the use of critical process analysis to reduce risk and ... · analysis to reduce risk and increase...

The Use of Critical Process Analysis to Reduce Risk and

Increase Biologics Product Quality

Howard L. Levine, Ph.D. BioProcess Technology Consultants, Inc.FIP Quality International 2007 Conference

Critical Process Parameters in the Manufacture of APIs, Biologicals, Tablets, and Parenterals

London, UKNovember 26 – 27, 2007

From Clone to CommercialTM

What is Quality by Design?“Means that product and process performance characteristics are scientifically designed to meet specific objectives, not merely empirically derived from performance of test batches.”The product is designed to meet patient needs and performance requirementsThe process is designed to consistently meet product critical quality attributesThe impact of starting raw materials and process parameters on product quality is well understoodThe process is continually monitored, evaluated and updated to allow for consistent quality throughout product life cycleCritical sources of variability are identified and controlled through appropriate control strategies

Ref: H. Winkle, BPI Conference, Oct 1 – 4, 2007


Defining Critical Quality Attributes (CQAs)“…those molecular and biological characteristics found to be useful in ensuring the safety and efficacy of the product…” (Q6B)Can these attributes be defined?• Often difficult due to complexity of biologic products• Default is to look at many attributes

For QbD need to focus only on critical product attributes and the impact of those attributes across their ranges on safety and efficacy• Product specifications based on mechanistic

understanding of how formulation and process factors impact product performance

Need to develop a design space to be documented in application which is based on CQAs


Critical Process Parameters (CPPs)

CPPs are the independent process parameters most likely to affect the quality attributesDetermined by sound scientific judgment and based on research, scale-up or manufacturing experienceControlled and monitored to confirm that the impurity profile iscomparable to or better than historical data from development and manufacturingQuality attributes that should be considered in defining CPPsinclude:• Chemical purity• Qualitative and quantitative impurities• Physical characteristics• Microbial quality


Ran

ge o

f Raw

Mat

eria

lA

ndFa

cilit

y A

ttrib

utes

Process Designed toLimit Product Variability

Critical Process Parameters Drive Critical Quality Attributes and Create a Design Space

Define API in terms of CQAsIdentify CPP that affect these CQAsDetermine range of each CPP that produces acceptable product to establish the Design Space


Antibody Variability and CQA Challenge

Ref: Kozlowski and Swan (2006)

What is the impact of each CPP on the numerous variations possible

Which variants are important for product efficacy and patient safety?


Critical Quality Attributes vs. Critical Process Parameters

Graphic adapted from Kozlowski and Swan (2006)

Process validation should provide “documented evidence that the process, operated within established parameters, can perform effectively and reproducibly to produce an intermediate or API meeting its predetermined specifications and quality attributes…” (ICH Q7A)

Critical Quality Attributes derive from …… Critical Process Parameters


Creating a Design Space: Identification of CPPs

Using data from development identify parameters that affect the defined product characteristics, for example• Level of Key Impurity• Desired Glycoform Content• Desired yield

Impurity 1

Gly

cofo

rm

Yield

Column Loading CapacityFl

ow R

ate

pH


Creating a Design Space: Optimize Process

Use Factorial Analysis without interactions to map boundary conditionsUse Full Factorial to fully define response surfaces and examine interactions

Column Loading Capacity

Flow

Rat

e

pH

Column Loading CapacityFl

ow R

ate

pH


Summarize results of multiple experiments to define response surface

Validate the process to demonstrate consistent production of product with desired characteristics

Creating a Design Space: Putting it All Together

Impurity 1

Gly

cofo

rm

Yield

[Fe] in Media

Agi

tatio

n R

ate

Final OD

Design Space


The Application of QbD to BiopharmaceuticalsQuality by Design is an important element in achieving desired state, however, we’re not there yet• Determining relationship between

−

Quality specifications and safety or efficacy results−

Clinical Activity and Critical Quality Attributes

−

Product Attributes and Critical Process Parameters−

Process Validation and the Design Space

• Insufficient Data on “Key” versus “Critical”• Strong Conservatism on both sides• Change must be regulated

“Traditional” process development and validation approaches can be applied to QbD, especially in identifying CPP and defining the Design Space


CPP Optimization in Cell Culture DevelopmentCritical Process Parameters in cell culture production…• Temperature• pH• Agitation• Dissolved oxygen• Medium constituents• Feed type and rate

Impact Critical Quality Attributes• Cell viability and number• Product titer• Product Characteristics (e.g. glycosylation)• Impurity profile

Two factorial design of experiment varying pH and temperature can help establish design space for product titer (yield)


Novo Nordisk Optimization of Cell Culture Conditions for Maximal MAb Yield

Optimization performed using SimCellTM technology from BioProcessors Corp.

2-Factor D.O.E.50 conditions (10 T x 5 pH)n=9 (450 total chambers)


Mapping a Downstream Process Design Space

Critical Process Parameters in column chromatography…• Column bed height and packing efficiency• Media selectivity• Dynamic capacity for product and total protein• Buffer conditions (pH, conductivity)• Temperature• Flow rate ranges• Sample load ranges• Media particle size and size range

All impact product purity and yield


Defining Ion Exchange Column Load ConditionsPurification of a natural protein by anion exchange chromatographyAnion exchange column equilibrated with 10 mM TRIS-Phosphate bufferVariation of load solution pH will impact product yield and purity

Product peak(s)


Anion Exchange Column Yield and Purity

Best yield at pH 7.0, however, additional contaminant present in pool not seen at higher pH’s

Can subsequent process steps remove this contaminant?

Product

Contaminant


Final Product PuritySubsequent purification of Anion Exchange Column pool removes process contaminant regardless of pH at which Anion Exchange column is run

A – Anion exchange column pool, pH 7.0B – Anion exchange column pool, pH 8.6

C – Column 2 pool following loading with “A”D – Column 2 pool following loading with “B”


Critical Process Parameters for MAb Purification

Parameter LoadDensity

FlowRate

BufferpH

BufferSalt

Specified Process

40 g/L 100 cm/hr 5.5 As specified

Load 10 g/L20 g/L30 g/L

Flow rate 50 cm/hr200 cm/hr

Buffer pH 5.45.6

Buffer salts + 5 mM- 5 mM

Ref: G. Blanks, Process Validation for Biologicals, 2001


Validation of Process Parameter RangesParameter CHOP (ppm) Monomer (%) DNA (log removal)

Control 10 99.5 >2.5Load:

10 g/L20 g/L30 g/L50 g/L

756

11

10010099.499.3

>1.9>2.2>2.42.3

Flow rate:50 cm/hr

200 cm/hr1311

99.499.3

>2.5>2.5

Buffer pH:5.45.6

1018

10098.5

>2.1>2.5

Buffer salts:+ 5 mM- 5 mM

520

99.199.5

>2.5>2.5

Ref: G. Blanks, Process Validation for Biologicals, 2001


Optimizing CPP in the Purification of Zevalin

Ref: Conley, McPherson and Thommes (2005)


Optimization of Process Parameter RangesCPPs identified during process development and operating ranges resulting in optimal purity and yield establishedDuring process validation product variability determined CPPswere operated at the center of and slightly beyond the normal operating range• Ensure process NOT operated at the edge of failure• Provides a data set for assessing potential manufacturing

excursions from the normal operating rangeExperiments run at the set point as well as upper and lower limit of the normal operating range for each CPP• CPPs normalized around the set point (X) ± desired operating

range • All of the CPPs for the other unit operations besides the unit

operation being investigated set at their defined “worst case” value (greatest chance of product failure)


Normalized CPPs by Unit Operation

Protein A• Linear velocity (1.05X cm/hr)• pH (X - 0.2)• Loading (1.2X mg/ml)

Viral Inactivation• pH (X - 0.15)• Time (X + 8 hr)

UF/DF #1• UF cross-flow (X - 20 L/m2/hr)• UF TMP (X + 3 psi)• Mass/surface area (0.75X g/m2)• Concentration (0.75X mg/ml)• NaCl adjustment (X - 50 mM)• DV (1.1X)• DF cross-flow (X - 20 L/m2/hr)• DF TMP (X + 1.5 psi)

Anion Exchange• Linear velocity (1.1X cm/hr)• pH (X + 0.1)• Loading (1.5X mg/ml)

HIC• Linear velocity (1.1X cm/hr)• pH (X - 0.1)• Load buffer (X + 50 mM)• Wash buffer (X - 50 mM)• Loading (1.44X mg/ml)

Viral Filtration• Concentration (2X)• Pressure (X + 0.2 psi)• Mass/surface area (1.66X g/m2)

Limits Shown as Worst Case


Routine OperationalParameters

Proven Acceptable Range

Edges of Process Failure Factorial

Experimental Design

DOE Can be Used to Optimize Antibody Purification


Use of Scale Down Models to Optimize

CPP and Establish a Design Space

OptimizeCPP

Product ForcedDegradation

Studies

Process / ProductImpurityAssays

Scale DownProcess Models

Design SpaceEstablished


Impact of Four CPPs on IgG Capture Chromatography

Column load concentrationLoad timeDynamic binding capacityFlow rate

Sunil Chhatre, IMRC Bioprocess program – UCL UK


Summary and ConclusionsApplication of QbD to biopharmaceutical products is difficult due to the complexity of these products

Process development of biologics has always included some aspects of QbD, including science-based decisions, the use of scale down process models, and the definition of CPP

DOE can a useful tool to develop, define, and evaluate CPP ranges

Once optimized, CPP ranges can be used to define the design space for biologic manufacturing processes

Combining DOE with science-based decisions can decrease the time required to optimize production, speed the development of robust processes, and reduce risk in biologics product development


AcknowledgementsBioProcess Technology Consultants

• Sheila Magil, Ph.D.

• Susan Dana Jones, Ph.D.

BioProcessors, Inc.

• Cell culture optimization

Neurobiological Technologies, Inc.

• Chromatography optimization

Protherics Corporation

• DOE and Scale down models


For more information, contact

Howard L. Levine, Ph.D.BioProcess Technology Consultants, Inc.

289 Great Road, Suite 303Acton, MA 01720

978-266-9153978-266-9152 (fax)

[email protected]

the use of critical process analysis to reduce risk and ... · analysis to reduce risk and increase...

Documents