SBIR Phase I: SAAS-based Mass Spectrometry Data Processing for Antibody Therapeutics for COVID-19 and Other Diseases

这项小企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力将是，为COVID-19和其他疾病开发有效且负担得起的新抗体疗法的可能性将增加。目前的软件是时间密集的，以推进抗体治疗的发展。拟议的软件将提供关于假定疗法的功能和安全性的快速、准确的信息，从而实现更快的部署。这个SBIR一期项目将开发新的算法和接口，以增强通过质谱分析抗体。该研究将通过从实验元数据、样品制备、仪器参数和仪器输出中获取信息，并创建模型来预测数据支持的可能结构，从而扩大检测、表征和定量抗体聚糖、二硫键和杂质的能力和准确性。本项目旨在增加可准确检测和定量的抗体亚基数量。通过使用新颖的算法、可定制的自动化过程和集成的报告工具，这个过程将比目前的方法节省十倍的时间。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be the increased likelihood of effective and affordable new antibody therapy development for COVID-19 and other diseases. Current software is time-intensive to advance antibody therapy development. The proposed software will provide rapid, accurate information about the function and safety of putative therapies, enabling faster deployment. This SBIR Phase I project will develop novel algorithms and interfaces for enhancing antibody analysis via mass spectrometry. The research will expand capacity and accuracy for detecting, characterizing, and quantifying antibody glycans, disulfide bonds, and impurities by capturing information from experimental metadata, sample preparation, instrument parameters, and instrument output and creating models for prediction of possible structures supported by the data. The proposed project aims to increase the quantity of antibody subunits that can be accurately detected and quantified. This process will take tenfold less time than current methods through the use of novel algorithms, customizable automatable processes, and integrated reporting tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.