Novartis Biomedical Research, Cambridge, Massachusetts 02139, United States.
Novartis Biomedical Research, San Diego, California 92121, United States.
ACS Chem Biol. 2024 Apr 19;19(4):938-952. doi: 10.1021/acschembio.3c00737. Epub 2024 Apr 2.
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.
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。