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The AAPS Journal

, 21:105 | Cite as

Retrospective Analysis of Bioanalytical Method Validation Approaches in Biosimilar Biological Product Development

  • O. N. Obianom
  • Theingi M. ThwayEmail author
  • S. J. Schrieber
  • O. O. Okusanya
  • Y. M. Wang
  • S. M. Huang
  • I. Zineh
Research Article
  • 390 Downloads

Abstract

Development and validation of a bioanalytical method for biosimilar biological product development (BPD) can be challenging. It requires the development of a bioanalytical method that reliably and accurately measures both proposed biosimilar and reference products in a biological matrix. This survey summarizes the current state of bioanalysis in BPD. Bioanalytical data from 28 biosimilar biologic license applications submitted to U.S. Food and Drug Administration (FDA) up to December 2018 were analyzed. The aim of the analysis was to provide (i) a summary of the bioanalytical landscape for BPD, (ii) a cumulative review of bioanalytical method validation approaches to aid in understanding how a specific method was selected, and (iii) a summary of data regarding bioanalytical bias differences between products. Results show diversity of the bioanalytical approaches used, as well as the observed differences in bioanalytical bias. Our findings highlight the need for understanding the critical aspects of BPD bioanalysis and clarifying BPD bioanalytical best practices, which could help ensure consistent method validation approaches in the BPD community.

Keywords

Biosimilar bioanalysis Therapeutic biologics 351(k) BLAs Bioanalytical method comparability 

Abbreviations

ARP

authentic reference product

Bias

%difference from accuracy

BLA

biologic license application

BPD

biosimilar biological development program

CRO

contract research organization

CV

coefficient of variation

ELISA

enzyme-linked immunosorbent assay

LBA

ligand binding assay

MSD

Meso Scale Discovery

PK

pharmacokinetic

QC

quality control

PBP

proposed biosimilar product

USRP

US reference product

WHO

World Health Organization

Notes

Acknowledgments

The authors thank Joanne Berger, FDA Library, and Daniel Sloper, NCTR, for manuscript editing assistance and Dr. Elimika Pfu Fletcher, Office of Clinical Pharmacology, FDA for her critical review of the manuscript.

References

  1. 1.
    Thway TM. Fundamentals of large-molecule protein therapeutic bioanalysis using ligand-binding assay. Bioanalysis. 2016;8(1):11–7.CrossRefGoogle Scholar
  2. 2.
    O’Hara DM, Theobald V, Egan AC, Usansky J, Krishna M, TerWee J, et al. Ligand binding assays in the 21st century laboratory: recommendations for characterization and supply of critical reagents. AAPS J. 2012;14(2):316–28.CrossRefGoogle Scholar
  3. 3.
    U.S. Food and Drug Administration’s Guidance for Industry titled Clinical Pharmacology Data to Support a Demonstration of Biosimilarity to a Reference Product published on December 2016.Google Scholar
  4. 4.
    U.S. Food and Drug Administration’s Guidance for Industry titled Bioanalytical Method Validation published on May 2018.Google Scholar
  5. 5.
    Marini JC, Anderson M, Cai XY, Chappell J, Coffey T, Gouty D, et al. Systematic verification of bioanalytical similarity between a biosimilar and a reference biotherapeutic: committee recommendations for the development and validation of a single ligand-binding assay to support pharmacokinetic assessments. AAPS J. 2014;16(6):1149–58.CrossRefGoogle Scholar
  6. 6.
    Islam R. Bioanalytical challenges of biosimilars. Bioanalysis. 2014;6(3):349–56.CrossRefGoogle Scholar
  7. 7.
    Wang X, Chen L. Challenges in bioanalytical assay for biosimilars. Bioanalysis. 2014;6(16):2111–3.CrossRefGoogle Scholar
  8. 8.
    Thway TM, Macaraeg C, Calamba D, Patel V, Tsoi J, Ma M, et al. Applications of a planar electrochemiluminescence platform to support regulated studies of macromolecules: benefits and limitations in assay range. J Pharm Biomed Anal. 2010;51(3):626–32.CrossRefGoogle Scholar
  9. 9.
    Wang YC, Wang Y, et al. Role of modeling and simulation in the development of novel and biosimilar therapeutic proteins. J Pharm Sci. 2019;108(1):73–77.Google Scholar
  10. 10.
    R Core Team. R: a language and environment for statistical computing. R Foundation for statistical computing. Vienna; 2013. http://www.R-project.org/
  11. 11.
    Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer-Verlag; 2016.CrossRefGoogle Scholar
  12. 12.
    Thway TM, Macaraeg C, Eschenberg M, Ma M. In silico evaluation of the potential impact of bioanalytical bias difference between two therapeutic protein formulations for pharmacokinetic assessment in a biocomparability study. AAPS J. 2015;17(3):684–90.CrossRefGoogle Scholar

Copyright information

© American Association of Pharmaceutical Scientists 2019

Authors and Affiliations

  • O. N. Obianom
    • 1
  • Theingi M. Thway
    • 1
    Email author
  • S. J. Schrieber
    • 2
  • O. O. Okusanya
    • 1
  • Y. M. Wang
    • 1
  • S. M. Huang
    • 1
  • I. Zineh
    • 1
  1. 1.Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research,U.S. Food and Drug AdministrationSilver SpringUSA
  2. 2.Office of New Drugs, Center for Drug Evaluation and ResearchU.S. Food and Drug AdministrationSilver SpringUSA

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