计算机模拟预测治疗性抗体聚集倾向性的进展及应用

Progress and Application on in silico Prediction of Aggregation of Therapeutic Antibodies

由于具有靶向性高、不良反应少等优势,治疗性抗体已成为生物技术产业的重要研究领域。诸多重大疾病如肿瘤、自身免疫性疾病、心血管疾病、糖尿病、老年性痴呆等都有望用抗体药物进行治疗,以抗体为基础的分子设计和靶向治疗也是近年生物医药产业的最大增长点。但目前在抗体药物的研发和生产过程中仍然存在一些问题亟待解决。其中,抗体分子聚集是影响药物成药性的关键因素之一,筛选和设计稳定高效的抗体药物是药物研发早期阶段的重要目标。相较于传统筛选方法,应用计算机模拟技术预测蛋白质的聚集倾向为抗体药物的稳定性和高度可溶性提供了重要参考信息,将有效地提高药物的研发效率。本文重点介绍了已成功应用于治疗性抗体领域的聚集预测方法,并对这些方法的原理与应用前景进行总结,为基于计算机模拟的抗体研究提供参考。

Due to precise specificities and low side-effects, therapeutic antibodies are becoming more and more important in the biotechnology industry. With promising application prospects in clinical practice, therapeutic antibodies are expected to treat diseases like cancers, autoimmune diseases, cardiovascular diseases, diabetes, Alzheimer’s diseases and so on. Antibody-based molecular design and targeted therapy is the fastest growing field of biomedical industry in recent years. However, there are still challenges for the development and production process of therapeutic antibodies. Among these challenges, antibody aggregation is one of the key determinants for antibody. Screening and rational design of potent antibody candidates without obvious aggregation propensity are critical in the earlier stage of antibody development. Comparing to conventional methods, in silico prediction of protein aggregation can provide key information on the stability and solubility of the antibody drugs and hence greatly improve the efficiency of drug development. Here, we summarize the approaches into sequence-based method and structure-based method. The former makes predictions from amino acid sequence only, while the latter approaches predict aggregation by calculating interactions on structures model. Furthermore, we illustrate their principles and prospects in therapeutic antibodies development which might provide paradigmatic reference for computation-based antibody design in the future. Depending on the different advantages of these approaches, a right choice could be potentially useful in the rational design of therapeutic candidates with not only high potency and specificity but also improved stability and solubility.