Quicker Ways to a Scalable Process Based on Sound-Science Based Methodologies
Christoph Herwig
February 11th 2015
18/04/16 Ch. Herwig
Our Mission in Bioprocess Technology
2
18/04/16 Ch. Herwig
Status quo of bioprocess design The Scaling Tasks
Investigate! Redo! Hope! Stomach decision !
Process Development
Piloting Manufacturing Screening
Scale-up Scale-up Scale-up
Productivity
Waste
Process Development Time Revenue Period
3
18/04/16 Ch. Herwig
Challenges that can be solved by
faster process development and
higher titers
prevent and identify scale-up
effects
prevent failed
batches
4
18/04/16 Ch. Herwig
How can these challenges be addressed?
โขโฏ Clear and proven workflows for data analysis
โขโฏ Tools to process data and ensure data quality
โขโฏ Efficient use of chemometric and mechanistic data science tools
โขโฏ10
18/04/16 Ch. Herwig
Presentation Workflow
โขโฏ Goal: Quicker Ways to a Scalable Process Based on Sound-Science Based Methodologies
โขโฏ Processing Strategies to Understand the Production Platform and to Allow Transferability from Scale to Scale and from Product to Product โ
6
Conclusions Model
Building Tutorial
Linking MVDA
results to metabolic models
Information extraction
and Statistical analysis
Big Data Processing
18/04/16 Ch. Herwig
GET A GRAB ON YOUR DATA
Big Data Processing
7
18/04/16 Ch. Herwig
Typical industrial process data
8
0 0.2 0.4 0.6 0.8 10
500
1000LEISTUNG MEAS (HK)
0 0.2 0.4 0.6 0.8 10
0.5
1F Druck
0 0.2 0.4 0.6 0.8 10
50
100Substrat 3 Menge
0 0.2 0.4 0.6 0.8 10
1
2Dosierung 2
0 0.2 0.4 0.6 0.8 10
1
2
3Summe Substrat
0 0.2 0.4 0.6 0.8 10
500
1000biol. Wรคrmeleistung
0 0.2 0.4 0.6 0.8 10
10
20
30Substrat 2
0 0.2 0.4 0.6 0.8 10
20
40
60Product
0 0.2 0.4 0.6 0.8 10
5
10Substrat 1
0 0.2 0.4 0.6 0.8 10
0.05
0.1Nebenprodukt
18/04/16 Ch. Herwig
Data import, data alignment and contextualization
โขโฏ USP & DSP automatic data import โขโฏ Excel spreadsheet, text documents, LIMS,
PIMS
โขโฏ Data contextualization โขโฏ measurement units โขโฏ campaign, phase definition โขโฏ operators
โขโฏ Data survey & overview โขโฏ overview plots โขโฏ data density plots
ร โฏ All data in one format that can easily be analyzed and explored
Exputec Software
Excel
LIMS PIMS
9
18/04/16 Ch. Herwig
Principal Component Analysis: Raw Data
โขโฏ Principal component analysis on individual runs to quantify variations and detect relationships among variables.
10
-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
Energieeintrag (HK)Druck
S3 Menge
Dosierung 2Summe Substrat
Bio Wรคrmeleistung
S2
Product
S1
Nebenprodukt
Component 1
Com
pone
nt 2
B169012
1 2 30
10
20
30
40
50
60
70
80
90
100
Principal Component
Var
ianc
e E
xpla
ined
(%
)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
18/04/16 Ch. Herwig
GET A HINT
Information extraction and statistical analysis
11
18/04/16 Ch. Herwig
Information extraction โ Completion of data sets
โขโฏ Tools to derive meaningful descriptors โขโฏ Control quality tools
โขโฏ Descriptors that describe the control quality of process parameters, such as pH, pO2
โขโฏ RMSE, outlier detection
โขโฏ Bioprocess suite deriving scalable descriptors โขโฏ ยต, qs, CER, OUR, โขโฏ Yields โขโฏ kinetic constants
โขโฏ Calculation of missing entities via combination of dvariables and sound science first principles
ร โฏ Automatic extraction of meaninful descriptors for different process phases, USP & DSP processes
0 5 10 15 20 250
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
feed time (h)
my
rec
calc
(1/h
)
0 5 10 15 20 250.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
feed time (h)
Yxs
rec
calc
(c-m
ol/-c
mol
)
12
18/04/16 Ch. Herwig
PCA on scalable parameters for individual runs
โขโฏ Principal component analysis on individual runs to โขโฏ build mechanistic
hypothesis and
โขโฏ detect number of independent mechanisms
-1 -0.5 0 0.5 1-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
qXqP
qS3
qNP
qSsum
Component 1Co
mpo
nent
2
B162254
1 20
10
20
30
40
50
60
70
80
90
100
Principal Component
Varia
nce
Expl
aine
d (%
)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
-1 -0.5 0 0.5 1-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
qXqP
qS3
qNP
qSsum
Component 1
Com
pone
nt 2
B163552
1 20
10
20
30
40
50
60
70
80
90
100
Principal ComponentVa
rianc
e Ex
plai
ned
(%)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
13
18/04/16 Ch. Herwig
Gathered data to statistical information
14
โขโฏ Raw data from DoE experiments transferred into information โขโฏ Influence of pH, pCO2 and pO2 on specific rates and yields
VCD
Glucose
SP
EC
IFIC
RAT
E
YIE
LD
18/04/16 Ch. Herwig
Hypothesis Generation using MVIA Results
โขโฏ Processing of data (concentrations, flows) into specific rates and yield coefficients (ยต, qs)
โขโฏ Tools โขโฏ Principal Component Analysis โขโฏ MLR โขโฏ PCR โขโฏ Factor Analysis โขโฏ Multi-way methods โขโฏ โฆ
High Performer Low Performer
ร โฏ Identify and understand trends and correlationsร โฏ Identify interactions across unit operations
18/04/16 Ch. Herwig
NEEDING A SEGREGATED VIEW OF YOUR CATALYST?
Cell-based Analytics
16
18/04/16 Ch. Herwig
Analytical methods
ยงโฏ Flow Cytometer ยงโฏ Producers / Non โ producers ยงโฏ Apoptotic cells
ยงโฏ Dead cells: viability staining (Cedex)
ยงโฏ Lysed cells (DNA, protein, LDH) ยงโฏ Product analytics (HPLC) ยงโฏ Product quality
800msec 25%
18/04/16 Ch. Herwig
Segregation of Biomass -Lysis-
18
Bio ma ss
l y sis
ph y sio l o g y
ma mma l ia
0 500 1000 1500 2000 2500-10
-8
-6
-4
-2
0
2
r dLDH
(mU/
(mL*
h))
Initial LDH (mU/mL)0 2000 4000 6000 8000 10000
-70
-60
-50
-40
-30
-20
-10
0
10
r dDNA
(ng/
(ml*h
))
Initial DNA (ng/mL)
a) b)
0
10000
20000
30000
40000
50000
r pLDH
(ยตU(
mL*
h))
rmeasLDH rcorrLDH
d)
0 50 100 150 200 250time (h)
0
50000
100000
150000
200000
250000
r pDNA
(pg/
(mL*
h))
rmeasDNA rcorrDNA
c)
0 50 100 150 200 250time (h)
Degradation rates of a) DNA and b) LDH in fermentation supernatants with respect to initially detected extracellular concentrations of DNA or
LDH. Volumetric rates of c) DNA release into culture supernatants and d) LDH release into culture supernatants from direct measurements of
the respective marker (rmeas) and corrected for marker degradation (rcorr).
18/04/16 Ch. Herwig
Segregation of Biomass -Lysis-
19
Bio ma ss
l y sis
ph y sio l o g y
ma mma l ia
b)
0 25 50 75 100 125 150 1750.00
2.50E6
5.00E6
7.50E6
1.00E7
1.25E7
1.50E7
1.75E7
Cel
ls/m
L
time (h)
VCC TCC LCC
d)
0 50 100 150 200 2500.00
2.50E6
5.00E6
7.50E6
1.00E7
1.25E7
1.50E7
Cel
ls/m
L
time (h)
VCC TCC LCC
c)
0 50 100 150 200 2500.0
5.0E6
1.0E7
1.5E7
2.0E7
2.5E7
Cel
ls/m
L
time (h)
VCC TCC LCC
a)
0 50 100 150 200 2500
2
4
6
8
10
12
Glc
(g/
L)
time (h)
0
1
2
3
Gln
, Lac
(g/
L)
GlucoseLactateGlutamine
4
pH, 1
pH, 2 pH, 3
pH, 1
18/04/16 Ch. Herwig
Segregation of Biomass -Lysis-
20
Bio ma ss
l y sislysis โ results
Lysis has a influence on the physiological interpretation! lysis should be considered
ph y sio l o g y
ma mma l ia
pH7.0 pH6.8 pH7.20
5
10
15
20
Yiel
d (1
08 cel
ls/g
Glc)
YVCC
YTCC
YLCC
b)a)
0 50 100 150 200 250-0.02
0.00
0.02
0.04
ยต (1
/h)
time (h)
ยตVCC
ยตLCC
18/04/16 Ch. Herwig
LINK THE HINT TO MECHANISTICS
Linking Cluster Analysis to Metabolic Models
21
18/04/16 Ch. Herwig
3 4 5 6 7 8 9-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
Process Time [h]
Varia
bles
Data Analysis to understand lactate production and consumption in Cell Culture
Prediction of qLac days by a PLS-R model Prediction itself not very useful, since it is very time consuming to measure all 66 variables However, the weights of the PLS-R model can be used to find mechanistic links
11 Batches from a DoE 66 variables (Specific rates, MFA-results etc.) 8 Samples each
-2 0 2 4 6 8 10-2
-1
0
1
2
3
4
5
6
7
8
Actual
Predicted
1 2 3 40
10
20
30
40
50
60
70
80
90
100
Number of PLS components
Perc
ent V
aria
nce
Expl
aine
d in
y
22
18/04/16 Ch. Herwig
0 2 4 6 8 100
1
2
3
4
Process Time [d]0 2 4 6 8 100
1
2
3
4
Process Time [d]
0 2 4 6 8 100
1
2
3
4
Process Time [d]
Cluster 1Cluster 2Cluster 3Cluster 4
PLS-R variable importance (VIP) for qLac
Cluster detection (k-means cluster analysis) based on PLS-R VIP to detect main correlations with response (e.g.: qLac) ร identify mechanistics VIP > 1 variable is significant VIP < 1 variable is insignificant Phase detection by k-means cluster analysis (blues lines)
Average VIPs
23
18/04/16 Ch. Herwig
Use simplified metabolic flux models
โขโฏ Only the Central Carbon Metabolism is considered
โขโฏ Redox and energy metabolism โขโฏ Amino acid metabolism
โขโฏ But which measurements are now really omportantn in which phase?
24
18/04/16 Ch. Herwig
Glc GPI ald GAPDH pgk eno pepkin g6Pdh prodh 1 2
3.14.15.26.27.38.3
Variables
Proc
ess
Tim
e [d
]
0
0.5
1
1.5
2
Ala Arg Asp Asn Glu Gln Gly His Ile Leu Lys Phe Pro Trp Tyr Val mu qp Formate NH4 Lip Anap PPP_nox gpt asp-got aspg gls leu cat ile cat shmt phe deg serc3 mehf sink 1 23.14.15.26.27.38.3
VariablesProc
ess
Tim
e [d
]
Met Ser Thr Cit Pyr Suc pdh cisac icdh akgdh succdh fum maldh val cat thr deg Resp 1 2
3.14.15.26.27.38.3
Variables
Proc
ess
Tim
e [d
]
0
0.5
1
1.5
2
arg cat glud his deg lys deg met deg Trp deg Tyr cat NH3 sink 1 2
3.14.15.26.27.38.3
Variables
Proc
ess
Tim
e [d
]
0
0.5
1
1.5
2
Clusters variable importance(VIP) for Lactate production/consumption
Cluster 1: Important variables for qLac for all time points; mainly related to glycolysis ร Overflow / Lactate
Cluster 3: Important variables for qLac at the early phase (plus late phase); mainly related to TCA cyle activity
Cluster 4: Important variables for qLac at the late time points; probably related to nutrient limitation / stationary phase
Red: important to predict qLac; a value > 1 means the variable is significant Blue: not important to predict qLac; a value < 1 means the variable is insignificant
Cluster 2: Less important variables for qLac
25
18/04/16 Ch. Herwig
Mechanistics insights by target oriented use of statistical tools
โขโฏ Highly target-oriented analysis of large data sets by combination of different data driven methods โขโฏ Do not get lost in the woods!
โขโฏ Automated clustering of process variables in to physiologically meaningful groups with regard to the response variable (e.g.: qLac)
โขโฏ Mechanistic insight can be acquired from data driven methods if the tools are applied appropriately
26
18/04/16 Ch. Herwig
GET IT UNDERSTOOD AND OPTIMIZED
Mechanistic Model Building Tutorial
27
18/04/16 Ch. Herwig
The Modelling Work Flow
StructureIdentification
ParameterEstimation
ModelValidation
ModelRefinement
ModelUse
YES
NO
ExperimentalData
PrioriKnowledge
28
18/04/16 Ch. Herwig
Mechanistic Model for Glucose Uptake Kinetics
29
ยงโฏ Propose hypothesis:
๐โ๐บ๐๐โ=๐(๐บ๐๐)
ยงโฏ Formulate equation:
๐โ๐บ๐๐โ= ๐โ๐ฎ๐๐,๐๐๐โโ [๐บ๐๐]/[๐บ๐๐]+ ๐ฒโ๐ฎ๐๐โโ ยงโฏ Estimate parameters
individually by minimizing cost
function โโโ( ๐ฆโ๐,๐๐๐๐ ๐ข๐๐๐โโ ๐ฆโ๐,๐๐๐๐๐โ)ยฒโ
ยงโฏ Compare with literature
ยงโฏ Define validity
18/04/16 Ch. Herwig
Toolset for model calibration
โขโฏ Model calibration: โขโฏ finding suitable estimates for model
parameters
โขโฏ Sensitivity and identifiability analysis: โขโฏ Link to your measurements and
processing platform
30
18/04/16 Ch. Herwig
Mechanistic optimization
โขโฏ Use a kinetic model for model-based optimization โขโฏ Development of mechanistic process
model โขโฏ In-silico optimization using optimization
algorithms โขโฏ Identification of optimal operating
conditions โขโฏ Control Implementation
ร higher productivity using less experiments ร Knowledge on mechanistic relationships can be transferred to the next product
31
18/04/16 Ch. Herwig
CONCLUSIONS
32
18/04/16 Ch. Herwig
Our approach for bioprocess design
33
18/04/16 Ch. Herwig
ProcessParameters
ProcessVariables
VfeedcfeedVgasincgasinrpmโฆ
.
.QAscproductcBDWcO2outcCO2outโฆ!
Predic?veProcessing
InverseAnalysis
Pred
ic?v
eProcessing
Structured
ProcessDevelop
men
tFrom good data via understanding
to prediction
34
CONSISTENT DATA SET
INFORMATION & EXPERIMENTAL DESIGN
KNOWLEDGE & MODELLING
OPTIMIZED & PREDICTIVE CONTROL
18/04/16 Ch. Herwig
Monitoring
ProcessParameters(Inputs)
ProcessVariables(Outputs)
VfeedcfeedVgasincgasinrpmโฆ
.
.QAscproductcXL
xO2outxCO2outโฆ
DataQualityandConsistencyPhysiologicalProcessControl
PhysiologicalExperimentalDesign
!Mul?variateControlalongDesignSpace(MIMO)Mechanis?cModeling
Real-?meImplementa?on
CELL
Plenty of tools available! Need to be transferred for industrial use!
35
PhysiologicalInforma?onExtrac?on
ExperimentalAutoma?on&ParallelApproaches
ModelPredic?veControl
Op?malControlModelBasedOp?miza?onPredic?ve
Processing
Mechanis?cHypothesesGenera?on
Mul?variateInforma?onProcessingSoRSensors InverseAnalysis
18/04/16 Ch. Herwig
Take Home Workflow
36
โขโฏ Bioprocess development and manufacturing challenges can be solved by process data science
โขโฏ Proven workflows and a tailored toolset for bioprocess data analysis and process optimization is necessary
Tranfer knowledge to
other process /
product / site
Optimize the process by mechanistic
models
Link your hint to
mechanistic hypotheses
Get a hint using
information extraction and statiscial tools
Get a grab on your data by
tailored workflows
18/04/16 Ch. Herwig
Investigate! Redo! Hope! Stomach decision !
Process Development
Piloting Manufacturing Screening
Scale-up Scale-up Scale-up
Productivity
Waste
Process Development Time Revenue Period
Benefits through shown Approach
37
Process
Development
Pilo?ng ManufacturingScreening
Scale-up
Increaseproduc1vitybysustainedop1miza1on,scalabilityand
elimina1onoffailbatches
CutProcessDevelopmentTimeby50% RevenuePeriod
Inves?gate! Verify! Control&predict!Explore!
Scale-up Scale-up
18/04/16 Ch. Herwig
Process-undisclosed
Design&CQAs-Freedomduringdesign-ProofofConceptforcontrollingQTPP
ProvenPa?entBenefit-QTPP-Efficacy,Safety-ClinicalPhaseI,II&III
Process-completedisclosure
New
Produ
ct
Biosim
ilar
ProofofConcept
Mechanis?calModelsviaPhysiologyโฏ FirstPrinciplesโฏ MetabolicFounda?onsโฏ PlaZormKnowledge
โฏ RiskManagementโฏ VerifiedScaleDownModels
DesignMethodology
Relevance for new products & biosimilars
Design&CQAs-FixedCQAlimitsfromini?alapproval
ProofofSimilarity
38
18/04/16 Ch. Herwig
Thank you for your attention!
Univ.Prof. Dr. Christoph Herwig Vienna University of Technology Institute of Chemical Engineering Research Division Biochemical Engineering Gumpendorferstrasse 1a/ 166 - 4 A-1060 Wien Austria emailto: [email protected] Tel (Office): +43 1 58801 166400 Tel (Mobile): +43 676 47 37 217 Fax: +43 1 58801 166980 URL : http://institute.tuwien.ac.at/chemical_engineering/bioprocess_engineering/EN/
https://www.facebook.com/BioVTatTUWien
39
18/04/16 Ch. Herwig
BACK UP SLIDES
40
18/04/16 Ch. Herwig
IMPLEMENTATION FOR PROCESS CONTROL
Get it ON-LINE!
41
18/04/16 Ch. Herwig
Computational environment for real-time implementation and control
โขโฏ Object oriented design of different data storage and processing components.
โขโฏ Classes are able to store data, perform specific functions, and communicate with each other
42
Process Information
Management System
OPC server
Calculations:
feeding rates offgas analysis
volume calculation outlier detection
โฆ
Observer
Bioprocess object
y u(t0)
y
Model-predictive controller & PID
u(t0)
xpred
u(t1)
y: measurements/outputs (e.g. offgas) u: system inputs (e.g. feeding rates) x: system states (e.g. concentrations)
18/04/16 Ch. Herwig
Calculation of respiratory rates and outlier detection
43
Bioprocess
Calculator offgas
Calculator outlier
Observer
FAIR,FO2,O2_offgas,CO2_offgas
OUR, CER, RQ
OUR, CER outliers removed
7.3565 7.3566 7.3566
x 105
0
0.1
0.2
0.3
0.4
Original signalNew signal 7.3565 7.3566 7.3566
x 105
0
0.5
1
G
Grubbs distance
7.3565 7.3566 7.3566
x 105
0
0.02
0.04
0.06stdandard deviation
7.3565 7.3566 7.3566
x 105
0
0.1
0.2
0.3
0.4
New signal
18/04/16 Ch. Herwig
Real-time implementation via particle filter estimations
0 50 100 1500
5
10
15
20
25
Time [h]
[g/l]
Biomass
0 50 100 1500
5
10
15
20A0 and A1 (soft-sensor)
[g/l]
Time [h]
0 50 100 1500
0.05
0.1
0.15
0.2
Time [h]
[C-m
ol/h
]
Biomass conversion rate (soft-sensor)
0 50 100 1500
0.2
0.4
0.6
0.8
1
1.2
1.4
Time [h]
[g/l]
Glucose (measured)
0 50 100 150-2
0
2
4
6
8
10
Time [h]
[g/l]
Gluconate (measured)
0 50 100 1500
1
2
3
4
5
6
Time [h]
[g/l]
Penicillin (measured)
0 50 100 150-0.12
-0.1
-0.08
-0.06
-0.04
-0.02
0
0.02
Time [h]
[mol
/h]
OUR (measured)
0 50 100 150-0.02
0
0.02
0.04
0.06
0.08
0.1
0.12
Time [h]
[C-m
ol/h
]
CER (measured)
measuredsoft-sensor
A0A1 measured
soft-sensor
measuredsoft-sensor
measuredsoft-sensor
measuredsoft-sensor
measuredsoft-sensor
measuredsoft-sensor
44
18/04/16 Ch. Herwig
LIFE CYCLE SOLUTION GET IT DONE YOURSELF
45
18/04/16 Ch. Herwig
CMLCM: Computational model life-cycle management
46
18/04/16 Ch. Herwig
Enabling Method Implementation
Customiza?on&Implementa?on
ofTools&Solu?ons
Quan?fica?on
DataInforma?onKnowledge
Scale-UpOp?miza?onRiskReduc?on
Quality
47
ManufacturersCMOs
Get There Faster.
Exputecโs Software Solutions by INCYGHT
ยงโฏ Efficient implementation of our solutions at your site using Exputec INCYGHT software
Batch 1 Batch 2 Batch N
Campaign analysis
. . .
MVIA (Multivariate Information Analysis) for extraction of mechanistic
information out of procee data
M-DoE (Mechanistic
Design of Experiments) for reduced number of experiments.
Multivariate statistical methods
for exploratory data analysis:
hypothesis generation and
testing
Proven workflows for identification of
sources of variability,
increasing process robustness, and
optimization.
48
18/04/16 Ch. Herwig
a)Tradi?onalfeedprofiledesign b)Physiologicalfeedprofiledesign
Accelerate!
Wechselbergeret.al.2012
Speed up by using physiological information in DoEs!
รโฏ Careful selection of physiological factors for the DoE significantly reduces number of experiments
49
18/04/16 Ch. Herwig
รโฏ Dynamic experiments increase information & throughput
Accelerate!
a)Quickiden?fica?onofscalablerela?onships
b)Dynamicfeedingprofilesbasedonspecificsubstrateuptakerate
Zalaiet.al.2012
Speed up by using physiological information & dynamics!
50
18/04/16 Ch. Herwig
NECESSARY TO LOOK MORE DETAILED INTO BIOMASS?
51
Bio ma ss
18/04/16 Ch. Herwig
Segregation of Biomass -Lysis-
52
Bio ma ssl y sisCell Lysis
deIinition: ๏ฟฝโloss of integrity of a cell (destruction of the cell membrane)โ
motivation:
lysed cells were once "produced" and can be included in the description of the growth kinetics lysed cells could serve as a nutrient source an inIluence on the product quality can not be excluded
measurement methods:
classiIication over intracellular substances in the supernatant mammalians:
โขโฏ LDH measurements in the supernatant โขโฏ DNA measurements in the supernatant
microbials โขโฏ C-balance
ph y sio l o g y
ma mma l iaM ic r o bia l s
18/04/16 Ch. Herwig
Quality by Design
Process Parameters Temperature Stirrer Speed Dissolved Oxygen pH Air Flow Pressure Feedrate Nutrient concentrations Inducer concentration Biomass concentration Induction Time Conductivity Redox level Strain Expression cassette โฆ
Product quality attributes Enzyme activity Titer Purity Stability Batch-to-batch variability Efficiency Cost of product Space-time-yield Protein folding Glycosylation pattern Viability Ease of further processing (downstream) Potential risks for end-user โฆ
???
18/04/16 53
18/04/16 Ch. Herwig
Process Parameters Temperature Stirrer Speed Dissolved Oxygen pH Air Flow Pressure Feedrate Nutrient concentrations Inducer concentration Biomass concentration Induction Time Conductivity Redox level Strain Expression cassette โฆ
Product quality attributes Enzyme activity Titer Purity Stability Batch-to-batch variability Efficiency Cost of product Space-time-yield Protein folding Glycosylation pattern Viability Ease of further processing (downstream) Potential risks for end-user โฆ
Quality by Design
???
54
18/04/16 Ch. Herwig
Current interpretation of QbD: Cooking Recipe: DoE
SpecificAc?vity
[kU/gbiomass]
Induc?onPhaseTemperature
[ยฐC]
Induc?onPhaseFeeding
Exponentk
โDesign Spaceโ
Data CPPs
CQA
CQA
55
CPPs
18/04/16 Ch. Herwig
DOE: Insignificant factors- all for nothing?
โขโฏ Factors turn out to be insignificant โขโฏ Variance cannot be explained by the
original factors โขโฏ Possible reasons:
โขโฏ 1) wrong factors were chosen for investigation
โขโฏ 2) noise on experiment higher than effects of factors
โขโฏ How to proceed?
ร Go beyond MLR analysis for the efficient exploitation of DoEs!
-0,10
-0,05
-0,00
0,05
x_
EF
B
ยต_
FB
ma
x s
pe
c tite
r su
p g
/g
N=9 R2=0,604 RSD=0,002799 DF=6 Q2=-0,347 Conf. lev.=0,95
-0,10
0,00
0,10
x_
EF
B
ยต_
FB
ma
x s
pe
c tite
r p
elle
t g
/g
N=9 R2=0,099 RSD=0,004055 DF=6 Q2=-1,568 Conf. lev.=0,95
Factor Transformation
MVDA
Knowledge 56
18/04/16 Ch. Herwig
-0,10
-0,05
-0,00
0,05
x_
EF
B
ยต_
FB
ma
x s
pe
c tite
r su
p g
/g
N=9 R2=0,604 RSD=0,002799 DF=6 Q2=-0,347 Conf. lev.=0,95
-0,10
0,00
0,10
x_
EF
B
ยต_
FB
ma
x s
pe
c tite
r p
elle
t g
/g
N=9 R2=0,099 RSD=0,004055 DF=6 Q2=-1,568 Conf. lev.=0,95
Run DoE
MLR analysis on original factors
Data Processing, Analyze more
descriptors of the process
MVDA Exploratory analysis
Hypythesis generation: New factors
Sort out co-linear factors (e.g.variance
inflation factor)
MLR analysis on transformed factors
Recycling of DoE Results
57
-1,5
-1,0
-0,5
0,0ยต
ma
x s
pe
c tite
r su
p g
/g
N=9 R2=0,510 RSD=0,002881 DF=7 Q2=0,313 Conf. lev.=0,95
-1,5
-1,0
-0,5
0,0
ยต
ma
x s
pe
c tite
r p
elle
t g
/g
N=9 R2=0,335 RSD=0,003226 DF=7 Q2=0,047 Conf. lev.=0,95
18/04/16 Ch. Herwig
Linking metabolite production and substrate uptake
58
Hypothesis:
overflow metabolism is coupled to high glucose consumption rate
a) Threshold ๐โ๐๐๐ โ๐๐๐โโ0.11 [๐๐๐๐/10โ9โ๐๐๐๐๐ โโโ] b) Linear equation ๐๐๐ ๐โ๐๐๐โ<๐โ๐๐๐ โ๐๐๐
๐โ๐๐๐โ=โ0.25โ2.21โ๐โ๐๐๐โ ๐ ๐๐๐ธ = 0.028 [๐๐๐๐/10โ9โโ๐๐๐๐๐ โโโ]
18/04/16 Ch. Herwig
Vom Labor in den Prozess
2x @50%
Toolset Integration
Data- Knowledge Management
Process Understanding
59
18/04/16 Ch. Herwig
Sensitivity of model outputs to variations in one model parameter
40 datasets were simulated by applying ยฑ2% variations on a process value
60
0 100 2000
10
20
30
40cXLtot
Time [h]0 100 200
0
0.5
1cS1
Time [h]0 100 200
0
5
10cPEN
Time [h]0 100 200
0
2
4
6cPOX
Time [h]
0 100 200-0.2
-0.15
-0.1
-0.05
0OUR
Time [h]0 100 200
0
1
2
3
4
5cA0
Time [h]0 100 200
0
10
20
30
40cA1
Time [h]0 100 200
0
2
4
6
8
10cS2
Time [h]
0 100 2000
0.05
0.1
0.15
0.2CER
Time [h]0 100 200
0
0.05
0.1
0.15
0.2rX
Time [h]0 100 200
-0.25
-0.2
-0.15
-0.1
-0.05rS1
Time [h]0 100 200
-0.3
-0.2
-0.1
0
0.1
0.2rS2
Time [h]
18/04/16 Ch. Herwig
Identifiability of parameter subsets
โขโฏ The colinearity index measures the degree of linear dependence of model parameters. โขโฏ It equals unity if the columns are linearly dependent. โขโฏ If it exceeds 10-15, then the corresponding parameter
subset is poorly identifiable.
61
0 1 2 3 4 5 6 70
5
10
15
20
25
30
35
40
45
50
min
imum
of
colli
near
ity in
dex
size of a parameter sets
Parameter
Unit
Optimized Value
Literature Value
Reference
๐๐ณ๐๐๐ฎ๐๐
๐๐๐๐/๐๐๐๐ โ1,11 โ1,10 (Lee et al., 2003)
๐๐ฎ๐๐,๐๐๐.๐๐๐ ๐๐ด ๐ฎ๐๐ ๐๐๐๐/(๐๐ โ ๐๐๐๐ โ โ) โ1,99 โ 10โ11 โ4 โ 10โ11 (Lee et al., 2003)
๐ฒ๐ฎ๐๐ ๐๐ 8,75 2,25 (Aehle et al., 2012)
๐๐ณ๐๐,๐ช๐๐๐๐๐๐๐๐๐๐ ๐๐๐๐/(๐๐๐๐ โ โ) โ1,80 โ 10โ10
๐๐ฎ๐๐,๐๐๐ ๐๐๐๐/(๐๐๐๐ โ โ) โ3,37 โ 10โ10 โ1,8 โ 10โ10 (Aehle et al., 2012)
ยต๐๐๐ โโ1 0,024 0,035 (Craven et al., 2013)
๐ฒยต,๐ฎ๐๐ ๐๐ 3,81 4,8 (Dhir et al., 2000)
ยต๐ ๐๐๐๐,๐๐๐ โโ1 0,015 0,019 (Dhir et al., 2000)
๐ฒ๐,๐ฎ๐๐ ๐๐ 1,58
ยต๐ ๐๐๐๐,๐๐๐๐๐ โโ1 0,0015 0,00266 (Borchers et al., 2013)
๐ฒ๐๐๐๐๐ โโ1 0,004 0,04 (Craven et al., 2013)
๐๐ต๐ฏ๐+/๐ฎ๐๐ ๐๐๐๐/๐๐๐๐ โ0,54 โ0,68 (Craven et al., 2013)
18/04/16 Ch. Herwig
Validation of parameter estimation via independent experiments
62