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: AKTTYVA THERAPEUTICS, INC.
• 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
• 起止时间: 2021-12-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2136307&HistoricalAwards=false
• 关键词: SBIR; Phase; AI; assisted; identification

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.

该小型企业创新研究（SBIR）I期项目的更广泛影响/商业潜力是确定血管渗漏疾病的治疗方法。不受控制的血管渗漏疾病是常见的病理过程，可导致多个器官的组织损伤和60多种临床疾病。目前，还没有基于药物的治疗方法来解决血管渗漏。现有的解决方案集中在提供支持性护理或减少炎症，而没有解决潜在的机制。该项目提出了一种新的方法来修复血管泄漏作为一种治疗干预。通过建立第一个用于鉴定修复血管渗漏的小分子的药物发现工作流程，该项目将能够开发用于多种疾病的药物管道。第一个目标是急性呼吸窘迫综合征（ARDS），它占重症监护室（ICU）入院人数的10%，是ICU死亡的主要原因。在全球范围内，它每年影响超过300万患者。这一小型企业创新研究（SBIR）第一阶段项目旨在验证一种新的基于结构的药物筛选平台，该平台旨在识别激活负责修复血管渗漏的分子通路的小分子。该平台由用于配体结合位点预测的新型机器学习方法的独特组合组成，快速对接算法能够在几分钟内（5，000种化合物/秒）筛选具有靶向吸收，分布，代谢和排泄毒性（ADME-Tox）特征的超大型（超过10亿个分子）化合物库。人工智能引导的对接方法与体外高通量测定相结合，测量人体组织生理相关微环境中的血管渗漏机制，以选择靶向血管渗漏疾病的候选者。在这个项目中，这个分层工作流程的步骤将被验证并应用于第一个目标，从而确定一组新的候选药物。这个奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。