A pharmacogenetic predictive model for paclitaxel clearance based on the DMET platform

Anne Joy M. De Graan, Laure Elens, Marcel Smid, John W. Martens, Alex Sparreboom, Annemieke J.M. Nieuweboer, Lena E. Friberg, Samira Elbouazzaoui, Erik A.C. Wiemer, Bronno Van Der Holt, Jaap Verweij, Ron H.N. Van Schaik, Ron H.J. Mathijssen*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

24 Citations (Scopus)

Abstract

Purpose: Paclitaxel is used in the treatment of solid tumors and displays high interindividual variation in exposure. Low paclitaxel clearance could lead to increased toxicity during treatment. We present a genetic prediction model identifying patients with low paclitaxel clearance, based on the drug-metabolizing enzyme and transporter (DMET)-platform, capable of detecting 1,936 genetic variants in 225 metabolizing enzyme and drug transporter genes. Experimental Design: In 270 paclitaxel-treated patients, unbound plasma concentrations were determined and pharmacokinetic parameters were estimated from a previously developed population pharmacokinetic model (NONMEM). Patients were divided into a training- and validation set. Genetic variants determined by the DMET platform were selected from the training set to be included in the prediction model when they were associated with low paclitaxel clearance (1 SD below mean clearance) and subsequently tested in the validation set. Results: A genetic prediction model including 14 single-nucleotide polymorphisms (SNP) was developed on the training set. In the validation set, this model yielded a sensitivity of 95%, identifying most patients with low paclitaxel clearance correctly. The positive predictive value of the model was only 22%. The model remained associated with low clearance after multivariate analysis, correcting for age, gender, and hemoglobin levels at baseline (P = 0.02). Conclusions: In this first large-sized application of the DMET-platform for paclitaxel, we identified a 14 SNP model with high sensitivity to identify patients with low paclitaxel clearance. However, due to the low positive predictive value we conclude that genetic variability encoded in the DMET-chip alone does not sufficiently explain paclitaxel clearance.

Original languageEnglish
Pages (from-to)5210-5217
Number of pages8
JournalClinical Cancer Research
Volume19
Issue number18
DOIs
Publication statusPublished - 15 Sept 2013

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