Abstract
Biological assay or bioassay is an analytical method used to measure the biological activity or potency of a biopharmaceutical or a vaccine. CMC statisticians contribute to the design, development, analysis and validation of bioassays. Skills in linear and nonlinear mixed effects modeling, design of experiments (DOE) and equivalence testing are essential to that support. This chapter describes statistical methods used to support bioassay and provides references for further explorations.
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References
Anscombe F (1973) Graphs in statistical analysis. Am Stat 27(1):17–21
Burdick RK, Borror CM, Montgomery DC (2005) Design and Analysis of Gauge R&R Studies. SIAM 143–148
Chatterjee S, Firat A (2007) Generating data with identical statistics but dissimilar graphics: a follow up to the Anscombe dataset. Am Stat 61(3):248–254
European Pharmacopeia 8th Edition, Chapter 5.3, Statistical analysis of results of biological assays and tests
Gelman A et al (2014) Bayesian data analysis. Chapman and Hall, Boca Raton
Gottschalk PG, Dunn JR (2005) The five-parameter logistic: a characterization and comparison with the four-parameter logistic. Anal Biochem 343(1):54–65
Gottschalk PG, Dunn JR (2005) Measuring parallelism, linearity, and relative potency in bioassay and immunoassay data. J Biopharm Stat 15:437–463
Hoffman D, Kringle R (2007) A total error approach for the validation of quantitative analytical methods. Pharm Res 24(6):1157–64
Hubert P et al (2007) Harmonization of strategies for the validation of quantitative analytical procedures A SFSTP proposal–part III. J Pharm Biomed Anal 45:82–96
ICH Q8(R2) Pharmaceutical development (2009)
Kirkwood T (1979) Geometric means and measures of dispersion, letter to the editor of biometrics 35: 908–909
Lansky D (2002) Strip-plot designs, mixed models, and comparisons between linear and non-linear models for microtitre plate bioassays. Dev Biol 107:11–23
Martin GP et al (2014) Stimuli to the revision process, lifecycle management of analytical procedures: method development, procedure performance qualification, and procedure performance verification Pharmacopeial Forum 39(5)
Nijius M, Van den Heuval ER (2007) Closed-form confidence intervals on measures of precision for an interlaboratory study. J Biopharm Stat 17(1):123–142
Tan C (2005) RSD and other variability measures of the lognormal distribution. Pharmaceopeial Forum 31(2):653–5
USP Chapter <111> (2015) Design and analysis of biological assays, USP 38 – NF30
USP General Chapter <1032> (2015) Design and development of biological assays, USP 38 – NF30
USP General Chapter <1033> (2015) Biological assay validation, USP 38 – NF30
USP General Chapter <1034> (2015) Analysis of biological assays, USP 38 – NF30
Acknowledgements
I would like to thank Rick Burdick for his guidance in formulating confidence bounds on intermediate precision and format variability as well as his comments throughout the chapter. I would also like to thank Stan Altan for his careful review of the chapter and for his suggestions which added further clarity to some of the content.
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Schofield, T. (2016). Lifecycle Approach to Bioassay. In: Zhang, L. (eds) Nonclinical Statistics for Pharmaceutical and Biotechnology Industries. Statistics for Biology and Health. Springer, Cham. https://doi.org/10.1007/978-3-319-23558-5_17
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DOI: https://doi.org/10.1007/978-3-319-23558-5_17
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23557-8
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