Deimmunization of protein therapeutics - Recent advances in experimental and computational epitope prediction and deletion.
| Autor: | Zinsli LV; Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland., Stierlin N; Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland., Loessner MJ; Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland., Schmelcher M; Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland. |
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| Jazyk: | angličtina |
| Zdroj: | Computational and structural biotechnology journal [Comput Struct Biotechnol J] 2020 Dec 29; Vol. 19, pp. 315-329. Date of Electronic Publication: 2020 Dec 29 (Print Publication: 2021). |
| DOI: | 10.1016/j.csbj.2020.12.024 |
| Abstrakt: | Biotherapeutics, and antimicrobial proteins in particular, are of increasing interest for human medicine. An important challenge in the development of such therapeutics is their potential immunogenicity, which can induce production of anti-drug-antibodies, resulting in altered pharmacokinetics, reduced efficacy, and potentially severe anaphylactic or hypersensitivity reactions. For this reason, the development and application of effective deimmunization methods for protein drugs is of utmost importance. Deimmunization may be achieved by unspecific shielding approaches, which include PEGylation, fusion to polypeptides (e.g., XTEN or PAS), reductive methylation, glycosylation, and polysialylation. Alternatively, the identification of epitopes for T cells or B cells and their subsequent deletion through site-directed mutagenesis represent promising deimmunization strategies and can be accomplished through either experimental or computational approaches. This review highlights the most recent advances and current challenges in the deimmunization of protein therapeutics, with a special focus on computational epitope prediction and deletion tools. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2020 The Author(s).) |
| Databáze: | MEDLINE |
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