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16 May 2014 – Hinxton

16 May 2014 – Hinxton. Kinetic CHO Cell Modelling and Simulations – The Modeling Cycle and Industrial Application. Sophia Bongard. Company Overview Insilico Biotechnology AG. Insilico Biotechnology. Founded in 2001, headquartered in Stuttgart, Germany

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16 May 2014 – Hinxton

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  1. 16 May 2014 –Hinxton Kinetic CHO Cell Modelling and Simulations – The Modeling Cycle and Industrial Application Sophia Bongard

  2. Company OverviewInsilico Biotechnology AG Insilico Biotechnology • 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 Biotech Process Analysis & Optimisation Pharma Testing of New Drugs Software Services Insilico quantifies and predictscellular processes forthe Life Science Industries.

  3. Technology Platform – Overview > 100,000 cores > 20 strains + organs/body > 8,000 reactions> 2,000 compounds Insilico Discovery™ Insilico Inspector™ High-Performance Computing Insilico Software Insilico Databases Insilico Cells Insilico Organs Insilico Technology Platform Insilico delivers (i) quantitative insightintobiotechnologicalprocessesand (ii) predictivesimulations.

  4. Why do we need CHO Cells?– Usability and industrial Application • What are CHO cells? • Immortalisedcellstrainfrom • chinesehamsterovaries • What do thecellsproduce? • Recombinantproteins • How aretheyproduced? • Fermentation processes Picture Source: wikipedia Sampling cProduct Feed cSubstrates

  5. Model-supportedCell Line Development • Cellline: host cell + recombinantexpressionconstruct 4. Expansion 5. Growth Evaluation & ProcessOptimization 1. Transfection 2. Amplification & Selection 3. Screening ManyClones (100‘s – 1000‘s) µL - mL 10 – 20 Clones 100 – 1.000 mL Host Cell Cell Line Engineering CloneSelection ProcessControl Computer-supportedProcesses

  6. Case Scenario for this session– IgGantibody Production • Most commonantibody in bloodneededfor immune defenseagainstbacteriaandviruses • Artificialgeneration via CHO cellstotargetproteins in the human body (used e.g. forcancertherapies) • Case scenarios • Producer straincomparisonwithstationary CHO models • Bioprocessoptimizationofbestcloneusingdynamic CHO model

  7. In silicoWorkflow 7 Hand over new Media Design to customer • 1 Model reconstruction and network adaption • 6 New Bioprocess Design • 2 Experimental Design 5 Kinetic parameter estimation • 3 Data integration • 4 Determination of stationaryflux distribution

  8. Model Reconstruction and Network Adaption • Challenge • Togain a consistentmodelwith all neededdatainformation in a short time andappropriatevisualisation • Solution • KEGG: Pathway Research • UNIPROT: stoichiometry • PubChem: chemicalcompoundstructures •  multiple databaseinformation (reactions, compounds), whichcanbeusedformodelreconstruction, e.g. in COPASI andCytoscape • Benefits • „All-in-one“ softwaresolutionwhichdeliversmodel in universal SBML format

  9. CHO Cell Metabolism IgG ERand Golgi AminoAcids Glucose O- and N-Glycosylation Glucose glycoprotein Pyruvate BIOMASS Protein Purine andPyrimidineMetabolism Mitochondrium DNA Lipid Metabolism RNA Glycogen Steroid Synthesis Lipid TCA Cycle AminoAcidMetabolism AKG Peroxi-somes O2 Nucleus OrganicAcids(e.g. Lactate) CO2 NH4

  10. CHO Model Network Adaption Merge Host Cellwith Novel Reactions »Super-Network« Integration ofKinetics • + Host cell + newstrain-specificreactions/pathway +knowngene knock outs + experimental data (extracellularandintracellular) Stationary Model Identification Dynamic Model Identification • Producer StrainComparison • Target Identification,Design New Media

  11. Definition of Model Kinetics • Ordinary Equation System: • Stationarymodel: with r=const. • Dynamic model: withr=f(c, p), nonlinearkinetics • Kinetics • linlogkinetics: +…), • with • Michaelis-Mentenkinetics: • Convenience kinetics, Massactionkinetics, Hill kinetics …

  12. In silicoWorkflow 7 Handover new Media Design to customer • 1 Model reconstruction and network adaption • 6 New Bioprocess Design • 2 Experimental Design 5 Kinetic parameter estimation • 3 Data integration • 4 Determination of stationary flux distribution

  13. Experimental Design • Challenge • To find theminimumsetofrequiredmeasurementsprovidingthemaximumofinformationformodelidentification • Solution • Optimal Experimental Design (CSIC, CWI, Joke Blom) • Benefits • Getmaximumquality/quantityofinformationfrom a minimumof experimental effort • Savesresources

  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/ContinuousProcess: • Feed Rates, Feed Concentrations, Bolus Shots • Processtime: 300 h • Sampling • Biomassdensitiy in cell • Biomassgrowth rate • Productproteincomposition (aminoacids)

  15. Identification of Optimal Experimental Conditions – Required inputs for CSIC Method • Mathematical Model Inputs: • Ordinary differential equationsincludingexternalconditions (e.g. feeding, temperature), kineticparameters, state variables (e.g. fermentervolume) • Auxiliaryfunctionsdescribingtherelationbetweenmodelstatesand experimental measurements (preliminarydata) • Measurement Inputs: • Measurement quality • Limitations (e.g. glucosesolubility) • Variables tobeconsidered in experimental design (samplingtimes, feeds, tobemeasuredmetabolites…)=Output ofoptimised experimental design

  16. In silicoWorkflow 7 Handover new Media Design to customer • 1 Model reconstruction and network adaption • 6 New Bioprocess Design • 2 Experimental Design 5 Kinetic parameter estimation • 3 Data integration • 4 Determination of stationary flux distribution

  17. In silicoWorkflow 7 Handover new Media Design to customer • 1 Model reconstruction and network adaption • 6 New Bioprocess Design • 2 Experimental Design 5 Kinetic parameter estimation • 4 Determination of • stationary flux distribution • 3 Data integration

  18. Determination of StationaryFlux Distributions • Challenge • Determinephase-dependentfluxdistributionswhichbestdescribemeasurements • Solution • COPASI: steady-stateanalysisandparameterestimation (UNIMAN) • Multiple Objective FBA (CSIC, Julio Banga) • Benefit • State-of-the-art parameterestimationandfluxbalanceanalysismakingintegrationof multiple objectivespossible • Considerationsof non-obviouscriteria (Example??)

  19. Case Study 1:Producer Strain Comparison

  20. Producer StrainComparison–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 StrainComparison–DecisionCriteria I Product Titer The final productconcentration in fermenter ProductYield The ratiobetweenproductproducedtoglucoseconsumed BiomassYield The ratiobetweengeneratedbiomasstoconsumedGlucose CellDensity Maximum viablecellconcentration in thefermenter Growth Rate Maximum oraveragegrowth rate (biomassformation rate) overtheprocess Productivity Maximum oraveragespecificproductgeneration rate overthewholeprocess Maintenance Cellular rate of ATP consumptionformaintainingthecell in a viablestate.

  22. Producer StrainComparison–DecisionCriteria II ProductYield Maximum Yield Producer Strain1 Producer Strain2 Producer Strain 3 BiomassYield Producer Strain 1 has best performance indicators, but high ammonia release  Strain 1 fornextgenerationstraindevelopment

  23. In silicoWorkflow 7 Handover new Media Design to customer • 1 Model reconstruction and network adaption 6 New Bioprocess Design • 2 Experimental Design 5 Kinetic parameter estimation • 3 Data integration • 4 Determination of stationary flux distribution

  24. Kinetic Parameter Estimation • Challenge • Complexityof large dynamicmodels (large numberofparameters, stabilityissues, modeltoo robust or fragile) • High resourcedemandofcalculations • Solution • AMIGO: ScatterSearchoptimisationmethod in combinationwithensemblemodelling(CSIC, Julio Banga) • COPASI: integratedoptimisationalgorithms • High performancecomputing (Super Computer) • Benefit • Improvedassessmentofpredictivevalue due toquantifieduncertainty • Saving time byreducingnumberofrequiredrestartsduringparameterestimation

  25. Dynamic Model Identification + dynamicmodel rate equations (usestationaryfluxdistributionasreferenceflux in dynamicmodel) + initial parameterguess + experimental data (extracellularandintracellular) Preliminary dynamic model Identified stationary model Parameter Estimation • Target Identification, New Media Design Identifieddynamicmodel

  26. In silicoWorkflow 7 Handover new Media Design to customer • 1 Model reconstruction and network adaption 6 New Bioprocess Design • 2 Experimental Design 5 Kinetic parameter estimation • 3 Data integration • 4 Determination of stationary flux distribution

  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 • Identificationofsourcesof NH4formation in different processphases • Identificationofintracellularandextracellularsubstratelimitations/bottlenecks • New media design forbetterperformancethroughfeedoptimisation • Benefits • ReduceNH4levels, improveviabilitylate in theprocessandproduct

  29. NH4 Reduction in a CHO Process AminoAcid Synthesis View in Insilico Inspector™ Asparagineandglutamineexhibitthehighestdegradationratesoftheaminoacidstakenup in Phase 2 >80% Degradation <40% Degradation Phase 2 Fluxes in µmol Carbon/(gDW*h)

  30. NH4 Reduction in a CHO Process– Case Study 2: Phase-specific identification of NH4 sources Nitrogen Metabolism View in Insilico Inspector™ Phase 2 Intracellular degradation of asparagine and glutamine is responsible for the majority of NH4 released until Phase 3 Fluxes in µmol Nitrogen/(gDW*h)

  31. Identification of New Media Designfor NH4 Reduction + definitionofobjectives •  reduceNH4, increaseproducttiter, … + constraintdefinitions Definition of elements to be optimized Identified dynamic model Optimization • Predictionofalteredcelldynamicsandnewperformanceindicatorvalues New Media Composition

  32. In silicoWorkflow 7 Handover new Media Design to customer • 1 Model reconstruction and network adaption 6 New Bioprocess Design • 2 Experimental Design 5 Kinetic parameter estimation • 3 Data integration • 4 Determination of stationary flux distribution

  33. NH4 Reduction in a CHO Process– Case Study 2: Implementation Customer reducedasparagine in thefeedby 38% Optimized Result: reducedammoniumlevels, improvedviability, andproducttiterincreasedby >30% relative to thereferencerun

  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. Manythanksto • Joachim Schmid, Dirk Müller, Klaus Mauch (InsilicoBiotechnology AG) • AndtotheBioPreDynconsortium!

  36. Contact Sophia Bongard InsilicoBiotechnology AG Meitnerstr. 8 70563 Stuttgart | Germany T +49 711 460 594-25 sophia.bongard@insilico-biotechnology.com

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