Buck Patrick M, Kumar Sandeep, Wang Xiaoling, Agrawal Neeraj J, Trout Bernhardt L, Singh Satish K
Biotherapeutics Pharmaceutical Research and Development, Pfizer, Inc, St. Louis, MO, USA.
Methods Mol Biol. 2012;899:425-51. doi: 10.1007/978-1-61779-921-1_26.
Protein based biotherapeutics have emerged as a successful class of pharmaceuticals. However, these macromolecules endure a variety of physicochemical degradations during manufacturing, shipping, and storage, which may adversely impact the drug product quality. Of these degradations, the irreversible self-association of therapeutic proteins to form aggregates is a major challenge in the formulation of these molecules. Tools to predict and mitigate protein aggregation are, therefore, of great interest to biopharmaceutical research and development. In this chapter, a number of such computational tools developed to understand and predict the various steps involved in protein aggregation are described. These tools can be grouped into three general classes: unfolding kinetics and native state thermal stability, colloidal stability, and sequence/structure based aggregation liabilities. Chapter sections introduce each class by discussing how these predictive tools provide insight into the molecular events leading to protein aggregation. The computational methods are then explained in detail along with their advantages and limitations.
基于蛋白质的生物治疗药物已成为一类成功的药物。然而，这些大分子在制造、运输和储存过程中会经历各种物理化学降解，这可能会对药品质量产生不利影响。在这些降解中，治疗性蛋白质不可逆地自缔合形成聚集体是这些分子制剂中的一个主要挑战。因此，预测和减轻蛋白质聚集的工具对生物制药研发具有极大的吸引力。在本章中，将描述为理解和预测蛋白质聚集所涉及的各个步骤而开发的一些此类计算工具。这些工具可大致分为三类：解折叠动力学和天然态热稳定性、胶体稳定性以及基于序列/结构的聚集倾向。本章各节通过讨论这些预测工具如何深入了解导致蛋白质聚集的分子事件来介绍每一类。然后详细解释计算方法及其优缺点。
