Design Space Development for Lyophilization Using DOE and Process Modeling - Develop a relevant design space without full factorial DoE. - BioPharm International


Design Space Development for Lyophilization Using DOE and Process Modeling
Develop a relevant design space without full factorial DoE.

BioPharm International
Volume 23, Issue 9


Figure 11. Final experimentally verified primary drying design space
Occasionally, during routine production, a process may undergo a short-term deviation outside the established design space (e.g., because of a mechanical failure). Here, we present an example of a manufacturing deviation to illustrate how a mathematical model could be a powerful guiding tool in such situations. One of the more frequent lyophilization cycle deviations occurs when, in response to a spike in chamber pressure, the lyophilizer goes into a safety shelf freeze to protect the product. We performed a planned deviation experiment at the pilot scale to mimic the above scenario. A pressure deviation to 200 mTorr was modeled at two points of interest in the cycle: at the beginning of the primary drying hold when the shelf temperature first reaches the primary drying set point, and toward the end of sublimation when the product temperature starts to ramp up to the shelf temperature. After the experiment was complete, we simulated the same planned deviation recipe using the mathematical model, and we compared the product temperature profiles predicted by the model with the experimentally obtained data (Figure 12). Based on both the model and the experiment, the product temperature stayed well below the collapse temperature, even during the pressure deviation. However, as the shelf temperature returned to the setpoint after each safety freeze event, the product temperatures needed an additional 0.75 h to reach the temperature value just before the pressure deviation. Thus, for a pressure excursion of this type in manufacturing, the recommendation would be to extend primary drying by 0.75 h for each safety freeze. During routine production at full scale, product temperature data typically are not available. This study demonstrates that the model can be used to predict what the product temperature profile would be under a deviation scenario, thus enabling in-line cycle modifications and batch release decisions based on a sound scientific rationale.


Figure 12. Comparison of measured product temperature profiles and the predicted profile (blue curve) for the pressure excursion case
One of the key elements of QbD is to prospectively develop in-depth process and product understanding, and to apply that knowledge to establish robust manufacturing processes. From industry perspective, although the QbD approach is expected to bring about significant long-term benefits, concerns remain around the increased amount of characterization work that is needed upfront. In this article, we presented an approach in which a combination of risk assessments and mathematical modeling of lyophilization was used to guide characterization studies. With this approach, we were able to obtain a lyophilization design space that is relevant for manufacturing through relatively fewer experiments compared to a purely empirical approach. We also discussed how mathematical modeling can be used to simulate the impact of process deviations on process attributes, thereby enabling a more informed response to process deviations. This article shows that mathematical models based on a good mechanistic understanding of unit operations are excellent tools for more efficient implementation of QbD elements during manufacturing process development.

Jagannathan Sundaram is engineer II, Chung C. Hsu is an engineer, and Samir U. Sane is an associate director, all in process research and development at Genentech Inc., South San Francisco, CA, 650.467.4798,
Yun-Hua Max Shay was a co-op at Genentech Inc. when this work was done.

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