A Batch of Opportunities
Continuous granulation allows for use of fewer materials and minimized scale-up risks
T he pharmaceutical industry uses numerous batch processes. Recently, because of their many advantages, continuous processes are becoming more prevalent. Continuous granulation offers opportunities for product and process development using small quantities of materials with minimized risks in scale-up. Characterization of the granulation process using design of experiment (DOE) techniques can be achieved using a smaller amount of active pharmaceutical ingredient (API). In the development of new drugs and excipients, small-scale continuous systems reduce time to market and employ processes comparable to production.
In manufacturing, continuous granulation can deliver increased efficiency and a throughput that is not only higher but also flexible. In addition, it allows for the monitoring of processing parameters in the granulation process, particularly important for process analytic technology (PAT). The following article compares batch and continuous processes with similar output rates. The cost of producing one sample is compared, and the product risks associated with each method are discussed.
Using only small quantities of new ingredients during formulation and process development for proof-of-concept studies can deliver significant cost savings. Compared to more traditional batch processes, in which mixing vessels can have volumes of between 3 and 600 liters, continuous twin screw granulators with throughputs ranging from 1 to 50 kg/hr can deliver similar production outputs. Less obvious is the time saved due to reduced material handling and smaller in-process inventory of expensive API and excipients. Additional benefits include reduced cleaning time and lower investments due to a smaller footprint. A comparison of a range of batch granulators with equivalent continuous twin screw granulators is instructive (See Figure 1, p. 28).
In the batch process, as the dry materials are introduced into the mixer, any ingredient errors, either in quantity or quality, will render the full batch unusable. Moreover, the addition of the liquid binder represents a risk, because the exact time and method of addition and the quantity added can critically affect the end product quality.
The individual components of a formulation made up of API, excipients, and binders can be added separately into different zones of the extruder. This means that expensive ingredients and APIs can be held separately until they are introduced to the granulator. In a DOE investigation, changes in formulation can be made easily, allowing the minimum-sized samples to be prepared.
Feeding ingredients separately into a continuous granulator means that the quantity of material at risk (mixed with other ingredients) is significantly reduced. All ingredients are added as controlled continuous feed streams. The quantity of material within the continuous granulator is very small. Compared with dry blending, the requirements of particle size and distribution are less critical because the twin screw granulator handles the mixing and wetting (See Figure 2, p. 30).
Transferring a batch process from a smaller to a larger mixer is a complex procedure. Motor powers and impellor speeds are governed by complex power laws for material flow. 1
Increasing the diameter (D) of the impellor has a significant effect on mixer power and flow pattern. Further complications arise when liquid is added. It is important that the liquid binder be added in a controlled manner at a rate that matches liquid addition in the smaller mixer. Work on the "flux density" of the liquid addition has shown how both rate and spray area can significantly affect the granulation process. 2
In comparison, scale-up of continuous processes is well characterized; similar residence times and energy inputs can be calculated in changing from a smaller extruder size to a larger version.
Using the same screw configurations and scaling throughputs directly in proportion to the relative free volumes, scientists find similar residence times across a range of screw speed and feed rates. 3 In the case of the 16 mm 25:1 L/D and the 24 mm 28:1 L/D, the free volumes are 68 ml and 255 ml, respectively. Given this information, if feed rates are scaled in a similar ratio (1: 3.75), we can see if the performance of the two extruders is similar. This shows that scale-up between different sized twin screw extruders can be confidently predicted in relation to time within the twin screw granulator (See Figure 3, p. 32).
It is worth noting that the free volumes shown in Figure 3 represent the total free volume within the extruder barrel. Since the extruder is rarely full, the volume of product actively within the granulation process is very small compared to the output rate that can be achieved.
Specific Energy Input
The specific energy input is the measure of how motor power is converted into energy in the product. In any driven system, there will be power losses from the motor. These losses, which take place, for example, in bearings and gears in the motor and gearbox, reduce the power absorbed in the process material. Taking these losses into account gives a more reliable measure of the energy imparted to the product through the mechanical work of the extruder screws. The "no-load" power is measured by rotating the motor across the speed range without screws fitted in the extruder: motor power minus no-load power = Ps (kW).
The output rate is the mass throughput. This can be measured by totaling the individual feeder's rates or by measuring the output at the extruder discharge: product output rate = Q (kg/h). Dividing the power input by the feed rate gives the measure called specific energy input. This energy is one of the most useful factors available to operators scaling up performance from smaller to larger extruders: specific energy input = P/Q = E (kWh/kg). By matching the process on two different extruders to achieve similar specific energy inputs, the operator can be confident that processing conditions and product quality will also be similar.
The specific energy value allows a calculation of the output from a larger extruder with motor power that is required to process a defined quantity of material Q: motor power of the large extruder = PL (kW). The specific energy E (kWh/kg) has been established from small scale tests; the estimated product output Q will be PL * E kg/h.
Scale-Up and Heat Transfer
For co-rotating twin screw extruders of different sizes, throughputs will scale up, as we have seen, in proportion to volume and motor power in the extruders. There is one factor that does not scale up in proportion to the volume, however, and that is the metal surface area available for heat transfer. This is often the most common source of scale-up problems.
Heat transfer in an extruder is dependent on the ratio of surface area to volume. This means that if you increase the diameter of the screws by 1.5 times, then volume (a cubic function)-and therefore throughput-will increase by 3.375 times. Surface area (a square function) and, consequently, heat transfer capability will increase only 2.25 times.
The ratio of the barrel surface area available for heat transfer to the volume of material in the extruder is inversely proportional to the barrel diameter.
In other words, the ratio of the barrel surface area available for heat transfer to the volume of material in the extruder is inversely proportional to the barrel diameter. In the two typical twin screws, there is 50% more barrel surface area available for heat transfer for every unit volume on the 16 mm twin screw in comparison to the 24 mm extruder (See Table 1, p. 28). So a key factor for reliable scale-up from a small to a large twin screw extruder is to run adiabatically on the small extruder. Simply put, this minimizes heat transfer.
In a truly adiabatic operation, there would be no heat transfer between the extruder barrel wall and the materials being processed. In reality, this ideal is difficult to achieve, but it can be approached with the following steps:
Allow the extruder to heat up to a temperature that matches the desired temperature profile.
Begin extrusion and monitor product temperatures either within the barrel or at the discharge of the extruder.
If these are very different from the actual barrel temperatures, align the barrel to match the product. A word of caution: product thermocouples embedded in the barrel wall are greatly influenced by the temperature of the metal itself. I have seen many operators try to reduce indicated processing temperatures in a twin-screw extruder by cooling the barrel. They see an apparent reduction in the product temperature, only to find that there is no difference in the actual product temperature at the discharge from the extruder. That is a process that will not scale-up reliably.
If product temperatures are still outside the desired limits, make use of the several degrees of freedom in a twin screw extruder to modify processing parameters such as screw speed and feed rate. For example, at a fixed feed rate, increasing screw speed will increase product specific energy and temperature because of increased shear rate. Alternatively, at a fixed screw speed, increasing feed rate will reduce product specific energy and temperature because of reduced residence time.
In the extreme, if these measures do not work, review step changes such as the screw configuration or dosing positions for the different ingredients.
Application of PAT Tools
On-line instrumented techniques used for monitoring key process parameters are replacing traditional methods of sampling and testing for quality control. 4-5 Ninety percent of the defined tests can be accomplished using near infrared (NIR) spectrometry. When linked to continuous granulation, NIR spectrometry can continuously monitor the granulation process. Savings in time and materials are realized quickly because any deviation from the established parameters can be readily identified, allowing the process to be controlled or even halted. The small quantity of material that is in process reduces product losses.
Quality by Design
An understanding of the manufacturing process allows for the design of equipment that delivers desired quality of product. Critical quality attributes are defined and controlled, and the impact of variables (e.g., raw materials, process, equipment, personnel) is defined. Product specifications are tied to "fit for use" and not empirically derived from batch analysis. 6
A number of factors play a role in quality by design. When applied to process equipment, knowledge space is defined from our understanding of the limitations of the equipment and characteristics of the materials being processed. Design space is defined according to critical and non-critical parameters, along with experimentation needed to define the relationship between different process parameters. Even using DOE techniques, a large number of experiments are required to define the design space based on the effects of different process parameters on product quality attributes.
In a batch method, there is always a compromise between experimenting on a small mixer-using less material and shorter cycles-or on a larger mixer where scale-up risks are minimized. In either case, generating over 200 samples, as required for a full evaluation, can take a long time.
Controlled space defines the operating window within which all critical process parameters can be controlled to deliver the required product quality attributes.
The need for multiple experiments in which several process parameters are varied places an enormous burden on the development group. In a batch method, there is always a compromise between experimenting on a small mixer-using less material and shorter cycles-or on a larger mixer where scale-up risks are minimized. In either case, generating over 200 samples, as required for a full evaluation, can take a long time.
By comparison, a continuous process with individual feed streams allows rapid formulation changes and minimum sample size. Material used and experimental time required can be significantly reduced. The advantages become clear when comparing outputs and cost of producing product and process samples (See Table 2, p. 30). �
Swanborough is the pharmaceutical leader at Thermo Fisher Scientific, Staffordshire, U.K. For more information call +44(0)1785-825200 or email firstname.lastname@example.org.
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