Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
J Pharm Sci. 2012 Jan;101(1):102-15. doi: 10.1002/jps.22758. Epub 2011 Sep 20.
Determining the aggregation propensity of protein-based biotherapeutics is an important step in the drug development process. Typically, a great deal of data collected over a large period of time is needed to estimate the aggregation propensity of biotherapeutics. Thus, candidates cannot be screened early on for aggregation propensity, but early screening is desirable to help streamline drug development. Here, we present a simple molecular computational method to predict the aggregation propensity via hydrophobic interactions, thought to be the most common mechanism of aggregation, and electrostatic interactions. This method uses a new quantity termed Developability Index. It is a function of an antibody's net charge, calculated on the full-length antibody structure, and the spatial aggregation propensity, calculated on the complementarity-determining region structure. Its accuracy is due to the molecular level details and the incorporation of the tertiary structure of the antibody. It is particularly applicable to antibodies or other proteins for which structures are available or could be determined accurately using homology modeling. Applications include the selection of molecules in the discovery or early development process, selection of mutants for stability, and estimation of resources needed for development of a given biomolecule.
确定基于蛋白质的生物治疗药物的聚集倾向是药物开发过程中的重要步骤。通常,需要大量长时间收集的数据来估计生物治疗药物的聚集倾向。因此,不能早期筛选候选药物的聚集倾向,但早期筛选是可取的,有助于简化药物开发。在这里,我们提出了一种简单的分子计算方法,通过疏水相互作用预测聚集倾向,这被认为是聚集的最常见机制,以及静电相互作用。该方法使用一种新的量,称为可开发性指数。它是抗体净电荷的函数,在全长抗体结构上计算,以及空间聚集倾向,在互补决定区结构上计算。其准确性归因于分子水平的细节和抗体三级结构的结合。它特别适用于具有结构或可以使用同源建模准确确定结构的抗体或其他蛋白质。应用包括在发现或早期开发过程中选择分子、选择稳定性突变体以及估计开发给定生物分子所需的资源。