Discovering existing medicines that abrogate cellular responses to SARS CoV-2 infection

发现消除细胞对 SARS CoV-2 感染反应的现有药物

• 批准号: 10199270
• 负责人: TODD R. GOLUB
• 金额: $ 165.28万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-21 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10199270
• 关键词: 2019-nCoV; Agonist; Androgens; Antiviral Agents; Autopsy; Binding; Biological; Biological Assay; Biology; COVID-19; CTSL gene; Cells; Clinical Trials; Communities; Data; Data Analyses; Data Set; Disease; Epithelial Cells; Expression Profiling; FDA approved; Failure; Functional disorder; Funding; Gene Expression Profile; Gene Expression Profiling; Generations; Glucocorticoids; Human; IL6 gene; Immune; Immune response; Individual; Infection; Inflammatory Response; Information Resources; Infrastructure; Institutes; Kidney Diseases; Learning; Libraries; Lung; Medicine; Modeling; Natural Immunity; Patients; Pharmaceutical Preparations; Physiological; Process; Production; Property; Renal carcinoma; Reporting; Research; Resources; System; TMPRSS2 gene; Testing; Ursidae Family; Viral; Viral Proteins; Virus Diseases; Visualization software; analytical tool; base; clinical development; cost effective; cytokine release syndrome; data sharing; drug discovery; drug resource; drug testing; experience; factor A; immune function; inhibitor/antagonist; loss of function; macrophage; monocyte; novel therapeutics; pre-clinical; research clinical testing; response; single-cell RNA sequencing; transcriptomics

PROJECT SUMMARY Our overall strategy is to screen existing drugs for their unexpected ability to abrogate key aspects of SARS- CoV-2 infection. The proposal will leverage the infrastructure we have created as part of the LINCS Consortium to use gene expression profiling to discover existing drugs that either block the cellular mechanisms required for viral infection, or block the cellular response to infection that causes disease. While there is much biological learning to be done, the focus of this proposal is not basic biology, but rather on the discovery of drugs that could be rapidly tested in patients. For this reason, we emphasize existing drugs (those that are either FDA-approved, EMA-approved, or are in clinical development) because of the pace at which they can be advanced to clinical trials. Our proposed approach leverages capabilities developed under our Common Fund-supported LINCS Center for Transcriptomics. First, we have created a “shovel-ready” production-scale data generation capability that can now be brought to bear on COVID-19. Second, we have assembled a drug repurposing library with ~10,000 drugs that are either FDA-approved or are in clinical development. Third, through our LINCS experience, we have learned that real power comes from unleashing the world’s scientific community by creating public resources that can be utilized by others. In this project, we will screen ~10,000 drugs in lung epithelial cells and monocytic immune cells, using gene expression profiling as the readout. We will make this Drug Repurposing LINCS dataset immediately publicly available, so that anyone in the research community can query it with relevant signatures -- including those that have yet to be discovered. High priority drugs identified through analysis of these data will be subjected to orthogonal tests of antiviral and immune function. An important aspect of this proposal will be the creation of a COVID-19 Drug Repurposing Portal which will house all of the raw and processed data generated by this proposal, together with biologist-friendly analytical and visualization tools that will enable the entire COVID-19 research community to identify top priority drugs for pre-clinical and eventually clinical testing as anti-COVID-19 therapies.

项目摘要 我们的总体战略是筛选现有药物，以发现它们具有意想不到的消除SARS关键方面的能力- CoV-2感染。该提案将利用我们作为LINCS联盟的一部分创建的基础设施 使用基因表达谱来发现现有的药物，这些药物可以阻断细胞机制， 病毒感染，或阻断细胞对导致疾病的感染的反应。虽然有很多生物 学习做，这个建议的重点不是基础生物学，而是发现药物， 在患者中快速检测。出于这个原因，我们强调现有的药物（那些要么是FDA批准， EMA批准，或正在临床开发），因为它们可以推进到临床 审判我们提出的方法利用了在我们共同基金支持的LINCS下开发的能力 转录组学中心。首先，我们已经创建了一个“铲准备”生产规模的数据生成能力 现在可以用来对付新冠肺炎其次，我们已经组装了一个药物再利用库， 约10，000种药物已获FDA批准或正在临床开发中。第三，通过我们的LINCS经验， 我们已经了解到，真实的力量来自于通过创造公众 可以被其他人利用的资源。在这个项目中，我们将在肺上皮细胞中筛选约10，000种药物， 单核细胞免疫细胞，使用基因表达谱作为读数。我们将使这种药物重新利用 LINCS数据集立即公开，以便研究社区中的任何人都可以使用 相关的特征，包括那些尚未被发现的特征。通过以下方式确定的高度优先药物 对这些数据进行抗病毒和免疫功能的正交试验分析。一个重要方面 该提案的一部分将是创建一个COVID-19药物再利用门户网站， 处理该提案产生的数据，以及生物学家友好的分析和可视化工具， 将使整个COVID-19研究界能够确定临床前和最终 作为抗COVID-19疗法的临床试验。

项目成果

The GCTx format and cmap{Py, R, M, J} packages: resources for optimized storage and integrated traversal of annotated dense matrices.

GCTX​​格式和CMAP {PY，R，M，J}软件包：用于优化注释密集矩阵的优化存储和集成遍历的资源。

• DOI: 10.1093/bioinformatics/bty784
• 发表时间: 2019-04-15
• 期刊: Bioinformatics (Oxford, England)
• 作者: Enache OM;Lahr DL;Natoli TE;Litichevskiy L;Wadden D;Flynn C;Gould J;Asiedu JK;Narayan R;Subramanian A
• 通讯作者: Subramanian A

A Library of Phosphoproteomic and Chromatin Signatures for Characterizing Cellular Responses to Drug Perturbations.

• DOI: 10.1016/j.cels.2018.03.012
• 发表时间: 2018-04-25
• 期刊: Cell systems
• 影响因子: 9.3
• 作者: Litichevskiy L;Peckner R;Abelin JG;Asiedu JK;Creech AL;Davis JF;Davison D;Dunning CM;Egertson JD;Egri S;Gould J;Ko T;Johnson SA;Lahr DL;Lam D;Liu Z;Lyons NJ;Lu X;MacLean BX;Mungenast AE;Officer A;Natoli TE;Papanastasiou M;Patel J;Sharma V;Toder C;Tubelli AA;Young JZ;Carr SA;Golub TR;Subramanian A;MacCoss MJ;Tsai LH;Jaffe JD
• 通讯作者: Jaffe JD

Genetic and transcriptional evolution alters cancer cell line drug response.

• DOI: 10.1038/s41586-018-0409-3
• 发表时间: 2018-08
• 期刊: Nature
• 影响因子: 64.8
• 作者: Ben-David U;Siranosian B;Ha G;Tang H;Oren Y;Hinohara K;Strathdee CA;Dempster J;Lyons NJ;Burns R;Nag A;Kugener G;Cimini B;Tsvetkov P;Maruvka YE;O'Rourke R;Garrity A;Tubelli AA;Bandopadhayay P;Tsherniak A;Vazquez F;Wong B;Birger C;Ghandi M;Thorner AR;Bittker JA;Meyerson M;Getz G;Beroukhim R;Golub TR
• 通讯作者: Golub TR

A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.

• DOI: 10.1016/j.cell.2017.10.049
• 发表时间: 2017-11-30
• 期刊: Cell
• 影响因子: 64.5
• 作者: Subramanian A;Narayan R;Corsello SM;Peck DD;Natoli TE;Lu X;Gould J;Davis JF;Tubelli AA;Asiedu JK;Lahr DL;Hirschman JE;Liu Z;Donahue M;Julian B;Khan M;Wadden D;Smith IC;Lam D;Liberzon A;Toder C;Bagul M;Orzechowski M;Enache OM;Piccioni F;Johnson SA;Lyons NJ;Berger AH;Shamji AF;Brooks AN;Vrcic A;Flynn C;Rosains J;Takeda DY;Hu R;Davison D;Lamb J;Ardlie K;Hogstrom L;Greenside P;Gray NS;Clemons PA;Silver S;Wu X;Zhao WN;Read-Button W;Wu X;Haggarty SJ;Ronco LV;Boehm JS;Schreiber SL;Doench JG;Bittker JA;Root DE;Wong B;Golub TR
• 通讯作者: Golub TR