Impact of CYP2C8*3 on paclitaxel clearance: a population pharmacokinetic and pharmacogenomic study in 93 patients with ovarian cancer

Bergmann, Troels K.; Brasch‐Andersen, Charlotte; Gréen, Henrik; Mirza, Mansoor Raza; Pedersen, Robert Smith; Nielsen, Flemming; Skougaard, Kristin; Wihl, Jessica; Keldsen, Nina; Damkier, Per; Friberg, Lena E.; Peterson, Curt; Vach, Werner; Karlsson, Mats O.

doi:10.1038/tpj.2010.19

Cited by 79 publications

(83 citation statements)

References 42 publications

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“…We would especially like to thank Mats O. Karlsson, Kajsa Harling, Anne-Gaëlle Dosne, Anders Kristoffersson, Oskar Alskär and Giulia Corn for discussions and extensive testing. In addition, we would like to thank the authors of (11)(12)(13)(14) who gave us permission to use their NONMEM model files and data sets for the numerical experiments.…”

Section: Acknowledgmentsmentioning

confidence: 99%

“…The data set also contains the covariate information of these subjects. The NONMEM model and the data set were provided by the authors of (11). We have added the ABS() statement to the model variables t h a t c a n n o t b e n e g a t i v e , a n d a l s o w e h a v e reparameterised the random effect parameters using fixed effect parameters (see Appendix C.1.1 for the details on how this reparameterisation can be performed) so that both fixed effect and random effect parameters can be preconditioned.…”

Section: C21 Model 1: Pharmacokinetics Model Of Paclitaxel Of Bergmmentioning

confidence: 99%

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Preconditioning of Nonlinear Mixed Effects Models for Stabilisation of Variance-Covariance Matrix Computations

Aoki

Nordgren

Hooker

2016

AAPS J

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Abstract. As the importance of pharmacometric analysis increases, more and more complex mathematical models are introduced and computational error resulting from computational instability starts to become a bottleneck in the analysis. We propose a preconditioning method for non-linear mixed effects models used in pharmacometric analyses to stabilise the computation of the variance-covariance matrix. Roughly speaking, the method reparameterises the model with a linear combination of the original model parameters so that the Hessian matrix of the likelihood of the reparameterised model becomes close to an identity matrix. This approach will reduce the influence of computational error, for example rounding error, to the final computational result. We present numerical experiments demonstrating that the stabilisation of the computation using the proposed method can recover failed variance-covariance matrix computations, and reveal non-identifiability of the model parameters.

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Section: Acknowledgmentsmentioning

confidence: 99%

Section: C21 Model 1: Pharmacokinetics Model Of Paclitaxel Of Bergmmentioning

confidence: 99%

Preconditioning of Nonlinear Mixed Effects Models for Stabilisation of Variance-Covariance Matrix Computations

Aoki

Nordgren

Hooker

2016

AAPS J

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“…The variants in CYP1B1, CYP3A5, ABCC1, ABCC10 and G767C in SLCO1B3, A-1G in ABCB1 and rs2273697 in ABCC2 were genotyped using TaqMan® pre-designed SNP genotyping assays by Applied Biosystems (Foster city, CA). SNPs in ABCG2 and rs17222723 and rs8187710 in ABCC2 were genotyped using SYBRgreen real time PCR assays as previously described [22].…”

Section: Genotyping Proceduresmentioning

confidence: 99%

Retrospective study of the impact of pharmacogenetic variants on paclitaxel toxicity and survival in patients with ovarian cancer

Bergmann

Gréen

Brasch‐Andersen

et al. 2011

Eur J Clin Pharmacol

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PurposePaclitaxel has a broad spectrum of anti tumor activity and is useful in the treatment of ovarian, breast and lung cancer. Paclitaxel is metabolized in the liver by CYP2C8 and CYP3A4 and transported by Pglycoprotein. The dose limiting toxicity is neuropathy and neutropenia, but the inter-individual variability in toxicity and also survival is large. The main purpose of this study was to investigate the impact of genetic variants in CYP2C8 and ABCB1 on the toxicity and survival. MethodsThe 182 patients previously treated for ovarian cancer with carboplatin and paclitaxel in either the AGO-OVAR-9 or the NSGO-OC9804 trial in Denmark or Sweden were eligible for this study.Genotyping was carried out on formalin fixed tissue. The patients' toxicity profiles and survival data were derived from retrospective data. CYP2C8*3, ABCB1 C1236T, G2677T/A and C3435T were chosen pre hoc for primary analysis; a host of other variants entered an exploratory analysis. ResultsClinical data and tissue was available from a total of 119 patients. Twenty-two single nucleotide polymorphism(SNPs) in 10 genes were determined. Toxicity registration was available from 710 treatment cycles. Primary analysis: no statistical significant correlation was found between CYP2C8*3, ABCB1 C1236T, G2677T/A and C3435T and neutropenia, sensoric neuropathy and overall survival. ConclusionCYP2C8*3 and the ABCB1 SNPs C1236T, G2677T/A and C3435T are not statistically significantly correlated to overall survival, sensoric neuropathy and neutropenia in 119 patients treated for ovarian cancer with paclitaxel/carboplatin.

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“…As a candidate gene approach, single-nucleotide polymorphisms (SNP) in CYP2C8 Ã 3, CYP3A5, and ABCB1 (3435C > T, 2677G > T and 1236C > T) have been tested, but these studies yielded contradictory results (5)(6)(7)(8)(9)(10).…”

Section: Introductionmentioning

confidence: 99%

A Pharmacogenetic Predictive Model for Paclitaxel Clearance Based on the DMET Platform

Graan

Elens

Smid

et al. 2013

Clinical Cancer Research

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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 ¼ 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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Impact of CYP2C8*3 on paclitaxel clearance: a population pharmacokinetic and pharmacogenomic study in 93 patients with ovarian cancer

Cited by 79 publications

References 42 publications

Preconditioning of Nonlinear Mixed Effects Models for Stabilisation of Variance-Covariance Matrix Computations

Preconditioning of Nonlinear Mixed Effects Models for Stabilisation of Variance-Covariance Matrix Computations

Retrospective study of the impact of pharmacogenetic variants on paclitaxel toxicity and survival in patients with ovarian cancer

A Pharmacogenetic Predictive Model for Paclitaxel Clearance Based on the DMET Platform

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