Building a Business Case for Biopharmaceutical QbD Implementation (Peer Reviewed) - The author describes a methodology for developing a per product qualitative and semi-qualitative business case for a

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Building a Business Case for Biopharmaceutical QbD Implementation (Peer Reviewed)
The author describes a methodology for developing a per product qualitative and semi-qualitative business case for applying QbD to a biopharmaceutical product.


BioPharm International
Volume 25, Issue 8, pp. 40-47

ESTABLISHING THE BUSINESS CASE

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: 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.
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).

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.


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