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Graphical methods for data from a fermentation process

Antje Christensen

Per Rexen

Novo Nordisk A/S

Slide No. 2 • 17 October 2002 • Fall Technical 2002

Agenda

• The Process

• The Project

• Data

• Graphs


The Process: Fermentation of a Pharmaceutical

• product: FVIIa(”activated factor seven”), a bloodclotting agent

• producer: geneticallymodified mammaliancells


Process steps

Cell culture

Working cellbank ampoule

Raw materials

Fermentation

Purification

Formulation offinished product


productionfermentor

Cell culture

cell bank ampoule

cellfactory

seedfermentor

I seed fermentor

II


Fermentation: Draw and Fill

• cell growth phase: 10 days

• production phase: up to 48 days

• harvest every 24 hours

• several fermentors at each step

• no fixed coupling between seed and productionfermentors


colu

mn

ste

p 1

colu

mn

ste

p 4

Purification

• chromatography

• 4 purification steps

• one column per step

• purpose:• volume reduction• removal of impurities• activation

• harvests from two days arepooled


The Project

• purpose• discovery - new knowledge from existing data• optimization of product yield• description of a normal state of production• prediction of an individual fermentation’s yield at an

early stage

• team• specialists from fermentation• specialists from purification• statistician


Data – Observations

• 26 fermentation batches

• up to 24 purification batches per fermentation batch

• two days per purification + days during cellculture

purification purification purification

day day dayday day dayday day

fermentation


Data – Time Intervals Between Observations

• one figure per fermentation batch• eg cell number in cell bank ampoule

• one figure per purification batch• eg product yield in mg

• one figure per 24 hour period• from samples

• eg cell concentration, laboratory measured pH

• virtually continuous data • from sensors

• eg temperature, pH


Data – Variables: Product Yield

• yield in mg before and after each purificationstep

• yield in % across each purification step

• concentration during cell culture and fermentation

• different measurement methods• chromatographical (HPLC)

• immunological (ELISA)


Data – Variables: Adjustable Input

• temperature

• pH / amount of added soda

• glucose concentration(not adjusted in data collection period)


Data – Variables: Cells

• cell numbers during cell culture and fermentation

• growth rate

• number of cells detached from the carrier

• proportion of dead cells among detached cells

• viability score in cell bank ampoule


Data – Variables: Product Variations and Product Related Impurities

• fermentation related• various incomplete molecules (eg propeptide still

attached)

• various molecules with different posttranslationalstructure (eg glycosylation)

• purification related• dimers, oligomers, polymers

• various degradation products

• degree of activation (purification step 2-4)


Data – Variables: Side Products and ProductUnrelated Impurities

• side products (fermentation related)• ammonium

• lactate

• impurities (purification related)• cell proteins

• antibodies introduced during affinity chromatography


Data – Variables: Non-Adjustable Physical Parameters

• volume of decanted liquid

• conductivity of decanted liquid

• load on purification columns

• time on purification columns


Graphical methods

• univariate• time series

• distributions

• bivariate• scatter plots

• follow groups of data points from one graph to another

• multivariate


Where are differences in yield generated?

• same analytical method! directly comparable

• coeff. of correlation 0,84

• for yield optimization, concentrate on thefermentation process and purification step 1

Yield per purification batch after step 1 and after completed purification

yield in mg after purification step 1

tota

l yie

ldin

mg


Influence of purification step 1 on yield

• different analyticalmethods

• after step 1: HPLC

• in decanted liquid: ELISA (much higher analyticalvariation)

• still a clear correlation

• for yield optimization, concentrate on thefermentation process

Yield per purification batch before and after step 1

yiel

din

mg

aft

erst

ep 1

yield in mg in decanted liquid


Development of yield over time

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Total yield in mg for individual purifications per fermentation

facility rebuilt

tota

l yie

ldin

mg

fermentation

!Any other parameters that change upon rebuilding?


Other parameters that change upon rebuilding I

Single chainHeavy chain degradation

enkk

0,00

0,25

0,50

0,75

1,00

23

4 678 9 10

1112 13

1415

1617 18

1920

2122

2324

2526

0

1

2

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26heav

y ch

ain

degr

adat

ion

sing

le c

hain

fermentation fermentation

Both phenomena are a result of higher concentration, as FVII has autocatalytic properties.


Other parameters that change upon rebuilding II

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Conductivity per fermentation

cond

uctiv

ity

fermentation


The Conductivity Story

• recent fermentations show high yield and lowconductivity

• graph is based onfermentation averages

Average yield in mg per purificationvs. average conductivity

average conductivity

aver

age

yiel

din

mg

yellow dots: after facility rebuilding


The Conductivity Story Contd.

• next graph is based onindividual purifications

• negative correlationvanishes

Yield in mg vs. conductivity

conductivity

yiel

din

mg

yellow dots: after facility rebuilding


Some Parameters Vary Systematically withthe Fermentation’s AgeResidual glucose Gla-35

0 10 20

Gla

-35

0 10 20 30 40 50

resi

dual

gluc

ose

day purification


Normal Curves I:Limits for Individual Observations

• mean curve: • mean per purification/day

• spline

• parametrical curve

• limits: mean curve ± 2 s, s2= s2

residual + s2between ferm.

from ANOVA withpurification/day fixed and fermentation random-10 0 10 20 30 40 50 60 70

Residual glucose

day

resi

dual

gluc

ose


Normal Curves II: Limits for Curves

• mean curve: as for individual observations

• limits: mean curve ± δwith minimum δ such thatthe limit curve is significantly different from the historical data at 2,5% level

-10 0 10 20 30 40 50 60 70

Residual glucose

day

resi

dual

gluc

ose


Normal Curves III: Limits for ParametersIndividual range charts for Gla-35 slopes and intercepts

inte

rcep

t

15 17 19 21 23 25

Avg=

LCL=

UCL• If the profile is modelled

by a parametrical curve:• Shewhart charts for

parameters

• Multivariate charts for correlated parameters

• If the profile is not modelled:

• Shewhart charts for principal components

fermentation

slop

e15 17 19 21 23 25

Avg=

LCL

UCL

fermentation


Handling Parameters with a Profile for Optimization

• Analysis on purification or day basis:Use residuals to normal curve rather than original observations

• Analysis on fermentation basis:Use parameters of parametrical curvesor principal components


Tracing Groups of Data Points

0 10 20 30 40 50day

lact

ate

0 10

yellow white blue

Distribution of lactatebased on days in fermentor


Lactate: The Top Branch

0 10 20 30 40 50 60

• most samples in thebranch come from thesame fermentation and are analyzed in the same analytical run

• additional lactate can beproduced in the sample ifit is not sterile

• presumably a problem ofsample handling

analytical run

day

lact

ate


Lactate: The Bottom BranchLactate Ammonium per l

0 10 20 30 40 50 0 10 20 30 40 50

lact

ate

amm

oniu

m p

er l

dayday


Lactate: The Bottom Branch Contd.Cell concentration FVII concentration by ELISA

0 10 20 30 40 50 0 10 20 30 40 50

cell

conc

entra

tion

dayFV

II co

nc. /

ELI

SAday


Do Higher Temperatures Cause LowerLactate Concentrations?

• temperature setpoint wasvaried in variousexperiments

• Why is there a distinguished shift in lactate when comparingnormal operation and experiments, but not between experiments?

Temperature setpoint

36

36,1

36,2

36,3

36,4

36,5

36,6

36,7

0 10 20 30 40 50day

tem

pera

ture


The Root Cause: A Process Change

0 10 20

• growth medium composition was changedafter fermentation 5

• no temperatureexperiments wereconducted afterfermentation 5

• lactate and ammonium measurements werediscontinued afterfermentation 15

Lactate by fermentation

fermentation

lact

ate


PCA for Discovering Covariances

• dataset based on fermentations

• variables:• fermentation parameters

• averages of purification parameters

• averages of daily measurements

• a rough-and-ready analysis of all available data


Score Plot of the First Two Principal Components


Loading Plot of the First Two Principal Components

medium

medium


PLS for Modelling Yield

• same dataset as for PCA

• all yield measures as Y

• model based on fermentations 12-18

• validate model on the other fermentations


PLS for Modelling YieldTotal yield in mg

model basis

fermentation

tota

l yie

ldin

mg

observed predicted


Conclusion I: Graphical Methods

• univariate graphs for process monitoring• time series along fermentations, across purifications/days• time series along purifications/days, across fermentations• distributions• control charts

• bivariate graphs for visualizing correlations• scatter plots• follow groups of data points from one graph to another

• multivariate methods for discovering correlations and for prediction• score plots• loading plots• overlay PLS-predicted and observed values


Conclusion II: The Role of Graphs

• Graphs facilitate discoveries in data

• Graphs facilitate communication of discoveries

• Graphs can give a feel for the process

• Graphs can be misleading –choose your graphs carefully!

Documents

Graphical methods for data from a fermentation process - ASQasq.org/cpi/2002/06/graphical-methods-for-data... · Graphical methods for data from a fermentation process ... • If