AB-Amy：机器学习辅助治疗性抗体轻链的淀粉样变性风险预测
AB-Amy: machine learning aided amyloidogenic risk prediction of therapeutic antibody light chains.
作者信息
Zhou Yuwei, Huang Ziru, Gou Yushu, Liu Siqi, Yang Wei, Zhang Hongyu, Dzisoo Anthony Mackitz, Huang Jian
机构信息
School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China.
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China.
出版信息
Antib Ther. 2023 Apr 12;6(3):147-156. doi: 10.1093/abt/tbad007. eCollection 2023 Jul.
Over 120 FDA-approved antibody-based therapeutics are used to treat a variety of diseases.However, many candidates could fail because of unfavorable physicochemical properties. Light-chain amyloidosis is one form of aggregation that can lead to severe safety risks in clinical development. Therefore, screening candidates with a less amyloidosis risk at the early stage can not only save the time and cost of antibody development but also improve the safety of antibody drugs. In this study, based on the dipeptide composition of 742 amyloidogenic and 712 non-amyloidogenic antibody light chains, a support vector machine-based model, AB-Amy, was trained to predict the light-chain amyloidogenic risk. The AUC of AB-Amy reaches 0.9651. The excellent performance of AB-Amy indicates that it can be a useful tool for the evaluation of the light-chain amyloidogenic risk to ensure the safety of antibody therapeutics under clinical development. A web server is freely available at http://i.uestc.edu.cn/AB-Amy/.
超过120种经美国食品药品监督管理局（FDA）批准的基于抗体的疗法被用于治疗各种疾病。然而，许多候选药物可能会因为不良的物理化学性质而失败。轻链淀粉样变性是一种聚集形式，可能会在临床开发中导致严重的安全风险。因此，在早期筛选出淀粉样变性风险较低的候选药物，不仅可以节省抗体开发的时间和成本，还可以提高抗体药物的安全性。在本研究中，基于742条淀粉样变性抗体轻链和712条非淀粉样变性抗体轻链的二肽组成，训练了一种基于支持向量机的模型AB-Amy，用于预测轻链淀粉样变性风险。AB-Amy的曲线下面积（AUC）达到0.9651。AB-Amy的优异性能表明，它可以成为评估轻链淀粉样变性风险的有用工具，以确保临床开发中的抗体疗法的安全性。可通过http://i.uestc.edu.cn/AB-Amy/免费访问一个网络服务器。
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