Coming to Biotech Near You: Quality by Design Part 1: Its Applicability in Biopharmaceuticals - The principles of QbD can be applied to biotech development and manufacturing to help resolve many


Coming to Biotech Near You: Quality by Design Part 1: Its Applicability in Biopharmaceuticals
The principles of QbD can be applied to biotech development and manufacturing to help resolve many common issues.

BioPharm International
Volume 21, Issue 5

Conrad J. Heilman, Jr., PhD
Biopharmaceutical scientists can be forgiven for thinking that they are already practicing the science-based approach to drug development promised by Quality by Design (QbD).

For more than 30 years, they have worked methodically to develop the science and technology required to design and genetically engineer systems and develop processes required to commercialize protein-based products—some of which are complex, large molecules with numerous polypeptide chains, conformational requirements, post-translational modifications, and other attributes. Despite the fact that such products are derived from living bacterial or mammalian cell culture systems that require careful handling and control, the industry has learned how to develop and manufacture many safe and effective products to address a great number of previously unmet medical needs.

Watching these scientists work, one develops an appreciation for their knowledge of these complex systems and skills required to control them. On the basis of their intimate familiarity with every facet of these processes, many scientists, like artists, can tell from visual inspection (e.g., of a cell culture) what is needed to complete the picture—a little more oxygen, a particular nutrient, or some other intervention—to "make the cells happy." Some manufacturers apply elements of QbD, as well as Process Analytical Technology (PAT). A lifecycle approach to validation, for example, closely resembles aspects of QbD. Some manufacturers have also carried out risk assessments in assuring quality. And some companies are using multivariate analysis to solve problems encountered in biotech processing.1 Little wonder, then, that some industry scientists regard QbD as redundant.

Far from being redundant, however, QbD is the next stage of scientific sophistication for an already very scientifically sophisticated industry in its quest to produce safer, more efficacious biopharmaceuticals and to produce them more rapidly, cost-effectively, and in a regulatory-compliant manner. In an often-quoted statement from 2004, FDA's Janet Woodcock said that QbD "means that product and process performance characteristics are scientifically designed to meet specific objectives, not merely empirically derived from performance of test batches." Further, she said, "good pharmaceutical quality represents an acceptably low risk of failing to achieve the desired clinical attributes." QbD then is two-pronged: (1) increased scientific understanding of products and processes and (2) risk-based compliance made possible by that understanding.

Table 1. Minimal approach contrasted with the QbD approach
Many current methods, scientific though they may be, do not meet those criteria. Table 1, developed by the International Committee on Harmonization (ICH), supplies some contrasts between traditional approaches to drug development, manufacturing, and control and approaches that leverage QbD. With QbD, quality is designed into the process and product instead of being "inspected" in after the fact. QbD scientifically provides a greater understanding of the complex relationships among product quality attributes, the manufacturing process, and clinical safety and efficacy by determining the various permutations of critical input variables that will keep the product within specification. With comprehensive knowledge of the multiple, complex interactions among variables, it is then possible to predict the outcome from particular permutations, thus opening the way for a risk-based approach to compliance. Process understanding increases dramatically and leads the way to regulatory flexibility and continuous validation as more and more experience is gained over time.

Consider, for example, the deceptively simple challenge of addressing variations in the attributes of raw materials. Buying in bulk and seeking the best prices for raw materials, companies may want to switch to and qualify another supplier of what is expected to be an identical product. However, what might be thought to be identical might have product attributes that may vary in subtle ways from the products of the original supplier. In fact, different batches of raw materials from the same supplier can also vary. If this raw material is critical, these variations may interact in myriad ways with all of the many other variables associated with producing the product and cause the process to fail. Far from simple, these multiple interactions are nearly impossible to get a handle on through laborious single-factor analyses, trial and error, or even the informed intuition of an experienced scientist.

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