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SBIR Phase I: AI-assisted identification of small molecules for targeted repair of vascular barrier dysfunctions

SBIR Phase I: AI-assisted identification of small molecules for targeted repair of vascular barrier dysfunctions
SBIR第一期:人工智能辅助识别小分子,用于靶向修复血管屏障功能障碍
批准号:
2136307
负责人:
Mario Dipaola
金额:
$25.55万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-12-01 至 2023-05-31

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英文摘要
The broader impact/commercial potential of this small Business Innovation Research (SBIR) Phase I project is the identification of treatments for vascular leak disorders. Uncontrolled vascular leak disorders are common pathological processes that lead to tissue damage across multiple organs and over 60 clinical conditions. Currently, there are no drug-based therapies that address vascular leaks. Available solutions are focused on providing supportive care or decreasing inflammation, without addressing the underlying mechanism. This project is proposing a new approach to repair vascular leaks as a therapeutic intervention. By establishing the first drug discovery workflow for the identification of small molecules that repair vascular leakage, this project will enable the development of a pipeline of drugs for multiple conditions. The first condition targeted will be acute respiratory distress syndrome (ARDS), which accounts for 10% of intensive care unit (ICU) admissions and is the leading cause of mortality in ICU. Globally, it affects more than 3 million patients yearly. The proposed solution will decrease the number of deaths and the costs for ICU.This Small Business Innovation Research (SBIR) Phase I project seeks to validate a new structure-based drug screening platform designed to identify small molecules that activate the molecular pathways responsible for repairing vascular leakage. The proposed platform consists of a unique combination of novel machine learning methods for ligand-binding site prediction, fast docking algorithm capable of screening ultra-large (over a billion molecule) compound libraries with targeted absorption, distribution, metabolism, and excretion-toxicity (ADME-Tox) profile within minutes (5,000 compounds/second). The AI-guided docking approach is combined with in vitro high-throughput assays measuring the mechanisms of vascular leak in a physiologically relevant microenvironment of human tissues to select candidates targeting vascular leak disorders. In this project, the steps of this tiered workflow will be validated and applied to the first target, leading to the identification of a set of new drug candidates.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.
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