Pharmacometabolomics of trabectedin in metastatic soft tissue sarcoma patients

Abstract

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.

Keywords: biomarkers; metabolomics; pharmacodynamics; pharmacokinetics; pharmacometabolomics; sarcoma; trabectedin.

Graphical abstract

FIGURE 1

Plasma-concentration time profile of trabectedin in 40 mSTS patients receiving 1.5 mg/m² dose of trabectedin administered as 24 h intravenous infusion.

FIGURE 2

Association between trabectedin AUC and clinical characteristics such as histotypes, tumour grading, PS, sex and age. AUC data are expressed as mean and SD. Age ≥65, PS 0 vs. 1–2. L, leimyosarcoma and lipoisarcoma; O, other sarcomas; PS, performance status; F, female; M, male; G2, G3, tumour grade. AUC normalize by total administered dose.

FIGURE 3

Refined PLS model based on pre-dose plasma metabolites (A); Variable importance projection (VIP) scores ranked, (B); Predicted vs. observed AUC/Dose plot, (C); internal validation goodness of fit (R², green) and predictability parameters (Q², blue) from the permutation analysis.

FIGURE 4

Partial least squares discriminant analysis (PLS-DA) score plot discriminated serum clinical-metabolomics profiles of PD (n = 20, red) and SD patients (n = 16, green) (A). Permutation test showed R2 (green) and Q2 (blue) validation parameters significantly different between permuted and original models (B). Variable importance in projection (VIP) of PLS-DA model ranked by increasing values (C).

FIGURE 5

(A); Plasma concentrations of variables significantly different between PD (blue) SD patients (dark blue) expressed as mean ± SE. SE: standard error. (B); Receiver operating characteristic (ROC) curve for single Hb (1) and cystahionine (2) with an AUROC of 0.78 and 0.8, respectively. The combination of Hb and cystahionone (3) and the integration of this latter model with C_max/Dose (4) achieved an AUROC of 0.87 (90% sensitivity, 75% specificity) and 0.95 (90% sensitivity, 81.3% specificity), respectively.