Qb D Akhilesh Dwivedi
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Qb D Akhilesh Dwivedi

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Quality by Design

Quality by Design

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  • 1. Presentation on Quality by Design
    ‘Pharmaceutical GMPs
    for the 21st Century’
    By: Akhilesh Kumar Dwivedi
    Dr. Reddy’s Labs. F.T.O-III
    Date: 18 Jan 2011
    QUALITY BY DESIGN
  • 2. QUALITY BY DESIGN
    processes
    products
  • 3. HISTORY:
    Quality by Design (QbD) is a concept first outlined by well-known quality expert Joseph M. Juran in various publications, most notably Juran on Quality by Design. Juran believed that quality could be planned, and that most quality crises and problems relate to the way in which quality was planned in the first place.
    QUALITY BY DESIGN
  • 4. What is Quality?
    Quality
    Requirements
    = need or expectations
    Target Product
    Quality Profile
    Patient
    (or surrogate)
    “Good pharmaceutical quality represents
    an acceptably low risk of failing to achieve
    the desired clinical attributes.”
  • 5. The quality mantra
    “Quality can not be tested into products; it has to be built in by design”
    “Quality is never an accident; it is always the result of high intention, sincere effort, intelligent direction and skillful execution; it represents the wise choice of many alternatives.”
  • 6. How can we modernize our industry?
    More knowledge of our products and processes, allowing better design and more control
    Better management:- introduction of quality risk management- expansion of GMP to more extensive pharmaceutical quality system
  • 7. The knowledge pyramid
    Desired State
    Knowledge based decisions
    First
    Principles
    Why?
    MECHANISTIC
    KNOWLEDGE
    How?
    “CAUSAL" KNOWLEDGE
    What “Causes” What?
    Need for regulatory oversight
    CORRELATIVE KNOWLEDGE
    What Is Correlated to What?
    Current State
    DESCRIPTIVE KNOWLEDGE:
    What?
  • 8. The New Quality Paradigm – The Evolving Regulatory Framework
    Product Life Cycle
    Product
    Design
    Process
    Design
    Scale-up &
    Transfer
    Commercial
    Manufacture
    Product
    ICH Q8/Q8(R) - Pharmaceutical Development
    PAT Guidance
    ICH Q9 – Quality Risk Management
    ICH Q10 – Pharmaceutical Quality Systems
  • 9. Definition: Quality by Design
    Quality by Design is
    a systematic approach to development
    that begins with predefined objectives
    and emphasizes - product and process understanding - and process control,
    based on sound science and quality risk management.
  • 10. Quality by Design approach can be used for
    • Simple dosage forms
    • 11. Advanced drug delivery systems
    • 12. Devices
    • 13. Combination products
    • 14. Active pharmaceutical ingredients
    • 15. Materials incl excipients
    • 16. Analytics
  • Impact of QbD
    • Companies re-organize their science
    • 17. Universities change their curriculum
    • 18. Health authorities change their assessment and inspection
  • QUALITY BY DESIGN
    Step 1. Agree on the Target Product Profile
    Step 2. Determine the Critical Quality Attributes (CQAs)
    Step 3. Link the drug and excipient attributes and the process parameters to the CQAs
    Step 4. Define the Design Space
    Step 5. Define the Control Strategy
    Step 6. Prepare QbD registration file
    Step 7. Product lifecycle management and continual improvement
  • 19. What are the steps in aQuality by Design approach?
    2. CRITICAL
    QUALITY
    ATTRIBUTES
    3. LINK
    MAs AND PPs
    TO CQAS
    1. TARGET
    PRODUCT
    PROFILE
    4. ESTABLISH
    DESIGN
    SPACE
    6. PRODUCT
    LIFECYCLE
    MNGMNT
    5. ESTABLISH
    CONTROL
    STRATEGY
  • 20. Step 1. Agree on the Target Product Profile
    Consider:
    dosage form
    route of administration
    strength
    release / delivery of the drug
    pharmacokinetic characteristics(e.g., dissolution; aerodynamic performance)
    drug product quality criteria(e.g., sterility, purity).
    Target Product Profile:- a prospective and dynamic summary of the quality characteristics of a drug product - that ideally will be achieved to ensure that the desired quality, and hence the safety and efficacy, of a drug product is realized.
    The TPP forms the basis of design of the product.
  • 21.
  • 22. CRITICAL QUALITY ATTRIBUTES - definition
    A critical quality attribute (CQA) is a - physical, chemical, biological, or microbiological property or characteristic - that should be within an appropriate limit, range, or distribution - to ensure the desired product quality.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29. Step 4. Define the Design Space
    • The linkage between - the process inputs (input variables and process parameters) and - the critical quality attributes
    • 30. can be described in the design space.
  • 31. Definition of Design Space
    Roll pressure
    Gap width
    Screen Size
    • The material attributes and process parameters that assure quality.
    • 32. The multidimensionalcombination and interaction of input variables (e.g. material attributes) and process parameters that have beendemonstrated to provide assurance of quality.
  • 33.
  • 34. Step 5. Define the Control Strategy
    The control strategy should describe and justify how
    • in-process controls and
    • 35. the controls of - input materials (drug substance and excipients), - container closure system, - intermediates and
    • 36. the controls of end products
    contribute to the final product quality
  • 37. 5. CONTROL STRATEGY
    Elements of a control strategy can include, but are not limited to, the following:
    • Control of input material attributes (e.g., drug substance, adhesive polymer, primary packaging materials) based on an understanding of their impact on processability or product quality
    • Product specification(s)
    • Controls for unit operations that have an impact on downstream processing or end-product quality (e.g., the impact of solvent on degradation)
    • In-process or real-time release in lieu of end-product testing
    • A monitoring program (e.g., full product testing at regular intervals) for verifying multivariate prediction models.