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New Binary Gas Integrity Test Improves Membrane Quality Assurance

BioPharm International, April 2011 Issue (PDF)

Psst. I've got a bit of gossip for you, a piece of news you won't hear on TMZ or Entertainment Tonight. Early reports seem to indicate that a huge celebrity couple, seen everywhere at all the big industry firms is headed for splitsville. That's right. That perennial couple, the so-called jewel in every pharma companies' crown—R&D—looks like they're on the outs. R seems to be leaving D. Or is it getting kicked out? An article in the Feb. 10, 2011 issue of 

 reported cuts to the research staffs at both Pfizer and GSK. Pfizer, the article said, is shutting down its Sandwich, UK, facility, thus cutting 2400 mostly research scientists. The company, according to the article, is also eliminating 1100 jobs from its Groton, CT, research facility, and is slashing research budgets from a previously stated target of $8.0 to $8.5 billion down to between $6.5 and $7.0 billion. For its part, GSK was quoted in the same article saying that it would be "externalizing parts of early-stage discovery; dismantling development in areas with low financial and scientific return." Judy Slinn, a business historian at Oxford Brooks University in UK was also quoted as saying "Pharma companies will still do development work. They won't do discovery." 

OK. But then who will do discovery? Pharma is looking to small biotechs to fuel its discovery engines. By most accounts, pharma will be shopping among later-stage biotech companies ready for development. Yet there still seems to be a significant funding gap to develop promising technologies out of the universities and into at least proof-of-concept stage; that is, there seems to be little money to develop technologies to the point that they become attractive to large pharma. 

This funding gap—the so-called Valley of Death—is nothing new. It's just that in an environment where everyone is cutting back on research, the problem of developing promising technologies seems more acute. The Biotechnology Industry Organization (BIO) is addressing the problem in what it is calling their "Big Thinking Project." BIO recently commissioned the firm headed by former NIH Director Elias Zerhouni to collect suggestions for optimal government and industry initiatives from key industry leaders. They asked for policy proposals that would support early innovation to a point where risk-averse venture investors and big pharma firms would pick up the tab for continued development. Their responses include a mix of reforms that would reduce regulatory and financial risks. 

The Obama administration is also keen on promoting innovation. In his 2011 State of the Union address, the president wasted little time pinpointing the source of America's future financial security. "The first step in winning the future is encouraging American innovation," he said. And the administration's 2012 budget backs that up with funding increases for biomedical research and FDA activities. But then it curiously sounds a counter-innovative note. The very same administration "now proposes to jettison the biotech exclusivity deal," that had previously been approved as part of the healthcare reform package, notes Jill Wechsler in Regulatory Beat this month. In shrinking the exclusivity period from 12 to 7 years, the move threatens to disincent investor support for the very innovation it otherwise hopes to stimulate. 

Interestingly, in the same State of the Union Address, the president warned of competition from overseas. "Meanwhile," he cautioned, "nations like China and India realized that with some changes of their own, they could compete in this new world. And so they started educating their children earlier and longer, with greater emphasis on math and science. They're investing in research and new technologies." Indeed they are. The Chinese government is spending a reported $2.4 billion to support drug development, while it introduces policies to promote its biotech sector, strengthen its intellectual property protection, and strengthen tax and lending policies. In other words, research, jilted by US pharma, may find solace in China's warm embrace. 

Sources: D. Cressey, Nature 470 (Feb. 10, 2011) p. 154; H. Jia, Nature Biotech. 28 (Oct. 2010), p. 990; Podcast, A Conversation with Jim Greenwood, 

From the Editor

Research and development headed for divorce.

Research and Development Headed for Divorce

Drug-substance uniformity is an important consideration for the final step in the manufacture of drug substance/active pharmaceutical ingredient. Uniformity studies are necessary to ensure that the entire contents of the batch are homogenous and that the drug substance specification sample is representative of the batch. This paper describes considerations for drug-substance uniformity, such as selection of appropriate test parameters and sample points, and approaches to establishing acceptance criteria. Additionally, operational considerations and best practices to ensure robust and consistent drug substance filtration and uniformity are described.

The final step in drug-substance (active pharmaceutical ingredient, API) manufacture is typically a 0.2 µm filtration step. This filtration step serves as a final clarification of the process pool and also as a bioburden control measure prior to the storage and further processing of drug substance (DS) to drug product (DP). There are many technical considerations to ensure a consistent and robust bulk filtration step, for example: 					

Materials of construction for the DS containers and tubing for transfers

Filter preparation to reduce extractable and leachable components 

One aspect that must not be overlooked is the DS uniformity (or homogeneity) which is discussed in this article. When designing validation studies, it is important to consider the stringent regulatory expectations for ensuring batch uniformity and integrity of drug products

The DS may be filtered into single or multiple vessels. In the former case (single agitated vessel), uniformity considerations are mixing speed and mixing time prior to taking a sample that is representative of the entire contents of the vessel. In the latter case, it is necessary to demonstrate uniformity between the DS containers. The initial concentration of product effluent from the filter may be expected to be slightly lower than the rest of the pool due to dilution with residual flush solutions in the filter pores and housing assembly, as well as due to non-specific protein or excipient adsorption to the filter. Thus, uniformity acceptance criteria should take into consideration an asymptotic increase in concentration during a reasonable initial product volume. This paper describes risk-based approaches to establish rigorous acceptance criteria and define operational parameters to ensure consistent DS uniformity.  

RELEVANT PERFORMANCE PARAMETERS FOR A DRUG-SUBSTANCE UNIFORMITY STUDY

The DS specification parameters for biological products confirm the identity, purity, potency, quality and safety of the API. The ultimate aim of a DS uniformity study is to ensure that individual DS containers are consistent with respect to all of these critical quality attributes (CQAs) or specification parameters. For the purpose of demonstrating uniformity, it is not necessary to test each of the DS specification parameters, since a subset of the parameters may be used as a surrogate for the others. Typically, the quantitative specification parameters can be used to demonstrate uniformity. Representative assays may include protein concentration by absorbance (e.g., UV280), high performance liquid chromatography (HPLC) or bioassay. Of these, the UV280 is the simplest and fastest measurement with an acceptable degree of accuracy and precision. Protein concentration also allows evaluation of possible mechanisms for introduction of non-uniformity to the DS batch (e.g., dilution due to residual flush liquid, protein adsorption to the filter material, or inherent uniformity challenges with the upstream pool). Other parameters, such as pH, osmolality, conductivity and/or purity (e.g., aggregate concentration) may also be used as measures for DS uniformity. It is important to consider the potential failure modes for uniformity when selecting performance parameters such that they are sensitive enough to pick up a lack of uniformity. For example, if dilution with residual water is a potential source for non-uniformity, pH may not be the best parameter to use because water for injection (WFI) may not significantly impact the buffering capacity of the drug substance formulation buffer. Additionally, stabilizing agents such as polysorbate may be a critical component of the DS and it may be necessary to demonstrate that the concentration of such agents is within required limits. Other factors, such as shear could impact DS product quality (particle size, aggregation, potency), and a risk assessment should be considered to identify and mitigate these potential outcomes. Validation provides evidence of sufficient uniformity such that any sample location across the fill is representative of the entire DS lot with respect to the CQAs. Validation allows batch release and stability testing to be performed on a representative sample collected from a single location within the DS fill operation. 

 Figure 1: Flow diagram of drug substance filtration. (FIGURE IS COURTESY OF THE AUTHOR)			

Another key question is when to take the samples to demonstrate uniformity and how to establish appropriate validation acceptance criteria. Typically, samples may be taken at the beginning, middle, and end of bulk filtration, and if they meet prospective acceptance criteria, the entire batch can be considered to be homogenous. The sample collection strategy is an important consideration: Does one take a point sample (i.e. directly from the filter bell), or a pool sample from the actual container? The latter is more relevant but needs to be balanced against the potential risk of contamination during sampling, and mixing considerations prior to taking the sample. Typically, the beginning sample is taken as a pool sample from the first container, as this reflects how the contents of the DS will be forward processed. If the DS lot-release and stability samples are taken as point samples at the middle of bulk filtration, the use of point samples during a uniformity study may be favorable for consistency and to minimize potential for contamination. 

There are several ways to establish acceptance criteria to demonstrate DS uniformity, which may be used in isolation or in combination, as appropriate. Prior to selecting a preferred strategy, it is important to understand how the DS will be used during the subsequent DP formulation and testing, and to ensure that the requirements for these steps are addressed in the DS approach. Several methods for DS testing, and their pros and cons, are briefly discussed below.  

Calculation based on DP specification limits

This method is based on a back calculation of the requirements for DP processing and DP specification criteria. The DS batch may be divided and used over several DP batches. In order to maintain this flexibility, it is necessary to ensure that any DS batch or part thereof used for DP processing will meet the DP specification requirements. It is necessary to take into account the smallest DP stock keeping unit (SKU), including product concentration and manufacturing volume, which will be used. The advantage of this method is that it is based on processing needs. However, it may be complicated by the existence of multiple SKUs and the desired safety margin between a passing uniformity result for DS compared to the associated specification range for DP. 

Requirements at or tighter than the DS specification limits

The criteria for DS uniformity may be established to be the same as the DS specification limits. While this is a relatively simple way of setting the uniformity process validation acceptance criteria (PVAC), it does carry the undesirable risk of failure for DP specification and uniformity limits if the sample measurements are at the DS specification limits, especially if there are stability concerns during storage. One way to reduce this risk is to set the validation acceptance criteria within the DS specification limits, thereby providing a safety margin. One should also ensure that the documented analytical method precision is able to achieve results of the reduced range. The advantage of this approach is its simplicity in applying a safety margin relative to the specification limits, which ensures robustness in meeting the process needs. The downside is that the safety margin allowance may be subjective and may not align with the method precision. 

Table I: Typical processing steps in drug substance filtration operation. (FIGURE IS COURTESY OF THE AUTHOR)			

Inclusion of analytical method variance (to account for potential measurement error)

This strategy takes into consideration all potential errors that could contribute variability to the uniformity results, such as analytical method variability and volume measurement for the product and additives, in order to ensure acceptable DS uniformity. The root mean squares of the potential errors are calculated to derive the acceptance criteria. The acceptance criteria may be established based on the analytical method variability or precision when the analytical method variance is significantly greater (more than one order of magnitude) than the process variance. It should be emphasized that the prospective method validation acceptance criteria should be used in the calculation, rather than the results from the analytical method validation exercise. One must also ensure that sound technical justification exists for how the method validation acceptance criteria are developed. The latter is typically based on a fairly small data set and could result in overly tight acceptance criteria, resulting in a failure of the uniformity study. The acceptance criteria is set so that the percentage relative standard deviation (RSD) of the sample points is less than or equal to the analytical method precision. An assessment on process impact in using this method is recommended to ensure that validation acceptance criteria are not set too wide. 

Percentage of the feed stream concentration

Another simple way to establish uniformity study validation acceptance criteria is to evaluate individual samples against a percentage of the feed material concentration. Selection of the actual percentage value could use a similar approach to that described the two previous methods. This method is based on the assumption that typically, there may be a small amount of product adsorption onto the bulk filter or dilution from flush water/buffer retained in the filter apparatus. As such, this source of variability is expected during the initial phase of the bulk filtration step. This method is applicable where the principal uniformity failure mode is based on dilution. It is fairly simple to implement, but can be problematic if the method variability is high. 

Figure 2: Concentration curves for surrogate  (0.1 M NaCl) and product filtrations. (FIGURE IS COURTESY OF THE AUTHOR)			

This approach may be used if adequate historical filtration data exists to calculate a tolerance interval and capture the expected long-term behavior of the DS fill process. Typically, the tolerance interval contains 99% of the population (coverage) with a 95% confidence limit. However, the confidence and population coverage depends on the size of the historical data set. Care should be taken in combining data from laboratory or pilot scale filtrations with full–scale or manufacturing data, as the non-recoverable volume of the systems may result in substantial differences in terms of batch uniformity. Additionally, the calculated uniformity limits should not be wider than the DS/DP specification limits. While this method is based on actual historical experience, it does require additional sampling and testing during clinical lots. 			

The objective of the equivalency approach is to test the null hypothesis of non-equivalence (non-uniformity) within a DS batch. If the null hypothesis is rejected, evidence of uniformity within a batch is demonstrated. Multiple samples are required for each sample point within the batch, and the means are calculated for each sample location. Uniformity within a batch is demonstrated when proscribed confidence intervals (typically at 90% or 95%) of the difference between the means are within the calculated acceptance criteria. The benefit of this equivalence acceptance criteria (EAC) approach is a statistically defined proof of uniformity within the allowable variability. However, this method does require a larger number of samples to be collected from each sample point in order to have sufficient statistical power to make the acceptance criteria meaningful.  

µ1, µ2, µ3 are the mean of the sample test parameter over the course of bulk filtration (e.g. protein concentration at the beginning, middle and end), and EAC is the equivalency acceptance criteria. It is beyond the scope of this paper to describe equivalence testing; References 2 and 3 provide general sources on statistical equivalence. 			

Prior to entering uniformity validation, it is necessary to have robust control of the preparation operations for the bulk filtration step. If the bulk filter is autoclaved or sterilized in place, this may result in retention of steam condensate on the filter, filter housing, and/or system piping, which in turn could result in dilution of the DS and increased variability in uniformity results. Additionally, the filter may be integrity tested prior to use (either prior to or after sterilization) or preflushed with buffer to remove potential extractables/leachables and equilibrate the filter. The buffer flush is especially important if the formulation buffer contains polysorbate or other agents which are known to bind to the filter. The buffer flush could alternatively be performed post autoclave/SIP of the filter, however, this adds complexity to the operation to ensure that the aseptic state of the equipment is not compromised during the flush. The integrity test procedure requires wetting of the filter with water. Although the majority of water is removed during the integrity test (diffusion or bubble point), there may be sufficient residual water retained in the filter. A good practice is to implement an in-process control point to ensure that the residual liquid from the integrity test flush, buffer flush, or steam condensate is reduced to an acceptable level prior to processing. This  approach could be carried out in the form of an air-purge step, a filter-drying step (in an oven),or a simple comparison of the filter weight before and after preparation steps. The allowable residual fluid in the filter can be calculated based on the uniformity requirements. Such control measures provide confidence that the potential sources of dilution are removed or minimized prior to validation.  

These controls are especially important to ensure that the DS meets specification limits in the event of reprocessing across the bulk filtration step (e.g., if the post-use integrity test fails). Table I highlights typical steps in preparation for the drug substance filtration operation. It should be noted that not all the steps shown in Table I  may be required; some steps may be eliminated depending on the need for operational simplicity, for example, by minimizing manipulations such as the air purge or buffer flush to the filter assembly post autoclave. The final decision on the number and order of steps should ensure that the potential failure modes are adequately addressed while maintaining a robust and consistent process to meet the uniformity requirements. 

In addition to the controls described above, it may be necessary to send a pre-determined product flush to drain in order to reduce dilution effects. While it is desirable to minimize this loss of product, it is imperative to ensure that yield optimization does not override the quality consideration for demonstrating the uniformity of the batch. Regarding uniformity performance parameters and acceptance criteria discussed previously, it should be noted that it is not necessary to demonstrate that the first drop emerging from the DS filter is absolutely identical/uniform with the rest of the batch as long as the lot release samples are not taken at this point. The quality consideration should take into account acceptance criteria that are scientifically justifiable and based on how the drug substance will be forward processed.  

Another important consideration with respect to the product flush is the size of the filter and filter housing. While it is appropriate to size the filter with a safety margin so that it can process batches in a robust manner and in a reasonable time frame, oversizing the filter could result in challenges from a bulk uniformity perspective. One way to address this issue is to perform a prefiltration step with a larger filter to remove particulates which may foul the 0.2 µm filter. The prefiltration can be performed as part of the preceding unit operation so as to collect a clarified feed stream for the bulk filtration step. A further variation is to perform a batch-wise bulk filtration into a single collection vessel, thereby allowing for a buffer chase to maximize product recovery and also any dilution that may be necessary to achieve the target product concentration. This option could also be adapted into a continuous mode by the use of a surge tank between the filter and collection containers to avoid dilution effects. Following adequate mixing, the product may be dispensed aseptically into appropriate collection vessels. This obviates the need for the product flush, and thus maximizes product recovery during processing. 

Other considerations to achieve uniformity at the bulk filtration step are the design and operation of the upstream feed vessel. Mixing studies should be used to establish set point and ranges for agitation speed and time to ensure thorough mixing of the contents to be filtered (e.g., top, middle and bottom of vessel). The design of the vessel is also important to ensure that dead zones and holdup volumes (both line and sample port) are minimized. This is especially important if dip tubes are present, which may be used for product introduction into the vessel and/or subsequent withdrawal for the bulk filtration step. Both examples may impact uniformity if lines are not efficiently flushed, or if hydrostatic pressure within the tube results in concentration changes over the course of the filtration. It is preferable to use separate routes for product introduction and to locate dip tubes such that they do not result in a dead zone within the tank. The vessel location and piping required to transfer the product from the feed vessel through the filter should also be evaluated to minimize holdup volume and ensure proper drainage (to account for condensate drainage following product transfer line steaming) in order to minimize the possibility of dilution. 

Characterization studies to confirm that preparation procedures are adequate and robust should be performed prior to performing uniformity studies with product. These can be performed using buffers or salt solutions with pH and/or conductivity as convenient indicators to assess uniformity through filtration. Careful assessment of the buffer used for such studies is necessary to ensure that the buffer selected is a representative model to use, and parameters such as density and viscosity which could impact the kinetics of filtration should be considered. Figure 2 shows the result from one such study where a sodium chloride solution was used to determine whether a product flush would be required. As expected, the initial samples during the filtration step have a slightly lower conductivity but this quickly stabilizes to greater than 97% of the initial concentration. The results from replicate studies are consistent, showing that the operation is reproducible. An alternative strategy would be to utilize a representative protein surrogate, if possible. 

It would be prudent to also perform a (non-GMP) engineering run with product prior to the process validation studies. This provides added assurance that the uniformity validation study acceptance criteria will be met. Figure 3 also compares the product profile with the surrogate salt filtration runs, confirming the results and conclusions drawn from the wet testing. If there is a potential for reprocessing at the bulk filtration step (e.g., as a result of failed filter integrity test post use or operational errors which may have compromised the aseptic nature of the batch), it is recommended to test the uniformity of the batch at the reprocessing step during the engineering run.  

The standard practice in industry is to perform uniformity validation during the conformance runs (also referred to as process performance qualification, and historically referred to as process validation). Future process changes also require an assessment of the validated state of the step and whether revalidation is necessary to confirm that uniformity is not affected. However, since ongoing uniformity testing is not typically performed, subtle shifts or trends in the process would not be detected. The use of the filter weight checks as inprocess controls along with robust maintenance of operational parameters provide assurance that DS uniformity is maintained. It may be desirable to perform uniformity testing on a periodic basis to provide further confirmation. The approach of using the "percentage of feed stream concentration," described above, provides a simple means to confirm uniformity on an ongoing basis. 

Features

The authors describe considerations and best practices for meeting drug substance uniformity.

Practical Considerations for Demonstrating Drug Substance Uniformity

The introduction of new technology often means a significant step forward in the performance of one subprocess, but only an incremental improvement in the full product development process. In the case of Next Generation Genomics (NGG) Technologies, a series of incremental technology advancements has brought about the ability to radically change the way bioprocesses are developed and optimized. Bioprocessing in pharmaceuticals and in industrial biotech have significantly different economic drivers, but both can realize significant economic benefit from the application of these new technologies. Pharmaceutical applications are driven foremost by the cost of development, regulatory approval, and compliance, and only secondarily by process productivity. In contrast, the primary market driver of industrial bioprocesses is productivity, particularly in commodity and biofuel applications. In this paper, we focus on the practical application to biopharmaceuticals, which need to increase emphasis on productivity of manufacturing due to the continual rise in health care costs, and the expansion of access to pharmaceuticals in developing countries. In addition, there is the potential for significant impact of NGG in the emerging FDA initiative, known as quality by design (QbD). 

For new biologics to be profitable, they must be developed in a cost-effective manner and optimized to produce the highest possible titers. For existing biologics to remain profitable, especially with the emergence of biosimilars, they must be efficiently optimized in order to improve productivity and scales, with the resultant lowering of cost–of–goods. Remarkably, most of the research currently conducted uses outdated tools and is performed generally on model cell lines that have been subjected to numerous population doubling events that, over time, induce extensive genetic polymorphisms, ultimately decreasing product quality and process stability (1).  When it comes to production of these newly developed biologics, total economic pressure is a key driver of success. Aside from the inherent complexity (structural, glycosylation, folding, stability, etc.) of the biopharmaceutical products themselves, bioprocess engineers are also faced with the intricacy of the production process itself. For each product, a cell line with sufficient production phenotypes has to be developed. Current strategies involve time consuming, labor–intensive steps,  from the introduction of the product genes to the isolation and characterization of candidate clones. Cell-line development spans several months, or in some cases, years, and involves the screening of several hundred cell clones for high productivity before a few dozen are selected as candidate production lines. The process typically lasts for up to six months for each candidate before it can enter the evaluation phase, where its efficacy and safety in animal and human subjects are determined (2). Once the process has been established and approved, follow–on improvements become very costly, each of which must address FDA's requirements for quality and safety. In the past, insufficient knowledge of the biology of the production organism and the impact of the conditions it is grown under made it difficult to maintain stabile product quality attributes when variables had changed. We believe that NGG provides a modern and comprehensive approach to address this gap. 

There is also a regulatory benefit to implementing NGG techniques. In an innovative and forward–thinking move, FDA's QbD initiative emphasizes the achievement of product quality by thorough process understanding, monitoring, and control. The approach allow manufacturers to identify critical process parameters (CPPs) and the direct effects they have on product quality. Adopting QbD principles and process analytical technology (PAT) guidelines, can help ensure an overall understanding of the bioprocess, ultimately assisting manufactures to achieve process robustness, stability and quality. PAT has been defined by FDA as a mechanism to design, analyze, and control pharmaceutical manufacturing processes through the measurement of CPPs that affect various critical quality attributes (CQA). The belief is that with more complete understanding comes the ability to not only develop products more quickly, but also to knowledgably and safely optimize products and processes downstream, while continuing to maintain higher levels of quality control than previously achievable. 

UNDERSTANDING THE BIOLOGY OF THE PRODUCTION ORGANISM

In contrast to small molecules, the production of biopharmaceuticals is a process–within–a–process, with the cultivation process supporting the metabolic production process within the cell.  In order to develop a detailed process understanding, one must understand the biology of the organism and its environment. In short, what is required is a systems biology approach, with a focus on building a solid understanding regarding the biology of the cells themselves. Ultimately, the cells are the primary production vehicles, and by following PAT guidelines and QbD principles, one can develop a clear understanding of the biology of the cells and control of the overall bioprocess. By identifying both genomic and metabolic factors that play into the production of therapeutic biologics, process engineers can identify CPPs and characterize the impact that each of the variable CPPs (i.e., media conditions, fermentation conditions, pH, temperature, dissolved oxygen content) has on titers and quality.  Process engineers can identify how well these variables can be controlled, and subsequently establish the criticality of these variables within the overall bioprocess. By understanding these CPPs and their importance in the overall bioprocess (e.g., effects on product yield, quality, and process stability), process engineers can rapidly and effectively improve production titers and process robustness. We took the approach of initially focusing on the biology of the production cells. By focusing on the genetic changes associated with the overall bioprocess, one develops a very clear understanding of how changes in CPPs influence production yields and overall product quality. Once manufacturers understand the intricacies of the overall bioprocess, one can consider bioprocess flexibility.  

For existing processes and products already in revenue–generating production, the cost of one change, even if it results in significant increases in yield and stability, can often be too high to be recuperated within the product lifecycle.  Process changes, even if they could be made technically, are rarely made because of the costs associated with requalification and validation of the overall process. Bioprocess flexibility in the recent past has been associated with increased safety risks, primarily due to our lack of understanding of the actual biology of the production cells and how it affects process quality and stability. Flexible manufacturing, implemented with a systems–biology understanding, fits well within FDA's current QbD initiative and allows for ongoing process improvement. We believe NGG is a key tool for the acquisition and rational use of this understanding. In a flexible manufacturing environment, once a design space has been defined, using NGG and other tools, manufacturers will have the flexibility to make process changes within that design space with no prior approval from FDA regulators. Manufacturers that adopt this approach will be able to regularly improve their performance and efficiencies, while maintaining higher stability and quality control, and while also reducing the costs of recertification.  

THE PRACTICAL IMPLEMENTATION OF NGG FOR ACCELERATING BIOPROCESS QBD 

One such project that the authors have been involved with is a collaborative partnership between a major pharmaceutical company and ArrayXpress, a contract genomics services company. While the specific organism and target compound are confidential, the tools, techniques and processes utilized provide a great example for demonstrating the benefits of the NGG systems biology approach. The primary objective of the project under discussion was to increase production titers of an essential target compound used in the manufacturing process of a current large revenue generating commercial product. The secondary objective was to build knowledge that will allow for faster and more efficient manufacturing of other products using the same organism/expression platform. As with all bioprocesses, the organism itself is only a single variable influencing productivity, with many environmentally tunable variables making up the remainder. We have CPPs that influence production, but due to prior technology limitations, the manufacturing engineers did not know their full impact on the metabolic processes and production efficiencies of the cells. Therefore, these parameters had previously simply been lumped together as an unknown called "process variability" or "biological variability", and as such, their manufacturing was completely at the mercy of the process itself, with limited process stability and dramatic product titer variability.  

By bringing together the cells and the CPPs in a systems model, we can now see the entire equation. The cells are the primary production machinery; therefore our approach was to evaluate the physiological condition and the state of the cells during the various media and fermentation development stages. We first generated a working hypotheses by developing a fishbone diagram that showed all the confirmed and putative CPPs associated with the overall target compound production process (see Figure 1). This allowed for the identification of critical areas to be characterized in more detail, which was subsequently experimentally tested.  

Figure 1: Fishbone diagram showing all the confirmed and putative CPPs associated with the overall target compound production process. 			

Our approach was to design highly focused and statistically sound microarray experiments with complementing standard analytical chemistry tests. We wish to emphasize the importance of having a very well thought out experimental design and analysis strategy prior to project initiation. This approach made it possible to identify key genes and their associated molecular pathways that were differentially affected due to changes of various CPPs in the overall production process. The use of DNA microarrays provides a detailed qualitative snapshot of the state of the transcriptome at the time of sampling, somewhat like a molecular fingerprint, that can reveal subtle process variations in great detail. This approach is especially useful in time course experiments like the ones we faced, to determine whole transcriptome changes associated with different CPPs, monitored across different growth phases (different time points) of the cells during the media and fermentation optimization stages.  

Strong bioinformatics, both in statistical design and data analysis and mining, are the next key to success. A particularly important aspect of statistical inference in high throughput problems, such as microarray experiments, is the assessment of statistical significance exhibited by the data in the presence of a tremendous multiplicity of hypotheses.  A single experiment can involve tens of thousands of hypothesis tests. This assessment requires efficient estimation of experimental error and careful control of false discovery rates. We applied two interconnected analysis–of–variance models: A normalization model that accounts for experiment–wide systematic effects that could bias inferences made from the data on individual genes, and a gene model that is fit to the normalized data from each gene, allowing inferences to be made using separate estimates of variability. Expression differences are then parameterized as factorial effects in linear mixed effects models appropriate to the experimental design.  These effects can be estimated efficiently using statistical softwaresuch as JMP Genomics or SAS PROC MIXED. Resulting least square estimates are then mapped onto their associated metabolic pathways using KEGG metabolic pathway maps (www.genome.jp/kegg/pathway.html) in combination with proprietary software mapping tools (3).  

The ability to map differentially expressed genes onto their associated biochemical pathways provides the opportunity to "zoom in" on each of the metabolic pathways associated with protein production. Key metabolites that are either depleted or produced are relatively easy to identify, but true process understanding comes from identifying how the compounds are used in the metabolic machinery.  Amino acids, for example, could be depleted by translation, interconversion to other amino acids, or detoxification by the cell.  Each of these routes has dramatically different impacts on cell health and productivity. With the application of NGG techniques you do not have to wait until the end of the project to begin seeing results. Each individual experiment contributes to the "systems" knowledge but in the short run provides specific information on variables that can be tuned for performance. Over the past three years we have completed numerous microarray experiments as part of our primary media and fermentation optimization objectives.  A few examples will be highlighted here that will demonstrate the power of microarray technology to improve bioprocess stability and production yields as part of a larger NGG initiative.  

In the manufacturing process of the target compound of interest, the original growth medium components were not well defined. As a result, different medium lots varied dramatically in protein yield and product titers. One of the primary objectives was to develop a chemically defined medium that would yield consistent titers. In our experiments, we evaluated whether stress response mechanisms of the production cells caused a reduction in titer during phase transition, and how media and fermentation conditions impacted these stress responses. We carefully designed time course experiments to cover transition through growth phases with trial versions of different defined media. Complimentary to this, we completed analytical chemistry tests to assign putative roles to transcription regulators that might be involved in stress response. By ultimately correlating differentially expressed genes of sigma factors with their associated biochemical pathways, we were able to optimize and change certain media components that led to improved protein production.  

 The real success of the microarray studies came, however, when we discovered a medium supplement that dramatically enhanced protein production for a chemically defined medium. This particular medium was characterized by high titers when monitored over time. However, at a particular stage during the fermentation process, protein yields suddenly dropped off. We carefully designed a time course microarray experiment, and mapped the resulting differentially expressed genes to their associated metabolic pathways. To our surprise, analyses of interconverting pathways led to the identification of a particular amino acid (see Figure 2). The ability to map differentially expressed genes to their associated pathways clearly made it possible to "zoom" in" and identify a key component that led to medium optimization and that targeted a truly essential amino acid. We also used microarrays to survey the dynamics of gene expression in media with varying productivities, as well as to examine process conditions that enhanced productivity. The results identified genes within the cells cultured in media yielding high product concentrations/titers that are related to growth and cell division and were expressed at significantly higher levels compared to those cells grown in media yielding lower titers. This enabled the cells to remain viable longer at the end of cultivation, when the cell concentration is highest, thus allowing more product molecules to accumulate.  

Figure 2: Mapping differentially expressed genes onto their associated metabolic pathways led to the identification of a truly essential amino acid directly involved with increased protein titers. 			

Despite the utility and versatility of DNA microarray technology, it only provides for a qualitative picture of the overall transcriptome and only reveals the activity of genes for which probes are present on the array. Furthermore, based on many recent research reports for both prokaryotic and eukaryotic organisms, we know that cell physiology, as well as many functional cellular and biological processes, including cell cycle progression and induction and suppression of apoptosis, are not entirely dependant on the level of gene expression, but rather controlled by upstream regulatory regions and the increasingly important "non-coding" regions of the genome (e.g., microRNAs/small RNAs) (4,5). This is where NGG comes to the forefront.   

One of the key technologies in NGG is Next Generation Sequencing (NGS). Recent technological advances in DNA sequencing have dramatically improved overall throughput and quality and have led to the development of methods to characterize whole transcriptomes of entire cell populations in a way that was never before possible (6–8). RNA–sequencing (mRNA–seq) involves the direct sequencing of complementary DNAs (cDNAs) using high throughput, massively parallel NGS technologies (Illumina's Genome Analyzer IIx; Illumina's HiSeq2000, Roche's 454 FLX system, to name a few), followed by mapping of the resulting sequencing reads to a reference genome (see Figure 3 for a detailed RNA–seq work-flow diagram).  

Figure 3: A detailed breakdown of a typical mRNA-seq work flow. This work flow is for a bacterial species for which comprehensive genome information is available. Partially adapted from Wilhelm and Landry (2009). 			

In a single RNA-Seq experiment, one can derive not only an accurate, quantitative measure of transcriptome-wide gene expression levels (as with real–time quantitative polymerase chain reaction technologies), but also discover novel transcribed regions (new exons/genes) in an unbiased manner (as with a whole genome tiling microarray approach),  map their boundaries, and identify the 5' and 3' ends of the genes (9,10).  In addition, this methodology enables a global survey of the usage of the alternative splice sites (similar to a custom designed splicing microarray). It allows for the identification of transcription start sites, the identification of new splicing variants, and the monitoring of allele expression (9,10). Based on the power of the RNA–Seq approach, it is clear, that at least for comprehensive studies in higher eukaryotes where surveys of differential splicing activity, antisense transcription, and discovery of novel regions of transcription are desired, high throughput sequencing of RNA has augmented and is beginning to supersede microarray-based methods (9,10). Not only do the economics of faster development and better optimization support it, but it also allows for a host of new quality control and bioprocess monitoring capabilities after the research and optimization is completed. All of this dovetails perfectly with the intent of FDA's QbD guidelines.  

We have developed methods to use various current and next generation genomics tools to increase the performance and cost effectiveness of bioprocesses during manufacturing.  NGS  technologies, in particular, have several potential applications in this scenario.  These include the generation of valuable genomics resources, development of molecular fingerprints for improved product yield and quality and bioprocess monitoring, quantitative expression analysis (RNA–seq), and identification of metabolic bottlenecks, all leading to evidence–based bioprocess optimization (see Figure 4).  For example:  

Figure 4: Integration of NGG technologies to develop a species specific genomics platform that can be used to optimize bioprocesses, while building a solid understanding of the biology of the particular production species. 			

 Sequencing genomic DNA, mRNA, micro/smallRNAs, and immunoprecipitated DNA fragments from production strains or cell lines provides genomic resources that have a direct impact on understanding the overall biology of an organism. Specifically, it enables the understanding of gene regulation (e.g., the role of noncoding regulatory RNA elements and transcription factors/sigma factors in gene regulation) and genome structure and dynamics (chromosomal rearrangements, alternative splicing events, etc.). These resources have a direct impact on understanding the genome architecture of the production species, laying the foundation for intelligent, biology-guided process development. 

 To develop functional gene-based markers, NGS of mRNA of contrasting phenotypes for the biomolecule of interest (for example, yield and quality) can be used to identify candidate genes involved in or associated with the production phenotype. These genetic markers can then be used as molecular fingerprints to assist with the selection of production lines, and to monitor process development and optimization with the goal of guiding early stage bioprocesses. At later stages, these molecular fingerprints can also be used to monitor process scaleup and manufacturing. Active monitoring throughout the entire process life cycle maximizes product yield and quality while minimizing associated costs.

 Coupled to metabolic pathway analysis, RNA–seq of production strains/cell lines in different growth and environmental conditions sheds light on key metabolic pathways controlling biomolecule production and identifies potential metabolic bottlenecks. The ability to zero in on the control points governing metabolic flow towards increased production allows for process manipulation based on empirical knowledge instead of large DOE fishing expeditions and brute force methods.  

Intelligently designed NGG experiments have become the hallmark of research and manufacturing design and optimization. With the advent of NGG technologies, the old and the new can be combined for very powerful results. Short term gains in production (in our case, sometimes more than 300%) were recognized from DNA microarray and/or mRNA-seq experiments that have provided foundational information for our NGG initiatives. We have dramatically improved our ability to rapidly optimize both growth media and fermentation conditions associated with the production of a key protein used in the manufacturing of a major commercial product. The technology has enabled us to better understand the overall bioprocess, as well as the physiology of the production cells themselves. We have incorporated various current and next generation genomics tools to form the basis of a bioprocessing genomics platform that will enable us to ultimately support FDA's QbD initiative. Not only will this improve our understanding of bioprocesses and the effect of all CPPs on protein yield, quality and process stability, but it will also make flexible bioprocessing possible and safe. 

 is the CEO and CSO of ArrayXpress and a faculty member at NC State University.  

is the chairman of the board at ArrayXpress Inc. 

 9. Z. Wang, M. Gerstein, and M. Snyder, 

A series of advancements has changed the way bioprocesses are developed and optimized.

Accelerating Bioprocess Optimization

I had dinner recently with an old and very insightful Big Pharma colleague of mine and after speaking together, he revealed an important concern. "Gregg, I'm worried," he said. "I think the complexities of our current global supply chain outweigh our capability to control the network," he continued. He was talking about his company's third-party external supply network.  

It's a scary thought, the idea of being too big and complex to adequately control an organization's supply chain. But as my colleague and I discussed this in more detail, I definitely understood why he felt that way. For the past 20 years and especially the past decade, the industry has watched the evolution into "gigantic pharmaceutical drug-makers," such as Johnson & Johnson, Pfizer, Roche, Merck & Co, Novartis GlaxoSmithKline, and sanofi-aventis, all $40-billion-plus annual revenue behemoths. 

These global giants are the results of mergers, acquisitions, and myriad comarketing, in-licensing, and other growth strategies to make up for the disappointing failures of traditional R&D. The resulting manufacturing "mash-ups" have created third-party external supply networks that are difficult to manage because of the number of CMOs involved, long-term contracts (some with substandard organizations), contracts with no provisions for ongoing cost reductions, and other nuances that result in the buying organization having a higher risk profile than desired.   

Now, there is no doubt that making changes to the network can be difficult and expensive due to product registrations and other regulatory hurdles on a country-by-country basis. But if a company is willing to play a longer game, with a forward vision, it is possible for it to optimize its third-party external network to reduce all types of supply risk and ensure the ability to manufacture products cost effectively. 

There is a new methodology, called collaborative optimization, which I've become familiar with through my work as senior advisor for A.T. Kearney Procurement and Analytic Solutions. The methodology has its roots in optimizing transportation logistics. It's been successfully used in many direct and indirect categories of spend as diverse as packaging, chemicals, and temporary labor and also to simultaneously optimize multiple categories of spend used in sequential manufacturing processes. It was designed for complex categories of spend when the complexity comes from many specifications, many potential suppliers with a multitude of capabilities, and regional or global manufacturing and distribution needs.  

Collaborative optimization has three key components. First, it establishes deep analytics in the cost makeup of a product/service through the creation of a detailed cost breakdown. Second, it gives suppliers the ability to quote in multiple ways, including creatively (i.e., to quote on parts of the business that play to an individual supplier's strengths or to provide new ways to meet requirements more cost efficiently). This function is called expressive bidding and makes ups the "guts" of a new type of request for quotation that has an abundance of cost-rich information and cost-reduction ideas.  

The real differentiator with collaborative optimization is the ability to use combinatorial optimization through an embedded combinatorial analyzer that allows for scenario comparisons to be rapidly generated. These comparisons determine the most desirable outcome based on what a corporation must have. This requirement might be the lowest total price or the best total cost when certain constraints are added. This method overcomes many of the limitations in today's procurement approaches by fostering creativity into the quotation process and handling large amounts of data in a fast and efficient way, thereby enabling the buying entity to determine the best solution for its specific needs. What I like the best about collaborative optimization is that it does not pit supplier against supplier the way traditional procurement practice often does. Instead, it identifies the best way forward that is beneficial to buyers and suppliers.    

So, how could this methodology be applied to a third-party external network? To start, a corporation would need to decide it wants to fix the "mess" (a word used by the late, great systems thinker, Russell L. Ackoff, PhD, to describe most current state situations). The company also would need to recognize that the fix might take 5 to 10 years to fully implement. For a corporation that was willing to play the long game using collaborative optimization, the benefits would be substantial. One benefit is that it allows a company to identify where new contract-manufacturing opportunities should be sourced. It also allows a company to understand where existing contract manufacturing should be sourced and if there is a financial or risk-based business case to support working through regulatory and contractual barriers. This method also ensures that the best CMO network had been identified to reduce risk and provide real cost competitiveness.   

When a company thinks about its the third-party external supply network, a crucial question to consider is whether the company is using a CMO's total capabilities across its total product requirements on a global basis. From my experience, which includes more than 30 years in procurement, the supply chain, and related operations, including 10 years as vice-president of procurement of global systems and operations at GlaxoSmithKline, I'm fairly confident that the answer would be "no." And that reply is really no fault of an individual or an organization. It goes back to what my colleague and I were discussing: the complexities of what an organization is dealing with are greater than its ability to control unless there is a commitment to develop a long-term vision and a willingness to use different approaches in managing a third-party external supply network. Collaborative optimization offers the potential for annual incremental savings and potentially a lower overall risk profile. 

The key factor for effective supplier management is that once a company has established what its end-state third-party external network should look like and what suppliers should constitute that network, it is imperative that there is an ongoing collaborative environment in place that ensures that future supply partners are always incented to improve and innovate. This evaluation may make an organization take a hard look at the skills (or lack thereof) that it has in place to support third-party external manufacturing. My guess is that there are some gaps in relationship management. Identifying and filling those gaps is a first crucial step in improving supplier management. 

 is CEO of Wildfire Commerce, and senior advisor for A.T. Kearney Procurement and Analytic Solutions, tel. 215-327-5739, 

Perspectives in Outsourcing

Mergers and acquisitions expected to increase, as big companies bolster piplines by acquiring smaller biotech companies.

A Hard Look at Third-Party External Supply Networks

The authors have developed a test for defects in filter membranes based on the principle of differing gas permeabilities through the liquid layer of a wetted membrane. Both the binary gas integrity test and the standard gas–liquid diffusion method were applied to a newly developed virus clearance filter. Results demonstrated that the binary gas test provided a significantly higher level of virus retention assurance compared to the air–water diffusion test.  The authors conclude that the binary gas test provides superior defect detection sensitivity in virus filters. 

Over the years, there have been a number of nondestructive integrity tests for filtration membranes conducted as part of manufacturing quality assurance. Tests are typically implemented by the filter manufacturer as an additional quality assurance test prior to shipping the product to users. 

Millipore has developed a test designed to be more sensitive to detection of defects. The new high sensitivity binary gas integrity test is based on the principle of differing gas permeabilities through the liquid layer of a wetted membrane that results in a concentration enhancement of the faster permeating gas.   

The test makes use of the fact that the permeate composition in an intact (integral) membrane, with no defects, can be predicted based on the transport properties of the gases permeating through the liquid layer and the known operating conditions. So, if the test spots a deviation from the expected concentration, it is a clear indication of a defect or the presence of open pores.  

The binary gas test has low sensitivity to membrane porosity, liquid layer thickness, and membrane area. This means that integral devices will exhibit a relatively narrow range of test values, making it easier to spot defects.  

To "test the test", researchers applied both the binary gas integrity test and the standard gas–liquid diffusion method to a newly developed virus clearance filter. Results demonstrated that the binary gas test provided a significantly higher level of virus retention assurance compared to the air–water diffusion test.  

TESTING FOR DEFECTS THAT COMPROMISE PERFORMANCE

Integrity testing of microporous or ultraporous filters is routinely used to detect the presence of oversized pores or defects that can compromise the filter's retention capability. Test choices include the particle challenge test, the liquid–liquid porometry test, the bubble point test, the gas–liquid diffusion test, and diffusion tests measuring tracer components. Although each test has benefits, there are also compromises that must be made.  

The particle challenge test is destructive and therefore not applicable as a pre–use test. The liquid–liquid porometry and bubble point tests are useful for ensuring that the user has selected a membrane with the proper nominal pore, but are not sensitive enough to identify small numbers of small defects, particularly for filters larger than 47 mm. With the gas–liquid bubble point test, a single or few small defects may add only a small amount of gas flow that cannot be distinguished from the background diffusive flow rate through the integral part of the membrane in a filter device.  

The most commonly applied nondestructive integrity test for membrane filters, especially virus filters, is the gas–liquid diffusion test. A wetted membrane provides a liquid layer across which diffusive air flow occurs (see Figure 1a). 

Figure 1: Gas diffusion through a wetted membrane: (a) integreal and (b) non-integral membrane.   

As pressure is increased, diffusive flow increases linearly until either the liquid layer begins to thin or until the bubble point is reached, whereupon robust bulk air flow commences. As shown in Figure 1, a measured gas flow rate more than that predicted for an integral membrane signals the presence of a defect.  

The sensitivity of this test is limited by the minimum detectable excess flow. There can be significant device–to-device variability in gas diffusion flow rates of integral membrane filter devices due to differences in membrane area, membrane thickness, membrane porosity, and pore tortuosity (twists and turns). Other factors such as thinning of the liquid due to evaporation, liquid retention of membrane support layers (porous non-wovens, for example), and membrane movement or compression can also affect measured gas diffusion rate. This variability in gas flow rate acts as "background noise" that can diminish the sensitivity of the gas–liquid diffusion test.  

The binary gas test was developed to increase the level of defect detection sensitivity, and has been found to be particularly useful for evaluating virus filtration membranes. It is based on the principle of differing gas permeabilites of a gas mixture's two components through the liquid layer of a wetted membrane.  

Unlike the single gas in the gas–liquid diffusion test, the binary gas test relies primarily on the measurement of downstream gas composition rather than downstream flow rate. In an integral membrane, the permeate gas is depleted of the slower permeating gas. However, if a defect is present, the leak through the membrane will contaminate the permeate stream, resulting in an elevated concentration of the slower permeating gas.  

A key advantage of the binary gas test is that the permeate concentration expected through an integral membrane is well defined and its sensitivity is not compromised by most of the background noise factors cited for the gas-liquid diffusion test. Figures 2a and 2b illustrate how the test works. 

Figure 2: Binary gas diffusion through a wetted membrane: (a) integral and (b) non-integral membrane.  

Diffusion can be specified for each component in a gas mixture permeating across a membrane. Assuming that the gas is completely mixed on both sides of the membrane, the composition of the permeate gas can be calculated from the ratio of diffusive flow rates of the two components, and the inlet side composition. 

The composition of the permeate gas is independent of membrane thickness, tortuosity, porosity, and area. It is also independent of the pressure difference across the membrane but instead is dependant on the pressure ratio. The permeate composition does, of course, depend on the feed side composition. To maintain a constant feed side composition, a constant sweep flow must be applied. 

A measured binary gas composition can be used to estimate a defect size in a device. The calculated defect size can, in turn, be used to estimate the liquid flow rate through the defect and the total volume of liquid passing through the defect for a given time period. Therefore, the permeate gas concentration can be used to predict the loss in virus log reduction value (LRV) due to the defect.  

The sensitivity of the binary gas test is related to the selectivity (ratio of permeabilites) of the gases through the liquid layer. To maximize the sensitivity of the binary gas test, the gas pair should have a high selectivity. Figure 3 shows normalized permeabilites of some common gases in water. For this study, researchers selected a carbon dioxide/ hexafluoroethane (CO2/C2F6) pair, with a selectivity of about 1000.  

Figure 3: Normalized permeability of  common gases in water at 25 Â°C.  

The selection of the concentration of the gases in the mixture was influenced by a number of factors, including the ease of composition measurement, gas flow rate through the membrane, and economic considerations. The concentration selected enables convenient flow and composition measurement for even relatively small membrane areas (as low as 3 cm

COMPARING HOW THE TESTS MEASURE CONTROLLED DEFECTS AND VIRUS RETENTION

The research included conducting integrity tests on virus filtration membranes using both the air–water diffusion test and the binary gas test. The virus filtration membrane used was Millipore Viresolve Pro (asymmetric PES) membrane in flat sheet or disc formats. 

Researchers introduced defects by laser drilling 2–10 µm diameter holes in the center of the membrane discs (see Figure 4). 

Figure 4: Laser hole drilled through a Viresolve Pro membrane disc.  

For the air diffusion test, pressurized air was applied to the upstream side of the membrane and downstream air flow rate was measured using a mass flow meter. 

For the binary gas test, the CO2/C2F6 test gas was introduced to the membrane at 345 kPa, and a constant sweep gas rate at a 4:1 ratio relative to the permeate flow rate was maintained through the vent port of the filter holder.  Gas composition was measured using fourier transform infrared spectroscopy (FTIR) (inDuct FTIR, MKS Instruments).  Measurements were recorded continuously until an essentially steady state permeate composition was achieved, typically within 15–20 minutes, which was the time required to fully flush out the residual air from the volume downstream of the membrane, the sample lines leading to the FTIR, and the FTIR sample chamber.  A small volume custom cell was procured for the FTIR in order to minimize the total internal volume downstream of the membrane and thereby reduce the overall test time.   

After the initial air diffusion and binary gas testing, researchers challenged the membrane devices with a solution consisting of a bacteriophage mixed with polyclonal human immunoglobulin in a buffer solution. The solution was filtered through the membrane until flux had declined by 75% compared to the clean buffer. They then collected feed and permeate samples, determined the infectious titer, and calculated the virus LRV. 

Figure 5: Defect detection by the air diffusion test.  

Figures 5 and 6 show the air diffusion and binary gas test results as functions of defect size. The solid lines are the model predictions for the air diffusion and binary gas tests. The shaded regions in each graph show typical test value ranges for integral membranes. These regions represent background noise against which a signal for a defect must be compared. Figure 5 shows that a 2 µm defect was not "visible" to the air diffusion test because the additional flow rate due to the defect was not large enough to increase the total flow rate beyond the range typically measured for integral membranes. In contrast, as shown in Figure 6, the elevated C2F6 concentration in the permeate is a clear signal for the same 2 µm defect. This result was an unambiguous demonstration of the binary gas test's superior defect detection sensitivity. For defect sizes larger than 2 µm, both tests provided a strong signal for a defect. 

Figure 6: Defect detection by the binary gas test.  

The research also showed that the loss in LRV compared to an integral membrane due to a single defect can be predicted from the binary gas value. This means that a maximum allowable binary gas value can be established for a desired level of LRV assurance based on a worst case assumption of a single defect. 

In addition to using the test in conjunction with controlled defects, researchers looked at defect detection sensitivity in manufactured devices. Both tests were applied to a set of prototype Viresolve Pro devices. A portion of the two–layered membrane used to manufacture the devices was tested for virus retention and the LRV of the three discs was determined to be 5.9. The LRV of the devices, however, ranged from 5.0 to 5.9. While the air diffusion test did not differentiate among these devices, the binary gas test showed clearly elevated values for the two devices with lower LRV values (see Table I). 

Table I: Comparison of integrity test sensitivity between the air-water and binary gas tests on prototype Viresolve Pro devices.  

Compared to the conventional air-water diffusion test, the binary gas test provides superior defect detection sensitivity in virus filters. While the air diffusion test provided an LRV assurance of about 4.5–5.0 for the virus filters studied, the binary gas test can provide an LRV assurance of greater than 6.0. 

The greater sensitivity of the binary gas test is due to a much more favorable signal–to–noise ratio than the air-water diffusion test. Unlike the gas–liquid diffusion test, the binary gas test has low sensitivity to membrane porosity, liquid layer thickness, and membrane area. Other factors that can confound the sensitivity of the air diffusion test such as thinning of the liquid due to membrane asymmetry or evaporation, liquid retention of membrane support layers (porous non–wovens, for example), and membrane movement or compression have a much lower impact on the sensitivity of the binary gas test.  

*is principal applications engineer, and 

is director of single use technologies, both at EMD Millipore, 

The authors developed a test for defects in filter membranes.

The biotech industry has followed in close step with the surging capital markets so far this year, and by close of trading business at the end of February, the Burrill Biotech Select Index was up 3.6 %, year-to-date. For the same period, the Dow Jones Industrial average was up 5.6 % and the Nasdaq Composite index was up almost 5 %. 

G. Steven Burrill (DIGITAL VISION/GETTY IMAGES)			

The industry got off on the right foot with a generally upbeat mood pervading the annual JPMorgan Healthcare Conference in San Francisco in early January, reflecting the fact that many biotech and pharma CEOs in attendance felt that 2011 would be a good year for their companies.  

There was also a general consensus at the meeting that biotech mergers and acquisitions activity would increase significantly as a result of larger biotechnology and Big Pharma companies looking to bolster their product pipelines and catalyze growth. We certainly didn't have to wait too long to see this scenario begin to unfold. In late January, Amgen announced that it would pay $1 billion to acquire BioVex, a privately held biotechnology company based in Woburn, Massachusetts, with additional operations in Abingdon, United Kingdom. The transaction provides Amgen with BioVex's lead product candidate, OncoVEXGM-CSF, an investigational oncolytic vaccine currently in phase III clinical development as a treatment for metastatic melanoma. A phase III multinational study in head and neck cancer is also ongoing. 

Biotech's second largest company by market cap, Gilead Sciences, also announced an acquisition, saying that it will pay $375 million cash to acquire privately-held Calistoga Pharmaceuticals, a company focused on cancer and inflammatory diseases. Factoring in potential milestones, the total deal could be worth around $600 million. Commenting on the rationale for the deal, Gilead said that it serves to further broaden their pipeline and expertise in the areas of oncology and inflammation.  

In February, sanofi-aventis and Genzyme finally reached an agreement approximately seven months after sanofi made its initial offer of $69 per share to acquire Genzyme. In the end, sanofi agreed to pay $74 per share in cash, and give each Genzyme shareholder one contingent value right for each share they own, which will entitle them to additional cash payments tied to specified milestones related to Genzyme's Lemtrada, Cerezyme, and Fabrazyme drugs, according to the released terms of the deal. The final closing of this deal caused a surge of investor interest in the sector and fueled speculation about other "blue-chip" biotech companies that could be in the crosshairs of Big Pharma companies. 

Another transaction of note was Novartis' acquisition of San Diego-based diagnostics company Genoptix for $470 million. Genoptix is focused on delivering personalized and comprehensive diagnostic services to community-based hematologists and oncologists.  

Table I: Performance of Biotech IPOs completed in 2010. 			

We have seen plenty of deals like this in the personalized medicine space during the previous 12 months. As described in the just released 25th anniversary issue of the annual report on the industry, 

*, this sector is attracting a great deal of attention. This is because we are rapidly moving to a world that embraces not only the possibility of using a person's genetic information to identify their risks for disease, but also being able to construct individualized strategies for prevention or treatment through the use of biomarkers, molecular diagnostics and targeted therapeutics. Not surprisingly, companies in the healthcare sector are positioning themselves for this evolving trend. A November 2010 study by the Tufts Center for the Study of Drug Development reported that personalized medicine is occupying a growing role in the clinical pipelines of drug developers and causing companies to change their research and development paradigms as a result. 

Not only are we seeing the impact that the field of personalized medicine is having on drug development, but also that it is part of a wider discussion on how these evolving technologies will have a direct impact on helping to bring healthcare costs under control. To drive healthcare costs down, we need to focus on encouraging wellness rather than simply treating sickness. Interestingly enough, personalized medicine is moving to a "participatory" phase as people become much more involved in decisions about their own healthcare. Driving this trend is the fact that healthcare is being impacted by the convergence of information technology, wireless technology, and the proliferation of mobile devices. WiFi-enabled devices are allowing patients to connect with their physicians remotely. Smartphones are becoming personal healthcare assistants capable of collecting and transmitting vital body signs for analysis and subsequent results. This "digital health" world is just evolving and will bring about a radical change in how medicine is practiced in the next decade.   

Several biotech companies made their US market debut in February. With the exception of renewable fuels and chemical developer Gevo, all other biotech companies had to significantly lower their pricing expectations to get IPO deals done. The average aftermarket performance of the biotech IPOs at the end of February was 8.6 %.  

 * Biotech 2011–Life Sciences: Looking Back to See Ahead is G. Steven Burrill's 25th annual publication on the state of the biotechnology industry. This special anniversary edition examines how the industry has developed into the global enterprise it is today. It describes what companies will need to do in order to remain competitive in a world being reshaped by technology, globalization and emerging markets. In addition, it provides comprehensive analysis on the global industry's performance in 2010. Details: 

 is chief executive officer at Burrill & Company, San Francisco, CA, 415.591.5400, 

Burrill on Biotech

Industry starts the year with a positive spin.

Biotech Starts the Year Positively

A little more than a year ago, leaders of the pharmaceutical industry negotiated a deal to provide billions of dollars in discounts and fees designed to make drugs more affordable to Americans. In return, manufacturers anticipated a larger market for prescription medicines in a reformed national healthcare system, plus favorable policies governing research and development (R&D) and marketing— without explicit price controls. 

Now there's considerable uncertainty about how the Obama healthcare-reform program will be implemented, and how well the system will support biomedical innovation and new drug development. Federal courts are weighing the constitutionality of the Affordable Care Act (ACA), while reform critics in Congress are challenging specific policies and curbing funds needed to implement reform initiatives. Some states face serious budget problems and are looking to limit Medicaid programs, including drug benefits. The Obama administration's budget plan for 2012 offers extra funding for biomedical research and for FDA operations, but it's uncertain whether these proposals will survive the budget-cutting battle on Capitol Hill.  

The search for additional funds to pay for healthcare-reform initiatives and government health programs, moreover, is driving the Obama administration to ask Big Pharma to ante up even more. During the healthcare-reform debate of 2009, the Pharmaceutical Research and Manufacturers of America (PhRMA) agreed to pay higher Medicaid rebates and additional taxes, and to subsidize the cost of drugs prescribed to seniors caught in the "doughnut hole" of the Medicare drug benefit— all adding up to some $80 billion over 10 years. A primary gain for biomedical companies was the promise of substantial protection for innovator biotech therapies in the face of more aggressive generic competition.  

The administration now proposes to jettison the biotech exclusivity deal and boost consumer access to generic drugs to help gain some of the $54 billion needed to support Medicare payments to physicians. Shrinking the exclusivity for innovator biologics from 12 to seven years and thus speeding less costly biosimilars to patients, according to Obama's 2012 budget plan, would save about $2.3 billion over 10 years. The Biotechnology Industry Organization (BIO) warned that such "questionable short-term budgetary savings" could jeopardize development of new breakthrough cures.  

John Castellani, president of PhRMA, said in a press release that the proposal "flies in the face" of the administration's talk about supporting "innovation, biomedical research, jobs and US competitiveness." But US Health and Human Services (HHS) Secretary Kathleen Sebelius told the House Energy and Commerce (E&C) Committee last month that the administration now feels that a seven-year exclusivity period will permit innovator firms to realize an appropriate return on investment, while ensuring that new breakthrough medicines are widely available and affordable. 

Another administration proposal would end pay-for-delay deals between brand-name and generic drug makers that postpone when a new generic product comes to market. The Generic Pharmaceutical Association (GPhA) applauded the shorter biotech exclusivity period, but criticized the curb on settlements as "misguided."  Castellani agreed with the generic-drug makers, noting that these "pro-consumer settlements" do not delay generic entry and often bring low-cost drugs to market sooner. Federal Trade Commission officials, however,  have been pushing hard to curb such arrangements, which they insist are anti-competitive and costly to consumers. The numbers-crunchers predict that banning pay-for-delay deals will save the government $540 million next year and nearly $8.8 billion through 2021.  

The generic drug gains together provide only a small portion of the resources needed to finance the "doc fix." Most of the money would come from proposed reductions in federal payments to state Medicaid programs, stiffer scrutiny of certain Medicare reimbursement to insurers, and proposals to reduce Medicare fraud and abuse. The plan also proposes to increase tracking of high prescribers in Medicaid programs to reduce excessive drug utilization by states. And manufacturers would be hit with additional penalties if they fail to pay appropriate Medicaid drug rebates and to comply with rebate rules and FDA policies for listing drugs on databases. But these policies generate virtually no tangible savings, and it's questionable whether the squeeze on biotech exclusivity is worth the rather small budgetary gain to the government.

Although pharmaceutical companies traditionally look to Republican allies in Congress to champion patent protection and product exclusivity, GOP leaders still are smarting over PhRMA's support for Obama's healtchare plan. As part of its investigation into HHS implementation of healthcare reform, House E&C Committee Chairman Fred Upton (R–MI) is looking hard at the "secret negotiations" between the White House and healthcare interest groups, including pharmaceutical companies, leading to enactment of the reform legislation. E&C Committee Republicans complained in a Feb. 18, 2011 letter to White House Aide Nancy-Ann DeParle (formerly head of the White House Office of Health Reform) that instead of the open and transparent debate on healthcare legislation that Obama had promised, deals were made behind closed doors with providers, drug companies, and others.   

Upton and his colleagues are scrutinizing the HHS process for determining whether states and healthcare providers and payers should receive waivers from complying with specific ACA rules, a process they believe indicates widespread problems associated with implementing the reform law. The E&C Committee also wants to know more about HHS support for establishing state-based insurance exchanges and for developing new rules governing insurers, including standards for "essential benefits" that will shape medical and drug coverage.  

FDA operations and policies are also under scrutiny by GOP leaders. The E&C Health subcommittee held a hearing in February to examine whether FDA's slow process for approving more complex medical devices for market is harming US device makers. And Republicans once more are examining the three-year-old heparin crisis, complaining in a Feb. 23, 2011 letter to FDA Commissioner Margaret Hamburg that still no one knows the source of the adulteration nor the Chinese culprits—all while imports in pharmaceutical ingredients from that China are booming.  

In addition to hauling administration officials up to Capitol Hill to explain their actions, Congress is moving forward with efforts to revise portions of the ACA, after failing in January to repeal the legislation. In the low-hanging fruit department, Democrats and Republicans agree on the need to repeal the 1099 reporting policy, a burdensome rule that requires businesses to report to the IRS any expenditure over $600—a requirement that has little to do with healthcare. President Obama has signaled support for killing the program, but the challenge is to find the $22 billion or so needed over the next 10 years to offset potential revenue gains from the policy. 

President Obama and Congressional leaders also are eyeing medical-liability reform as a way to reduce spending on unnecessary healthcare services incurred as a defense against malpractice charges. A bill recently approved by the House Judiciary Committee would cap noneconomic and punitive damages, limit the time for filing suits and curb attorneys' contingency fees. Such proposals face considerable opposition from lawyers and some patient advocates, yet Obama expressed interest in revising malpractice policy in his State of the Union speech and included $250 million in his 2012 budget plan to support grants to help states rewrite their malpractice laws and establish health courts. Reform could move forward if there's clear evidence that damage caps would save money by reducing defensive medicine and other costs.  

But there's little bipartisan support for improving ACA implementation. President Obama recently voiced support for legislation that permits states to opt out of exchanges and the individual mandate, provided the state can devise alternative ways to cover more uninsured individuals. Although this  action was considered a major concession by ACA supporters,  Republicans labeled it a "fig leaf" that didn't increase local choices. 

The battle continues over Republican efforts to limit federal spending overall. Policymakers avoided a government shutdown in early March by agreeing to a short-term fix on funding the federal government for the current (2011) fiscal year. But Republicans still demanded some $100 billion in cuts for this year, as well as curbs on many ACA provisions, including individual coverage requirements, medical-loss ratio rules, health insurance exchanges, and Medicaid expansion plans.  

Critics also are looking to eliminate a number of entities established by ACA, but seen by Republicans as examples of a federal government over-reach into state and private sector activities. Although health prevention has broad appeal, Republicans want to dissolve the Prevention and Public Health Fund, which is supposed to dispense some $15 billion over 10 years to support state and local prevention and health initiatives. This prevention "slush fund," say Republicans, is excessive—and its resources could be tapped to offset the cost of repealing the 1099 IRS reporting requirement.  

Republicans also don't see a need to spend $10 billion over 10 years to fund the Center for Medicare and Medicaid Innovation (CMMI), which was established by ACA to support research and testing of reimbursement and coverage approaches for Medicare and other health programs. There's also skepticism among Republicans about federal investment in comparative effectiveness research (CER), when private plans and local organizations have funded technology assessment on their own. Some Republicans would like to shutter the Patient-Centered Outcomes Research Institute (PCORI) and use its $500 million a year in appropriated funds for other purposes. Pharma companies generally support development of standards and policies for CER research, but probably won't expend much effort fighting to preserve PCORI.  

There's not much good news for the pharmaceutical industry regarding the battle to repeal and revise healthcare  reform. Manufacturers already are paying stiffer Medicaid rebates and absorbing 50% discounts on drugs for Medicare Part D beneficiaries caught in the coverage gap. The IRS is establishing rules for collecting some $2 to $3 billion in new industry taxes beginning this year, based on company sales of branded drugs. At the same time, more onerous legislation is on the table. Once again, there is bipartisan support for a Senate bill to permit reimportation of high-cost medicines from Canada. And Democrats want to eliminate tax deductions for direct-to-consumer advertising.  

Stiffer FDA user fees are on the horizon as well. The Obama administration has requested a $4.3 billion budget for the agency in 2012, a reasonable increase given the tight funding environment in Washington. But most of the gain would come from higher user fees on medical product manufacturers, and most of any added appropriations would fund a huge expansion in food safety oversight authorized by Congress, but without support from food industry fees.  

Any extra money for FDA's Center for Drug Evaluation and Research is designated to support development of medical countermeasures and biosimilars, to expand monitoring of  imported medical products, and to improve the safety of certain high-risk products such as vaccines and human tissue. New generic-drug user fees, which at long last appear to be moving toward reality, would improve the review of generic drugs, while other proposed fees would support more field inspections. There's a small amount of money earmarked for improving regulatory science at FDA, which would primarily complete work on a new laboratory complex for drugs and biologics at the agency's White Oak, Maryland campus. Without an extra $24 million to get the new laboratory operational, FDA would have a new facility with no equipment, and still have to pay rent on an old, obsolete laboratory.  

FDA is negotiating with manufacturers on its next five-year plan for collecting fees from industry under by the Prescription Drug User Fee Act (PDUFA). Meanwhile, the agency wants drug and biotech firms to ante up some $850 million in PDUFA fees in 2012, up more than $275 million over 2010. About $600 million will fund drug oversight, and $125 million will support regulatory activities involving vaccines and other biologics. Combined with all the added costs imposed by healthcare reform, and apparent threats to expanding healthcare coverage to some 30 million uninsured Americans, manufacturers have to worry about the erosion of resources to support new drug development.  

Washington editor, Chevy Chase, MD, 301.656.4634, 

Regulatory Beat

Courts and Congress seek to reshape policies and programs affecting drug costs and access.

Health-Reform Controversies

As the pharmaceutical industry weathers some of its biggest challenges in decades, supply-chain analytics may offer the solution to some of the industry's biggest problems. Accenture's Global Pharma Industry Supply Chain & Tech Ops study—completed in May 2010—offers some key insights into how analytical capabilities could help transform supply chain operations.  

While most study participants—25 pharma and biotech companies from around the globe and across industry segments—would agree that "supply chain analytics are a crucial part of our strategic priorities," their efforts are largely focused on developing greater visibility into supply chain performance. There is certainly room to improve in this area, but the true value of analytics goes far beyond simple performance management.  

Indeed, companies that indicated stronger analytical capabilities (e.g., closer integration with customers on demand forecasting) also consistently demonstrated higher margins. Data availability is the most fundamental requirement for strong analytic capabilities. This is an area in which the pharma industry continues to struggle. Supply-chain data is typically scattered throughout a fragmented landscape of manufacturing, enterprise resource planning (ERP), and laboratory information management systems (LIMS), which do not exchange information. Pharma companies' ability to pull information from outside the organization is not much better. Few have been able to develop tight links with customers, and even where these links are in place, the customers' data are often of less-than-sterling quality.  

But having the data, while necessary, is far from sufficient to develop strong analytics. In fact, organizational factors which break the link between data and decisions are often the biggest obstacles to overcome. Too often, supply-chain organizations in the pharma industry operate in disconnected functional silos which encourage decision making based on tradition, rather than data.  

Traditional thinking, for example, might dictate a decision to build inventory to ensure that all orders are filled. Analytical thinking might instead suggest tracking customer inventory levels and patient demand to optimize order performance.  

As organizational capabilities mature and data quality improves, focus will shift from using analytics to enhance the effectiveness of traditional processes to building new ways of operating. In the consumer goods industry, for instance, manufacturers are increasingly turning to point-of-sale data from their retail customers to design algorithms which allow product manufacturing and replenishment strategies to be tailored to the stages of the product life cycle in real time.  

The utility of such an approach for pharma companies facing tougher generic competition and lengthening research and development timeframes is obvious. What's more, the industry's current focus on improving product traceability and supply-chain security will tend to build exactly the kind of links with customers and distribution partners that can provide the data to drive more analytically oriented forecasting and replenishment. 

For guidance on how analytics can be best deployed in the pharma industry, it's helpful to look at how analytics have driven improved performance in other industries: 					

 A leading big-box retailer in the United States has been able to leverage two decades of experience in collecting and reporting on product data to radically democratize decision-making, pushing decisions on reorder points, product mix and discounting to a local level and allowing store employees to custom fit sale items to conditions in the community.

One of the world's largest manufacturers of building materials uses a predictive model of traffic and weather conditions which allows them to guarantee a 20-minute arrival window for perishable mixed cement, a capability which has enabled them to charge premium prices for the most basic of commodities.

Such high performers have a quantitative mindset, constantly using data to challenge assumptions and separate "what we know" from "what we think we know." Equally important is a focus on using analytics to seek out prospective sources of competitive advantage, rather than just measuring past performance. Finally, these companies have moved beyond internal data to draw information from the outside world where necessary. All these factors come together to make analytically advanced companies more customer-centric than their competitors.  

If the challenges facing the pharma industry are large, so are the opportunities. The recent wave of merger and acquisition activity offers especially tantalizing opportunities for the consolidated companies.

 For example, tax-optimized supply-chain analytics can be used to help rationalize manufacturing and distribution networks in the most efficient means possible while increasing margins. Improved analytics in the areas of business simulation, network optimization, and risk modeling offer the potential for greatly enhanced synergies and a quantum jump in supply- chain capability.  

Send your Final Word story ideas to Editorial Director Michelle Hoffman at 

, is a senior executive at Accenture, a global management consulting, technology services, and outsourcing company. He leads the supply-chain practice for Accenture's Life Sciences industry group.  

Final Word

Supply-chain analytics can lead to increased profitability.

BioPharm International-04-01-2011