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- de Graan, Anne-Joy M., et al.
(författare)
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CYP3A4*22 Genotype and Systemic Exposure Affect Paclitaxel-Induced Neurotoxicity
- 2013
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Ingår i: Clinical Cancer Research. - 1078-0432 .- 1557-3265. ; 19:12, s. 3316-3324
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Paclitaxel is used for the treatment of several solid tumors and displays a high interindividual variation in exposure and toxicity. Neurotoxicity is one of the most prominent side effects of paclitaxel. This study explores potential predictive pharmacokinetic and pharmacogenetic determinants for the onset and severity of neurotoxicity. Experimental Design: In an exploratory cohort of patients (n = 261) treated with paclitaxel, neurotoxicity incidence, and severity, pharmacokinetic parameters and pharmacogenetic variants were determined. Paclitaxel plasma concentrations were measured by high-performance liquid chromatography or liquid chromatography/tandem mass spectrometry, and individual pharmacokinetic parameters were estimated from previously developed population pharmacokinetic models by nonlinear mixed effects modeling. Genetic variants of paclitaxel pharmacokinetics tested were CYP3A4*22, CYP2C8*3, CYP2C8*4, and ABCB1 3435 C>T. The association between CYP3A4*22 and neurotoxicity observed in the exploratory cohort was validated in an independent patient cohort (n = 239). Results: Exposure to paclitaxel ((log)AUC) was correlated with severity of neurotoxicity (P < 0.00001). Female CYP3A4*22 carriers were at increased risk of developing neurotoxicity (P = 0.043) in the exploratory cohort. CYP3A4*22 carrier status itself was not associated with pharmacokinetic parameters (CL, AUC, C-max, or T->0.05) of paclitaxel in males or females. Other genetic variants displayed no association with neurotoxicity. In the subsequent independent validation cohort, CYP3A4*22 carriers were at risk of developing grade 3 neurotoxicity (OR = 19.1; P = 0.001). Conclusions: Paclitaxel exposure showed a relationship with the severity of paclitaxel-induced neurotoxicity. In this study, female CYP3A4*22 carriers had increased risk of developing severe neurotoxicity during paclitaxel therapy. These observations may guide future individualization of paclitaxel treatment.
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| 2. |
- de Graan, Anne-Joy M., et al.
(författare)
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A Pharmacogenetic Predictive Model for Paclitaxel Clearance Based on the DMET Platform
- 2013
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Ingår i: Clinical Cancer Research. - 1078-0432 .- 1557-3265. ; 19:18, s. 5210-5217
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Tidskriftsartikel (refereegranskat)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.
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