SBIR Phase I: Automated Humanization of Antibodies Directed to Protein and Carbohydrate Antigens

这个小型企业创新研究(SBIR)项目的更广泛的影响/商业潜力将是开发一个在线的、全自动的平台，用于设计用作潜在候选药物的高亲和力抗体。基于抗体的药物的成功引起了人们对发现和优化抗体的更快、更有效方法的兴趣。该项目的目标将是开发和实施一种计算方法，用于产生人源化抗体的蛋白质序列。这将通过提供软件来实现，该软件允许科学家将他们的一些初始实验转移到云中，并通过使用计算方法更快地获得结果。将被这种方法取代的典型实验需要大约一个月的时间，耗资数万美元。在这种方法的帮助下，这一时间可以缩短到只需点击一个按钮。预计在新药开发方面节省的时间和成本将改善候选药物的特征，提高临床研究的成功率，并加速新药的商业化。这项SBIR第一阶段项目提出开发一个最大限度地增加人体含量、保留亲和力、增强可开发性和产生知识产权的抗体设计平台。在开发基于抗体的药物时，免疫原性是一个关键问题。人源化的目的是增加最初从人类以外的来源获得的抗体的人类含量，如鼠或兔杂交瘤。其目的是简化人源化抗体的设计，目前这涉及到许多劳动密集型步骤。该公司的战略是为客户提供精心设计并专注于抗体分析的在线预测服务。其次，目标是解决针对蛋白质和碳水化合物抗原的人源化抗体的新需求。目前的预测软件偏向于针对蛋白质抗原的训练系列。该计划是利用抗伯克霍尔德氏菌的小鼠候选单抗从蛋白质和碳水化合物中衍生出来的靶标。这将导致评估正在开发的培训系列的效用，以执行针对这两类抗原的抗体的人源化，并使用所获得的信息来进一步优化预测程序。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project will be to develop an online, fully automated platform for designing high-affinity antibodies for use as potential drug candidates. The success of antibody-based drugs has generated interest in faster and more efficient methods to discover and optimize antibodies. The goal of this project will be to develop and implement a computation method for producing protein sequences of humanized antibodies. This will be achieved by providing software that allows scientists to move some of their initial experiments into the cloud, and achieve results much more quickly by using computational methods. The typical experiments that will be replaced by this method take about a month of time and cost tens of thousands of dollars. With the aid of such a method, this time can be cut back to the click of a button. The expected savings in time and cost for new drug development will improve the features of drug candidates, enhancing the success rate of clinical studies, and accelerating the commercialization of new drugs.This SBIR Phase I project proposes the development of a platform for antibody design that maximizes human content, retention of affinity, enhancement of developability, and generation of intellectual property. Immunogenicity is a critical concern when developing an antibody-based drug. Humanization is designed to increase the human content of an antibody originally obtained from sources other than human, such as mouse or rabbit hybridomas. The objective is to simplify the design of humanized antibodies, which currently involves many labor-intensive steps. The strategy is to offer clients online prediction services precisely designed and focused on antibody analysis. Secondly, the goal is to address the emerging need for humanizing antibodies directed against both protein and carbohydrate antigens. Current prediction software is biased towards training series that are specific for protein antigens. The plan is to use a pool of anti-Burkholderia mouse monoclonal antibody candidates have been derived from protein and carbohydrate targets. This will result in the ability to assess the utility of the training series under development to perform humanization of antibodies directed against both classes of antigens, and use the information obtained to further optimize prediction procedures.