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The AAPS Journal

, 21:96 | Cite as

TCPro: an In Silico Risk Assessment Tool for Biotherapeutic Protein Immunogenicity

  • Osman N. Yogurtcu
  • Zuben E. Sauna
  • Joseph R. McGill
  • Million A. Tegenge
  • Hong YangEmail author
Research Article

Abstract

Most immune responses to biotherapeutic proteins involve the development of anti-drug antibodies (ADAs). New drugs must undergo immunogenicity assessments to identify potential risks at early stages in the drug development process. This immune response is T cell-dependent. Ex vivo assays that monitor T cell proliferation often are used to assess immunogenicity risk. Such assays can be expensive and time-consuming to carry out. Furthermore, T cell proliferation requires presentation of the immunogenic epitope by major histocompatibility complex class II (MHCII) proteins on antigen-presenting cells. The MHC proteins are the most diverse in the human genome. Thus, obtaining cells from subjects that reflect the distribution of the different MHCII proteins in the human population can be challenging. The allelic frequencies of MHCII proteins differ among subpopulations, and understanding the potential immunogenicity risks would thus require generation of datasets for specific subpopulations involving complex subject recruitment. We developed TCPro, a computational tool that predicts the temporal dynamics of T cell counts in common ex vivo assays for drug immunogenicity. Using TCPro, we can test virtual pools of subjects based on MHCII frequencies and estimate immunogenicity risks for different populations. It also provides rapid and inexpensive initial screens for new biotherapeutics and can be used to determine the potential immunogenicity risk of new sequences introduced while bioengineering proteins. We validated TCPro using an experimental immunogenicity dataset, making predictions on the population-based immunogenicity risk of 15 protein-based biotherapeutics. Immunogenicity rankings generated using TCPro are consistent with the reported clinical experience with these therapeutics.

KEY WORDS

anti-drug antibodies (ADA) computational approaches immunogenicity protein-based therapeutics 

Notes

Acknowledgments

This project was supported in part by an appointment to the Research Participation Program at CBER, US Food and Drug Administration, administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the US Department of Energy and FDA.

Author Contributions

Conceptualization: ONY, ZES, JRM, MAT, HY.

Data curation: ONY, ZES, JRM.

Formal analysis: ONY.

Funding acquisition: HY, ZES.

Investigation: ONY, ZES.

Methodology: ONY, ZES, JRM, MAT, HY.

Project administration: HY, ZES, MAT.

Resources: HY, ZES.

Software: ONY.

Supervision: HY, ZES, MAT.

Validation: ONY.

Visualization: ONY.

Writing—original draft: ONY, ZES, JRM.

Writing—review and editing: ONY, ZES, JRM, MAT, HY.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Disclaimer

This article reflects the views of the authors and should not be construed to represent FDA's views or policies.

Supplementary material

12248_2019_368_MOESM1_ESM.docx (697 kb)
ESM 1 (DOCX 696 kb)

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Copyright information

© American Association of Pharmaceutical Scientists 2019

Authors and Affiliations

  • Osman N. Yogurtcu
    • 1
  • Zuben E. Sauna
    • 2
  • Joseph R. McGill
    • 2
  • Million A. Tegenge
    • 1
  • Hong Yang
    • 1
    Email author
  1. 1.Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and ResearchUS FDASilver SpringUSA
  2. 2.Office of Tissues and Advanced Therapy, Center for Biologics Evaluation and ResearchUS FDASilver SpringUSA

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