Li Chen1, Ciao-Sin Chen1, Yihan Sun1, N Lynn Henry2,3, Kathleen A Stringer1,4, Daniel L Hertz5,6. 1. Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI, USA. 2. Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI, USA. 3. Division of Hematology/Oncology, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA. 4. The NMR Metabolomics Laboratory, College of Pharmacy, University of Michigan, Ann Arbor, MI, USA. 5. Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI, USA. DLHertz@med.umich.edu. 6. Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI, USA. DLHertz@med.umich.edu.
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.
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.
Authors: Daniel L Hertz; Kelley M Kidwell; Kiran Vangipuram; Feng Li; Manjunath P Pai; Monika Burness; Jennifer J Griggs; Anne F Schott; Catherine Van Poznak; Daniel F Hayes; Ellen M Lavoie Smith; N Lynn Henry Journal: Clin Cancer Res Date: 2018-04-27 Impact factor: 12.531
Authors: M T Huizing; G Giaccone; L J van Warmerdam; H Rosing; P J Bakker; J B Vermorken; P E Postmus; N van Zandwijk; M G Koolen; W W ten Bokkel Huinink; W J van der Vijgh; F J Bierhorst; A Lai; O Dalesio; H M Pinedo; C H Veenhof; J H Beijnen Journal: J Clin Oncol Date: 1997-01 Impact factor: 44.544
Authors: M Joerger; J von Pawel; S Kraff; J R Fischer; W Eberhardt; T C Gauler; L Mueller; N Reinmuth; M Reck; M Kimmich; F Mayer; H-G Kopp; D M Behringer; Y-D Ko; R A Hilger; M Roessler; C Kloft; A Henrich; B Moritz; M C Miller; S J Salamone; U Jaehde Journal: Ann Oncol Date: 2016-08-08 Impact factor: 32.976
Authors: Anne-Joy M de Graan; Laure Elens; Jason A Sprowl; Alex Sparreboom; Lena E Friberg; Bronno van der Holt; Pleun J de Raaf; Peter de Bruijn; Frederike K Engels; Ferry A L M Eskens; Erik A C Wiemer; Jaap Verweij; Ron H J Mathijssen; Ron H N van Schaik Journal: Clin Cancer Res Date: 2013-05-02 Impact factor: 12.531
Authors: R Peto; C Davies; J Godwin; R Gray; H C Pan; M Clarke; D Cutter; S Darby; P McGale; C Taylor; Y C Wang; J Bergh; A Di Leo; K Albain; S Swain; M Piccart; K Pritchard Journal: Lancet Date: 2011-12-05 Impact factor: 79.321