Pardon Marie, Chapel Soraya, de Witte Peter, Cabooter Deirdre
Laboratory for Pharmaceutical Analysis, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Herestraat 49 Box 923, 3000, Leuven, Belgium.
Laboratory for Molecular Biodiscovery, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Herestraat 49 Box 824, 3000, Leuven, Belgium.
Anal Bioanal Chem. 2025 Aug 30. doi: 10.1007/s00216-025-06071-z.
Pharmaceutical residues in freshwater systems constitute a growing environmental problem. An important point source of these pharmaceuticals is hospital wastewater. The characterization of hospital wastewater is challenging because of its complex nature. Pharmaceuticals and their metabolites display a large variety of physicochemical properties, while matrix compounds create additional complexity. Innovative analytical approaches are hence needed to characterize these challenging samples. A promising technique is online comprehensive two-dimensional liquid chromatography (LC × LC), combining two orthogonal separation modes to increase the separation power significantly. Because of the many optimization parameters involved, method development in online LC × LC is complicated. It is difficult to predict which combinations will result in the highest peak capacity for a specific sample. In this work, different separation systems are evaluated for the online LC × LC analysis of pharmaceuticals, using an in-house developed Python-based 2D combination selector (PCS) tool. Practical peak capacities of different combinations, determined using an orthogonality score based on 12 different orthogonality metrics and predicted peak capacities, are used to select promising LC × LC conditions, including reversed-phase (RPLC) and hydrophilic interaction (HILIC) LC. Three promising combinations are further optimized, with special focus on their mobile phase incompatibility. To deal with these incompatibility issues, both active solvent modulation and flow splitting are investigated. After optimization, the RPLC × RPLC method displays the best D-peak shapes and highest effective peak capacity (1877) in line with predictions made by the PCS tool, highlighting its effectiveness for online LC × LC method development. The RPLC × RPLC method is successfully applied to identify 36 pharmaceuticals of various classes in real hospital wastewater.
淡水系统中的药物残留构成了一个日益严重的环境问题。这些药物的一个重要点源是医院废水。医院废水的特性分析具有挑战性,因为其性质复杂。药物及其代谢物表现出多种多样的物理化学性质,而基质化合物又增加了复杂性。因此,需要创新的分析方法来表征这些具有挑战性的样品。一种有前景的技术是在线全二维液相色谱(LC×LC),它结合了两种正交分离模式,显著提高了分离能力。由于涉及许多优化参数,在线LC×LC中的方法开发很复杂。很难预测哪种组合会为特定样品带来最高的峰容量。在这项工作中,使用内部开发的基于Python的二维组合选择器(PCS)工具,对不同的分离系统进行评估,用于药物的在线LC×LC分析。使用基于12种不同正交性指标的正交性得分确定的不同组合的实际峰容量和预测峰容量,用于选择有前景的LC×LC条件,包括反相(RPLC)和亲水相互作用(HILIC)液相色谱。进一步优化了三种有前景的组合,特别关注它们的流动相不相容性。为了解决这些不相容问题,研究了主动溶剂调制和分流。优化后,RPLC×RPLC方法显示出最佳的D峰形状和最高的有效峰容量(1877),与PCS工具的预测一致,突出了其在在线LC×LC方法开发中的有效性。RPLC×RPLC方法成功应用于实际医院废水中36种各类药物的鉴定。