Department of Medicinal Chemistry, Collegium Medicum in Bydgoszcz Nicolaus Copernicus University in Toruń, Jurasza 2, 85-094 Bydgoszcz, Poland; Department of Chemistry, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1.
Anal Chim Acta. 2013 Oct 3;797:13-9. doi: 10.1016/j.aca.2013.08.025. Epub 2013 Aug 20.
Quantitative structure-retention relationship (QSRR) is a technique capable of improving the identification of analytes by predicting their retention time on a liquid chromatography column (LC) and/or their properties. This approach is particularly useful when LC is coupled with a high-resolution mass spectrometry (HRMS) platform. The main aim of the present study was to develop and describe appropriate QSRR models that provide usable predictive capability, allowing false positive identification to be removed during the interpretation of metabolomics data, while additionally increasing confidence of experimental results in doping control area. For this purpose, a dataset consisting of 146 drugs, metabolites and banned compounds from World Anti-Doping Agency (WADA) lists, was used. A QSRR study was carried out separately on high quality retention data determined by reversed-phase (RP-LC-HRMS) and hydrophilic interaction chromatography (HILIC-LC-HRMS) systems, employing a single protocol for each system. Multiple linear regression (MLR) was applied to construct the linear QSRR models based on a variety of theoretical molecular descriptors. The regression equations included a set of three descriptors for each model: ALogP, BELe6, R2p and ALogP(2), FDI, BLTA96, were used in the analysis of reversed-phase and HILIC column models, respectively. Statistically significant QSRR models (squared correlation coefficient for model fitting, R(2)=0.95 for RP and R(2)=0.84 for HILIC) indicate a strong correlation between retention time and the molecular descriptors. An evaluation of the best correlation models, performed by validation of each model using three tests (leave-one-out, leave-many-out, external tests), demonstrated the reliability of the models. This paper provides a practical and effective method for analytical chemists working with LC/HRMS platforms to improve predictive confidence of studies that seek to identify small molecules.
定量构效关系（QSRR）是一种能够通过预测分析物在液相色谱柱（LC）上的保留时间和/或其性质来提高其鉴定能力的技术。当 LC 与高分辨率质谱（HRMS）平台相结合时，这种方法特别有用。本研究的主要目的是开发和描述适当的 QSRR 模型，这些模型提供可用的预测能力，允许在解释代谢组学数据时去除假阳性鉴定，同时在兴奋剂控制领域增加实验结果的可信度。为此，使用了由世界反兴奋剂机构（WADA）名单中的 146 种药物、代谢物和禁用化合物组成的数据集。对反相（RP-LC-HRMS）和亲水相互作用色谱（HILIC-LC-HRMS）系统分别进行了 QSRR 研究，为每个系统采用了单一方案。基于多种理论分子描述符，应用多元线性回归（MLR）构建线性 QSRR 模型。每个模型的回归方程都包含一组三个描述符：用于反相和 HILIC 柱模型分析的 ALogP、BELe6、R2p 和 ALogP(2)、FDI、BLTA96。统计上显著的 QSRR 模型（模型拟合的平方相关系数，RP 为 0.95，HILIC 为 0.84）表明保留时间与分子描述符之间存在很强的相关性。通过使用三种测试（留一法、留多法、外部测试）对每个模型进行验证来评估最佳相关模型，证明了模型的可靠性。本文为使用 LC/HRMS 平台的分析化学家提供了一种实用有效的方法，可提高旨在识别小分子的研究的预测置信度。
