Literature DB >> 16299241

Association of CYP2C8, CYP3A4, CYP3A5, and ABCB1 polymorphisms with the pharmacokinetics of paclitaxel.

Anja Henningsson1, Sharon Marsh, Walter J Loos, Mats O Karlsson, Adam Garsa, Klaus Mross, Stephan Mielke, Lucia Viganò, Alberta Locatelli, Jaap Verweij, Alex Sparreboom, Howard L McLeod.   

Abstract

PURPOSE: To retrospectively evaluate the effects of six known allelic variants in the CYP2C8, CYP3A4, CYP3A5, and ABCB1 genes on the pharmacokinetics of the anticancer agent paclitaxel (Taxol). EXPERIMENTAL
DESIGN: A cohort of 97 Caucasian patients with cancer (median age, 57 years) received paclitaxel as an i.v. infusion (dose range, 80-225 mg/m(2)). Genomic DNA was analyzed using PCR RFLP or using Pyrosequencing. Pharmacokinetic variables for unbound paclitaxel were estimated using nonlinear mixed effect modeling. The effects of genotypes on typical value of clearance were evaluated with the likelihood ratio test within NONMEM. In addition, relations between genotype and individual pharmacokinetic variable estimates were evaluated with one-way ANOVA.
RESULTS: The allele frequencies for the CYP2C8*2, CYP2C8*3, CYP2C8*4, CYP3A4*3, CYP3A5*3C, and ABCB1 3435C>T variants were 0.7%, 9.2%, 2.1%, 0.5%, 93.2%, and 47.1%, respectively, and all were in Hardy-Weinberg equilibrium. The population typical value of clearance of unbound paclitaxel was 301 L/h (individual clearance range, 83.7-1055 L/h). The CYP2C8 or CYP3A4/5 genotypes were not statistically significantly associated with unbound clearance of paclitaxel. Likewise, no statistically significant association was observed between the ABCB1 3435C>T variant and any of the studied pharmacokinetic variables.
CONCLUSIONS: This study indicates that the presently evaluated variant alleles in the CYP2C8, CYP3A4, CYP3A5, and ABCB1 genes do not explain the substantial interindividual variability in paclitaxel pharmacokinetics.

Entities:  

Mesh:

Substances:

Year:  2005        PMID: 16299241     DOI: 10.1158/1078-0432.CCR-05-1152

Source DB:  PubMed          Journal:  Clin Cancer Res        ISSN: 1078-0432            Impact factor:   12.531


  45 in total

Review 1.  Part 2: pharmacogenetic variability in drug transport and phase I anticancer drug metabolism.

Authors:  Maarten J Deenen; Annemieke Cats; Jos H Beijnen; Jan H M Schellens
Journal:  Oncologist       Date:  2011-05-31

Review 2.  Recent developments in the clinical pharmacology of classical cytotoxic chemotherapy.

Authors:  Alan V Boddy
Journal:  Br J Clin Pharmacol       Date:  2006-07       Impact factor: 4.335

Review 3.  Population pharmacokinetics and pharmacodynamics for treatment optimization in clinical oncology.

Authors:  Anthe S Zandvliet; Jan H M Schellens; Jos H Beijnen; Alwin D R Huitema
Journal:  Clin Pharmacokinet       Date:  2008       Impact factor: 6.447

4.  Comparison of model-based tests and selection strategies to detect genetic polymorphisms influencing pharmacokinetic parameters.

Authors:  Julie Bertrand; Emmanuelle Comets; France Mentre
Journal:  J Biopharm Stat       Date:  2008       Impact factor: 1.051

5.  Influence of CYP3A4 genotypes in the outcome of serous ovarian cancer patients treated with first-line chemotherapy: implication of a CYP3A4 activity profile.

Authors:  Joana Assis; Deolinda Pereira; Mónica Gomes; Dânia Marques; Inês Marques; Augusto Nogueira; Raquel Catarino; Rui Medeiros
Journal:  Int J Clin Exp Med       Date:  2013-08-01

Review 6.  Clinical Pharmacokinetics of Paclitaxel Monotherapy: An Updated Literature Review.

Authors:  Tore B Stage; Troels K Bergmann; Deanna L Kroetz
Journal:  Clin Pharmacokinet       Date:  2018-01       Impact factor: 6.447

7.  Limitations in Adjuvant Breast Cancer Therapy: The Predictive Potential of Pharmacogenetics and Pharmacogenomics.

Authors:  Patrick Thurner; Christian Nanoff
Journal:  Breast Care (Basel)       Date:  2008-11-25       Impact factor: 2.860

8.  Polymorphisms of drug-metabolizing enzymes (GST, CYP2B6 and CYP3A) affect the pharmacokinetics of thiotepa and tepa.

Authors:  Corine Ekhart; Valerie D Doodeman; Sjoerd Rodenhuis; Paul H M Smits; Jos H Beijnen; Alwin D R Huitema
Journal:  Br J Clin Pharmacol       Date:  2008-11-17       Impact factor: 4.335

9.  Metabolomics Analysis of Hormone-Responsive and Triple-Negative Breast Cancer Cell Responses to Paclitaxel Identify Key Metabolic Differences.

Authors:  Delisha A Stewart; Jason H Winnike; Susan L McRitchie; Robert F Clark; Wimal W Pathmasiri; Susan J Sumner
Journal:  J Proteome Res       Date:  2016-08-03       Impact factor: 4.466

10.  Global variation in CYP2C8-CYP2C9 functional haplotypes.

Authors:  William C Speed; Soonmo Peter Kang; David P Tuck; Lyndsay N Harris; Kenneth K Kidd
Journal:  Pharmacogenomics J       Date:  2009-04-21       Impact factor: 3.550

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.