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

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nlim

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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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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

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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