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Observational Study
. 2021 Sep;88(3):475-483.
doi: 10.1007/s00280-021-04300-7. Epub 2021 Jun 5.

Feasibility of pharmacometabolomics to identify potential predictors of paclitaxel pharmacokinetic variability

Li Chen  1 , Ciao-Sin Chen  1 , Yihan Sun  1 , N Lynn Henry  2   3 , Kathleen A Stringer  1   4 , Daniel L Hertz  5   6
Affiliations
Observational Study

Feasibility of pharmacometabolomics to identify potential predictors of paclitaxel pharmacokinetic variability

Li Chen et al. Cancer Chemother Pharmacol. 2021 Sep.

Abstract

Purpose: Paclitaxel is a commonly used chemotherapy drug with substantial variability in pharmacokinetics (PK) that affects treatment efficacy and toxicity. Pharmacometabolomic signatures that explain PK variability could be used to individualize dosing to improve therapeutic outcomes. The objective of this study was to identify pretreatment metabolites or metabolomic signatures that explain variability in paclitaxel PK.

Methods: This analysis was conducted using data previously collected on a prospective observational study of 48 patients with breast cancer receiving weekly 80 mg/m2 paclitaxel infusions. Paclitaxel plasma concentrations were measured during the first infusion to estimate paclitaxel time above threshold (Tc>0.05) and maximum concentration (Cmax). Metabolites measured in pretreatment whole blood by nuclear magnetic resonance spectrometry were analyzed for an association with Tc>0.05 and Cmax using Pearson correlation followed by stepwise linear regression.

Results: Pretreatment creatinine, glucose, and lysine concentrations were positively correlated with Tc>0.05, while pretreatment betaine was negatively correlated and lactate was positively correlated with Cmax (all uncorrected p < 0.05). After stepwise elimination, creatinine was associated with Tc>0.05, while betaine and lactate were associated with Cmax (all p < 0.05).

Conclusion: This study identified pretreatment metabolites that may be associated with paclitaxel PK variability demonstrating feasibility of a pharmacometabolomics approach for understanding paclitaxel PK. However, identification of more robust pharmacometabolomic predictors will be required for broad and routine application for the clinical dosing of paclitaxel.

Keywords: Biomarkers; Breast cancer; Metabolomics; Nuclear magnetic resonance; Oncology; Personalized treatment.

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Conflict of interest statement

6. Conflicts of Interest

DLH has an unpaid, informal scientific collaboration with Saladaax Inc., a company that provides paclitaxel concentration measurement for therapeutic drug monitoring. Saladax had no role in this clinical study or secondary analysis.

Figures

Fig. 1
Fig. 1
Scatterplots for metabolites correlated with Tc>0.05. Three metabolites were associated with Tc>0.05: creatinine (r=0.382, p=0.007, Panel a), glucose (r=0.335, p=0.020, Panel b), and lysine (r=0.293, p=0.043, Panel c)
Fig. 2
Fig. 2
Scatterplots for metabolites correlated with Cmax. Two metabolites were associated with Cmax: betaine (r= −0.294, p=0.045, Panel A)and lactate (r=0.299, p=0.041 Panel B)
Fig. 3
Fig. 3
Scatterplot of the final linear regression model including betaine and lactate with Cmax. There was a positive correlation between the Cmax predicted by the final multivariable linear regression model and that observed (r=0.450, p=0.007).
See this image and copyright information in PMC

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