Automated open-access liquid chromatography high resolution mass spectrometry to support drug discovery projects

被引:4
|
作者
Fontana, Alberto [1 ]
Iturrino, Laura [1 ]
Corens, David [2 ]
Crego, Antonio L. [3 ]
机构
[1] Janssen Res & Dev, Analyt Sci, SA C Jarama 75A, Toledo 45007, Spain
[2] Janssen Res & Dev, Analyt Sci, Turnhoutseweg 30, B-2340 Beerse, Belgium
[3] Univ Alcala, Fac Sci, Dept Analyt Chem Phys Chem & Chem Engn, Km 33-600, Madrid 28871, Spain
关键词
Automation; Open-access liquid chromatography; Open-access high resolution mass; spectrometry; Drug Discovery; IONIZATION;
D O I
10.1016/j.jpba.2019.112908
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
The need of a continuous productivity increases in medicinal chemistry laboratories of the pharmaceutical industry motivated the development, over the years, of new software solutions to enable Open-Access in many analytical techniques such as NMR or LC, among others, to characterize and assess the purity of new molecules. These approaches have been widely spread in LC with low resolution MS systems, but similar automated platforms have been rather less explored with high resolution MS. In this work, an improved Automated Open-Access methodology on an UHPLC with DAD coupled to ESI and quadrupole time-off-light MS system is described. Detailed reports from standard UHPLC-MS runs containing chromatograms and different spectra (MS with different fragmentation) are automatically sent to the chemists. High resolution MS data is typically achieved within +/- 1 mDa mass accuracy regardless of sample concentration. Upon training, chemists log-in samples into the system by selecting appropriate methods, being able to interpret the results by themselves in 95% of the cases. The instrument is working unattended, except for a limited number of samples (5%) which require more complex experiments. To the best of our knowledge, this is the first time a completely automated Open-Access LC-HRMS approach has been implemented for medicinal chemists of a pharmaceutical industry. (C) 2019 Elsevier B.V. All rights reserved.
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页数:11
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