The Time Value of Information in Designing Downstream Purification Processes

To speed up the downstream process, you must get the right data, in the right amount, at the right time. Here's how.
Mar 02, 2010

ImClone Systems
Over the years, much has been learned about the downstream purification of monoclonal antibodies (MAbs). Standard processes are well established and there is extensive literature that describes methods and approaches. At the same time, there is a need to improve the process. In recent years, the US Food and Drug Administration has been influential in steering process improvement efforts through its 21st Century, Quality by Design (QbD), and process analytical technology (PAT) initiatives. This has led to industry reports such as, "A-MAb: A Case Study in Bioprocess Development," and novel methods for defining the design space.

Speeding up Knowledge Development

Figure 1
One driver for improvement is the desire to accelerate the process of getting products to market, which is a direct result of the speed of knowledge development. The speed of obtaining knowledge (especially quantitative knowledge) affects timing, cost, quality, and the ability to meet regulatory requirements (Figure 1). Costs go down with more knowledge to asymptote steady state cost. Speeding up knowledge acquisition means costs are lowered more quickly. So the time value of information grows fastest at the early stage of a project.

Strategies that emphasize the highest payout in quantitative knowledge per development time segment provide a significant increase in the time to review findings. The result is better decisions and better product quality.

QbD and Downstream Process Design

Figure 2
Stephen Covey teaches, "Begin with the end in mind."3 Doing so will increase the likelihood that you end up where you want to be as quickly as possible. The building blocks of QbD (Figure 2) show us what needs to be done to get to the end result. QbD shows the sequence and linkage of steps in knowledge development, allowing the right data to be collected when you need them.

Along the way, four critical aspects of the downstream development process must be addressed:
1. development of process and product understanding (risk assessment, experimentation method development, defining the design space)
2. development of the process control (process control strategy, change control strategy)
3. regulatory filing and approval
4. continuous improvement.

The development of appropriate measurement systems is central to the QbD building blocks. Measurement can be thought of as part of the process modeling work shown in Figure 2, which cannot be done effectively without good inputs. QbD clearly is more than just creating the design space.

Process Understanding: How Do We Know When We Have Sufficient Data?

A commonly asked question is, "How much data?" It can't be said early on whether QbD will require more or fewer data; the answer is situational and depends on the approach currently being used. In the early stages of QbD implementation, you will likely collect more data than needed. Also, regulatory agencies are reviewing QbD approaches and will become more comfortable with data collected in ways different than in past filings. Over time, the amount of data required will decrease as we learn how to effectively target the most critical data. The FDA, and indeed good science, stress that to produce quality products on a sustainable basis, you need to understand your process. Process understanding exists when you can accurately predict the performance of your process. This leads to the conclusion that there are sufficient data when performance can be accurately predicted. The required accuracy (quality) and precision of the prediction vary over the life of the development process; it is higher at the end of development than at the beginning.

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