Potency Assay Variability Estimation in Practice.
CMC statistics
bioassay
linear mixed model
method variability
potency
Journal
Pharmaceutical statistics
ISSN: 1539-1612
Titre abrégé: Pharm Stat
Pays: England
ID NLM: 101201192
Informations de publication
Date de publication:
08 Jul 2024
08 Jul 2024
Historique:
revised:
26
03
2024
received:
29
09
2023
accepted:
22
05
2024
medline:
9
7
2024
pubmed:
9
7
2024
entrez:
9
7
2024
Statut:
aheadofprint
Résumé
During the drug development process, testing potency plays an important role in the quality assessment required for the manufacturing and marketing of biologics. Due to multiple operational and biological factors, higher variability is usually observed in bioassays compared with physicochemical methods. In this paper, we discuss different sources of bioassay variability and how this variability can be statistically estimated. In addition, we propose an algorithm to estimate the variability of reportable results associated with different numbers of runs and their corresponding OOS rates under a given specification. Numerical experiments are conducted on multiple assay formats to elucidate the empirical distribution of bioassay variability.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : AstraZeneca
Informations de copyright
© 2024 AstraZeneca. Pharmaceutical Statistics published by John Wiley & Sons Ltd.
Références
FDA, “Biological Products: General, 21 C.F.R. 210.3(b)(16),” 2023 https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?fr=210.3.
USP, “<1032> Design and Development of Biological Assay,” https://doi.org/10.31003/USPNF_M1354_01_01.
C. Robinson, M. Sadick, S. Deming, S. Estdale, S. Bergelson, and L. Little, “Assay Acceptance Criteria for Multiwell‐Plate‐Based Biological Potency Assays Draft for Consultation,” Bioprocess International 12 (2014): 30–41.
J. R. White, M. Abodeely, S. Ahmed, et al., “Best Practices in Bioassay Development to Support Registration of Biopharmaceuticals,” BioTechniques 67, no. 3 (2019): 126–137.
T. A. Little, “Essentials in Bioassay Design and Relative Potency Determination,” BioPharm International 29, no. 4 (2016): 49–52.
USP, “<1034> Analysis of Biological Assays,” https://doi.org/10.31003/USPNF_M5677_02_01.
ICH, “Q14 Analytical Procedure Development. Draft Version,” 2022 https://database.ich.org/sites/default/files/ICH_Q14_Document_Step2_Guideline_2022_0324.pdf.
USP, “<111> Design and Analysis of Biological Assays,” https://doi.org/10.31003/USPNF_M98860_03_01.
ICH, “Q6B Specifications: Test Procedures and Acceptance Criteria for Biotechnological/Biological Products,” 1999 https://www.ema.europa.eu/en/documents/scientific‐guideline/ich‐q‐6‐b‐test‐procedures‐acceptance‐criteria‐biotechnological/biological‐products‐step‐5_en.pdf.
ICH, “Q2 (R1) Validation of Analytical Procedures: Text and Methodology,” 2005 https://database.ich.org/sites/default/files/Q2%28R1%29%20Guideline.pdf.
N. M. Laird and J. H. Ware, “Random‐Effects Models for Longitudinal Data,” Biometrics 38, no. 4 (1982): 963–974.
H. T. Thai, F. Mentré, N. H. Holford, C. Veyrat‐Follet, and E. Comets, “A Comparison of Bootstrap Approaches for Estimating Uncertainty of Parameters in Linear Mixed‐Effects Models,” Pharmaceutical Statistics 12, no. 3 (2013): 129–140, https://doi.org/10.1002/pst.1561.
H. Brown and R. Prescott, Applied Mixed Models in Medicine, 3rd ed. (West Sussex, UK: John Wiley & Sons, 2015).
B. G. Francq, D. Lin, and W. Hoyer, “Confidence and Prediction in Linear Mixed Models: Do Not Concatenate the Random Effects. Application in an Assay Qualification Study,” Statistics in Biopharmaceutical Research 12, no. 3 (2020): 262–272, https://doi.org/10.1080/19466315.2020.1776762.
J. Duan, M. Levine, J. Luo, and Y. Qu, “Estimation of Group Means in Generalized Linear Mixed Models,” Pharmaceutical Statistics 19, no. 5 (2020): 646–661, https://doi.org/10.1002/pst.2022.
USP, “<1033> Biological Assay Validation,” https://doi.org/10.31003/USPNF_M912_01_01.
USP, “<1010> Analytical Data—Interpretation and Treatment,” https://doi.org/10.31003/USPNF_M99740_05_01.