Application of PAT in Product Development - Control Global · Virtual Plant Structure Demonstrated [ ] [ ] ... Model is Constructed Using Process Building Blocks. March 26-28, 2008

March 26-28, 2008

Pennsylvania Convention Center

Application of PAT in Product Development

Trish Benton - Broadley-JamesTerry Blevins, Michael Boudreau – Emerson

Process ManagementYang Zhang – University of Texas

March 26-28, 2008


Agenda• On-line Process Analytics - Terry Blevins

– Research in On-line Analytics– Fault detection – Quality parameter Prediction

• High Fidelity Dynamic Process Modeling – Michael Boudreau– Modeling Mammalian cell culture– Implementation, advantages of approach

• Model Parameter Identification – Yang Zhang– Design of Experiments– Example Results

• Beta Installation at Broadley-James – Trish Benton– Cell Line Utilized – Equipment and process – Analyzer for on-line sampling– Plans for scale-up

• Summary – References

March 26-28, 2008


PAT Framework

The PAT framework defines the following tool categories:• Multivariate data acquisition and analysis tools• Modern process analyzers and process analytical

chemistry tools• Process and endpoint monitoring and control tools• Continuous improvement and knowledge management

toolsAn appropriate combination of some, or all, of these tools

may be applicable to a single unit operation or to an entire manufacturing process and its quality assurance

March 26-28, 2008


Process Analytics

• Emerson Process Management established a research project at University of Texas, Austin in September, 2005 to investigate advanced process analytics

• The primary objective of this project is to explore the on-line application of Multivariate Analytic for prediction and fault detection and identification in batch operations

• The research grant given to UT is funding the work of a PhD graduate student, Yang Zhang, under the supervision of Professor Tom Edgar

March 26-28, 2008


Why Multivariate Analysis?UCL

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LCL

X2

March 26-28, 2008


T2 – Multivariate SPC Chart

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UCLUCL

March 26-28, 2008


Detection of Abnormal Operation• Measured Disturbances – The Score space associated with

Principal Component Analysis, PCA, captures contributions that can be associated with process measurements. Deviations in the principal component subspace score may be quantified through the application of Hotelling’s T2 statistic

• Unmeasured Disturbances – The Residual space that is not captured by the score space reflects changes in unmeasured disturbances that impact the operation. The Q statistic, Squared Prediction Error (SPE), is a measure of deviations in process operation that are captured by the residual subspace

• Identification of the primary measurements that contribute to a process deviation will be done using contribution plots

March 26-28, 2008


Quality Parameter Estimation

• The detection of deviations in quality parameters will be addressed through the use of Projection to Latent Structures, PLS. Through this technique, it is possible to maximize the covariance (between the predictor (independent) variables X and the predicted (dependent) Y parameters

• The fault detection, identification, techniques that may be used with PCA can be applied in exactly the same way for PLS e.g. Q and T2 statistics, contribution plots

March 26-28, 2008


Dynamic Time Warping

Reference trajectoryTrajectory to be synchronizedSynchronized trajectory

March 26-28, 2008


Interface for Testing PCA Model• Historian data may

be played back faster than real time.

• Testing is done with data not used in model development.

March 26-28, 2008


Interface for Testing PLS Model• Predicted value of

multiple quality parameters may be examined

• Confidence interval is displayed for the prediction.

March 26-28, 2008


Operator Interface to Process AnalyticsPCA – Fault Detection PLS – Quality Parameter Prediction

Contribution Plot

March 26-28, 2008


First Principles versus Data Driven Model

Model type First Principles Data DrivenSimulation usage Interpolation and

extrapolationInterpolation only

Physical/Chemical Basis Yes; experimental data are used to estimate the model parameters

No; statistical methods (PCA, PLS) are used

Areas of application Control; Monitoring; Optimization (reduce batch time); Diagnosis

Monitoring; sometimes Control and Diagnosis

Ease to build model Usually hard to build a ‘universal’ model, which may be too complicated; need a model with a specific purpose

Fast and relatively easy to build

March 26-28, 2008


Process Modeling• First principal bacterial model

presented in New Directions in Bioprocess Modeling and Control

• Model developed for fungal, bacterial, and mammalian cell process

• Using a process model, it is possible to evaluate input step techniques and control strategies in the product development (PD) stage

March 26-28, 2008


Basis for Bioreactor Models• Bioreactor models are based on:

– First Principles Model - fundamental laws– Energy Balance - broth temperature– Mass Balances - broth level and head pressure– Component Mass Balances - ammonia, carbon dioxide, lactate,

oxygen, product, sodium carbonate, substrates, and viable and dead biomass

– Charge Balance – pH – Phases - liquid, bubble, and overlay vapor space– Kinetics - biomass growth and death, product formation,

ammonia formation, and lactate formation rates– Mass Transfer - effect of gassed power and superficial gas

velocity on dissolved gases

March 26-28, 2008


Virtual Plant Structure Demonstrated

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March 26-28, 2008


Michaelis-Menten Composite Template

March 26-28, 2008


Michaelis Menton Combined in Component Kinetics Blocks

March 26-28, 2008


Model is Constructed Using Process Building Blocks

March 26-28, 2008


Example Interface - Bioreactor Model

March 26-28, 2008


March 26-28, 2008


March 26-28, 2008


Bioreactor Model Parameters

March 26-28, 2008


Bioreactor Model Parameters (Cont.)

March 26-28, 2008


Model Parameter Identification

• The mammalian cell kinetic model is based on 5 ordinary differential equations supported by 29 algebraic equations with 22 kinetic parameters that can vary with cell line.

• The model parameter values that depend on the cell line and media must be identified utilizing process data.

• Other model parameters that depend on the physical equipment e.g. impeller size may be specified based on the process design.

March 26-28, 2008


Parameter Estimation• Least Square (MLE under N IID Noise Condition):

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March 26-28, 2008


Design of Experiment ( DOE) Utilized for Parameter Estimation

• Bioprocess model structure is available with moderate number of parameters and satisfactory precision – DOE is feasible

• Mammalian cell culture dynamics are slow and a limited number of samples can be taken – DOE is a requirement

March 26-28, 2008


Design Of Experiments (DOE)

• DOE Definition:A structured, organized method for determining the relationship between factors (Xs) affecting a process and the output of that process (Y).

• DOE Classification:– Model Free – Factorial Design (Qualitively)– Model Based DOE (Quantitively)

March 26-28, 2008


Model Based DOE• Why First Principles Model is important?

– Better Understanding of a Type of Process– Process Optimization;– Operator Training;– Nonlinear Process Control;

• Challenges of First Principles Modeling in Pharmaceutical Industry:– High Nonlinearities:

Complex Kinetics & Large Number of Model Parameters.– Lack of Process Measurements:

Model Parameter Unobservable (large estimation error).– Long Duration:

Time Consuming in Data Collection

March 26-28, 2008


DOE for CHO Cell

• P&ID Control:– Glucose, Glutamine, Dissolved

Oxygen, pH, and Temperature.

• Pseudo Random Binary Sequence (PRBS):– Signal generated by MPC to

change the set points of the PID controller.

• Sampling rate is 3.3 hours and a whole batch will last 20 – 25 days.

March 26-28, 2008


Things to Design• PRBS Amplitude

– Large: unsteady operation condition; – Small: process noise;

• PRBS Frequency– High: system does not have time to response;– Low: lose dynamic information;

• Sampling Rate– Low: not enough information;– High: financial and experimental limitation;

• Stimulate Phase

March 26-28, 2008


Input & Output Profile

PRBS sequence – Process Inputs Process Outputs

March 26-28, 2008


Input & Output Profile

PRBS sequence – Process Inputs Process Outputs

March 26-28, 2008


Parameter Estimation

Phase 1 Phase 2Glucose

Glutamine

Temperature

pH

Cell Growth

Lactate

Ammonia

Cell Death

Hydrolysis

Product Formation

March 26-28, 2008


Beta Installation – Broadley-James

• Work will focus on fed batch using a CHO cell line

• Demonstrate on-line prediction of quality and economic parameters

• Show value of high fidelity process models for testing alternate control strategies

• Evaluate different means of on-line fault detection and identification i.e. multi-way PCA/PLS

March 26-28, 2008


Beta Test CHO Cell Line

• CHO cell line – donated by Peregrine Pharma to Broadley-James

• Product – antibody• Expression System – Glutamine synthase /

methylsulfoximine selection (Lonza)• Emerson’s mammalian cell culture model has

been modified for feed glutamate feed

March 26-28, 2008


Lab Established by Broadley-James• Initial work is being done

using four(4) seven liter bioreactors

• Online measurements and at-line measurements are automatically collected in DeltaV historian

• Beta station allows remote terminal access to batch data for analytic and first principal model development

March 26-28, 2008


Lab Established by Broadley-James(Cont)

March 26-28, 2008


Product Analyzer – Nova Biomedical

GlucoseLactateGlutamineGlutamateAmmoniumpHPCO2PO2

SodiumPotassiumCalciumOsmolality Cell DiameterCell DensityCell Viability

• BioProfile FLEX is provided for lab analysis.

• Latest technology available

• 1 ml sample size• All 15 tests in 6.25

minutes• Automatic

sampling of four(4) bioreactors

March 26-28, 2008


Prove Scaleup to 100 liters - Hyclone

• Hyclone single-use bioreactor have been donated for the beta test

• Available in sizes of 50, 100, 250 and 1000 liters

• 100 liter bioreactor will be used to prove scale-up of Emerson’s mammalian cell model

March 26-28, 2008


Learning More About This PAT Initiative

• See March issue of BioProcessInternational “PAT Tools for Accelerated Process Development and Improvement”

• Article addresses our work in bioreactor modeling and process analytics

March 26-28, 2008


Other References – PAT Initiative in process monitoring and control, analytics, and modeling• Gregory McMillan, Trish Benton, Yang Zhang, and Michael Boudreau,

“PAT Tools for Accelerated Process Development and Improvement”, BioProcess International, Process Design Supplement, March, 2008

• Terry Blevins and James Beall, “Monitoring and Control Tools for Implementing PAT, Pharmaceutical Technology, Monitoring, Automation , & Control, 2007

• Robert Wojewodka, Philippe Moro, Terry Blevins, “Coupling Process Control Systems and Process Analytics to Improve Batch Operations”, Emerson Exchange, 2007

• Video: Scott Broadley, Trish Benton, Terry Blevins, Emerson - Broadley James Beta: http://www.controlglobal.com/articles/2007/309.html .

• Video: Robert Wojewodka, Philippe Moro, Terry Blevins Emerson -Lubrizol Beta: http://www.controlglobal.com/articles/2007/321.html

• Michael Boudreau, Gregory McMillan, and Grant Wilson, “Maximizing PAT Benefits from Bioprocess Modeling and Control”, Pharmaceutical Technology, IT Innovations, 2006

• Michael Boudreau and Gregory McMillan, New Directions in Bioprocess Modeling and Control – Maximizing Process Analytical Technology Benefits, Instrumentation, Automations, and Systems (ISA), 2006

http://www.controlglobal.com/articles/2007/309.html

http://www.controlglobal.com/articles/2007/321.html

Documents

Application of PAT in Product Development - Control Global · Virtual Plant Structure Demonstrated [ ] [ ] ... Model is Constructed Using Process Building Blocks. March 26-28, 2008