Doe Presentation

7/27/2019 Doe Presentation

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Interferon -1 Production

in CHO Cells and Growth

Optimization in Orbitally

Shaken Bioreactors

FE 536 DesignProje

mran ZER, Damla TAYKO


2/28

Project Outline

Background Information

Methods of the Study

Results and Discussion

Concluding Remarks


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Multiple Sclerosis (MS) is an autoimmun

Multiple sclerosis, progresses with demyelination of nerve tissu

the brain.

May be caused by genetic or environmental factors.


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MS may affect several body functions


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FDA approved drugs: Rebif, Cin

Annovex.

These drugs aim to relapse d

progression by reducing inflammation.

This protein may be produced

recombinant product in various org

such as bacteria or mice.

Interferon -1, is a frequently used drug m

MS disease


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Protein production is a result of cellgrowth. In recombinant protein productio

several factors directly affect the yield.

Chemical inducer

Growth factors

Agitation

Inhibitor concentration

Production parameters matter


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Chinese Hamster Ovary cells - in orbitallyshaken bioreactors (50 ml).

ProCHO5 medium - contains Fetal Bovine

Serum (FBS).

5.105 cells/ml initial cell concentration.

36 hours of cultivation. Viable cells are counted with

hamocytometer, to measure cell growth

level.

Methods of Study


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Factors Level 1 Level 2 Level 3

Agitation speed (rpm) 0 40 80

Serum Concentration (% of total

working volume)

1 5.5 10

Inducer Concentration (ng/ml) 10 505 1000

Face centered central composite design.

31 runs analyzed with Design Expert 8.0 program

Regression model of the system was validated

with extra runs.

Methods of Study


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Results and Discussion

Face-centered CCD contains 16 factorial terms, 12 axial terms and 3


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Quadratic effect was suggested due to lack of fit test

Design Suggestion by Design Expert Pro


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The lack of fit is significant.This is an undesiredsituation for the analysis,therefore modifying themodel by removinginsignificant parameters isapplied, but aninsignificant lack of fit

value for the test modelcould not be obtained.Therefore, secondsuggested model for thesystem that is cubic modelusing aliased terms wasused.

ANOVA of Quadratic Design


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10-1

2,0

1,5

1,0

0,5

10-1

10-1

2,0

1,5

1,0

0,5

A

Mean

B

C

Main Effects Plot for Viable Cell Count

Data Means

ANOVA of Cubic Design and Main

MINITAB 15


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Design-Expert SoftwareFactorCoding: CodedViablecell count

CIBandsDesignPoints

X1= A: serumX2= B: agitation

CodedFactorC: inducer= -1.000

B--1.00B+ 1.00

B: agitation

-1.00 -0.50 0.00 0.50 1.00

A: serum

V

ia

ble

cellcou

nt

-1

0

1

2

3

4

22

22

Interaction Design-Expert SoftwareFactorCoding: CodedViablecell count

CIBandsDesignPoints

X1= A: serumX2= B: agitation

CodedFactorC: inducer= 0.000

B--1.00B+ 1.00

B: agitation

-1.00 -0.50 0.00 0.50 1.00

A: serum

V

iable

cellcount

-1

0

1

2

3

4

223

Interaction

Design-Expert SoftwareFactorCoding: CodedViablecell count

CIBandsDesignPoints

X1= A: serum

X2= B: agitation

CodedFactorC: inducer= 1.000

B--1.00B+ 1.00

B: agitation

-1.00 -0.50 0.00 0.50 1.00

A: serum

V

iable

cellcount

-1

0

1

2

3

4

4422

4

2

Interaction

Design-Expert SoftwareFactorCoding: ActualViablecell count

CIBandsDesignPoints

X1= B: agitationX2= C: inducer

Actual FactorA: serum = -1.68

C--1.68C+ 1.68

C: inducer

-1.68 -0.84 0.00 0.84 1.68

B: agitation

V

iab

le

ce

llcount

-1

0

1

2

3

4

2222 22

InteractionDesign-Expert SoftwareFactorCoding: ActualViablecell count

CIBandsDesignPoints


Actual FactorA: serum = 0.00

C--1.68C+ 1.68

Design-Expert SoftwareFactorCoding: ActualViablecell count

CIBandsDesignPoints


Actual FactorA: serum = 1.68

C--1.68C+ 1.68

-1.68 -0.84

V

iab

le

ce

llcoun

t

-1

0

1

2

3

4

22

I

AB interaction is more significant than BC interaction because AB lines intersect eac

lines have less intersection. ANOVA p values also confirm the evaluation about the inte

Interactions Plots


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Regression formula that is based on significant term generated by the Design ExpeEquation below:

= 2,44 + 0,43 + 0,73 0,30 + 0,10 0,052 + 0,066

1,02 0,54 + 0,15 (0,36 )

Regression Analysis


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Design-Expert SoftwareViable cell count

Color points by value ofViable cell count:

2.95

0.0125

Internally Studentized Residuals

N

orm

al%

Probability

Normal Plot of Residuals

-2.00 -1.00 0.00 1.00

1

510

20

30

50

70

80

90

95

99

Residuals are normally distributed because all the points are very close to the centerline ethe last runs.

Residual Analysis


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2.95

0.0125

Run Number

Intern

ally

S

tudentizedR

esidual

Residuals vs. Run

-3.00

-2.00

-1.00

0.00

1.00

2.00

3.00

1 6 11 16 21

There is no particular pattern in the residual distribution.


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2.95

0.0125

2 2

2

Actual

Predicted

Predicted vs. Actual

0.00

0.50

1.00

1.50

2.00

2.50

3.00

0.00 0.50 1.00 1.50 2.00

Model adequately represents the system.


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Design-Expert SoftwareFactor Coding: CodedViable cell count

Design Points2.95

0.0125

X1 = A: serumX2 = B: agitation

Coded FactorC: inducer = -1.000

-1.00 -0.50 0.00 0.50 1.00

-1.00

-0.50

0.00

0.50

1.00Viable cell count

A: serum

B

:

a

g

ita

tio

n

0

0.5

1

1

1.5

2

2 2

2 2

2

Design-Expert SoftwareFactorCoding: CodedViable cell count

Design points above predicted valueDesign points belowpredicted value

2.95

0.0125


Coded FactorC: inducer= -1.000

-1.00

-0.50

0.00

0.50

1.00

-

-1

0

1

2

3

4

V

ia

b

le

ce

llco

u

nt

B: agitation

@

Optimization


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Design Points2.95

0.0125


Coded FactorC: inducer = 0.000

-1.00 -0.50 0.00 0.50 1.00

-1.00

-0.50

0.00

0.50


A: serum

B

:

a

g

ita

tio

n

1

1.5

2

2

2

2.5

2 2

2

2

3


Design points above predicted valueDesign points below predicted value

2.95

0.0125



-1.00

-0.50

0.00

0.50

1.00

-1

-1

0

1

2

3

4

V

iable

cellcount

B: agitation

@ m


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Design Points2.95

0.0125



-1.00 -0.50 0.00 0.50 1.00

-1.00

-0.50

0.00

0.50


A: serum

B

:

a

g

ita

tio

n

-0.5

0

0.5

0.5

1 1.5

2 2

2 2

2


Design points above predicted valueDesign points below predicted value2.95

0.0125



-1.00

-0.50

0.00

0.50

1.00

-1

0

1

2

3

4

Vi

ab

le

cellcount

B: agitation

@


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Numerical Optimization


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Coded Terms Actual TermsRuns A B C Serum Agitation Inducer

1 -1 0 -1 1 40 102 -1 0 +1 1 40 10003 -1 +1 0 1 80 5054 +1 -1 0 10 40 105 +1 0 -1 10 40 10

Runs Predicted value Observed v1 1,206 1,252 0,906 0,9753 1,516 1,54 2,066 25 1,076 1,125

Average 1,354 1,37

The error is 2% throughout the experiment and it is thought th

value is an acceptable low value with respect to the literature

values (Tissot 2011).

Validation


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This study should have done by measuring two

viable cell count and recombinant protein concen

inadequate number of ELISA kits, recombinant pr

measuring could not be completed. Therefore, sy

using only one response that

The results had a normal distribution and each ma

to have significantly different effects on cell grow

and inducer concentration had two-sided effects

optimization for these two factors had gre

maximized cell growth. On the other hand, s

factor had a comparably linear

Serum concentration-agitation speed interaction

speed-inducer concentration interactions (BC) hav

Significant Effects


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This study sho

Chinese Hamst

culture

concentration, 40and 505 ng/ml in

at small scale b

optimum viable cel

of recombin

Therefore more t

may have been obta

Optimized Parameters


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As a result of regrvalidation runs, stu

This is a very sbiological systems

usually many more fbe controlled

humidity changes inhuman-related mista

Therefore, it can be model fits well fo

set-up and h

Validation and Precision


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This study reveals optimized

1 production in recombin

first optimization study for-1 production in CHO

bio

This study also promises new, small-scale animal cell culture production tbioreactors that is concluded a

Additionally, these optimization results belong to small-scale production of -1. These results cannot be generalized to different scale production due t

process parameters but they may lead another optimization studies in

Novelty and Importance


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disease progression in relapsing multiple sclerosis. The Multiple Sclerosis Collaborative Research

Neurol. 1996;39(3):285-94.

Tenembaum SN, Banwell B, Pohl D, Krupp LB, Boyko A, Meinel M, et al. Subcutaneous InterferonMultiple Sclerosis: A Retrospective Study. J Child Neurol. 2013;10:10.

Shekhter, II, Beiko VP, Bulenkov MT, Khodova OM, Kolevatykh MA, Izotova LS, et al. [Obtaining

(serine-17) beta-interferon by the method of oligonucleotide-directed mutagenesis and its exp

coli]. Antibiot Khimioter. 1991;36(8):25-8.

Abdul-Ahad AK, Galazka AR, Revel M, Biffoni M, Borden EC. Incidence of antibodies to interf

treated with recombinant human interferon-beta 1a from mammalian cells. Cytokines Cell Mol Th

Houdebine LM. Production of pharmaceutical proteins by transgenic animals. Comp Immunol

2009;32(2):107-21.Kieseier BC, Calabresi PA. PEGylation of interferon-beta-1a: a promising strategy in multiple

2012;26(3):205-14.

Zhang X, Stettler M, De Sanctis D, Perrone M, Parolini N, Discacciati M, De Jesus M, Hacker D, Qu

Use of orbital shaken disposable bioreactors for Mammalian cell cultures from the milliliter-sc

scale. Adv Biochem Eng Biotechnol. 2010;115: 33-53.

Ikura K, Nagao M, Masuda S, Sasaki R, Animal Cell Technology: Challenges for the 21st CentuPublishers, 1998; 314-387.

Tissot S, OrbShake Bioreactors for Mammalian Cell Cultures: Engineering and Scale-up,2011, EPFLFederale de Lausanne) PhD Thesis.

References

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