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A STRATEGIC VISION OF AN INTEGRATED PAT IMPLEMENTATION FRAMEWORK IN

BIOTECH MANUFACTURING: Lessons from the Chemical Industry

Babatunde A. OgunnaikeChemical Engineering Department

University of Delaware

Wednesday 26 March 2008

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OUTLINE1. MOTIVATION & INTRODUCTION 2. PROCESS & PRODUCT QUALITY CONTROL: AN

OVERVIEW3. VISION FOR BIO-PHARMA MANUFACTURING4. ILLUSTRATIVE INDUSTRIAL EXAMPLES5. UNIVERSITY OF DELAWARE PROGRAM6. SUMMARY AND CONCLUSIONS

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1. MOTIVATIONFDA DRUG QUALITY INITIATIVE (2002)(Pharmaceutical cGMPs for the 21st Century)

To enhance and modernize the regulation of pharmaceutical manufacturing and product quality;To bring a 21st century focus to this critical FDA responsibility

PROCESS ANALYTICAL TECHNOLOGY GUIDANCE (2004)

to improve process understanding; to enable manufacturers measure, control, and/or predict quality and performance..

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Definition:A system for designing, analyzing, and controlling the manufacturing enterprise,through timely measurements of

key process variables andcritical quality and performance attributes of raw and in-process materials and end products

Goal: Ensuring final product qualityQuality cannot be tested into products; it should be built-in by design and maintained by appropriate systems and strategies

Process Analytical Technology (PAT)

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Ingredients for achieving world-class qualityComprehensive understanding (for world-class product design)

Intended therapeutic objectives; patient population; route of administration; …Chemical, physical, pharmacological, toxicological, and pharmacokinetic characteristics of a drug

World-class manufacturingSound process design using modern process engineering principles, Robust process operation and quality assurance

to ensure acceptable, reproducible product quality, and performance throughout a product's shelf life

World-class quality in Pharma products

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Key IssuesUnderstanding the Challenge

Product Quality Assurance for Bio-PharmaAn increasingly important factor;Availability of appropriate sensors and analyzers (no longer major hurdle?)Utilizing available information appropriately (particularly for Biotech manufacturing)?

Devising appropriate strategiesCan we draw lessons from the Chemical Industry?

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FORCES SHAPING CHEMICAL MANUFACTURING*

Intensified Global CompetitionMore stringent Safety & Environmental regulationsMaturing IndustryExpanding horizons:

Fine chemicals; pharmaceuticalsBio-materials; general biotechnology, etc.

Increased investor scrutiny of short-term earningsIncreased customer demand for consistent attainment of high product quality.

*Harold and Ogunnaike, AIChE J., 46, 11, 2123-2127, (2000)

Introduction

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Chemical Industry response

“ … Aggressive global competition and increased customer expectations combined with rising societal demands for environmentally benign plants will require new and better chemical processes for current and future products. With costs increasing and price decreasing, productivity must increase to justify the capital investments needed. These productivity improvements can only be obtained with continuing advances in [process] technology and through their more effective implementation.”

J. A. Miller, DuPont CTO*, 1997

*Currently Senior VP & CTO, Corning

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An Integration of Processing Units for Converting Raw Materials and Energyinto Finished Products (and Energy).

CHEMICALPROCESS

Raw materials

Finished Products

Energy

Energy

The Chemical Process

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Example: VINYL ACETATE PROCESS

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An Integration of Processing Units for Converting Raw Materials and Energyinto Finished Products (and Energy).

BIOLOGICPROCESS

Raw materials

Finished Products

Energy

Energy

The Biologic Process

CytokinesGrowth HormonesGrowth FactorsMonoclonal Antibodies

Mammalian Cell Cultures

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Example: Penicillin

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Example: Penicillin

PRODUCTION OF L-AMINO ACIDSBY AMINOACYLASE: SATO and TOSATanabe Seiyaku Company, Ltd.Osaka, Japan

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Fundamental Objectives of Process Operation

must be operated safely;specified production rates must be maintained;desired product quality specs must be met.

Main Obstacles to meeting objectives:Manufacturing processes are dynamic: (Process variables are always changing with time)

Process Operation

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ConsequencesMust be able to monitor, andinduce change in key variables related tosafety, production rateand product quality.

Process Control System needed for these purposes

Process Operation

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ANATOMY OF THE MANUFACTURING PROCESS (A Control Viewpoint)

Consists of Input, Output, (and State) VariablesInput Variables (u):

independently stimulate system; can thus induce change in internal conditions subdivided into MANIPULATED and DISTURBANCE Variables

Output Variables (y):by which information about internal states of the process are obtained

End-use/Product Quality Variables (z):Provide information about product characteristics

2. PROCESS & PRODUCT QUALITY CONTROL: AN OVERVIEW

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Chemical Process ExamplesDistillation Column

Manipulated, u: Reflux flow rates; Reboiler Heat input;Disturbance, d: Feed composition;Output, y: Select Top and Bottom tray temperaturesProduct Quality, z: Pour Point; Overhead Mole fraction of light material, …

Polymer ReactorManipulated u: Initiator flow rates; Jacket cooling water flow;Disturbance, d: Feed composition;Output, y: Reactor temperature and Conversion;Product Quality, z: Co-polymer Composition; Molecular weight distribution; Melt index; Mooney viscosity; …

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Particulate Examples

(PAN) GRANULATION

PROCESSManipulated Variables, uMoisture flow rate;Pan Angle;Pan Speed.

Controlled Variables, yBulk Density;Key Percentiles of the PSD.

Disturbance Variables, dFeed Rate

End-use Quality Variables, zDispersion Rate;Flowability;Attrition resistance;Size uniformity

R(.)

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Particulate Examples

(SUSPENSION) CRYSTALLIZER

PROCESSManipulated Variables, uFines flow rate;Product flow rate;Total heat input.

Controlled Variables, yNumber density (of fines);Magma density;Solute Concn;Occlusion;Mean Crystal Size.

Disturbance Variables, dFeed Composition

End-use Quality Variables, zFlowability;Filtrability;Xtal shape;Purity;

R(.)

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Issues for Bio-PharmaVariable Characterization

What are the “product quality indicators” z?How are they measured (if at all)?Can they be “predicted” from available measurements?

What are the measurable “critical-to-quality” variables q?What are the process variables of importance, y, whose control will lead to “stable process operation”?What are the manipulated variables, u?

Control-relevant mathematical relationshipsHow are the variables related quantitatively across the “hierarchical” u-y-q-z chain?

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Example: mAbProcess variables of interest

Temp, pH, DO2, RPM agitation, media composition, CO2;

Product quality?Aggregation/degradationProtein profilingGlycoform signatureGlycoform mapping Peptide mapping

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Fundamental Concept of Process Control

Concept of “Variability Transfer”

From where it will “hurt”

To where it will NOT.

Steam Flow rate judiciously adjusted (absorbing variability) to keep Tank temperature on target

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The Effective Control System

Main ObjectiveExpeditious transfer of variability

away from product attributes (where we cannot afford them) to appropriate process manipulated variables (where they are harmless)

HowBy appropriate overall design and implementation of all components

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BASIC STRUCTURE of Contemporary Industrial Process Control

THE PROCESS

Sensors, Analyzers, Transmitters, Actuators

Regulatory Control

Model-Based Control

Product Quality Control

Monitoring

ProdPlanning,

Sched. & Opt.

Process and Operation Optimization

Performance Assessment

Quality Assurance

Instrumentation

Design and Operating Procedure

ProcessControl

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3. VISION FOR BIO-PHARMA MANUFACTURING

Key determining factorsProcessing involves biological (living) entities;

more complex, more fragile, and possibly more hazardous?

Requisite process understanding still rudimentaryCellular and genetic processing (transduction, transcription, translation, post-translation…;)Influence of cellular processing on macro-scale protein production

Repercussions for poor/inconsistent quality could be fatalManufacturing costs becoming more of an issue?

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MeasurementOff-line determination of product quality (after the fact) no longer good enough;On-line, direct determination, or inference, now necessary for acceptable performance

ControlOn-line direct or inferred product measurement (if available) should be used for control to take full advantage.

Central Issues

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Ideal Configuration for (Bio) Pharmaceuticals

INFORMATIONPROCESSING

PROCESS MONITORING/IMPROVEMENT

PROCESS & PROD. QUALITY CONTROL

AdvancedModel-basedBase Regulatory

SPC6-SigmaQuality By Design

PCA/PLSFilteringProcess ModelState estimation

Raw data

Process Optimization

PROCESS

PAT

Sensors/Analyzers

Process Variable Adjustments

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ROLEGeneration of required data

PRIMARY ISSUESDesign, selection, implementation of appropriate types;Frequency of data acquisition;Has been key focus of many PAT initiatives

EXAMPLESField instruments; NIR Spectroscopy; …

Sensors and Analyzers

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ROLEConverting data

first to information, then to knowledge and ultimately to understanding

PRIMARY ISSUESData processing and analysis (information acquisition);Process model development (to encapsulate acquired knowledge) and utilization;State estimation (continuous model update in the face of new information).

Information Processing

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ROLEActive manipulation of appropriate process variables to ensure acceptable product quality

PRIMARY ISSUESTranslating product quality requirements to measurable (and controllable) CTQ variables;Design and implementation of appropriate control strategies to meet quality demands effectively

Base regulatory control (for process flow temperature, pH, etc);Advanced hierarchical model-based (inferential) control for product quality and attributes control

Process & Product Quality Control

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ROLEUtilizing acquired process information, knowledge and understanding for “Process Optimization”

PRIMARY ISSUESProcess operation diagnostics (fault detection, identification and rectification via Multivariate SPC)Continuous improvement (via SPC, 6-Sigma; Quality by Design concepts)

Process Monitoring/Improvement

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Advanced Control in Chemical manufacturing

Input, uuopt

PLANTOutput, y

AppropriateSet-point

ESTIMATOR

Full State Estimates

CONTROLOPTIMIZER

Auxiliary Input, (For Process Optimization)

Desired ProductCharacteristics

FIRST-PRINCIPLES MODEL

MODEL-BASEDCONTROLLER

MODEL-BASEDPROCESSOPTIMIZER

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4. ILLUSTRATIVE EXAMPLEPOLYMER REACTOR

On-line process measurement system coordination with lab quality measurementAdvanced modeling and quality variable estimationAdvanced Control

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THE PROCESSLarge evaporatively cooled reactor;Makes 10,000 – 25,000 lbs/hr of Terpolymer “P” from monomers “A”, “B” and “C”.

MAIN PRODUCTION OBJECTIVESProduction RateCopolymer composition (Wt % A, %B in polymer)Melt Viscosity Indicator Variable (Mw, and MWD)

Illustrative Example 1*

*B. A. Ogunnaike: “On-line Modeling and predictive control of an industrial terpolymerization reactor.” International Journal of Control, vol 59, no 3, pp 711-729, (1994).

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Main Problems

MEASUREMENTSProduct quality measurements available every 2 hrs; (with 30 – 45 mins inherent analysis delay);Gas Chromatographic analysis of reactor content available every 5 mins (with 15 mins inherent delay)

GRADE TRANSITIONS AND START-UPSFrequent (15 + different grades manufactured with same process);

TYPICAL INDUSTRIAL REACTORMultivariable; Complex,Nonlinear, …

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Solution Strategy

CONCEPTUALMeasurement structure induces natural two-tier character (Reactor Content control at 1st Tier; Product Quality Control at 2nd Tier);Utilize “First-Principles” model to estimate product properties in between lab samples;

CONTROL TECHNIQUESUse MPC for controller at each tier.

RESULTSBetter control; improved product quality;Significant savings; financial returns

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Measurement and Control Strategy

REACTORCONTENTSANALYSIS

- XA, XB, XC,- Xcat

PRODUCTCHARACTERISTICS

- Production Rate,- Polymer Composition YA, YB, YC- Viscosity- etc.

CURRENTCAMPAIGNGOALS

REACTORCONTENTSTARGETS

SECOND TIER(Reactor Product Control)

Production Rate and Product Properties

FIRST TIER(Reactor Contents Control)

LEVEL 0Rx Feed Flow��������

The Terpolymerization reactor strategy.

Gas Chromatographs

Gel-Permeation ChromatographLab ViscosityLab Copolymer Composition

Field Instruments

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Reactor Control “Block Diagram”

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Sample Results

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Sample Results

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Sample Results

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Sample Results

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5. UD Program: ResearchScope:

Limit to “Production”; expand to upstream purification later

ComponentsMulti-scale modeling

Cellular and genetic processing (UPR; transduction, changes in gene expression…)Influence of Cellular processing on macroscale protein production

Systems Analysis TechnologyDevelopment of techniques for implementing the three “boxes”

Process Development and Optimization

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UD Program: TrainingDemonstrating for PAT Implementation

Modeling Bioreactors for Process understanding Systems AnalysisProcess Control and Optimization

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Current specsProtein:

30 kDa or less; one site glycosylation

Cell Line: mammalianBioreactor 2L or greater size

Process monitoring: pH, Temp, DO2, agitation?

Controls: Gas sparging, Temp, pH, RPM agitation, media composition

UDExperimentalSystem

Biopharmaceutical Applications:- Therapeutic Value- Immunotherapy- Detection and Purification of Bio-Chemicals

Production Methods/Limitations:- Mammalian Cell Culture in Batch Reactor Setup- Yield and Quality are Often Competing Issues

FDA Initiative:- Implement Process Analytical Technology (PAT) to ImproveProduction Quality and Yield

- PAT Approaches will Impact Cost,Turnaround Time and Regulatory Compliance

Batch Production Process “Not under Feedback Control”

Production Unit Operation of Interest: Glycosylation

Monoclonal Antibody

Glycosylation:Enzymatic process that results inthe addition of a saccharide to aprotein at a specific location- Occurs in Endoplamic Reticulum

(ER) and Golgi Apparatus

Critical Process Parameter:Varying levels of glycosylation in antibody production- Heterogeneity increases; multiple iso-forms of a protein

affect product quality- An imperfect glycosylation of an antibody results in

incomplete antigen binding and signaling

Variables Enabling Better Control of This Process:- ER/Golgi Residence Time, pH, Cell Culture Conditions

Post Translational Modifications

http://oregonstate.edu/instruction/bb450/stryer/ch11/Slide62.jpg

Ribosomes

Examples of Byproduct Effects:– Lactate can change osmolarity, inhibit cell growth, and increase

protein production– Aspartate and glutamate

consumption can increase withhigh ammonia concentrations

– High lactate concentrations canfreeze lactate production and decrease specific ammonia and alanine production

– Increased ammonia concentration in culture can increase heterogeneity in proteins secreted by CHO cells

– Glycosylation and sialylation can be directly altered by increased byproduct accumulation, resulting in varying protein quality

Cell Culture Conditions: Byproduct Accumulation

Batt, 1988Hanahan, 2000

OUTPUTS

LactateAmmonia

Cell ViabilityCell Growth

Implementing Feedback Control

Chemical Industry TodayINPUTS

ConcentrationTemperature

PolymerProduction

In Situ Feedback

OUTPUTS

Product QualityYield

Biopharmaceutical Industry Today

INPUTS

GlucoseGlutamine

Antibody Production

Batch-Type Production Method Is Rate Limiting Step!

Glycosylatio

In Situ Feedback

INPUTS

GlucoseGlutamine

Antibody Production

OUTPUTS

LactateAmmoniaCell Viability

Cell Growth

Implementing Feedback Control

Mammalian Cell Culture BioreactorMX4/4 parallel gas mixing stationcan gas four separate CHO cell cultures– Air, N2, O2, and CO2– DasGip Control Software

MP8 Multi Pump Module is capable of eight total feed linesVariomag BioModule 40B blade type agitation system maintains suspension mixing Facilities in Colburn Lab

Glycosylation

Current Experimental System

Suspension Cell Culture System

• 160ml suspension culture of CHO Cells• 5% CO2, 37 C, 30 rpm agitation• Ex-cell serum free media• Glucose, Glutamine, Lactate, andUV/Vis Assays were conducted

Mammalian Cell CultureCapabilities at the

University of Delaware

• Laminar Flow Hood

• 4 Incubator Banks

• Fluorescence Microscope

• UV/Vis Spectrophotometer

• Fluorescence Plate Reader

• Flow Cytometer

• Experienced faculty members(M. Sullivan, M. Antoniewicz)

• Confocal Microscope

• Mass Spectrometry

• HPLC

First Attempt – Analyzing Metabolic Activity “By Hand”

KEY LEARNINGS: Difficult to measure variouslevels effectively with existing assays

Need in situ measurement capabilityso that adjustments can be performed

Samples taken at various time pointsafter Glucose “pulse” at t=0 hrs

Inconsistent

Expected Trend

Example of Control Capability

KEY LEARNINGS: Pre-programmed gas level control can enable effective return to setpoint in our bioreactor

Spike in Gas Concentratio

n

Return toSetpoint

KEY LEARNINGS: Analytical testing of pH in situ enables effective control & return to setpoint in our bioreactor

Example of Control Capability

Spike inCulture pH

Return toSetpoint

Conclusions

Mammalian Cell Culture system is in place, enabling examination of the various parameters of interest

Antibody Production Critical Process Parameters are known

Process Analytical Technology approaches that can effectively achieve this goal are well established

Control strategies necessary to facilitate proper reactor control have been mastered

GOALMeasure and Control Metabolic Activity

In Situ to Improve Product Quality and Yield

CONCEPTINCEPTION

PRACTICALIMPLEMENTATION

WHERE WEARE NOW

Acknowledgements

Undergraduate Research StudentsMark StitzTom PaviaDoug Behrens

Grad StudentsPete MilliliMonica Branco

Partial FundingFDADasGIP (Equipment Upgrade/Computer System)

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6. SUMMARY AND CONCLUSIONS

MAIN POINTSProduct quality assurance (has become) a priority for Bio-Pharma;Proper implementation of PAT (should be) more than just measurement acquisition;Ideal vision for integrated system presented

Involves 3 interconnected components (in addition)Information processingProcess and Product Quality ControlProcess Monitoring and improvement

Illustrated with application examples from industrial chemical manufacturing.

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ConclusionsProcess Control for Chemicals and Biologics

Many similarities so that what works in one can be translated (appropriately) to the other Key difference

Primarily dealing with living organisms (“manufacturing plant” within manufacturing plant).

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Conclusions

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ConclusionsMany issues to be resolved with “biologics”

Measurements (Process and Product Quality)Inference from available process measurementsModel development

Fundamental/mechanistic modeling Molecular, Cellular, Population, Macro-scale

Empirical modelingControl theory suitable to such processesImplementation details

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Ideal Configuration for (Bio) Pharmaceuticals

INFORMATIONPROCESSING

PROCESS MONITORING/IMPROVEMENT

PROCESS & PROD. QUALITY CONTROL

AdvancedModel-basedBase Regulatory

SPC6-SigmaQuality By Design

PCA/PLSFilteringProcess ModelState estimation

Raw data

Process Optimization

PROCESS

PAT

Sensors/Analyzers

Process Variable Adjustments