Pharmacy Department of Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, PR China.
State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100050, PR China.
J Pharm Biomed Anal. 2021 Sep 10;204:114224. doi: 10.1016/j.jpba.2021.114224. Epub 2021 Jun 24.
The pharmacokinetic parameters of paroxetine vary between individuals, leading to differences in efficacy. The focus of our research was to predict responses to paroxetine using a pharmacometabonomic approach combining metabolic phenotypes and pharmacokinetic parameters. The pharmacokinetics of 12 healthy volunteers treated with paroxetine over two cycles was determined using high-performance liquid chromatography tandem mass spectrometry. Metabolic profiling and phenotyping were performed on the blood samples that remained after pharmacokinetic studies, using ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry. Thirty-nine metabolites (p < 0.05) increased or decreased after treatment with paroxetine. Vitamin B6 metabolism; valine, leucine, and isoleucine biosynthesis; phenylalanine metabolism; pantothenate and coenzyme A biosynthesis; tyrosine metabolism; and glyoxylate and dicarboxylate metabolism were likely to be relevant for the effects of paroxetine. The two-stage partial least squares (PLS) strategy was used to screen potential biomarkers and predict the pharmacokinetic parameters of paroxetine. An orthogonal PLS discriminant analysis strategy was then applied to separate the high- and low-value groups based on the screened biomarkers. Pearson correlation test and receiver operating characteristic curve analysis confirmed the key prediction biomarkers. Seven common biomarkers were able to predict both the area under the curve (AUC) and the maximum concentration (C): cortisone, l-tyrosine, phenylpyruvate, l-valine, 2-oxoglutarate, l-lactate, and glycerate. Furthermore, homoprotocatechuate and l-glutamate were unique biomarkers for AUC, and citicoline and fumarate were unique biomarkers for C. The selected biomarkers were able to predict the AUC and C and discriminate good responders from poor responders to paroxetine treatment. This trial was registered with http://www.chinadrugtrials.org.cn/ (no. CTR20171590).
帕罗西汀的药代动力学参数在个体之间存在差异，导致疗效不同。我们的研究重点是使用代谢组学结合代谢表型和药代动力学参数的方法来预测帕罗西汀的反应。通过高效液相色谱串联质谱法测定 12 名健康志愿者在两个周期内接受帕罗西汀治疗后的药代动力学。在药代动力学研究后剩余的血液样本上进行代谢组学和表型分析，使用超高效液相色谱与高分辨率质谱联用。帕罗西汀治疗后，有 39 种代谢物（p<0.05）增加或减少。维生素 B6 代谢；缬氨酸、亮氨酸和异亮氨酸生物合成；苯丙氨酸代谢；泛酸和辅酶 A 生物合成；酪氨酸代谢；以及乙醛酸和二羧酸代谢可能与帕罗西汀的作用有关。采用两阶段偏最小二乘法（PLS）策略筛选潜在生物标志物并预测帕罗西汀的药代动力学参数。然后应用正交偏最小二乘法判别分析策略，根据筛选出的生物标志物将高值组和低值组分开。Pearson 相关检验和受试者工作特征曲线分析证实了关键的预测生物标志物。7 种常见生物标志物能够同时预测 AUC 和 Cmax：皮质酮、l-酪氨酸、苯丙酮酸、l-缬氨酸、2-氧代戊二酸、l-乳酸和甘油酸。此外，高香草酸和 l-谷氨酸是 AUC 的独特生物标志物，而胞磷胆碱和延胡索酸是 Cmax 的独特生物标志物。所选生物标志物能够预测 AUC 和 Cmax，并区分帕罗西汀治疗的良好反应者和不良反应者。本试验在中国临床试验注册中心注册（编号：CTR20171590）。
