In the Quality by Design framework, there is a significant emphasis on the robust characterization of manufacturing processes
and prospectively identifying manufacturing design space that ensures product quality. In this article, we discuss an approach
that leverages risk assessments, design of experiments, and mechanistic modeling of unit operations to establish a design
space that is relevant for manufacturing. We present an overview of our methodology and its application to lyophilization
process characterization for a monoclonal antibody formulation.
Lyophilization is a drying process in which a formulation containing the active pharmaceutical ingredient of interest is frozen
at low temperatures (well below 0 °C) and water is then sublimed by subjecting it to low pressure. Removal of water in this
manner preserves the physical and biochemical attributes of the product, thus resulting in good long-term stability. In a
typical lyophilization process, the drying consists of a primary drying step, in which low pressure provides the driving force
for frozen water in the product to start subliming; and a secondary drying step to remove any residual adsorbed water by desorption.
Figure 1 shows a schematic of a lyophilizer with three main components—the drying chamber in which vials are heated or cooled;
the vacuum pump, which provides low pressure in the chamber; and a condenser, which traps water vapor leaving the vials.
The Quality by Design (QbD) philosophy is increasingly being adopted in the pharmaceutical and biopharmaceutical industries.1,2 From a manufacturing perspective, this translates into leveraging a sound, scientific understanding of unit operations to
design a manufacturing process with prospectively defined multivariate parameter ranges that produce acceptable product quality
attributes. In the QbD framework, the multidimensional combination and interaction of input variables that have been demonstrated
to provide assurance of quality is termed the design space. Approaches for identifying the design space in the pharmaceutical industry that are discussed in the scientific literature
or at conferences generally rely on statistical design of experiments (DOE), which can be resource intensive.3–5 In cases where the number of input variables is large, performing a full-blown statistical DOE may simply be impractical.
Figure 1. Schematic of a lyophilization unit operation
In this article, we present a methodology in which a combination of a statistical DOE and a mechanistic mathematical model
was used to arrive at larger and practically relevant design space with fewer experiments than a full factorial DOE. Figure
2 presents a flowchart illustrating our approach to developing a design space for lyophilization.
Figure 2. Methodology for developing a design and excursion space for lyophilization. Details of the use of the model to expand
the design space are shown in Figure 7.
In the subsequent sections, we will further illustrate some of the steps in this methodology using a case study for lyophilization
of a monoclonal antibody (MAb).
Figure 3. Freeze-drying microscopy of a model antibody formulation