SBIR Phase I: Implementation of an In Silico Antibody Engineering Environment to engineer therapeutic antibodies

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是为抗体工程师提供一个用户友好的计算平台，以协助治疗性抗体的设计和工程。基于抗体的药物正在成为多种疾病的治疗选择，如多发性硬化症、风湿性关节炎和几种癌症。在美国和欧洲，超过40种治疗性抗体的成功是由于它们与其他生物药物相比具有精致的特异性、高效力、稳定性、溶解度、临床耐受性和相对便宜的制造过程。因此，随着2015年市场销售额预计超过500亿美元，开发更快，更有效的方法来发现和优化用于治疗应用的抗体的兴趣已经获得了动力。一个用户友好的计算平台，产生更可预测和强大的抗体设计，应该显著降低后期开发的损耗率，并导致显著的成本节约。这个SBIR一期项目提出了一个自动化抗体人源化平台的开发。人源化方法是用于人类治疗的非人抗体工程的关键，其发展有三个主要目标：(1)增加人源化含量以减少免疫原性反应；(2)保留结合谱以保留效力，并在进一步的工程过程（如亲和成熟）中将成本降至最低；(3)保护知识产权。提议的平台旨在最大限度地实现以下三个目标。首先，基于知识的规则将用于选择人类种系基因作为框架区域（FR）供体，用于非人类互补决定区域（cdr）嫁接。期望所选择的fr保持结合并促进治疗性抗体的进一步开发。二是话单人性化。话单嫁接协议通常不修改话单；然而，根据PI发表的工作，预计cdr中非人残基的数量可以在不影响结合的情况下显着减少。如果是这样，最终产物将与从转基因小鼠或噬菌体展示库中分离的人抗体无法区分。作为概念的证明，由我们的计算平台生成的设计的生化和生物物理概况将进行实验评估。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to provide antibody engineers with a user-friendly computational platform to assist in the design and engineering of therapeutic antibodies. Antibody-based drugs are becoming the treatment of choice for diverse diseases such as multiple sclerosis, rheumatoid arthritis, and several types of cancers. The success of therapeutic antibodies with over forty approved or in review in the United States and Europe is due to their exquisite specificity, high potency, stability, solubility, clinical tolerability and relatively inexpensive manufacturing process in comparison with other biologic drugs. Thus, with a market forecast of over $50 billion in sales for 2015, the interest in developing faster and more efficient methods for discovering and optimizing antibodies for therapeutic applications has been gaining momentum. A user-friendly computational platform that generates more predictable and robust antibody designs should significantly reduce the attrition rate in late development, and result in significant cost savings. This SBIR Phase I project proposes the development of an automated antibody humanization platform. Humanization methods are key to engineering nonhuman antibodies for human therapy, and their development has been driven by three main goals: (1) Increase human content to minimize immunogenic reactions; (2) preserve binding profile to retain potency and minimize costs in further engineering processes, e.g., affinity maturation; and (3) secure intellectual property. The proposed platform aims to maximize all three of these goals as follows. First, knowledge-based rules will be used to select human germline genes as framework region (FR) donors for nonhuman Complementary-Determining Regions (CDRs) grafting. It is expected that the selected FRs preserve binding and facilitate further development of the therapeutic antibody. Second, the CDRs will be humanized. CDR grafting protocols typically do not modify the CDRs; however, based on work published by the PI, it is anticipated that the number of nonhuman residues in the CDRs can be reduced significantly without impacting binding. If so, the final product will be indistinguishable from human antibodies isolated from transgenic mice or phage display libraries. As proof of concept, the biochemical and biophysical profiles of designs generated by our computational platform will be assessed experimentally.