SBIR Phase II: Automated Design Methods of Antibodies Directed to Protein and Carbohydrate Antigens

SBIR II 期：针对蛋白质和碳水化合物抗原的抗体的自动化设计方法

• 批准号: 1632399
• 负责人: Monica Berrondo
• 金额: $ 75万
• 依托单位: Macromoltek
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1632399&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Automated; Design

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II 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 computational 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 saving time and cost for new drug development. In addition, it is anticipated that this will improve the features of antibody-based drug candidates, enhance the success rate of clinical studies, and accelerate the commercialization of new drugs.This SBIR Phase II project aims to develop and implement computational tools for designing antibodies that are more effective, have fewer side effects, and have fewer problems in manufacturing. Current computational design methods rely almost entirely on the expertise of scientists iterating between experimental and bioinformatics approaches. An automated, systematic approach will help researchers design better antibodies with desired features in a shorter amount of time. The final platform will allow researchers to incorporate experimental and structural information to develop better drugs by determining which experiments will be necessary, assessing the viability of a potential candidate, and identifying structural features responsible for the molecule?s stability, immune response, and binding properties. The typical antibody design process takes many months and tens of thousands of dollars. With the aid of a computational process, this time can be cut back to the click of a button.

这个小企业创新研究（SBIR）二期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平台，用于设计高亲和力抗体，作为潜在的候选药物。基于抗体的药物的成功引起了人们对更快、更有效的方法来发现和优化抗体的兴趣。该项目的目标将是开发和实施一种计算方法来生产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端，并通过使用计算方法更快地获得结果，从而节省新药开发的时间和成本。此外，预计这将改善基于抗体的候选药物的特性，提高临床研究的成功率，并加速新药的商业化。该SBIR二期项目旨在开发和实施计算工具，用于设计更有效、副作用更少、制造过程中问题更少的抗体。目前的计算设计方法几乎完全依赖于科学家在实验和生物信息学方法之间迭代的专业知识。一种自动化、系统化的方法将帮助研究人员在更短的时间内设计出具有所需特征的更好的抗体。最终的平台将允许研究人员将实验和结构信息结合起来，通过确定哪些实验是必要的，评估潜在候选药物的可行性，并确定分子的结构特征来开发更好的药物。S稳定性、免疫反应和结合特性。典型的抗体设计过程需要几个月和数万美元。在计算过程的帮助下，这个时间可以缩短到点击一个按钮。