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Development of CE-SDS (reducing)
method for an antibody by following
a QbD approach
Siméoni Philippe
San Francisco – September 2018
Novartis Pharma
© 2018 Novartis Pharma AG
Novartis Pharma
• Gerald Gellermann
• Christoph Roesli
• Roland Bienert
• Manuela Gritsch
• Markus Bluemel
• John den Engelsman
• These slides are intended for educational purposes only and for the personal use of the audience.
• These slides are not intended for wider distribution outside the intended purpose without presenter approval.
• The content of this slide deck is accurate to the best of the presenter’s knowledge at the time of production.
Acknowledgement and disclaimer
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Background information:
When using a plateform version of the CE-SDS method under reducing conditions, a strong
heavy chain peak tailing was recognized for an IgG1.
Method development to address:
1. Heavy chain tailing issue linked to electrodispersion: when the sample zone
has a lower mobility than the running buffer, the tailing edge will be diffuse
source: https://doi.org/10.1146/annurev.fluid.38.050304.092053
2. Baseline jump issue (observed for outlet injection / dilution of the running
buffer)
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Quality by Design for Analytics
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Adapted from: http://www.usp.org/sites/default/files/events/stakeholder_forums/2016/face-face-meeting/3b-life-cycle-
management-analytical-methods-industry-perspective-2016-10-13.pdf
Analytical Target Profile (ATP)
Technology selection
Predefined objectives
Critical Method Parameters
Method Development
SDS-PAGE -> CE
Fishbone / Risk assessment
Critical QualityAttribute
DoE : screening / optimization
Design & Understanding
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Analytical Target Profile (ATP)
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Attribute Target
Intended
purposeQuantitative determination of HC, LC,
NG-HC and sum of impurities
Accuracy Purity (LC+HC): 98.5-101.5% of
assumed true value
Precision Purity (LC+HC): Srel ≤ 0.35%
business
requirementsRun time below 45 min
No harmful reagents used (dyes, toxic)Operating demands relevant
for potential use in QC
Deliverables
(= the CQA to be controlled)
Measurement requirements
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What are the required precision and
accuracy to efficiently control a CQA?
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A simple fit for use tool was developed to define the quality of reportable results
Probability of potential OOS for each deliverable is assessed by the tool
Delivrable: Purity (time-corrected area-%)
Specification limit: > 96%
Mean: 98.35 %
Accuracy - systematic error: 1.5%
Precision - standard deviation: 0.35%
Overall failure rate: 0.76%
Statistical distribution: normal
Sp
ecific
atio
nlim
it
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Factors selection
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fishbone diagram &
literature research
9 potential impacting factors
(capillary / electrophoretic conditions /
sample preparation / running and
sample buffers / injection settings)
Expert round table
& Risk assessment
Factors selected for the DoE
1. pH of sample buffer
2. running buffer dilution / composition
3. applied voltage during the migration
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3 quantitative responses were define (and ranked):
HC peak tailing (converted to a numerical value by acquisition software)
Critical peak resolution
Separation performance in the Low Molecular Weight region
1. HC peak tailing2. Critical peak
resolution3. Separation performance
in the LMW region
Software peak asymetrySoftware peak resolution
(USP)
Responses setting
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Readout of the screening DoE
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Factors impacting the HC peak tailing:
Dilution of the running buffer (response not linear)
Factors impacting the resolution of NG peak:
1. Dilution of the running buffer (response not linear)
2. Voltage
Factors impacting the separation
performance for LMW:
1. pH of the sample buffer
2. Dilution of the running bufferthe variability of this response is increasing
significantly if 2 different running buffer
batches are used
Basis for the optimization DoE
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Contour plots bring valuable information regarding the method behavior. Combining
Contour plots of all responses helps to:
Set final analytical method settings
Define MODR(*) (method operable design region)
Readout of the optimization DoE
if operated in the green
area, the method will
always meet the ATP
requirements
(*) MODR = the range of operating parameters that produce reportable results with adequate quality
Running Buffer Dilution
Voltage
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Method development: before vs after
Learnings: Running buffer dilution is the most critical parameter, it impacts every response
Voltage « only » impacts the resolution between critical peaks
Sample buffer’s pH has a limited impact on the overall performance of the method
LCHC
Starting point
End of development
Peak
tailing
Critical
resolution
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The baseline jump issue
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LCHC
NG-HC
last buffer peak baseline jump
Phenomenon description: A baseline jump is observed after 3 to 4 injections using the same buffer set
Using a new buffer set, the baseline jump disappears (for the 2 first injections)
Reproducible for all instruments, capillaries and buffer sets
1st idea was to decrease the number of injections using the same running buffer vial
Possible explanation: depletion most likely due to the electrolysis of the running buffer
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4
3
2
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The baseline jump occurance time depends on the running buffer dilution
The height of the baseline jump is propotional to its dilution
Decision: move the baseline jump outside the integration range, keeping the required
performance of the method (stay in the MODR)
Correlation
study
RB dilution /
baseline jump RMT
The baseline jump issue
Jump
dilution 3
Jump
dilution 2
Jump
dilution 1
3
2
1
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Confirmation experiment was combined to a risk assessment study, to make sure
this phenomenon is well understood and under control.
Occurance time of the baseline jump chosen to disable possible interaction between
with the first observed degradation peak
The baseline jump issue
RISK ASSESSMENT
worst case scenario for baseline jump: 1.018 (*)
worst case scenario for first degradation peak: 1.055 (*)
Conclusion: No interference (1.020 < 1.055)
(*) Migration Time Ratio to the last buffer peak
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Conclusion of the method
development under QbD
Benefits of a development under use of QbD principles:
ATP guides the method development
DoEs reduce the number of experiments and save resources
Method parameters optimization is easy and fast
Best settings for the method can not be missed
Better understanding and prediction of the method behavior
Knowledge gained for the critical method parameters
MODRs: produce reliable results with required quality (predifined in the ATP)
= Method robustness improved
Baseline jump issue solved
HC peak tailing solved
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Thank you
© 2018 Novartis Pharma AG