Considering Quality by Design (QbD) in Analytical Development for Protein Therapeutics

Considering Quality by Design
(QbD) in Analytical Development
for Protein Therapeutics
Weijun Li
Global Biological Development
Bayer Pharmaceuticals

Disclaimer
• The contents of this presentation are based upon the
opinion and professional experience of the presenter, and
the statements and approaches do not represent the
official position of Bayer Pharmaceuticals. The actual
results may differ materially from the contents of the
presentation due to various factors. The presenter and
Bayer have no obligation to update the contents
contained in this presentation.
Bioprocess International WEST 2016, Weijun Li

Overview
QbD concept expanded from process development to
analytical development
Consider QbD in the method life cycle
Include risk assessment and prior experience in QbD
Evaluate the method robustness in early development
QbD case study with SEC method development

Analytical Procedures and Methods Validation for
Drugs and Biologics
Guidance for Industry, FDA, July 2015
III. Analytical Methods Development
“During early stages of method development, the robustness of
methods should be evaluated because this characteristic can help
you decide which method you will submit for approval.”
“You should submit development data within the method validation
section if they support the validation of the method.”
“To fully understand the effect of changes in method parameters on
an analytical procedure, you should adopt a systematic
approach for a method robustness study (e.g., a design of
experiments with method parameters). You should begin with an
initial risk assessment and follow with multivariate
experiments. Such approaches allow you to understand
factorial parameter effects on method performance.”
Bioprocess International West 2016, Weijun Li

Key steps in implementation of QbD
for a biotech product
• ICH Q8 defines QbD as a systematic approach to development that begins with
predefined objectives and emphasizes product and process understanding and
process control, based on sound science and quality risk management.
Rathore & Winkle, Nature Biotech,
2009, 27(1): 26-34

Comparison of QbD Terminologies in
Process and Analytical Development
Items Process QbD Example Analytical QbD Example
Target Quality Target
Product Profile
(QTPP)
A liquid product
stable at 100
mg/mL for at
least 24 months
Quality Target
Method Profile
(QTMP)
An accurate
impurity method to
determine the size
distribution of
release and stability
samples
Input Critical Process
Parameter
(CPP)
Flow rate,
conductivity, pH,
etc. in
purification
process
Critical Method
Parameter (CMP)
Flow rate, mobile
phase pH, salt
concentration, etc.
in HPLC analysis
Output Critical Quality
Attribute
(CQA)
Max. 5%
Aggregates
Critical Method
Attribute (CMA)
Peak Resolution
>1.5
Deliverable High quality
GMP material
1.5 KG DS GMP compliant
testing results
%Aggregates
%Monomer
%LMW

QbD Based Analytical Development in
Method Life Cycle
Initiation:
QTMP (intended use, spec,
sample matrix, assay
throughput), risk assessment
Scouting & DoE:
Platform experience,
variables, responses,
CMP, CMA
Modeling & Confirmation:
Design space, effect on
reportable results, robustness,
system suitability, SOP
Method Validation:
Phase appropriate- validation,
ICH Q2
Method Improvement:
Continuous monitoring method
performance, control trending,
failure investigation

SEC Method Case Study:
QbD workflow is broken down to 9 steps
Initiation:
• QTMP
• Plan the development based on the
protein molecular characteristics:
Mw, pI, PTM, hydrophobicity, domain
specific property, etc.
• Platform assay experiences
• Formulation study
• Material availability
Scouting:
• Columns
• Mobile phase components
(appropriate for the target pH
range; compatible with sample)
• Select the MP and CMA for DoE
Initial DoE / Modeling:
• Wide range DoE
• Modeling to identify CMP, study
CMP-CMA correlation and design
space
Confirmatory DoE:
• Small range DoE within
design space
• Robustness confirmed using
reportable results
Additional Confirmation
Studies:
• 3 column gel lots
• Multiple instruments

Step 1 - Define QTMP
Step 2 - Define Method Parameters (MP)
Step 3 - Define Critical Method Attribute (CMA)
Step 4 - Initial DoE
Step 5 - Identify the CMP
Step 6 - Model the Effect of CMP on CMA
Step 7 - Design Space
Step 8 - Confirm the Robustness in Confirmatory DoE
Step 9 - Additional Studies to Finalize SOP
QbD workflow in 9 steps

Chromatographic Profiles Are Dependent
on Method Parameters
min7 8 9 10 11 12 13 14
mAU
60
80
100
120
140
160
*DAD1B,Sig=280,8Ref=330,8(RHA072015ARHA072015A2015-07-2013-38-49002-1503.D)
Monomer
Aggregates
• Peak Resolution and
Efficiency (Plate
Number) are
improved, when the
mobile phase salt
concentration is
changed

Step 1- Define QTMP
QTMP should be in line with the process control strategy
• Intended use:
A quantitative impurity method
• Sample type:
DS/DP release and stability samples
• Reportable results:
Aggregates%, main peak% and LMW%
• Sample matrix:
DS/DP formulation buffer
• Target specification:
Assay range should cover the product specification
• Throughput:
Consider assay turnaround time

Step 2- Define Method Parameters (MP)
• Columns from different vendors
• Column temperature
• Flow rate
• Buffer pH: protein recovery, normally avoid pH at pI
• Salt type and concentration: recovery and secondary interaction
• Additives and organic modifiers
• Detection: ideally UV280 preferred
• Injection volume/load: S/N ratio
Select the suitable MP (e.g. pH and Salt Conc.) for DoE
study based on risk assessment and prior experience

Step 3 – Define Critical Method Attribute
(CMA)
• Resolution (Aggregates & Monomer)
• Protein recovery (total peak area)
• USP tailing factor
• Plate number
• Peak height
Not all CMAs are variable; If the CMAs do not change
significantly during the DoE, they could be robust enough

Traditional Approach vs. DoE Approach
Parameter 1
Parameter2
One factor at a time
Full factorial or fractional
factorial design
(Darker spots symbolize better method condition)

• Full Factorial Design
• pH (4) X Salt (6) = 24 sets
• Triplicate injection per set = 72
injections
• 1 column, 1 HPLC
• Divide the study into 4 sequences
per pH
• Include additional column
conditioning/equilibration steps
between the salt levels to
minimize potential impact by
buffer change
• Method Parameters: pH and Salt
conc
• Critical Method Attributes:
Resolution (monomer-
aggregates), plate number,
monomer peak height, and USP
tailing
Step 4 - Initial DoE
6.0 6.5 7.0 7.5
500
mM
x3 x3 x3 x3
400
mM
x3 x3 x3 x3
300
mM
x3 x3 x3 x3
200
mM
x3 x3 x3 x3
100
mM
x3 x3 x3 x3
0 mM x3 x3 x3 x3
pH
SaltConc.

Step 5 - Identify the CMP
- Use Resolution (between aggregates
and monomer) as an example CMA for
evaluation
- Use JMP to screen the effect of MP (pH
and Salt Conc.) on CMA (Resolution)
with the 24 sets of data
- A screening plot will help identify the
critical MP (CMP) that has STRONG
effect on particular CMA. The further
away from the fitting line, the more
significant effect on the CMA.

Good fit
Good correlation coefficient (R) indicates clear
effect of CMP (pH, Salt Conc) on CMA (Resolution)

Lack of fit
“Bad” correlation coefficient (R) indicates lack of correlation
between the CMP (pH, Salt Conc) and CMA (USP Tailing).
The data are still useful!!

Step 7 - Design Space
Design
Space
Confirmatory DoE:
• Small range DoE
within design space
• pH (3 levels) x Salt
(3 levels) = 9 sets

Step 8 – Confirm the Robustness in
Confirmatory DoE
• Evaluation of the reportable results using the confirmatory DoE
• Results could be used to set system suitability (control strategy)

Step 9 – Additional Studies to Finalize SOP
• Guard column / pre-column filter
• Column maintenance procedure
• Tubing and fittings
• Carry-over
• Sample preparation procedure
• Injection volume / protein load
• Forced degradation
• …

Typical CMP and CMA for protein HPLC
Assay Critical Method
Parameter (CMP)
Critical Method Attribute
(CMA)
Points to Consider
General
HPLC
methods
• Protein load
• Column types
• Instrument model
• Total Protein Peak Area –
indicator of recovery
• Run the same condition in
replicate injections (NLT 3) –
get the %CV and carry-over
info
SEC- • Buffer pH
• Salt conc
• Organic modifier
• Resolution – between the
monomer and HMW or LMW
• Tailing factor (TF) – monomer
• Peak height – surrogate of TF
• Temperate normally fixed at
25ºC
• High flow rate may damage
the column
IEX- • Buffer pH-start
• Buffer pH-end
• Buffer salt-start
• Buffer salt-end
• Temperature
• Resolution – choose two peaks
• Tailing factor – main peak (may
not apply if not well separated)
• Peak height – surrogate of TF
• Consider the protein pI in
the buffer selection
• Use low salt buffer in the
sample prep
RP- • TFA%
• Phase B%-start
• Phase B%-end (could
be two solvents)
• Temperature
• Flow rate
• Resolution
• Tailing factor
• Peak Area Change in replicate
injections
• Retention time
• Interaction between mobile
phases and stationary phase
is critical
• Minimize carry-over
• Consider the capacity factor
k’

Summary
Regulatory Agencies expect the product license
applicants to consider QbD in analytical development
A practical QbD approach could be a combination of
knowledge-based empirical assessment and DoE
One main goal of QbD is to understand and improve the
method robustness
Due to the complexity of biological product development,
the QbD approach for each method has to be product-
specific and method-specific

References
• FDA Guidance (2015): Analytical Procedures and Methods
Validation for Drugs and Biologics
• ICH Q2 (R1): Validation of Analytical Procedures
• ICH Q8 (R2): Pharmaceutical Development
• Agilent Report 5991-2166EN (2014): “Gain Greater
Confidence, Agilent Solutions for Quality-by-Design
Implementation in Pharmaceutical Development”

Acknowledgement
 Quan Yuan
 Eric Helgeson
 Lisa Regan

Considering Quality by Design (QbD) in Analytical Development for Protein Therapeutics

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