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利用灰物质——高通量筛选图谱中具有新颖作用机制的化合物,拓展已知化学生物学文库之外的小规模可筛选生物空间。

Enhancing the Small-Scale Screenable Biological Space beyond Known Chemogenomics Libraries with Gray Chemical Matter─Compounds with Novel Mechanisms from High-Throughput Screening Profiles.

机构信息

Novartis Biomedical Research, Cambridge, Massachusetts 02139, United States.

Novartis Biomedical Research, San Diego, California 92121, United States.

出版信息

ACS Chem Biol. 2024 Apr 19;19(4):938-952. doi: 10.1021/acschembio.3c00737. Epub 2024 Apr 2.

DOI:10.1021/acschembio.3c00737
PMID:38565185
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11040606/
Abstract

Phenotypic assays have become an established approach to drug discovery. Greater disease relevance is often achieved through cellular models with increased complexity and more detailed readouts, such as gene expression or advanced imaging. However, the intricate nature and cost of these assays impose limitations on their screening capacity, often restricting screens to well-characterized small compound sets such as chemogenomics libraries. Here, we outline a cheminformatics approach to identify a small set of compounds with likely novel mechanisms of action (MoAs), expanding the MoA search space for throughput limited phenotypic assays. Our approach is based on mining existing large-scale, phenotypic high-throughput screening (HTS) data. It enables the identification of chemotypes that exhibit selectivity across multiple cell-based assays, which are characterized by persistent and broad structure activity relationships (SAR). We validate the effectiveness of our approach in broad cellular profiling assays (Cell Painting, DRUG-seq, and Promotor Signature Profiling) and chemical proteomics experiments. These experiments revealed that the compounds behave similarly to known chemogenetic libraries, but with a notable bias toward novel protein targets. To foster collaboration and advance research in this area, we have curated a public set of such compounds based on the PubChem BioAssay dataset and made it available for use by the scientific community.

摘要

表型分析已成为药物发现的一种既定方法。通过具有更高复杂性和更详细读数的细胞模型(如基因表达或高级成像),通常可以实现更大的疾病相关性。然而,这些测定的复杂性质和成本对其筛选能力施加了限制,通常将筛选限制在经过良好表征的小化合物集,如化学基因组文库。在这里,我们概述了一种计算化学方法,用于识别具有可能新作用机制(MoA)的一小部分化合物,从而扩展了通量有限的表型测定的 MoA 搜索空间。我们的方法基于挖掘现有的大规模表型高通量筛选(HTS)数据。它能够识别在多个基于细胞的测定中表现出选择性的化学型,这些化学型的特征是持久且广泛的结构活性关系(SAR)。我们在广泛的细胞分析测定(细胞绘图、DRUG-seq 和启动子特征分析)和化学蛋白质组学实验中验证了我们方法的有效性。这些实验表明,这些化合物的行为类似于已知的化学遗传学文库,但具有明显的新型蛋白质靶标偏向。为了促进该领域的合作和研究进展,我们根据 PubChem BioAssay 数据集整理了一组此类化合物,并供科学界使用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bb9/11040606/8f7b5f110311/cb3c00737_0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bb9/11040606/92283feb7525/cb3c00737_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bb9/11040606/7a9ac04e0bfa/cb3c00737_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bb9/11040606/75ad560c704e/cb3c00737_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bb9/11040606/84ac8d7fbfe5/cb3c00737_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bb9/11040606/93c89a44ff2c/cb3c00737_0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bb9/11040606/8f7b5f110311/cb3c00737_0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bb9/11040606/92283feb7525/cb3c00737_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bb9/11040606/7a9ac04e0bfa/cb3c00737_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bb9/11040606/75ad560c704e/cb3c00737_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bb9/11040606/84ac8d7fbfe5/cb3c00737_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bb9/11040606/93c89a44ff2c/cb3c00737_0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bb9/11040606/8f7b5f110311/cb3c00737_0006.jpg

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2
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J Chem Inf Model. 2024 Apr 8;64(7):2331-2344. doi: 10.1021/acs.jcim.3c00799. Epub 2023 Aug 29.
3
Optimizing the Cell Painting assay for image-based profiling.
优化细胞染色法进行基于图像的分析。
Nat Protoc. 2023 Jul;18(7):1981-2013. doi: 10.1038/s41596-023-00840-9. Epub 2023 Jun 21.
4
DRUG-seq Provides Unbiased Biological Activity Readouts for Neuroscience Drug Discovery.DRUG-seq 为神经科学药物发现提供了无偏的生物活性检测结果。
ACS Chem Biol. 2022 Jun 17;17(6):1401-1414. doi: 10.1021/acschembio.1c00920. Epub 2022 May 4.
5
Collaborative Profile-QSAR: A Natural Platform for Building Collaborative Models among Competing Companies.协同 Profile-QSAR:在竞争公司之间构建协同模型的自然平台。
J Chem Inf Model. 2021 Apr 26;61(4):1603-1616. doi: 10.1021/acs.jcim.0c01342. Epub 2021 Apr 12.
6
The chemfp project.化学指纹项目。
J Cheminform. 2019 Dec 5;11(1):76. doi: 10.1186/s13321-019-0398-8.
7
PubChem in 2021: new data content and improved web interfaces.PubChem 在 2021 年:新增数据内容和改进的网络界面。
Nucleic Acids Res. 2021 Jan 8;49(D1):D1388-D1395. doi: 10.1093/nar/gkaa971.
8
Evolution of Novartis' Small Molecule Screening Deck Design.诺华小分子筛选库设计的演变。
J Med Chem. 2020 Dec 10;63(23):14425-14447. doi: 10.1021/acs.jmedchem.0c01332. Epub 2020 Nov 3.
9
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10
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Nat Chem Biol. 2020 Nov;16(11):1189-1198. doi: 10.1038/s41589-020-0557-2. Epub 2020 Jun 22.