Regulatory support...economic pressures... the complexity of protein-based products. As we detailed in the previous installment in this series, (

, May 2008) all of those trends are coming together to bring Quality by Design (QbD) to biotech companies. Because QbD envisions designing-in product and performance characteristics from the first rather than deriving them through testing after the fact, it opens the way to a risk-based approach to quality. The key to ensuring an acceptably low risk of failing to achieve the desired clinical attributes lies in determining the Design Space, defined in ICH Q8 as "the multi-dimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality."

But as we also pointed out previously, one of the biggest obstacles to adopting QbD is the industry's inconsistent understanding and application of the tools and methods that are essential for accurately determining Design Space. In this article, we show how those tools are typically used in describing the relationships between the manufacturing process inputs and the critical quality attributes (CQAs). Used correctly, these tools help make it possible to map the multidimensional Design Space in which quality is ensured, and to carve out an efficient operating space in the center of that Design Space where process robustness resides. 

The CQAs are the desired outputs of the manufacturing process. In mapping out Design Space, the goal is to understand the relative impact on CQAs of input variables—process steps, process parameters, and raw materials. For example, in a large-scale mammalian cell culture bioreactor, there are any number of potential CQAs, which could include buffer components, media components, supplements (e.g., nutrients), dissolved gases, etc. One surrogate measure for these CQAs in a biologic operation is yield, and the case study detailed below focuses on this surrogate measure. The Design Space encompasses numerous permutations of the input variables in relation to each other that still produce the desired outputs. In other words, the goal is to achieve a profound and comprehensive understanding of the process and then carefully monitor and control those critical elements.  

In a traditional approach to process understanding, every parameter could potentially receive the same degree of scrutiny. However, given all the possible permutations of process steps, process parameters, and raw material components, such an approach is highly inefficient and sometimes impossible to achieve. Although manufacturers using one-factor-at-a-time analysis are able to produce an in-specification product by locking down individual process parameters, such success is usually short-lived. Differences in raw material batches and drift in other parameters can soon bring new problems. By contrast, QbD's risk-based approach seeks to determine the critical parameters and their combinations and control for them in a flexible manner in the Design Space.    

APPLYING THE STATISTICAL TOOLS TO HISTORICAL DATA      

The first step in achieving process understanding is to make sure that you have gathered in one place all of the historical data about the development of the product. Ideally, a database and all development reports to date will already exist. But if the data are lacking or spotty, it's necessary to compile the data before trying to map the Design Space. Once all of the historical data is on hand, you can then apply and sequence the appropriate tools. 

It's important to acknowledge that the amount of data available in a development setting is often limited. Therefore, the ability to conduct statistical analysis is affected and a modified approach to analysis may be required. However, as a product moves into manufacturing and production runs mount, the amount of data available for statistical analysis increases and improves one's understanding of the process and one's ability to detect CQAs and understand how they interact. In the example below, we applied QbD tools to a manufacturing process to solve a problem with bulk substance yield at a company we will refer to as PharmaCo. The product being produced was a monoclonal antibody produced in a standard mammalian cell expression system in a bioreactor. 

 First, the historical data on yield were plotted on a run chart in order to graphically represent the batch-to-batch variability and shift in output over time. The chart depicted a sequence of batches, at different scales, with the standardized yield in grams

on the vertical (y) axis and the time series, represented by the batch numbers, on the horizontal (x) axis (as in Figure 1, a control chart that has the run chart as its basis). In the underlying run chart, we see a few indications of atypical behavior. First, there is a shift in the average yield from about 900 grams to about 700 grams. Something, unknown to this point, has happened in the process to cause this shift. We also see instances of outliers—or points that breached the lower control limit (LCL). These points also point to some special cause. 

Figure 1.  A control chart that plots batch-to-batch variability in yield. 

Control charts add the control limits of the process to support a wider range of analytical techniques. The control limits, as in Figure 1, are statistically derived based on the data. An analysis of the control chart showed that over time a significant shift in the process occurred that resulted in a drop in yield of 18%. Moreover, the chart shows a high number of out-of-control points, including three extreme outliers that occurred three consecutive times, which were traced to a single raw material lot. In other words, the process is not in "statistical control." Further, patterns that the chart uncovers—in this case, problems with a raw material lot, among other things—may offer some clues to the source of the out-of-control variations in the process. 

PharmaCo's manufacturing personnel acknowledged the three extreme outliers in the data points but were unable to provide an explanation of them because those three batches had been produced in precisely the same fashion as the three preceding batches. This is not an uncommon situation, but it is telling that there are elements of the process that are not well understood. In the language of QbD, there is a lack of process understanding.  

 To help understand the sources of the variation indicated by the control chart, the available data were subjected to bivariate analysis in which pairs of variables extracted from the historical data were examined and depicted on a scatter plot. In PharmaCo's case, bivariate analysis might plot yield against raw material lots or against process parameters to find correlations. Figure 2 plots the yield of the PharmaCo fermentation process against one of the key raw material parameters and shows that this process parameter correlates strongly with yield. At this point, however, this correlation does not imply cause and effect; further study is warranted. In fact, bivariate analysis conducted over a number of pairs of variables typically finds only weak correlations, not a strong correlation between two variables that might point to a magic bullet to solve the problem.  

Figure 2.  A scatter plot of a bivariate analysis comparing a key raw material parameter to yield.

Of course, it would be possible to create a bivariate analysis for every conceivable pair of parameters, but it would be extremely inefficient to do so. Also, this method cannot uncover interaction effects, which could be important. Thus, bivariate analysis can be used as a prioritization step to reduce the number of variables for further study. 

. Having identified several potentially critical variables through bivariate analysis, we applied multivariate analysis to look simultaneously at many variables that drive yields.  

Multivariate analysis can be done through many commercially available software packages. The goal is to create statistically valid models that describe the behavior of output variables (Ys) from input variables (Xs). A word of caution: these methods require some advanced statistical training to produce valid results.  

In this case, multivariate analysis of historical data revealed three variables that accounted for 76% of the variability in yield. Further, the analysis showed that all three variables were related to raw materials.  

Figure 3.  Multivariate analysis of three variables.

Figure 3 shows the results of the multivariate analysis. A perfect model, which would account for 100% of the variability in yield, would show all of the data falling on the solid diagonal line.  

BEYOND PURELY HISTORICAL DATA: DESIGN OF EXPERIMENTS 

All of the previous steps focus on historical data. The next step—Design of Experiments (DoE)—uses the results of that historical analysis as a foundation on which to begin building experiments that will further refine understanding of the process. However, the three important variables uncovered by the multivariate analysis are not the end of the story. Before embarking on a DoE study, you should make sure that you haven't overlooked other variables that might be important.  

For example, in biopharmaceutical manufacturing, variables that are typically important include mixing time, temperature, and the size of the bioreactor. Any such variable that you know to be important in a particular case should be included in the DoE study. In addition, you should solicit the input of personnel who are thoroughly familiar with the process and the product. For example, it might emerge from these conversations that certain variables known to be important were kept at the same setting for all batches. As a result, those potential variables might not have shown up as important in the previous analysis. Nevertheless, they should be included in the statistically designed experiments.  

Once you've determined all of the relevant variables to be included, you then design experiments that are scaled correctly, balanced, orthogonal, and correctly sized. You must also have a statistically valid sampling plan to make sure you're making the right number of measurements in each batch. Then you can carefully execute the experimental trials.  

The DoE study must include any parameters that were found to be important as a result of the bivariate and multivariate analyses. It should also include other variables that are found to be important in similar processes. Depending on the number of variables to be studied and the amount of variability one might expect to see in the response variables (CQAs), the DoE can be designed and sized. 

For PharmaCo, this DoE might include batches representing different levels of raw material properties: RM1, RM2, and RM3. Since we are referring to only three variables, these could be arranged in a central composite design consisting of as few as 16 trials. 

Using widely available statistical software, you can then perform multiple regression analysis on the results of the DoE to arrive at estimates of the effects of variables on the CQAs, their interactions, and their statistical significance. The model can then be refined manually based on such diagnostics as risk probability and others. The final regression model for yield should then have an excellent fit to the data—indicating that the model explains nearly all of the variation in the desired outcome. You can then produce a multidimensional picture of the Design Space (Figure 4).  

Figure 4.  A contour profiler matrix plot.

This "contour profiler matrix plot" was created using the optimum settings for each of the three significant variables in the PharmaCo example. This tool brings together three-dimensional response surface plots, each of which was originally created in the modeling software. The X and Y axes are made up of the DoE variables, and the Z axis (the contour curves) represent yield (the response variable). In the white regions, yield is in specification, and in the shaded regions it falls outside specification. 

Each of the cells is a three-dimensional plot in which the Y variable is to the left of the cell, the X variable is below the cell, and the Z variable is the measured response, yield. For example, the top left cell has raw material attribute 1 as the Y axis and raw material attribute 2 as the X axis; the surface depicted in the plot represents yield.  

The goal is to find an available Design Space in which the variables can produce in-specification yield. That is: what values for the variables offer the optimum path to achieving the desired yield? The "contour matrix profiler" shows the areas (in white) in which you can operate successfully and those (shaded) in which you cannot. Those white areas are the Design Space for PharmaCo's fermentation process. In that Design Space, one can identify an area in the center as the operating space, in which the process or outcome will be in control. By choosing this "sweet spot" for yield, and again for the other CQAs as the operating space, they can be sure that the process won't drift into inoperable regions.  

THE ADVANTAGES OF UNDERSTANDING DESIGN SPACE

Successfully defining the Design Space means you have achieved a full understanding of the various permutations of input variables and process parameters that ensure an in-specification product. As a result, you gain far more flexibility in changing process parameters and other variables. For example, when raw material batches vary, PharmaCo can make the necessary adjustments in the manufacturing process to compensate for the effects of differing properties and be confident that the resulting product will achieve the desired results.  

Despite the significant operational and business benefits that the FDA's QbD initiative offers, only a small number of organizations know how to take full advantage of it. Many continue to use one-variable-at-a-time analysis, even though critical pro- cesses may depend on the complex interactions of several variables. The result: poorly understood processes, an inability to demonstrate adequate control, costly delays in development, and many processes that are neither robust nor reliable. But with carefully structured data analysis through statistical tools, biotech organizations can achieve the robust and reliable processes required to streamline scale-up, technology transfer, and validation, and produce high-quality biologic products. At a time when the pharmaceutical industry is under intense pressure to make safe products and reduce costs, these tools and approaches offer tremendous value. 

As illustrated in the PharmaCo example, it's never too late to apply these statistical tools to a process. In fact, it's a straightforward process and can be accomplished in a relatively short period of time. It offers significant enhancement to process understanding, improves scientific rigor, and typically results in significantly enhanced qualitative and quantitative performance as well as cost savings. 

, is senior vice president, both at  Tunnell Consulting, King of Prussia, PA, 610.337.0820, 

 1. International Conference on Harmonization. Q8, Pharmaceutical development. Geneva, Switzerland; 2005. 

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Quality by Design and Design Space can be used by companies to enhance process understanding, improve scientific rigor, and enhanced qualitative and quantative performance, as well as cost savings.

Coming to a Biotech Near You: Quality by Design Part 2: Design Space in Development and Manufacturing

The US orphan drug (OD) program has demonstrated that economic incentives and regulatory flexibility can spur development of treatments for small patient populations. Since enactment of the Orphan Drug Act (ODA) in 1983, the US Food and Drug Administration has approved more than 300 medicines that have been used worldwide by some 12 million patients with rare conditions. However, there are some 6,000 to 8,000 rare diseases, so "we still have a very long way to go," commented Janet Woodcock, director of the Center for Drug Evaluation and Research, at a May conference commemorating ODA's 25th anniversary, sponsored by the Drug Information Association. Woodcock noted that genomic discoveries can expand understanding of the science behind many serious conditions, and personalized medicine promises an increased treatment effect and decreased side effects for innovative medicines. At the same time, high prices for many orphan therapies create challenges for insurance coverage and patient access. 

Before the ODA, biomedical companies seldom invested in products unlikely to make a profit, noted Abby Meyers, prime mover behind ODA and founder of the National Organization for Rare Disorders (NORD). That changed with legislation sponsored by Rep. Henry Waxman (D-CA) offering powerful economic incentives for OD development. The most important is seven years of exclusivity for an approved orphan product; this blocks other companies from selling the same drug to treat the same rare disease. In addition, there is a 50% tax credit for clinical study costs, grants to support clinical research, and FDA protocol assistance, all of which are particularly important for small companies. Further legislation in 1985 added biologics to the program and more clearly defined ODs as treatments for diseases affecting fewer than 200,000 people in the US. 

The incentives for industry have generated some controversy. Certain orphan therapies such as human growth hormone became immensely profitable, raising questions about exclusivity and "salami slicing" by manufacturers seeking orphan status for a very narrow indication with an eye to promoting the drug to broader populations. Patient advocates helped fight off changes and kept the program intact. 

The ODA gave FDA the job of defining orphan diseases and proposed treatments. During the last 25 years, FDA's Office of Orphan Products Development (OOPD) has received 2,622 requests for OD designation and has awarded that status to 1,850 products. Of those, 326 ODs have been approved, and 41 of those approvals were supported by FDA grants. OOPD has awarded more than 400 grants (of 1,500 applications); 15–20 new grants are made each year, and some 70 grants are active at any one time. 

In addition to exclusivity, sponsors may gain a 50% tax credit for clinical research expenses that can apply to past taxes or be carried forward up to 20 years. Designated ODs also are exempt from the user fee for filing a new drug application, which currently runs over $1 million.  

The US program also has inspired similar strategies around the world, which were discussed at the 2008 meeting of the International Conference on Rare Diseases and Orphan Drugs  that also took place in Washington in May. The European Union (EU) adopted an OD program in 2000, which has resulted in 28 product approvals. Now, FDA and the European Medicines Agency (EMEA) seek to harmonize relevant regulatory processes. The two authorities have developed a joint application for OD designation. Both regions already had similar approaches and benefited from political support for "trans-Atlantic administrative simplification" strategies, commented Kerstin Westermark, chair of EMEA's Committee for Orphan Medicinal Products (COMP).  

The common application may simplify the designation process, but each regulatory agency still carries out its own assessment and follows policies that differ on some key points. EMEA requires manufacturers to show that a new OD will have significant benefit over any available alternative therapy. And there is variation in standards for determining the prevalence threshold for an orphan disease: it can affect only 200,000 patients in the US but the EU uses a formula that sets the threshold at about 240,000 to reflect changing population levels.  

Further harmonization may involve developing a common annual report on OD development that aims to reduce the paperwork burden on small companies. There is a desire for common guidelines on OD designation and terminology and similar standards for clinical data requirements in designation applications.  

In both the US and EU, separate offices review the resulting market application for an OD. At the FDA, there are differences among new drug review divisions in how much leeway they grant manufacturers in modifying clinical testing to reflect small populations available for clinical trials. OOPD doesn't "meddle" in safety and efficacy assessments, says OOPD director Timothy Cote, but his office often provides input requested by review divisions.  

In addition to research and regulatory issues, formulation and production difficulties can stymie OD development. Many orphan treatments involve complex biotech manufacturing systems that can be difficult to scale up or move to new facilities. The FDA recently blocked Genzyme's plans to shift production of its treatment for Pompe disease to a larger facility, a decision illustrating that even life-saving ODs are not exempt from quality standards. The agency's concern was that Myozyme (alglucosidase alfa) from Genzyme's 2000-L facility (in Allston, MA) exhibited differences in the carbohydrate structures of the molecule compared to batches from its 160-L plant (in Framingham, MA).  

The FDA requested data from larger clinical trials to ensure that the differences do not affect product quality, and Genzyme filed a new biologics license application with additional clinical data. But the delay will cost Genzyme some $50 million in lost sales plus the cost of providing Myozyme for free to some patients pending scale-up to the larger plant. The silver lining for Genzyme and other biotech manufacturers is that the FDA's demand for additional clinical data to support a manufacturing change illustrates the hurdles that will face companies seeking market approval for follow-on biotech therapies in the future. 

The Genzyme scale-up problem also points to the need to provide researchers and small companies with assistance in product formulation and clinical supply production. The National Institutes of Health's  Rapid Access to Interventional Development project offers toxicology testing, scale-up production for clinical trial lots, and other services to support the development of novel treatments by academics and nonprofit organizations. Similarly, the Center for Orphan Drug Research at the University of Minnesota College of Pharmacy provides assistance in drug synthesis, formulation, pharmacometrics, and other processes to small companies that lack in-house expertise in these areas.  

To spur more OD development, advocates want to expand the OD grants program, which has been stuck at a $14-million budget level for years and suffers from eroded buying power. Congress regularly authorizes additional funding, only to see the number cut by appropriators.  

Another FDA project is to rescue abandoned orphans. The designation (1,850) and OD approval (326) numbers mean that some 1,500 drugs identified as ODs have never come to market. The FDA plans to cull through those abandoned applications to "find the diamonds hidden in all that gravel," Cote said.  

OOPD also is implementing a new priority review voucher program established by the FDA Amendments Act to spur development of drugs to treat tropical diseases that are rare in the US. A company developing a treatment for malaria, for example, could sell the voucher to another company and use the proceeds to offset development costs. It's a complicated program, but could help spur innovative approaches for tackling neglected diseases worldwide.   

's Washington editor, 7715 Rocton Avenue, Chevy Chase, MD 20815, 301.656.4634, 

The US Food and Drugs Administration is boosting its efforts for orphan drugs development.

Biotech Innovation Boosts Orphan Drug Development

Clear strategy and flawless execution are key to business success, but recent studies suggest that most companies' execution efforts fail to yield the right results: 80% of pharmaceutical companies engage in some form of strategic planning, but less than 10% achieve their strategic goals. The following are 10 key ways to help align your strategy with execution:  

Help translate the strategy into clear guidance for managing daily choices and trade-offs. Strategy gains its meaning through dialogue with the people who need to implement it, yet typical executive teams spend less than an hour per month in a strategic dialogue with project teams. 

Find the gaps that will hinder execution. The capacity to execute is what transforms a company with creative ideas into an innovator. Becoming a world-class product or service provider requires the right talent and the cross-functional leadership and processes in place for rapid execution. 

The translation of the strategy to day-to-day resource allocations defines the speed of execution. Achieving strategic targets requires regular interaction between corporate and local decision-makers to resolve conflicts between operational and strategic priorities. 

Communicate expectations constantly: up, down, and horizontally. Especially, ensure that the middle managers are on board and support the role and practices needed of project teams. Teams that cannot make decisions and take necessary action to resolve issues build frustration not products.  

5. Adjust Processes for the Right Outcomes. 

The new product development (NPD) process brings the right people together to deliver high-quality products that make the business profitable. Insist on process reviews, not just product and project reviews, and make the improvements. 

Functional silos continue to be the top reason for project slippage, as most cross-functional teams do not know how to integrate their activities. Build cross-functional project management capability by educating core teams, functional managers, and sponsors on how to make projects work. 

 Bringing diverse perspectives together can transform the impossible to achievable, but cooperation is not a natural act for entities with competing agendas, goals, and backgrounds. Build proactive processes and skills for partnering, managing conflict, and preventing emotional shutdowns. 

8. Focus on the Outcomes You Want to Generate. 

Define the NPD success indicators for your organization and stay focused on the basics:  

The customer and the market: Know how to create the most value to the end-user of the product, by uncovering unmet needs (medical and other) and learning how the care–delivery system behaves.

Cross-functional participation: Bring all the key players together (R&D, manufacturing, marketing, legal, regulatory, and quality) to define the project strategy upfront.

 Finding fundamental solutions to complex problems requires time. Agile companies create time (in the project schedule) for individual and team reflection, creative thinking, and sharing across disciplines.

 Take an honest look at mindsets and behaviors:  Are they in alignment with what you want to create?  Visions are not created in the future—they are the product of daily practices. Start with self reflection and then engage your team in defining what needs to stop, start, or continue. Collective energy channeled toward the right goals is what makes change happen. 

 is the CEO of Action for Results, Inc., Andover, MA, 800.613.9358, 

Best practices a pharmaceutical company can follow to execute their strategy sucessfully.

10 Key Ways to Align Your Strategy with Execution

This paper discusses an approach for the establishment and lifecycle management of biological and biotechnology-derived product specifications. The views presented are consistent with the concept of Quality by Design (QbD), in which critical quality attributes (CQAs) are distinguished from parameters used to monitor process consistency. Specifications and the corresponding limits as applied to CQAs serve to ensure that the product is fit for use, whereas control limits are a manufacturer's tool to monitor shifts and trends in the manufacturing process. In the current paradigm, inappropriate use of specifications creates a disincentive for continuous process understanding; more suitable approaches to analyzing development and manufacturing data are discussed. Statistical methods are presented for deriving and interpreting data against specifications that better manage the risk to the customer of receiving product with diminished safety or efficacy, as well as the risk to the manufacturer of earmarking a satisfactory lot as unacceptable. The recommendations are presented as a rational approach to setting and maintaining specifications, while recognizing that their applicability may not be suitable in all cases, given the heterogeneity of types of regulated biological and biotechnology-derived products and their unique challenges.

The purpose of this paper, which has been developed by the Working Group on Specifications and Formulations of the Pharmaceutical Research and Manufacturers of America (PhRMA) Biologics and Biotechnology Leadership Committee, is to provide guidance on a lifecycle approach to setting global specifications for biological and bio technology-derived products. In the pharmaceutical industry, specifications are legally binding criteria that a product must meet in order to be marketed. They ensure the consistency and quality of the product and help ensure that it is safe and efficacious over the shelf life of the product. Specifications evolve during product development and ideally should embrace future process capability. This is true for biological and biotechnology-derived products for which there may be limited experience at the time of regulatory filings (including the marketing application), and for which early commercial production often is necessary to gain a better understanding of product quality attributes, methods, and limits.

Part 1 of this article, published in the June issue of 

 included three sections: Terminology, Stages of the Lifecycle of a Product, and Components of a Biological and Biotechnology Product Specification. This part 2 covers the next section, Current Issues Related to the Development of Specifications. The final section, the Suggested Approach for Developing and Maintaining a Total Quality System, will be published in Part 3, in the August issue.

CURRENT ISSUES RELATED TO THE DEVELOPMENT OF SPECIFICATIONS

Several issues require resolution to implement a rational approach for setting specifications. These issues relate to the interpretation of measurements relative to limits, and the meaning and use of various types of limits.

Current strategies for developing and maintaining specifications and for interpreting data against specifications do not adequately acknowledge the risks to both the customer and the manufacturer. Although most manufacturers strive to attain an acceptable quality level for their processes and products, a common understanding of the business risks as well as the risks to the customer is necessary to achieve a 21st century vision of quality. Many practices for setting limits are arbitrary and fail to factor in these risks. Some practices strive to achieve unattainable goals, such as guaranteeing that every dosage unit will meet specifications. Other practices are overly restrictive, leading to either disincentives for collecting data or hardships on the part of the manufacturer to investigate and explain apparent out-of-specification results. Still other practices, such as adopting control limits as specifications, hinder process understanding and improvement. The following is a discussion of the practices impeding a rational approach to setting and maintaining specifications for biological and biotechnology-derived products.

Reportable value versus individual measurements

Currently, there are conflicts in thinking and in regulatory guidances regarding whether specifications are applied to individual samples, individual measurements on the same sample, or the average of multiple samples. The International Conference on Harmonization's (ICH) Q1E guideline, 

 instructs that shelf life be determined from a lower 95% confidence interval on the mean regression line from the analysis of a quality attribute over time, whereas ICH Q6A, 

Specifications: Test Procedures and Acceptance Criteria for New Drug Substances and New Drug Products: Chemical Substances

 stipulates that individual measurements be held to shelf-life specifications. The recently issued US Food and Drug Administration 

Guidance for Industry on Investigating Out-of-Specification (OOS) Results for Pharmaceutical Production

"It may be appropriate to specify in the test method that the average of these multiple assays is considered one test and represents one reportable result," and in another section,

"In cases where a series of assay results (to produce a single reportable result) are required by the test procedure and some of the individual results are OOS, some are within specification, and all are within the known variability of the method, the passing results are no more likely to represent the true value for the sample than the OOS results. For this reason, a firm should err on the side of caution and treat the reportable average of these values as an OOS result, even if that average is within specification." 

These incongruities create undue concern on the part of manufacturers and regulators alike. Although manufacturers strive to obtain reliable information on products through strategic study design and adequate replication, they are likely to be penalized when a single measurement is OOS. This situation creates a disincentive for collecting data.

 The regulator, faced with the question of whether or not to question an OOS measurement, will likely deem a satisfactory result OOS in accordance with the more restrictive of these rules. 

Quality control measures should guarantee that the product on the market conforms to the attributes of materials tested during development. In development, clinical lots are tested and described by the average of measurements made on the lot. The average may be obtained from a single dosage unit or multiple dosage units. In this way, quality attributes of the lot are mapped to clinical response in the population of patients receiving the lot.

Figure 1 illustrates this relationship for a bioassay. A sample of dosage units is taken from each of three clinical lots and then tested in a bioassay. The reportable value for each lot is the average of the measurements on these dosage units, and these reportable values are used to set the specifications for the product (represented as a normal curve). Another sample of dosage units is administered to patients in the clinical trial, and the clinical response to these is reported as the clinical outcome (here, 95% response). Post-licensure, samples are taken from a commercial lot and compared to the specifications derived during development. When the reportable value for the commercial lot falls within the specifications, it is inferred that patients receiving this lot will experience the same clinical benefit as those studied in the clinical trial (i.e., 95% response). 

Holding commercial product to the same standard as development materials will help guarantee that the clinical response will be similar to that observed during development. However, measurements on individual dosage units or individual measurements on the same dosage unit should not be held to specifications established during development, because clinical doses were not tested prior to clinical administration, and thus there is no link between measurements made on individual doses and clinical outcome. 

Using control limits as specifications will hinder a manufacturer's ability to monitor product and to make process improvements. The advantage of maintaining a separation between specifications and control limits is that the customer is adequately protected through properly defined specifications, and the manufacturer is protected from discarding product that is fit for use. This distinction promotes attention to process shifts or trends without affecting product distribution, and the manufacturer is encouraged to improve its process to reap the benefits of improved process capability. Improved process capability means fewer failures, and thus greater capacity to provide safe and effective product to the market.  

Furthermore, the vision of Quality by Design and design space cannot be achieved when control limits are used as specifications. The design space may be viewed as the region of process settings that yields acceptable product (i.e., product that meets specifications). When control limits are used as specifications, the design space reverts to the control space for the process, leaving no opportunity for process improvement. 

The ideal relationship between limits and process spaces is illustrated in Figure 2. Two response factors (X

) are studied across the knowledge space, yielding a response surface in the quality attribute (panel 1). The response surface intersects the lower specification limits (LSL, panel 2) and upper  specification limits (USL, panel 3) to yield the design space (panel 4). The control space represents a normal operating range for the factors, falling well within the design space (panel 5). Product that is manufactured within this control space will yield measurements falling within the upper and lower control limits (UCL and LCL in panel 6). As long as a control space falls within the design space, the associated control limits will fall within the specifications, yielding good process capability and the opportunity for process improvement. 

Interpreting data from annual stability studies and process validation

Another conflict exists in the guidance documents regarding establishing shelf life and interpreting individual stability results. ICH Q1E, 

 promotes the use of statistical methods for establishing the shelf life of the product. As mentioned previously, this approach uses the confidence bound on the mean regression line. However, individual stability measurements are more variable than is predicted by the confidence bound, and are thus likely to become OOS throughout shelf life. This is illustrated in Figure 3. Following ICH Q1E, a regression analysis is performed on development stability data, yielding a lower 95% confidence bound (dashed curve) that intersects a minimum requirement for potency (dotted line), predicting 24-month shelf life for the product. However, measurements from an annual stability lot are not restricted  by the confidence bound but instead by a prediction bound (dotted curve). The probability of experiencing one or more OOS measurements throughout the shelf life of the annual lot is predicted from this example assuming the annual lot degrades like the development lot, and the assay variability is the same as that observed from the development study. The proportions of measurements that are predicted to fall below the minimum requirement at 12, 18, and 24 months are 5%, 9%, and 18% respectively. The probability of experiencing one or more OOS measurements is calculated as: 

Thus, in this case, material that was judged to have 24-month shelf life by ICH Q1E has a ~30% probability of yielding an OOS result over the course of an annual stability study. 

This contradiction has fostered strategies that protect against stability OOS results that have little or negative impact on product quality. A minimalist approach to stability testing is engendered during development, because frequent measurements may result in early OOS results that shorten the apparent shelf life of the product. Often, companies file shelf life limits (which should contain stability measurements through shelf life) that are artificially wide because of adjustment for statistical multiplicity (i.e., the increased probability of failure, with multiple testing; one adjusts for multiplicity by making the interval wider), and are thus unable to capture meaningful changes in product stability. As with release testing, the current environment discourages enhanced designs to study the stability of the final product or to monitor the stability of product on the market. Stability data should be analyzed in a way that promotes data collection to achieve better product understanding.

This risk is likewise borne in validation studies. Samples are taken from multiple locations of the production process, or at multiple levels of a process parameter, and subject to specifications. As with stability testing, in which multiple samples are taken over time, validation samples are subject to excess risk of OOS due to multiplicity, which acts as a disincentive to collecting data for better process understanding.

The purpose of this paper, which has been developed by the Biologics and Biotechnology Working group on specifications of the Pharmaceutical Research and Manufacturers of America (PhRMA), is to provide guidance on a lifecycle approach to setting global specifications for biological and biotechnology-derived products.

is senior director, nonclinical statistics, at Merck Research Laboratories, Merck & Co., West Point, PA, and the corresponding author, 

is scientific director,  analytical and formulation sciences, at Amgen Inc., Thousand Oaks; 

is vice president, quality and compliance biopharmaceuticals, at Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany; 

is biotech quality technology leader, Wyeth Pharmaceuticals, Collegeville ,PA; 

is associate director, regulatory affairs biopharm CMC, at GlaxoSmithKline, Collegeville, PA, and 

 is director and team leader, biopharma operations, at Pfizer Global Manufacturing, Chesterfield, MO.

12.  International Conference on Harmonization (ICH).  Q1E, Evaluation of stability data. Geneva, Switzerland; 2003. 

13.  ICH. Q6A, Specifications for new drug substances and products. Geneva, Switzerland; 1999. 

14.  US Food and Drug Administration. Guidance for Industry: Investigating out of specification (OOS) test results for pharmaceutical production. Rockville, MD: 2006. 

15.  Foust L, Diener M, Gorko MA, Hofer J, Larner G, LeBlond D, Lewis J, Sandell D, Schofield T, Vukovinsky K, Warner E.  Overcoming disincentives to process understanding in the pharmaceutical CMC environment. Pharm Technol. 2007 Sept;31(9):108–115.

Features

A Rational Approach for Setting and Maintaining Specifications for Biological and Biotechnology–Derived Products—Part 2

Bioinformatics is one of the fastest-expanding fields in India's biotechnology sector today. There are over 200 companies in Bangalore, Hyderabad, Pune, Chennai, and Delhi that are in some way involved in bioinformatics. Large IT organizations such as Intel, IBM, and Wipro are also getting into this sector in India. Just as important, over 300 college-level institutes across India now offer degrees in biotechnology, bioinformatics, and biological sciences. These facilities are educating millions of students annually. This educational base is creating a solid future for India's bioinformatics industry. 

Much of this growth is because of the linkages between IT and biotechnology. India's software engineers are established around the globe. This breadth of expertise is, in turn, creating an engine for global biopharmaceutical growth. In fact, because of India's global presence in bioinformatics, Indian scientists and bioinformatics professionals are in high demand. 

Today, the country's talented software engineers are establishing themselves as more than just inexpensive. As a result, salary differentials today are shrinking quickly. Engineers in India today command salaries around half those in the US, compared with as little as one fourth in 2001, according to John Morrow, PhD, president of Newport Biotech (Newport, KY), and a contributing editor to the recent joint BioPlan Associates and Society for Industrial Microbiology publication, 

Advances in Biopharmaceutical Technology in India

  Thus, as India heads toward salary parity with the West, the country's bioinformatics sector is expected to provide innovative technology advantages, not just cost advantages. Morrow notes, "Innovation in bioinformatics is driving internal demands, and R&D in India will ultimately produce technologies that are unavailable in the EU and US."  

India's government recognizes the opportunities and is investing heavily. India's Department of Biotechnology, for example, has supported improvements in intellectual property protection. The government also supports the industry through financial incentives, tax benefits, and loans for R&D activities to both the public and private sectors. Manju Sharma, PhD, former director of the Department of Biotechnology, says that the biotech industry could become the "single largest sector for employment of skilled human resources in the years to come."

India was one of the first countries in the world to establish a nationwide bioinformatics network. The Department of Biotechnology (DBT) initiated a program on bioinformatics in 1986. The Biotechnology Information System Network (BTIS), a division of DBT, now connects 57 key research centers, covering the entire country. More than 100 databases for biotechnology have been developed. Two databases, namely one that covers data regarding the coconut genome and another that contains the complete genome of the white spota syndrome of shrimp, have been released for public use. In addition, several major international databases with applications for genomics and proteomics have been established under the National Jai Vigyan Mission.  

BTIS also has decided to establish five advanced research and training centers. These Centers of Excellence (COE) in Bioinformatics undertake advanced research in bioinformatics, provide PhD and postdoctoral training, develop new solutions to support the Indian Bioinformatics industry and its academic institutions in India, help in solving complex biological problems, and retain required high-end manpower.  

The core R&D strength in Indian biotechnology is its relatively well-educated and trained labor force, with a strong base of English-speaking scientists who are well versed in mathematics, physics, and chemistry. Thus, the country has the scientific skills that encompass capabilities for handling all aspects of biological information acquisition, processing, analysis, and interpretation.  

India's well-known software skills are, of course, another key advantage in global bioinformatics. For example, notes Nanda Kumar, PhD, an attorney at Reed Smith LLP (Philadelphia, PA), India's professionals have the capabilities to build tools such as biochips (particularly biochips patented outside India). Lack of government interference is another advantage. "There is freedom to operate in India and perform data analysis related to genomic sequencing, functional genomics, and proteomics fields," says Kumar. "These advantages are fueling the outsourcing of bioinformatics services to India."  

India also offers pro-pharma government policies. The New Millennium Indian Technology Leadership Initiative (NMITLI), launched by the Council of Scientific & Industrial Research (CSIR), provides financial support as grants-in-aid to institutional and industrial partners. Since 2003, it has funded 37 projects involving 65 industry partners and 175 R&D institutions with an estimated outlay of $67.5 million. Of the 37 on-going projects, four are in bioinformatics.  

A full understanding of the complex IT requirements and biotechnology processes is key for IT suppliers if they are to bridge the gap between IT and biotechnology. From the BioPlan study, some major opportunity areas for IT companies include:   

Creating mechanisms to support effective approaches for producing robust software that can be widely shared

As major pharmaceutical and genome-based biotech companies invest heavily in software, Indian IT companies have the opportunity to offer complete database solutions

Improving the content and utility of databases 

Developing better tools for data generation, capture, and annotation

Developing and improving tools and databases for comprehensive functional studies

Developing and improving tools for representing and analyzing sequence similarity and variation.

Because of the small size of most organizations in the field, the lack of sufficient educational programs, and inadequate financial rewards, commercialization of basic science discoveries into industrial products is limited.  

A major issue in India is the level of skills required to develop custom applications to integrate geographically and functionally separate databases and programs. Many biotech professionals have superficial IT knowledge, according to the study. Often, those who opt for bioinformatics have life sciences backgrounds, and have not been exposed to the IT side of bioinformatics.  

This problem, however, is not limited to India. According to Kristina Obom, PhD, associate program chair, Biotechnology, at Johns Hopkins University (Baltimore, MD), "Bioinformatics is an important part of R&D in Biotechnology. Training and education are essential in bioinformatics as it requires complex skills in computer science, bioscience, and IT. Most scientists in biotech today simply do not have all the necessary skills to be successful in the field. This lack of education tends to prolong R&D and increase costs of discovery." 

Indian IT companies have an opportunity to offer complete database solutions to major pharmaceutical and genome-based biotech companies. The IT industry can take advantage of opportunities in bioinformation through offerings in hardware, database packages, implementation and customization of software, and functional database enhancements.  

Venture capital and other funding sources are still needed, though. This may require concurrent support from government agencies, in the form of building infrastructure and funding of small- and medium-sized entrepreneurs. Foreign companies can offer knowledge Indian companies may lack, so partnering will likely become an important element in this segment's growth, as Indian companies increasingly recognize that expertise in bioinformatics is not enough without expertise in  areas such as lead generation, toxicology studies, regulatory affairs, and patenting.   

 is president and managing partner at BioPlan Associates, Inc., Rockville, MD. He is also the editor of 

 1. Sawant D. Life sciences education in India. In: Langer ES, editor. Advances in Biopharmaceutical Technology in India. Rockville, MD: BioPlan Associates Inc. and Society for Industrial Microbiology. 2008. 

The bioinformatics industry is currently one of the fastest growing fields in India's biotechnology sector. Indian IT companies have several advantages in the bioinformation field and can continue to grow their opportunities worldwide.

India's Bioinformatics Industry: An Engine for Global Growth

The BIO convention is always a fun place to explore the future of the biopharmaceutical industry. Amid the talks on industry developments, the announcements of innovative therapies, and the energy of 20,000 networking professionals, it's easy to feel optimistic about the promise of biotech. 

At this year's event, though, a different set of conversations, focused on manufacturing, is what got me excited. The editors of 

conducted two days of live podcast interviews at BIO to shed light on evolving bioprocessing trends. In those conversations, experts discussed manufacturing advances, best practices in areas like supply chain management and technology transfer, and the development of emerging technologies and emerging markets. 

What struck me through all of these conversations was how the frontiers of biotech continue to move.  

One recurring theme, for example, was the maturation of biotech manufacturing, particularly for highly platformed antibody production. This has led to a significant emphasis on efficiency, in sharp contrast to 10 years ago. "In the '90s, when the race was to bring products to market . . . you had a very difficult time justifying a project on cost-of-goods reduction," noted Peter Watler of JM Hyde Consulting. "That's no longer the case today." 

A much-cited strategy for improving efficiency is making manufacturing plants more flexible. For new facilities, Watler points out, this means modular designs that incorporate a lot of disposable equipment. For older sites, as Mike Kowolenko of Wyeth explained, it means retrofitting to convert plants into modern, multipurpose facilities. This is an important advance, given that most biotech plants are used to manufacture products that are 

 from the products for which they were designed. 

But just as antibody production is showing signs of maturity, other areas, like personalized medicine, are only emerging and present difficulties that don't arise with traditional drugs. For example, with small batches of product, it is difficult to follow the FDA's guidance on out-of-specification results, which suggests re-testing seven times. The lack of a potency test also makes ensuring efficacy very difficult. "It may conceptually take a different approach to product design, development, and clinical studies, for patient-specific drugs to be successful," said Stephan Krause of Favrille.  

Other product types, like vaccines, are re-emerging and expanding. Roger Lias of Eden Biodesign notes that there is a "vast array" of vaccine candidates in development, making process development much more challenging. "There's really no standard platform for producing many of these novel vaccines," he said. "They all need different production processes." 

Meanwhile, geographical frontiers are opening up. When it comes to quality, for example, be prepared for China to catch up, said Ian Sellick of Pall Corporation. "I think the perception, based on perhaps some outdated experience, is that GMP is not a great thing in China," he said. "But the reality is that the SFDA has a mandate to promote GMP culture in China." His words are backed up by experience; his company has been manufacturing equipment in China for more than 15 years. 

By conducting these live podcast interviews, we are doing our own bit of exploring, pushing out to the frontiers of publishing. Everything new brings uncertainty, however. In this case, one concern is foremost for us: audience acceptance. Based on the number of people who listened during only the first few days, the response is excellent. But we would like to know more. Is a podcast interview a useful way for you to keep up with industry developments? We encourage you to listen to a few of these short interviews (at  

biopharminternational.findpharma.com/BIOPodcasts

Pushing frontiers in a maturing biotechnology industry.

Biotech's New Frontiers

Selection and sizing of membrane flat-sheet cassette devices are critical to a successful tangential flow filtration (TFF) process. To maximize return on investment, the devices should boost product yields while offering cleaning and reuse results that provide superior membrane lifetime cost benefits. In this case, small-scale models were developed and executed to test and evaluate selected membrane devices for a protein concentration–diafiltration application, in which the current device delivered unacceptable product recovery and cleaning and reuse results. The application involved the last purification step of a recombinant bacterial protein. Characterization and optimization of operating process parameters were followed by performance verification at the pilot plant, and by full commercial-scale use of the device selected.

Optimization of tangential flow filtration (TFF) processes and membrane lifetime studies have been previously reported in detail.

 However, the application to a final purification step involving a bacterial protein for product yield and membrane lifetime enhancements requires an evaluation based on the nature of the feedstock and the purpose of the purification step. Wang, et al., proposed a 10-cycle lifetime for membranes used in a primary recovery TFF process.

 However, that TFF process was designed to handle human monoclonal antibody (HuMab) cell culture harvest material at the start of purification. In a separate review, Rathore, et al., affirmed the importance of reusing membrane filters for multiple cycles before replacing them, and these researchers provided a basis for conducting validation activities for up to 100 cycles of high-volume monoclonal antibody product.

 To improve a TFF process economically, product yield and membrane lifetime should be maximized. 

The commercial-scale process for the application in question did not previously have a reuse strategy in place, and product recovery was not maximized. Historical data for the application showed average product yields of approximately 94% with the current polyethersulfone-based (PES-based) membrane. The PES-based membrane was also too difficult to clean to the required normalized clean water permeability (NCWP) standards to implement a satisfactory reuse strategy. In the current study, selected membrane types were tested in laboratory models and their performances were assessed based on product yields and NCWP recoveries. Laboratory studies demonstrated that regenerated cellulose membranes offer better yields and better reuse potential than PES-based membranes for this application. Optimization of the operating parameters focused on product load per surface area, crossflow rate, and transmembrane pressure (TMP). The combination of selecting a new membrane type and optimizing operating parameters led to significant increases in yield. The regenerated cellulose membranes and the new process delivered up to 100% yields in the laboratory and 99% yields at the pilot-plant scale. The low protein-binding nature of the regenerated cellulose membranes also facilitated simple caustic cleaning regimens and stable NCWP values. 

In the end, laboratory-scale membrane lifetime studies of the characterized regenerated cellulose membrane processes showed sustained product yields and satisfactory performance over 30 use cycles. The development, characterization strategy, and summary of results are presented here. 

The molecular weight cut-off (MWCO) of a membrane is crucial to the retention capability of the target molecule. The recommended choice is generally a membrane with an MWCO rating of less than 1/3 the size of the molecule to recover.

 Following this rule allows the process characterization to proceed efficiently to select the best operating parameter ranges and to optimize for large-scale trials. In the current study, the existing application used a PES-based membrane with an MWCO of 8 kDa, which is greater than 1/3 the size of the molecule to recover. The new membranes tested had MWCO ratings of 1/3 or less of the size of the molecule of interest. 

To demonstrate reproducibility of the laboratory model to the commercial process, the commercial TFF process of interest was scaled down to the appropriate laboratory scale and process conditions. The product load per surface area is a critical parameter to consider when designing the proper small-scale model. Conducting experiments at various loads per surface area is important for studying membrane performance over different product-load ranges and its effects on process cycle times.  

During the initial phase of laboratory-scale process development, three types of membranes were tested to screen out the two with the lowest performance potential. The following membrane devices were tested: a PES-based 8 kD device (current); a PES-based 5 kD (new); and a regenerated cellulose–based 5 kD device (new). 

Flux (JF) is the permeate flow rate per unit surface area (in this case, in L/min.h or simply, LMH). To assess process optimization performance in regard to crossflow rates, TMPs, and their associated permeate flow rates, JF-versus-TMP curves were constructed for each membrane. Crossflow rates and TMP ranges were selected according to commercial process capabilities. To ensure performance assessments over the entire process range, the JF-versus-TMP curves were developed at both the initial and final protein concentration levels. Figures 1 and 2 show the JF-versus-TMP relationships for the three membranes types at the approximate initial and final concentration levels, respectively. 

As shown in Figures 1 and 2, the PES-based 8 kDa membranes exhibited the highest permeabilities across the crossflow rates and TMP ranges studied. Both 5 kDa membranes had lower flux profiles, with the regenerated cellulose device showing the least permeability. Flux profiles are a good predictor of process cycle times. Flux profiles should be evaluated for decreasing trends, which could signal fouling or excessive polarization across the membrane surface. In this case, when comparing both PES-based devices, researchers noted lower permeabilities with the higher concentration profiles shown in Figure 2. This fact translates into a flux decrease during the concentration process, as a result of membrane polarization or fouling. Although the regenerated cellulose membranes led to slower cycles times because of their lower permeabilities, they exhibited more stable flux profiles than did the PES-based membranes. The flux profiles were essentially the same for all evaluated crossflow rates and protein concentration levels. This fact shows that the regenerated cellulose membranes are less susceptible to membrane polarization or fouling with the protein of interest. In addition, because the PES-based 8 kDa membrane pore size is the largest of the three membranes tested, its flux is the most dependent on crossflow rates; hence, the PES-based 8 kDa membrane showed a greater variability in performance. 

Higher permeabilities could also lead to product losses through the membrane. Transmissibility refers to the permeability, or transmission, of the product component through the membrane layers. Figures 3 and 4 show the transmissibility profiles according to permeate absorbance measurements taken during the JF-versus-TMP trials. The absorbance measurements are given in milli-absorbance units (mAU). 

As the PES-based membranes exhibit binding of the protein along the membrane surface when the membrane is exposed to the protein at high TMPs, the transmissibility across the membrane increases and is noticeably higher for the PES-based 8 kDa device. The transmissibilities are even higher at the high protein concentration levels (Figure 4). Data show less transmissibility for the 5 kDa devices, particularly the regenerated cellulose 5 kDa, for which data suggest zero transmissibility. The nonpositive values were the result of the online ultraviolet (UV) meter not being zero-adjusted to provide the correct absorbance baseline for the process buffer solution; however, the trend differences are significant among the devices. The higher the transmissibility, the higher the risk of product loss across the membrane during full concentration–diafiltration processes. As the data show, PES-based 5 kDa membranes are also favorable in terms of product retention capability; however, for our application, historical data have established that PES-based devices pose cleaning and reuse challenges because of their high protein binding. This excessive polarization of PES-based membranes is discussed in detail in the following section of this article. 

Small-scale concentration–diafiltration runs were executed to test membrane performance in accordance with operating parameter data from the JF-versus-TMP tests. Based on the JF-versus-TMP trials, crossflow rates and TMP parameter values can be assessed to perform small-scale concentration–diafiltration runs. For the PES-based devices, crossflow rates of 3.0 LPM/m

 at a TMP of 20 psig were selected. For the regenerated cellulose runs, crossflow rates of 2.0 and 3.0 LPM/m

 were selected along with TMPs of 20 and 25 psig. Product load levels of 25, 34, and 51 g/m

 were initially tested to provide a range of the expected protein load to the system. When combining all these options into the experimental design, it is important to note the expected outcomes in terms of process cycle times and large-scale system capacity, because processing at 3.0 LPM/m

 at a TMP of 25 psig may not be possible at large scale if pressure drops are extreme in a large-scale skid. The size and capacity of the large-scale pump should also be considered. 

Product recovery schemes are also of critical importance when designing and conducting small-scale model experiments. The initial transfer out of concentrated bulk material from the system was the first step, as it should be for every recovery scheme. A single flush or a series of flushes with the appropriate buffer solution followed the initial transfer out of the runs. Some of the flushes were circulated for times ranging from 5 to 15 minutes at no TMP to allow sweeping of the membrane and release of additional material. Careful attention should be given not to overshoot the final target concentration (if specified) when performing the flushes. 

The cassettes used in the scale-down models had membrane areas of 50 cm

. The large-scale commercial process is based on 20 m

 total area; therefore, scale-down factors of 4,000 and 200 were used. The amount of diluted protein material was fixed to provide target product loads of 25, 34, and 51 g/m

 for each case. The benchtop TFF processes were performed with an äKTA crossflow system equipped with Unicorn software to collect and analyze process data. The quality attributes of the final product were verified by analytical methods designed to assess purity, identity, and potential byproducts such as dimers and aggregates. Verification of these attributes is important to investigate potential detrimental effects of the membranes or of the process on the nature of the final product. 

Figure 5 shows resulting trends for a concentration process with a PES-based 8 kDa membrane system. The trends show a steady decline of the process flux while exhibiting a steady rise of the permeate UV curve. The rise is particularly distinctive during the latter stages of the concentration. The process was run at a product load of 51 g/m

; however, similar trends were obtained at 34 and 25 g/m

. The mass balance showed a 2.5% loss of product through the permeate stream and a total recovery of 96%. Product recoveries for the PES-based 8 kDa systems ranged from 91.2 to 96.0%, and permeate losses ranged from 2.5 to 6.2%. Given this data, PES-based 8 kDa devices clearly are not the best option to maximize yield for this application. With flux values from 80.1 to 53.3 LMH, for the PES-based 8 kDa systems the only benefit observed was the fast processing time. However, permeate losses and decreases in flux performance were the main factors considered to reject the devices. In addition, with simple caustic regimes it was impossible to clean the soiled membranes and restore their permeability to the acceptable criteria for further reuses. 

Figure 6 shows the resulting trends for a PES-based 5 kDa concentration at a 34 g/m

 protein load. A steady flux decrease was also noted. However, the permeate absorbance profile did not show a trend indicative of product loss. The absorbance oscillated around values well below the ones observed for the PES-based 8 kDa device, indicating better retention for the PES-based 5 kDa membrane. Recoveries for this latter device ranged from 95.3 to 100%. The average flux values ranged from 34.6 to 24.2 LMH for this system, leading to slower process cycle times in comparison to the PES-based 8 kDa device; this was obviously related to the smaller pore size. 

Figures 7 and 8 show trends for regenerated cellulose 5 kDa TFF small-scale processes. Figure 7 corresponds to a concentration run at 25 g/m

 and a TMP of 20 psig. Figure 8 shows a concentration–diafiltration run at a 34 g/m

 protein load. Crossflow rate and TMP were set at 2.0 LPM/m

 and 25 psig for this system. As the figures show, the flux profiles were very stable with minor flux degradation. Permeate absorbance trends were also very stable and were contained under values leading to nondetectable protein losses. The permeate flux values ranged from 13.0 LMH to 14.5 LMH for the regenerated cellulose 5 kDa systems, leading to the longest cycle times. The stable flux profiles were a result of the low binding nature of the membranes, which led to minimal fouling. This benefit, combined with the absence of permeate product losses, outweighed that of longer cycle times for commercial implementation of this application. Provided a satisfactory cleaning and reuse result, the longer cycle time was offset by the time savings of not needing to install new cassettes for every lot processed. Table 1 shows a performance comparison summary of the three membrane devices. 

Based on the performance observed during bench-scale experimentation, the regenerated cellulose 5 kDa devices offer the best qualities and risk-benefit profile. Quality testing showed no detrimental effect on the purity and identity of the product. The regenerated cellulose membranes at the 5 kDa MWCO specification offered the best product recovery and cleaning and reuse potential; therefore, development and scale-up activities continued with a pilot-plant trial. 

Table 1. Summary of laboratory scale developmental runs for PES 8 kDa, PES 5 kDa, and regenerated cellulose 5 kDa systems 

Regenerated cellulose cassettes with 2.5 m

 membrane area were used for the pilot-plant runs. The protein load target was set at 34 g/m

. To process the available material at this product load, 10 m

Best methods to maximize product yield and membrane lifetime to enhance a tangential flow filtration process.