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

这个小型企业创新研究(SBIR)第二阶段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，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征，提高临床研究的成功率，并加速新药的商业化。这项SBIR第二阶段项目旨在开发和实现计算工具，以设计更有效、副作用更少、制造问题更少的抗体。目前的计算设计方法几乎完全依赖于在实验和生物信息学方法之间迭代的科学家的专业知识。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结合实验和结构信息来开发更好的药物，方法是确定哪些实验是必要的，评估潜在候选者的生存能力，并确定对分子负责的结构特征-S的稳定性、免疫反应和结合属性。典型的抗体设计过程需要数月和数万美元。在计算过程的帮助下，这一时间可以缩短到只需点击一个按钮。

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.