graphical methods for data from a fermentation process -...
TRANSCRIPT
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
Slide No. 3 • 17 October 2002 • Fall Technical 2002
The Process: Fermentation of a Pharmaceutical
• product: FVIIa(”activated factor seven”), a bloodclotting agent
• producer: geneticallymodified mammaliancells
Slide No. 4 • 17 October 2002 • Fall Technical 2002
Process steps
Cell culture
Working cellbank ampoule
Raw materials
Fermentation
Purification
Formulation offinished product
Slide No. 5 • 17 October 2002 • Fall Technical 2002
productionfermentor
Cell culture
cell bank ampoule
cellfactory
seedfermentor
I seed fermentor
II
Slide No. 6 • 17 October 2002 • Fall Technical 2002
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
Slide No. 7 • 17 October 2002 • Fall Technical 2002
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
Slide No. 8 • 17 October 2002 • Fall Technical 2002
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
Slide No. 9 • 17 October 2002 • Fall Technical 2002
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
Slide No. 10 • 17 October 2002 • Fall Technical 2002
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
Slide No. 11 • 17 October 2002 • Fall Technical 2002
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)
Slide No. 12 • 17 October 2002 • Fall Technical 2002
Data – Variables: Adjustable Input
• temperature
• pH / amount of added soda
• glucose concentration(not adjusted in data collection period)
Slide No. 13 • 17 October 2002 • Fall Technical 2002
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
Slide No. 14 • 17 October 2002 • Fall Technical 2002
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)
Slide No. 15 • 17 October 2002 • Fall Technical 2002
Data – Variables: Side Products and ProductUnrelated Impurities
• side products (fermentation related)• ammonium
• lactate
• impurities (purification related)• cell proteins
• antibodies introduced during affinity chromatography
Slide No. 16 • 17 October 2002 • Fall Technical 2002
Data – Variables: Non-Adjustable Physical Parameters
• volume of decanted liquid
• conductivity of decanted liquid
• load on purification columns
• time on purification columns
Slide No. 17 • 17 October 2002 • Fall Technical 2002
Graphical methods
• univariate• time series
• distributions
• bivariate• scatter plots
• follow groups of data points from one graph to another
• multivariate
Slide No. 18 • 17 October 2002 • Fall Technical 2002
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
Slide No. 19 • 17 October 2002 • Fall Technical 2002
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
Slide No. 20 • 17 October 2002 • Fall Technical 2002
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?
Slide No. 21 • 17 October 2002 • Fall Technical 2002
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.
Slide No. 22 • 17 October 2002 • Fall Technical 2002
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
Slide No. 23 • 17 October 2002 • Fall Technical 2002
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
Slide No. 24 • 17 October 2002 • Fall Technical 2002
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
Slide No. 25 • 17 October 2002 • Fall Technical 2002
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
Slide No. 26 • 17 October 2002 • Fall Technical 2002
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
Slide No. 27 • 17 October 2002 • Fall Technical 2002
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
Slide No. 28 • 17 October 2002 • Fall Technical 2002
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
Slide No. 29 • 17 October 2002 • Fall Technical 2002
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
Slide No. 30 • 17 October 2002 • Fall Technical 2002
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
Slide No. 31 • 17 October 2002 • Fall Technical 2002
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
Slide No. 32 • 17 October 2002 • Fall Technical 2002
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
Slide No. 33 • 17 October 2002 • Fall Technical 2002
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
Slide No. 34 • 17 October 2002 • Fall Technical 2002
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
Slide No. 35 • 17 October 2002 • Fall Technical 2002
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
Slide No. 36 • 17 October 2002 • Fall Technical 2002
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
Slide No. 37 • 17 October 2002 • Fall Technical 2002
Score Plot of the First Two Principal Components
Slide No. 38 • 17 October 2002 • Fall Technical 2002
Loading Plot of the First Two Principal Components
medium
medium
Slide No. 39 • 17 October 2002 • Fall Technical 2002
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
Slide No. 40 • 17 October 2002 • Fall Technical 2002
PLS for Modelling YieldTotal yield in mg
model basis
fermentation
tota
l yie
ldin
mg
observed predicted
Slide No. 41 • 17 October 2002 • Fall Technical 2002
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
Slide No. 42 • 17 October 2002 • Fall Technical 2002
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!