Tackling the data analysis challenge for characterisation of

biotherapeutics Carsten P Sönksen, Ph.D., Novo Nordisk

CASSS AT 2015 Berlin March 2015 1

Experience:

• ~19 years with mass spectrometry

• ~14 years in an industrial setting

Responsibility:

• Senior Research Scientist, responsible for MS-based protein characterisation of biopharmaceuticals

• Dept.: CMC - analytical support, Novo Nordisk

• Implemented Genedata Expressionist for vendor independent data processing and analysis

Personal background: Carsten P Sönksen

2 Tackling the data analysis challenge…

• Stressed IgG4 sample was analysed by iCIEF

• Preparative isoelectric focusing (Agilent) of fractions for characterisation by MS

• MS-characterisation by

• Intact mass analysis LC-MS (Waters)

• Tryptic peptide map LC-MS/MS (Thermo)

• Raw data processing, analysis, and reporting

• Genedata Expressionist for Mass Spectrometry

Case study: Characterisation of the charged isoforms of stressed IgG4

3 Tackling the data analysis challenge…

iCIEF electropherograms of stressed sample and isolated preparative fractions

4 Tackling the data analysis challenge

Challenge: Several peaks in each fraction

-0.01

0.09

0.19

0.29

0.39

0.49

0.59

6.4 6.6 6.8 7 7.2 7.4

Load

Acidic 3

Acidic 2

Acidic 1

Major 1

Major 2

Basic 1

Basic 2

Basic 3

pI

Intact LC-MS data: Single sample view vs. overlay Weak patterns become clearer

Presentation title 5

Acidic 3

Acidic 3 Acidic 2 Acidic 1 Major 1 Major 2 Basic 1 Basic 2

-F: -146 Da

Main form: +2 G0F, - 2 K, 2 Pyro-glu

- 2 Pyro-glu: + 34 Da

+ K: +128 Da

+2 K: +256 Da

+ G: + 162

+2 G: + 324 Da

Mass

Quality check of tryptic peptide maps (LC-MS/MS):

Box Plot analysis

Presentation title 6

1: Average signal intensity analysis - check for differences - check for abnormal runs 2: Set “Acidic 2” and “Basic 3” on the “watch list”

Acid

ic 3

A

cid

ic 2

A

cid

ic 1

M

ajo

r 1

M

ajo

r 2

B

asic

1

Basic

2

Basic

3

Guided vs. blind analysis: Only a fraction of the peaks are identified (red crosses)

Tackling the data analysis challenge 7

“Acidic 3” peptide map TIC chromatogram “Acidic 3” peptide map TIC 2D heat map

• ~ 345 charge clusters not identified out of 530!

• A need for further identification analysis.

Many unidentified high intense peaks still ask for identification

Tackling the data analysis challenge 8

Mass 5556.89 Charge 4 Mass 5556.89 Charge 3

Are the detected modification valid? Check modifications in the mass spectral data

Presentation title 9

Raw Data Cleaned Data

Dominant form HC aa 309-324

Succinimide Form

Dehydrated Form?

Deamidated Form

Absence of the C-terminal lysine (K)

Presentation title Date 10

Peptide with C-terminal lysine on the HC

Peptide without C-terminal lysine on the HC

Acid

ic 3

A

cid

ic 2

A

cid

ic 1

M

ajo

r 1

M

ajo

r 2

B

asic

1

Basic

2

Basic

3

Acid

ic 3

A

cid

ic 2

A

cid

ic 1

M

ajo

r 1

M

ajo

r 2

B

asic

1

Basic

2

Basic

3

Unbiased Statistic Analysis of Charged Isoforms

Presentation title Date 11

Contrast analysis: Only the HC C-terminal lysine peptides describe the difference in the preparative fractions with a significance of ~ P<0.05

Distribution of deamidated and succinimide forms

Tackling the data analysis challenge Date 12

Acid

ic 3

A

cid

ic 2

A

cid

ic 1

M

ajo

r 1

M

ajo

r 2

B

asic

1

Basic

2

Basic

3

Acid

ic 3

A

cid

ic 2

A

cid

ic 1

M

ajo

r 1

M

ajo

r 2

B

asic

1

Basic

2

Basic

3

Check signal in raw data

Δ = 0.984 Da

Δ = 1.003 Da

Deamidated

Succinimide

13 Presentation title Date

Distribution of N-glycosylation forms

G0F G1F

No N-glyco G0

• Loss of lysine on the C-terminal of the heavy chain explains the basic isoforms

• Deamidation and succinimide explain only part of the acidic isoforms

• Neutral modifications like G0F are fractionated as well

Conclusion

Presentation title 14

-0.05

0

0.05

0.1

0.15

0.2

0.25

6.4 6.5 6.6 6.7 6.8 6.9 7 7.1 7.2 7.3 7.4

C-term Lysine

+2 G0F, - 2 K, 2 Pyro-glu

Deamidation

Deamidation

G1F G0

Genedata Expressionist has become our standard platform for biopharmaceutical characterisation

15 Presentation title Date

Why did we look beyond vendor software • So much data, so little time … • Instruments from 5 vendors -> 5 softwares to learn … • Automating standardized work and using free time to dig

deeper into interesting peaks ... Workflow-based system enables: - Reporting of data, results and parameters - Swift analysis of samples in parallel - Fast iterations for reanalysis - Excellent visualisation of data - Confirmation of results in raw data

• Overlaying of parallel processed data is a simple powerful approach to verify patterns

• Box plot analysis is a simple and fast analysis to check peptide load integrity between samples

• Automation saves time on the routine tasks which can be spent on high intense unidentified peaks

• Always verify conclusion by looking at both the processed and raw data

• Sophisticated visualization aids unbiased and complete characterization

Data analytical recommendations

16 Tackling the data analysis challenge

• Brian Kristensen, Novo Nordisk

• Ingelise Fabrin, Novo Nordisk

• Leif H. Bagger, Novo Nordisk

• Arnd Brandenburg, Genedata

Thanks to

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