Mattei Aimee E, Gutierrez Andres H, Martin William D, Terry Frances E, Roberts Brian J, Rosenberg Amy S, De Groot Anne S
EpiVax, Inc., Providence, RI, United States.
Front Drug Discov (Lausanne). 2022;2. doi: 10.3389/fddsv.2022.952326. Epub 2022 Oct 10.
The prediction of T cell epitopes within any peptide or biologic drug candidate serves as an important first step for assessing immunogenicity. T cell epitopes bind human leukocyte antigen (HLA) by a well-characterized interaction of amino acid side chains and pockets in the HLA molecule binding groove. Immunoinformatics tools, such as the EpiMatrix algorithm, have been developed to screen natural amino acid sequences for peptides that will bind HLA. In addition to commonly occurring in synthetic peptide impurities, unnatural amino acids (UAA) are also often incorporated into novel peptide therapeutics to improve properties of the drug product. To date, the HLA binding properties of peptides containing UAA are not accurately estimated by most algorithms. Both scenarios warrant the need for enhanced predictive tools. The authors developed an method for modeling the impact of a given UAA on a peptide's likelihood of binding to HLA and, by extension, its immunogenic potential. assessment of immunogenic potential allows for risk-based selection of best candidate peptides in further confirmatory and assays, thereby reducing the overall cost of immunogenicity evaluation. Examples demonstrating immunogenicity prediction for product impurities that are commonly found in formulations of the generic peptides teriparatide and semaglutide are provided. Next, this article discusses how HLA binding studies can be used to estimate the binding potentials of commonly encountered UAA and "correct" estimates of binding based on their naturally occurring counterparts. As demonstrated here, these in vitro binding studies are usually performed with known ligands which have been modified to contain UAA in HLA anchor positions. An example using D-amino acids in relative binding position 1 (P1) of the PADRE peptide is presented. As more HLA binding data become available, new predictive models allowing for the direct estimation of HLA binding for peptides containing UAA can be established.
预测任何肽或生物药物候选物中的T细胞表位是评估免疫原性的重要第一步。T细胞表位通过HLA分子结合槽中氨基酸侧链与口袋之间特征明确的相互作用与人白细胞抗原（HLA）结合。免疫信息学工具，如EpiMatrix算法，已被开发用于筛选能与HLA结合的肽的天然氨基酸序列。除了常见于合成肽杂质中，非天然氨基酸（UAA）也经常被纳入新型肽疗法中以改善药物产品的性质。到目前为止，大多数算法都不能准确估计含UAA肽的HLA结合特性。这两种情况都需要增强预测工具。作者开发了一种方法，用于模拟给定UAA对肽与HLA结合可能性的影响，并进而模拟其免疫原性潜力。对免疫原性潜力的评估有助于在进一步的确认性研究和试验中基于风险选择最佳候选肽，从而降低免疫原性评估的总体成本。本文提供了在特立帕肽和司美格鲁肽等仿制药肽制剂中常见的产品杂质的免疫原性预测示例。接下来，本文讨论了如何利用HLA结合研究来估计常见UAA的结合潜力，并根据其天然对应物“校正”结合估计值。如此处所示，这些体外结合研究通常是用已知配体进行的，这些配体已被修饰以在HLA锚定位置含有UAA。本文给出了在PADRE肽的相对结合位置1（P1）使用D-氨基酸的示例。随着更多HLA结合数据的获得，可以建立新的预测模型，直接估计含UAA肽的HLA结合情况。
