Computational methods for optimized biologics formulation

项目总结： 以蛋白质为基础的生物制剂-其有效成分是蛋白质，最常见的是单抗 抗体(MAb)-形成一个2000亿美元/年的市场，预计到2025年规模将翻一番。一位批评者 生物制品的安全性和有效性的组成部分是需要长期保持活性的蛋白质 储存和随后的注射/输注。为此，辅料和缓冲液的选择被称为 “配方。”以蛋白质为基础的药物的正确配方对于稳定活性蛋白质的展开是必不可少的。 并阻断折叠蛋白上可能造成聚集风险并导致粘度升高的部位，这是由于 不良蛋白质-蛋白质相互作用(PPI)。重要的是，公式可以在不改变序列的情况下完成 因此，它是将生物疗法推向市场的独立工具。 目前选择优化配方的方法要么是低通量试验，要么是 没有考虑辅料-蛋白质相互作用的分子细节的计算方法。这个 用配基竞争饱和(SILCS)计算平台技术确定已建立的位点 绘制了蛋白质完整的3D表面对各种化学官能团的亲和力模式。 然后使用官能团亲和模式来确定能够结合并稳定活性的赋形剂， 蛋白质的折叠构象，并结合到可能参与PPI的蛋白质区域，从而抑制 凝聚降粘。该提案的总体目标是继续发展和 将SILCS-Biologics作为行业就绪的工作流程和图形用户界面进行验证，以进行管理和应用 SILCS辅料筛选和PPI分析产生的大量信息。在拟议的研究中 将进行试验性工作，以生成数据，用于各种类型的模型训练和验证 蛋白质和常用辅料。然后，该数据将与计算的SILCS指标相结合，包括 辅料结合位置和亲和力以及可参与完整PPI的潜在区域 蛋白质表面，包括pH值的影响和精氨酸作为赋形剂的独特性质。此信息 然后在机器学习的背景下应用，以开发预测辅料将被阻止的模型 PPI从而降低粘度和减缓聚集以及稳定折叠的生物活性状态 蛋白质的含量。建议的模型将在马里兰大学巴尔的摩分校和工业大学进行验证 和政府合作伙伴反对对一系列蛋白质进行既定的实验方法 治疗适应症。项目成功完成后，新的产品将添加到现有的 SILCS软件套件，将最大限度地减少生物制剂配方的时间和成本要求 AS可改善配方，从而改善临床结果。这些功能将在 直接销售给制药公司并在合同中使用的行业就绪工作流的背景 生物制品的优化配方研究。

Project Summary: Protein-based biologics – therapeutics whose active ingredient is a protein and most commonly a monoclonal antibody (mAb) – make up a $200 billion/year market that is expected to double in size by 2025. A critical component in the safety and efficacy of biologics is the need to maintain the active protein during long-term storage and subsequent injection/infusion. The selection of excipients and buffers toward this end is termed “formulation.” Proper formulation of a protein-based drug is essential to stabilize the active protein from unfolding and to block sites on the folded protein that may pose an aggregation risk and lead to elevated viscosity due to undesirable protein-protein interactions (PPI). Importantly, formulation can be done without altering the sequence of (i.e. re-engineering) the protein and is thus an independent tool for bringing a biologic therapeutic to market. Current approaches to choosing an optimized formulation are either low-throughput experiments or computational methods that do not take into account the molecular details of excipient-protein interactions. The established Site Identification by Ligand Competitive Saturation (SILCS) computational platform technology maps the affinity pattern of the complete 3D surface of a protein for a wide diversity of chemical functional groups. The functional group affinity pattern is then used to determine excipients that can bind to and stabilize the active, folded conformation of a protein and bind to regions of the protein that may participate in PPI, thereby inhibiting aggregation and decreasing viscosity. The broad goal of the proposal is the continued development and validation of SILCS-Biologics as an industry-ready workflow and a graphical user interface to manage and apply the extensive information generated by SILCS excipient screening and PPI analysis. In the proposed studies experimental efforts will be undertaken to generate data for model training and validation across a variety of proteins and commonly used excipients. That data will be then combined with computed SILCS metrics including excipient binding locations and affinities and potential regions that can participate in PPI across the complete protein surface, including the impact of pH and the unique properties of Arginine as an excipient. This information will then be applied in the context of machine learning to develop models that will predict excipients that will block PPI thereby lowering viscosity and slowing aggregation as well as stabilize the folded, biologically active state of the protein. The proposed models will be validated at the University of Maryland, Baltimore and with industrial and government partners against established experimental methods on a range of proteins with various therapeutic indications. Upon successful completion of the project new offerings will be added to the existing SILCS software suite that will minimize the time and costs requirements for the formulation of biologics as well as lead to improved formulations thereby improving clinical outcomes. These capabilities will be implemented in the context of industry-ready workflows for direct sale to pharmaceutical companies and for use in contract research for the optimized formulation of biologics.