Department of Chemical Engineering, Pukyong National University, Busan 48-513, Korea.
Department of Pharmaceutical Chemistry, Medical University of Gdańsk, Al. Gen. Hallera 107, 80-416 Gdańsk, Poland.
Int J Mol Sci. 2019 Jul 12;20(14):3443. doi: 10.3390/ijms20143443.
In this work, we employed a non-linear programming (NLP) approach via quantitative structure-retention relationships (QSRRs) modelling for prediction of elution order in reversed phase-liquid chromatography. With our rapid and efficient approach, error in prediction of retention time is sacrificed in favor of decreasing the error in elution order. Two case studies were evaluated: (i) analysis of 62 organic molecules on the Supelcosil LC-18 column; and (ii) analysis of 98 synthetic peptides on seven reversed phase-liquid chromatography (RP-LC) columns with varied gradients and column temperatures. On average across all the columns, all the chromatographic conditions and all the case studies, percentage root mean square error (%RMSE) of retention time exhibited a relative increase of 29.13%, while the %RMSE of elution order a relative decrease of 37.29%. Therefore, sacrificing %RMSE() led to a considerable increase in the elution order predictive ability of the QSRR models across all the case studies. Results of our preliminary study show that the real value of the developed NLP-based method lies in its ability to easily obtain better-performing QSRR models that can accurately predict both retention time and elution order, even for complex mixtures, such as proteomics and metabolomics mixtures.
在这项工作中，我们采用了非线性规划 (NLP) 方法，通过定量构效关系 (QSRR) 建模来预测反相液相色谱中的洗脱顺序。通过我们快速有效的方法，牺牲了保留时间预测的误差，以降低洗脱顺序的误差。评估了两个案例研究：(i) 在 Supelcosil LC-18 柱上分析 62 种有机分子；(ii) 在 7 根具有不同梯度和柱温的反相液相色谱 (RP-LC) 柱上分析 98 种合成肽。平均而言，在所有的柱上，所有的色谱条件和所有的案例研究中，保留时间的均方根误差的相对增加了 29.13%，而洗脱顺序的均方根误差相对减少了 37.29%。因此，牺牲保留时间的均方根误差（%RMSE）导致 QSRR 模型在所有案例研究中的洗脱顺序预测能力有了显著提高。初步研究结果表明，所开发的基于 NLP 的方法的真正价值在于其能够轻松获得性能更好的 QSRR 模型，这些模型能够准确预测保留时间和洗脱顺序，即使对于复杂的混合物，如蛋白质组学和代谢组学混合物也是如此。
