1

16 May 2014 –Hinxton

Kinetic CHO Cell Modelling and Simulations – The Modeling Cycle and Industrial Application

Sophia Bongard

2

PharmaTesting of

New Drugs

Insilico Biotechnology

BiotechProcess Analysis& Optimisation

Services

Company OverviewInsilico Biotechnology AG

Founded in 2001, headquartered in Stuttgart, Germany

Inter-disciplinary team comprised of biologists, chemists, computer scientists, physicists, and bioprocess engineers

Expertise in modelling and simulation of biochemical networks

Solution provider for 40+ international companies and academic research institutes

Insilico quantifies and predictscellular processes forthe Life Science Industries.

Software

3

Technology Platform– Overview

Insilico Technology Platform

High-Performance Computing

InsilicoSoftware

Insilico CellsInsilico Organs

Insilico Databases

> 8,000 reactions> 2,000 compounds

> 20 strains+ organs/body

Insilico Discovery™Insilico Inspector™

> 100,000 cores

Insilico delivers (i) quantitative insight into biotechnological processes and (ii) predictive simulations.

4

Why do we need CHO Cells? – Usability and industrial Application

What are CHO cells?

Immortalised cell strain from

chinese hamster ovaries

What do the cells produce?

Recombinant proteins

How are they produced?

Fermentation processes

Picture Source: wikipedia

FeedcSubstrates

SamplingcProduct

5

Host Cell

Model-supportedCell Line Development

Cell line: host cell + recombinant expression construct

1. Transfection 2. Amplification & Selection

3. Screening 4. Expansion 5. Growth Evaluation & Process Optimization

Many Clones (100‘s – 1000‘s)µL - mL

10 – 20 Clones100 – 1.000 mL

Cell Line Engineering Clone Selection Process Control

Computer-supported Processes

6

Case Scenario for this session– IgG antibody Production

Most common antibody in blood needed for immune defense against

bacteria and viruses

Artificial generation via CHO cells to target proteins in the human body

(used e.g. for cancer therapies)

Case scenarios

Producer strain comparison with stationary CHO models

Bioprocess optimization of best clone using

dynamic CHO model

7

In silico Workflow

1 Model reconstruction and network adaption

2 Experimental Design

4 Determination of stationaryflux distribution

5 Kinetic parameter estimation

7 Hand over new Media Design to customer

3 Data integration

6 New Bioprocess Design

8

Model Reconstruction and Network Adaption

Challenge

To gain a consistent model with all needed data information in a short time and appropriate visualisation

Solution

KEGG: Pathway Research

UNIPROT: stoichiometry

PubChem: chemical compound structures

multiple database information (reactions, compounds), which can be used for model reconstruction, e.g. in COPASI and Cytoscape

Benefits

„All-in-one“ software solution which delivers model in universal SBML format

9

CHO Cell Metabolism

Nucleus

ER and Golgi

Mitochondrium

Glucose

Glucose

Pyruvate

AKG

Amino Acids

ProteinDNARNA

TCA Cycle

O- and N-Glycosylation

LipidGlycogen

O2

CO2

BIOMASS

glycoprotein

IgG

Purine and Pyrimidine Metabolism

Lipid Metabolism

Steroid SynthesisAmino Acid Metabolism

NH4

Organic Acids(e.g. Lactate)

Peroxi-somes

10

CHO Model Network Adaption

Merge Host Cellwith Novel Reactions

»Super-Network«

+ Host cell+ new strain-specific reactions/pathway+ known gene knock outs

Integration of Kinetics

Stationary Model Identification

Dynamic Model Identification

+ experimental data (extracellular and intracellular)

Producer Strain Comparison

Target Identification,Design New Media

11

Definition of Model Kinetics

Ordinary Equation System:

Stationary model: with r=const.

Dynamic model: with r=f(c, p), nonlinear kinetics

Kinetics

linlog kinetics: +…),

with

Michaelis-Menten kinetics:

Convenience kinetics, Mass action kinetics, Hill kinetics …

12

In silico Workflow

1 Model reconstruction and network adaption

2 Experimental Design

4 Determination of stationary flux distribution

5 Kinetic parameter estimation

7 Handover new Media Design to customer

3 Data integration

6 New Bioprocess Design

13

Experimental Design

Challenge

To find the minimum set of required measurements providing the maximum of information for model identification

Solution

Optimal Experimental Design (CSIC, CWI, Joke Blom)

Benefits

Get maximum quality/quantity of information from a minimum of experimental effort

Saves resources

14

Implementation of General conditions– Definitions of constraints, variables and parameters

Cell concentration in fermenter (500,000 cells/ml)

Fermenter Volume (6 Liters)

Fed-Batch/Batch/Continuous Process:

Feed Rates, Feed Concentrations, Bolus Shots

Process time: 300 h

Sampling

Biomass densitiy in cell

Biomass growth rate

Product protein composition (amino acids)

15

Identification of Optimal Experimental Conditions – Required inputs for CSIC Method

Mathematical Model Inputs:

Ordinary differential equations including external conditions (e.g. feeding,

temperature), kinetic parameters, state variables (e.g. fermenter volume)

Auxiliary functions describing the relation between model states and

experimental measurements (preliminary data)

Measurement Inputs:

Measurement quality

Limitations (e.g. glucose solubility)

Variables to be considered in experimental design (sampling times, feeds,

to be measured metabolites…) =Output of optimised experimental design

16

In silico Workflow

1 Model reconstruction and network adaption

2 Experimental Design

4 Determination of stationary flux distribution

5 Kinetic parameter estimation

7 Handover new Media Design to customer

3 Data integration

6 New Bioprocess Design

17

In silico Workflow

1 Model reconstruction and network adaption

2 Experimental Design

4 Determination of stationary flux distribution

5 Kinetic parameter estimation

7 Handover new Media Design to customer

3 Data integration

6 New Bioprocess Design

18

Determination of StationaryFlux Distributions

Challenge

Determine phase-dependent flux distributions which best describe measurements

Solution

COPASI: steady-state analysis and parameter estimation (UNIMAN)

Multiple Objective FBA (CSIC, Julio Banga)

Benefit

State-of-the-art parameter estimation and flux balance analysis making integration of multiple objectives possible

Considerations of non-obvious criteria (Example??)

19

Case Study 1:Producer Strain Comparison

20

Producer Strain Comparison– Strategy

Input:

Time Series Data of extracellular Metabolites, Offgas Data, Feeds, Samples

Procedure:

Calculation of according phase-dependent uptake/consumption rates

Flux Balance Analysis for intracellular distribution for multiple process phases

Analysis:

Either phase-wise comparison of performance indicators or over whole process

21

Producer Strain Comparison– Decision Criteria I

Cell Density

Maximum viablecell concentration in the fermenter

Product Titer

The final product concentration in fermenter

Product Yield

The ratio between product produced to glucose consumed

Growth Rate

Maximum or average growth rate (biomass formation rate) over the process

Productivity

Maximum or average specific product generation rate over the whole process

Maintenance

Cellular rate of ATP consumption for maintaining the cell in a viable state.

Biomass Yield

The ratio between generated biomass to consumed Glucose

22

Producer Strain Comparison– Decision Criteria II

Producer Strain 1 has best performance indicators, but high ammonia release Strain 1 for next generation strain development

Product Yield

Producer Strain 1

Producer Strain 2 Producer

Strain 3

Biomass Yield

Maximum Yield

23

In silico Workflow

1 Model reconstruction and network adaption

2 Experimental Design

4 Determination of stationary flux distribution

5 Kinetic parameter estimation

7 Handover new Media Design to customer

3 Data integration

6 New Bioprocess Design

24

Kinetic Parameter Estimation

Challenge

Complexity of large dynamic models (large number of parameters, stability issues, model too robust or fragile)

High resource demand of calculations

Solution

AMIGO: ScatterSearch optimisation method in combination with ensemble modelling (CSIC, Julio Banga)

COPASI: integrated optimisation algorithms

High performance computing (Super Computer)

Benefit

Improved assessment of predictive value due to quantified uncertainty

Saving time by reducing number of required restarts during parameter estimation

25

Dynamic Model Identification

Identified stationary model

Preliminary dynamic model

+ dynamic model rate equations (use stationary flux distribution as reference flux in dynamic model)+ initial parameter guess

Parameter Estimation

Identified dynamic model

+ experimental data (extracellular and intracellular)

Target Identification, New Media Design

26

In silico Workflow

1 Model reconstruction and network adaption

2 Experimental Design

4 Determination of stationary flux distribution

5 Kinetic parameter estimation

7 Handover new Media Design to customer

3 Data integration

6 New Bioprocess Design

27

Case Study 2: NH4 Reduction in CHO Strain 1

28

NH4 Reduction in a CHO Process– Case Study 2: Summary

Challenge

NH4 accumulation in a CHO fed-batch processes for monoclonal antibody

production impairs process performance

Solution

Identification of sources of NH4 formation in different process phases

Identification of intracellular and extracellular substrate limitations/bottlenecks

New media design for better performance through feed optimisation

Benefits

Reduce NH4 levels, improve viability late in the process and product

29

Asparagine and glutamine exhibit the highest degradation rates of

the amino acids taken up in Phase 2

NH4 Reduction in a CHO Process

>80% Degradation

<40% Degradation

Amino Acid Synthesis View in Insilico Inspector™

Fluxes in µmol Carbon/(gDW*h)

Phase 2

30

Intracellular

degradation of

asparagine and

glutamine is responsible

for the majority of NH4

released until Phase 3

NH4 Reduction in a CHO Process– Case Study 2: Phase-specific identification of NH4 sources

Fluxes in µmol Nitrogen/(gDW*h)

Nitrogen Metabolism View in Insilico Inspector™

Process Phase Phase 1 Phase 2 Phase 3

Amino acids with significant degradation (>30% of uptake)

Asn > Gln > Leu > Val > Tyr

Asn > Gln > Leu > Val > Tyr

Asn > Gln > Ser

Phase 2

31

Identification of New Media Designfor NH4 Reduction

Identified dynamic model

Definition of elements to be

optimized

+ definition of objectives reduce NH4, increase product titer, …

Optimization

New Media Composition

+ constraint definitions

Prediction of altered cell dynamics and new performance

indicator values

32

In silico Workflow

1 Model reconstruction and network adaption

2 Experimental Design

4 Determination of stationary flux distribution

5 Kinetic parameter estimation

7 Handover new Media Design to customer

3 Data integration

6 New Bioprocess Design

33

Customer reduced asparagine in the feed by 38%

NH4 Reduction in a CHO Process– Case Study 2: Implementation

Result: reduced ammonium levels, improved viability, and product titer increased by >30% relative to the reference run

Optimized

34

Benefits of kinetic CHO models

Gain quantitative insight

Save experimental studies and reduce development time

Improve yield, productivity, and quality of biotech products

Generate new know-how and intellectual property

Taking decisions based on quantified processes

35

Many thanks to

Joachim Schmid, Dirk Müller, Klaus Mauch (Insilico Biotechnology AG)

And to the BioPreDyn consortium!

36

Sophia Bongard

Insilico Biotechnology AG

Meitnerstr. 8

70563 Stuttgart | Germany

T +49 711 460 594-25

sophia.bongard@insilico-biotechnology.com

Contact