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ALARM NMR: A Rapid and Robust Experimental Method To Detect Reactive False Positives in Biochemical Screens

化学 假阳性悖论 药物发现 分析物 组合化学 质子核磁共振 计算生物学 色谱法 计算机科学 生物化学 人工智能 立体化学 生物
作者
Jeffrey R. Huth,Renaldo Mendoza,Edward T. Olejniczak,Robert W. Johnson,Darlene A. Cothron,Yaya Liu,Claude G. Lerner,Jun Chen,Philip J. Hajduk
出处
期刊:Journal of the American Chemical Society [American Chemical Society]
卷期号:127 (1): 217-224 被引量:224
标识
DOI:10.1021/ja0455547
摘要

High-throughput screening (HTS) of large compound collections typically results in numerous small molecule hits that must be carefully evaluated to identify valid drug leads. Although several filtering mechanisms and other tools exist that can assist the chemist in this process, it is often the case that costly synthetic resources are expended in pursuing false positives. We report here a rapid and reliable NMR-based method for identifying reactive false positives including those that oxidize or alkylate a protein target. Importantly, the reactive species need not be the parent compound, as both reactive impurities and breakdown products can be detected. The assay is called ALARM NMR (a La assay to detect reactive molecules by nuclear magnetic resonance) and is based on monitoring DTT-dependent (13)C chemical shift changes of the human La antigen in the presence of a test compound or mixture. Extensive validation has been performed to demonstrate the reliability and utility of using ALARM NMR to assess thiol reactivity. This included comparing ALARM NMR to a glutathione-based fluorescence assay, as well as testing a collection of more than 3500 compounds containing HTS hits from 23 drug targets. The data show that current in silico filtering tools fail to identify more than half of the compounds that can act via reactive mechanisms. Significantly, we show how ALARM NMR data has been critical in identifying reactive compounds that would otherwise have been prioritized for lead optimization. In addition, a new filtering tool has been developed on the basis of the ALARM NMR data that can augment current in silico programs for identifying nuisance compounds and improving the process of hit triage.
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