Suppr超能文献

一种结合节省材料的高通量氢核磁共振的自动化纯化工作流程,用于并行药物化学。

An Automated Purification Workflow Coupled with Material-Sparing High-Throughput H NMR for Parallel Medicinal Chemistry.

作者信息

Bellenger Justin, Koos Martin R M, Avery Melissa, Bundesmann Mark, Ciszewski Gregory, Khunte Bhagyashree, Leverett Carolyn, Ostner Gregory, Ryder Tim F, Farley Kathleen A

机构信息

Medicine Design, Pfizer Inc., 445 Eastern Point Rd, Groton, Connecticut 06340, United States.

出版信息

ACS Med Chem Lett. 2024 Jul 26;15(9):1635-1644. doi: 10.1021/acsmedchemlett.4c00245. eCollection 2024 Sep 12.

Abstract

In medicinal chemistry, purification and characterization of organic compounds is an ever-growing challenge, with an increasing number of compounds being synthesized at a decreased scale of preparation. In response to this trend, we developed a parallel medicinal chemistry (PMC)-tailored platform, coupling automated purification to mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR) on a range of synthetic scales (∼3.0-75.0 μmol). Here, the generation and acquisition of 1.7 mm NMR samples is fully integrated into a high-throughput automated workflow, processing 36 000 compounds yearly. Utilizing dead volume, which is inaccessible in conventional liquid handling, NMR samples are generated on as little as 10 μg without consuming material prioritized for biological assays. As miniaturized PMC synthesis becomes the industry standard, we can now obtain quality NMR spectra from limited material. Paired with automated structure verification, this platform has the potential to allow NMR to become as important for high-throughput analysis as ultrahigh performance liquid chromatography (UPLC)-MS.

摘要

在药物化学中,有机化合物的纯化和表征是一项日益艰巨的挑战,合成的化合物数量不断增加,而制备规模却在减小。针对这一趋势,我们开发了一个针对平行药物化学(PMC)的平台,在一系列合成规模(约3.0 - 75.0 μmol)下,将自动纯化与质谱(MS)和核磁共振光谱(NMR)相结合。在这里,1.7 mm NMR样品的生成和采集完全集成到高通量自动化工作流程中,每年可处理36000种化合物。利用传统液体处理中无法利用的死体积,只需10 μg的样品即可生成NMR样品,而不会消耗优先用于生物测定的材料。随着小型化PMC合成成为行业标准,我们现在可以从有限的材料中获得高质量的NMR光谱。与自动结构验证相结合,该平台有可能使NMR在高通量分析中变得与超高效液相色谱(UPLC)-MS一样重要。

相似文献

本文引用的文献

2
Augmenting DMTA using predictive AI modelling at AstraZeneca.在阿斯利康使用预测性 AI 模型增强 DMTA。
Drug Discov Today. 2024 Apr;29(4):103945. doi: 10.1016/j.drudis.2024.103945. Epub 2024 Mar 8.
4
High-Throughput Purification in Drug Discovery: Scaling New Heights of Productivity.药物研发中的高通量纯化:提升生产力至新高度。
ACS Med Chem Lett. 2023 May 31;14(7):916-919. doi: 10.1021/acsmedchemlett.3c00073. eCollection 2023 Jul 13.
6
9
Has Artificial Intelligence Impacted Drug Discovery?人工智能是否影响了药物发现?
Methods Mol Biol. 2022;2390:153-176. doi: 10.1007/978-1-0716-1787-8_6.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验