Spots Global Cancer Trial Database for Pharmacometabolomic of Trabectedin in Soft Tissue Patients

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Trial Identification

Brief Title: Pharmacometabolomic of Trabectedin in Soft Tissue Patients

Official Title: Research of Serum and Urine Metabolomic Biomarkers Predictive Pharmacokinetic Parameters of Trabectidin in Patients With Soft Tissues Sarcomas

Study ID: NCT04394728

Conditions

Sarcoma

Interventions

Trabectedin

Study Description

Brief Summary: This perspective, mono institutional study is addressed to find potential serum and urine biomarkers predictive of the pharmacokinetic and pharmacodynamic profile of soft tissue sarcomas patients treated with trabectedin.

Detailed Description: This investigation enrolled patients with unresectable and/or metastatic soft tissue sarcoma not responsive to the first-line treatment based on anthracycline/ifosfamide. Patients underwent trabectedin monotherapy that was administered intravenously at the dose of 1.3 mg/m2 every 21 days. Single overnight fasting urine and blood samples were collected on day-1 of the first trabectedin administration. Plasma pharmacokinetics was performed during cycle 1. Blood samples, drawn from a site separate from the drug infusion site, were obtained prior to the infusion (basal) at 2, 8, 24 (end of infusion) and 0.5, 1.0, 4.0, 8.0, 24.0 after the end of the infusion. Plasma concentrations of trabectedin were measured by liquid chromatography, tandem mass spectrometry assay (LC-MS/MS) and the pharmacokinetic parameters (Cmax, Clearance, AUC and T1/2) were calculated from the concentration-time curve using a non-compartmental model. Metabolomics profiles were explored by LC-MS/MS in predose urine and serum and encompassed a total of 192: a) 45 amino acid derivatives, virtually involved in a wide set of biochemical pathways; b) 40 different acylcarnitines, principally involved in the cellular energy metabolism; c) 15 lysophosphatidylcholine metabolites, 77 phosphatidylcholine derivatives, and 15 sphingomyelins, involved in fatty acid metabolism and cellular signaling. The identification of predictive metabolomics biomarkers is performed using univariate and multivariate statistical analyses.