The Challenges Of Applying QbD In Biopharma Development

By Hanna Jankevics Jones

With much of the science of biological molecules now more firmly understood, there is an increasing drive for the biopharmaceutical sector to follow small molecule pharma down the QbD path. The gradual adoption of QbD for biopharmaceutical development is being prompted not only by experience from conventional pharma, but also by the expectations of regulatory agencies. This is reflected in regulatory guidelines such as ICH Q11: Development and Manufacture of Drug Substances (Chemical and Biotechnological/Biological entities). Furthermore, the greater understanding required to drive QbD has the added advantage of building transferable knowledge, a particularly valuable benefit for an industry at a relatively early stage of development. However, implementation presents some substantial challenges – starting with understanding what exactly QbD is.

THERE’S LIKELY MORE YOU CAN LEARN ABOUT QbD

The underlying principle of QbD is that quality is built into a product from the outset rather than tested for during manufacture. A generally accepted definition is taken from the International Conference of Harmonization document Q8(R2) (ICHQ8) which describes QbD as ‘A systematic approach to development that begins with predefined objectives and emphasizes product and process understanding, based on sound science and quality risk management’. Central to the implementation of QbD is a structured and rigorous approach to product and process development that results in a detailed understanding of the factors that influence clinical efficacy, and a manufacturing control strategy based on the mitigation of risk.

QbD was first introduced as a concept more than a decade ago, but uptake in the conventional pharma industry was initially quite slow. The absence of a regulatory requirement, in combination with the need for broader and more rigorous experimentation, discouraged early adoption.  However, interest increased steadily as the industry recognized the potential value of the greater manufacturing flexibility delivered by a QbD-based approach. A further driver for implementation came with regulatory expectation of the application of QbD in Abbreviated New Drug Applications (ANDA), which was put in place in 2013. Today QbD is embedded firmly in both innovator and generic development and shapes their associated workflows. Figure 1 shows the basic steps associated with QbD application.

QbD begins with the definition of a quality target product profile (QTPP) which typically includes specifications relating to quality, safety, and efficacy. Key questions that must be addressed to securely identify the QTPP include:

• How will the drug be delivered -- in what form and at what concentration?
• What is the required bioavailability of the drug and how will this be controlled?
• How will stability be maintained, both during manufacture and in the finished product?

Once the QTPP has been established, it is possible to identify critical quality attributes (CQAs) for the product, variables that directly influence clinical efficacy and quality.  Examples of CQAs for biological products include post-translational modifications, microbiological properties, and product purity.  The focus of QbD subsequently moves to assessment of the risk associated with each CQA, the identification of an appropriate manufacturing process and the evolution of an effective control strategy for risk mitigation. These steps include definition of the design space, and raise questions such as:

• What is the magnitude of risk associated with a given CQA – is there a broad or a narrow window of acceptability that will result in the QTPP being met?
• What analytical techniques can be used to provide timely, sufficiently accurate data to monitor CQAs?
• What controls need to be put in place to ensure that defined specifications for the CQAs are adhered to?

Though the steps associated with product development and commercialization are the same, with or without QbD, the process directly impacts the rigor and approach that is required. For example, a QbD approach to investigating the CQAs of the drug product might extend to developing functional relationships between these CQAs and critical material attributes (CMAs) and critical process parameters (CPPs). This requirement for additional rigor may weigh against the uptake of QbD, where there remains a choice.

THE STEEP LEARNING CURVE OF QbD FOR BIOLOGICS

Currently, the biopharma industry faces some of the same practical difficulties that inhibited the early implementation of QbD in conventional pharma. Silo departmentalization complicates the justification of additional investment in R&D on the basis of enhanced return during commercial manufacture. And there is, as yet, relatively little experience of applying QbD to biologics, meaning that there is a steep learning curve to climb.

In addition, there are also some unique difficulties in applying QbD to biopharmaceutical development. A primary issue is the complexity and heterogeneity of biopharmaceutical products, which far exceeds that of their small molecule counterparts. In addition, the science underpinning the successful development and application of biologics is still evolving. Both of these factors make it difficult to robustly identify CQAs and securely link them to CMAs and CPPs, and impact scale up and technology transfer which are more complex with biologics, and at the same time far less well-established. Other factors impacting the application of QbD within biopharma include:

• Ease of product characterization – as cells from living organisms exhibit much higher variability than their small molecule, chemical counterparts, it can be difficult to securely quantify the potency of a biologic and/or to characterize any impurities present.
• Process variability – biopharmaceutical manufacturing processes tend to be associated with far higher variability than those used in conventional pharma. This is an issue further complicated by the lack of established and effective process monitoring tools.
• Stability – a major issue in the biopharmaceutical industry, where processes such as protein aggregation may not only diminish the therapeutic effect of a drug but also trigger an immunogenic response. While chemical drug substances often exhibit high stability, biologics can be extremely sensitive to the environment in which they are held.

The net result of this complexity is that the implementation of QbD within biopharma calls for a significant level of detailed information gathering. The next challenge, therefore, is identifying the analytical instrumentation that can deliver those necessary insights.

Analytical protocols for the biopharma industry continue to evolve in order to meet the need for increasing insight, not only for QbD but, more broadly, for industry progression. Identifying a certain issue as important (e.g., protein aggregation or the potential for subvisible particles to trigger an immunogenic response) creates a requirement for reliable detection. However, understanding the mechanisms that give rise to a problem and learning how to control them calls for additional layers of insight. As our understanding of the fundamentals of biologic behavior grows, so too does the requirement to dig deeper for data to support further progress. Traditional, well-established techniques, as well as innovative newcomers and hybrids that lie somewhere between the two, all have a role to play in helping regulators and developers reach a level of understanding and control that will ensure the continued innovation of safe and effective products.

BIO:

Dr. Hanna Jankevics Jones received her Masters degree in Physics and Engineering from KTH Royal Institute of Technology in Stockholm, Sweden and her PhD in Physical Chemistry from EPF Lausanne in Switzerland. She then worked at the National Institute for Medical Research in Mill Hill, London on biophysical characterization of proteins. In 2008, Hanna joined Malvern Instruments, initially as a product technical specialist for the Zetasizer range of instruments, supporting the biopharmaceutical industry and academia in applications for biophysical characterization. Hanna later moved into her current position as Principal Applications Scientist for Malvern’s novel product introduction department, where she guides the engineering teams’ efforts to create user-friendly and application-relevant instrumentation and software.