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Quality By Design

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Quality by design and Design of experiment

Quality by design and Design of experiment


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  • 1. Quality by Design
    Development, optimization and robustness by Design
    Mayank
  • 2. Global initiatives
  • 3. Global initiatives
    References
    1. ICH, Q8(R1) Pharmaceutical Development (Geneva, Switzerland, Nov. 10, 2005; Rev. 2008).
    2. ICH, Q9 Quality Risk Management (Geneva, Switzerland, Nov. 9. 2005).
    3. J. Agalloco et al., "FDA's Guidance for Industry: Process Validation: General Principles and Practices," presented at PDA, Jan. 14, 2009.
    4. FDA, Draft Guidance for Industry—Process Validation: General Principles and Practices (Rockville, MD, Nov. 2008).
    5. W. Charlton, T. Ingallinera, and D. Shive, "Validation of Clinical Manufacturing," and Validation Chapter, in Validation of Pharmaceutical Process, J. Agalloco and F. Carleton, eds. (Informa Healthcare, New York, 3rd ed., 2008), pp. 542–544.
  • 4. Quality by design (QbD)
    What is QbD?
    Product and process performance characteristics are scientifically designed to meet specific objectives, not merely empirically derived from performance of test batches
    Focus during development
    Critical Quality Attributes (CQA)
    eg. USP
    DSP
    • Cell viability
    • 5. Cell count
    • 6. Titre
    • 7. Product characteristics (egGlycocylation)
    • 8. Impurity profile
    • 9. Overall purity
    • 10. Type of impurity (eg HCP, endotoxins, DNA,)
    • 11. Yield
    Critical Process Parameter (CPP)
  • Quality by design (QbD)
    Tools for successful implementation of QbD
    Team:
    Analytical equipments
    Powerful Statistical tools
  • 47. Quality by design (QbD)
    Process flow:
    Screening
    Characterization range
    Identification of significant parameters
    Acceptable range
    Finding parameter ranges
    Operating range
    Optimization
    Finding interactions of parameters
    Defining models
    Set point
    Validation
    Process design space
    Identification of CPP
    Identification of noise factors
    Process/ product Development:
    Robust
    Cost effective
    Feasible
    Defining control strategies
    Production
    Continuous monitoring
    and development
  • 48. Quality by design (QbD)
    Defining Design space
  • 49. Quality by design (QbD)
    Defining Design space
  • 50. Quality by design (QbD)
    Defining Design space
  • 51. Screening
    Parameter selection
    Physical
    Chemical
    Raw material
    Component/Equipment
    Process (time, type)
    Environmental
    Facility
    Categorical
    Continuous
  • 52. Screening
    Response
    Level selection
    Parameter
    Digging for a fossil
  • 53. Screening
    Fractional Factorial
    22
  • 54. Screening
    Fractional Factorial
    22
  • 55. Screening
    Fractional Factorial
    23-1
    C=AB
    C is confounding with AB
  • 56. Screening
    Fractional Factorial
    23-1
    C=AB
    C is confounding with AB
    B=AC
    B is confounding with AC
  • 57. Screening
    Fractional Factorial
    23-1
    C=AB
    C is confounding with AB
    B=AC
    B is confounding with AC
    A=BC
    A is confounding with BC
  • 58. Screening
    Fractional Factorial
    PlackettBurman
    2 level fractional factorial designs
    Resolution III design
    Efficient estimations
    Interactions between factors ignored
    Used in Matrix form
    Multiple of 4 not power of 2
    Saturated orthogonal array
  • 59. Screening
    Fractional Factorial
    PlackettBurman
    Matrix
  • 60. Screening
    Lack of fit
    Before deciding whether to build a response surface model, it is important to assess the adequacy of a linear model
    The error term ε in the model is comprised of two parts:
    modeling error, (lack of fit, LOF)
    experimental error, (pure error, PE), which can be calculated from replicate points
    The lack of fit test helps us determine if the modeling error is significant different than the pure error
  • 61. Screening
    Lack of fit
    Before deciding whether to build a response surface model, it is important to assess the adequacy of a linear model
    The error term ε in the model is comprised of two parts:
    modeling error, (lack of fit, LOF)
    experimental error, (pure error, PE), which can be calculated from replicate points
    The lack of fit test helps us determine if the modeling error is significant different than the pure error
  • 62. DOE and Experiments
    RS Model
    1
    x2
    x1
    Response surface methodology
    Input
    Response
    Black Boxed
    System
    Original System
  • 63. Response surface methodology
    RSM characteristics
    Models are simple polynomials
    Include terms for interaction and curvature
    Coefficients are usually established by regression analysis with a computer program
    Insignificant terms are discarded
    Model equation for 2 factors
    Y = β0constant
    + β1X1 + β2X2 main effects
    + β3X12 + β4X22 curvature
    + β5X1X2 interaction
    + ε error
    Model equation for 3 factors
    Y = β0constant
    + β1X1 + β2X2 + β3X3 main effects
    + β11X12 + β22X22 + β33X32 curvature
    + β12X1X2 + β13X1X3 + β23X2X3 interactions
    + ε error
    Higher order interaction terms
    are not included
  • 64. Response surface methodology
    Central composite design (CCD)
    eg. 2 factor
    Central composite circumscribed (CCC)
    5 Levels
    α (star point) are beyond levels
    Central composite face centered (CCF)
    3 Levels
    α (star point) are within levels (center)
    Central composite inscribed (CCI)
    5 Levels
    α (star point) are within levels
    Scale down of CCC
  • 65. Response surface methodology
    Central composite design (CCD)
    Central composite circumscribed (CCC)
    3 factors
    Total exp: 20
    Full factorial 8
    Axial points 6
    Center points 6
    +++
    -+-
    ---
    +--
    -++
    ++-
    --+
    +-+
  • 66. Response surface methodology
    Central composite design (CCD)
    Central composite circumscribed (CCC)
    Randomization:
    To avoid effect of uncontrollable nuisance variables
    +++
    -+-
    ---
    +--
    -++
    ++-
    --+
    +-+
  • 67. Response surface methodology
    Central composite design (CCD)
    Central composite circumscribed (CCC)
    Blocking:
    To avoid effect of controllable nuisance variables
    -++
    +++
    +-+
    --+
    ++-
    -+-
    +--
    ---
  • 68. Response surface methodology
    Box Behnen
    • It is portion of 3k Factorial
    • 69. 3 levels of each factor is used
    • 70. Center points should be included
    • 71. It is possible to estimate main effects and second order terms
    • 72. Box-Behnken experiments are particularly useful if some boundary areas of the design region are infeasible, such as the extremes of the experiment region
    eg. 3 factor
    12 experiments
  • 73. Response surface methodology
    Comparison of RSM experiments
    * One third replicate is used for a 3k factorial design and one-half replicate is used for a 2k factorial design with the CCD for 5, 6 and 7 factors.
  • 74. Robust process development
    Who is better shooter?
    B
    A
  • 75. Robust process development
    Goal post vs Taguchi view
    LSL
    USL
    LSL
    USL
  • 76. Robust process development
    Reducing variation
  • 77. Robust process development
    Objective of robust process
    Smaller-the-Better S/N Ratio  = – 10 Log10 ( 1/n  Yi2 )
    e.g. defects, impurity, process time, cost
    Larger-the-Better S/N Ratio  = – 10 Log10 ( 1/n  1/Yi2 )
    e.g. titre, yield, resolution, profit
    Nominal-the-BestS/N Ratio  = – 10 Log10[1/n(YIDEAL- Yi ) 2 ]
    e.g. target
    Signal-to-Noise S/N Ratio =10log[μ2/σ2]
    e.g. trade-off
  • 78. Robust process development
    Identification of Signal and noise
    eg: Fermentation
    Signal:
    What can be controlled in plant and laboratory
    Noise:
    What can not be controlled in plant but in laboratory
  • 79. Robust process development
    Developing robust process
    To find a signal settings in presence of noise that minimize response variation while adjusting of keeping the process on target
    Taguchi approach
    Signal:
    Inner array
    Noise:
    Outer array