Only Time Will Tell With Biotech QbD

By James Netterwald, Ph. D.

When manufacturing a pharmaceutical product, there are various attributes that contribute to overall quality, including solubility, toxicity, and uniformity. These attributes are affected by process parameters, such as temperature inside of a bioreactor or gas chamber, internal pressure of a bioreactor, concentration of catalysts or raw materials, and diffusion rates. These variables are what are commonly referred in the QbD (quality by design) concept as attributes. The combination of, and interaction between, all of these attributes is referred to as the design space.

QbD basically deals with the questions: What is the basis of a company’s process decisions in terms of process controls, and how does the control of the process determine the quality of the product? From the standpoint of QbD, there is a theoretical three-dimensional (design) space around a manufacturing process. The view is that as long as a manufacturer keeps the process within that space, the process control as well as the product quality should be safe. “However, it turns out that it’s a lot of work for a developer to create that kind of data package to describe this three-dimensional design space,” says Wolfgang Noe, senior executive at Biogen Idec and an expert in QbD. “It is pretty difficult to implement QbD and make a long-term effort not only to look for individual parameters but also to have an integrated QbD approach in which you change one parameter at a time or multiple parameters simultaneously.”

QbD requires a thorough understanding of how quality can be impacted by fluctuations that occur in the production process. QbD ensures that scientists and engineers jointly have an understanding of the impact of various process parameters on overall quality. That is, rather than follow a narrow biomanufacturing process definition repeatedly, the QbD approach requires that scientists and engineers build or design changes in critical parameters in the process in order to improve the quality of the final product. The net result of implementing a QbD approach into a pharmaceutical manufacturing process is “an ability to mitigate the aftereffects and create higher quality products with lower amounts of scrap and waste,” says Arvindh Balakrishnan, VP of Life Sciences Business Unit at Oracle.

FDA’s Interest In QbD
One of the ways a regulatory agency can ensure consistent quality of a pharmaceutical product is by regulating the manufacturing of that product, but this has not always been the case. “In the early days of process manufacturing, you developed a process, and the agency was not very involved in process decisions or proposing new development parameters,” says one QbD industry expert. Nowadays, the regulatory agency wants to monitor manufacturers’ plans for validation runs more closely to make sure that the process and any changes in those parameters are completely controlled and do not negatively impact the quality of the end product. “Regulatory agencies are very much interested in seeing your early development data, which could be data from many more runs to full-scale runs, pilot scale runs, etc., to show that your process is consistent and stable,” says the QbD expert.

The FDA has been quite interested in seeing that biopharmaceutical companies show at least a fundamental understanding of this design space. Incorporating QbD approaches from the start ensures that the organization has collectively understood and optimized the design space for best human health outcomes. Given the FDA’s recent focus on QbD, it could be argued that incorporating these approaches will lead to improved drug and process approval, assuming that all other criteria (e.g. efficacy, safety, and toxicity profiles) remain the same.

“I think, overall, the purpose of QbD, from a regulatory perspective, was to design manufacturing processes for biologics that are more science-based,” says the QbD industry expert. “So, the major point from the regulatory agency is and was to know why, for example, you are cultivating the producer cell lines at 36 or 37 degrees, that is, to know the scientific basis of what you’re doing in your manufacturing process.” But not only does the regulatory agency want to be notified about any changes to the manufacturing process, more importantly they want to know how those changes will impact the quality of the final product — the drug.

Influence On Investors
QbD may be the ideal approach for developing and ensuring a high-quality pharmaceutical or biopharmaceutical product, but how does it translate into best practices for business? For example, does implementing a QbD process help these companies attract investors, improve their bottom line, or even improve their best practices? One thing is for certain: “It is logical that a biotechnology company with a promising pipeline and a well-documented product and process development paradigm will be more attractive than a biotech with a promising pipeline but somewhat disparate product life cycle management [PLM] processes,” says Balakrishnan. “I think a much more tangible correlation is that biotechs with a more robust pipeline tend to obtain better investment valuations, and those with more consistent PLM processes have better rationale for why their pipelines should be valued higher.”

Overall, QbD helps the biopharmaceutical industry face challenges in a changing investment environment by ensuring that the process development process is optimized for the intended use and efficacy of the product. “Adopting QbD techniques helps biotechnology companies and biopharmaceutical companies bring product to market more efficiently, with a detailed evidence repository for compliance and submissions,” says Balakrishnan.

Does implementing a QbD approach save money and improve the bottom line? “I think one of the original ideas of QbD was that it will save money because we thought that we could do most of the validation work in small scale, in pilot scale, and in a non-GMP [Good Manufacturing Process] environment,” says the QbD industry expert. ”Just to construct those kinds of complex QbD models, it takes quite some time to provide the data.” And, of course, providing all this additional data to regulatory agencies delays a drug product’s time to market, which translates to greater financial cost to the drug developer.

“I think the upside of QbD is that you now have more wiggle room from the manufacturer’s perspective to provide material which is acceptable from a quality perspective. However, you need to do much more to create the data that supports your stable position in that three-dimensional space. So, I think QbD definitely turns out to be more work on one side, and on the other side it brings for the patients more safety and more consistency around product quality,” says the QbD industry expert. “I believe in five years, if we have sort of a standard submission for QbD, we may know more about the general trends in QbD. Right now, there are no standards in QbD — everyone in the industry is doing their own thing and not sharing their data with their colleagues. So, only time will tell how successful QbD will be in the bio/pharmaceutical industry.”