Protein therapeutics typically require a concentrated protein formulation, which can lead to self-association and/or high viscosity due to protein–protein interaction (PPI). Excipients are often added to improve stability, bioavailability, and manufacturability of the protein therapeutics, but the selection of excipients often relies on trial and error. Therefore, understanding the excipient–protein interaction and its effect on non-specific PPI is important for rational selection of formulation development. In this study, we validate a general workflow based on the site identification by ligand competitive saturation (SILCS) technology, termed SILCS-Biologics, that can be applied to protein therapeutics for rational excipient selection. The National Institute of Standards and Technology monoclonal antibody (NISTmAb) reference along with the CNTO607 mAb is used as model antibody proteins to examine PPIs, and NISTmAb was used to further examine excipient–protein interactions, in silico. Metrics from SILCS include the distribution and predicted affinity of excipients, buffer interactions with the NISTmAb Fab, and the relation of the interactions to predicted PPI. Comparison with a range of experimental data showed multiple SILCS metrics to be predictive. Specifically, the number of favorable sites to which an excipient binds and the number of sites to which an excipient binds that are involved in predicted PPIs correlate with the experimentally determined viscosity. In addition, a combination of the number of binding sites and the predicted binding affinity is indicated to be predictive of relative protein stability. Comparison of arginine, trehalose, and sucrose, all of which give the highest viscosity in combination with analysis of B₂₂ and k_D and the SILCS metrics, indicates that higher viscosities are associated with a low number of predicted binding sites, with lower binding affinity of arginine leading to its anomalously high impact on viscosity. The present study indicates the potential for the SILCS-Biologics approach to be of utility in the rational design of excipients during biologics formulation.

中文翻译：

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使用通过配体竞争饱和 (SILCS) 进行位点识别的合理赋形剂选择的抗体-赋形剂相互作用的计算表征 - 生物制剂方法。

蛋白质治疗剂通常需要浓缩的蛋白质制剂，由于蛋白质-蛋白质相互作用 (PPI) 会导致自缔合和/或高粘度。通常添加辅料以提高蛋白质治疗剂的稳定性、生物利用度和可制造性，但辅料的选择通常依赖于反复试验。因此，了解赋形剂-蛋白质相互作用及其对非特异性 PPI 的影响对于合理选择配方开发非常重要。在这项研究中，我们验证了基于配体竞争饱和 (SILCS) 技术（称为 SILCS-Biologics）进行位点识别的一般工作流程，该工作流程可应用于蛋白质治疗以进行合理的赋形剂选择。美国国家标准与技术研究院单克隆抗体 (NISTmAb) 参考与 CNTO607 mAb 一起用作模型抗体蛋白来检查 PPI，NISTmAb 用于进一步检查赋形剂-蛋白质相互作用，在计算机上。SILCS 的指标包括赋形剂的分布和预测亲和力、缓冲液与 NISTmAb Fab 的相互作用，以及相互作用与预测 PPI 的关系。与一系列实验数据的比较表明，多个 SILCS 指标具有预测性。具体而言，与预测的 PPI 相关的赋形剂结合的有利位点的数量和赋形剂结合的位点的数量与实验确定的粘度相关。此外，结合位点的数量和预测的结合亲和力的组合表明可以预测相对蛋白质的稳定性。精氨酸、海藻糖和蔗糖的比较，结合B ₂₂和k _D以及 SILCS 指标表明，较高的粘度与预测的结合位点数量较少相关，而精氨酸的结合亲和力较低，导致其对粘度的异常高影响。本研究表明 SILCS-Biologics 方法在生物制剂制剂过程中合理设计辅料的潜力。