Discovering Affective Drug Combinations for Treating Covid-19

发现治疗 Covid-19 的有效药物组合

• 批准号: 10262603
• 负责人: Eytan Ruppin
• 金额: $ 67.26万
• 依托单位: DIVISION OF BASIC SCIENCES - NCI
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10262603
• 关键词: 2019-nCoV; Address; Affective; Antiviral Agents; COVID-19; Cell Line; Cells; Clinical; Clinical Trials; Collaborations; Coronavirus; Data; Data Set; Drug Combinations; Drug Screening; Drug Targeting; Human; In Vitro; Infection; Metabolic; Metabolic Pathway; Metabolism; Modeling; Patients; Performance; Pharmaceutical Preparations; Publishing; Sampling; Testing; Viral; Virus; base; clinical development; clinical investigation; clinical predictors; clinically relevant; computational pipelines; drug candidate; drug discovery; experimental study; genome-wide; high-throughput drug screening; in vivo; nonhuman primate; novel; pre-clinical; remdesivir; tool; transcriptome sequencing; transcriptomics

Efforts of anti-COVID-19 drug discovery encompass hundreds of ongoing clinical trials and published pre-clinical drug screening studies, with many employing drug repurposing to meet urgent clinical needs. It is well established that effective viral therapies frequently employ combinations of drugs, however, as the space of possible drug combinations is prohibitively large, rational strategies for predicting effective combinations are needed. Viruses, including coronaviruses, are known to hijack various host metabolic pathways to facilitate their own proliferation, making targeting host metabolism an interesting anti-viral approach. Here, we aim to harness our expertise in genome-scale metabolic modeling (GEM) to address these challenges. Using both published data and our own RNA sequencing data on SARS-CoV-2-infected samples of cell lines and patients, we shall apply our published high-performance GEM-based computational pipeline to predict host metabolism-targeting anti-COVID-19 drugs and their combinations. We first aim to find treatments that effectively reverse the metabolic alterations induced by SARS-CoV-2 in the human host cells. Second, we shall predict their most synergistic combinations, and predict the combinations of metabolic drugs with emerging non-metabolic antiviral drugs under clinical investigation, such as remdesivir. In close collaboration with the Sumit Chanda lab, which has performed the largest high-throughput drug screening to date, we shall experimentally validate our predictions in vitro and in vivo. Our pipeline is the first that integrates drug screening and GEM analysis for antiviral drug discovery focusing on host metabolism. It will contribute to the anti-COVID-19 endeavor but also elucidate host metabolism-targeting as a novel and potentially generalizable antiviral strategy.

抗 COVID-19 药物发现工作包括数百项正在进行的临床试验和已发表的临床前药物筛选研究，其中许多研究采用药物再利用来满足紧急的临床需求。众所周知，有效的病毒疗法经常采用药物组合，然而，由于可能的药物组合空间非常大，因此需要合理的策略来预测有效的组合。众所周知，包括冠状病毒在内的病毒会劫持各种宿主代谢途径以促进自身增殖，这使得针对宿主代谢成为一种有趣的抗病毒方法。在这里，我们的目标是利用我们在基因组规模代谢模型（GEM）方面的专业知识来应对这些挑战。利用已发表的数据和我们自己的关于感染 SARS-CoV-2 的细胞系和患者样本的 RNA 测序数据，我们将应用我们已发表的基于 GEM 的高性能计算管道来预测宿主代谢靶向抗 COVID-19 药物以及它们的组合。我们的首要目标是找到有效逆转 SARS-CoV-2 在人类宿主细胞中引起的代谢改变的治疗方法。其次，我们要预测它们最具协同作用的组合，预测代谢药物与临床研究中的新兴非代谢抗病毒药物（如瑞德西韦）的组合。 Sumit Chanda 实验室进行了迄今为止最大规模的高通量药物筛选，我们将与该实验室密切合作，通过体外和体内实验验证我们的预测。我们的产品线是第一个将药物筛选和 GEM 分析相结合的产品线，用于专注于宿主代谢的抗病毒药物发现。它将有助于抗击 COVID-19 的努力，同时也阐明宿主代谢靶向作为一种新颖且可能普遍推广的抗病毒策略。