Authors: Arnaud Périlleux, Yolande Rouiller, Martin Jordan and Matthieu Stettler

Biotech Process Sciences, Upstream Development Group

Merck Serono S.A. Vevey, Switzerland

Speeding up media design: a novel high throughput approach for rapid cell culture media development Cell Culture World Congress 2013, Munich, 26 February 2013

Merck Serono at a glance

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Merck Serono SA is the largest division of Merck KGaA – Established in January 2007,17’000 employees,

EUR 5.9 billion in 2011, headquarter in Darmstadt, Germany

– In the United States and Canada, Merck Serono operates under the name of EMD Serono (a separately incorporated affiliate of Merck Serono)

Merck Serono SA process development and production site in Vevey, Switzerland – One of the largest and most technologically

advanced biotech centers in the world (4 x 5K and 8 x 15K production capacity)

– Production of Rebif® (INF beta-1a) since 1999

– Production of Erbitux® (Cetuximab) since 2011 Merck Serono’s headquarter in Darmstadt (top) and development and production site in Vevey (bottom)

Presentation scope

Overview of Merck Serono’s high throughput cell culture methods

Case study #1: High throughput media blending – How to obtain a new high performance medium in one single experiment?

Case study #2: Product quality optimization – How are quality analyses possible with micro-scale culture systems?

Perspectives – Strategies to quickly develop processes with strong quality requirements

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Integrated development approach Predictive, scalable and comprehensive set of tools

Merck Serono’s HT cell culture methods Overview

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High performance cell lines

High performance processes

Deliver the right product quality

High throughput cell culture methods for fed-batch processes assessing

24 to 400+ cultures in parallel

Media Blending

Feed Blending

DoE

CFD

Cell line evaluation

Medium development

Feed development

Process development

Process validation Quality by Design

Merck Serono’s HT cell culture methods High throughput evaluation of cell lines in fed-batch

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A B

C D

C

D

Cloning in static 384 well plate

Adaptation in shaking 96DWP

Fed-batch in shaking 96DWP

Scale up to shake tubes

Fed-batch in shake tubes

Fed-batch in lab-scale bioreactors

Fed-batch in micro-scale bioreactors

A

B

Transfection and selection

0.5 mL

0.5 mL

10 mL

30 mL

15 mL

3 000 mL

Up to 500 clonal cell

lines

12 cell lines

4 candidate cell lines

Authors:

Yolande Rouiller, Arnaud Périlleux, Natacha Collet,

Martin Jordan, Matthieu Stettler, Hervé Broly

Manuscript accepted, to be published in mAbs

Case study #1: High throughput media blending A high-throughput media design approach for high performance mammalian fed-batch cultures

Case study #1: High throughput media blending Workflow

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0

1000

2000

3000

0 2 4 6 8 10 12 14

Tite

r (m

g/L)

Elapsed time (days)

16 media formulations varying 43 out of 47 components

47 components

376 media blends tested

3 passages prior a 14-days fed-batch Data

analysis

Predicted vs. Actual

Blends automatically mixed in 96DWP

Data acquisition

0

5

10

15

20

25

-8 -6 -4 -2 0 2 4 6 8 10 12 14

Viab

le C

ell D

ensi

ty

(x10

6 cel

ls/m

L)

Time (days)

Case study #1: High throughput media blending Evaluation of 376 media blends

Protocol – 3 passages prior fed-batch inoculation, an

antibody expressing cell line is diluted in each of the 376 blends

• Guaranty to obtain a medium suitable for both expansion and fed-batch process

– Each of the 376 cultures is diluted individually targeting a specific cell density

• Standardized and controlled experimental setup

– On day 2, 4, 7 and 10, a reference proprietary feed is added

• Guaranty to obtain a medium adapted to the feed system

– Performance assessment

• Cell count, Cell viability with a Guava Easycyte

• Titer quantification with an Octet KQe

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0

500

1000

1500

2000

2500

3000

3500

-8 -6 -4 -2 0 2 4 6 8 10 12 14

Tite

r (m

g/L)

Cell expansion 3 passages

Fed-batch process

Ctrl 1 Ctrl 2

Case study #1: High throughput media blending Data analysis opportunities

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Data analysis using three approaches

1st approach: Excel spreadsheet 2nd approach: DoE (Design Expert)

Identification of key media formulations

3rd approach: MVA (Simca P++)

Identification of key components

List of key components to be further optimized

Design of predicted best formulation

Ranking of various tested conditions

Selection of promising formulations

Case study #1: High throughput media blending Data analysis with Design of Experiment (DoE)

DoE analysis allows – To identify the key formulations (out of the 16)

– To design new media formulations

DoE analysis does not allow – To understand why some media are better than others

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Compositions of best predicted media based on the 16 formulations

Pred

icte

d va

lues

Observed values

Titer at Day 14

Predicted Values

Prediction PDL IVC Titer

1st 10.43 226 3960

2nd 10.47 238 3933

3rd 10.42 226 3908

4th 10.45 230 3902

5th 10.39 224 3885

Average 10.43 228 3910

0

1000

2000

3000

0 1000 2000 3000

R2 = 0.88 Adj. R2 = 0.84

Pred. R2 = 0.76

0% 5%

10% 15% 20% 25% 30% 35%

F1

F2

F3

F4

F5

F6

F7

F8

F9

F10

F11

F12

F13

F14

F15

F16 %

of e

ach

form

ulat

ion

in th

e pr

edic

ted

med

ium

Prediction 1 Prediction 2 Prediction 3 Prediction 4 Prediction 5 Average

Case study #1: High throughput media blending Data analysis with MultiVariate Analysis (MVA)

MVA analysis allows – To rank the 43 components in terms of influence on performance

– To identify the key components that could be interesting for further optimization (in orange and yellow)

MVA analysis does not allow – To have a strong confidence in the conclusions due to the relatively low number of conditions

tested compared to the high number of factors evaluated

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Influence of increased levels of the tested components in the PLS regression of titers at day 14

-0.3 -0.2 -0.1

0 0.1 0.2 0.3 0.4

L-S

erin

e D

-Bio

tin

L-A

rgin

ine

Thym

idin

e L-

Asp

artic

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Leuc

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L-G

luta

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d H

ypox

anth

ine

Zinc

Sul

fate

m

yo-In

osito

l N

aH2P

O4

L-H

istid

ine

Sod

ium

Sel

enite

P

utre

scin

e L-

Tyro

sine

R

ibof

lavi

n C

holin

e C

hlor

ide

Pyr

idox

ine

L-Ly

sine

x H

Cl

L-P

heny

lala

nine

L-

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Cal

cium

Chl

orid

e Fo

lic a

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Vita

min

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Th

iam

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roni

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than

olam

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L-A

spar

gine

C

upric

sul

fate

L-

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tein

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iaci

nam

ide

(B3)

G

lyci

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L-Tr

ypto

phan

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line

Mag

nesi

um S

ulfa

te

L-A

lani

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L-M

ethi

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odiu

m p

yruv

ate

Pot

assi

um C

hlor

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L-V

alin

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-Pan

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enic

aci

d x

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a Fe

rric

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mon

ium

citr

ate

Coe

ffici

ents

sca

led

& c

ente

red

(com

pone

nts

1 to

3 o

f PLS

mod

el)

Ferric Ammonium Citrate (mg/L)

Tite

r Day

14

(mg/

L)

Components that correlate by design with key components Components with strong influence in MVA

Case study #1: High throughput media blending Scale-up, confirmation and conclusions

8 media from the ranking approach and 1 from the DoE approach were scaled up in shake tubes – data not shown

1 medium was selected for scale up in bioreactor and evaluated on several cell lines – The 3 cell lines showed from 30 to 60% titer

improvement

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Conclusions – Media blending allows in one single experiment to identify new media formulations with high

potential for performance improvement

– Same approach can be made with feeds

– An approach combining medium and feed blending can be designed

Cell line 1 Cell line 2 Cell line 3

Reference medium

New medium

0

1000

2000

3000

4000

5000

6000

0 2 4 6 8 10 12 14 16 18

Tite

r (m

g/L)

Time (days)

Case study #2: Product quality optimization using high-throughput cell culture methods

Case study #2: Product quality optimization Are quality analyses possible with micro-scale cultures?

Context – Cell culture process development required to optimize a large number

of critical quality attributes (CQAs)

• Isoforms (deamidation, oxydation, …), glycoforms, (galactosylation, mannosylation, fucosylation, sialylation), product integrity, process impurities

– Most of the CQAs are linked to the recombinant cell line, the medium and feeds, and the process (physical parameters)

– To ensure a successful process development (e.g. next generation process), it is important that the new process provides a product with the same quality

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Case study #2: Product quality optimization Are quality analyses possible with micro-scale cultures?

Challenges – High throughput cell culture methods (i.e.

96DWP) provide small amounts of product

– Analytical lab should be able to analyze 400+ samples with 5+ analytical methods

• Capture, glycan analysis, charge profile, integrity, …

Achievements – A combination of media blending and DoE

generated 400 samples of about 400 µL

– Samples were captured using Phytips®

– Samples were analyzed in 2.5 weeks on 5 analytical methods

– 8800 results were generated

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Fed batch process in 96DWP

Capture on Phytips®

Charge profile iCE280

Glycan analysis CGE-LIF

Integrity SE-HPLC Caliper NR

400-500 µL 225-1500 µg

Case study #2: Product quality optimization Experiment design

Combine media blending and DoE design – 5 new Basal Media (BM) mixed to obtain 11 media

– 17 factors tested in DoE by addition on day 5

– Standard platform feeding strategy

– Analytics performed on day 14

• Biacore • CGE-LIF, iCE280 • Caliper NR, SE HPLC

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Factors tested in DoE Zn N-acetylcystein

Cu pH HCl Fe NaCl

Se NaButyrate Mn Hydrocortisone

Galactose Spermine Fucose Ascorbic acid + Retinol

+ 4-aminobenzoic acid + a-tocopherol Uridine

ManNAc Lipids

0 3 10 14 7

Main Feed

Glucose

2ndary feed

5

Additional factors

Case study #2: Product quality optimization Results

For each CQA – Distribution of results was compared to

Target Product Profile (TPP)

– Models were defined

– Lists of key media and factors were established from models

Conclusions – For most of the CQAs, at least some

conditions were able to match the TPP

– Models allow

• to fix the levels of 4 factors

• to select 4 factors and 2 media for further optimization

– After optimization, the confirmation and scale up of a new manufacturing process version was performed

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10

15

20

25

30

Cha

rge

Prof

ile

Clu

ster

4 (%

)

All

data

B

M1

BM

2 B

M3

BM

4 B

M5

Mix

1 M

ix2

Mix

3 M

ix4

Mix

5 M

ix6

BM

1 B

M2

BM

3 B

M4

BM

5

Control (n=4) 32 DoE conditions

-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

Cu

NaC

l

Spe

rmin

e

Mix

1

Mix

4

Mix

5

BM

3

BM

4

Std.

Coe

f. (C

lust

er 4

) Model: R2 = 74.9% / R2 adj. = 71.2%

HT cell culture – Speeding up media design Conclusions

High throughput tools are now available for cell culture, but also for purification and analytics

Designing the right experiments allows to get the maximum of these tools and to reduce development timelines

Their use has been mainly directed to media design, but their application to feed design could even provide more interesting results

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Acknowledgements

Biotech Process Sciences (BPS), Merck Serono SA Vevey, Switzerland

– Hervé Broly - Head of BPS – Upstream Development Group – Flavie Robert - Head of Analytical

Group

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Questions

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