The characterization and validation of equipment cleanliness are key requirements for biopharmaceutical facilities to assure that the cleaning process can meet predetermined cleanability criteria consistently and reproducibly. For a multi-product facility, the cost of performing such process characterization at large-scale could be substantial. This article describes how a robust bench-scale model can be used to characterize the key operating parameters of the cleaning process. A scale-down model that evaluates the cleanability of various protein drug products on stainless steel coupons was used to explore the process performance over a wide design space. The bench-scale cleaning model is also a useful tool for comparing the cleanability of various products and for developing new cleaning cycles. 

The design space concept as introduced by the International Committee on Harmonization (ICH) for a unit operation can be defined as the linkage between the input variables or process parameters and critical quality attributes.

  Characterizing the design space involves understanding these linkages and identifying variables and ranges within which consistent quality can be achieved. For the cleaning process, the design space can be considered the interactions among various operating parameters (e.g., temperature, soilant, cleaning agent concentration, dirty hold time) and their effect on performance parameters such as residual soil levels at the end of the cleaning process. 

Drug product processing involves various process parameters that can affect the safety, quality, efficacy, and purity of the final dosage form.

  Cleaning processes also can have a direct effect on the critical quality attributes of a product by means of contaminant carryover through product contact equipment surfaces. Regulatory expectations call for biopharmaceutical companies to have a sound, documented cleaning validation approach that uses effective and consistent cleaning and keeps the carryover below the acceptable limits.

  Such an approach warrants a cleanability assessment before the introduction of a new product into the manufacturing equipment. Bench-scale cleaning studies are a useful model to evaluate the relative cleanability of new products with very low material requirements, well in advance of technology transfer to the manufacturing facility.

 Bench-scale models also provide the benefit of performing cleaning evaluations under controlled simulated conditions, thereby offering a useful tool to characterize the process design space. 

There are several elements of cleaning process characterization, control, and validation including, but not limited to, cleaning cycle, equipment, sampling techniques, analytical assays, and acceptance criteria.

  This article focuses on the characterization of the cleaning process with respect to process parameters. A bench-scale model is used to evaluate four different protein drug product formulations over a wide range of operating conditions. Both single parameter and cross interaction among different operating conditions are studied to fully characterize the design space associated with the cleaning process. Results show that temperature and cleaning agent concentration are strongly coupled, and different protein products behave differently with respect to their cleanability because the process conditions are varied. The work also provides useful insights into the development of an optimized cleaning cycle for biopharmaceuticals.  

In a typical cleaning process, cleaning agents such as alkaline and acidic reagents are used at elevated temperatures in combination with mechanical action to achieve removal of protein soilants from equipment surfaces. A combination of water and caustic acid rinses is used to achieve the desired level of cleanability needed to ensure minimal product carryover to the next product lot. A hot alkaline wash is considered the critical cleaning step in which conditions of high pH and high temperature are used to remove the protein by degrading it into smaller fragments, and to solubilize any hydrophobic residues. 

A cleaning cycle relies on two pathways for soilant removal from equipment surfaces.

  The first pathway is the physical removal commonly achieved through mechanical action resulting from the convective action of the fluid flow. The efficacy of this mechanism is governed by the soil–surface interactions and the extent of adhesion. The second mechanism uses chemical interaction between the cleaning agents and the protein soils such as protein degradation, solubilization, wetting, and emulsification to remove the soilant from the surface.

  There are various inputs to the cleaning process that affect these cleaning mechanisms and thereby the overall performance of the cleaning cycle. These key operating parameters include the temperature of the cleaning solution, the concentration of the cleaning agent, the strength of the mechanical action, and contact time with the cleaning agent. In addition, other factors such as soil type, dirty hold time (i.e., how long the soiled equipment is held before cleaning), material of construction (stainless steel, glass, Teflon), and surface finish also affect the cleanability of the equipment. 

All the experiments in this study were conducted using a bench-scale cleaning model comprising the following components:   

Reciprocating shaking water bath: A Precision Scientific model 25 shaking water bath (Precision Scientific, Winchester, VA) was used to control temperature and agitation. Oscillation frequency and amplitude were adjusted to obtain the desired linear velocity. The temperature in the water bath was maintained with an accuracy of ±1°C.

Stainless steel coupons: 6 x 3-inch stainless steel (304L) nonelectropolished coupons (Q-Lab Corp., Cleveland, OH) were used in this study. Although the equipment used in biopharmaceutical manufacturing is typically made from 316-grade stainless steel, for the purpose of these cleanability experiments, it was assumed that the grade of steel (304 versus 316) would not have a significant effect on the relative cleanability of drug products. Differences in stainless steel surface finishes were not included in the scope of this evaluation either. 

Cleaning agents: The cleaning solution used in this study was either de-ionized water or a CIP-100 solution (Steris, Mentor, OH). In addition, CIP-200 (Steris) and the alcohol solution Septihol (Steris) were used for precleaning the coupons. 

Four protein drug products (labeled as products A, B, E, and H in this article) were evaluated in these studies. Products A and B are antibodies and products E and H are protein products. These aqueous products contained different excipients and stabilizers, and were formulated at various concentrations (≥100 mg/mL).  

Stainless steel coupons were precleaned in a cycle that comprised a CIP-100 wash, CIP-200 wash, and water for injection (WFI) rinses, and dried before use. This precleaning procedure helped to cleanse any residual coating from the coupon fabrication process and also ensured that all new coupons were in the same initial condition before starting the experiment. Of each protein product, 250 μL was spotted on the coupon (with a maximum of six spots per coupon). Sufficient resolution was offered by the 250 μL to differentiate among the cleaning times of all four products in reasonable experiment duration. Each protein soil was spread into a circular spot that was 1.35 cm in diameter. Variability in spot size and shape and in the measured cleaning time was minimized by using new coupons for each evaluation. The spotted coupons were air dried at room temperature for 24 h before cleaning. It should be noted that the selection of 250 μL over a circular spot 1.35 cm in diameter might not be completely representative of the typical product monolayer observed on the manufacturing equipment. As a result, the absolute cleaning times reported in this study are not reflective of a large-scale cleaning cycle. 

Before starting the cleaning experiment, the water bath tank was filled with the appropriate cleaning solution. This volume was large enough (>1,000 times the protein load) to ensure that the performance of the cleaning process was not limited by the amount of cleaning solution. The temperature and agitation speed of the water bath were set based on the selected cleaning conditions. Based on the amplitude and frequency of the reciprocal shaking motion of the coupons, the cleaning fluid velocity relative to the product spot was computed. The product spots on the coupons were monitored visually and the total time taken by the spot to appear visually clean was taken as the cleaning time for that spot. At least three spots were studied for each of the four products to obtain an estimate of standard error in the reported cleaning time. Visual inspection is an important element of cleaning validation and offers a simple, noninvasive technique for surface cleanability assessment.

  If needed, the technique can be easily coupled with more sensitive assays (gravimetric or total organic carbon). 

Design of Experiments: Single Parameter Evaluation

The key parameters explored in this evaluation included: temperature, concentration of cleaning solution, dirty hold time, and agitation during cleaning. The first phase of this study was designed to identify the parameters that have a significant impact on the cleaning process while minimizing cross interactions among input variables. Each parameter was varied within a specified range that was preselected to be much larger than the normal operating range. One parameter was varied while others were kept constant at the baseline cleaning conditions. These baseline conditions were selected to mimic the manufacturing cleaning cycle. Table 1 shows the baseline cleaning conditions and the selected evaluation range for each parameter. 

Table 1.  List of process operating parameters and the operating ranges for large-scale and bench-scale cleaning process 

Augmented Design of Experiments: Cross-Interaction Among Parameters

After the first phase of experiment was completed, an augmented design of experiments was constructed using the JMP statistical software (SAS, Cary, NC), and additional experiments were conducted to assess the effect of variable cross interactions on the cleaning process.

  A leverage plot analysis showed that the cross-interactions were limited to two parameters: concentration and temperature of the cleaning solution. 

 The conventional approach to cleaning processes may drive us to believe that it is always better to use a cleaning solution at a higher temperature. Although this may be the case for products where solubility increases with temperature (mainly small-molecule based pharmaceuticals), protein products exhibit a  different trend. Figure 1a shows how the cleaning time changed for four products as the temperature of the 1% v/v CIP-100 solution was increased while keeping other operating parameters the same as the baseline listed in Table 1. The longest cleaning time occurred at 55°C. Interestingly, the shortest cleaning time for all products is observed at the lower temperatures. We attribute this trend to the unique behavior of proteins, for which cleaning time is a combination of two competing phenomena: the dissolution of protein soil in the cleaning solution (controlled by solubility, wetability, etc.), and protein degradation under high pH and temperature conditions though a series of chemical reactions including hydrolysis, oxidation, and de-amidation. Protein solubility is maximized in the lower temperature range (20–30°C) where protein molecules are dissolved in the aqueous solution while maintaining their structure. As the temperature increases, protein products lose their structure, start to denature, stick to the surface, and become increasingly more difficult to dissolve. The onset of denaturation is driven by the melting temperature of each product. Product H, with the lowest denaturation temperature, shows an increase in cleaning time even for a temperature of 40°C. As the temperature is increased, CIP-100 solution (pH >11) also starts to degrade the product molecules into smaller fragments.

  At an intermediate temperature of approximately 55°C, although protein degradation has started, it is the protein denaturation phenomenon that dominates, resulting in an overall increase in the cleaning time, the effect being more noticeable for antibody products (A and B).  

Figure 1.  Relationship between cleaning time and various key operating parameters: (a) temperature, (b) CIP-100 concentration, (c) dirty hold time and (d) agitation. Only one parameter was changed at a time, while others were kept constant at the baseline conditions listed in Table 1. 

As the temperature increases beyond 70 °C, the alkaline solution degrades the protein molecules more effectively into smaller fragments and also enhances protein removal from the surface. It is therefore concluded that although protein dissolution is high near ambient temperatures, a temperature of ≥70°C is needed to degrade the protein molecules into smaller fragments. Cleaning validation may seek product degradation as a requirement in addition to product removal from surfaces because smaller peptides can be considered less immunogenic than native product.

Similar experiments conducted with cleaning in de-ionized water at various temperatures also showed ambient-temperature water resulted in shorter cleaning times for all four products. Higher temperatures (≥55°C) caused protein denaturation, making the cleaning solution (water) turbid. In the absence of degradation action of the CIP-100 solution, none of the four products could be cleaned within the experimental duration of 2 h when subjected to water at 70°C. It is therefore recommended that the prerinse step, often used before the equipment hold, should be performed using ambient-temperature water to remove the majority of the protein load from the equipment.  

 The concentration of the cleaning agent also plays a critical role in governing the ability of the cleaning fluid to remove protein products from equipment surface. Although high temperatures favor denaturation (loss of secondary and tertiary structure), a higher CIP-100 concentration increases the OH¯ concentration, which results in increased degradation rates. The primary effect of alkaline agents is peptide bond hydrolysis, which results in protein molecules being broken down into smaller peptide fragments.  

Figure 2 compares the formation of such smaller peptide fragments over time for products A and H when subjected to two different CIP-100 concentrations at 70°C. As the figure shows, product degradation is much faster for 5% v/v CIP-100 than for 1% v/v solution. The hot alkaline wash achieves its cleaning action through a combination of protein degradation and enhanced dissolution. It is therefore expected that the cleaning time would decrease as the CIP-100 concentration is increased. Figure 1b shows that cleaning times, in general, are lower at higher CIP-100 concentrations.  

Figure 2.  SDS-PAGE gel to study degradation of protein products A and H during cleaning at 70 Â°C in (a) 1% v/v CIP-100 and (b) 5% CIP-100. The first lane represents molecular weight markers. The numbers on the tops represents the reaction time divided by contact time (in min) with CIP-100 solution corresponding to that sample. 

However, the trend for the change in cleaning time is also product specific. Product H, for instance, exhibited a minimal cleaning time at an intermediate concentration of 1% CIP-100 (Figure 1b). Other products, such as B and E, also show an improved performance at 1% CIP-100 compared to 2% CIP-100. Such an increase in cleaning time at higher caustic concentration can potentially be attributed to factors such as a change in fluid properties and a change in surface morphology of the deposited product affecting the fluid diffusion into the deposit. This variability highlights the product-specific nature of the cleaning performance and the importance of characterization work in determining optimum cleaning conditions. However, this trend, established in Figure 1, is specific to a cleaning temperature of 70°C. As we report later, temperature and cleaning agent concentration are strongly coupled parameters and it is necessary to understand their cross interaction to understand the complete behavior of the performance of the cleaning process. 

Another factor governing the cleanability of equipment under manufacturing conditions is dirty hold time—the duration of time the soiled equipment and equipment parts are held before subjecting them to the cleaning cycle. Cleaning validation is often performed using the worst-case scenario of maximum expected dirty hold time. We studied the effect of air-drying at ambient temperature for different hold times on the observed cleaning time for the four products. Figure 1c shows that there is a significant effect on cleanability during the early hours of hold time (<16 h) because the protein soil is not completely dried. We observed a small increment in cleaning time once the coupons have been dried for 24 h at room temperature. This also justifies the choice of 24 h as a baseline drying time for the remaining evaluations in this study. 

 One of the key operating parameters that contributes to the removal of the product soils from the equipment surface is the mechanical action generated by the cleaning fluid. The impingement action of the cleaning fluid spray ball is often used to generate this effect in automated clean-out-of-place (COP) baths. Because of the specificity of the cleaning action generated by a spray ball and its dependence on the location in the COP bath, this remains one of the most challenging operating parameters to scale down. In fact, in the scaled-down model we only aspire to mimic the worst-case scenario of lowest shear expected to be observed in the equipment holes, cavities, or corners in the COP bath with minimal fluid flow. Figure 1d shows how the cleaning times changed over four different agitation speeds of the shaking water bath.  

 Protein products tend to get more difficult to clean as their concentration increases. Generally, a rise in concentration is accompanied by an increase in product viscosity and a stronger propensity to form a thicker soilant layer on the surface. To assess the challenges posed by new high concentration formulations on the cleaning process, it is important to evaluate how the cleanability is affected by the increase in protein concentration. We studied three different products: A, B, and H. A range of product concentrations was prepared by diluting the products in their respective buffers and concentrating them through 10-K centrifugal filters. Figure 3 shows that the cleaning time can be significantly higher for these products as the concentration is increased. It is also observed that the increase in cleaning time was more significant for the difficult-to-clean antibody products (A and B) compared to protein product (H). 

Figure 3.  Dependence of cleaning time on product concentration, using baseline cleaning conditions for temperature, CIP-100 concentration, dirty hold time, and agitation. Cleaning time increases with product concentration. 

After completion of the single parameter study, an augmented design of experiments was constructed to evaluate the cross interactions between the two critical parameters: temperature and CIP-100 concentration. Additional experiments were conducted to sample the two-parameter space. JMP software was used to perform the statistical analysis using various operating parameters as effects and the cleaning time as a response. Linear regression analysis was used to fit the model to the experimental data. The model included both first order terms (temperature and concentration), and second order terms (self- and cross-interaction). 

Leverage plots were constructed to graphically view the significance of each term's effect on cleaning time. A P-value analysis shows a strong correlation between the cleaning time and the cross interaction between temperature and concentration (Figure 4). The cleaning time was modeled as a response to both first- and second-order interactions between temperature and concentration. Table 2 lists the scaled estimates for this model and provides a more relevant scale-invariant effect size. As shown in Table 2, the cross interaction between temperature and concentration has the maximum effect on cleaning time. This suggests that the effect of a temperature change on cleaning time is strongly dependent on the value of CIP-100 concentration, and vice-versa.  

Table 2.  Scaled estimates for various first- and second-order terms of the fitted model 

A similar analysis was performed for the remaining three products (B, E, and H) and the cross interaction term was consistently found to be the most significant parameter with respect to its effect on cleaning time. 

Figure 4.  Leverage plot analysis for dependence of cleaning time on the most significant term: the cross interaction between temperature (T) and concentration (C)  

Interaction Plots and Surface Response Profiles

The interaction plots offer a graphical view of any prospective interaction among the various factors. Any evidence of interaction between effects is depicted as nonparallel lines in the interaction plots. Figure 5 shows that the impact of CIP-100 concentration varies depending on the temperature of the cleaning fluid. At high temperatures, a high CIP-100 concentration shortens the cleaning time, whereas at low temperatures, improved cleaning is achieved at lower CIP-100 concentrations. Such interaction profiles as well as the process response surface may be product specific and should be established for each product during the development of the cleaning cycle.  

Articles

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A method to evaluate the relative cleanability of new products.

Bench-Scale Characterization of Cleaning Process Design Space for Biopharmaceuticals

The optimization of a large-scale chromatography operation involved a strategy combining bench and pilot scale packing stability studies, and evaluation of hardware limitations at bench and large scales.

  The effects of resin amount on column stability, hydrodynamic properties of compressible media, process resolution, and targeted recovery were evaluated at the bench scale. The process was scaled up for hardware and process performance evaluation. A robust packing procedure was developed at pilot scale to optimize system suitability, reduce the time required for packing, and improve process performance. The packing optimization provided an increased bed stability, which offered the basis to extend the number of chromatography cycles per packed column. The lifetime extension reduced resin costs per lot. The faster packing method and reduced frequency for packing reduced labor requirements for this step at large scale. 

This case study relates to protein purification through ion exchange chromatography with a start of collection based on ultraviolet (UV) absorbance and end of collection based on reaching a volumetric endpoint. This approach mitigates the potential impact of impurities that elute on the back side of the main peak, but led to higher yield losses for runs, in which peak resolution was broader because of nonoptimized packing conditions. 

Inconsistent performances of the chromatography step for the large-scale process led to high variability in process yields and purity. Therefore, basic process development techniques are required to assess and resolve the cause of variability and thereby improve and optimize the chromatography process.  

The quantity of resin required to achieve targeted compression on a packed bed is a factor that significantly affects the chromatography elution profile. Achieving targeted compression is critical to avoid headspace formation and overcompression in stainless-steel columns, in which this cannot be visually observed. Previously, the resin concentration on slurry was determined by the gravity settling (GS) method, which is a simple way to determine resin concentration as a volumetric ratio. The method calls for pouring a determined amount of homogenous slurry into graduated cylinders and allowing the resin to settle by gravity over an extended period, which is dependant on the storage solution. There are, however, certain drawbacks associated with this approach such as a long settling time before reading. The calibration resolution of the graduated cylinder and wall support effects provided misleading readings of bed volume. The resin concentration varied because of intricacies of the technique related to visual interpretation of the cylinder calibration resolution and an unstable resin settling using the previous storage solution. 

Figure 1.  Experimental bed height and predicted height  at different aspect ratios 

An alternative to the GS method is the flow consolidation (FS) method, which takes into consideration the hydrodynamic properties of compressible media, showing a linear relationship between height and flow (Figure 1). The linearity between bed height and flow can be expressed in terms easily read on a chromatography column tube (Equation 1):

 is the bed height as a function of flow velocity (u) and 

 represents the initial settled bed height at a flow velocity of 0 cm/h. 

 can be obtained as the slope constant and 

, the curve intercept from a linear regression analysis (Figure 2). The linearity of bed height to flow appears at various aspect ratios (AR) defined as the ratio between initial bed height (

) and column diameter (D). By allowing the resin to consolidate by flow, this approach provides the advantage of reducing wall support effects that lead to variability in the resin quantification and allow for a homogeneous settling with results obtained much faster than the GS method. 

For a constant cross-sectional area (AC) of the column, the volumetric concentration of the resin can be determined from Equation 2 by associating the intercept 

 from linear regression analysis with the initial amount of slurry poured in the column as follows: 

 is the slurry height poured into the column. 

This method requires using a complex and expensive setup to quantify resin, which is not ideal for a manufacturing environment because of sensitivity. 

The quantification of solids by weight through centrifugation was also developed for this application.

  After a resin sample is loaded on the top section, centrifugal force pushes the liquid through the filter into a receiver. Centrifugal speed and time are controlled to minimize variability. The amount of resin is then determined based on net mass retained in the centrifuge concentrator. The mass is then normalized against the total slurry weight poured into the concentrator defining the weight ratio 

 are the net masses of the resin (top reservoir) after centrifugation and the total slurry (top and bottom reservoirs) respectively. Although there are concentrators with volumetric marks for volume determination, this approach provided less accuracy than using an analytical scale for measuring resin amount.  

The disadvantage of this method is that a volumetric relationship must be established to adapt the model to a manufacturing environment, in which packing procedures call for volumetric amounts. In addition, the developed correlation is specific to the type of resin and its interactions with the storage media and its physical properties. 

Therefore, a relationship between volumetric and mass ratio was established to optimize the advantages of methods described earlier. The relationship was generated through a plot of volumetric ratio (GS and FC) versus mass retained (Figure 3). The data showed a region of linear relationship between volumetric and mass retained ratios. Given that the GS method was historically unstable, the centrifuge method was related to the FC method. A plot of mass retained versus volumetric ratios demonstrate a linear relationship within the tested range. 

Figure 3.  Volumetric versus mass ratio  

Using the centrifuge method for resin determination significantly reduced the packing procedure cycle time from greater than 70 hours down to 60 minutes. This method was important in the process characterization strategy because it paved the way to rapidly complete numerous packs at bench and large scales and thereby reduced the characterization timeframe and raw material consumption. 

COMPRESSIBLE MEDIA PRESSURE-FLOW CHARACTERISTICS

A small-scale study was established to understand the hydrodynamic properties to forecast effects of resin amount before consuming expensive raw materials and efforts at large-scale. This provided the advantage to predict the column performance at given conditions of resin amount, fluid properties, and flow rates at various aspect ratios. Pressure-flow studies were executed at small scale. The model was tested with resin slurry at applicable process temperatures. The experimental plan was based on the work performed by Jonathan Stickel and Alexandros Fotopoulos.

  Pressure and bed height for various aspect ratios were collected on flow-rate increments until a maximum flow rate was achieved. Bench-scale experiments on a 100-mm diameter column reached maximum compressions of approximately 20.4% average at various aspect ratios. 

The results from small-scale experiments provided a linear expression on the plot of the critical velocity (

) versus aspect ratio with slope and intercept values of 349.97 cm

 is the critical flow velocity as a function of AR given in cm/h, 

 is the gravity settled bed height given in cm, and 

 is the column inner diameter given in cm. This expression is capable to predict maximum flow rates as a function of column aspect ratio, which is dictated by the desired compression on a column (Figure 4). 

Figure 4.  Critical velocity versus aspect ratio 

The relation of aspect ratio and flow rate was embedded into the Blake-Kozeny equation below (Equation 5) to predict pressure drop as a function of flow rate.  

Finally, the  correlation  was developed from a curve fit analysis of the expression with the experimental data. The reliability of the model developed at a 100-mm scale was challenged by comparing the model with large-scale column experimental data (Figure 5) with promising results. 

Figure 5. Large-scale experimental data versus model 

Process parameters could be predicted at given conditions providing insights into the stability of the packed bed and settings to achieve optimum performance. Significant influence of the interstitial bed porosity and particle size on pressure drop (Figure 6) shows that a nonhomogeneous packed column will yield a larger pressure drop than expected from a homogeneous pack in which the interstitial porosity or void fraction is smaller. Therefore, a typical pressure drop may be an indicator of column bed deterioration. Increased interstitial porosity suggests an unstable pack because of partial motility of the beads in the column. Decreased bead size or particle diameter for unstable packs increases the pressure drop substantially at any given flow rate. 

Figure 6.  Pressure drop of packed beds of different interstitial bed porosities 

The bench-scale results confirmed headspace formation at process flow rate with previous resin compression parameter thus marking a relationship of resin amount to the pack instability and performance variability seen on large scale. To optimize the chromatography operation, it was considered to pack at higher compression to increase resin stability at process flow rates. The use of more resin rather than lowering the bed height to compress further the resin or decreasing flow rates to minimize head space was avoided to prevent resolution loss and additional changes to regulatory filing. 

The pressure-flow model predicted that incrementing compression to 1.18 (as recommended by manufacturer) will require a minimum flow velocity of 191.6 cm/h to achieve a bed height of 12 cm. For large-scale columns using flow-packing techniques, in which the resin is compressed by increasing the mobile phase velocity, this flow is not enough because of the resin expansion experienced once the flow is stopped to lower the top plate adaptor to the desired height of 12 cm, and therefore, a higher flow rate is required.

It was determined from the pressure-flow simulation data that, for an optimum packing flow (85% of 

), the required flow rate to flow-pack the resin to 1.18 compression is close to 226 cm/h. For the existing 1,000-mm column hardware, this represented a limitation because the expected pressure drop of the resin plus the pressure drop of the hardware exceeded the maximum allowable working pressure of the column. Axial compression was introduced to the packing procedure to compensate for system limitations of the flow-pack technique at higher compression. 

Packing studies were performed at large scale (1,000 mm) to understand the influence of column hardware, flow cell geometry effects, and variable resin amount on the pack stability. The studies consisted of a series of experiments that include dye and cleaning studies, and evaluation of various packing approaches.

Dye studies using phenol red revealed equipment limitations of flow distribution under the top slurry nozzle through inspection of bed cross-sectional cuts at various angles, indicating that the flow velocity under the slurry nozzle is lower than the average velocity of the sample bands (~2.5 CV) or dye injection. A low compression pack revealed low stability underneath mobile phase inlet across bed diameter. By increasing the resin amount and thus the compression, the sample band distribution was more homogeneous throughout the bed under the top slurry nozzle. 

A cleaning study evaluated the column cleaning using phenol red as a visual indicator for cleaning aptness. The packed column was buffer exchanged from packing buffer into a 0.1 g/L phenol red in packing buffer for 2 CV duration. The equilibration followed a five-hour static hold to allow diffusion toward stagnant zones at the edge of the packed bed. Once hold was completed, the bed was washed with packing buffer at a 3 CV duration followed by a 1.5 static hold and storage. These set of conditions were selected as a stress-case scenario when compared to current hold parameters. Inspection of cross-sectional cuts of the packed bed revealed no traces of phenol red. This study provided cleaning capability of the column as retention volume basis (it is not an evaluation of impurity or carryover clearance that could bind into the resin during the chromatography step). 

The large-scale column pack qualification pulse injection testing used flow rates different from the chromatography elution step flow rate. In addition, the previous pulse injection and elution phase compositions had ionic interactions with the media. The ionic interactions led to qualification results that deviated from ideal asymmetry for the resulting peak. The pulse injection and elution flow rates were changed to match that of the process and a different testing media consisting of a 0.1 M sodium chloride solution/1.0 M sodium chloride pulse injection. This combination reduced the ionic interaction effects on the elution profile. By minimizing these effects HETP and peak asymmetry values measured by this method gave a greater sensitivity to the quality of flow distribution. This procedure was used to test all column packs in this study.  

Robust packing procedures can improve process performance and increase resin lifetime.

Chromatography Optimization Strategy

The benefits of single-use manufacturing systems have been widely touted by their suppliers and by early adopters in the pharmaceutical industry. Their implementation has been shown to have many advantages: reduced initial capital investment, accelerated project execution through the use of off-the-shelf systems versus highly customized skids with a challenging support infrastructure, reduced labor for preparing equipment for re-use, and energy savings from reduced cleaning and sterilization of fixed equipment. The trade-offs are also well-documented: potential supply-chain security issues with lack of redundancy of supply and managing supplier change control, the ability to manage large volumes and high flowrates, waste management for plastics that are difficult to recycle and, of course, the fear of change itself. In this assessment of a potential technology change, we often overlook some of the underlying challenges of the fixed equipment, perhaps because as we have accepted them as the price of doing business in a current good manufacturing practices (cGMP) environment. 

For a plant manager, the elimination of a number of typical facility issues may be a good incentive to implement single-use systems. In the two years I have worked with disposables, I've learned that there are new challenges with single-use manufacturing systems, but there are also a few things I do not worry about anymore. 

For example, there is a long list of preventative maintenance items that need to be managed routinely (and sometimes in an emergency basis) with stainless-steel based facilities, including replacement of diaphragm valves, mechanical seals, and pipe gaskets. We cannot forget the associated qualification and validation of repairs that are non-routine and often unplanned.  

Stainless steel cannot be entirely eliminated in the production plant. Even with single-use systems for manufacturing, you will likely still need a stainless steel water for injection (WFI) system. This pure water source is corrosive and will deteriorate the piping (and welds) over time. The systems are periodically purged with acid to remove the corrosion and are re-inspected to confirm weld integrity on a regular basis. Although problems with WFI cannot be eliminated with the use of single-use systems, the associated issues can be isolated to your WFI and clean steam systems; your product path can be single-use tubing. The elimination of fixed equipment allows you to shrink your WFI use for equipment cleaning, reduce water sampling points, and eliminate much of your WFI distribution systems, which will lead to a simpler facility. 

The elimination of long runs of stainless process piping also reduces product hold-up in the equipment, thereby improving product recovery. A bag of product and the connecting lines between processes can be drained completely because they are easily manipulated by the operator at the end of a process step. The headaches of designing, confirming, and maintaining pipe slope through the equipment lifecycle, which is key to cleaning and sterilization performance for fixed equipment, can now become a distant memory. 

In considering single-use systems, you may face the natural resistance to change and fear of the unknown. But just as personal computers, PDAs, and other new equipment of the last few decades have changed (and hopefully improved) the way we live and work, there is a whole new world of single-use manufacturing technology that is worth exploring. Someday, you may wonder how you lived without it. 

 is vice president, technical and quality operations, Novavax, Inc., Rockville, MD, 240.268.2019, 

Columns and Departments

Single-use systems have greatly improved and simplified common manufacturing issues.

Tanks for the Memories

Over the past several years, biopharmaceuticals have taken their place among the promising therapies approved for human use. Between 2000 and 2004, for example, regulators in the US and Europe approved 75 biopharmaceuticals, including hormones, therapeutic enzymes, vaccines, antibodies, and other therapeutics. The growing importance of biotherapeutics is further evidenced by the market's estimated size: $40 billion and still growing.

Among the leading life sciences companies that have announced intentions to stake out a position in biologics is Pfizer, which is investing $237 million in a monoclonal antibody (MAb) process development and clinical-scale pilot production plant in Shanbally, Ireland, adjacent to its existing facility in Ringaskiddy. Becoming a top-tier provider of biotherapeutics is one of Pfizer's priorities. 

Currently, Pfizer ranks 19th in the world in biotherapeutics but the company has announced its intentions to be among the top five by 2015. Central to Pfizer's achieving this leadership position is the recent creation of a new research and development (R&D) division, the Biotherapeutics and Bioinnovation Center, which will advance leading edge biologic technologies and new biotherapeutics into Pfizer's biotherapeutics pipeline and product portfolio. With five biological-based products on the market and a strong, growing biotherapeutics-based pipeline, adequate capacity and capability to manufacture the new compounds will be critically important to the company's growth strategy. 

The MAb pilot-plant site at Shanbally, Ireland, before any construction activity (circa 1969). The tree still blossoms there every year. 

Designing and building a facility for the production of MAb drug substance is a major undertaking. Not only is the investment substantial, but also the various elements of facility design must be carefully tailored to accommodate processes that may still be developing. Pfizer's Shanbally project offers an excellent illustration of the complex facility design challenges and requirements dictated by the flexibility, speed, and economic value now required by a competitive business environment. 

FROM COW PASTURE TO MONOCLONAL ANTIBODIES

Under construction on a 30-acre site adjacent to the Ringaskiddy facility, and on target for completion in late 2009, Shanbally sits 10 miles from the city of Cork in what used to be a cow pasture. Pfizer chose Ireland as the site for the facility because of the favorable business climate, accessibility of an experienced biotechnology talent pool, and close proximity to the company's drug product plant in Dublin, which formulates products produced in mammalian cell culture. The adjacent Ringaskiddy plant was one of the first pharmaceutical plants established in Ireland, initially making citric acid in the 1970s, and shortly afterwards adding an active pharmaceutical ingredient (API) manufacturing facility. Today, it has expanded both its API and drug product presence in Ireland. 

Aerial view of the site under construction, from the first pour of the Pfizer small-scale facility in October 2007 to the ongoing progress of steel erection of the main production area and laboratory administration block in February 2008. 

The new site in Shanbally will provide MAb drug substance for Phase 2b and Phase 3 clinical studies and perform process validation studies, drug substance and drug product release testing, and stability program testing. Shanbally will play a pivotal role for Pfizer's network in applying MAb platform technologies and producing small-volume commercial supplies for potential therapies in oncology, chronic pain, diabetes, and autoimmune diseases. 

Because of the unpredictability of the development portfolio, two of the most important design considerations include the need for maximum operational flexibility and the speed at which changes can be made. Because site operators will be working with a number of processes in the course of a year, the ability to easily and quickly reconfigure operations and equipment is vital. These two factors affect nearly all design decisions at Shanbally—from how automation is initially configured to how buffers and media are dispensed. 

Assessing project requirements was a critical initial step that covered a sweep of key factors and design and build elements carefully mapped to Shanbally's future use. Again, speed and flexibility topped the list of requirements. Designing in the ability for rapid changeover from one product to another meant the use of less hard piping in the facility. Instead, the project team opted for the use of disposable bag technology for both buffer and media, where possible. This greatly reduced the amount of piping that needs clean in place (CIP) and steam in place (SIP) treatment, which will make changeover faster and easier. 

Single Downstream Processing Equipment Train

The relatively small production capacity needed for clinical quantities of drug substance dictated that the facility include two 2,500 L bioreactors and a single downstream processing equipment train. Much of the downstream equipment is mobile and the skids can be configured to suit differing product needs. 

Completion of cladding and roofing in September 2008.  

A key factor that influenced the plans for Shanbally was the need to achieve a design similar to the company's Phase 1 and Phase 2a pilot plant in St. Louis, MO. Because St. Louis is the focal point supporting Pfizer's biologics research and development efforts, plant similarities will permit colleagues at the two facilities to more easily share workload, improve technology transfer, and work to develop new technology as required. This will help ensure smooth technology transfer in the future from the St. Louis Pfizer Global Research & Development site to Shanbally for MAb therapeutics. 

The physical design includes a single building consisting of a manufacturing plant with a laboratory and administration block and a warehouse block configured at each end. The laboratories include a quality control (QC) laboratory and a dedicated technical services laboratory where process development work using small-scale bioreactors and purification equipment will take place. The manufacturing block has a ground floor where all utilities are located. The first floor is the main cleanroom suite where the inoculum laboratories, cell culture, harvest, purification, buffer and media prep, bulk fill, and various support activities are located. The cleanroom suite has an interstitial area over the walk-on ceiling. The third floor contains the main heating, ventilation, and air conditioning (HVAC) units. 

Final construction phase of the facility with the installation of utility equipment in October 2008. 

Another consideration was effective planning for warehousing space that would be adequate for storage needs of the raw materials used in manufacturing. Storage was assessed based on the inventory requirements for our products and benchmarks with other Pfizer sites. 

Perhaps the most significant element of the plant's design is the high degree of automation, an approach driven by the need for operational flexibility, and allowing for easy changeover on a batch-by-batch basis. The complex and sophisticated process for identifying appropriate automation elements addressed the following:  

Distributed control system (DCS) software was developed with flexibility in mind—in small operating blocks associated with unit operations rather than large recipes that would control the complete process.

The programmable logic controller (PLC) associated with specific vendor skids were an important part of the automation solution.

Definition of a user requirement specification (URS) before the software bid process, enabling a more tailored approach in matching requirements with software solutions.

An overall philosophy of seeking out value-added automation when selecting what and how to automate key functions.

Use of the global automation solutions core modules from the Pfizer Global Manufacturing IT software library. These standard units of automation code can be drawn down and used by any site, minimizing software development and testing efforts.

A multistep approach in separating the functional and detailed design specification documents into a functional specification and a detailed design specification (DDS). This enabled the process subject matter experts to develop process requirements upfront; it also allowed the automation resources to ensure the functional requirements are met in the DDS and relayed on to the software.

Conducting a thorough Pfizer quality audit of the automation integrator quality systems, enabling use of their quality system and resources. This means that the team can use supplier test methods and documentation without the need to develop project-specific documentation.

Planning ahead for postproject system support by tapping a nearby Pfizer Ireland Pharmaceuticals Operations support group for colleagues that could be assigned to key project positions and developed for ongoing support. (This group is a central automation and information technology function that supports all Pfizer manufacturing sites in Ireland.)

The Shanbally project is a complex coordination of interdependent considerations that must balance a variety of inputs: strategic considerations for Pfizer's biologics infrastructure; operational requirements needed for future clinical-scale production work; and logistical elements that center on time, space, and budgetary constraints. To meet the challenge, Pfizer assembled an approach based on best practices in facility planning, cross-functional team input, and company-wide initiatives in design and operational excellence. 

From the start, the new facility drew on multiple disciplines. The design and build process required global input from a project team of individuals with specialized backgrounds. The project leverages the know-how of specialists in technical services and production, quality and regulatory issues, laboratory operations, and engineering. Various subject matter experts from corporate support groups and operating plants in the Pfizer network are also involved to provide input on key design disciplines ranging from quality control laboratory size and design, to the specifics of downstream processing platform technology. 

This core team of 10 people will work through design construction, commissioning, and qualification, and will stay through the whole process, right up to the point where the plant is making its first batches. At every step, Pfizer needed the involvement of people who understood the project processes and what is required to deliver an operating plant. Rounding out the project team are seven key stakeholders, representing Pfizer Global Manufacturing, Pfizer Global Research & Development, Quality functions, and appropriate Pfizer manufacturing plants. 

Because the Shanbally plant will form a key component in the company's global biologics infrastructure, the design and build process had to address a number of important Pfizer-wide operational excellence initiatives. 

Among these is Right First Time (RFT), the company's program for encouraging colleagues to apply Six Sigma and other tools to the task of understanding and improving key processes—with the goal of reducing process variations, making them stable, and allowing for future optimization. 

Together, RFT and Lean manufacturing were used for assessing the facility layout. For example, the laboratory area was evaluated in depth to ensure its layout supported the test cell philosophy for efficiently carrying out future activities, requiring optimization that affected availability of utilities, sinks, storage space, and instrument location. Process equipment was designed with the use of process analytical technology (PAT) systems in mind. 

Quality risk management tools were used to assess the facility design with regard to European Union (EU) and US regulatory requirements. The team worked to ensure the plant design complies with requirements of the EU Annex 2 Guidance on Manufacture of Biological Medicinal Products for Human Use and with all relevant FDA requirements. Shanbally also will meet the requirements of the Irish Medicines Board. The project team concentrated on reviewing the 3D model to ensure it met the quality expectations for the overall design. 

Another worldwide Pfizer initiative, called the Green Buildings program, helped guide the planning of the Shanbally facility. 

The program seeks to reduce the environmental impact of business operations through the innovative design, construction, and operation of Pfizer facilities, and seeks opportunities to apply these best practices to additional company locations worldwide. Key areas scrutinized in Shanbally's design include energy performance, water conservation, renewable resources, recycling plans, waste management, indoor air quality, and innovation. 

One key tenet of the project team was the need to recognize and manage the different project phases, and never to get out of sequence with the design effort. For example, proceeding with the detailed design of a system before the plant piping and instrumentation diagram (P&ID) development has advanced enough would not be allowed because past experience shows the time and cost of rework far outweighs any delay in getting to the appropriate standard before proceeding. Benchmarking also played a role in the design. The team analyzed other biologics facilities, both within the company's manufacturing network and outside it, for key parameters such as cost, schedule, equipment size, and other parameters. 

A few other considerations in Shanbally's design process are worth mentioning, especially the fact that good design requires a project team culture oriented around solid design principles. A key principle in this case was to plan for more than simply the ongoing operation of the site. With this facility, design considerations also take construction and commissioning requirements into account. The fact that the designer will commission and qualify the systems ensures that the appropriate high- and low-point vents and system isolation breaks are built into the design, not engineered-in late, after construction is complete. Layouts that maximize construction trade access to equipment are another benefit of designers' being familiar with how construction can be made easier. 

Cover

A close-up look at Pfizer's biotherapeutics plant in Shanbally, Ireland.

Speed and Efficiency in Designing and Building a Monoclonal Antibodies Pilot Plant

"I used to feel proud that I was part of one of America's lasting manufacturing industries," an executive from a biotech company confessed recently. "Now, however, I am not so sure that's true." 

Presumably, a number of trends have influenced his outlook. First, pharmaceutical companies are outsourcing more than ever. Pfizer is just one example of a Big Pharma company that has given up being a fully integrated pharmaceutical company, or FIPCO, to become a FIPNet, a fully integrated 

. Gone are the days when they assumed they would make all drugs in-house; today, make-or-buy decisions are made strategically. Pfizer now outsources around 17% of its manufacturing, and its goal for three years from now is 30%. Similarly, Merck has announced plans to double outsourcing by 2010, to approximately 35% overall. AstraZeneca's David Smith famously went to the extreme, with his 2007 statement (later retracted) that the company would outsource all drug manufacturing in 10 years. These percentages apply mainly to small molecules, and in-house cell culture capacity is currently high, which should delay this trend in biotech. But it looms out there as a possibility. 

Second, site competition for new facilities is fierce, and tax incentives can make foreign locations appealing. Ireland has attracted big players like Wyeth and Pfizer (see "Speed and Efficiency in Designing and Building a Monoclonal Antibodies Pilot Plant"), and now Genentech and Lonza are setting up plants in Singapore.  

Third, as we all know, low-cost countries like India and China are ramping up their biotech capabilities. A recent 

 article highlighted the fact that penicillin API, once produced at many US plants, now is made exclusively in China. Sure, antibiotics are an off-patent, low-margin business, but it wasn't always that way. For my parents' generation, the broad availability of these drugs following World War II was a life-changing innovation.  

Fourth, mammalian cell culture titers are now doubling about every four years. One day, the standard six-pack of 15,000-L bioreactors may be replaced by a set of 2,000-L single-use fermenters for commercial-scale production. Presuming downstream processing catches up (see this month's supplement on purifying high titers), new plants will be smaller and require less upfront capital. That will make it easier for small companies, here and abroad, to build them. And the simpler cleaning validation offered by single-use technologies will make it cheaper and easier to run these plants, too. At some point, the heavy investments in large, stainless-steel based facilities that US companies have made (to hedge the risk of running short on capacity) may become a burden. 

Of course, it is not clear how far these trends will go, or how quickly. And indeed, managing an increasingly globalized network is just one of the issues manufacturing executives face as they plan for the future. Other important concerns include the broader issue of manufacturing economics—of which global outsourcing is just one piece—supply-chain security, decisions about investing in Quality by Design, and the ever-present puzzle of capacity planning, only made trickier by ever-increasing upstream yields.  

So what is the future of biopharmaceutical manufacturing? On March 17 at Interphex, we will explore the issues involved with an expert panel of senior executives from Genentech, Eli Lilly, and Wyeth. Join me as I ask the panel to weigh in on these challenges and try to predict what lies ahead.  

In the meantime, you can hear preview interviews at 

www.biopharminternational.com/InterphexKeynote

. I hope you'll join us for the discussion. 

At some point, will heavy investments in large, stainless-steel based facilities become a burden to US companies?

Where We'll Be in 10 Years

The use of centrifuges to remove cells in combination with depth filters to remove colloids has become widespread. High colloid concentrations and allowance for process variability can lead to large single-use filter assemblies of several hundred square meters. This study measures the performance differences and variability between multilayer depth filters of different formats and sizes using a Chinese hamster ovary (CHO) centrate challenge.

Cell debris and colloids are removed from centrifuged harvested cell culture fluid (HCCF) by depth filters to extend the capacity of the downstream sterile filters. The successful efforts to improve factory productivity by increasing bioreactor titers often leads to higher cell densities, cell lysis, and higher colloid concentrations that can lower depth filter capacity. Allowance for process variability and scaling can require large, single-use assemblies of several hundred square meters. 

Depth filters that show high capacities for this application are composed of cellulose fibers impregnated with diatomaceous earth and a polymeric amine binding resin. These depth filters are typically fed by the centrifuge and operated at a constant flow. As particle laden fluid is passed through these depth filters, the submicron colloids are removed and the depth filters show an increase in pressure drop across them. When the pressure drop reaches an operational limit, here taken as 15 psi, the filters are considered to have plugged. The amount of fluid that has passed through the filter is then considered to be the design capacity, expressed here as liters per square meter of filter area. 

This study measures the performance differences and variability between multilayer depth filters of different formats and scales using a CHO centrate challenge. Variability from other sources was minimized to see the effect of scale and format more clearly. A single 12,000-L bioreactor production-scale batch of Chinese hamster ovary (CHO) cells expressing a monoclonal antibody product was used to challenge all the filters. Use of a single lot removes lot-to-lot harvest variability so that capacity differences between filters can be seen more readily. The centrifugation of the harvest took several hours, and therefore, controls were taken to assess if differences in hold time before centrifugation affected filter capacity. 

Figure 1.  Depth filters in a) Lenticular stack, b) Pod, c) Mini capsule formats 

The different filter formats and sizes used in this study are shown in Figure 1 and Table 1. The Millistak+ A1HC filter used in this study contains multiple layers of cellulose filtration media and a cast membrane. A single manufacturing lot of filter cellulose media was used for this study. Use of a single lot removes lot-to-lot filter media variability so that capacity differences between filters of different formats and scales can be seen more readily. 

Table 1.  Test filter device formats and sizes 

  lenticular stack device (Stack) and a 1.65 m

  Pod device in parallel), allowed a more direct comparison of the different formats at comparable sizing. Large-area filters and assemblies were run in the manufacturing plant while small area filters were run in the pilot plant. Replicate 0.11 m

  Pod controls were run in both the pilot and manufacturing plants to assess any differences in performance between the two test locations. 

Test operating conditions were the same for each filter device and size to minimize operating condition variability. Each filter was flushed with 50 L/m

  of water for injection (WFI) at a flux of 100 LMH (liters per m

  per hour). This was lowered from the recommended 600 LMH because of pump limitations. HCCF centrate was pumped at a constant flux of 111 LMH, representative of manufacturing-scale operation. Feed pressure and liters filtered were recorded using a data acquisition system during processing for small-area devices and recorded manually for large-scale devices. Filtrate quality was assessed by measuring turbidity every 40 L/m

. The run was stopped when the feed pressure reached 25 psid, past the 15 psid design limit. Pooled depth filter filtrates from each test were also filtered through a 0.2 μm sterile filter. These sterile filter tests were run at a constant 10 psi feed pressure and the filtered volume recorded over time. All depth filter formats were tested simultaneously and three replicates of the test plan were completed sequentially. Replicate testing was used to determine variability in results. There were 27 separate depth-filter tests as summarized in Figure 2. 

Figure 2.  Experimental design: there were 27 separate depth-filter tests 

The pressure drop across each filter was calculated by subtracting the measured filtrate pressure from the feed pressure at each time point. This pressure drop was then divided by the constant filtrate flux of 111 LMH to calculate a hydraulic resistance as psid/LMH. Figure 3 shows the increase in filter resistance with loading for the 27 tests. 

Figure 3. Depth filter resistance profiles 

The filter resistance profiles showed a consistent shape for most filters, starting at around 0.025 psid/LMH and remaining fairly flat, then rising fairly rapidly. The initial variability is attributed to some air locking which disappears after a 0.05 psi/LMH resistance once the membrane intrusion pressure has been reached. The Mini capsule filters (Mini), however, tended to show increasing resistances at higher loading compared with other filters that tended to lie together. 

Figure 4 shows the filtrate turbidity profiles for the 27 tests. These values are much lower than the average HCCF feed turbidity of 91 NTU showing that particles are being removed by the filters. The turbidity profiles for most filtrates showed the typical shape, a slow initial rise followed by a steady value. Contributing to the lower initial values is dilution by held-up WFI used for flushing at about 10 L/m

  Stack was an outlier with higher turbidities from the outset. 

Figure 4.  Depth filtrate turbidity profiles 

Filtrate quality was also assessed by running the pooled depth filter filtrate through a 0.2 μm sterilizing filter. Sterile filter plugging is assessed by calculating the ratio of the sterile filter hydraulic resistance at each time point divided by its initial hydraulic resistance for each test. This ratio is obtained by numerically differentiating the weight versus time data.  Figure 5 shows the calculated ratio for 21 tests. Run 3 for the 0.45 m

  Stack was an outlier with rapidly increasing resistances with loading. This mirrors the higher turbidity results. The remaining resistance ratios show consistently low resistance increases. There are some sporadic spikes in the ratio attributed to accidental bumping of the scales.  

Figure 5.  Sterile filter resistance ratio profiles 

The housing was disassembled for the 0.45 m

  Stack that was an outlier on turbidity and sterile filter profiles. The top gasket seal had failed to contact the adjacent seal as shown in Figure 6. The nonintegral seal is considered a source of feed bypass and the data from this run was not considered in further variability analysis. 

Figure 6.  Lenticular stack seal failure 

The resistance profiles for the different filter formats and sizes were fit to different plugging models (see Appendix for details): gradual pore plugging, cake filtration, intermediate plugging, complete pore blockage, cake-complete combined model, second-order polynomial, and third-order polynomial. Figure 7 shows an example of the model fits for one resistance profile. The empirical polynomial models fit the data best using more adjustable parameters. Of the mechanistic models, the combined cake-complete plugging model provided the best fit of the data. This is consistent for the multilayer filter used in this study that has a cast membrane layer in series with the cellulose media. 

Table 2 shows that the combined cake-complete plugging model provided a good fit for all devices, and the magnitudes of the best-fit parameters were comparable between formats. This provides a more quantitative assessment that the resistance curves are similar for all devices and suggests that all format types plugged by similar mechanisms with similar magnitudes of feed and filter interactions. 

Filter capacity was assessed for each test by determining the L/m

  loading when the depth-filter resistance reached 0.15 psid/LMH (~15 psid). As shown in Figure 8, the depth-filter capacities were comparable and repeatable at about 400 L/m

  for all filter types with the exception of the Mini filters, which consistently showed 25% higher capacities of 500 L/m

. If the Mini filters are excluded, there is no statistical difference in capacity between the remaining devices. The Mini filter has more variability than the other devices. 

Figure 8.  Depth filter capacities at 0.15 psi/LMH resistance versus format and scale 

Figure 9 shows that the turbidities were comparable and repeatable at about 5 NTU with the exception of the Mini filters, which showed 15% lower turbidities at 4.25 NTU. The magnitude of the difference was small (<1 NTU) and its result on the downstream sterile filter capacity appears to have been negligible. It is likely this difference is because of the fluid composition averaging that occurs in slowly filling the vial for turbidity measurement using a Mini filter as opposed to somewhat instantaneous filling using larger sizes. Some effects could also be because of real differences in the devices or in measuring equipment or operator procedures. The 25% higher capacity of the Mini filters cannot be attributed to nonintegral devices by turbidity measurement. 

Figure 9.  Depth filter filtrate turbidities at 0.15 psi/LMH resistance quality versus format and size 

Batch-to-batch variability in capacity at the manufacturing scale includes variability in the HCCF centrate feed, filter lot, and operation. For this study, we minimized variability in the HCCF centrate, limiting it to hold-time differences and labeled here as sequence. We restricted filter variability to different formats, sizes, and the variability within a single lot of filter media labeled as device. Variability in operation was limited to test location. The capacity data was analyzed by ANOVA using Minitab software. The sources of variability are summarized in Table 3. 

Table 3.  Sources of capacity variability 

Data on the performance and variability of different formats.

BioPharm International-03-01-2009

Bench-Scale Characterization of Cleaning Process Design Space for Biopharmaceuticals

Chromatography Optimization Strategy

Tanks for the Memories

Speed and Efficiency in Designing and Building a Monoclonal Antibodies Pilot Plant

Where We'll Be in 10 Years

Considerations for Scaling-up Depth Filtration of Harvested Cell Culture Fluid

New Expression Systems Create Opportunities for Innovators, CMOs, and Product Developers

Biotech Therapies Face Disclosure Requirements