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The AAPS Journal

, Volume 18, Issue 4, pp 793–800 | Cite as

Monitoring Quality of Biotherapeutic Products Using Multivariate Data Analysis

  • Anurag S. Rathore
  • Mili Pathak
  • Renu Jain
  • Gaurav Pratap Singh Jadaun
Commentary

Abstract

Monitoring the quality of pharmaceutical products is a global challenge, heightened by the implications of letting subquality drugs come to the market on public safety. Regulatory agencies do their due diligence at the time of approval as per their prescribed regulations. However, product quality needs to be monitored post-approval as well to ensure patient safety throughout the product life cycle. This is particularly complicated for biotechnology-based therapeutics where seemingly minor changes in process and/or raw material attributes have been shown to have a significant effect on clinical safety and efficacy of the product. This article provides a perspective on the topic of monitoring the quality of biotech therapeutics. In the backdrop of challenges faced by the regulatory agencies, the potential use of multivariate data analysis as a tool for effective monitoring has been proposed. Case studies using data from several insulin biosimilars have been used to illustrate the key concepts.

Keywords

biosimilars multivariate data analysis process monitoring product quality quality by design 

Notes

Disclaimer

ASR presently serves as the Chairman of the Committee for Advising the Drug Controller General of India on Regulation of Biotech Products. The approach stated in this article does not have any regulatory implications. The article presents merely a scientific perspective.

References

  1. 1.
    Walsh G. Biopharmaceutical benchmarks. Nat Biotechnol. 2010;28:917–24.CrossRefPubMedGoogle Scholar
  2. 2.
    DeFrancesco L. Drug pipeline 2Q13. Nat Biotechnol. 2013;31:780.CrossRefGoogle Scholar
  3. 3.
    Committee for Medicinal Products for Human Use. Guideline on similar biological medicinal products. The European Medicines Agency Evaluation of Medicines for Human Use. European Medicines Agency. EMEA/CHMP/437/04, UK, 2005.Google Scholar
  4. 4.
    Ashton G. Growing pains for biopharmaceuticals. Nat Biotechnol. 2001;19:307–12.CrossRefPubMedGoogle Scholar
  5. 5.
    Hincal F. An introduction to safety issues in biosimilars/follow-on biopharmaceuticals. J Med CBR Def. 2009;7:1–17.Google Scholar
  6. 6.
    Rathore AS. Follow-on protein products: scientific issues, developments and challenges. Trends Biotechnol. 2009;27:698–705.CrossRefPubMedGoogle Scholar
  7. 7.
    Ebbers HC, Crow SA, Vulto AG, Schellekens H. Interchangeability, immunogenicity and biosimilars. Nat Biotechnol. 2012;30:1186–90.CrossRefPubMedGoogle Scholar
  8. 8.
    Eon-Duval A, Broly H, Gleixner R. Quality attributes of recombinant therapeutic proteins: an assessment of impact on safety and efficacy as part of a quality by design development approach. Biotechnol Prog. 2012;28:608–22.CrossRefPubMedGoogle Scholar
  9. 9.
    International Conference of Harmonisation (2008) ICH Harmonised Tripartite Guideline: Q8(R1) Pharmaceutical Development (http://www.ich.org/LOB/media/MEDIA4986.pdf).
  10. 10.
    U.S. Department of Health and Human Services, Food and Drug Administration (2004) Guidance for industry: PAT—a framework for innovative pharmaceutical development, manufacturing and quality assurance (http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm070305.pdf).
  11. 11.
    Guidance for Industry (2008) Q10 Quality Systems Approach to Pharmaceutical CGMP Regulations, ICH Harmonised Tripartite Guideline, Step 4, June.Google Scholar
  12. 12.
    U.S. Department of Health and Human Services, Food and Drug Administration (2011) Guidance for industry, process validation: general principles and practices, current good manufacturing practices (CGMP), Revision 1, (http://www.fda.gov/downloads/Drugs/Guidances/UCM070336.pdf).
  13. 13.
    WHO Expert Committee on Biological Standardization. (2012). WHO Expert Committee on Biological Standardization: fifty-ninth report. World Health Organization.Google Scholar
  14. 14.
    Woodcock J, Woosley R. The FDA critical path initiative and its influence on new drug development. Annu Rev Med. 2008;59:1–12.CrossRefPubMedGoogle Scholar
  15. 15.
    Selvarasu S, Kim DY, Karimi IA, Lee DY. Combined data preprocessing and multivariate statistical analysis characterizes fed-batch culture of mouse hybridoma cells for rational medium design. J Biotechnol. 2010;150:94–100.CrossRefPubMedGoogle Scholar
  16. 16.
    Xing Z, Li Z, Chow V, Lee SS. Identifying inhibitory threshold values of repressing metabolites in CHO cell culture using multivariate analysis methods. Biotechnol Prog. 2008;24:675–83.CrossRefPubMedGoogle Scholar
  17. 17.
    Thomassen YE, van Sprang EN, van der Pol LA, Bakker WA. Multivariate data analysis on historical IPV production data for better process understanding and future improvements. Biotechnol Bioeng. 2010;107:96–104.CrossRefPubMedGoogle Scholar
  18. 18.
    Rathore AS, Mittal S, Pathak M, Mahalingam VJ. Chemometrics application in biotech processes: assessing comparability across processes and scales. J Chem Technol Biotechnol. 2014;89:1311–6.CrossRefGoogle Scholar
  19. 19.
    Bhushan N, Hadpe S, Rathore AS. Chemometrics applications in biotech processes: assessing process comparability. Biotechnol Prog. 2012;28:121–8.CrossRefPubMedGoogle Scholar
  20. 20.
    Gunther JC, Baclaski J, Seborg DE, Conner JS. Pattern matching in batch bioprocesses—comparisons across multiple products and operating conditions. Comput Che Eng. 2009;33:88–96.CrossRefGoogle Scholar
  21. 21.
    Rathore AS, Bhushan N, Hadpe S. Chemometrics applications in biotech processes: a review. Biotechnol Prog. 2011;27:307–15.CrossRefPubMedGoogle Scholar
  22. 22.
    Kirdar AO, Conner JS, Baclaski J, Rathore AS. Application of multivariate analysis toward biotech processes: case study of a cell‐culture unit operation. Biotechnol Prog. 2007;23:61–7.CrossRefPubMedGoogle Scholar
  23. 23.
    Mercier SM, Diepenbroek B, Wijffels RH, Streefland. Multivariate PAT solutions for biopharmaceutical cultivation: current progress and limitations. Trends Biotechnol. 2014;32:329–36.CrossRefPubMedGoogle Scholar
  24. 24.
    Ündey C, Ertunç S, Mistretta T, Looze B. Applied advanced process analytics in biopharmaceutical manufacturing: challenges and prospects in real-time monitoring and control. J Process Control. 2010;20:1009–18.CrossRefGoogle Scholar
  25. 25.
    Lennox B, Kipling K, Glassey J, Montague G, Willis M, Hiden H. Automated production support for the bioprocess industry. Biotechnol Prog. 2002;18:269–75.CrossRefPubMedGoogle Scholar
  26. 26.
    Rathore AS, Mittal S, Lute S, Brorson K. Chemometrics applications in biotechnology processes: predicting column integrity and impurity clearance during reuse of chromatography resin. Biotechnol Prog. 2012;28:1308–14.CrossRefPubMedGoogle Scholar
  27. 27.
    Rathore AS, Mittal S, Pathak M, Arora A. Guidance for performing multivariate data analysis of bioprocessing data: pitfalls and recommendations. Biotechnol Prog. 2014;30:967–73.CrossRefPubMedGoogle Scholar
  28. 28.
    McCamish M, Woollett G. Worldwide experience with biosimilar development. mAbs. 2011;3:212–20.CrossRefGoogle Scholar
  29. 29.
    World Health Organization. Guidelines on evaluation of similar biotherapeutic products (SBPs). 2009. http://www.who.int/biologicals/areas/biological_therapeutics/BIOTHERAPEUTICS_FOR_WEB_22APRIL2010.pdf.
  30. 30.
    Thelwell C, Longstaff C. Biosimilars: the process is the product. The example of recombinant streptokinase. J Thromb Haemost. 2014;12:1229–33.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© American Association of Pharmaceutical Scientists 2016

Authors and Affiliations

  • Anurag S. Rathore
    • 1
  • Mili Pathak
    • 1
  • Renu Jain
    • 2
  • Gaurav Pratap Singh Jadaun
    • 2
  1. 1.Department of Chemical EngineeringIndian Institute of TechnologyNew DelhiIndia
  2. 2.National Institute of BiologicalsNoidaIndia

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