The Use of Critical Process Parameters and Quality By Design to Improve Biopharmaceutical Product Quality
Howard L. Levine, Ph.D. BioProcess Technology Consultants, Inc.
Presented at BioProcess International Asia Pacific Mumbai, India October 21, 2008 and 2008 PDA Development and Regulation of Clinical Trial Supplies Conference Cambridge, MA November 11, 2008
What is Quality by Design? ¾ “Means that product and process performance characteristics are scientifically designed to meet specific objectives, not merely empirically derived from performance of test batches.” ¾ The product is designed to meet patient needs and performance requirements ¾ The process is designed to consistently meet product critical quality attributes ¾ The impact of starting raw materials and process parameters on product quality is well understood ¾ The process is continually monitored, evaluated and updated to allow for consistent quality throughout product life cycle ¾ Critical sources of variability are identified and controlled through appropriate control strategies From Clone to Commercial®
Ref: H. Winkle, BPI Conference, Oct 1 – 4, 2007
Defining Critical Quality Attributes (CQAs) ¾ “…those molecular and biological characteristics found to be useful in ensuring the safety and efficacy of the product…” (Q6B) ¾ Can these attributes be properly defined for biologics? • Often difficult due to complexity of biologic products • Default is to look at many attributes ¾ QbD focuses 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 ¾ Need to develop a design space to be documented in application which is based on CQAs
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What are Critical Process Parameters (CPPs) ¾ CPPs are independent process parameters most likely to affect the quality attributes of a product or intermediate ¾ CPPS are determined by sound scientific judgment and based on research, scale‐up or manufacturing experience ¾ CPPS are controlled and monitored to confirm that the impurity profile is comparable to or better than historical data from development and manufacturing ¾ Quality attributes derived from CPPs include: • Chemical purity • Qualitative and quantitative impurities • Physical characteristics • Microbial quality
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QbD for Small Molecules vs. Biologics Small Molecule Drugs
Biologic Products
Variability derived from formulated Drug Product
Variability derived from Drug Substance
Quality Attributes determined early in product development
Quality Attributes difficult to determine; defined late in product development
Readily Characterized
Characterization Complex
Process control readily defined and achieved
Process control more difficult to define and implement
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The Application of QbD to Biologic Products ¾ Quality 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 • “Traditional” development and validation approaches can be applied to QbD, especially in identifying CPP and defining the Design Space
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Knowledge Space
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Ref: B. Davis, FIP Conference 2007
Defining CQAs Throughout a Product Life Cycle Critical aspects throughout product life cycle define what is needed: ¾ To release product ¾ To control the process ¾ For post‐approval changes Preclinical
Estimates Based on Experience
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Phase 1
Refined Based on Preclinical and Safety Data
Phase 2
Phase 3
Refined Based on Clinical and Process Data
Post Approval
Continually Reviewed and Refined Based on Increased Clinical
Linking Design Space and Control Strategies Design space is based on our Knowledge Space ¾ Control Strategy: Maintaining the process within the Design Space ¾ Design Space is not intended to define critical attributes, rather these will follow from the “process flow” ¾
¾
The control strategy for a CQA is the selection and combination of different types of controls applied to the manufacturing process and associated systems to assure the right product quality at an acceptably low level of risk of manufacturing failure
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Defining Control Strategies
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Range of Raw Material And Facility Attributes
CPP Drive CQA to Create the Design Space
Process Designed to Limit Product Variability
Define API in terms of CQAs ¾ Identify CPP that affect the CQAs ¾ Determine range of each CPP that produces acceptable product to establish the Design Space ¾
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Comparing CQAs and CPPs 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 From Clone to Commercial®
Graphic adapted from Kozlowski and Swan (2006)
Flow Rate
Glycoform
Design Space: Identification of CPPs
eld Yi
Impurity 1
pH
Column Loading Capacity
¾ Using data from development identify parameters that affect the defined product characteristics, for example • Level of key impurity • Desired glycoform content • Desired yield From Clone to Commercial®
Flow Rate
Flow Rate
Design Space: Process Optimization
pH
pH
Column Loading Capacity
Column Loading Capacity
¾ Use Factorial Analysis without interactions to map boundary conditions ¾ Use Full Factorial to fully define response surfaces and examine interactions From Clone to Commercial®
Design Space: Putting it All Together
Agitation Rate
Glycoform
¾ Summarize results of multiple experiments to define response surface
eld Yi
[Fe] in Media
lO na Fi
D
¾ Validate the process within the Design Space to demonstrate consistent production of product with desired characteristics
Impurity 1
Design Space
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Application of QbD to Cell Culture ¾ Optimizing clone selection to achieve maximal product titer within a Design Space ¾ Potential Critical Process Parameters in cell culture production… • Temperature • pH • Agitation • Dissolved oxygen • Medium constituents • Feed type and rate
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Optimization of Cell Culture ¾ Many potential process parameters can impact the Critical Quality Attributes of the cell culture process, including: • Cell viability and number • Product titer • Product Characteristics (e.g. glycosylation) • Impurity profile ¾ Identify those which are critical through process development evaluating impact of each parameter on the CQA ¾ Create Design Space by optimization of these parameters through a two factorial design of experiment
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Optimization of Cell Culture Conditions Two factorial design monitoring product titer (yield) as a function of pH and temperature ¾ 50 conditions (10 T x 5 pH) ¾ n=9 (450 total chambers)
Optimization performed using SimCellTM technology from BioProcessors Corp.
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Mapping Downstream Process Design Space ¾ 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 ¾ All impact product purity and yield
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Defining Ion Exchange Column Conditions ¾ Purification of a natural protein by anion exchange chromatography ¾ Anion exchange column equilibrated with 10 mM TRIS‐Phosphate buffer ¾ Variation of load solution pH will impact product yield and purity
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Anion Exchange Column Yield and Purity ¾
Best yield at pH 7.0, however, additional contaminant present in pool not seen at higher pH’s
Product Contaminant
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¾
Can subsequent process steps remove this contaminant?
Final Product Purity Subsequent 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 7.0 B – Anion exchange column pool, pH 8.6 C – Column 2 pool following loading with “A” D – Column 2 pool following loading with “B”
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Summary and Conclusions ¾ Application of QbD to biopharmaceutical products is often difficult reflecting the complexity of these products ¾ Process development of biologics has always included some aspects of QbD, including science‐based decisions and the use of scale down process models ¾ Once optimized, CPP ranges can be used to define the design space for biologic manufacturing processes
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Summary and Conclusions ¾ 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 ¾ Using QbD can facilitate the technology transfer by describing the design space for complex products and allowing process variability
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Acknowledgements ¾ BioProcess Technology Consultants • Sheila Magil, Ph.D. • Susan Dana Jones, Ph.D. • Alex Kanarek, Ph.D. ¾ BioProcessors, Inc. • Cell culture optimization ¾ Neurobiological Technologies, Inc. • Chromatography optimization
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THANK YOU! For more information, contact Howard L. Levine, Ph.D. BioProcess Technology Consultants, Inc. 289 Great Road, Suite 303 Acton, MA 01720 978‐266‐9153 978‐266‐9152 (fax)
[email protected] www.bioprocessconsultants.com
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