Huang Bo-Cheng, Lu Yun-Chi, Liao Jun-Min, Liu Hui-Ju, Hong Shih-Ting, Hsieh Yuan-Chin, Chuang Chih-Hung, Chen Huei-Jen, Liao Tzu-Yi, Ho Kai-Wen, Wang Yeng-Tseng, Cheng Tian-Lu
Institute of Biomedical Sciences, National Sun Yat-Sen University Kaohsiung Taiwan
Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University Kaohsiung Taiwan.
Chem Sci. 2021 Jun 14;12(28):9759-9769. doi: 10.1039/d1sc01748a. eCollection 2021 Jul 21.
The on-target toxicity of monoclonal antibodies (Abs) is mainly due to the fact that Abs cannot distinguish target antigens (Ags) expressed in disease regions from those in normal tissues during systemic administration. In order to overcome this issue, we "copied" an autologous Ab hinge as an "Ab lock" and "pasted" it on the binding site of the Ab by connecting a protease substrate and linker in between to generate a pro-Ab, which can be specifically activated in the disease region to enhance Ab selectivity and reduce side effects. Previously, we reported that 70% of pro-Abs can achieve more than 100-fold blocking ability compared to the parental Abs. However, 30% of pro-Abs do not have such efficient blocking ability. This is because the same Ab lock linker cannot be applied to every Ab due to the differences in the complementarity-determining region (CDR) loops. Here we designed a method which uses structure-based computational simulation (MSCS) to optimize the blocking ability of the Ab lock for all Ab drugs. MSCS can precisely adjust the amino acid composition of the linker between the Ab lock and Ab drug with the assistance of molecular simulation. We selected αPD-1, αIL-1β, αCTLA-4 and αTNFα Ab as models and attached the Ab lock with various linkers (L1 to L7) to form pro-Abs by MSCS, respectively. The resulting cover rates of the Ab lock with various linkers compared to the Ab drug were in the range 28.33-42.33%. The recombinant pro-Abs were generated by MSCS prediction in order to verify the application of molecular simulation for pro-Ab development. The binding kinetics effective concentrations (EC-50) for αPD-1 (200-250-fold), αIL-1β (152-186-fold), αCTLA-4 (68-150-fold) and αTNFα Ab (20-123-fold) were presented as the blocking ability of pro-Ab compared to the Ab drug. Further, there was a positive correlation between cover rate and blocking ability of all pro-Ab candidates. The results suggested that MSCS was able to predict the Ab lock linker most suitable for application to αPD-1, αIL-1β, αCTLA-4 and αTNFα Ab to form pro-Abs efficiently. The success of MSCS in optimizing the pro-Ab can aid the development of next-generation pro-Ab drugs to significantly improve Ab-based therapies and thus patients' quality of life.
单克隆抗体（Abs）的靶向毒性主要是由于在全身给药过程中，抗体无法区分疾病区域表达的靶抗原（Ags）与正常组织中的靶抗原。为了克服这个问题，我们“复制”了一种自体抗体铰链作为“抗体锁”，并通过在其间连接蛋白酶底物和接头，将其“粘贴”到抗体的结合位点上，生成一种前体抗体，该前体抗体可以在疾病区域被特异性激活，以提高抗体的选择性并减少副作用。此前，我们报道70%的前体抗体与亲本抗体相比，可实现超过100倍的阻断能力。然而，30%的前体抗体没有如此高效的阻断能力。这是因为由于互补决定区（CDR）环的差异，相同的抗体锁接头不能应用于每种抗体。在这里，我们设计了一种方法，使用基于结构的计算模拟（MSCS）来优化抗体锁对所有抗体药物的阻断能力。MSCS可以在分子模拟的辅助下，精确调整抗体锁与抗体药物之间接头的氨基酸组成。我们选择αPD-1、αIL-1β、αCTLA-4和αTNFα抗体作为模型，分别通过MSCS将带有各种接头（L1至L7）的抗体锁连接到抗体上，形成前体抗体。与抗体药物相比，各种接头的抗体锁覆盖率在28.33%-42.33%之间。通过MSCS预测生成重组前体抗体，以验证分子模拟在前体抗体开发中的应用。与抗体药物相比，αPD-1（200-250倍）、αIL-1β（152-186倍）、αCTLA-4（68-150倍）和αTNFα抗体（20-123倍）的结合动力学有效浓度（EC-50）作为前体抗体的阻断能力。此外，所有前体抗体候选物的覆盖率与阻断能力之间存在正相关。结果表明，MSCS能够预测最适合应用于αPD-1、αIL-1β、αCTLA-4和αTNFα抗体以有效形成前体抗体的抗体锁接头。MSCS在优化前体抗体方面的成功有助于下一代前体抗体药物的开发，从而显著改善基于抗体的治疗方法，进而提高患者的生活质量。
