Pharmaceutical Research

, Volume 20, Issue 11, pp 1885–1900

Recommendations for the Bioanalytical Method Validation of Ligand-Binding Assays to Support Pharmacokinetic Assessments of Macromolecules

  • Binodh DeSilva
  • Wendell Smith
  • Russell Weiner
  • Marian Kelley
  • JoMarie Smolec
  • Ben Lee
  • Masood Khan
  • Richard Tacey
  • Howard Hill
  • Abbie Celniker
Article

Abstract

Purpose.With this publication a subcommittee of the AAPS Ligand Binding Assay Bioanalytical Focus Group (LBABFG) makes recommendations for the development, validation, and implementation of ligand binding assays (LBAs) that are intended to support pharmacokinetic and toxicokinetic assessments of macromolecules.

Methods. This subcommittee was comprised of 10 members representing Pharmaceutical, Biotechnology, and the contract research organization industries from the United States, Canada, and Europe. Each section of this consensus document addresses a specific analytical performance characteristic or aspect for validation of a LBA. Within each section the topics are organized by an assay's life cycle, the development phase, pre-study validation, and in-study validation. Because unique issues often accompany bioanalytical assays for macromolecules, this document should be viewed as a guide for designing and conducting the validation of ligand binding assays.

Results. Values of ±20% (25% at the lower limit of quantification [LLOQ]) are recommended as default acceptance criteria for accuracy (% relative error [RE], mean bias) and interbatch precision (%coefficient of variation [CV]). In addition, we propose as secondary criteria for method acceptance that the sum of the interbatch precision (%CV) and the absolute value of the mean bias (%RE) be less than or equal to 30%. This added criterion is recommended to help ensure that in-study runs of test samples will meet the proposed run acceptance criteria of 4-6-30. Exceptions to the proposed process and acceptance criteria are appropriate when accompanied by a sound scientific rationale.

Conclusions. In this consensus document, we attempt to make recommendations that are based on bioanalytical best practices and statistical thinking for development and validation of LBAs.

best practices bioanalytical assay biological matrices consensus document immunoassay 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    V. P. Shah, K. K. Midha, S. V. Dighe, I. J. McGiveray, J. P. Skelly, A. Yacobi. T. Laylogg, C.T. Viswanathan, C.E. Cook, R.D. McDowall, K.A. Putman, and S. Spector. Analytical methods validation: bioavailability, bioequivalence, and pharmacokinetic studies. J. Pharm. Sci. 81:309-312 (1992).Google Scholar
  2. 2.
    V. P. Shah, K. K. Midha, S. Dighe, I. J. McGilveray, J. P. Skelly, A. Yacobi, T. Layloff, C. T. Viswanathan, C. E. Cook, R. D. McDowall, K. A. Pittman, and S. Spector. Analytical methods validation: bioavailability, bioequivalence, and pharmacokinetic studies. Pharm. Res. 9:588-592 (1992).Google Scholar
  3. 3.
    J. W. A. Findlay, W. C. Smith, J. W. Lee, G. D. Nordblom, I. Das, B. S. DeSilva, M. N. Khan, and R. R. Bowsher. Validation of Immunoassays for bioanalysis: A pharmaceutical industry perspective. J. Pharm. Biomed. Anal. 21:1249-1273 (2000).Google Scholar
  4. 4.
    C. M. Riley and T. W. Rosanke. Development of validation of analytical methods: progress in pharmaceutical and biomedical analysis (vol 3) Elsevier (Pergamon), NY 1996.Google Scholar
  5. 5.
    V. P. Shah, K. K. Midha, S. Dighe, I. J. McGilveray, J. P. Skelly, A. Yacobi, T. Layloff, C. T. q Viswanathan, C. E. Cook, and R. D. McDowall. Analytical methods validation: bioavailability, bioequivalence and pharmacokinetics studies. Conference Report. Eur J Drug Metabol Pharmacokinetics 16:249-255 (1991).Google Scholar
  6. 6.
    Guideline on validation of analytical procedures: definitions and terminology International Conference of Harmonization (ICH) of Technical Requirements for the Registration of Pharmaceuticals for Human Use Geneva 1995 (1996).Google Scholar
  7. 7.
    V. P. Shah, K. K. Midha, J.W.A Findlay, H. M Hill, J. D. Hulse, I. J McGilvary, G. McKay, K. J. Miller, R. N. Patnaik, M.L. Powell, A. Tonnelli, C. T. Viswanathan, and A. Yacobi. Bioanalytical method validation. A revisit with a decade of progress. Pharm. Res. 17:1551-1557 (2000).Google Scholar
  8. 8.
    K. J. Miller, R. R. Bowsher, A. Celniker, J. Gibbons, S. Gupta, J. W. Lee, J. S. J. Swanson, W. C. Smith, and R. S. Weiner. Workshop on Bioanalytical Methods Validation for Macromolecules: Summary Report. Pharm. Res. 18:1373-1383 (2001).Google Scholar
  9. 9.
    Guidance for the Industry. Bioanalytical Method Validation US Department of Health and Human Services FDA (CDER) and (CVM) May 2001.Google Scholar
  10. 10.
    J. O. Westgard. Points of care in using statistics in method comparison studies. Clin. Chem. 44:2240-2242 (1998).Google Scholar
  11. 11.
    H. Hubert, P. Chiap, J. Crommen, B. Boulanger, E. Chapuzet, N. Mercier, S. Bervoas-Martin, P. Chevalier, D. Grandjean, P. Lagorce, M. Lallier, M. C. Laparra, M. Laurentie, and J. C. Nivet. The SFSTP guide on the validation of chromatographic methods for drug analysis: from the Washington Conference to the laboratory. Analytica Chimica Acta. 391:135-148 (1999).Google Scholar
  12. 12.
    R. Kringle and D. Hoffman. Stability methods for assessing stability of compounds in whole blood for clinical bioanalysis. Drug Info J. 35:1261-1270 (2001).Google Scholar
  13. 13.
    U. Timm, M. Wall, and D. Dell. A new approach for dealing with the stability of drugs in biological fluids. J. Pharm. Sci. 74:972-977 (1985).Google Scholar
  14. 14.
    D. Rodbard, Y. Feldman, M. L. Jaffe, and L. E. M. Miles. Kinetics of Two-Site Immunoradiometric (Sandwich) Assays-II. Immunochem. 15:77-82 (1978).Google Scholar
  15. 15.
    B. D. Plikaytis, P. F. Holder, L. B. Pais, S. E. Maslanka, L. L. Gheesling, and G. M. Carlone. Determination of parallelism and nonparallelism in bioassay dilution curves. J. Clin. Microbiol. 32:2441-2447 (1994).Google Scholar
  16. 16.
    R. L. Placket and J. P. Burman. The design of optimum multifactorial experiments. Biometrica 33:305-325 (1946).Google Scholar
  17. 17.
    J. M. Bland and D. G. Altman. Measuring agreement in method comparison studies. Stat Meth Med Res 8:135-160 (1999).Google Scholar
  18. 18.
    C. Hartmann, J. Smeyers-Verbeke, W. Penninckx, Y. Vander Heyden, P. Venkeerberghen, and D. L. Massart. Reappraisal of hypothesis testing for method validation; Detection of systematic error by comparing the means of two methods or two laboratories. Analytical Chem. 67:4491-4499 (1995).Google Scholar
  19. 19.
    S. R. Searle, G. Casella, and C. E. McCulloch. Variance Components Chapter 3. John Wiley & Sons, Inc, New York, NY (1992).Google Scholar
  20. 20.
    R. W. Mee. β-expectation and β-content tolerance limits for balanced one-way ANOVA random model. Technometrics 26:251-254 (1984).-Google Scholar

Copyright information

© Plenum Publishing Corporation 2003

Authors and Affiliations

  • Binodh DeSilva
    • 1
  • Wendell Smith
    • 2
  • Russell Weiner
    • 3
  • Marian Kelley
    • 4
  • JoMarie Smolec
    • 5
  • Ben Lee
    • 6
  • Masood Khan
    • 7
  • Richard Tacey
    • 8
  • Howard Hill
    • 9
  • Abbie Celniker
    • 10
  1. 1.Amgen IncThousand Oaks
  2. 2.Lilly Research LaboratoriesGreenfield
  3. 3.Bristol-Myers SquibbPrinceton
  4. 4.Johnson and Johnson PharmaceuticalRaritan
  5. 5.Alta Analytical LaboratorySan Diego
  6. 6.Pfizer Global Research and DevelopmentAnn Arbor
  7. 7.Covance LaboratoriesChantilly
  8. 8.PPD DevelopmentRichmondVirginia
  9. 9.HLSCambridgeshireUK
  10. 10.Millennium PharmaceuticalsCambridge

Personalised recommendations