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The author describes a methodology for developing a per product qualitative and semi-qualitative business case for applying QbD to a biopharmaceutical product.
The aim of this paper is to describe a methodology for developing a per product qualitative and semi-quantitative business case for application of quality-by-design (QbD) for a biopharmaceutical product. Previous authors have not frequently approached the business case topic on a per product basis. Instead, they have examined benefits across a portfolio for a company or aggregated across the pharmaceutical industry as a whole (1). In contrast, this effort focuses on a per product basis of business benefit estimate.
The primary purpose of a quality-by-design (QbD) business case is to demonstrate and elucidate the expected value that QbD is to deliver (2). Lack of belief in QbD's business case has been cited as a key challenge within pharmaceutical companies preventing successful QbD implementation (3, 4). Regardless, many companies already have adopted QbD concepts as their standard business practice for process and analytical development execution (5). Recently, momentum has increased based on QbD's recognized role as a new approach to improve product manufacturing and quality (6).
In 2010, FDA approved only six new biologics, giving few opportunities for QbD submissions (7). Overall, there have been few biopharmaceutical QbD submissions to date despite seven elapsed years from the first draft of the International Conference on Harmonization (ICH) Q8 guidance in November 2004 and longer since the first mention of risk-based quality approaches by FDA's Janet Woodcock in October 2002 (8, 9). Although submissions containing enhanced development data and parameter interaction studies have increased, there have been few, if any, design-space claims (10). Several biopharmaceutical QbD efforts have focused on retrospective QbD for licensed processes, leveraging larger amounts of manufacturing data, experience, and knowledge compared with processes for pipeline products (5).
Often, business cases are based on return-on-investment (ROIs) with minimal ROIs of less than a few years considered most attractive. During the early stages of QbD introduction, the business case was based on qualitative potential benefits in a few key areas: minimized manufacturing-scale development studies, fewer quality issues, greater flexibility to optimize postlicensure, improved patient-focus for the product, more clinically meaningful product specifications, and reduced effort in regulatory interactions. The business case needs to be updated based on the current level of biopharmaceutical QbD implementation maturity, considering that some of the potential benefits achievable for current pipeline products now may be substantially different.
Costs of goods
Selling costs for a product are lowered by lowering production costs, albeit not proportionally. In turn, production costs are fixed by Phase III clinical production processes. These processes are often developed to meet tight clinical trial timelines that do not permit substantial optimization for efficient licensed manufacturing, nor the ability to incorporate new process or analytical technologies (1). Simulated production costs, including indirect/fixed costs, for a typical aglycosylated protein ranged from $100–800/g depending on production host and selected yield assumptions (11). Lower production costs arise from reduced wastes (i.e., fewer rejected batches, deviations, or reprocessing), higher yields, and better utilization of assets (ie, greater overall equipment effectiveness) (12, 13). Specifically, the true aggregated cost of poor quality (COPQ) can often be greater than the more readily quantifiable cost of waste (14).
Development timelines directly affect the product's net present value (NPV). In one model, a 6-month delay to launch (type of product not given) translated to a $100-million loss in NPV (15, 16). In contrast, an 18 month acceleration increased NPV by $180 million by the model (15, 16). The average internal rate of return (RIR) for R&D for a biologic is 13% and NPV is $1.26 billion (15, 16).One author estimates a cost of $1 million in expenses for each day of product development and > $0.5 million in losses for each day delay in product commercialization (17).
Later parts of clinical phase timelines to final filing are primarily affected by assembly of clinical data, but timelines in early clinical phases can be highly affected by chemistry, manufacturing, and controls (CMC) activities. Even the availability of nonclinical bulk material can be rate-limiting because often the goal is to start pharmacology and toxicity studies as soon as possible (18). In addition, the entire timeline benefit associated with reducing CMC risks likely is under-estimated because some lurking CMC issues never are uncovered when clinical issues halt product candidate development.
Quantifying cost and timeline benefits
Table I shows a sample matrix of QbD-related benefits and proposed steps for how to evaluate them. Ultimately all benefits can be related to a cost, either directly or indirectly, because timeline extensions are converted into opportunity costs. Direct costs can further be broken down into cost reductions or cost avoidance, based on the ability to either allocate fewer resources initially to complete a deliverable or request fewer resources to revise or redo a deliverable. Overall, the cost avoidance category serves as the most frequently estimated QbD benefit, primarily because it focuses on minimizing additional unexpected resources not already allocated.
Table I: Sample matrix for quantifying biopharmaceutical QbD benefits.
A business case is based on ROI in the form of costs for QbD implementation versus benefits of having applied QbD principles to the product and process during either product development or post-licensure. The business case for QbD can be difficult to establish because QbD has several tangible and intangible benefits that are challenging to quantify. Interestingly, this situation is similar for other business frameworks such as process safety (19). In these cases, it is both hard to predict which benefits are most likely to materialize and difficult to develop leading indicator measures that can show early evidence of improvements. In addition, QbD benefits can take 3–5 years to materialized, and sometimes are not apparent until the product has been licensed (1).
Some industry members feel that benefits are harder to estimate than costs because benefits are based on what may happen with a pipeline product by applying existing product quality performance data. Thus, benefit predictions for reduced costs to address atypical investigations and manufacturing deviations based on existing products can be used to develop the business case for a pipeline product. However, predictions can only be replaced by actual data several months after commercial production begins for the new product.
Benefits include not only reductions in the numbers and types of issues (i.e., less proven, cost avoidance estimates; see Table I) but also the size of supporting quality organizations (ie, committed, cost reduction estimates; see Table I) (2). In addition to quality, business measures, such as titers and yields, are becoming important indicators of QbD benefits. The list of other benefits (often less quantifiable) has expanded from the early stages of QbD implementation. These examples include supply chain reliability, process robustness, process variability, inspection focus, post-approval changes, regulatory expectations/benefits, globalization, and faster time to market. Furthermore, reducing development risks has benefits both in minimizing unnecessary product/process development work and ensuring that prudent development work is not overlooked (20). Thus, it can be difficult to clearly depict how QbD benefits product development costs while pipeline efforts are still underway, especially during early stages of development (21).
Table II shows typical QbD benefits and examples of ways to quantify them based on the cost associated with current wasteful, nonvalue added activities and a typical target reduction of that waste specifically associated with QbD implementation. The quantitative categories typically used for QbD benefits (see Table II) heavily overlap with the categories of risk reduction (i.e., reducing number and severity of events) and sustained value (i.e., reliable processes) developed for quantifying process safety benefit (19). Similarly, the qualitative categories of corporate responsibility (i.e., planning and doing things right) and business flexibility (i.e., greater freedom and self-determination) also appear relevant for examining qualitative QbD benefits (19).
Table II: Benefit categorization and example quantifications for a single product (data are self-generated and drawn from sources including 2, 21, 22, 34). Values are expressed as amount per year.
Lost sales revenue can reach up to $3 million/day for a $1 billion blockbuster product, and this number does not include wasted marketing efforts or lost patient confidence (2). Although QbD is one framework that can minimize the occurrence and magnitude of a supply interruption, assigning QbD such a large cost avoidance benefit is not generally agreed upon since other factors likely share responsibility. Similar reasoning applies to the anticipated QbD benefit of avoiding a typical $50 million clinical comparability trial. In both cases, a reduced cost avoidance benefit assumption of 5% of the total benefit has been used (see Table II).
Using a reasonable set of cost avoidance benefit assumptions for all examples, the estimated QbD benefit per category can be calculated for a typical product (see Table II). Highest benefits are expected in productivity, followed by quality, then technology and personnel, and finally regulatory. Interestingly, cost avoidance for the productivity category, and not the quality category, stands well above the other categories. Further refined, product-specific estimates in each of these categories can be made based on the mitigated outcomes of process risk assessments.
Based on a relatively short list of example benefits, the total annual product-specific benefit was calculated at over $3.5 million/year (see Table II), or about 3.5% of the $100 million cost of goods for a product with $1 billion annual sales (23). This value is low in comparison with the benefit of 10–20% reduction in cost of goods (which included benefits of reduced defects, cycle time, compliance, and commercialization costs) from improved better product/process development practices estimated elsewhere (1). However, the identification and inclusion of additional examples is likely to increase these quantifiable benefits.
The cost of QbD implementation generally has been easier to estimate, but not without its own estimation challenges. Implementation costs for effective QbD execution can differ within and among companies (21). There is a perception that smaller companies have a greater burden especially for high throughput process and analytical equipment and data management systems that enable efficient multivariate analysis (21). Some questions remain regarding whether the significant investments in scale-down models, along with execution of additional univariate and multivariate lab-scale studies, are worth the effort (20). However, others believe that the cost to start QbD-related experimentation is less than $1 million and the incremental effort to perform it during initial clinical development phases was about 6 person-days (24–26). These divergent costs perceptions may largely depend on adopted level of pre-investment associated with the desired product-specific QbD strategy, for example the extent of process understanding. At a minimum, however, QbD can be used successfully to more effectively perform the existing scope of process development work.
Table III shows the cost estimate for the additional CMC workload for an illustrative bioprocess, assumed to be executed using internal company resources. Estimates have been made in areas of staff, expenses, and capital which add up to $6–10 million over the course of a typical 6-year product CMC development cycle. Timeline impact from QbD-related workload was assumed neutral because benefit of reduced time from less redoing of deliverables owing to CMC failures was felt to be offset by the extended time needed for additional net effort increases. The value of $10 million was taken for the QbD implementation cost for this analysis.
Table III: Illustrative "bottom-up" bioprocess QbD cost analysis for additional CMC workload on a per product basis. FTE is full-time employee, DOE is design of experiments.
Even if the incremental costs associated with initial QbD workload are not as significant as perceived, they are paid during drug development stages and the payback is largely postlicensure in manufacturing (27). Overall, QbD is a near-term investment of staff, expenses, and some capital, funded by process and analytical development, for a long-term, postlicensure gain in current operations or expected improvements that is realized by manufacturing (e.g., supporting knowledge for atypical investigations). Because most companies have structurally distinct development and manufacturing organizations, it can be hard to orchestrate this preinvestment across development stages and through to manufacturing. Thus, pre-investment must be carefully managed, especially for higher risk pipeline products. More success may have been achieved to date solely by manufacturing applications, for example the use of standardized work to reduce variability for licensed products (28). This success is reflected in the productivity cost-avoidance benefits estimated in Table II.
Initial high implementation cost estimates for the first few products in a company's process platform are expected to significantly decrease as experience is gained for similar platform products, and quickly produce observable process development efficiencies. Implementation costs are not expected to decrease, and may even increase, as the biopharmaceutical industry undertakes new therapeutic modalities, often bringing in new platforms through partnering with or buying small biotechnology companies. Regardless, because QbD is based heavily on design-for-six-sigma (DFSS) principles, there is still substantial net benefit in its application even to novel aspects of a biopharmaceutical product or complex bioprocess steps where less prior knowledge is available to be leveraged (29).
Table IV: Published rough biopharmaceutical base numbers for "top-down" calculation of QbD implementation costs.
QbD implementation costs, estimated from adding up the incremental costs for the additional CMC workload (see Table III) compared reasonably well with the estimate obtained from published ranges for other typical biopharmaceutical industry costs for an example of a product with $1 billion in annual sales (see Table IV). In both cases, the QbD implementation cost was 10–20% of process/CMC development costs. Although the $10 million QbD implementation cost estimated for per-product QbD implementation is a significant chunk (around 10–20%) of CMC development costs, it is a very small percentage (around 1–2%) of total product development costs (see Table V).
Table V: Biopharmaceutical QbD costs relative to total chemistry, manufacturing, and controls (CMC) costs and total development costs. FTE is full-time employee.
With both potential QbD implementation costs and cost avoidance benefits being loosely quantified, a rough return-on-investment can be calculated and thus the question of whether QbD is worth the investment can begin to be examined, albeit with some uncertainty. Consequently, an illustrative business case was constructed to examine sensitivity of ROI to assumptions in product sales, batch cost-of-goods, and the percentage of batches discarded owning to reasons of insufficient understanding (see Table VI). The cost of QbD implementation was estimated at $10 million (see Table III). An overall discard rate (based on all reasons for failure) of 5% and fraction of sales that is COGS of 25% (15% cost of goods only, plus 10% for cost of services) was used to calculate potential benefit (compared with the more detailed estimates presented in Table II)(23). The simple benefit calculation does not include reduced lost sales, reduced market share, regulatory implications, or other detriments associated with higher discard rates/uncertain supply for a licensed product.
Table VI: Illustrative business case return on investment (ROI) showing linear variation with product sales, discards arising from insufficient understanding and batch cost.
ROI varied linearly and directly with product sales, discards arising from insufficient understanding, and batch cost. Although this set of calculations suggested attractive ROIs for processes with high discards, improving low discard processes also had merit (see Table VI). Specifically, for a product with $1 billion in annual sales and $250 million COGs, a decrease in discard rate from 5% to 1% translated to a $2.5 million annual savings opportunity, or a 4-year ROI.
Nature of QbD-related change
Engaging large organizations in QbD-related change implementation can be a challenging change-management exercise. There are several key areas of impact:
1. Structure: The disciplined and structured QbD approach uses paper analysis to drive experimental work. Additional staff member time spent on this analysis delays initiation of wet-lab work, but eventually it translates into less time spent on lower value-added work on less critical areas. Such risk assessments drive work towards what is really important to product quality and process performance (27). The design space established influences post-licensure regulatory strategies, tempered by the degree of regulatory comfort in a company's quality systems to control changes within ranges of demonstrated acceptable performance.
2.Technology: Although there is little change in the required science, there is a change in technology that challenges QbD implementation (3). High-throughput process and analytical technologies are linked to experimental planning and design to minimize resources and speed up the time required to address identified high-risk areas.
3. Knowledge: There is a prescribed knowledge build associated with QbD implementation. An information management strategy must maintain ongoing and current links to documentation and access to justifications for decisions. Increased understanding includes a primary focus on design space definition, specifically the extent of multivariate study linking multiple input parameters and raw material attributes (often from multiple steps) to CQAs. Data obtained are subsequently assembled into mathematical models that can readily predict outcomes for varied input ranges.
Understanding QbD implementation challenges
Many companies currently are struggling with QbD implementation (5). These challenges directly affect estimation of both costs and benefits, which in turn assist in fully creating and understanding the business case (21).
One main challenge is insufficient understanding of QbD itself. Training, mentoring, and overall integration into process development deliverables serves to improve this understanding. There are differences in levels of understanding among regulators within FDA and internationally (3, 5). Insufficient understanding among company scientists and regulators is potentially exacerbated by the lack of a significant number of actual success stories demonstrating the "value add" for large molecules. A bit of "flying the plane while building it" exists because many of the QbD implementation specifics are still being worked out and acceptance by FDA is not guaranteed. Interestingly, despite a similar assessment given by FDA's Woodcock about FDA's situation with the biosimilar pathway, several companies worldwide have managed to develop compelling business cases to undertake work in this area (30).
A second challenge relates to the cultural change required to adopt this new way of thinking (2), especially the increased requirements for cross-functional alignment (21). Several competing business factors limit the time, effort, and commitment required at all levels (ie, senior management, middle management, and staff). Upper management needs to consistently ask about risks to quality and timeline, and how can QbD mitigate these risks (1, 24, 31). The most mature QbD implementations have strong active and consistent senior management support, standardized and rigorous development tools/processes, and cooperation between development and manufacturing (25, 26). Generally, both regulatory and corporate scientists are more comfortable adding resources to address a high risk rather than subtracting resources when they do not add value to address a low risk. It thus has become difficult to redistribute resources while maintaining a "zero sum" outcome.
A third challenge is the unclear nature about whether QbD is a regulatory expectation, along with the "lack of tangible guidance for industry" (3). For example, some parts of ICH Q8 have been recommended to be part of inspections (32). Other parts of ICH Q8 have been incorporated in the newly approved revised process validation (PV) guidance, although without overtly using the term "quality-by-design" (33). Even so, some have observed that the new PV guidance requires "a wholesale shift towards a QbD approach to achieve compliance" and "greater characterization early in the product development cycle" (14). The co-existence of traditional and enhanced (i.e., QbD) approaches in these guidances suggests that a minimum level of QbD investment is expected by regulators. Potentially these incorporations eventually help develop a clear and unified picture of the information content and depth expected by regulators (5).
Ultimately, QbD adoption boils down to a company- and product-based decision about what has the perceived lower risk and cost profile: to understand the process before licensure and be able to leverage this knowledge to address variability, or to license a less understood process used to produce the initial batches for validation and then use investigations and corrective actions to improve it in a limited fashion over time within the bounds of the license (21). The calculation of ROI is highly sensitive to its assumptions and their projected accuracy. This dependence makes it exceedingly difficult to match QbD investment to its eventual benefits to assess ROI. Consequently, the pursuit of QbD during product and process development often has been rationalized based on qualitative improvements and the (often invaluable) benefits of systematic approaches, especially for delivable tracking, technical reviews, and communication. A key example is an organized, aligned, and justified approach to risk mitigation, pre-investment, and decision-making. A well-defined QbD strategy, and its implementation prior to incorporation into a submission, can be used to support and justify QbD costs during the early phases of product/process development.
Continued refinement of quantitative and qualitative ROI analysis is needed, especially based on actual case studies. Most urgently, several significant benefits remain to be quantified. Specifically, the evolution of QbD consensus among both industry and regulators is likely to highly influence ROI. Thus, outcomes from the FDA Office of Biotechnology Products pilot program, as well as industry case studies/mock submissions, are eagerly awaited.
The primary uses of a solid QbD busines case spans all levels of the organization:
1. Manage benefit expectations to justify resource allocations based on product and process risks and timing for pre-investments on the probability of technical and regulatory success (PTRS)
2. Match the expected QbD benefits to the company's vision of its future QbD state including its risk management strategy
3. Gain solid sponsorship from senior and middle management
4. Sustain enthusiasm through benefit examples for staff at all levels, especially at the bench.
Although a solid business case is important for sustaining strong management support, some argue that solid case study application examples may outweigh the business case in encouraging initial adoption at the bench level. This tilt in the balance might be because QbD heavily overlaps with traditional approaches to bioprocess development in some companies. Regardless, it remains necessary to continue to collect information to improve and refine business case estimation for sustained application.
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