Role of Statistics in Pharmaceutical Development Using Quality-by-Design Approach

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Role of Statistics in Pharmaceutical Development
Using Quality-by-Design Approach an FDA
Perspective
Chi-wan Chen, Ph.D.Christine Moore, Ph.D.Office
of New Drug Quality AssessmentCDER/FDA
FDA/Industry Statistics WorkshopWashington
D.C.September 27-29, 2006
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Outline

• FDA initiatives for quality
• Pharmaceutical CGMPs for the 21st Century
• ONDQAs PQAS
• The desired state
• Quality by design (QbD) and design space (ICH Q8)
• Application of statistical tools in QbD
• Design of experiments
• Model building evaluation
• Statistical process control
• FDA CMC Pilot Program
• Concluding remarks

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21st Century Initiatives

• Pharmaceutical CGMPs for the 21st Century a
  risk-based approach (9/04) http//www.fda.gov/cder
  /gmp/gmp2004/GMP_finalreport2004.htm
• ONDQA White Paper on Pharmaceutical Quality
  Assessment System (PQAS) http//www.fda.gov/cder/g
  mp/gmp2004/ondc_reorg.htm

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The Desired State(Janet Woodcock, October 2005)
A maximally efficient, agile, flexible
pharmaceutical manufacturing sector that reliably
produces high-quality drug products without
extensive regulatory oversight
A mutual goal of industry, society, and regulator
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FDAs Initiative on Quality by Design

• In a Quality-by-Design system
• The product is designed to meet patient
  requirements
• The process is designed to consistently meet
  product critical quality attributes
• The impact of formulation components and process
  parameters on product quality is understood
• Critical sources of process variability are
  identified and controlled
• The process is continually monitored and updated
  to assure consistent quality over time

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Quality by Design
FDAs view on QbD, Moheb Nasr, 2006
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Design Space (ICH Q8)

• Definition The multidimensional combination and
  interaction of input variables (e.g., material
  attributes) and process parameters that have been
  demonstrated to provide assurance of quality
• Working within the design space is not considered
  as a change. Movement out of the design space is
  considered to be a change and would normally
  initiate a regulatory post-approval change
  process.
• Design space is proposed by the applicant and is
  subject to regulatory assessment and approval

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Current vs. QbD Approach to Pharmaceutical
Development
Current Approach QbD Approach
Quality assured by testing and inspection Quality built into product process by design, based on scientific understanding
Data intensive submission disjointed information without big picture Knowledge rich submission showing product knowledge process understanding
Specifications based on batch history Specifications based on product performance requirements
Frozen process, discouraging changes Flexible process within design space, allowing continuous improvement
Focus on reproducibility often avoiding or ignoring variation Focus on robustness understanding and controlling variation
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Pharmaceutical Development Product Lifecycle
Product Design Development
Process Design Development
Manufacturing Development
Continuous Improvement
ProductApproval
Candidate Selection
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Pharmaceutical Development Product Lifecycle
Statistical Tool
Design of Experiments (DOE)
Product Design Development Initial
Scoping Product Characterization Product
Optimization
Model Building And Evaluation
Process Design Development Initial
Scoping Process Characterization Process
Optimization Process Robustness
StatisticalProcess Control
Manufacturing Development and Continuous
Improvement Develop Control Systems Scale-up
Prediction Tracking and trending
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Process Terminology
Process Step
Output Materials (Product or Intermediate)
Input Materials
Input ProcessParameters
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Design Space Determination

• First-principles approach
• combination of experimental data and mechanistic
  knowledge of chemistry, physics, and engineering
  to model and predict performance
• Statistically designed experiments (DOEs)
• efficient method for determining impact of
  multiple parameters and their interactions
• Scale-up correlation
• a semi-empirical approach to translate operating
  conditions between different scales or pieces of
  equipment

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Design of Experiments (DOE)

• Structured, organized method for determining the
  relationship between factors affecting a process
  and the response of that process
• Application of DOEs
• Scope out initial formulation or process design
• Optimize product or process
• Determine design space, including multivariate
  relationships

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DOE Methodology

• (1) Choose experimental design
• (e.g., full factorial, d-optimal)

(2) Conduct randomized experiments
Experiment Factor A Factor B Factor C
1 - -
2 - -
3
4 -
A
B
C
(4) Create multidimensional surface
model (for optimization or control)
(3) Analyze data
www.minitab.com
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Model Building Evaluation - Examples

• Models for process development
• Kinetic models rates of reaction or degradation
• Transport models movement and mixing of mass or
  heat
• Models for manufacturing development
• Computational fluid dynamics
• Scale-up correlations
• Models for process monitoring or control
• Chemometric models
• Control models
• All models require verification through
  statistical analysis

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Model Building Evaluation - Chemometrics

• Chemometrics is the science of relating
  measurements made on a chemical system or process
  to the state of the system via application of
  mathematical or statistical methods (ICS
  definition)
• Aspects of chemometric analysis
• Empirical method
• Relates multivariate data to single or multiple
  responses
• Utilizes multiple linear regressions
• Applicable to any multivariate data
• Spectroscopic data
• Manufacturing data

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Statistical Process Control - Definitions

• Statistical process control (SPC) is the
  application of statistical methods to identify
  and control the special cause of variation in a
  process.
• Common cause variation random fluctuation of
  response caused by unknown factors
• Special cause variation non-random variation
  caused by a specific factor

Upper Specification Limit
Upper Control Limit
3s
Target
Lower Control Limit
Lower Specification Limit
Special cause variation?
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Process Capability Index (Cpk)
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Quality by Design Statistics

• Statistical analysis has multiple roles in the
  Quality by Design approach
• Statistically designed experiments (DOEs)
• Model building evaluation
• Statistical process control
• Sampling plans (not discussed here)

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CMC Pilot Program

• Objectives to provide an opportunity for
• participating firms to submit CMC information
  based on QbD
• FDA to implement Q8, Q9, PAT, PQAS
• Timeframe began in fall 2005 to end in spring
  2008
• Goal 12 original or supplemental NDAs
• Status 1 approved 3 under review 7 to be
  submitted
• Submission criteria
• More relevant scientific information
  demonstrating use of QbD approach, product
  knowledge and process understanding, risk
  assessment, control strategy

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CMC Pilot - Application of QbD

• All pilot NDAs to date contained some elements of
  QbD, including use of appropriate statistical
  tools
• DOEs for formulation or process optimization
  (i.e., determining target conditions)
• DOEs for determining ranges of design space
• Multivariate chemometric analysis for
  in-line/at-line measurement using such technology
  as near-infrared
• Statistical data presentation and usefulness
• Concise summary data acceptable for submission
  and review
• Generally used by reviewers to understand how
  optimization or design space was determined

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Concluding Remarks

• Successful implementation of QbD will require
  multi-disciplinary and multi-functional teams
• Development, manufacturing, quality personnel
• Engineers, analysts, chemists, industrial
  pharmacists statisticians working together
• FDAs CMC Pilot Program provides an opportunity
  for applicants to share their QbD approaches and
  associated statistical tools
• FDA looks forward to working with industry to
  facilitate the implementation of QbD