BILS 2015 Christoph Herwig

Quicker Ways to a Scalable Process Based on Sound-Science Based Methodologies

Christoph Herwig

February 11th 2015

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Our Mission in Bioprocess Technology

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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

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Challenges that can be solved by

faster process development and

higher titers

prevent and identify scale-up

effects

prevent failed

batches

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How can these challenges be addressed?

•  Clear and proven workflows for data analysis

•  Tools to process data and ensure data quality

•  Efficient use of chemometric and mechanistic data science tools

• 10

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Presentation Workflow

•  Goal: Quicker Ways to a Scalable Process Based on Sound-Science Based Methodologies

•  Processing Strategies to Understand the Production Platform and to Allow Transferability from Scale to Scale and from Product to Product “

6

Conclusions Model

Building Tutorial

Linking MVDA

results to metabolic models

Information extraction

and Statistical analysis

Big Data Processing

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GET A GRAB ON YOUR DATA

Big Data Processing

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Typical industrial process data

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1000LEISTUNG MEAS (HK)

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Data import, data alignment and contextualization

•  USP & DSP automatic data import •  Excel spreadsheet, text documents, LIMS,

PIMS

•  Data contextualization •  measurement units •  campaign, phase definition •  operators

•  Data survey & overview •  overview plots •  data density plots

à  All data in one format that can easily be analyzed and explored

Exputec Software

Excel

LIMS PIMS

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Principal Component Analysis: Raw Data

•  Principal component analysis on individual runs to quantify variations and detect relationships among variables.

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GET A HINT

Information extraction and statistical analysis

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Information extraction – Completion of data sets

•  Tools to derive meaningful descriptors •  Control quality tools

•  Descriptors that describe the control quality of process parameters, such as pH, pO2

•  RMSE, outlier detection

•  Bioprocess suite deriving scalable descriptors •  µ, qs, CER, OUR, •  Yields •  kinetic constants

•  Calculation of missing entities via combination of dvariables and sound science first principles

à  Automatic extraction of meaninful descriptors for different process phases, USP & DSP processes

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PCA on scalable parameters for individual runs

•  Principal component analysis on individual runs to •  build mechanistic

hypothesis and

•  detect number of independent mechanisms

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Gathered data to statistical information

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•  Raw data from DoE experiments transferred into information •  Influence of pH, pCO2 and pO2 on specific rates and yields

VCD

Glucose

SP

EC

IFIC

RAT

E

YIE

LD

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Hypothesis Generation using MVIA Results

•  Processing of data (concentrations, flows) into specific rates and yield coefficients (µ, qs)

•  Tools •  Principal Component Analysis •  MLR •  PCR •  Factor Analysis •  Multi-way methods •  …

High Performer Low Performer

à  Identify and understand trends and correlationsà  Identify interactions across unit operations

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NEEDING A SEGREGATED VIEW OF YOUR CATALYST?

Cell-based Analytics

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Analytical methods

§  Flow Cytometer §  Producers / Non – producers §  Apoptotic cells

§  Dead cells: viability staining (Cedex)

§  Lysed cells (DNA, protein, LDH) §  Product analytics (HPLC) §  Product quality

800msec 25%

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Segregation of Biomass -Lysis-

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Bio ma ss

l y sis

ph y sio l o g y

ma mma l ia

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rmeasDNA rcorrDNA

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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


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Bio ma ss

l y sis

ph y sio l o g y

ma mma l ia

b)

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GlucoseLactateGlutamine

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pH, 2 pH, 3

pH, 1

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Bio ma ss

l y sislysis – results

Lysis has a influence on the physiological interpretation! lysis should be considered

ph y sio l o g y

ma mma l ia

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µLCC

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LINK THE HINT TO MECHANISTICS

Linking Cluster Analysis to Metabolic Models

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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

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Perc

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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) à identify mechanistics VIP > 1 variable is significant VIP < 1 variable is insignificant Phase detection by k-means cluster analysis (blues lines)

Average VIPs

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Use simplified metabolic flux models

•  Only the Central Carbon Metabolism is considered

•  Redox and energy metabolism •  Amino acid metabolism

•  But which measurements are now really omportantn in which phase?

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Glc GPI ald GAPDH pgk eno pepkin g6Pdh prodh 1 2

3.14.15.26.27.38.3

Variables

Proc

ess

Tim

e [d

]

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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

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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

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Clusters variable importance(VIP) for Lactate production/consumption

Cluster 1: Important variables for qLac for all time points; mainly related to glycolysis à 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

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Mechanistics insights by target oriented use of statistical tools

•  Highly target-oriented analysis of large data sets by combination of different data driven methods •  Do not get lost in the woods!

•  Automated clustering of process variables in to physiologically meaningful groups with regard to the response variable (e.g.: qLac)

•  Mechanistic insight can be acquired from data driven methods if the tools are applied appropriately

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GET IT UNDERSTOOD AND OPTIMIZED

Mechanistic Model Building Tutorial

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The Modelling Work Flow

StructureIdentification

ParameterEstimation

ModelValidation

ModelRefinement

ModelUse

YES

NO

ExperimentalData

PrioriKnowledge

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Mechanistic Model for Glucose Uptake Kinetics

29

§  Propose hypothesis:

𝑞↓𝐺𝑙𝑐 =𝑓(𝐺𝑙𝑐)

§  Formulate equation:

𝑞↓𝐺𝑙𝑐 = 𝒒↓𝑮𝒍𝒄,𝒎𝒂𝒙 ∙ [𝐺𝑙𝑐]/[𝐺𝑙𝑐]+ 𝑲↓𝑮𝒍𝒄   §  Estimate parameters

individually by minimizing cost

function ∑↑▒( 𝑦↓𝑖,𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑 − 𝑦↓𝑖,𝑚𝑜𝑑𝑒𝑙 )² 

§  Compare with literature

§  Define validity

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Toolset for model calibration

•  Model calibration: •  finding suitable estimates for model

parameters

•  Sensitivity and identifiability analysis: •  Link to your measurements and

processing platform

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Mechanistic optimization

•  Use a kinetic model for model-based optimization •  Development of mechanistic process

model •  In-silico optimization using optimization

algorithms •  Identification of optimal operating

conditions •  Control Implementation

à higher productivity using less experiments à Knowledge on mechanistic relationships can be transferred to the next product

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CONCLUSIONS

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Our approach for bioprocess design

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ProcessParameters

ProcessVariables

VfeedcfeedVgasincgasinrpm…

.

.QAscproductcBDWcO2outcCO2out…!

Predic?veProcessing

InverseAnalysis

Pred

ic?v

eProcessing

Structured

ProcessDevelop

men

tFrom good data via understanding

to prediction

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CONSISTENT DATA SET

INFORMATION & EXPERIMENTAL DESIGN

KNOWLEDGE & MODELLING

OPTIMIZED & PREDICTIVE CONTROL

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Monitoring

ProcessParameters(Inputs)

ProcessVariables(Outputs)

VfeedcfeedVgasincgasinrpm…

.

.QAscproductcXL

xO2outxCO2out…

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

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Take Home Workflow

36

•  Bioprocess development and manufacturing challenges can be solved by process data science

•  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

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Process-undisclosed

Design&CQAs-Freedomduringdesign-ProofofConceptforcontrollingQTPP

ProvenPa?entBenefit-QTPP-Efficacy,Safety-ClinicalPhaseI,II&III

Process-completedisclosure

New

Produ

ct

Biosim

ilar

ProofofConcept

Mechanis?calModelsviaPhysiology  FirstPrinciples  MetabolicFounda?ons  PlaZormKnowledge

  RiskManagement  VerifiedScaleDownModels

DesignMethodology

Relevance for new products & biosimilars

Design&CQAs-FixedCQAlimitsfromini?alapproval

ProofofSimilarity

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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

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BACK UP SLIDES

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IMPLEMENTATION FOR PROCESS CONTROL

Get it ON-LINE!

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Computational environment for real-time implementation and control

•  Object oriented design of different data storage and processing components.

•  Classes are able to store data, perform specific functions, and communicate with each other

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Process Information

Management System

OPC server

Calculations:

feeding rates offgas analysis

volume calculation outlier detection

…

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)

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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

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G

Grubbs distance

7.3565 7.3566 7.3566

x 105

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0.02

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7.3565 7.3566 7.3566

x 105

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New signal

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Real-time implementation via particle filter estimations

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measuredsoft-sensor

A0A1 measured

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measuredsoft-sensor

measuredsoft-sensor

measuredsoft-sensor

measuredsoft-sensor

measuredsoft-sensor

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LIFE CYCLE SOLUTION GET IT DONE YOURSELF

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CMLCM: Computational model life-cycle management

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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.

Exputec‘s Software Solutions by INCYGHT

§  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.

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a)Tradi?onalfeedprofiledesign b)Physiologicalfeedprofiledesign

Accelerate!

Wechselbergeret.al.2012

Speed up by using physiological information in DoEs!

Ø  Careful selection of physiological factors for the DoE significantly reduces number of experiments

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Ø  Dynamic experiments increase information & throughput

Accelerate!

a)Quickiden?fica?onofscalablerela?onships

b)Dynamicfeedingprofilesbasedonspecificsubstrateuptakerate

Zalaiet.al.2012

Speed up by using physiological information & dynamics!

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NECESSARY TO LOOK MORE DETAILED INTO BIOMASS?

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Bio ma ss

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52

Bio ma ssl y sisCell Lysis

deIinition: �“loss of integrity of a cell (destruction of the cell membrane)”

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:

•  LDH measurements in the supernatant •  DNA measurements in the supernatant

microbials •  C-balance

ph y sio l o g y

ma mma l iaM ic r o bia l s

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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 …

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 …

???

18/04/16 53

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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 …

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 …

Quality by Design

???

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Current interpretation of QbD: Cooking Recipe: DoE

SpecificAc?vity

[kU/gbiomass]

Induc?onPhaseTemperature

[°C]

Induc?onPhaseFeeding

Exponentk

“Design Space”

Data CPPs

CQA

CQA

55

CPPs

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DOE: Insignificant factors- all for nothing?

•  Factors turn out to be insignificant •  Variance cannot be explained by the

original factors •  Possible reasons:

•  1) wrong factors were chosen for investigation

•  2) noise on experiment higher than effects of factors

•  How to proceed?

à Go beyond MLR analysis for the efficient exploitation of DoEs!

-0,10

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x_

EF

B

µ_

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

µ_

FB

ma

x s

pe

c tite

r p

elle

t g

/g


Factor Transformation

MVDA

Knowledge 56

18/04/16 Ch. Herwig

-0,10

-0,05

-0,00

0,05

x_

EF

B

µ_

FB

ma

x s

pe

c tite

r su

p g

/g


-0,10

0,00

0,10

x_

EF

B

µ_

FB

ma

x s

pe

c tite

r p

elle

t g

/g


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

0,0µ

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

µ

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 𝑇ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑≈−0.11 [𝑚𝑚𝑜𝑙/10↑9 𝑐𝑒𝑙𝑙𝑠 ∗ℎ ] b) Linear equation 𝑓𝑜𝑟 𝑞↓𝑔𝑙𝑐 <𝑇ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑

𝑞↓𝑙𝑎𝑐 =−0.25−2.21∙𝑞↓𝑔𝑙𝑐  𝑅𝑀𝑆𝐸 = 0.028 [𝑚𝑚𝑜𝑙/10↑9 ∗𝑐𝑒𝑙𝑙𝑠∗ℎ ]

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 ±2% 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

•  The colinearity index measures the degree of linear dependence of model parameters. •  It equals unity if the columns are linearly dependent. •  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

𝒀𝑳𝒂𝒄𝑮𝒍𝒄

𝑚𝑚𝑜𝑙/𝑚𝑚𝑜𝑙 −1,11 −1,10 (Lee et al., 2003)

𝒒𝑮𝒍𝒏,𝒂𝒗𝒓.𝒑𝒆𝒓 𝒎𝑴 𝑮𝒍𝒏 𝑚𝑚𝑜𝑙/(𝑚𝑀 ⋅ 𝑐𝑒𝑙𝑙 ⋅ ℎ) −1,99 ⋅ 10−11 −4 ⋅ 10−11 (Lee et al., 2003)

𝑲𝑮𝒍𝒄 𝑚𝑀 8,75 2,25 (Aehle et al., 2012)

𝒒𝑳𝒂𝒄,𝑪𝒐𝒏𝒔𝒖𝒎𝒑𝒕𝒊𝒐𝒏 𝑚𝑚𝑜𝑙/(𝑐𝑒𝑙𝑙 ⋅ ℎ) −1,80 ⋅ 10−10

𝒒𝑮𝒍𝒄,𝒎𝒂𝒙 𝑚𝑚𝑜𝑙/(𝑐𝑒𝑙𝑙 ⋅ ℎ) −3,37 ⋅ 10−10 −1,8 ∗ 10−10 (Aehle et al., 2012)

µ𝒎𝒂𝒙 ℎ−1 0,024 0,035 (Craven et al., 2013)

𝑲µ,𝑮𝒍𝒄 𝑚𝑀 3,81 4,8 (Dhir et al., 2000)

µ𝒅𝒆𝒂𝒕𝒉,𝒎𝒂𝒙 ℎ−1 0,015 0,019 (Dhir et al., 2000)

𝑲𝒊,𝑮𝒍𝒄 𝑚𝑀 1,58

µ𝒅𝒆𝒂𝒕𝒉,𝒄𝒐𝒏𝒔𝒕 ℎ−1 0,0015 0,00266 (Borchers et al., 2013)

𝑲𝒍𝒚𝒔𝒊𝒔 ℎ−1 0,004 0,04 (Craven et al., 2013)

𝒀𝑵𝑯𝟒+/𝑮𝒍𝒏 𝑚𝑚𝑜𝑙/𝑚𝑚𝑜𝑙 −0,54 −0,68 (Craven et al., 2013)

18/04/16 Ch. Herwig

Validation of parameter estimation via independent experiments

62