When DoE fails: Mechanistic modeling for chromatographic manufacturability
Tobias Hahn, Thiemo Huuk, Teresa Beck, Jürgen Hubbuch
INTRODUCTION
Convection
Film Diffusion
Pore Diffusion
Dispersion
Adsorption
S
S
S
S
S
S
Repulsion
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
Shielding
In contrast, mechanistic models,
that describe the transport of
sample components using fluidand thermodynamic principles,
allow efficient process optimization in silico.
MATERIALS AND METHODS
In this case study, a high-capacity
cation-exchange resin was used.
The target component was an intermediately eluting antibody. One
low molecular weight (LMW) and
two high molecular weight (HMW)
species were to be removed.
All simulations, parameter estimation and process optimization were
performed with GoSilico's software
ChromX (top right).
While the impurities are hardly visible in the chromatogram (right),
an SEC fraction analysis (bottom)
shows that the LMW impurity
strongly overlaps with the target
antibody such that LMW removal
would result in less than 70% yield.
The ChromX user interface with examplary model.
Initial reference chromatogramm exported from GE Unicorn.
The model was calibrated using
two tracer runs with and without
the column connected and three
gradient elutions in non-linear
mode. Additional information
was obtained by offline SEC fraction analyses. ChromX automatically generated pseudo chromatograms from the relative
fractions content determined by
SEC, shown as dashed lines in
the plots on the right.
All unknown model parameters
were determined by curve fitting.
The plots show the overall result
and a close-up of the impurities.
While the model fits are very good by visual inspection, only the calculated 95% confidence
intervals provide information, if the parameters
are well identified.
For the Steric Mass Action adsorption model,
all charge and equilibrium parameters have low
relative confidence intervals. The kinetics have
larger but still acceptable values. Only, the steric
shieding parameters of the low concentrated
species could not be determined and are, thus,
set to zero.
LMW
Simulated single-component curves (solid lines) and fraction data (dashed lines).
MODEL-BASED PROCESS DEVELOPMENT
Once the model is calibrated, subsequent process development
and optimization can be done
solely in the computer.
Compared to the DoE experiments, the load volume was reduced by 75% and the column
length increased by 50%.
Together with optimized salt
elution steps, a high yield and
purity could be achieved.
The box below explains why the
high capacity of the resin could
not be utilized in this case.
The plot on the right shows the best
process set-up when load volume and
column length are kept constant.
When only buffer salt concentrations
and step/gradient shapes can be optimized, the target antibody and the
LMWs co-elute, such that the necessary
LMW removal results in a poor product
yield. Even a subsequent salt gradient
elution cannot avoid co-eluting HMWs.
L
LMW Reduction
STOP EXPERIMENTING.
L
LMW Reduction
Simulated chromatogram and predicted KPIs of the partly optimized process set-up.
IMPROVED PROCESS UNDERSTANDING
bound the higher the mobile phase
concentration. This results in a faster
moving concentration front of the
high concentrated target protein during elution.
In fact, the target peak was overtaking
the LMW and only a load reduction
could solve this problem.
HMW 2
The parameter uncertainity can be used to determine the uncertainty of the predicted KPI's.
This is accomplished by calculating statistics
from a sampling of the confidence intervals:
This confirms that the performed DoE
was condemned to a failure.
Simulated chromatogram and predicted KPIs of the optimized process set-up.
The mechanistic model also explains
why the DoE failed: The surface
charge of the LMW is indeed lower
than the target antibody, such that
it should elute earlier. Because of
the convex shape of the adsorption
isotherm (plot on the right), a higher
relative amount of protein cannot be
HMW 1
Simulated impurity curves (solid lines) and fraction data (dashed lines).
10
7
-5
6
5
Solid phase concentration [mol/L]
In a platform approach, only few
parameters of a chromatographic
purification step are adjusted for a
new molecule. The remaining design space is then explored using
high-resolution screening experiments based on Design-of-Experiments (DoE).
While this approach works for
most platform molecules, it requires a high sample amount, elaborate offline analytics, and is restricted to the calibrated experimental range.
In this industrial case study, DoE
failed to find a feasible process.
MODEL QUALITY
MODEL CALIBRATION
4
3
2
1
0
0
0.5
1
1.5
2
2.5
Mobile phase concentration [mol/L]
3
3.5
4
10
-6
Exemplary batch adsorption isotherm measurements and fitted
isotherm model curves for different salt concentrations.
SILICO.
GoSilico is a spin-off company of Karlsruhe
Institute of Technology (KIT) that develops
software and methods for computer-aided –
in silico – bioprocess development.
www.gosilico.com