Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Protocol
  • Published:

Comprehensive multi-attribute method workflow for biotherapeutic characterization and current good manufacturing practices testing

Nature Protocols volume 18, pages 1056–1089 (2023)Cite this article

Subjects

Abstract

The multi-attribute method (MAM) is a liquid chromatography-mass spectrometry (LC-MS)-based method that is used to directly characterize and monitor numerous product quality attributes (PQAs) at the amino acid level of a biopharmaceutical product. MAM enables identity testing based on primary sequence verification, detection and quantitation of post-translational modifications and impurities. This ability to simultaneously and directly determine PQAs of therapeutic proteins makes MAM a more informative, streamlined and productive workflow than conventional chromatographic and electrophoretic assays. MAM relies on proteolytic digestion of the sample followed by reversed-phase chromatographic separation and high-resolution LC-MS analysis in two phases. First, a discovery study to determine quality attributes for monitoring is followed by the creation of a targeted library based on high-resolution retention time plus accurate mass analysis. The second aspect of MAM is the monitoring phase based on the target peptide library and new peak detection using differential analysis of the data to determine the presence, absence or change of any species that might affect the activity or stability of the biotherapeutic. The sample preparation process takes between 90 and 120 min, whereas the time spent on instrumental and data analyses might vary from one to several days for different sample sizes, depending on the complexity of the molecule, the number of attributes to be monitored and the information to be detailed in the final report. MAM is developed to be used throughout the product life cycle, from process development through upstream and downstream processes to quality control release or under current good manufacturing practices regulations enforced by regulatory agencies.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of the MAM workflow for analysis of biotherapeutic proteins from process development to product characterization.
Fig. 2: In-solution trypsin digestion workflow summarized for IgG1 type monoclonal antibodies.
Fig. 3: Automated trypsin digestion workflow summarized for IgG1 type monoclonal antibodies.
Fig. 4: Comparison of in-solution versus automated digestion workflows.
Fig. 5: SET summary.
Fig. 6: Peptide mapping analysis for three different biopharmaceuticals used to develop the MAM workflow.
Fig. 7: Data processing summary screenshot.
Fig. 8: SST performed on two different C18 columns by using NISTmAb tryptic digest.
Fig. 9: Evaluation of NISTmAb peptide mapping after in-solution trypsin digestion on three different UHPLC systems.
Fig. 10: Identification and integration of the oxidized peptide WQQGNVFSCSVMHEALHNHYTQK observed for an investigational in-house–produced chimeric IgG1 monoclonal antibody for the duration of the cell culture batch process.

Similar content being viewed by others

Data availability

All LC-MS data used in this paper are publicly available at the GNPS-MassIVE repository under the following accession numbers: MSV000088774 (chimeric IgG1 monoclonal antibody drug product; three replicates are provided as 1, 2 and 3); MSV000088775 (chimeric IgG1 monoclonal antibody drug product spiked with different levels of HCPs, cathepsin and rHLPL, at three different levels: 10, 100 and 1,000 ppm; three replicates are provided for each spiked level as 1, 2 and 3); and MSV000089060 (chimeric IgG1 monoclonal antibody investigational biosimilar; three replicates are provided as 1, 2 and 3).

References

  1. Yasunaga, M. Antibody therapeutics and immunoregulation in cancer and autoimmune disease. Semin. Cancer Biol. 64, 1–12 (2020).

    Article  CAS  PubMed  Google Scholar 

  2. Shim, H. Bispecific antibodies and antibody-drug conjugates for cancer therapy: technological considerations. Biomolecules 10, 360 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Shu, S. A., Wang, J., Tao, M. H. & Leung, P. S. Gene therapy for autoimmune disease. Clin. Rev. Allergy Immunol. 49, 163–176 (2015).

    Article  CAS  PubMed  Google Scholar 

  4. Islam, M. A., Kundu, S. & Hassan, R. Gene therapy approaches in an autoimmune demyelinating disease: multiple sclerosis. Curr. Gene Ther. 19, 376–385 (2020).

    Article  PubMed  Google Scholar 

  5. World Preview 2018, Outlook to 2024. Evaluate Pharma, 1–47 Available at https://www.evaluate.com/thought-leadership/pharma/evaluatepharma-world-preview-2018-outlook-2024?gclid=Cj0KCQiAmaibBhCAARIsAKUlaKTZfRwuRaj58cikLdbczZL8vgBdGJpJX2DJ2GQKWpFuGn8BKbN9ZWAaAm8OEALw_wcB (2018).

  6. Mullard, A. FDA approves 100th monoclonal antibody product. Nat. Rev. Drug Discov. 20, 491–495 (2021).

    Article  CAS  PubMed  Google Scholar 

  7. Jefferis, R. Posttranslational modifications and the immunogenicity of biotherapeutics. J. Immunol. Res. 2016, 5358272 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Xu, Y. et al. Structure, heterogeneity and developability assessment of therapeutic antibodies. MAbs 11, 239–264 (2019).

    Article  CAS  PubMed  Google Scholar 

  9. Virág, D. et al. Current trends in the analysis of post‑translational modifications. Chromatographia 83, 1–10 (2020).

    Article  Google Scholar 

  10. Graf, T. et al. Recent advances in LC-MS based characterization of protein-based bio-therapeutics - mastering analytical challenges posed by the increasing format complexity. J. Pharm. Biomed. Anal. 186, 113251 (2020).

    Article  CAS  PubMed  Google Scholar 

  11. Rogstad, S. et al. A retrospective evaluation of the use of mass spectrometry in FDA biologics license applications. J. Am. Soc. Mass Spectrom. 28, 786–794 (2017).

    Article  CAS  PubMed  Google Scholar 

  12. Apostol, I. et al. Enabling development, manufacturing, and regulatory approval of biotherapeutics through advances in mass spectrometry. Curr. Opin. Biotehcnol. 71, 206–215 (2021).

    Article  CAS  Google Scholar 

  13. Rogers, R. S. et al. Development of a quantitative mass spectrometry multi-attribute method for characterization, quality control testing and disposition of biologics. MAbs 7, 881–890 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Rogers, R. S. et al. A view on the importance of “multi-attribute method” for measuring purity of biopharmaceuticals and improving overall control strategy. AAPS J. 20, 7 (2018).

    Article  Google Scholar 

  15. Xu, W. et al. A quadrupole Dalton-based multi-attribute method for product characterization, process development, and quality control of therapeutic proteins. MAbs 9, 1186–1196 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Rogstad, S. et al. Multi-attribute method for quality control of therapeutic proteins. Anal. Chem. 91, 14170–14177 (2019).

    Article  CAS  PubMed  Google Scholar 

  17. Buettner, A., Maier, M., Bonnington, L., Bulau, P. & Reusch, D. Multi-attribute monitoring of complex erythropoetin beta glycosylation by GluC liquid chromatography-mass spectrometry peptide mapping. Anal. Chem. 92, 7574–7580 (2020).

    Article  CAS  PubMed  Google Scholar 

  18. Liu, Y. et al. Simultaneous monitoring and comparison of multiple product quality attributes for cell culture processes at different scales using a LC/MS/MS based multi-attribute method. J. Pharm. Sci. 109, 1–11 (2020).

    Article  Google Scholar 

  19. Ren, D. Advancing mass spectrometry technology in cGMP environments. Trends Biotechnol. 38, 1051–1053 (2020).

    Article  CAS  PubMed  Google Scholar 

  20. Sokolowska, I. et al. Implementation of a high-resolution liquid chromatography-mass spectrometry method in quality control laboratories for release and stability testing of a commercial antibody product. Anal. Chem. 92, 2369–2373 (2020).

    Article  CAS  PubMed  Google Scholar 

  21. Tajiri-Tsukada, M., Hashii, N. & Ishii-Watabe, A. Establishment of a highly precise multi-attribute method for the characterization and quality control of therapeutic monoclonal antibodies. Bioengineered 11, 984–1000 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Zhang, Z., Chan, P. K., Richardson, J. & Shah, B. An evaluation of instrument types for mass spectrometry-based multi-attribute analysis of biotherapeutics. MAbs 12, 1783062 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Mouchahoir, T. et al. New peak detection performance metrics from the MAM Consortium Interlaboratory Study. J. Am. Soc. Mass Spectrom. 32, 913–928 (2021).

    Article  CAS  PubMed  Google Scholar 

  24. Song, Y. E. et al. Automated mass spectrometry multi-attribute method analyses for process development and characterization of mAbs. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 1166, 122540 (2021).

    Article  CAS  Google Scholar 

  25. Evans, A. R., Hebert, A. S., Mulholland, J., Lewis, M. J. & Hu, P. ID-MAM: a validated identity and multi-attribute monitoring method for commercial release and stability testing of a bispecific antibody. Anal. Chem. 93, 9166––9173 (2021).

    Article  PubMed  Google Scholar 

  26. Jakes, C. et al. Tracking the behavior of monoclonal antibody product quality attributes using a multi-attribute method workflow. J. Am. Soc. Mass Spectrom. 32, 1998–2012 (2021).

    Article  CAS  PubMed  Google Scholar 

  27. Atouf, F., Chess, E. & McCarthy, D. Shaping tomorrow’s solutions to today’s biologics quality challenges. USP Biologics Open Forum, April 28, 2021. Available at https://www.usp.org/get-involved/provide-input/stakeholder-forums/bio-mam (2021).

  28. Liu, Y. et al. A fully integrated online platform for real time monitoring of multiple product quality attributes in biopharmaceutical processes for monoclonal antibody therapeutics. J. Pharm. Sci. 111, 358–367 (2022).

    Article  CAS  PubMed  Google Scholar 

  29. Melmer, M. et al. HILIC analysis of fluorescence-labeled N-glycans from recombinant biopharmaceuticals. Anal. Bioanal. Chem. 398, 905–914 (2010).

    Article  CAS  PubMed  Google Scholar 

  30. Fekete, S., Beck, A., Veuthey, J. L. & Guillarme, D. Ion-exchange chromatography for the characterization of biopharmaceuticals. J. Pharm. Biomed. Anal. 113, 43–55 (2015).

    Article  CAS  PubMed  Google Scholar 

  31. Rustandi, R. R., Washabaugh, M. W. & Wang, Y. Applications of CE SDS gel in development of biopharmaceutical antibody-based products. Electrophoresis 29, 3612–3620 (2008).

    Article  CAS  PubMed  Google Scholar 

  32. Wang, Y. et al. Simultaneous monitoring of oxidation, deamidation, isomerization, and glycosylation of monoclonal antibodies by liquid chromatography-mass spectrometry method with ultrafast tryptic digestion. MAbs 8, 1477–1486 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Wang, T. et al. Application of a quantitative LC-MS multiattribute method for monitoring site-specific glycan heterogeneity on a monoclonal antibody containing two N-linked glycosylation sites. Anal. Chem. 89, 3562–3567 (2017).

    Article  CAS  PubMed  Google Scholar 

  34. Bomans, K. et al. Multi-attribute monitoring of antibody modifications by semi-automated liquid chromatography mass spectrometry peptide mapping. Am. Pharm. Rev. Available at https://www.americanpharmaceuticalreview.com/Featured-Articles/331529-Multi-Attribute-Monitoring-of-Antibody-Modifications-by-Semi-Automated-Liquid-Chromatography-Mass-Spectrometry-Peptide-Mapping/ (2016).

  35. Ogata, Y. et al. eAutomated multi-attribute method sample preparation using high-throughput buffer exchange tips. Rapid Commun. Mass Spectrom. 36, e9222. (2021).

    PubMed Central  Google Scholar 

  36. Hao, Z. et al. Multi-attribute method performance profile for quality control of monoclonal antibody therapeutics. J. Pharm. Biomed. Anal. 205, 114330 (2021).

    Article  CAS  PubMed  Google Scholar 

  37. Dong, J. et al. High-throughput, automated protein A purification platform with multiattribute LC-MS analysis for advanced cell culture process monitoring. Anal. Chem. 88, 8673–8679 (2016).

    Article  CAS  PubMed  Google Scholar 

  38. Millán-Martín, S. et al. Inter-laboratory study of an optimised peptide mapping workflow using automated trypsin digestion for monitoring monoclonal antibody product quality attributes. Anal. Bioanal. Chem. 412, 6833–6848 (2020).

  39. Arndt, J. R. et al. High-resolution ion-mobility-enabled peptide mapping for high-throughput critical quality attribute monitoring. J. Am. Soc. Mass Spectrom. 32, 2019–2032 (2021).

    Article  CAS  PubMed  Google Scholar 

  40. Jakes, C. et al. Consistent results for peptide mapping and monitoring across three systems of the Vanquish UHPLC platform (AN-001123). Thermo Fisher Scientific Available at https://www.thermofisher.com/es/en/home/products-and-services/promotions/industrial/simplify-biopharma-hr-mam-workflow.html (2022).

  41. Qian, C., Niu, B., Jimenez, R. B., Wang, J. & Albarghouthi, M. Fully automated peptide mapping multi-attribute method by liquid chromatography-mass spectrometry with robotic liquid handling system. J. Pharm. Biomed. Anal. 198, 113988 (2021).

    Article  CAS  PubMed  Google Scholar 

  42. Rogers, R., Swenson, S., Khuu-Duong, K. & Zhang, T. Mass spectrometry-based process analytical technologies for cell therapies. MAM Consortium Available at http://mamconsortium.org/ (2021).

  43. Carillo, S., Criscuolo, A., Fussl, F., Cook, K. & Bones, J. Intact multi-attribute method (iMAM): a flexible tool for the analysis of monoclonal antibodies. Eur. J. Pharm. Biopharm. 177, 241–248 (2022).

    Article  CAS  PubMed  Google Scholar 

  44. Yu, L. X. et al. Understanding pharmaceutical quality by design. AAPS J. 16, 771–783 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Zhang, Y. & Yang, H. Bringing Multi-Attribute Method (MAM) to the next level. ASMS 2021, Pennsylvania Convention Center (November 2021). Available at https://www.asms.org/publications/abstracts-and-proceedings (2021).

  46. Yang, H. et al. The MAM 2.0 workflow enables seamless transition from research and development to quality control (AN000463). Thermo Fisher Scientific Available at https://www.thermofisher.com/es/en/home/products-and-services/promotions/industrial/simplify-biopharma-hr-mam-workflow.html (2022).

  47. Robinson, C. J. & Jones, C. Quality control and analytical techniques for biopharmaceuticals. Bioanalysis 3, 81–95 (2011).

    Article  CAS  PubMed  Google Scholar 

  48. Beck, A., Wagner-Rousset, E., Ayoub, D., Van Dorsselaer, A. & Sanglier-Cianferani, S. Characterization of therapeutic antibodies and related products. Anal. Chem. 85, 715–736 (2013).

    Article  CAS  PubMed  Google Scholar 

  49. Berkowitz, S. A., Engen, J. R., Mazzeo, J. M. & Jones, G. B. Analytical tools for characterizing biopharmaceuticals and the implications for biosimilars. Nat. Rev. Drug Discov. 11, 527–540 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Zhang, X., Chemmalil, L., Ding, J., Mussa, N. & Li, Z. Imaged capillary isoelectric focusing in native condition: a novel and successful example. Anal. Biochem. 537, 13–19 (2017).

    Article  CAS  PubMed  Google Scholar 

  51. Zhang, L. et al. Characterization and elimination of artificial non-covalent light Chain dimers in reduced CE-SDS analysis of pertuzumab. J. Pharm. Biomed. Anal. 190, 113527 (2020).

    Article  CAS  PubMed  Google Scholar 

  52. Esterman, A. L., Katiyar, A. & Krishnamurthy, G. Implementation of USP antibody standard for system suitability in capillary electrophoresis sodium dodecyl sulfate (CE-SDS) for release and stability methods. J. Pharm. Biomed. Anal. 128, 447–454 (2016).

    Article  CAS  PubMed  Google Scholar 

  53. Fekete, S., Guillarme, D., Sandra, P. & Sandra, K. Chromatographic, electrophoretic, and mass spectrometric methods for the analytical characterization of protein biopharmaceuticals. Anal. Chem. 88, 480–507 (2016).

    Article  CAS  PubMed  Google Scholar 

  54. Zhu-Shimoni, J. et al. Host cell protein testing by ELISAs and the use of orthogonal methods. Biotechnol. Bioeng. 111, 2367–2379 (2014).

  55. EMA. ICH Topic Q 6 B Specifications: Test Procedures and Acceptance Criteria for Biotechnological/Biological Products. Available at https://www.ema.europa.eu/en/documents/scientific-guideline/ich-q-6-b-test-procedures-acceptance-criteria-biotechnological/biological-products-step-5_en.pdf (1999).

  56. Sitasuwan, P. et al. Enhancing the multi-attribute method through an automated and high-throughput sample preparation. MAbs 13, e1978131 (2021).

    Article  Google Scholar 

  57. Dong, M. W. Tryptic mapping by reversed phase liquid chromatography. Adv. Chromatogr. 32, 21–51 (1992).

    CAS  PubMed  Google Scholar 

  58. Cho, I. H. et al. Evaluation of the structural, physicochemical, and biological characteristics of SB4, a biosimilar of etanercept. MAbs 8, 1136–1155 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Stavenhagen, K. et al. Site-specific N- and O-glycosylation analysis of atacicept. MAbs 11, 1053–1063 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Guapo, F., Strasser, L., Millán-Martín, S., Anderson, I. & Bones, J. Fast and efficient digestion of adeno associated virus (AAV) capsid proteins for liquid chromatography mass spectrometry (LC-MS) based peptide mapping and post translational modification analysis (PTMs). J. Pharm. Biomed. Anal. 207, 1–7 (2021).

    Google Scholar 

  61. Toole, E. N. et al. Rapid highly-efficient digestion and peptide mapping of adeno-associated viruses. Anal. Chem. 93, 10403–10410 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Ren, D. et al. An improved trypsin digestion method minimizes digestion-induced modifications on proteins. Anal. Biochem. 392, 12–21 (2009).

    Article  CAS  PubMed  Google Scholar 

  63. Zhou, M., Gucinski, A. C. & Boyne, M. T. 2nd Performance metrics for evaluating system suitability in liquid chromatography—Mass spectrometry peptide mass mapping of protein therapeutics and monoclonal antibodies. MAbs 7, 1104–1117 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. FDA. Part 11, Electronic Records; Electronic Signatures - Scope and Application. Guidance for Industry. (September 2003). Available at https://www.fda.gov/regulatory-information/search-fda-guidance-documents/part-11-electronic-records-electronic-signatures-scope-and-application (2003).

  65. Mesmin, C., Manache-Alberici, L. & Jones, J. Validation of LC-MS Multi-Attribute Method (MAM) Supporting Biopharma Process Characterization. Available at https://www.biopharminternational.com/view/validation-lc-ms-multi-attribute-method-mam-supporting-biopharma-process-characterization (2020).

  66. Rogers, R. Case Study: Automated Software-Driven Implementation of the Multi-Attribute Method (MAM) for Biotherapeutic Characterization. Available at https://www.genedata.com/fileadmin/4.Resources/Case-studies/Ex_Just_MAM_19E03.pdf (2019).

  67. Dong, Q. et al. The NISTmAb tryptic peptide spectral library for monoclonal antibody characterization. MAbs 10, 354–369 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Schiel, J. E., Davis, D. L. & Borisov, O. V. State-of-the-Art and Emerging Technologies for Therapeutic Monoclonal Antibody Characterization Volume 2. Biopharmaceutical Characterization: The NISTmAb Case Study. ACS Symposium Series. Vol. 1201 Available at https://pubs.acs.org/doi/10.1021/bk-2015-1201 (2015).

  69. Mouchahoir, T., Rogers, R. & Schiel, J. Interlaboratory performance metrics from the MAM Consortium New Peak Detection Round Robin Study. 15th Symposium on the Practical Applications of Mass Spectrometry in the Biotechnology Industry (San Francisco, CA) Available at https://www.nist.gov/publications/new-peak-detection-performance-metrics-mam-consortium-interlaboratory-study (2018).

  70. Rogers, R. MS in QC: a single multi-attribute method for quality control and release testing of biologics. CASSS MS, Boston, 24 September 2013.

  71. Yandrofski, K. et al. Interlaboratory studies using the NISTmAb to advance biopharmaceutical structural analytics. Front. Mol. Biosci. 9, 876780 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge Thermo Fisher Scientific for instrument access and financial support.

Author information

Authors and Affiliations

  1. National Institute for Bioprocessing Research and Training, Dublin, Ireland

    Silvia Millán-Martín, Craig Jakes, Sara Carillo & Jonathan Bones

  2. School of Chemical and Bioprocess Engineering, University College Dublin, Dublin, Ireland

    Craig Jakes & Jonathan Bones

  3. Umoja Biopharma, Seattle, WA, USA

    Richard Rogers

  4. Amgen Inc., Process Development, Thousand Oaks, CA, USA

    Da Ren

Authors
  1. Silvia Millán-Martín
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Craig Jakes
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sara Carillo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Richard Rogers
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Da Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jonathan Bones
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.J. and S.C. codeveloped the analytical method, acquired data and reviewed and edited the manuscript. S.M.-M. performed data analysis and interpretation and wrote the manuscript. J.B. codeveloped the analytical method, performed data interpretation, edited the manuscript and was the academic lead. R.R. and D.R. contributed to the writing and edited the manuscript.

Corresponding author

Correspondence to Jonathan Bones.

Ethics declarations

Competing interests

J.B. received funding to support undertaking this study as part of a funded collaboration between NIBRT and Thermo Fisher Scientific. S.C., C.J. and S.M.-M. are employed on this collaborative project. Beyond this, the authors are not aware of any affiliations, memberships, funding or financial holdings that might be perceived as affecting the objectivity of this article. The authors declare no competing financial interests.

Peer review

Peer review information

Nature Protocols thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Key references using this protocol

Jakes, C. J. Am. Soc. Mass Spectrom. 32, 1998–2012 (2021): https://doi.org/10.1021/jasms.0c00432

Supplementary information

Supplementary Information

Supplementary Figs. 1–4 and Table 1

Supplementary Data 1

Data processing walk-through details

Supplementary Data 2

SST report obtained for Hypersil Gold 150 × 2.1-mm, 3-µm column, including all evaluated parameters and monitored quality attributes

Supplementary Data 3

SST report obtained for Acclaim C18, 250 × 2.1-mm, 2.2-µm column, including all evaluated parameters and monitored quality attributes

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Millán-Martín, S., Jakes, C., Carillo, S. et al. Comprehensive multi-attribute method workflow for biotherapeutic characterization and current good manufacturing practices testing. Nat Protoc 18, 1056–1089 (2023). https://doi.org/10.1038/s41596-022-00785-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-022-00785-5

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing