Process modeling can be a useful tool to aid in process development, process optimization, and process scale-up. When modeling
a chromatography process, one must first select the appropriate models that describe the mass transfer and adsorption that
occurs within the porous adsorbent. The theoretical understanding of chromatographic behavior can augment available experimental
data and aid in the design of specific experiments to develop a more complete understanding of the behavior of a unit operation.
Biotech unit operations, including process chromatography, are complex because of the large number of factors that affect
process performance. A typical process chromatography step has 50–100 operating parameters that can impact its performance.
In addition, a similar number of raw materials are used during the process step. It is not feasible to evaluate the effect
of all of these variables on step performance. The current practice is to use risk analysis to identify process parameters
that need to be examined during process characterization with the extent of process modeling limited to use of linear statistical
Avecia Biologics Limited
An example of such statistical modeling has been published before in this series.5 The case study involved the design of a process analytical technology-based control scheme for a cation-exchange chromatography
step. A design of experiments (DoE) study consisting of 17 experiments was conducted with purity of the load material, start
point of pooling (start collect), and end point of pooling (stop collect) as variables. Pool purity and step yield were monitored
for each experiment. The results were analyzed using statistical software and are presented in Figure 1. It is seen that load
purity and stop collect have a significant impact, where as start collect does not have a significant impact on pool purity.
This model was then used to calculate the pool purity for all the experiments that were performed. Figure 2 illustrates a
comparison of the pool purities calculated using the statistical model and the actual pool purity as measured by reversed-phase
high-performance liquid chromatography (RP HPLC). It is seen that the two numbers correlate very well and support feasibility
of this scheme. While this example highlights the usefulness of an empirical model, there are significant limitations of this
approach. First, because the model is not based on fundamental principles that govern the unit operation, its predictions
are limited to the ranges of data for process parameters that the model is based on, e.g., load impurity of 1.8–5.8% for the
case study illustrated in Figure 1. Second, just like any other modeling approach, the underlying assumptions have to hold
true for the model to be accurate.
If possible, it is always preferable to create a process model based on fundamental principles. Such a process model can be
used as a tool for optimization, scale-up, and manufacturing support. This article is the thirteenth in the Elements of Biopharmaceutical Production series and focuses on process modeling tools that can be useful in capturing the process understanding. We review some of
the significant and recent contributions in the area of process modeling of process chromatography along with examples from
existing biotech processes.1–5
Figure 1.Statistical analysis of data from chromatography experiments