Monoclonal antibody drugs are popular therapeutics for a plethora of disease conditions, from cancer to autoimmune disorders. Antibodies administered as drugs are still immunogenic, meaning that they elicit an immune response from the body. Several factors contribute to the immunogenicity of a drug, including the product origin; purity, mechanism, and stability; and sequence of biologic product, mode of administration, and dose (Lu et.al., 2020). Evaluating a drug’s immunogenicity in-vitro helps researchers understand a drug’s safety and efficacy in vivo.
ADAs and Adverse Immune Reactions
Anti-drug antibodies (ADAs) are formed when the body recognizes a drug as a foreign antigen, triggering a humoral immune response. ADAs can inactivate the drug and increase drug clearance, lowering its efficacy and increasing the risk of an adverse immune reaction to the treatment. These responses vary depending on the person. However, in silico and in vitro assessment of immunogenicity can help evaluate risk of an antibody.
In silico prediction of adverse reactions
In silico assessment uses computer modeling and predictive algorithms to forecast the drug epitopes likely to be presented by T cells to elicit an immune response. T cell epitopes are found in either the therapeutic antibody itself or as impurities in the formulation. Efforts to predict T cell epitopes focus on characterizing those epitopes as CD8 or CD4 epitopes, as well as their potential binding affinity to major histocompatibility complexes (MHC class I and II).
There are multiple algorithms, both open-access and commercial, that are commonly used for the prediction of T cell epitopes based on the amino acid sequence of the antibody. NetMHCPan is one of the more common open-access tools, while EpiMatrix is a commercially available algorithm to evaluate the immunogenicity of therapeutics in silico. These algorithms are trained on vast amounts of data to calculate the relative immunogenicity score (Mattei et.al., 2022).
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Figure 1: Generation of immunogenicity scores by predictive algorithms. Used under CC BY from Mattei et.al., 2022. |
Limitations of the in silico approach
The in silico approach cannot replicate the complexity of antigen processing events that take place within an antigen-presenting cell like a T cell. It is also not yet possible for these algorithms to model individually-specific factors, such as previous exposures to that specific antigen, pre-existing conditions, etc. that can vary widely between recipients.
In vitro prediction of drug immunogenicity
The goal of in vitro prediction of immunogenicity is to validate the findings from the in silico assessments, as well as analyze the contributing factors that in silico prediction algorithms fail to capture. In vitro evaluation of therapeutic immunogenicity is typically performed using traditional 2D assays with immune cell lines or peripheral blood mononuclear cells (PBMC) in 3D models of the human lymphatic system. PBMCs consist of various immune cells, and can be used as a whole or can be used to isolate specific subsets like CD4+ or CD8+ T-cells or monocytes. PBMCs can be further differentiated into dentritic cells or macrophages through cytokine induction (Groell et.al, 2018). Thus, microscopic in-vitro models can be built that contain various components of the immune system.
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Figure 2: In-vitro models for immunogenicity prediction of therapeutic antibodies. Used under CC BY fromGroell et.al, 2018. |
3D models are somewhat of a middle ground between 2D assays and in vivo animal models. In these cases, they are designed to mimic the human lymphatic system or even the human skin and subcutaneous models (Groell et.al, 2018). Treating these cells with the therapeutic antibody can help us assess the effects of the drug on the actual cells or organ system. This allows scientists to get a more definitive grasp on the immunogenicity of the drug product while ensuring the safety and the efficacy.
Better together
Combining in silico and in vitro approaches enables researchers to develop safer and more effective mAb therapeutics. The in silico method of immunogenicity assessment can help in determining the T-cell epitopes that will likely bring about unwanted immunogenicity. The 3-D models can be applied to corroborate the occurrence and extent of the adverse drug reaction. This system would somewhat act like a 2-step verification.
All approaches have their own limitations. Using methods together can inform decisions about in vivo application and clinical testing. This would pave the way for improved patient outcomes and the advancement of precision medicine in the future.
Madhura Acharya is a former co-op and current Scientist, DNA Sequencing and QC at Addgene.
References and resources
References
Mosch, R., and Guchelaar, H.-J. (2022). Immunogenicity of Monoclonal Antibodies and the Potential Use of HLA Haplotypes to Predict Vulnerable Patients. Frontiers in Immunology, 13. https://www.frontiersin.org/articles/10.3389/fimmu.2022.885672
Piché, M.-S., Mounho-Zamora, B., LeSauteur, L., & Burns-Naas, L. A. (2015). Chapter 27—Immunotoxicology Testing of Monoclonal Antibodies in Macaca fascicularis: From Manufacturing to Preclinical Studies. In J. Bluemel, S. Korte, E. Schenck, & G. F. Weinbauer (Eds.), The Nonhuman Primate in Nonclinical Drug Development and Safety Assessment (pp. 519–542). Academic Press. https://doi.org/10.1016/B978-0-12-417144-2.00027-5
van Brummelen, E. M., Ros, W., Wolbink, G., Beijnen, J. H., & Schellens, J. H. (2016). Antidrug Antibody Formation in Oncology: Clinical Relevance and Challenges. The oncologist, 21(10), 1260–1268. https://doi.org/10.1634/theoncologist.2016-0061
WHO. International monitoring of adverse reactions to drugs: adverse reaction terminology. WHO Collaborating Centre for International Drug Monitoring, Uppsala, Sweden, 1992.
Mattei, A. E., Gutierrez, A. H., Martin, W. D., Terry, F. E., Roberts, B. J., Rosenberg, A. S., & De Groot, A. S. (2022). In silico Immunogenicity Assessment for Sequences Containing Unnatural Amino Acids: A Method Using Existing in silico Algorithm Infrastructure and a Vision for Future Enhancements. Frontiers in drug discovery, 2, 952326. https://doi.org/10.3389/fddsv.2022.952326
Joubert, M. K., Deshpande, M., Yang, J., Reynolds, H., Bryson, C., Fogg, M., Baker, M. P., Herskovitz, J., Goletz, T. J., Zhou, L., Moxness, M., Flynn, G. C., Narhi, L. O., & Jawa, V. (2016). Use of In Vitro Assays to Assess Immunogenicity Risk of Antibody-Based Biotherapeutics. PloS one, 11(8), e0159328. https://doi.org/10.1371/journal.pone.0159328
Groell, F., Jordan, O., & Borchard, G. (2018). In vitro models for immunogenicity prediction of therapeutic proteins. European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V, 130, 128–142. https://doi.org/10.1016/j.ejpb.2018.06.008
Lu, RM., Hwang, YC., Liu, IJ. et al. Development of therapeutic antibodies for the treatment of diseases. J Biomed Sci 27, 1 (2020). https://doi.org/10.1186/s12929-019-0592-z
Resources
Antibodies 101: Introduction to Antibodies
Topics: Antibodies
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