Klagkou Katerina, Pullen Frank, Harrison Mark, Organ Andy, Firth Alistair, Langley G John
Department of Chemistry, University of Southampton, Southampton SO17 1BJ, UK.
Rapid Commun Mass Spectrom. 2003;17(11):1163-8. doi: 10.1002/rcm.987.
Combinatorial chemistry is widely used within the pharmaceutical industry as a means of rapid identification of potential drugs. With the growth of combinatorial libraries, mass spectrometry (MS) became the key analytical technique because of its speed of analysis, sensitivity, accuracy and ability to be coupled with other analytical techniques. In the majority of cases, electrospray mass spectrometry (ES-MS) has become the default ionisation technique. However, due to the absence of fragment ions in the resulting spectra, tandem mass spectrometry (MS/MS) is required to provide structural information for the identification of an unknown analyte. This work discusses the first steps of an investigation into the fragmentation pathways taking place in electrospray tandem mass spectrometry. The ultimate goal for this project is to set general fragmentation rules for non-peptidic, pharmaceutical, combinatorial compounds. As an aid, an artificial intelligence (AI) software package is used to facilitate interpretation of the spectra. This initial study has focused on determining the fragmentation rules for some classes of compound types that fit the remit as outlined above. Based on studies carried out on several combinatorial libraries of these compounds, it was established that different classes of drug molecules follow unique fragmentation pathways. In addition to these general observations, the specific ionisation processes and the fragmentation pathways involved in the electrospray mass spectra of these systems were explored. The ultimate goal will be to incorporate our findings into the computer program and allow identification of an unknown, non-peptidic compound following insertion of its ES-MS/MS spectrum into the AI package. The work herein demonstrates the potential benefit of such an approach in addressing the issue of high-throughput, automated MS/MS data interpretation.
组合化学在制药行业中被广泛应用，作为快速鉴定潜在药物的一种手段。随着组合文库的不断增加，质谱（MS）因其分析速度、灵敏度、准确性以及与其他分析技术联用的能力，成为了关键的分析技术。在大多数情况下，电喷雾质谱（ES-MS）已成为默认的电离技术。然而，由于所得光谱中缺乏碎片离子，因此需要串联质谱（MS/MS）来提供结构信息，以鉴定未知分析物。本文讨论了对电喷雾串联质谱中发生的碎裂途径进行研究的初步步骤。该项目的最终目标是为非肽类、药物类组合化合物制定通用的碎裂规则。作为辅助手段，使用了一个人工智能（AI）软件包来帮助解释光谱。这项初步研究集中于确定符合上述范围的某些化合物类型的碎裂规则。基于对这些化合物的几个组合文库所进行的研究，已确定不同类别的药物分子遵循独特的碎裂途径。除了这些一般性观察结果外，还探索了这些系统的电喷雾质谱中涉及的具体电离过程和碎裂途径。最终目标是将我们的研究结果纳入计算机程序，并在将未知非肽类化合物的ES-MS/MS光谱插入AI软件包后，实现对其的鉴定。本文的工作证明了这种方法在解决高通量、自动化MS/MS数据解释问题方面的潜在益处。
