Download Analytical Ultracentrifugation for Protein Analytical

Analytical Ultracentrifugation for Protein
Aggregate Analysis
John P Gabrielson
2010 Workshop on Protein Aggregation and Immunogenicity
July 22, 2010
Aggregation is a Common Protein Degradation
Pathway
•
Aggregation (soluble and insoluble) is one of the most prevalent, complex
and problematic degradation pathways
•
Challenge during protein drug development, storage and delivery
•
Potential for immunogenic responses
Aggregation
Heat
Freeze – Thaw
Agitation
Shear
Formulation
Conditions
Protein aggregates are critical product quality attributes due to their
immunogenicity potential and typically low bioactivity
The Particle Landscape
1016
≥ 100% protein by mass
SEC
Able to quantify
mass concentration
1014
# / mL
trillion 1012
AUC
1010
Large enough to
measure #/mL
108
million 106
≤ pg/mL
104
Light
obscuration
102
0 001
0.001
0 01
0.01
oligomer
monomer
01
0.1
1
Diameter (µm)
10
100
sub-visible
visible
With Distinct Advantages Compared to SEC, AUC is a
Regulatory Expectation for Aggregate Characterization
• Sample analysis in product formulation
• Expanded detection size range
• Essentiallyy free from sample-matrix
p
interactions resulting
g
in reduced disturbance of self-associated proteins1
• Enhanced resolution of size variants
AUC is a regulatory expectation for product characterization
and comparability
1Philo,
J.S., (2006). The AAPS Journal. 8(3). p. E564-E571.
C
Capability
bili off AUC
Method Capability Should Define Appropriate
Applications of AUC in Biopharmaceutical Development
• Method development and optimization
• Identify and control key sources of method variability
• Examples: channel alignment, centerpiece differences, and
solution conditions1,2
• Method characterization and qualification
• Characterize method operating space and demonstrate method
fitness for purpose
• ICH Q2(R1) recommends establishing performance of product
purity methods based on demonstration of specificity, linearity,
range accuracy,
range,
accuracy precision,
precision and detection limit
Development
1 Gabrielson,
2
Optimization
Characterization
J.P., et al., (2009). Journal of Pharmaceutical Sciences. 98(1). p. 50-62.
Schuck, P., (2004). Biophysical Chemistry. 108(1-3). p. 187-200.
Qualification / validation
Channel Misalignment Leads to Artifact
Aggregates that Contribute to AUC Variability
Channel aligned:
All forces act in radial
direction
Channel misaligned:
Convective transport
induces artifact peaks
Aggregate
gg g
(larger
( g than dimer))
sedimentation coefficient (S)
•
At 0° offset aggregate quantitation
matches expectation based on known
blend
•
Slopes indicate sensitivity to
misalignment
•
g
is a significant
g
source of
Misalignment
aggregate measurement variability
Arthur, K.K., et al., (2009). Journal of Pharmaceutical Sciences. 98(10). p. 3522-39.
Small Differences Between Centerpieces Can
Significantly Affect Measured Aggregate Levels
• Slight differences between centerpieces can arise from
aging, surface defects, or production imprecision
Tighter precision with improved
centerpiece manufacturing
Exc
cess dimer (% dim
mer)
Lower quality centerpieces
0.5
-0
0.5
5
0.0
0.5
1.0
1.5
Source of Variance
Std Deviation
(% aggregate)
Rotor
< 0.1
Cell housing
< 0.1
Centerpiece
From ~ 0.2 to < 0.1
R id l
Residual
~ 0.2
02
Std deviation of dimer levels (% dimer)
Pekar, A., and M. Sukumar, (2007). Analytical Biochemistry. 367(2). p. 225-237.
Gabrielson, J.P., et al., (2009). Analytical Biochemistry. 396(2). p. 231-241.
AUC Method Performance is Improved by
Applying Best Practices to Control Variability
Method
Precision
(% aggregate)
Detection Limit
(% aggregate)
SEC
0.1
0.3
AUC
(prior to improvements1)
0.5
1.7
AUC
(with improvements2)
0.3
1.0
Prior to
improvements
With
improvements
50x magnified
c(s)
c(s)
50x magnified
2
6
10
14
Sedimentation Coefficient (Sved)
2
6
10
Sedimentation Coefficient (Sved)
1 Obtained from multi-laboratory, multi-product intermediate precision study (Gabrielson, J.P., et al., (2009). Analytical
Biochemistry. 396(2). p. 231-241.)
2 Improved
14
method includes using more precisely manufactured centerpieces, more consistent alignment control, and more
accurate data fitting approaches (Gabrielson, J.P., et al., (2009). Journal of Pharmaceutical Sciences. 98(1). p. 50-62.)
Method Precision is Largely ProductIndependent
We analyzed AUC measurement variance of 7 protein products
over a 3 year period
•
•
Multiple instruments, analysts, centerpieces / cell assemblies, products, and
product lots
Partition total variance into contributions from product, lot, run, and residual
variance
5
Aggregate ((%)
4
Product 1 (n = 149)
Product 2 (n = 75)
Histograms
3
2
1
0
Date of Analysis
Jan 2006
Jan 2009
Std deviation (% a
aggregate)
•
This protein exhibits reversible selfassociation on an AUC relevant time-scale
1.0
0.8
52
0.6
0.4
149
77
Std Deviation
(% aggregate)
Inter-run
0.27
Intra-run
0.35
Total
0.44
0
44 a
9
75
74
0.2
Source of
Variance
11
0.0
Variability of 0.57% aggregate prior
to method improvements
a
Protein Product
Universal AUC qualification is a possibility for proteins with sufficiently stable
aggregate species
The Detection Limit of AUC Remains Unclear
There are two common approaches to determine the detection limit
Fi d – based
Fixed
b
d on std
td deviation
d i ti
D
Dynamic
i – based
b
d on signal-to-noise
i
lt
i
20
DL =
15
Absorbance
e (AU)
Meas
sured Aggregatte (%)
25
3.3σ
S
10
signal
RMSE
6.0
6.5
Radius (cm)
5
0
0
7.0
5
10
15
20
100
25
Expected Aggregate (%)
0.7 to 1.3% aggregate
DL (%)
DL fixed during method
qualification
DL varies for
each sample
40 krpm
60 krpm
10
1
0.5 to 3%
aggregate
0.1
0.01
1
10
s20,w / s20,w monomer
100
A li i
Applications
off AUC
Applications of AUC in the Biopharmaceutical
Industry
AUC is NOT useful for product lot release or stability testing due to:
•
Precision = ~ 0.3% aggregate
•
DL = ~ 1.0% aggregate
•
Low throughput
•
Extensive analyst training
BUT the advantages of AUC make it an attractive method for:
• Product characterization (case study #1)
• SEC method development (case study #2)
• Binding interactions
Case Study #1 AUC Can Detect Weakly
Associated Aggregates Undetectable by SEC
Protein Sample
Dimer measured by
AUC (%)
Dimer measured by
SEC (%)
Drug substance lot 1
3.9
1.0
Drug substance lot 2
4.1
1.2
Drug substance lot 3
4.3
1.4
Drug product
< 0.1
< 0.1
monomer
AUC allows direct analysis in various solutions
3.9% dimer
100x magnified
c(s)
Drug substance
Drug substance diluted into SEC mobile phase (agrees with SEC result)
Drug substance diluted into drug product formulation
Drug product
2
4
6
8
Sedimentation Coefficient (Sved)
Case Study #1 AUC Allows Characterization of
Monomer-Dimer
Monomer
Dimer Equilibrium
Reaction:
Α+Α
k on
⎯⎯→
k
←⎯⎯
dA
= −kon A2 + koff A2
dt
A2
off
Kd =
koff
kon
A2
=
A2
SEC:
Dimer (%))
1.5
• Dimer dissociates to monomer within 12 hrs
at 37°C or upon addition of PS80
• Kinetics: koff ~ 10-4 M-1s-1
• Equilibrium: Kd ~ 1 M
4°C
4
C, - PS80
1.0
4°C, + PS80
37°C, - PS80
0.5
0.0
0
2
4
6
8
Time (hr)
12
10
15
10
Dimer (%)
D
AUC:
Dimer (%)
D
20
Kd ~ 1 mM
5
8
6
4
2
0
0
2
4
6
8
Protein Concentration (g/L)
10
0
3
4
5
pH
6
7
8
10
12
Case Study #2 SEC Method Validated for Quantitation
of Aggregates Generated by Multiple Pathways
•
AUC and SEC results are
well correlated
•
AUC confirms SEC
adequately quantifies
aggregates generated from
multiple pathways
AUC (% aggregate)
AUC is a valuable tool in SEC method development and
qualification
Conclusions
•
AUC offers several advantages compared to other size separation
techniques
• Sample analyzed in product formulation
• Reduced disturbance of self-associated proteins
• Enhanced resolution
•
Recent advances have helped to define and improve AUC method
capability
• Identified key contributors to method variability
• Universal precision is approximately 0.3% aggregate
• “Best practices” DL is approximately 1.0% aggregate
•
AUC is an important orthogonal complement to SEC and other size
separation techniques
• Product and process development / characterization
• SEC method development / evaluation
• Binding interactions
Acknowledgments
Amgen
Beckman
•
K ll Arthur
Kelly
A h
•
Paul Voelker
•
Michael Stoner (formerly Amgen)
•
Cledwyn Fernandes
•
Brent Kendrick
•
Ron Ridgeway
•
Brad Winn
•
Ed Towers
•
Rick Burdick
•
George Svitel
•
Vladimir Razinkov
•
Yijia Jiang
•
Linda Narhi