Laboratory of Analytical and Bio-analytical Chemistry, School of Pharmaceutical Sciences, University of Shizuoka, 52-1 Yada, Suruga-ku, Shizuoka 422-8526, Japan.
J Chromatogr A. 2013 Jan 18;1273:95-104. doi: 10.1016/j.chroma.2012.11.082. Epub 2012 Dec 7.
An optimization procedure of ternary isocratic mobile phase composition in the HPLC method using a statistical prediction model and visualization technique is described. In this report, two prediction models were first evaluated to obtain reliable prediction results. The retention time prediction model was constructed by modification from past respectable knowledge of retention modeling against ternary solvent strength changes. An excellent correlation between observed and predicted retention time was given in various kinds of pharmaceutical compounds by the multiple regression modeling of solvent strength parameters. The peak width of half height prediction model employed polynomial fitting of the retention time, because a linear relationship between the peak width of half height and the retention time was not obtained even after taking into account the contribution of the extra-column effect based on a moment method. Accurate prediction results were able to be obtained by such model, showing mostly over 0.99 value of correlation coefficient between observed and predicted peak width of half height. Then, a procedure to visualize a resolution Design Space was tried as the secondary challenge. An artificial neural network method was performed to link directly between ternary solvent strength parameters and predicted resolution, which were determined by accurate prediction results of retention time and a peak width of half height, and to visualize appropriate ternary mobile phase compositions as a range of resolution over 1.5 on the contour profile. By using mixtures of similar pharmaceutical compounds in case studies, we verified a possibility of prediction to find the optimal range of condition. Observed chromatographic results on the optimal condition mostly matched with the prediction and the average of difference between observed and predicted resolution were approximately 0.3. This means that enough accuracy for prediction could be achieved by the proposed procedure. Consequently, the procedure to search the optimal range of ternary solvent strength achieving an appropriate separation is provided by using the resolution Design Space based on accurate prediction.
描述了一种使用统计预测模型和可视化技术优化 HPLC 方法中三元等度流动相组成的程序。在本报告中,首先评估了两个预测模型以获得可靠的预测结果。保留时间预测模型是通过对过去关于三元溶剂强度变化的保留建模知识进行修改而构建的。通过对溶剂强度参数进行多元回归建模,为各种药物化合物提供了出色的保留时间观测值和预测值之间的相关性。采用保留时间的多项式拟合来构建半峰宽预测模型,因为即使考虑了基于矩法的柱外效应的贡献,半峰宽与保留时间之间也没有线性关系。通过该模型可以获得准确的预测结果,显示出观测到的和预测到的半峰宽之间的相关系数大多超过 0.99。然后,尝试了一种可视化分辨率设计空间的程序作为次要挑战。采用人工神经网络方法将三元溶剂强度参数与预测分辨率直接联系起来,预测分辨率是通过准确的保留时间和半峰宽预测结果确定的,并在轮廓图上以 1.5 以上的分辨率范围可视化适当的三元流动相组成。通过在案例研究中使用类似的药物化合物混合物,我们验证了预测的可能性,以找到最佳条件范围。在最佳条件下观察到的色谱结果与预测结果大多匹配,观测到的和预测到的分辨率之间的平均差异约为 0.3。这意味着所提出的程序可以达到足够的预测精度。因此,通过使用基于准确预测的分辨率设计空间,提供了一种搜索实现适当分离的三元溶剂强度最佳范围的程序。