1). Implementing such processes can alleviate the problems associated with batch-to-batch variability and, provided that the process is tightly controlled, assures highly consistent output. The challenge is to successfully develop continuous processes—using the principles of QbD and appropriate PAT—that deliver these benefits. This is a complex undertaking that relies on comprehensive understanding and robust control of both the product and the process.

Continuous manufacture of oral solid dosage forms


Many unit operations within pharmaceutical manufacturing can already be thought of as semi-continuous. Considering the production of oral solid dosage forms, routine operations such as roller compaction, milling and tableting, are already continuously fed and run. However, the finished tablets are the end product of a number of sequential unit operations that together should combine APIs and excipients to deliver a finished tablet with the required critical quality attributes. 

 illustrates the stages in a typical tablet production process, which in batch manufacture are operated and controlled on a stand-alone, discrete basis. 

Typically, the first step in the tableting process is wet or dry granulation. This converts fine, but often dissimilarly sized, excipient and API particles into a homogeneous, granulated mass to avoid downstream component segregation and improve flowability through the process. In wet granulation, the exiting mass is dried to remove excess moisture prior to milling to ensure uniform, optimally sized granules that will perform well in the press. Flow additives, such as magnesium stearate, are often then added to the mixture in order to ease flow through the tablet press and lubricate interactions between the press and the blend during compaction and ejection of the finished tablet. Dry granulation is an alternative approach, especially for moisture-sensitive blends, and avoids the need for drying. 

The quality of the finished tablets relies on the output of each operation meeting the required specification to ensure success in the next stage of the process. Batch processes are heavily reliant on manual intervention and off-line analysis to support decision-making throughout an operation. The approach is to apply thoroughly defined processes, test the result, take any remedial action required, and then move material on to the next step. 

In contrast, continuous manufacture involves the successful integration of each of the process steps. It becomes essential to control each in real time to produce a suitable output that can then be fed to the following stage. The objective is to eliminate the need to intervene between each stage. Achieving this objective requires a comprehensive understanding of how materials and processes influence the output of each intermediate stage, and how to impose the necessary control. 

For example, in wet granulation, screw speed, feed rate, and water content all contribute to the physical properties of the granules. Successful integration of a continuous wet granulation process, therefore, relies on understanding how to manipulate these variables to produce granules that will reliably deliver high quality tablets. 

This need for more informed process design and operation requires a detailed understanding of powder behavior at every point of the manufacturing process. The diverse conditions that are applied during a tableting process subject the powder to a range of stresses and flow regimes, such as forced flow through an extruder, unconstrained flow into an empty tablet die, and compression. These different environments cause the powder blend or granules to behave in often unpredictable ways. The development of successful continuous processes is, therefore, supported by comprehensive powder characterization.


It is acknowledged that simple powder testing methods, such as angle of repose, flow through an orifice, and tapped density measurements, which are routinely used within the pharmaceutical industry, are relatively limited when it comes to developing efficient manufacturing practice (2). While the single number data these test methods generate give some insight into powder behavior, these techniques are often compromised by poor repeatability, reproducibility, and sensitivity. In addition, the test conditions do not represent the environment a powder is subjected to in a process. Consequently, the ability of these methods to deliver the relevant and reliable information needed to advance processing is limited. 

Dynamic powder testing is designed specifically to overcome these limitations and provide process relevant information. Dynamic flow parameters are generated by measuring properties of a powder in motion. Critical behavioral properties, such as flowability, can be measured under different conditions that more closely simulate the process environment and provide information that directly correlates with in-process behavior (3-4). 
Basic flowability energy (BFE) is a dynamic powder property that is determined from measurements of the axial and rotational forces acting on a rotating blade as it descends through a powder (see 

). BFE is a valuable parameter that reflects how a powder will flow under forced conditions, such as those that are applied by an extruder. Measuring the BFE of powders in different states enables quantification of the impact of different processing conditions, such as consolidation, aeration, and fluidization.

The experimental work detailed below explores how dynamic powder testing, and specifically measurements of BFE, supports the fast and efficient development of wet granulation via a design of experiments approach. 

As part of an on-going collaboration between Freeman Technology and GEA Pharma Systems, studies were undertaken to investigate whether dynamic powder properties could be usefully employed to quantify the quality of granules produced during wet granulation with respect to varying manufacturing conditions. The ultimate aim of the work was to establish a direct correlation between the BFE of granules and critical quality attributes of the tablets produced from them, such as hardness.

The trial focused on the performance of the ConsiGma 1 (GEA Pharma Systems), a lab-based, continuous high-shear wet granulation and drying system that can run samples from a few hundred grams up to 5 kg or more. The BFE of the granules produced using the ConsiGma 1 was measured at four different points: immediately upon exit from the wet granulator; after drying; after subsequent milling; and then after the milled granules were blended to form a feed for subsequent tableting. Experiments were carried out using two different trial blends: one based on paracetamol (APAP) the other on dicalcium phosphate (DCP) . All BFE measurements were made using an FT4 Powder Rheometer (Freeman Technology).

Impact of processing variables on the granule properties


The first set of experiments assessed the impact of the wet granulation operating conditions on the BFE of the resulting granules. The processing parameters that were varied were: amount of water added (15-25% water addition); powder feed rate (15-25 kg/hr); and granular screw rate (450 - 750 rpm). 

 shows how the BFE of the APAP granules produced at different screw speeds varies as a function of water content. 

Increasing water content, at constant screw speed, results in a higher BFE. Decreasing screw speed produces granules with a lower BFE, for a given water content. This trend is consistent with the observation that higher water content and lower screw speeds tend to produce larger, denser, and more cohesive granules that present substantial resistance to blade movements. Interestingly, the data indicates that granules produced at a water content of 11% and screw speed of 600 rpm have similar BFE values to those generated using a screw speed of 450 rpm and a water content of 8%. This suggests that it is possible to produce granules with similar properties using different operating conditions, an important finding when it comes to scoping the design space for a wet granulation process.

 shows the impact of varying the dry-powder feed rate. The BFE of granules produced with the DCP formulation was measured as a function of increasing feed rate with a fixed water content of 15% and screw speed held constant at 600 rpm. The BFE of the DCP granules was found to be indirectly proportional to feed rate, decreasing significantly with increasing feed rate.

To confirm the earlier finding that granules of similar properties can be formulated using different sets of operating conditions, additional research was carried out using the DCP formulations. Granules with 15% water content produced at a feed rate of approximately 18 kg/hr were found to have similar properties to granules containing 25% water made at a feed rate of 25 kg/hr. 

 shows the flowability measurements taken for two pairs of granules, each pair having similar BFE values produced using different process conditions. Conditions 1 and 2 result in granules with a BFE ≈ 2200 mJ, while 3 and 4 have a BFE ≈ 3200. The BFE of the granules was measured at each of four points in the process as outlined previously.

 shows how the flow properties of the granules change at each stage of the manufacturing process. The granules made using conditions 3 and 4 show an increase in BFE upon drying while the properties of those produced using conditions 1 and 2 stay relatively consistent as granule work up progresses. This increase can be attributed to the granule’s relatively large size, which in combination with increased density and hardness, results in increased mechanical interlocking in the dry sample, and greater resistance to forced flow. The granules produced using conditions 1 and 2 have a weaker structure, lower density, and relatively small size and are, therefore, less prone to mechanical interlocking. The BFE of these samples, therefore, remains comparable or slightly lower following drying. Following milling, particle sizes are more similar and flowability for the four batches converge--although difference in granule density, shape, and stiffness still exist--rationalizing the observed differences in BFE, which are retained following lubrication. 

Experimental work on wet granulation demonstrates the use of dynamic powder characterization to support continuous processing for tablet manufacturing.