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Pharmaceutical Research

, Volume 28, Issue 7, pp 1465–1479 | Cite as

Statistical Thinking and Knowledge Management for Quality-Driven Design and Manufacturing in Pharmaceuticals

  • Evdokia Korakianiti
  • Dimitrios RekkasEmail author
Expert Review
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ABSTRACT

The purpose of this article is to present the evolution of quality principles and how they have been implemented in the pharmaceutical industry. The article discusses the challenges that the FDA PAT Guidance and the ICH Q8, Q9 and Q10 Guidelines present to industry and provides a comprehensive overview of the basic tools that can be used to effectively build quality into products. The principles of the design of experiments, the main tools for statistical process analysis and control, and the requisite culture change necessary to facilitate statistical, knowledge-based management are also addressed.

KEY WORDS

design of experiments knowledge management process analytical technologies process variability quality quality by design statistical process control and analysis statistical thinking 
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Notes

ACKNOWLEDGMENTS

The authors would like to thank Mr S. Politis M.Sc. for his useful contribution.

REFERENCES

  1. 1.
    Abboud L, Henslye S. New prescription for drug makers: update the plant. Wall Street J. September 3, 2003.Google Scholar
  2. 2.
    Smith A, Martin R. Pharmaceutical quality: build it into the process. AMR research report. May 12 (2004) http://www.pharmamanufacturing.com/articles/2007/119.html (accessed on 01/18/2010).
  3. 3.
    FDA Pharmaceutical Quality for the 21st century a risk-based approach Progress Report May 2007 http://www.fda.gov/AboutFDA/CentersOffices/CDER/ucm128080.htm#intro (accessed 01/18/2010).
  4. 4.
    FDA Guidance for Industry AT—A framework for innovative pharmaceutical development, manufacturing, and quality assurance, September 2004 http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM070305.pdf (accessed 01/18/2010).
  5. 5.
    ICH Q8(R2): Pharmaceutical Development November 2005, http://www.ich.org/LOB/media/MEDIA4986.pdf (accessed 01/18/2010).
  6. 6.
    ICH Q9 Quality risk management 9 November 2005, http://www.ich.org/LOB/media/MEDIA1957.pdf (accessed 01/18/2010).
  7. 7.
    ICH Q10 Pharmaceutical quality system, June 2008, http://www.ich.org/LOB/media/MEDIA3917.pdf (accessed 01/18/2010).
  8. 8.
    Pirsig RM. Zen and the art of motorcycle maintenance. New York: William Morow; 1974.Google Scholar
  9. 9.
    Hoyer RW, Hoyer BY. What is quality? Qual Prog. 2001;53–62.Google Scholar
  10. 10.
    Feigenbaum AV. Total quality control. 3rd ed. New York: McGraw-Hill; 1991.Google Scholar
  11. 11.
    Montgomery DC. Introduction to statistical quality control. New Jersey: Wiley; 2001.Google Scholar
  12. 12.
    Deming WE. Out of the crisis. Cambridge: MIT Press, Center for Advanced Engineering Study; 1988.Google Scholar
  13. 13.
    Garvin DA. Competing on the eight dimensions of quality. Harvard Bus Rev. 1987;101–9.Google Scholar
  14. 14.
    ICH Q6A. Specifications: test procedures and acceptance criteria for new drug substances and new drug products: Chemical substances. October 1999. http://www.ich.org/LOB/media/MEDIA430.pdf (accessed 01/18/2010).
  15. 15.
    ASQ Glossary http://www.asq.org/glossary/q.html (accessed 01/18/2010).
  16. 16.
    Ishikawa K. Introduction to quality control. Japan: 3A Corporation; 1991.Google Scholar
  17. 17.
    Smith LR. Six sigma and the evolution of quality in product development. Six Sigma Forum Mag. 2001;1(1):28–35.Google Scholar
  18. 18.
    Shewhart WA. Economic control of quality of manufactured product. New York: D. Van Norstrand Co; 1931.Google Scholar
  19. 19.
    Juran JM. Juran on quality by design: the new steps for planning quality into goods and services. New York: Free; 1992.Google Scholar
  20. 20.
    Blomquist HJ. A missing step to better quality. Deming Electronic Network Website. http://deming-network.org/deming_blomquist1.htm (accessed 01/05/2010).
  21. 21.
    ASQ. The history of quality—overview. http://www.asq.org/learn-about-quality/history-of-quality/overview/overview.html (accessed 01/05/2010).
  22. 22.
    Georgaki T, Kouroupi K, Politis S, Rekkas DM. Total quality management. Athens: Sideris; 2010.Google Scholar
  23. 23.
    Kane VE. Process capability indices. J Qual Technol. 1986;18(1):41–52.Google Scholar
  24. 24.
    Bohte D. Measuring process capability. New York: McGraw-Hill; 1997.Google Scholar
  25. 25.
    Deming WE. The new economics—for industry, government, education. Cambridge: MIT; 1993.Google Scholar
  26. 26.
    Oakland JS. Total quality management. 2nd ed. Oxford: Heinemann; 1993.Google Scholar
  27. 27.
    Roethlen C, Mangiameli P, Ebrahimpour M. Quality in US manufacturing industries: an empirical study. QMJ. 2002;9(3):48–66.Google Scholar
  28. 28.
    Gunasekaran A, Goyal SK, Martikainen T, Yli-Olli P. Total quality management: a new perspective for improving quality and productivity. Int J Qual Reliab Manage. 1998;15(8/9):947–68.CrossRefGoogle Scholar
  29. 29.
    Dooley K. The paradigms of quality: evolution and revolution in the history of the discipline. In: Fedor D, Ghosh S, editors. Advances in the management of organizational quality, vol. 5. JAI Press; 2000. p. 1–28.Google Scholar
  30. 30.
    Hooper JH. The process approach to QMS in ISO 9001 and ISO 9004. Qual Prog. 2001;70–3.Google Scholar
  31. 31.
    Von Bertanlaffy L. An outline of general system theory. Br J Philos Sci. 1950;I(2):134–65.CrossRefGoogle Scholar
  32. 32.
    West J. ISO 9001: 2000’s process approach. Qual Prog. 2002;35:103–4.Google Scholar
  33. 33.
    Prevette S. Systems thinking an uncommon answer. Qual Prog. 2003;32–5.Google Scholar
  34. 34.
    Ackoff R. Towards a system of systems concepts. Manage Sci. 1971;17(11):661–71.CrossRefGoogle Scholar
  35. 35.
    Mohammed MA. Using statistical process control to improve the quality of health care. Qual Saf Health Care. 2004;13:243–5.PubMedCrossRefGoogle Scholar
  36. 36.
    Assaf AF, Schmele JA. The textbook of total quality in health care. Florida: St. Lucie; 1993.Google Scholar
  37. 37.
    Lewis RG, Smith DH. Total quality in higher education. Florida: St Lucie; 1994.Google Scholar
  38. 38.
    Foster ST, Howard LW, Shannon P. The role of quality tools in improving satisfaction with government. QMJ. 2002;9(3):20–31.Google Scholar
  39. 39.
    Pyzdek T. The Six sigma handbook. New York: McGraw& Hill; 2003.Google Scholar
  40. 40.
    Goldratt EM, Fox J. The goal. Great Barrington: North River; 1992.Google Scholar
  41. 41.
    Nave D. How to compare six sigma, lean and the theory of constrains. Qual Prog. 2002;35(3):73–8.Google Scholar
  42. 42.
    Kovach J, Stringfellow P, Turner J, Chao BR. The house of competitiveness: the marriage of agile manufacturing, design for six sigma and lean manufacturing with quality considerations. JIT. 2005;21(3):2–9.Google Scholar
  43. 43.
    Kourti T. Process analytical technology and multivariate statistical process control, wellness index of product and process—Part1. Process Anal Technol. 2004;1(1):13–9.Google Scholar
  44. 44.
    Shanley A, Thomas P. Flexible Pharma: puzzling out the plant of the future. Pharma Manufacturing.com http://www.pharmamanufacturing.com/articles/2009/170/html (accessed 01/08/2010).
  45. 45.
    Pselk PE. Creativity, innovation and quality. Wisconsin: ASQ Quality; 1977.Google Scholar
  46. 46.
    Brocklebank MP, Lam J, Mehta P. A hub layout concept for oral solid dosage facilities. Pharm Eng. 2006;26(1):1–8.Google Scholar
  47. 47.
    Shanley A. Lurching towards agility. PharmaManufacturing.com http://www.pharmamanufacturing.com/articles/2009/179.html (accessed 01/26/2010).
  48. 48.
    Yu LX. Pharmaceutical quality by design: product and process development, understanding and control. Pharm Res. 2008;25(4):781–91.PubMedCrossRefGoogle Scholar
  49. 49.
    Antony J, Taner T. A conceptual framework for the effective implementation of statistical process control. Bus Process Manag J. 2003;9(4):473–89.CrossRefGoogle Scholar
  50. 50.
    Goh TN. The role of statistical design of experiments in Six Sigma perspectives of a practitioner. Qual Eng. 2002;14(4):659–71.CrossRefGoogle Scholar
  51. 51.
    DeFeo JA. The tip of the Iceberg. Qual Prog. 2001;29–37.Google Scholar
  52. 52.
    Benneyan JC, Pselk PE. Statistical process control as a tool for research and healthcare improvement. Qual Saf Health Care. 2003;12:458–64.PubMedCrossRefGoogle Scholar
  53. 53.
    Liberatore R. Teaching the role of SPC in industrial statistics. Qual Prog. 2001;89–94.Google Scholar
  54. 54.
    Rys R, Abel J. PAT: gateway to advanced control for operational excellence. Pharma Manufacturing.com http://www.pharmamanufacturing.com/articles/2006/061.html (accessed 01/20/2010).
  55. 55.
    Joiner BL, Gaudard MA. Variation, management, and W. Edwards Deming. Qual Prog. 1990;23(12):29–37.Google Scholar
  56. 56.
    Prahalad CK, Krishnan MS. The new meaning of quality in the information age. Harvard Bus Rev. 1999;109–18.Google Scholar
  57. 57.
    Morris K. Making the most of drug development data. PharmaManufacturing.com http://www.pharmamanufacturing.com/articles/2005/399.html?page=full (accessed 01/25/2010).
  58. 58.
    Rockwell Automation. White paper. Making sense of e-manufacturing: a roadmap for manufacturers. May 2001. http://www.instrumentation.co.za/article.aspx?pklArticleId=1201 (accessed 01/20/10).
  59. 59.
    FDA Innovation and continuous improvement in pharmaceutical manufacturing. Pharmaceutical cGMPs for the 21st century. http://www.fda.gov/ohrms/dockets/ac/04/briefing/2004-408b1_01_manufSciWP.pdf (accessed 01/24/10).
  60. 60.
    FDA Pharmaceutical cGMPs for the 21st century—A risk-based approach—final report: Executive Summary: Key Accomplishments. http://www.fda.gov/Drugs/DevelopmentApprovalProcess/Manufacturing/QuestionsandAnswersonCurrentGoodManufacturingPracticescGMPforDrugs/ucm137175.htm (accessed 01/19/2010).
  61. 61.
    Burr T. The tools of quality. Part IV: Pareto charts. Qual Prog. 1990;23(11):59–61.Google Scholar
  62. 62.
    Lucas JM. The essential six sigma. How successful implementation can improve the bottom line. Qual Prog. 2002;27–31.Google Scholar
  63. 63.
    Macher J, Nickerson J. Pharmaceutical Manufacturing research project. September 2006. http://apps.olin.wustl.edu/faculty/nickerson/results/PMRPFinalReportSept2006.pdf (accessed on 01/19/2010).
  64. 64.
    Maes I, Van Liedekerle B. The need for a broader perspective if process analytical technology implementation is to be successful in the pharmaceutical sector. J Pharm Innov. 2006;19–21.Google Scholar
  65. 65.
    Economist Intelligence unit. Quality manufacturing: a blockbuster opportunity for pharmaceuticals. September 2005 (http://graphics.eiu.com/files/ad_pdfs/eiu_oracle_pharma_wp.pdf).
  66. 66.
    Marcher and Nickerson. Pharmaceutical Manufacturing Research Project. Sep. 2006 http://apps.olin.wustl.edu/faculty/nickerson/results/PMRPFinalReportSept2006.pdf (accessed 12/10/2009).
  67. 67.
    Makrymichalos M, Antony J, Antony F, Kumar M. Statistical thinking and its role for industrial engineers and managers in the 21st century. Manag Audit J. 2005;20(4):354–63.CrossRefGoogle Scholar
  68. 68.
    Bjerke F, Hersleth M. Introducing statistical thinking to the food industry, facilitating and inhibiting factors. QMJ. 2001;8(3):49–59.Google Scholar
  69. 69.
    Woodall WH, Montgomery DC. Research issues and ideas in statistical process control. J Qual Technol. 1999;31(4):376–86.Google Scholar
  70. 70.
    Griffith GK. Statistical process control methods for long and short runs. Milwaukee: ASQC Quality; 1996.Google Scholar
  71. 71.
    Swift JA. Introduction to modern statistical quality control and management. Florida: St Lucie; 1995.Google Scholar
  72. 72.
    Okes D. Organize your quality tool belt. Qual Prog. 2002;25–9.Google Scholar
  73. 73.
    Woodall W, Spitzner DJ, Montgomery D, Gupta S. Using Control charts to monitor process and product quality profiles. J Qual Technol. 2004;36(3):309–19.Google Scholar
  74. 74.
    Blankenship B, Petersen P. W. Edwards Deming’s mentor and others who made a significant impact on his views during the 1920s and 1930s. J Manag Hist. 1999;5(8):454–67.CrossRefGoogle Scholar
  75. 75.
    AT&T Hanbook. Statistical quality control handbook. USA: AT&T Technologies; 1956.Google Scholar
  76. 76.
    Nelson LS. Technical aids: the Shewart control charts tests for special causes. J Qual Technol. 1984;16(4):237–9.Google Scholar
  77. 77.
    Hunter JS. The exponentially weighted moving average. J Qual Technol. 1986;18(4):203–10.Google Scholar
  78. 78.
    Wiklund SJ. Process adjustment when using the EWMA charts. Int J Qual Reliab Manage. 1995;12(2):8–27.CrossRefGoogle Scholar
  79. 79.
    Jackson J. A user’s guide to principal components. New York: Wiley; 1991.CrossRefGoogle Scholar
  80. 80.
    Wold S, Esbensen K, Geladi P. Principal components analysis. Chemom Intell Lab Syst. 1987;2:37–52.CrossRefGoogle Scholar
  81. 81.
    Abdi H, Wiliams LJ. Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics. 2010;2:in press.Google Scholar
  82. 82.
    Abdi H. Partial Least Square Regression. In: Salkind NJ, editor. Encyclopedia of measurement and statistics. Thousand Oaks: Sage; 2007. p. 740–4.Google Scholar
  83. 83.
    Eriksson L, Johansson E, Kettaneh-Wold N, Wold S. Multi- and megavariate data analysis- principles and applications. Umea: Umetrics Academy; 2001.Google Scholar
  84. 84.
    Wold S. PLS for multivariate linear modeling QSAR. In: van de Waterbeemd H, editor. Chemometric methods in molecular design. Methods and principles in medicinal chemistry. Verlag-Chemie; 1994.Google Scholar
  85. 85.
    Geladi P, Kowalski BR. Partial least squares regression: a tutorial. Anal Chim Acta. 1986;185:1–17.CrossRefGoogle Scholar
  86. 86.
    Wehrlé P, Stamm A. Process control and quality improvement in the pharmaceutical industry. Drug Dev Ind Pharm. 1994;20(2):141–64.CrossRefGoogle Scholar
  87. 87.
    Kresta JV, MacGregor JF, Marlin TE. Multivariate statistical monitoring of process operating performance. Can J Chem Eng. 1991;69:35–47.CrossRefGoogle Scholar
  88. 88.
    MacGregor J. Using on-line process data to improve quality; Youden Address. ASQC Stat Div Newslett. 1996;16(2):6–13.Google Scholar
  89. 89.
    Kourti T, MacGregor JF. Process analysis, monitoring and diagnosis, using multivariate projection methods. Chemom Intell Lab Syst. 1995;28(1):3–21.Google Scholar
  90. 90.
    Kourti T, Nomikos P, MacGregor JF. Process analysis, monitoring and diagnosis, using multivariate projection methods. Chemom Intell Lab Syst. 1995;28:3–21.Google Scholar
  91. 91.
    Nomikos P, MacGregor JF. Multivariate SPC charts for monitoring batch processes. Technometrics. 1995;37:41–59.CrossRefGoogle Scholar
  92. 92.
    Kourti T. Abnormal situation detection, three-way data and projection methods; robust data archiving and modeling for industrial applications. Annu Rev Control. 2003;27(2):131–9.CrossRefGoogle Scholar
  93. 93.
    Kourti T. Process analytical technology and multivariate statistical process control, Part 3. Process Anal Technol. 2006;3(3):18–24.Google Scholar
  94. 94.
    Box G, Kramer T. Statistical process monitoring and feedback adjustment—A discussion. Technometrics. 1992;34(3):251–67.CrossRefGoogle Scholar
  95. 95.
    Box GP, MacGregor JF. The analysis of closed-loop dynamic-stochastic systems. Technometrics. 1974;16(3):391–8.CrossRefGoogle Scholar
  96. 96.
    Montgomery DG, Keats JB, Runger GC, Messina WS. Integrating statistical process control and engineering process control. J Qual Technol. 1994;26(2):79–87.Google Scholar
  97. 97.
    Tucker W, Faltin F, Wiel SV. Algorithmic statistical process control: an elaboration. Technometrics. 1993;35(4):363–75.CrossRefGoogle Scholar
  98. 98.
    Wiel SV, Faltin F, Doganaksoy N. Algorithmic statistical process control: concepts and an application. Technometrics. 1992;34(3):286–97.CrossRefGoogle Scholar
  99. 99.
    Bisgaard S. Industrial use of statistically designed experiments: case study references and some historical anecdotes. Qual Eng. 1992;4(4):547–62.CrossRefGoogle Scholar
  100. 100.
    Lewis GA, Matieu D, Phan-Tan-Luu R. Pharmaceutical experimental design. New York: Marcel Dekker; 1999.Google Scholar
  101. 101.
    Montgomery DC. Design and analysis of experiments. 4th ed. New York: Wiley; 1997.Google Scholar
  102. 102.
    Lepore J, Spavins J. Product quality lifecycle implementation (PQLI) innovations. PQLI design space. J Pharm Innov. 2008;3:79–87.CrossRefGoogle Scholar
  103. 103.
    Anderson-Cook CM, Goldfarb HB, Borror CM, Montgomery DC, Canter KG, Twist JN. Mixture designs and mixture—process variable experiments for pharmaceutical applications. Pharm Stat. 2004;3:247–60.CrossRefGoogle Scholar
  104. 104.
    Bhote KR. World class quality. New York: Amacon; 1991.Google Scholar
  105. 105.
    Blake S, Launsby RG, Weese DL. Experimental design meets the realities of the 1990s. Qual Prog. 1994;27(10):99–101.Google Scholar
  106. 106.
    Chatterjee B. Embracing risk. PharmaManufacturing.com. http://www.pharmamanufacturing.com/articles/2007/175.html (accessed 01/24/10).
  107. 107.
    Stamatis DH. Failure mode and effect analysis: FMEA from theory to execution. WI: ASQ Quality; 1995.Google Scholar
  108. 108.
    Bhuiyan N, Baghel A. An overview of continuous improvement: from the past to the present. Manage Decis. 2005;43(5):761–71.CrossRefGoogle Scholar
  109. 109.
    Pearson A. Measurements and the knowledge revolution. Qual Prog. 1999;31–7.Google Scholar
  110. 110.
    Buckman RH. Building a knowledge driven organization. NY: McGraw-Hill; 2004.Google Scholar
  111. 111.
    Mohan P, Glassey J, Montague GA. Pharmaceutical operations management: manufacturing for competitive advantage. New York: McGraw-Hill; 2006.Google Scholar
  112. 112.
    Lapre MA, Van Wassenhove LN. Learning across lines. The secret to more efficient plants. Harvard Bus Rev. 2002;107–11.Google Scholar
  113. 113.
    Womack JP, Jones DT, Roos D. The machine that changed the world. New York: Free; 2007.Google Scholar
  114. 114.
    Czarnecki H, Schroer BJ, Adamas M, Spann MS. Continuous process improvement when it counts most. Qual Prog. 2000;33(5):74–80.Google Scholar
  115. 115.
    Whitney D. Agile pathfinders in the aircraft and automobile industries—a progress report. MIT 1995. http://web.mit.edu/ctpid/www/agile/atlanta.html#r1 (accessed 01/24/10) part of Fast and Flexible communication projects at MIT http://web.mit.edu/ctpid/www/agile (accessed 01/24/10).
  116. 116.
    Sanchez LM, Nagi R. A review of agile manufacturing systems. Int J Prod Res. 2001;39(16):3561–600.CrossRefGoogle Scholar
  117. 117.
    Tolve A. How lean manufacturing can cut costs. Eye for Pharma. http://social.eyeforpharma.com/story/how-lean-manufacturing-can-cut-costs (accessed 01/27/2010).

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© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.European Medicines AgencyQuality Sector, ChemicalsLondonUK
  2. 2.School of Pharmacy, Division of Pharmaceutical TechnologyUniversity of AthensAthensGreece

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