Abstract
Analytical similarity is the foundation for demonstration of biosimilarity between a proposed biosimilar product and a reference product. For this assessment, the FDA has recommended a tiered system in which quality attributes are categorized into three tiers commensurate with their risk ranking. Different approaches of varying rigor have been recommended to analyze the tiered quality attributes. Two of these approaches are the equivalence test of means, and a quality range approach that requires individual biosimilar lot values to fall in a range based on the reference product lots. However, lack of knowledge of the reference product such as target specifications, process changes, and sources of bulk materials used to produce the final product lots makes it extremely challenging to set the acceptance criteria for both of these approaches. Further confounding the issue is that there is limited published literature on the subject and the FDA draft guidance published in September 2017 was withdrawn in June 2018. In this chapter, we provide an in-depth discussion of practical issues concerning analytical similarity and statistical remedies. Focus is on (1) Statistical criteria for equivalence of means testing and quality ranges, (2) Sample size considerations; and (3) Statistical strategies to mitigate risk of correlation among the reference products lots. Finally, a list of goals is provided to be considered when developing criteria to demonstrate analytical similarity.
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References
Apostol I, Brooks PD, Mathews AJ. Application of highprecision isotope ratio monitoring mass spectrometry to identify the biosynthetic origins of proteins. Protein Sci. 2001;10:1466–9.
Berger R, Hsu J. Bioequivalence trials, intersection-union tests and equivalence confidence sets. Stat Sci. 1996;11(4):283–319.
Berkowitz SA. Analytical characterization: structural assessment of biosimilar. In: Endrenyi L, Declerck P, Chow S-C, editors. Biosimilar drug product development. Boca Raton: CRC Press; 2017.
Burdick RK. Comments on analytical similarity session. FDA-Industry Statistics Workshop, Washington DC; 2015.
Burdick RK, Coffey T, Gutka H, Gratzl G, Conlon HD, Huang C-T, Boyne M, Kuehne H. Statistical approaches to assess biosimilarity from analytical data. AAPS J. 2016;19(1):4–14. https://doi.org/10.1208/s12248-016-9968-0.
Burdick RK, Thomas N, Cheng A. Statistical considerations in demonstrating CMC analytical similarity for a biosimilar product. Stat Biopharm Res. 2017;9(3):249–57. https://doi.org/10.1080/19466315.2017.1280412.
Chow SC, Song F, Bai H. Analytical similarity assessment in biosimilar studies. AAPS J. 2016;18(3):670–7.
Dong X, Weng Y-T, Tsong Y. Adjustment for unbalanced sample size for analytical biosimilar equivalence assessment. J Biopharm Stat. 2017;27(2):220–32. https://doi.org/10.1080/10543406.2016.1265544.
EMA. Guideline on similar biological medicinal products containing biotechnology-derived proteins as active substance: non-clinical and clinical issues. 2014. http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2015/01/WC500180219.pdf. Accessed Dec 19, 2017.
EMA. Reflection paper on statistical methodology for the comparative assessment of quality attributes in drug development. 2017. http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2017/03/WC500224995.pdf. Accessed Dec 25, 2017.
FDA. Pharmaceutical cGMPs for the 21st century: a risk-based approach: Final report. 2004.
FDA. Guidance for industry: scientific considerations in demonstrating biosimilarity to a reference product. Silver Spring: The United States Food and Drug Administration. 2015. https://www.fda.gov/downloads/drugs/guidances/ucm291128.pdf. Accessed Dec 19, 2017.
FDA. FDA briefing document. Arthritis Advisory Committee Meeting; 2016 Jul 13, BLA 761042, GP2015, a proposed biosimilar to Enbrel(etanercept). 2016. https://www.fda.gov/downloads/AdvisoryCommittees/CommitteesMeetingMaterials/Drugs/ArthritisAdvisoryCommittee/UCM510493.pdf. Accessed Dec 25, 2017.
FDA. Draft Guidance to industry: statistical approach to evaluate analytical similarity. 2017. https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM576786.pdf (Link is no longer available).
FDA. FDA homepage. 2018. https://www.fda.gov/ (Search for biosimilar advisory committee). Accessed Feb 12, 2018.
Giacoletti K, Heyse J. Using proportion of similar response to evaluate correlates of protection for vaccine efficacy. Stat Methods Med Res. 2011; https://doi.org/10.1177/0962280211416299.
Hannig J, Iyer H, Patterson P. Fiducial generalized confidence intervals. J Am Stat Assoc. 2006;101:254–69.
ICH. Q1E Evaluation for stability data. 2003. http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Quality/Q1E/Step4/Q1E_Guideline.pdf.
ICH. Q9 Quality risk management. 2005. http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Quality/Q9/Step4/Q9_Guideline.pdf. Accessed Dec 19, 2017.
ICH. Q8(R2) Pharmaceutical Development. 2009a. http://www.fda.gov/downloads/Drugs/.../Guidances/ucm073507.pdf. Accessed Dec 19, 2017.
ICH. Q10 Pharmaceutical quality systems. 2009b. http://www.fda.gov/downloads/Drugs/.../Guidances/ucm073517.pdf. Accessed Dec 19, 2017.
ICH. Q11 Development and manufacture of drug substances (Chemical entities and biotechnological/biological entities). 2012. https://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Quality/Q11/Q11_Step_4.pdf. Accessed Dec 19, 2017.
Kelley K. Confidence intervals for standardized effect sizes: theory, application, and implementation. J Stat Softw. 2007;20(8):1–22.
Montes RO. Analytical similarity assessment: practical challenges and statistical perspectives. Oral presentation at 2016 Midwest Biopharmaceutical Statistics Workshop. 2016. http://www.mbswonline.com/presentationyear.php?year=2016. Accessed Jul 26, 2018.
Satterthwaite FE. An approximate distribution of estimates of variance components. Biom Bull. 1946;2:110–4.
Schuirmann DJ. A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability. J Pharmacokinet Biopharm. 1987;15:657–80.
Shen M, Wang T, Tsong Y.Statistical considerations regarding correlated lots in analytical biosimilar equivalence test. J Biopharm Stat. 2016. https://doi.org/10.1080/10543406.2016.1265541.
Tsong Y Dong X, Shen M. Development of statistical methods for analytical similarity assessment. J Biopharm Stat. 2016. https://doi.org/10.1080/10543406.2016.1272606.
Tsui K, Weerahandi S. Generalized p-values in significance testing of hypotheses in the presence of nuisance parameters. J Am Stat Assoc. 1989;84:602–7. (Corrections: 86 (1991), p. 256).
Weerahandi S. Generalized confidence intervals. J Am Stat Assoc. 1993;88:899–905. (Corrections: 89 (1994), p. 726).
Yang H, Novick S, Burdick R. On statistical approaches for demonstrating analytical similarity in the presence of correlation. PDA J Pharm Sci Technol. 2016;70(6):547–59.
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Yang, H., Burdick, R.K., Cheng, A., Montes, R.O. (2018). Statistical Considerations for Demonstration of Analytical Similarity. In: Gutka, H., Yang, H., Kakar, S. (eds) Biosimilars. AAPS Advances in the Pharmaceutical Sciences Series, vol 34. Springer, Cham. https://doi.org/10.1007/978-3-319-99680-6_17
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DOI: https://doi.org/10.1007/978-3-319-99680-6_17
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