eBook: Analytical Method and QC Testing Strategies for Biologics Production - 6

Herein I will describe a case study for how to use DoE
principles to optimize the analytical unit operation of
sample preparation for a platform analytical procedure
for novel biopharmaceutical molecular formats.
The use of platform analytical methods for common
product types such as, monoclonal antibodies
(mAbs) has reduced the uncertainty/risk for the
manufacturing organization leading to less resource
being required for method development, qualification
and eventually validation. With the industry producing
products with an ever-increasing complexity e.g.
fusion proteins, antibody drug conjugates and
bispecifics to patients in the form of new medicines
the use of platform methods becomes an increasingly
challenging
approach to continue
with. Kovacs
et al.6 presented that an analytical method can
be thought of as three analytical unit operations;
sample preparation, measurement and replicate
strategy. For platform methods, the measurement
and the replicate strategy are defined in the standard
operating procedure (SOP) leaving only the product
specific analytical unit operations to be investigated
and in this situation, this leaves only the sample
preparation as the analytical unit operation requiring
optimization for analysis of these novel molecular
formats. The sample preparation information can be
captured in an associated technical document to the
SOP for the analyst to follow in the laboratory.
After generation of the ATP, where we have defined
the requirements of the analytical procedure the first
step towards developing a robust analytical procedure
is to screen for the initial method conditions and to
determine what are the main factors that affect the
method performance, followed by optimizing these
factors and then demonstrating robustness of the final
conditions. A Placket Burman design is an economical
screening design and is an appropriate approach for
screening multiple factors simultaneously. For this
initial step of the process it is important to choose
appropriate ranges for the factors under assessment.
Typically, the ranges selected should be wide enough
to see an effect but not so wide so as to not be relevant
to the method. Characteristically, the ranges should
be two to three times the level of process control. For
example, if pH is to be controlled to 7.4 ± 0.2 then
we would examine a range from 7.0 to 7.8 as part of
the screening DoE. By using a screening design such
as a Plackett Burman model we will not gather any
information if there are any secondary interactions
between factors. However, we will be able to identify
the main factors that significantly affect the results
obtained and this can allow us to remove any factors
from future designs that are shown not to be significant
for the reportable result obtained from the procedure.
Once the main factors have been identified the next
step is to optimize these factors so that we can meet the
method performance criteria started in the predefined
ATP and then define our method operable design region
(MODR). A fractional factorial model is an appropriate
design to provide specific combinations of the factors
under investigation over the ranges to efficiently
test their impact as main effects and the interactions
between these factors and is therefore very well suited
to factor optimization. A fractional factorial design
consists of the factors under investigation at two levels
(-1, +1) and with at least one center point, so as to be
able to determine if there is curvature in the model. It is
important to assess the model fit for the data obtained
from this optimization step is good as this will provide
confidence that there is predictive power within the
design space for between the actual and predicted
results (Figure 2). A poor model fit does not afford us
this predictive power therefore, additional levels will
6
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