protein quaternary structure characterization: the effect of ...0.06 0.08 0.10 0.12 0.14 0.16 35 o...

Protein Quaternary Structure Characterization: The Effect of Protein Self-association on Analytical Method Development

Qin ZouAnalytical Research & Development, Pfizer, Inc.,2nd International Symposium on Higher Order Structure of Protein Therapeutics,Baltimore, Maryland, February 11-13, 2013

Protein Structure: History & Methods• Primary Structure: sequence of amino acids connected via peptide bonds

• First determination of a protein primary structure (bovine insulin) by Frederick Sanger in 1953• Prerequisite for many other structural elucidation studies, including PTM, X-ray crystal

structure, NMR structure• Important for classification of protein families• Understanding genetic diseases• Major analytical techniques: mass spectrometry, sequencing, amino acid composition analysis.

• Secondary and Tertiary Structure: local conformation of the backbone and overall topology of the folded peptide chain

• Linus Pauling and Robert Corey in 1950s proposed α-helix and β-sheet structures.• The first protein X-ray structure (sperm whale myoglobin) determined in late 1950s by John

Kendrew, et al.• NMR structural determination started in mid 1980s• Major low resolution techniques: circular dichroism, fluorescence emission, FT-IR, Raman

spectroscopy• Quaternary Structure: aggregation of separate chains through non-covalent

interactions• The Svedberg discovered multi-polypeptide protein by ultracentrifugation• Protein oligomers, protein-DNA (RNA) complexes, viral particles and aggregates (!?)• Analytical methods include X-ray, analytical ultracentrifugation, electron microscopy, etc.

1

Protein-protein Association• Forces involved in association: electrostatic, hydrophobic, etc.• Native association: often form small oligomers• Non-native association: tend to form large aggregates

2Horton & Lewis, (1992) Protein Sci.

Dimerization

3

Aggregate: a Critical Quality Attribute for Biologics

• Negative effect on clinical studiesPotential immunogenecityPossible impact on potency

• Physicochemical propertiesReversible vs. irreversibleCovalent vs. non-covalentSmall oligomers vs. large aggregatesSoluble vs. insoluble

• Lack of standardsAggregates can be heterogeneousAggregates can be dynamic

SEC: Method of Choice for Aggregate Evaluation

Pros:High throughput, high precision, low cost, less demanding in training, QC-compatible

Cons:Loss of aggregate due to precolumn filtration, resin interaction and dilution; formation of new aggregate due to mobile phase buffer

References:Carpenter et al. (2010) Journal of Pharmaceutical SciencesJohn S. Philo (2009) Current Pharmaceutical BiotechnologyMahler et al (2008) Journal of Pharmaceutical Sciences

4

SEC for Protein Self-association

5

Yu et al (2006) J. Chrom. A

Lou et al. (2004) J. Chrom. A

The strength and kinetics of association affect peak shape

Regulatory Expectation for Analytical Method Development and Validation (Q2A & Q2B)

6

• “An objective of an analytical procedure is to demonstrate that it is suitable for its intended use.”

• “Due to their complex nature, analytical procedures for biological and biotechnological products in some cases may be approached differently…”

• As to specificity, “It is not always possible to demonstrate that an analytical procedure is specific for a particular analyte (complete discrimination). In this case, a combination of two or more analytical procedures is recommended to achieve the necessary level of discrimination”.

• For accuracy, “Comparison of the results of the proposed analytical procedure with those of a second well-characterized procedure, the accuracy of which is stated and/or defined…”.

Case Study 1: Self-association of an Fc-fusion Protein

• Significant aggregates were present in stressed samples

• Aggregate trending in stability samples was desultory

• Is the SEC method suitable for release and stability-indicating?

7

Modeled Structure of the Fc-fusion Protein

8

Fc domain

Fused Protein

Linker region

SEC Results on Stability SamplesAU

0.000

0.010

0.020

0.030

0.040

0.050

0.060

0.070

0.080

0.090

0.100

0.110

Minutes7.00 7.50 8.00 8.50 9.00 9.50 10.00 10.50 11.00 11.50 12.00 12.50 13.00 13.50 14.00 14.50 15.00 15.50 16.00

Sample (1 month)

Total HMMS

HMMSPeak 1

HMMSPeak 2

5oC (red) 1.6% 0.2% 1.4%

25oC (blue) 3.7% 1.8% 1.8%

35oC (green) 20.9% 18.5% 2.4%

Monomer

sedimentation coefficient (S, 20oC, water)

0 10 20 30 40 50 60 70 80 90 100

norm

aliz

ed c

(s)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

5oC 25oC1month 35oC1month

sedimentation coefficient (S, 20oC, water)

0 200 400 600 800 1000 1200 1400 1600

norm

aliz

ed c

(s)

0.000

0.005

0.010

0.015

0.020monomer?

dimer?

large aggregate (~7MDa)

large aggregates (~7MDa)

other higher order aggregates

AUC-SV Analysis at 20,000 RPM

Size and Abundance from AUC-SV Analysis

Samples

Monomer Dimer (?) Large aggregate Other aggregates

M.W. (kDa)

sed.coeff. (S20

oC,w) % M.W.

(kDa)sed.coeff. (S20

oC,w) % M.W.

(MDa)sed.coeff. (S20

oC,w) % %

5oC 85 4.1 21.5% 222 7.7 78.0% 1.2%*

25oC1month 86 4.7 28.5% 202 8.0 68.7% 3.0%*

35oC1month 7 78.2 33.9% 27.8%**

*species that are larger than the “dimer” peak ** aggregates that are other than the 7 MDa peak

Gravitational Sweep AUC for Large Aggregates

12

ln(s*)

0 1 2 3 4 5 6 7 8 9 10 11

s*g(

s*)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

5oC 25oC1month 35oC1month

245S

55S

4.5S

15S

Speed varied from 3,000 to 45,000 RPM

Gravitational Sweep vs. Single Speed AUC

13

ln(s*)

0 1 2 3 4 5 6 7 8 9 10 11

s*g(

s*)

0.0

0.2

0.4

0.6

0.8

1.0

c(s)

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

35oC1month, gsAUC35oC1month, AUC,20K RPM

1800S

245S

55S

4.5S

15S

• There is a reasonable agreement between two different AUC methods• Single speed AUC may also leave some vary large aggregates undetected

sed. coeff. (S, 20oC, water)

3 4 5 6 7 8 9 10 11 12 13 14 15

c(s)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0.11mg/mL 0.25 mg/ml 0.5mg/mL 1.0 mg/mL

Concentration-dependent AUC-SV of 5oCsample at 50K RPM

The concentration-dependent AUC-SV profile is consistent with Gilbert-Jenkins theory about the rapid association & dissociation at the time-scale of sedimentation.

Co-migration of Monomer and Dimer during Sedimentation according to Gilbert -Jenkins

Sedimentation tim

e

Mixture of monomer/dimer under rapid equilibrium in the same boundary

DMM ⇔+

Boundary formation during centrifugation Monomer Dimer

Moderate Association Strength

Global fitting the concentration-dependent AUC data in Sedphat for dimerization based on Gilbert-Jenkins Theory

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kcal/mol 9.5

ln

1M 40

−=Δ

−=Δ

=

=

oa

oD

a

D

G

KRTG

KK

K μ

Justification for Intended Use of the SEC Method

• Self-association has no significant effect on SEC performance due to relatively weaker association and high on-column dilution (0.006 mg/mL in the main peak calculated from flow rate and column load).

• At low stress condition, there seems to be comparable estimate of aggregate level between SEC and AUC analysis.

• At high stress condition (35oC), there is an underestimate of aggregates by SEC analysis. It is found later that there is precipitation in the stability samples. Stability issue may be more important.

17

Case Study 2: Strong Aggregation of an Fc-fusion Protein in Early Process Development

1.High level of large aggregate in early process development

2.Various SEC methods provide different levels of aggregates

3.Need orthogonal methods to confirm the SEC results for process development.

4.Need to support SEC development for lot release and stability testing

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Variation in Aggregate Quantification

19

sample 1 sample 2 sample 3

Tota

l agg

rega

tes

(%)

0

20

40

60

80

SEC Method 1 SEC Method 2 SEC Method 3 SEC Method 4

Preparative SEC profile

20

0

200

400

600

800

mAU

1500 2000 2500 mlF3 F4 F5 F6 F7 F8 F2 A3A4A5A6B7B6B5B4B3B2B1 C2 C4 C6 D7 D5 D3 D1 Waste

Aggregates

Fractions: F3 F4 F5 F6 F7

SEC Fraction F6 by Analytical SEC

21

min6 8 10 12 14 16 18 20 22 24

Norm.

0

20

40

60

80

100

120

140

9.08

5 -

HM

MS

12.8

99 -

Mon

omer

0

200

400

600

800

mAU




22min6 8 10 12 14 16 18 20 22 24

Norm.

0

20

40

60

80

100

120

140

9.03

6 -

HM

MS

12.9

04 -

Mon

omer

0

200

400

600

800

mAU




min6 8 10 12 14 16 18 20 22 24

Norm.

0

20

40

60

80

100

120

140

9.02

8 -

HM

MS

12.9

26 -

Mon

omer

0

200

400

600

800

mAU



sed. coff. (S, 20oC, water)

0 25 50 75 100 125 150 175 200 225 250 275 300 325 350 375 400

g(s*

)

0.00

0.01

0.02

0.03

0.04

0.05

0.06

F6 F5 F3

~800 kDa

AUC Profiles for SEC Fractions

24

Very large aggregates

Monomer

AUC Profiles for SEC Fractions

25

sed. coff. (S, 20oC, water)

0 2 4 6 8 10 12 14 16 18 20 22 24

g(s*

)

0.00

0.01

0.02

0.03

0.04

0.05

0.06

c(s)

0

1

2

3

4

5

6

7F6 F5 F3 Control

monomer,113 kDa,99.5%

Comparison of Aggregate Level

ControlFraction 6

Fraction 5Fraction 4

Fraction 3

Agg

rega

te (%

)

0

2

4

80

85

90

95

100SEC AUC

Size Distribution and Size Trend by Dynamic Light Scattering (DLS)

0

2

4

6

8

10

12

0.1 1 10 100 1000 10000 100000

Inte

nsity

(%)

Size (d.nm)

Size Distribution by Intensity

Record 43: F5 avg27

Control F7 F6 F5 F4 F3

Hyd

rody

nam

ic D

iam

eter

(nm

)

0

20

40

60

80

100

120

DLS results demonstrate more larger soluble aggregates in earlier aggregate fractions in preparative SEC

Further Development of SEC Method

• The SEC method may be suitable for early process development.

• This SEC method appears to be stability-indicating, but may overestimate the aggregate level.

• Further comparison of the aggregate level by SEC and AUC at different conditions, such as mobile phase buffer, may be needed.

28

Summary

• SEC is the work horse for aggregate detection and quantification.

• Orthogonal methods such as AUC can facilitate further characterization and confirm the SEC results.

• Difference in size profile between SEC and AUC does not automatically disqualify the SEC method for its intended use.

• Other biophysical methods, such as DLS, can further support the aggregate characterization and detection.

29

Acknowledgements

Jeff RyczekSuzanne DeMarco

Scott CookSydney Hoeltzli

Yin LuoTom Porter

Jason Rouse

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