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

SBIR 第一阶段：实施计算机抗体工程环境来设计治疗性抗体

• 批准号: 1448206
• 负责人: Juan Almagro
• 金额: $ 15万
• 依托单位: GlobalBio, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-01-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1448206&HistoricalAwards=false
• 关键词: SBIR; Phase; Implementation; Silico; Antibody

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.

这个小型企业创新研究（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，以帮助设计和工程化治疗性抗体。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，类风湿性关节炎和几种类型的癌症。在美国和欧洲获得超过40种批准或审查的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、溶解性、临床耐受性和相对便宜的制造工艺。因此，随着2015年销售额超过500亿美元的市场预测，开发更快和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，产生更可预测的和强大的抗体设计应显着降低后期开发的损耗率，并导致显着的成本节约。该SBIR I期项目提出了一个自动化抗体人源化平台的开发。人源化方法是工程化用于人类治疗的非人抗体的关键，并且它们的开发由三个主要目标驱动：（1）增加人含量以使免疫原性反应最小化;（2）保留结合谱以保持效力并使进一步工程化过程中的成本最小化，例如，亲和力成熟;（3）保护知识产权。拟议的平台旨在最大限度地实现这三个目标，具体如下。 首先，将使用基于知识的规则来选择人类生殖系基因作为非人互补决定区（CDR）移植的框架区（FR）供体。预期所选FR保留结合并促进治疗性抗体的进一步开发。第二，CDR将被人源化。CDR移植方案通常不修饰CDR;然而，基于PI发表的工作，预计CDR中的非人残基数量可以显著减少，而不影响结合。 如果是这样，最终产物将与从转基因小鼠或噬菌体展示文库分离的人抗体无法区分。 作为概念验证，我们的计算平台生成的设计的生化和生物物理特征将通过实验进行评估。