目的： 特拉贝昔单抗是一种常用于治疗转移性软组织肉瘤（mSTS）患者的抗癌药物。尽管其疗效得到认可，但在mSTS患者中观察到药物反应的显著变异。为了解决这个问题，本药物代谢组学研究旨在确定能够解释特拉贝昔单抗药代动力学和整体临床疗效个体差异的预剂量血浆代谢组学特征。方法：本研究纳入了40例接受1.5mg/m2特拉贝昔单抗通过24小时静脉注射给药的mSTS患者。利用多反应监测液相色谱-串联质谱（LC-MS/MS）分析患者的基线血浆代谢组学谱，包括氨基酸和胆汁酸的衍生物，同时分析其特拉贝昔单抗的药代动力学谱。采用多元偏最小二乘回归和单变量统计分析方法，以确定基线代谢物浓度与特拉贝昔单抗药代动力学之间的相关性；采用偏最小二乘判别分析，评估其与临床疗效之间的关联。结果：基于特拉贝昔单抗曲线面积（AUC）与预剂量代谢组学之间的相关性，得出的多元回归模型表现最佳，包括半胱氨酸、血红蛋白、牛磺胆酸、柠檬酸和苯丙氨酸/酪氨酸比值。该模型在预测药物AUC方面表现出4.6％的偏差和17.4％的精确度，有效解释了70％的个体间药代动力学变异。通过偏最小二乘判别分析，发现半胱氨酸和血红蛋白作为特定的代谢标志物，可以有效区分疾病稳定组与疾病进展组的患者。结论：本研究的发现提供了有力的证据，支持在发现特拉贝昔单抗药代动力学变异的潜在原因以及确定哪些患者最有可能从该治疗中获益方面利用预剂量代谢组学的应用。版权所有©2023 Corona, Di Gregorio, Buonadonna, Lombardi, Scalone, Steffan and Miolo.

Objective: Trabectedin is an anti-cancer drug commonly used for the treatment of patients with metastatic soft tissue sarcoma (mSTS). Despite its recognized efficacy, significant variability in pharmacological response has been observed among mSTS patients. To address this issue, this pharmacometabolomics study aimed to identify pre-dose plasma metabolomics signatures that can explain individual variations in trabectedin pharmacokinetics and overall clinical response to treatment. Methods: In this study, 40 mSTS patients treated with trabectedin administered by 24 h-intravenous infusion at a dose of 1.5 mg/m2 were enrolled. The patients' baseline plasma metabolomics profiles, which included derivatives of amino acids and bile acids, were analyzed using multiple reaction monitoring LC-MS/MS together with their pharmacokinetics profile of trabectedin. Multivariate Partial least squares regression and univariate statistical analyses were utilized to identify correlations between baseline metabolite concentrations and trabectedin pharmacokinetics, while Partial Least Squares-Discriminant Analysis was employed to evaluate associations with clinical response. Results: The multiple regression model, derived from the correlation between the AUC of trabectedin and pre-dose metabolomics, exhibited the best performance by incorporating cystathionine, hemoglobin, taurocholic acid, citrulline, and the phenylalanine/tyrosine ratio. This model demonstrated a bias of 4.6% and a precision of 17.4% in predicting drug AUC, effectively accounting for up to 70% of the inter-individual pharmacokinetic variability. Through the use of Partial least squares-Discriminant Analysis, cystathionine and hemoglobin were identified as specific metabolic signatures that effectively distinguish patients with stable disease from those with progressive disease. Conclusions: The findings from this study provide compelling evidence to support the utilization of pre-dose metabolomics in uncovering the underlying causes of pharmacokinetic variability of trabectedin, as well as facilitating the identification of patients who are most likely to benefit from this treatment.Copyright © 2023 Corona, Di Gregorio, Buonadonna, Lombardi, Scalone, Steffan and Miolo.