Quality By Design

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Quality by design and Design of experiment

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Quality By Design

  1. 1. Quality by Design<br />Development, optimization and robustness by Design<br />Mayank<br />
  2. 2. Global initiatives<br />
  3. 3. Global initiatives<br />References<br />1. ICH, Q8(R1) Pharmaceutical Development (Geneva, Switzerland, Nov. 10, 2005; Rev. 2008). <br />2. ICH, Q9 Quality Risk Management (Geneva, Switzerland, Nov. 9. 2005). <br />3. J. Agalloco et al., &quot;FDA&apos;s Guidance for Industry: Process Validation: General Principles and Practices,&quot; presented at PDA, Jan. 14, 2009. <br />4. FDA, Draft Guidance for Industry—Process Validation: General Principles and Practices (Rockville, MD, Nov. 2008). <br />5. W. Charlton, T. Ingallinera, and D. Shive, &quot;Validation of Clinical Manufacturing,&quot; and Validation Chapter, in Validation of Pharmaceutical Process, J. Agalloco and F. Carleton, eds. (Informa Healthcare, New York, 3rd ed., 2008), pp. 542–544. <br />
  4. 4. Quality by design (QbD)<br />What is QbD?<br />Product and process performance characteristics are scientifically designed to meet specific objectives, not merely empirically derived from performance of test batches<br />Focus during development<br />Critical Quality Attributes (CQA)<br />eg. USP<br />DSP<br /><ul><li>Cell viability
  5. 5. Cell count
  6. 6. Titre
  7. 7. Product characteristics (egGlycocylation)
  8. 8. Impurity profile
  9. 9. Overall purity
  10. 10. Type of impurity (eg HCP, endotoxins, DNA,)
  11. 11. Yield </li></ul>Critical Process Parameter (CPP)<br /><ul><li>Column bed height and packing efficiency
  12. 12. Media selectivity
  13. 13. Media particle size
  14. 14. Dynamic capacity
  15. 15. Buffer conditions (eg pH, conductivity)
  16. 16. Temperature
  17. 17. Flow rate
  18. 18. Sample load
  19. 19. Temperature
  20. 20. pH
  21. 21. Agitation
  22. 22. DO
  23. 23. Medium composition
  24. 24. Osmolarity
  25. 25. Feed type
  26. 26. Process type (eg Batch, fed batch or perfustion)</li></li></ul><li>Quality by design (QbD)<br />Tools for successful implementation of QbD<br />Team:<br /><ul><li>Engineers
  27. 27. Biologists
  28. 28. Analysts
  29. 29. Chemists
  30. 30. Industrial pharmacist
  31. 31. Satiations</li></ul>Analytical equipments<br /><ul><li>Online/Atline
  32. 32. NIR detectors
  33. 33. Methanol sensors
  34. 34. CO2/O2 probes
  35. 35. Conductivity probes
  36. 36. Osmolarity probes
  37. 37. Turbidity sensor
  38. 38. Cell count/contamination analyzer
  39. 39. Offline
  40. 40. HPLC/UPLC*
  41. 41. LC/MS
  42. 42. Ion analyzer*
  43. 43. C/N analyzer
  44. 44. Gel doc
  45. 45. Multi well plate reader
  46. 46. ELISA</li></ul>Powerful Statistical tools<br />
  47. 47. Quality by design (QbD)<br />Process flow:<br />Screening<br />Characterization range<br />Identification of significant parameters<br />Acceptable range<br />Finding parameter ranges<br />Operating range<br />Optimization<br />Finding interactions of parameters<br />Defining models<br />Set point<br />Validation<br />Process design space<br />Identification of CPP<br />Identification of noise factors<br />Process/ product Development:<br />Robust<br />Cost effective<br />Feasible<br />Defining control strategies<br />Production<br />Continuous monitoring <br />and development<br />
  48. 48. Quality by design (QbD)<br />Defining Design space<br />
  49. 49. Quality by design (QbD)<br />Defining Design space<br />
  50. 50. Quality by design (QbD)<br />Defining Design space<br />
  51. 51. Screening<br />Parameter selection<br />Physical<br />Chemical<br />Raw material<br />Component/Equipment<br />Process (time, type)<br />Environmental<br />Facility<br />Categorical<br />Continuous<br />
  52. 52. Screening<br />Response<br />Level selection<br />Parameter<br />Digging for a fossil<br />
  53. 53. Screening<br />Fractional Factorial<br />22<br />
  54. 54. Screening<br />Fractional Factorial<br />22<br />
  55. 55. Screening<br />Fractional Factorial<br />23-1<br />C=AB<br />C is confounding with AB<br />
  56. 56. Screening<br />Fractional Factorial<br />23-1<br />C=AB<br />C is confounding with AB<br />B=AC<br />B is confounding with AC<br />
  57. 57. Screening<br />Fractional Factorial<br />23-1<br />C=AB<br />C is confounding with AB<br />B=AC<br />B is confounding with AC<br />A=BC<br />A is confounding with BC<br />
  58. 58. Screening<br />Fractional Factorial<br />PlackettBurman<br />2 level fractional factorial designs<br />Resolution III design<br />Efficient estimations<br />Interactions between factors ignored<br />Used in Matrix form<br />Multiple of 4 not power of 2<br />Saturated orthogonal array<br />
  59. 59. Screening<br />Fractional Factorial<br />PlackettBurman<br />Matrix<br />
  60. 60. Screening<br />Lack of fit<br />Before deciding whether to build a response surface model, it is important to assess the adequacy of a linear model<br />The error term ε in the model is comprised of two parts:<br /> modeling error, (lack of fit, LOF)<br /> experimental error, (pure error, PE), which can be calculated from replicate points<br />The lack of fit test helps us determine if the modeling error is significant different than the pure error<br />
  61. 61. Screening<br />Lack of fit<br />Before deciding whether to build a response surface model, it is important to assess the adequacy of a linear model<br />The error term ε in the model is comprised of two parts:<br /> modeling error, (lack of fit, LOF)<br /> experimental error, (pure error, PE), which can be calculated from replicate points<br />The lack of fit test helps us determine if the modeling error is significant different than the pure error<br />
  62. 62. DOE and Experiments<br />RS Model<br />1<br />x2<br />x1<br />Response surface methodology<br />Input<br />Response<br />Black Boxed<br />System<br />Original System<br />
  63. 63. Response surface methodology<br />RSM characteristics<br />Models are simple polynomials<br />Include terms for interaction and curvature<br />Coefficients are usually established by regression analysis with a computer program<br />Insignificant terms are discarded<br />Model equation for 2 factors <br />Y = β0constant<br />+ β1X1 + β2X2 main effects<br /> + β3X12 + β4X22 curvature<br />+ β5X1X2 interaction<br />+ ε error<br />Model equation for 3 factors <br />Y = β0constant<br /> + β1X1 + β2X2 + β3X3 main effects<br />+ β11X12 + β22X22 + β33X32 curvature<br /> + β12X1X2 + β13X1X3 + β23X2X3 interactions<br />+ ε error<br />Higher order interaction terms<br />are not included<br />
  64. 64. Response surface methodology<br />Central composite design (CCD)<br />eg. 2 factor<br />Central composite circumscribed (CCC)<br />5 Levels<br />α (star point) are beyond levels<br />Central composite face centered (CCF)<br />3 Levels<br />α (star point) are within levels (center)<br />Central composite inscribed (CCI)<br />5 Levels<br />α (star point) are within levels<br />Scale down of CCC<br />
  65. 65. Response surface methodology<br />Central composite design (CCD)<br />Central composite circumscribed (CCC)<br />3 factors<br />Total exp: 20<br />Full factorial 8<br />Axial points 6<br />Center points 6<br /> +++<br /> -+-<br /> ---<br /> +--<br /> -++<br />++-<br /> --+<br /> +-+<br />
  66. 66. Response surface methodology<br />Central composite design (CCD)<br />Central composite circumscribed (CCC)<br />Randomization:<br />To avoid effect of uncontrollable nuisance variables<br /> +++<br /> -+-<br /> ---<br /> +--<br /> -++<br />++-<br /> --+<br /> +-+<br />
  67. 67. Response surface methodology<br />Central composite design (CCD)<br />Central composite circumscribed (CCC)<br />Blocking:<br />To avoid effect of controllable nuisance variables<br />-++<br />+++<br />+-+<br />--+<br />++-<br />-+-<br />+--<br />---<br />
  68. 68. Response surface methodology<br />Box Behnen<br /><ul><li> It is portion of 3k Factorial
  69. 69. 3 levels of each factor is used
  70. 70. Center points should be included
  71. 71. It is possible to estimate main effects and second order terms
  72. 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</li></ul>eg. 3 factor<br />12 experiments<br />
  73. 73. Response surface methodology<br />Comparison of RSM experiments<br />* 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.<br />
  74. 74. Robust process development<br />Who is better shooter?<br />B<br />A<br />
  75. 75. Robust process development<br />Goal post vs Taguchi view<br />LSL<br />USL<br />LSL<br />USL<br />
  76. 76. Robust process development<br />Reducing variation<br />
  77. 77. Robust process development<br />Objective of robust process<br />Smaller-the-Better S/N Ratio  = – 10 Log10 ( 1/n  Yi2 )<br />e.g. defects, impurity, process time, cost<br />Larger-the-Better S/N Ratio  = – 10 Log10 ( 1/n  1/Yi2 )<br />e.g. titre, yield, resolution, profit<br />Nominal-the-BestS/N Ratio  = – 10 Log10[1/n(YIDEAL- Yi ) 2 ]<br />e.g. target<br />Signal-to-Noise S/N Ratio =10log[μ2/σ2]<br />e.g. trade-off <br />
  78. 78. Robust process development<br />Identification of Signal and noise<br />eg: Fermentation<br />Signal:<br />What can be controlled in plant and laboratory<br />Noise:<br />What can not be controlled in plant but in laboratory<br />
  79. 79. Robust process development<br />Developing robust process<br />To find a signal settings in presence of noise that minimize response variation while adjusting of keeping the process on target<br />Taguchi approach<br />Signal:<br />Inner array<br />Noise:<br />Outer array<br />

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