Cellular assays to bridge the gap in immunogenicity prediction 

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Dr. Urmi Roy, Global Product Manager for Flow Cytometry Reagents, Miltenyi Biotec, explains why immunogenicity prediction is a key consideration for therapeutic development. 

Biologic drugs as therapy have improved the treatment of many diseases, but their success is often tempered by antidrug antibodies (ADAs), which specifically recognise the therapeutic molecule as a foreign antigen. Often clinically benign, ADAs can impact not only the pharmacokinetics and pharmacodynamics, but also efficacy of the biotherapeutic1,2, and can result in hypersensitivity reactions or even anaphylactic shock3. Key for successful development of therapeutics, therefore, is immunogenicity prediction. 

Broadly speaking, these unwanted immune responses are caused by the product, or by the patients themselves. The former can include dose, route, or frequency of administration, as well as its formulation, stability, and degree of humanisation; the latter comprises such factors as host immune system, genetic background, history of infection, and other medications. 

Many in silico, in vitro and in vivo models have been developed to predict different aspects of immunogenicity of therapeutic proteins5–7, but current understanding of ADA induction complicates the determination of risk factors. Prediction of the clinical consequences of a new biologic drug can therefore be challenging. 

Reliable in vitro models can complement in silico prediction and avoid over- or underestimation of safety risks in preclinical studies to support early and better immunogenicity prediction. Such models allow high-throughput screening in line with the 3Rs principle, and may enable a better long-term understanding of the human immune system. For this reason, regulatory authorities like the EMA and FDA have recently highlighted their importance and benefits in assessing immunogenicity before clinical trials8,9 

Commonly used in vitro assays and considerations

Usually, human primary immune cells are employed for in vitro biological assays for immunogenicity prediction, and though they are generally obtained from healthy donors, samples from specific subpopulations of patients can be of interest in the study of disease-specific immunogenic reactions. It’s convenient to divide these assays into four types based on the cell subset used: 

  • Whole blood or PBMC
  • Isolated CD4+ or CD8+ T cells
  • Monocyte-derived dendritic cell (MoDCs)
  • Mixtures of MoDCsand T cells, using different ratios 

These cells allow different types of assays to be performed, covering the T cell-mediated immune response from antigen uptake to T cell proliferation. So, for example, HLA binding (i.e. MAPPS) assays use purified HLA-II peptides of the protein of interest to complement in silico identification and evaluation of the binding affinity of HLA-II peptide epitopes. A T cell activation and proliferation (i.e. PBMC, DC:T) assay makes use of APC surface expression of specific antigens, eg. MHC-II peptides. These are recognised by T cells, resulting in proliferation. The antigen priming of CD4+ T cells by mature MoDCs leads to the proliferation of CD4+ T cells in in-vitro PBMC assays. In the case of cytokine release, pro-inflammatory cytokines, such as TNFα, IFNɣ, IL-2, IL-4, IL-6, IL-8, and IL-10 are released consecutive to the co-activation of DCs and T cells. Such cytokine secretion is involved in the induction of immune reactions to foreign proteins at the injection site. 

Of course, the vast diversity of HLA alleles makes it difficult to perfectly predict the immunogenicity of a therapeutic with respect to the entire human population. Hence, most in vitro risk prediction should of necessity involve a population of donors with diverse HLA alleles. Another challenge is that  extended culturing of fresh whole blood is problematic in assessing donors due to high background and increased cell death. Isolated PBMCs are more useful, since these can be freshly isolated from a donor or thawed from a cryopreserved stock. PBMC stimulation requires less hands-on work, but on the other hand makes controlling the ratio of antigen-presenting cells to T cells difficult10, 11. Isolated cellular subsets can allow better control in terms of assay set-up, but care must be taken to ensure minimal cellular damage, maximum yield, and standardised processing. Magnetic-based cell isolation (MACS technology) is state-of-the-art here.  

Flow cytometry can be superior to commonly used thymidine incorporation in measuring cell proliferation since the former can provide additional information through characterising and differentiating between T effector subsets based on the surface marker expression11. This increases the accuracy of the risk assessment because it can be used to identify Treg cells capable of suppressing immune responses12. Moreover, modern flow cytometers (such as the  MACSQuant X) allow higher throughput and standardisation by minimising operator variability. Meanwhile, use of recombinant antibodies (like REAfinityTM Recombinant Antibodies) significantly reduces reproducibility-related risks. 

Qualitative assessment of T cell activation-related cytokines can also be performed simultaneously using techniques like ELISA and multiplex fluorescence-based analysis. Multiplex cytokine-assay methods are preferable, however, since they allow for the screening of many different cytokines concurrently. For the purposes of immunogenicity risk assessment, the levels of pro-inflammatory innate and T cell activation-related cytokines, such AS IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-13, IL-17, IFN-γ, and TNF-α, can all be assessed simultaneously with commercially available single or multiplexed cytokine detection kits, including MACSPlex kits, which also support automated data analysis. 

Naturally, in vitro assays are not an identical model of the many and complex processes occurring in a living organism, but they are an invaluable first indicator of possible inherent immunogenic risk. Each of the assays outlined can provide crucial insight into the potential for clinical immunogenicity and, accordingly, should form a standard part of risk assessment and bioanalytical strategy planning in efforts to mitigate this unwanted immune response. 

Volume 22, Issue 4 – Fall 2021 

Figure 1: Immune cells involved in ADA generation and in-vitro immunogenicity prediction assays (inspired by Cohen & Chung, 20214)

About the author 

Before joining Miltenyi Biotec as a Global Product Manager for Flow Cytometry Reagents in 2018, Dr. Urmi Roy worked as a Post Doc at the Helmholtz Centre Braunschweig, Germany, where she specialised in mucosal immunology/gut microbiota. She received her Ph.D. in Immunology at the TU Braunschweig in 2017. 


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  2. Gunn G III, Sealey DC, Jamali F, Meibohm B, Ghosh S, Shankar G. From the bench to clinical practice: understanding the challenges and uncertainties in immunogenicity testing for biopharmaceuticals. Clin. Exp. Immunol. 184(2), 137–146 (2016). 
  3. Scherer K, Spoerl D, Bircher AJ. Adverse drug reactions to biologics. J. Dtsch. Dermatol. Ges. 8(6), 411–426 (2010). 
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  7. Wullner D, Zhou L, Bramhall E, Kuck A, Goletz TJ, Swanson S, et al. Considerations for optimization and validation of an in vitro PBMC derived T cell assay for immunogenicity prediction of biotherapeutics. Clin Immunol. 137(1):5–14 (2010). 
  8. EMA. Guideline on Immunogenicity Assessment of Therapeutic Proteins (2017). https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-immunogenicity-assessment-therapeutic-proteins-revision-1 en.pdf 
  9. US FDA. Immunogenicity Assessment for Therapeutic Protein Products (2014). https://www.fda.gov/media/85017/download 
  10. Brodin P, Davis MM. Human immune system variation. Nat Rev Immunol. 17(1):21–9 (2017). 
  11. Wullner D, Zhou L, Bramhall E, Kuck A, Goletz TJ, Swanson S, et al. Considerations for optimization and validation of an in vitro PBMC derived T cell assay for immunogenicity prediction of biotherapeutics. Clin Immunol. 137(1):5–14 (2010). 
  12. Dominguez-Villar M, Hafler DA. Regulatory T cells in autoimmune disease. Nat Immunol. 19(7):665–73 (2018).

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