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BioPharm International, November 2009 Issue (PDF)

You may never have considered this, but pharmaceutical manufacturing and professional football have a core similarity. No, this is not Tuesday-morning delirium from someone who stayed up too late watching Monday Night Football. And I'm not saying that validation managers have multimillion dollar contracts, or that the process development and manufacturing teams celebrate their successful tech transfers by pouring a cooler of Gatorade over the senior manager's head. Nor am I suggesting that the 3–4 defense is a good strategy to apply to downstream processing. 

What football and bioprocessing have in common is that in both cases, success is a minimum requirement. Bill Parcells, one of the most successful coaches in the history of the NFL, captured this perfectly when he said, "There is winning and there is misery." 

What Parcells is alluding to is not just the torture of losing for the highly competitive people who play or coach professional sports, but also the pressure of expectations. When you win consistently, everyone—the media, the fans, the team owners—starts to think that it's easy for you. You win another game, and they don't give it a second thought. They don't appreciate it. 

For manufacturing, of course, it's even worse, because unlike football, where at least a big win can be the subject of endless television coverage and water-cooler conversation, manufacturing success rarely gets noticed. The CEO doesn't stop by to say "Hey, nice execution on those batch records." The local TV reporter doesn't put a microphone in front of the director of process development and ask, "How did you achieve such tight process control?"  

When problems arise, however, it's another story. Of course, on the gridiron or the plant floor, a few things can go wrong along the way without excessive criticism—a missed tackle, a sack, an interception (an out-of-specification result, a deviation inspection, even a discarded lot)—as long as the end result is positive: another "W" on the record (only high quality product gets released). But when actual games are lost, or warning letters arrive, the scrutiny begins.  

 Do they have the right defensive scheme? Their undersized offensive line can't protect the quarterback. And their execution is so sloppy. Why do they commit so many penalties? 

 Do they have a good downstream processing scheme? Maybe they didn't design their scale-up studies properly. And the operators must not be following SOPs. Otherwise, why do they keep getting so many 483s? 

In drug manufacturing, of course, errors can cause more than just disappointment. Patients' lives and wellbeing are at stake, which is why quality is paramount. What is less recognized, however, is that the company's financial health also depends on manufacturing. A consent decree can involve millions of dollars in fines. A plant shutdown can cause drug shortages that can lead to long-term market share loss, as patients switch to, and continue with, a competitor's drug. Either situation can lead to lost investor confidence, declining stock prices, or even a failed start-up company. That brings up another Parcells quote: "Success is never final, but failure can be." 

So even though fan expectations about football may be overblown, excellence really 

 to be a minimum requirement in drug manufacturing. Given those high stakes, then, it would be nice if the larger world acknowledged the everyday successes of those who make sure that the manufacturing department keeps on winning, week after week. Because clearly, failure is not an option, even if there is no Super Bowl glory at the end. 

From the Editor

What football and bioprocessing both have in common is that in both cases, success is a minimum requirement.

Where Winning is the Only Thing

To commercialize DNA medicines, industrial plasmid DNA manufacturing processes that meet the quality, economy, and scale requirements projected for future products are needed. We have developed cell bank and fermentation process unit operation innovations that reduce plasmid-mediated metabolic burden, enabling improved upstream production of optimal plasmids to 2.6 g/L. Application of these processes also facilitated production of otherwise unstable direct repeat containing vectors in standard 

 host strain DH5α, eliminating the need for specialized stabilizing strains. Fermentation yields with low yield plasmids also were improved up to three-fold by using a simple fermentation process development method requiring only one to two fermentations. 

Plasmid DNA is increasingly finding its way into new, experimental non-viral gene therapeutics, including DNA vaccines,

  short hairpin RNA (shRNA) gene knockdown therapeutics, gene replacement vectors, and seed constructs for viral vector production.  

 strains for plasmid production require recA and endA mutations.

  Several studies on various plasmid host strains indicate that plasmid yield and quality are significantly affected by the choice of host strain. However, these studies also demonstrate that plasmid production in shake flasks is poorly predictive of plasmid production in fermentation, and that a strain's performance can be affected by the fermentation process.

  DH5α is a good host strain because it consistently produces high-quality plasmid DNA in both shake flask and fermentation culture. Evaluation of the individual contributions of the DH5α host strain, plasmid backbone, and production process to plasmid production has demonstrated high-yield plasmid fermentation is largely determined by process and vector-intrinsic factors, not strain-intrinsic factors.

Constitutive, high plasmid copy number throughout the fermentation process is not necessary or desirable. Maintaining a high copy number during biomass accumulation creates an environment in which plasmid-free cells have a significant growth advantage.  

Plasmid-mediated metabolic burden can inhibit biomass growth and may lead to stability or quality problems (e.g., deletions or dimers) with many plasmids. Thus, maintaining low cell stress or metabolic burden during biomass accumulation, and inducing high plasmid copy numbers for only the final portion of the process, leads to superior volumetric plasmid yields while preserving plasmid quality.  

Gene therapy or DNA vaccine plasmids typically contain the pUC (temperature-sensitive) origin of replication. This temperature sensitivity is especially useful for inducing high-yield plasmid production in fermentation using a temperature-inducible, fed-batch process (Figure 1) with an optimized semi-defined medium.

  The initial setpoint temperature of 30 °C maintains the plasmid at a low copy number. This reduces metabolic burden and cell stress during bacterial growth such that the majority of the final biomass is formed under low stress conditions.  

Figure 1.  Illustration of the temperature-inducible fed-batch process. The initial temperature setpoint is 30 Â°C to keep the plasmid copy number low during growth. Feed medium containing concentrated glycerol is added according to an exponential feeding strategy to control cellular growth at about 0.12/h. 

After sufficient biomass accumulation, the temperature is shifted to 42 °C and growth continues for up to approximately one doubling of cell mass. This process has resulted in volumetric plasmid yields up to 2,590 mg/L, and specific plasmid DNA yields up to 51 mg/g dry cell weight (DCW), or 5% of the total DCW.

  Figure 2 shows a time profile from a fermentation that reached a plasmid yield of 2,100 mg/L.  

Figure 2.  Time profile of a fermentation of DH5Î± containing a 6.5 kb DNA vaccine plasmid, temperature induced at 29 h 

Critically, use of the low metabolic burden process was essential to produce otherwise unstable retroviral plasmids. For example, pVLTrap, a 6.7 kilobase (kb) retrotransposon plasmid vector containing two long terminal repeats (LTRs, known to cause stability problems), was produced successfully in DH5α at 785 mg/L using the aforementioned low metabolic burden fermentation process (Table 1). However, when grown under identical conditions but at 37 °C, the region between the LTRs was entirely deleted by the end of the fermentation (Figure 3), and the truncated plasmid yield only reached 214 mg/L (Table 1).  

Figure 3.  Agarose gel electrophoresis of samples from DH5Î±/pVLTrap fermentations from Table 1. Lane 1 is plasmid prepared directly from the seed stock, and lane 2 is plasmid from the 30â42 Â°C fermentation harvest (70 OD600). Lanes 3â8 are from the 37 Â°C throughout process, at 16 (14 OD600), 20, 24, 37, 38, and 40 (113 OD600 harvest sample) h post induction, respectively. Note the deletion product (arrow) is detected in all samples from the 37 Â°C fermentation. 

For production of the various DNA medicines currently under investigation, it is essential that the fermentation process functions with a variety of different vector backbones and gene inserts. However, many plasmid vectors are unpredictably toxic or otherwise low yielding in standard fermentation processes. Numerous inserts also can confer low-yield plasmid production on an otherwise high-yielding plasmid backbone. 

Importantly, with toxic plasmids, maintaining a low metabolic burden should begin as early as the transformation process. Propagation of cultures at 30 °C rather than 37 °C during seed stock manufacture can dramatically improve downstream fermentation yields when producing plasmids containing toxic inserts,

  presumably by reducing the copy number to limit plasmid-mediated metabolic burden during this critical step.  

Use of the low metabolic burden process with 30 °C manufactured seed stocks further improved yield. A two-fold yield improvement was observed with the low metabolic burden at 30–42 °C inducible fermentation process (compared to fermentation at 37 °C throughout) with low-yielding short-hairpin RNA (shRNA) plasmids containing multiple short palindromic repeats (Table 1).  

Table 1. Comparison of plasmid quality and yield from 37 Â°C throughout and 30â42 Â°C inducible fermentations 

Modest yield improvement was observed with the 30–42°C inducible fermentation process compared to 37 °C throughout with a vector containing a 43 basepair (bp) palindromic repeat. Specific yields of this vector were reduced three-fold compared to a control vector without the repeat (Table 1). Reduced yields are caused by an inherent difficulty of DNA polymerases in replicating self-associating hairpin structures.  

Palindromes may be deleted by intramolecular or intermolecular recombination events. The observed sequence stability of the palindromes (in both processes; Table 1) establishes that shRNA plasmids are inherently stable in DH5α fermentation cells. 

Collectively, these results demonstrate plasmid production processes that use 37 °C continuously do not perform well with many unstable or suboptimal plasmids because of detrimental metabolic burden from plasmid maintenance at constitutively high levels. Using the low metabolic burden cell bank and fermentation process facilitated production of unstable plasmids such as inverted or direct repeat containing vectors in standard strains such as DH5α, eliminating the need to use specialized stabilizing host strains. 

Some plasmids are low yielding in the 30–42 °C inducible fermentation process because of cell lysis after plasmid induction. We hypothesize that these plasmids are toxic to the production cell at moderate copy number, such that lysis occurs at a threshold copy number reached shortly after plasmid induction. Consistent with this, fermentation yields (and cell bank viability) from low yielding plasmid-containing cell lines were much higher when seed stocks were created at 30 °C, rather than at 37 °C.

We have determined that fermentation yield with such plasmids can be improved through simple process modifications to: 1) Increase biomass before induction by performing temperature induction at a higher OD

; or 2) Induction for a shorter duration to limit cell lysis; or 3) Induction at a lower temperature (37 °C) to reduce copy number amplification.  

The following case studies using seed stocks manufactured at 30 °C in strain DH5α illustrate rapid process improvement using combinations of these modifications. In all cases, culture lysis and poor plasmid yields after the first pilot fermentation using standard induction conditions were indicative that the plasmid was toxic to the host at moverate copy number. Volumetric yield (mg/L plasmid) improvements ranged from 60–170% (Table 2: one process development run) to 194% (Table 3: three-fold yield increase with two process development runs).  

Table 2. Improved plasmid yield after fermentation process modification 

In this optimization with a proprietary 9-kb plasmid, fermentation run 1 induction was at 42 °C at 50 OD

 for 10.5 h. After the observed extensive lysis at harvest, the second fermentation was induced at 55 OD

 for 10 h (shorter induction at higher OD

). This resulted in improved harvest biomass and specific yield, resulting in a 60% improvement in volumetric yield (Table 2). 

In this optimization with a proprietary 12-kb plasmid, induction at 42 °C was at 50 OD

 for 10.5 h in fermentation run 1. After the observed extensive lysis and very poor yield at harvest, the second fermentation was induced at 37 °C at 60 OD

 for 9 h (shorter, reduced temperature induction at higher OD

). This resulted in improved harvest biomass and specific yield, resulting in a 170% improvement in volumetric yield (Table 2). 

In this optimization with a proprietary 3.5-kb eukaryotic GFP expression plasmid, fermentation run 1 induction was at 42 °C at 50 OD

 for 9 h. After the observed extensive lysis and poor yield at harvest, the second fermentation was induced at 55 OD

). This resulted in improved harvest biomass and specific yield, resulting in a 72% improvement in volumetric yield (Table 2). 

This was a proprietary 6-kb DNA vaccine plasmid containing a toxic influenza serotype H1 hemagglutinin gene. In the first fermentation (run 1), culture lysis was visible after 4 h induction at 42 °C (starting OD

 was 60). Only modest plasmid specific yield improvement (1.2 to 3.2 mg/L/OD

) was observed by 30–42 °C induction resulting in a final volumetric yield of 260 mg/L (Table 3).  

Table 3. Improved plasmid yield after fermentation process modification 

For the first process development run (run 2), a strategy to more modestly induce copy number (using 37 °C rather than 42 °C induction) was attempted. The 37 °C induction was performed at 53 OD

 for 6 h. However, specific yield during the 37 °C stage increased from 1.0 to only 2.3 mg/L/OD

. Therefore the 37 °C stage was followed by a shift to 42 °C at 83 OD

 for 4 h. Specific yields further increased to 4.6 mg/L/OD

 (44% improvement compared to run 1) after the 42 °C stage. This improved overall volumetric yield compared to run 1 by 68% to 436 mg/L (Table 3).  

For the second process development run (run 3) the fermentation was induced for 9 h at 42 °C (starting OD

 was 50). Despite culture lysis during the last several hours of induction resulting in overall lower biomass yields, three-fold improved overall plasmid yields were obtained (Table 3: 194% improvement in volumetric yield, and 253% improvement in specific yield compared to run 1). This demonstrates that, although a small subpopulation of cells is sensitive to plasmid accumulation and lysis early, the bulk of the cells are high producers with this plasmid. Optimization of the process to favor accumulation of the high producing cells led to dramatic overall process yield improvement.  

Thus, consistent with the hypothesis that these plasmids are toxic to the production cell at moderate copy number, simple process modifications that increased biomass and delayed high copy plasmid induction dramatically improved yield.  

Expression from cryptic promoters in plasmid sequences could drive copy number-dependent expression of toxic peptides (from the sense or antisense strand) in 

. Alternatively, RNA sequences or structures may be involved. Surprisingly, two classes of toxic plasmids were identified that are either 1) universally toxic or 2) conditionally toxic to a subset of cells (e.g., influenza serotype H1 hemagglutinin plasmid, case study 4). Interestingly, an influenza serotype H3 hemagglutinin plasmid that was toxic at moderate copy number in freshly transformed cells, but not fermentation cells, has also been identified.

  Although universal toxicity may be attributed to the production of cryptic proteins, peptides, or RNA structures that disrupt core metabolic functions or membrane integrity, the cellular targets for conditional plasmid toxicity are unknown.  

The temperature-inducible fermentation process has been successfully scaled-up to 300 L and used for GMP production of DNA vaccine plasmids. Fermentation cells are compatible with a variety of vectors,

  and downstream purification strategies.

  This low metabolic burden process is ideal as a generic plasmid DNA production platform to produce previously unstable and toxic plasmid DNA, as well as optimized plasmids at high yields in standard host strain DH5α, thus eliminating the need to use specialized stabilizing host strains. Contract manufacturers can use the base process with simple defined process optimization steps if necessary, to produce a wide range of customer-developed plasmids.

We thank Sheryl Anderson, Sarah Langtry, Justin Vincent, and Angela Schukar for their tireless efforts cleaning, batching, and operating fermenters. This paper described work supported by NIH grant R44GM072141. 

Manufacturing Economics Series

How to reduce plasmid-mediated metabolic burden for higher yields.

Rapid Process Development for High Yield Plasmid DNA Fed-batch Fermentation

Biotech and pharmaceutical manufacturing has for years witnessed and experienced the struggle between two opposing forces—supply chain efficiency and supply quality. This is often characterized by the disconnect between C-level executives, concerned with boosting the bottom line by an efficient product lifecycle, and quality managers, responsible for enforcing strict procedures and ensuring product quality. This doesn't imply that the C-level has been collectively negligent of product quality, or that quality managers are in no way concerned with the companies' overall financial performance. It simply means that the responsibilities and perspectives of these parties are often not in complete alignment, and between these parties, quality can easily take a backseat to production needs and profit.  

Ensuring that products are manufactured with the highest quality components and ingredients means keeping tabs on which suppliers offer the most consistent level of high-quality raw materials, and prioritizing future procurement from these suppliers accordingly. This requires tracking and management of supplier qualifications, audits, nonconformance, corrective actions, and other processes. As companies continue to rely both on outsourcing and internal manufacturing operations as crucial elements of the global supply chain, they also realize that incorporating processes for managing the supplier quality into their overall quality management systems is required to reduce risk and decrease costs.     

CURRENT CHALLENGES TO MANAGING SUPPLY QUALITY

To better understand the critical need for managing supply quality as part of a broader quality management system, it is necessary to examine where things stand currently. There are several challenges that companies face when implementing an effective process for managing supply quality.   

Biotech and pharmaceutical companies must conduct intensive audits of myriad suppliers and write detailed reports of their suppliers' operations. Yet, even some of the largest organizations have failed to implement a centralized system for tracking these relationships; instead they rely on rudimentary methods such as Excel spreadsheets or paper-based documentation to track these issues. Only by implementing a centralized system for comprehensive tracking of supplier data can organizations streamline their reporting efforts and realize the benefits of a more efficient and compliant process.   

Lack of C-Level Involvement in Managing Supply Quality    

Executives at biotech and pharmaceutical organizations, both large and small, generally are responsible for overseeing multiple facets of their company's global operations. Supply quality, therefore, often is left to departments that lack data about the direct correlation between supply quality management and the company's ability to grow revenues. Executives must play a more active role in ensuring that supplier relationships are effectively maintained and that the correct procedures are in place for gauging supply quality and evaluating processes.      

Lack of Risk-Based Analysis for Supplier Quality 

Companies must deploy comprehensive tools for analyzing and grading suppliers based on the level of risk they pose for the greater manufacturing operation.  Without a risk-based solution, too much valuable time and too many resources are allocated to assessing the risk of reliable suppliers that pose no threat to the larger operation, although the more suspicious suppliers are less scrutinized than they should realistically be.   

Companies face extreme challenges managing increasingly global supply chains. In 2008, supply quality related problems reached a boiling point with multiple deaths resulting from tainted heparin, which stemmed from China-based supply quality problems. Between holding companies and distributors, determining the true material suppliers and managing global supply chains has never been more challenging.   

Biotech and pharmaceutical organizations must establish practices for handling supplier audits, ensuring organizational efficiency, and supply quality, while driving profitability. 

BALANCING INTERNAL PROCESSES AND THIRD-PARTY PROVIDERS

The biotech and pharmaceutical industries depend on both internal manufacturing and compliance processes as well as third-party suppliers. Between these intertwined parties, it's critical for companies to find a way to hold themselves and their suppliers to stringent standards for quality and compliance while driving supply chain efficiencies and profits rather than impeding them.   

Companies must take a proactive approach and increase visibility into potential supplier problems by obtaining a global view of all quality issues and supplier scorecards. This has far-reaching benefits, as it 1) ensures compliance with industry regulations, good manufacturing practices (GMPs), and ISO standards; 2) eliminates high costs related to scrap, rework, and delays caused by supplier quality problems; and 3) anticipates and eliminates repeat problems by improving supplier investigation and root cause analysis processes. 

Although implementing a holistic program can require significant upfront resources, including system and infrastructure investments, training costs, and implementation time, the end gain is worth more than the initial outlay. With an enterprise-wide quality management program, companies also can improve quality trending and management reporting by integrating purchasing and inventory systems with the quality management system.   

CRITICAL COMPONENTS FOR SUPPLIER QUALITY MANAGEMENT 

An enterprise-wide centralized process must encompass myriad quality management and compliance components, including:   

: Manage qualification and approvals of new suppliers, including all necessary qualification steps, which may vary based on the supplier's risk assessment, and may include tasks such as self-assessment and onsite audit.

: Handle audits based on the risk level, audit frequency, and the supplier rating or score established in a supplier's profile. When a trend in supplier quality problems is identified, companies must schedule ad-hoc supplier audits. The audit process, related audit findings, and subsequent supplier corrective and preventive actions must be managed and tracked to ensure quick and effective resolution.

Create inspection records when materials are received to ensure tracking of all inspection activities, including inspection type, received date, material information, quantity, inspection results, and related information.

: Enable the organization to maintain and monitor approved or preferred supplier lists. Approved supplier lists can be customized reports or online databases that can be reviewed and updated by qualified staff.

Directly generate nonconforming material reports (NCMRs) from the inspection record or independently within the manufacturing operation when the materials are below quality control standards. Guarantee that the supplier investigations and root cause analyses are tracked through completion.

: Track all supplier corrective and preventive actions (CAPAs) through completion. Provide third-party suppliers with secure access to view only their specific NCMRs, and audit findings and record corresponding corrective actions.

: Maintain or link to documentation of important specifications, including inspection plans, delivery windows, and acceptance sampling for received items.  

Managing supply quality effectively serves not only to drive compliance, but also to reduce the cost of poor quality, which, with both tangible and intangible ramifications (costs of product recalls, tarnished reputations or brand image), can ultimately be a detriment to any biotech or pharmaceutical organization. Without a stringent, holistic, and centralized supply quality management initiative that drives numerous supply chain and organizational efficiencies, companies' bottom-line objectives will be undermined, eroding any previous organizational successes. 

 is the vice president of product management at Sparta Systems, Holmdel, NJ, 732.203.0400, 

Perspectives on Outsourcing

An enterprise-wide quality management initiative is required to maintain supplier quality without sacrificing bottom-line objectives.

Finding the Balance in Managing the Quality and Efficiency of the Supply Chain

The International Conference on Harmonization (ICH) Q8(R2), Q9, and Q10 guidelines provide the foundation for implementing Quality by Design (QbD). Applying those concepts to the manufacture of biotech products, however, involves some nuances and complexities. Therefore, this paper offers guidance and interpretation for implementing QbD for biopharmaceuticals, from early-phase development steps such as identifying critical quality attributes and setting specifications, followed by the development of the design space and establishing the process control strategy; to later stages, including incorporating QbD into a regulatory filing and facilitating efficient commercial processes and manufacturing change flexibility post licensure. 

Quality by Design (QbD) is a concept applied to the design and development of biopharmaceutical molecules and manufacturing that entails building quality into the process and product in a systematic, science- and risk-based manner. The QbD concepts outlined in the International Conference on Harmonization (ICH) Q8(R2), Q9, and Q10 guidelines provide the foundation for implementing QbD during specific stages of product development.

  A critical component of QbD is understanding the needs of the patient and the specific quality attributes of the product that are linked to safety and efficacy. Thus, to implement QbD, it is critical to have a fundamental understanding of the functional relationships between patient needs, product quality attributes, analytical capabilities, and the manufacturing process.  

Pharmaceutical product sponsors begin by identifying product requirements to meet patient needs and then determine the quality target product profile (QTPP) and the critical quality attributes (CQAs) required to meet those patient needs. Based on this information, the sponsor then designs the molecule, the manufacturing process, and the control strategy to ensure that the desired product quality is met consistently.  

The QbD approach can be maintained throughout the lifecycle of the product to facilitate innovation and continuous improvement based on expanded knowledge and new technologies. This knowledge is gained during development and grows with more manufacturing experience through process characterization, scale-up, technology transfer, and manufacture, as well as through increased patient exposure to the product. This approach encourages incorporating prior process and product knowledge and experience-based methodologies (i.e., platform knowledge) into manufacturing throughout the life of the product. 

The QbD approach was introduced into the US Food and Drug Administration's chemistry, manufacturing, and controls (CMC) review process in 2004 with the aim of enhancing and modernizing the regulation of pharmaceutical manufacturing and product quality.

 The goal of QbD is to develop robust, well-understood processes that 1) deliver a product meeting the QTPP and 2) are controlled by defined steps within the manufacturing process and that allow for manufacturing process changes within an established design space of input variables and operating parameters without negatively affecting process attributes or identified CQAs. When a product is developed using a QbD approach, the impact of raw and starting materials and process parameters on product quality are well understood and the sources of process and product variability are well-known and controlled. In contrast, traditional pharmaceutical manufacturing relies heavily on end-product testing and the process typically lacks the flexibility needed to respond to variables encountered during manufacturing. The outcome is often "quality by conformance" and a rigid, static manufacturing operation that is more susceptible to multiple manufacturing changes post licensure, thus requiring continuous updating of the application through burdensome global regulatory supplements, variations, or amendments.  

This paper focuses on the factors to consider when applying the QbD concepts outlined in ICH Q8(R2), Q9, and Q10 to biotechnology products. The paper is intended to capture and reflect both the current and future state of QbD implementation. Therefore, new tools and terminology (e.g., expanded change protocol and the post-approval management plan) have been included with an expectation that the paper will be revised in the future based on new information and experiences, such as the mock biotech common technical document (CTD) sections being developed by the European Federation of Pharmaceutical Industries and Associations (EFPIA) and the CMC Biotech Working Group (formerly Conformia), and the US FDA's CMC Biotech Pilot Program.  

This position paper begins by addressing patient needs and product quality attributes, followed by a discussion of characterization development studies and the development of the design space and control strategies. It then addresses how to incorporate QbD into a regulatory filing and finally, how to facilitate efficient, cost-effective commercial processes and manufacturing change flexibility post licensure. Although biologic and biotechnology products often present a higher level of complexity than small molecules in terms of manufacturing process or product structure, the concepts of QbD are the same as those for small molecules. This paper describes the nuances and complexities involved in implementing QbD in the manufacture of biotech products and offers guidance and interpretation for doing so. The scope of this paper is limited to well-characterized protein products, in which the natural molecular heterogeneity, impurity profile, and potency can be defined with a high degree of confidence.  

ESTABLISHING CRITICAL QUALITY ATTRIBUTES, SPECIFICATIONS, DESIGN SPACE, AND CONTROL STRATEGY FOR A BIOTECH PRODUCT

QbD is centered on the patient.Understanding the patient's needs for managing his or her disease state helps define the QTPP. Safety, efficacy, convenience, compliance, and cost effectiveness of the product must be considered. Next, CQAs of the product are identified, based on the understanding of the impact of various quality attributes on the safety and efficacy of the product. The initial assessment is based on molecular design and known attributes of the molecule. These include not only a comprehensive understanding of the molecular structure but also data collected from clinical and nonclinical studies that have been performed with the molecule and with other relevant molecules, as well as other applicable published knowledge. This process of assessing quality attributes and determining their criticality is ongoing, and is revisited continuously throughout the product lifecycle as more data become available. These data are also used to set appropriate specifications for the drug substance and the drug product, and to develop the overall control strategy.  

The CQAs are also used to design the molecule and the manufacturing process to meet patient needs and endpoints for safety and efficacy, as well as to control process and product quality. Further, the process is optimized and characterized, and the cumulative data set is used to define the critical process parameters, process design space, and the process control strategy, which together ensure that appropriate quality is maintained and variability is managed during the manufacturing, storage, and distribution of the product. This approach is schematically illustrated in Figure 1. A more detailed discussion of these steps is presented below.  

Figure 1. Steps taken toward establishing critical quality attributes, specifications, design space, and control strategy 

Products that are early in the development process are in a unique position to participate fully in prospective QbD during development. Consequently, companies may be able to develop an efficient, cost-effective commercial process for such products, and benefit from a more flexible reporting mechanism for changes to the application after approval. This situation is expected to provide an opportunity for manufacturing to incorporate new technology more quickly and easily and to make changes within the design space without regulatory agency review and approval.  

Products in early stages of development usually have a limited amount of manufacturing history and limited commercial-scale experience. As commercial-scale manufacturing data are gathered post-approval, new knowledge can be continuously integrated into the pre-approval understanding. The resulting comprehensive knowledge can then be used to support process changes and the assessment of their potential impact on critical quality attributes based on all available lifecycle data.  

For biotechnology products, a key element in implementing QbD is engineering the molecule itself. A number of strategies are currently used by investigators to alter the properties of the molecule to achieve the desired balance among efficacy, stability, safety (such as immunogenicity), and manufacturability. These strategies include engineering the primary sequence to incorporate chemical and post-translational modifications for improved stability; recombinant and chemical fusion approaches for improved half-life and potency; and affinity maturation by phage display or transgenic mouse (e.g., xenomouse, Humax, and velocity mouse) technologies. Understanding the structure and functional attributes of therapeutic proteins, including monoclonal antibodies (MAbs), is key to developing the design space, because that understanding facilitates the selection of desirable quality attributes during molecular design while ensuring that bioactivity of the protein therapeutic is maintained.  

Technologies based on combinatorial libraries and transgenic mice can generate a diverse panel of antibodies and protein-based candidates selected for binding and specificity for a target. Further selection and screening may involve recombinant cloning from hybridomas, humanization, and fusions (e.g., Fc or human serum albumin, PEGylation) and the use of stringent parameters like 

 assessment of biological activity. Other key factors in the selection of an efficacious molecule include affinity and the rate of dissociation from the target. Protein engineering also can be applied to increase tissue penetration and distribution. 

Switching the isotype of an antibody product to gain desired effector functions is one approach that has been practiced. In some cases, an IgG1 isotype is ideal for cell killing, whereas IgG2 is a favored isotype for eliminating the effector function of an antibody on target binding. For further enhancement of cell killing by an antibody, technologies focused on antibody Fc modifications by mutations or glycoengineering as well as fusions to toxin or drug conjugates have emerged and been taken to clinical trials for some targets. 

Engineering the protein sequence is also used to reduce the risk of an immune response. Murine and chimeric antibodies used as human therapeutics frequently produce an immune response that can result in a reduction in or loss of activity and an unfavorable pharmokinetic (PK) profile requiring frequent administration of a drug. Humanization, transgenic mice, and phage display approaches make it possible to select antibody sequences bearing high homology to human germline sequences with the goal of reducing the risk of immunogenicity. Nevertheless, the risk of immune response to a fully human antibody and other self proteins necessitates screening therapeutic protein candidates for immunogenicity using 

assays measuring T-cell responses. Sequence engineering also can be used to eliminate T-cell epitopes in the product.  

Besides rational design for efficacy and safety, science-based hypothesis-driven approaches are used for selection and early engineering to achieve balance in the optimal physical and chemical properties of proteins with respect to product stability during and after manufacturing. A common observation during the development and commercialization of protein products is the susceptibility of certain amino acids (e.g., asparagine and methionine) to chemical modifications. Deamidation of asparagines and oxidation of methionines in regions important for activity can affect efficacy in some products. Sequence engineering also may be applied to minimize risks of chemical instability.  

In summary, protein design and engineering are powerful tools for enhancing efficacy, modulating immunogenicity, altering proteolytic stability, introducing chemical modification sites, and improving expression. Rational design approaches make it possible to identify and engineer better protein therapeutic candidates right from the start, which combined with process optimization can pave the way for less frequent dosing, increased efficacy, decreased manufacturing costs, and an improved safety profile.

Using Laboratory, Nonclinical, and Clinical Studies to Identify and Assess CQAs

An important step in the bioproduct development process is the identification of the CQAs of the product that drive the assessment of the ability of the bioprocess and bioproduct to deliver the intended performance of the product.

  The initial step toward identifying the CQAs is achieving a full understanding of the molecular structure and product variants. Next, all the molecular quality attributes and potential quality attributes are ranked using risk-based tools. The objective of this exercise is to determine which quality attributes may have the potential to affect the performance (safety and efficacy) of the product. A typical monoclonal antibody product may have dozens of such quality attributes and all are considered and assessed. They include but are not limited to product-related variants, process-related impurities, formulation parameters, and essential attributes such as appearance. Examples of process-related impurities include host cell DNA, host cell protein, adventitious agents, residual medium supplements such as protein hydrolysates, and components such as nucleases or residual Protein A from chromatography resins used in the purification process. 

There are multiple approaches to assessing the criticality of quality attributes. One such approach is to focus on the severity and uncertainty of impact (to safety and efficacy), with the goal of process and product design and the control strategy to minimize the probability of occurrence to reduce overall risk. The ability to detect a quality attribute during product development is required to assess its occurrence, thus necessitating the development of methods suitable for this detection and quantitation. The need to detect a specific attribute can decrease as its severity and occurrence are eliminated or minimized to an acceptable level with product knowledge and process control. Other approaches do not incorporate an assessment of occurrence, because CQAs are not tied to the ability of the process to control an attribute.  

Prior product knowledge plays a key role in the risk assessment and consists of the accumulated laboratory, nonclinical, and clinical experience for any specific product quality attribute. It also can include relevant data from similar molecules and data from literature references. With the increasing use of platforms in the development of biotech products and process, such as monoclonal antibody products, it is expected that investments in creating this knowledge can be capitalized by applying it to several products once the platform has been established.

  This combined knowledge provides a rationale for relating the attribute to product safety and efficacy. As mentioned above, the assessment of the severity of the impact (to safety or efficacy) results in a list of quality attributes in order of criticality. The list may evolve as more product and process knowledge are accumulated.  

Process-related components may be handled by considering their potential safety risk. One approach to assessing this safety risk is to evaluate an impurity safety factor (ISF) with an appropriate target or lower limit. The ISF is the ratio of the impurity LD

 to the maximum amount of an impurity potentially present in the product dose: 

 is the dose of an impurity that results in lethality in 50% of the animals tested, and the 

 refers to the maximum amount of an impurity that could potentially be present and co-administered in a dose of product. The ISF is a normalized measure of the relationship between the level of an impurity resulting in a quantifiable toxic effect and the potential exposure of a patient to an impurity in the product. In the absence of an assay to detect an impurity, a conservative assumption is that all of the impurity in the process co-purifies with the product, and no clearance is achieved by the purification process. In cases where a sufficiently sensitive assay is available, the actual level of an impurity in the product is measured. Impurities can be eliminated from further consideration at any step where the safety risk is determined to be minimal, with an eye toward any impurity being removed or eliminated to as low a level as possible. An alternative strategy for process-related impurities, such as DNA or host cell proteins, is to demonstrate multi-log removal by validation that is conducted in a fashion similar to the validation of viral reduction or removal, thus obviating the need for a specification. 

The criticality of quality attributes may be explored in appropriately designed 

 studies. An example of the assessment of the affect of deamidation on the bioavailability, potency, and immunogenicity of a monoclonal antibody has been detailed elsewhere.

  A combination of tools may be used to establish correlations between quality attributes and preclinical or clinical performance. Examples include:  

analytical tools such as native isoelectric focusing (IEF), ion-exchange chromatography (IEC), and online mass spectrometry to quantify the level of deamidation

preclinical pharmacokinetics studies with the native and deamidated molecules

 potency studies with the native and deamidated molecules

preclinical immunogenicity studies using extended dosing with the deamidated molecule

determining the deamidation rate that would occur 

 and assessing its relevance compared to the circulating half-life of the molecule.

All or a combination of some of these studies may help qualify the criticality of an attribute such as deamidation. 

Setting Drug Substance and Drug Product Specifications

Drug substance and drug product specifications are two components of the control strategy that enable delivery of the QTPP and represent the ranges within which the product is considered to meet the desired product quality. It should be pointed out that there is not a one-to-one correspondence between a list of CQAs and specifications. Although some CQAs will have specifications (i.e., acceptance criteria) associated with them, there may be others (such as host cell proteins or DNA) that will not have associated specifications if it has been demonstrated that the process has satisfactory clearance through multi-log removal validation carried out in small-or large-scale studies. 

Figure 2.  Illustration of an approach towards setting specifications for product quality attributes. 

Product specifications can be proposed using a combination of approaches, consistent with the guidance in ICH Q6B (Figure 2).

  Where appropriate, certain attribute specifications are based on existing regulatory guidance. For other attributes, relevant limits or ranges are established based on several sources of information that link the attributes to the safety and efficacy of the product, similar to the way this information is used to assess the criticality of these attributes. These sources include but are not limited to:   

nonclinical studies with the product or the isolated variant or impurity such as binding assays, 

clinical and nonclinical studies with similar platform products

published literature on other similar products, variants, or impurities

the process control strategy and the extent of variability observed in the manufactured lots. 

The difference between actual experience in the clinic and product specifications depends on our level of understanding of the impact that the attribute under consideration can have on the safety and efficacy of the product. This is illustrated in Figure 3 for two attributes, purity by IEC and host cell proteins. In the case of purity, the proposed specifications of >95% compared to the clinical experience of >98% would require either nonclinical studies relating the impurity or impurities to safety and efficacy, or clinical or nonclinical data on these species from another similar platform molecule or the literature.  

Figure 3.  Illustration of differences between clinical experience and filed specifications for a product-related impurity (% purity by ion exchange chromatography, or IEC) and a process-related impurity (host cell protein, or HCP) for a typical monoclonal antibody product 

The proposed specification for host cell proteins (<100 ng/mg) is significantly broader than the clinical experience (<10 ng/mg). This may be justified based on our understanding of the host cell proteins or data from other molecules within a platform, as it is recognized that the types of host cell proteins present can be as important as the level of host cell proteins.  

In summary, identifying the CQAs and setting appropriate specifications and acceptable limits (or validating removal if applicable) are the foundational activities of implementing QbD for biologics. The former requires a risk assessment process to estimate the criticality of each quality attribute. Both activities depend heavily on prior knowledge from the sources listed above.  

Cover

First in a three-part series that discusses the complexities of QbD implementation in biotech product development.

Quality by Design for Biotechnology Products—Part 1

July 2, 2008, marked a significant milestone for the biopharmaceutical industry. That's when the US Food and Drug Administration announced its pilot program for the submission of quality information for biotechnology products consistent with the principles of Quality by Design (QbD).  

The greater product and process understanding provided by QbD can help companies in a variety of ways. Greater process understanding means more accurate and thorough validation and more robust processes, thus improving quality. More robustness also helps lower manufacturing costs by increasing yield, reducing manufacturing downtime, and decreasing the amount of rework and rejected batches. By strengthening the manufacturing process, QbD can speed time to market, thus increasing the return on investment. In addition, QbD holds out the promise of reduced regulatory burden, including fewer postapproval submissions, reduced end-product release testing, and the possibility of introducing process improvements without further agency review.  

Achieving those many benefits, however, will require adopting new ways of working and a new outlook on good manufacturing practices (GMPs). What will QbD mean in that regard? The answer lies in the convergence of science and compliance at the heart of QbD: (1) robust processes designed to provide statistically defined performance characteristics that ultimately result in products with a defined target product profile, and (2) good biopharmaceutical quality, defined as an acceptably low risk of failing to achieve the target profile. In other words, QbD combines increased scientific understanding of products and processes with the risk-based compliance that such understanding makes possible. 

This convergence of science and compliance will profoundly affect all areas of GMPs, including the nearly 20 such areas covered in the International Conference on Harmonization (ICH) Q7 guideline.

  Each GMP could, of course, be the subject of extended discussion in light of QbD, but more generally, the convergence of science and compliance will mean:  					

more integrated, coordinated activities across departments that have traditionally worked in separate silos

fundamental changes in focus for many personnel in development, manufacturing, and quality

changes in training to establish these new ways of working.

Under QbD, GMPs will be reconceived as a framework, rather than as the driver, for the performance of risk-based, flexible processes. In other words, if biopharmaceutical companies are to maximize the benefits of QbD, the technical and compliance revolution it embodies will need to be accompanied by a cultural and organizational revolution.  

Most companies have spent years carefully establishing and elaborating the steps involved in the manufacturing of biopharmaceutical products—from the initial cell culture vial through scale-up, bioreactor production, downstream processing, formulation, filling, and packaging. Not surprisingly, this approach has encouraged the development of functional silos, each narrowly focused on its area of expertise, inhibiting the diffusion and integration of knowledge throughout the organization.  

This model of drug production, with little crossfunctional involvement after each hand-off to another function, and with quality ensured through analytical testing near the end of the process, has served biopharmaceutical companies reasonably well for decades. But with tighter regulation and cost pressures that now reach into development and manufacturing, companies are seeking more efficiency in manufacturing processes by improving process understanding and control. 

QbD, with its aim of achieving a scientific understanding of manufacturing processes as early in the development process as possible, and of enabling continuous improvement in manufacturing, requires a much more integrated and holistic approach by staff in all functional areas. For example, in elucidating and, in particular, documenting the process design space (defined in ICH Q8

), development groups may need to operate with an increased attention to GMP guidelines because the data they develop by using design of experiments (DOE) to define the design space will be used to support manufacturing decisions to the FDA. Analytical and bioanalytical support functions will share in the responsibilities for this key element of process design and control.  

In essence, succeeding with QbD requires creating a continuous feedback loop between development and manufacturing, with upstream, downstream, and analytical personnel increasingly looking as far down the production process as possible, and manufacturing personnel reaching back into earlier stages of development, all tied tightly together by improved communication and documentation facilitated through IT groups.  

This holistic way of operating will mean some fundamental changes in focus by staff in key functions. For example, consider the age-old disjunction between development and manufacturing. It's often been said that development personnel "threw their creations over the wall" to manufacturing who would say, in effect, "thanks, we'll take it from here." GMPs meant simply following the rules established during validation and then performing quality assurance (QA) to make sure each batch of product met product specifications. 

But validation under a QbD regime aims to establish the design space within which changes may be made to process or product inputs while keeping the product within specifications. As a result, development and manufacturing will have to look together over the entire lifecycle of the process. Development personnel will therefore widen their scope to include issues of manufacturability. Conversely, manufacturing personnel will be looking at development data. Further, risk-based compliance means that manufacturing personnel, who have traditionally focused on issues of efficiency, will have to become decision-makers and risk managers, deciding which parameters to change when necessary or desirable and understanding the associated compliance risk.  

QA personnel also will need to be involved in defining the design space during validation, working closely with development staff. QA will change its focus from procedures and investigations to ensuring that the design space isn't breached and that risks are appropriately identified, assessed, managed, or mitigated. QA will likely ensure that the appropriate personnel have weighed in on a situation and that there is documented justification for decisions and actions taken. Often, justifications will come from technical reports, which in most organizations are not as regulated as GMP documents. This may require a new flexibility on the part of QA, because non-GMP documents may take on a new level of credence in the approval process and beyond. 

From one point of view, it's possible that flexibility for QA could come in the form of fewer deviations and investigations, because the design space will provide clear boundaries to define these. Under QbD, there could be fewer release data to be scrutinized, and acceptance criteria for release specifications would be more scientifically justified. Upstream process data could become more essential to the product quality equation, rather than only quality control (QC) end product testing. Batch records themselves might be less directive and more prescriptive. QA will need to ensure the appropriate data and justifications are documented during processing, rather than simply recording actions taken. All of this may require more process knowledge on the part of QA, but more likely requires only a different outlook on data credibility. For example, during vendor audits, the job will be to assess the risk that materials and services rendered pose to the valid design space. This will require an approach that is more scientifically based than a GMP-checklist style audit, which is currently common in the industry. 

From another point of view, it's possible that the FDA's evaluation of GMPs will not accommodate a completely flexible approach to document requirements. Because data produced by development staff must help define the space in which the manufacturing group is free to operate and make changes, quality oversight may in fact increase under QbD. How these issues are resolved between the industry and the agency will be important for conferring the freedom to operate within a design space. The prospect of having such freedom is a major appeal of QbD to the industry in the first place. 

Changes in the nature of the QC group's work will be driven by the trend toward real-time quality control embodied in initiatives like process analytical technology (PAT).

  This carries two implications for QC. First, insofar as such control becomes increasingly automated, some QC staff will be freed up to focus on attributes deemed critical to product quality. Second, smart companies will not simply throw new technology at process controls. Instead, armed with greater process understanding, they will be able to determine precisely where the greatest risks and opportunities lie in their operations and to address them strategically. Personnel supporting the manufacturing process therefore will need to become adept at creating, maintaining, and refining PAT and control strategies in line with the goal of continuous improvement embodied in QbD.  

These changes in the ways various functions do their part in ensuring GMPs will require additional training, which under ICH Q7 is itself a GMP.

 Different functions will, of course, require different kinds of training. For example:  									

QC staff will need training in PAT technology and strategy and in complex upstream analytics.

QA and manufacturing personnel will need training in risk-based assessment and compliance, and in appropriate process and analytical understanding 

Development staff will need help with DOE, and effective, or even compliant, documentation, and manufacturing constraints such as cost-of-goods efficiencies, GMPs, and risk analyses. 

But the convergence of science and compliance in QbD lends a common theme to all such training: the understanding of the design space. For some personnel, that will mean understanding the design space conceptually and, in specific instances, knowing the relevant parameters for purposes of control and the like. For others, particularly those who actually determine the design space, it means familiarity with the advanced statistical methods that are used to map the complex relationships among product quality attributes, the manufacturing process, and clinical safety and efficacy, and to determine the various permutations of critical input variables that will keep the product within specifications.  

It is precisely because biological processes involve many raw materials, numerous upstream and downstream processing steps, numerous types of equipment and operating conditions, and high levels of variation that the analytical and statistical rigor of QbD are required for designing highly robust and reliable systems. Many biopharmaceutical companies already use many of these statistical tools but not necessarily in a full-fledged QbD program. However, these tools are rarely subject to compliance oversight, and they are not easily amenable to the current procedural GMPs practiced in most biotech companies. 

Achieving GMPs in a QbD environment will also require that personnel from diverse functions learn how to work in crossfunctional teams. Such teamwork will be required to ensure product quality and that each function achieves some degree of crossfunctional understanding. For example, development, IT, and statistics personnel will need an appreciation of GMPs, while the quality and manufacturing groups ensure that the spirit of GMPs is captured as part of the project and lot histories. If effective and accountable team leaders are deployed, then crossfunctional teams can function smoothly as long as they have management support.  

In essence, the focus on the design space leads to what we might call a new workspace. Crossfunctional teamwork, the changing focus of work in specific functions, and a more holistic approach to quality are all part of this new workspace. Just as science and compliance converge in QbD, teams, functions and organizational processes will converge in this new workspace to realize the vision of more efficient development and production of biopharmaceuticals. 

 is a senior consultant, both at Tunnell Consulting, King of Prussia, PA, 610.337.0820, 

 1. International Conference on Harmonization. Q7. Good manufacturing practice guide for active pharmaceutical ingredients. Geneva, Switzerland; Nov 2000, p. 6 

 2. International Conference on Harmonization. Q8(R2). Pharmaceutical development. Geneva, Switzerland; Nov 2005, p. 11. 

 3. US Food and Drug Administration. Guidance for Industry. PAT—A framework for innovative pharmaceutical development, manufacturing, and quality assurance. Rockville, MD; 2004. 

The focus on the design space will lead to a new workspace, and will affect staff in the development, manufacturing, and quality functions.

BioPharm International-11-01-2009

BioPharm International, November 2009 Issue (PDF)

Where Winning is the Only Thing

Rapid Process Development for High Yield Plasmid DNA Fed-batch Fermentation

Finding the Balance in Managing the Quality and Efficiency of the Supply Chain

Quality by Design for Biotechnology Products—Part 1

QbD and GMPs: How the Convergence of Science and Compliance Will Change the Way We Work

To FIT or Not to FIT, That is the Question

Safety First in Biotech Development