Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
J Pharm Sci. 2011 Dec;100(12):5081-95. doi: 10.1002/jps.22705. Epub 2011 Jul 24.
Because of their large, complex, and conformationally heterogeneous structures, biotherapeutics are vulnerable to several physicochemical stresses faced during the various processing steps from production to administration. In particular, formation of protein aggregates is a major concern. The greatest risk with aggregates arises from their potential to give rise to immunogenic reactions. Hence, it is desirable to bring forward biotherapeutic drug candidates that show low propensity for aggregation and, thus, improved developability. Here, we present a comprehensive review of computational studies into the sequence and structural factors that underpin protein and peptide aggregation. A number of computational approaches have been applied including coarse grain models, atomistic molecular simulations, and bioinformatic approaches. These studies have focused on both the mechanism of aggregation and the identification of potential aggregation-prone sequence and structural motifs. We also survey the computational tools available to predict aggregation in therapeutic proteins. The findings communicated here provide insights that could be potentially useful in the rational design of therapeutic candidates with not only high potency and specificity but also improved stability and solubility. These sequence-structure-based approaches can be applied to both novel as well as follow-on biotherapeutics.
由于生物治疗药物结构庞大、复杂且构象多样，因此在生产到给药的各个处理步骤中，它们容易受到多种物理化学应激的影响。特别是蛋白质聚集的形成是一个主要关注点。聚集物的最大风险来自于它们引发免疫反应的潜力。因此，希望能够提出具有低聚集倾向的生物治疗候选药物，从而提高其可开发性。在这里，我们全面回顾了关于支持蛋白质和肽聚集的序列和结构因素的计算研究。已经应用了许多计算方法，包括粗粒度模型、原子分子模拟和生物信息学方法。这些研究不仅关注聚集的机制，还关注潜在的易于聚集的序列和结构基序的识别。我们还调查了可用于预测治疗性蛋白质聚集的计算工具。这里传达的研究结果提供了一些见解，这些见解可能在合理设计具有高效力和特异性以及提高稳定性和溶解度的治疗候选物方面具有潜在的用处。这些基于序列-结构的方法可应用于新型和后续生物治疗药物。
