Purpose Docetaxel is extensively metabolized by CYP3A4 in the liver, but mechanisms by which the drug is taken up into hepatocytes remain poorly understood. We hypothesized that (i) liver uptake of docetaxel is mediated by the polymorphic solute carriers OATP1B1 and OATP1B3, and (ii) that inherited genetic defects in this process may impair systemic drug elimination. Methods Transport of docetaxel was studied in vitro using various cell lines stably transfected with OATP1B1*1A (wildtype), OATP1B1*5 [c.521T>C (V174A); rs4149056], OATP1B3, or the mouse transporter Oatp1b2. Docetaxel clearance was evaluated in wildtype and Oatp1b2-knockout mice, as well as in 141 white patients with multiple variant transporter genotypes. Results Docetaxel was found to be a substrate for OATP1B1, OATP1B3, and Oatp1b2, but was not transported by OATP1B1*5. Deficiency of Oatp1b2 in mice was associated with an 18-fold decrease in docetaxel clearance (P=0.0099), which was unrelated to changes in intrinsic metabolic capacity in mouse liver microsomes. In patients, however, none of the studied common reduced-function variants in OATP1B1 or OATP1B3 were associated with docetaxel clearance (P>0.05). Conclusions The existence of at least two potentially redundant uptake transporters in the human liver with similar affinity for docetaxel supports the possibility that functional defects in both of these proteins may be required to confer substantially altered disposition phenotypes. In view of the established exposure-toxicity relationships for docetaxel, we suggest that extreme caution is warranted if docetaxel has to be administered together with agents that potently inhibit both OATP1B1 and OATP1B3.

PURPOSE Paclitaxel is used for the treatment of several solid tumors and displays a high inter-individual 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 HPLC or LC-MS/MS, and individual pharmacokinetic parameters were estimated from previously developed population pharmacokinetic models by non-linear mixed effects modeling (NONMEM). 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 (logAUC) 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, Cmax, 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 (odds ratio = 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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Purpose: Cigarette smoke is known to interact with the metabolism of several anticancer drugs. It may also affect the incidence and severity of adverse events and efficacy of chemotherapy. The main objective of this study was to examine the effects of smoking on the pharmacokinetics and toxicities of patients treated with docetaxel or paclitaxel.Experimental Design: Smoking status, toxicity profiles, and pharmacokinetic parameters (calculated by nonlinear mixed-effect modeling population analysis) were determined in 566 patients (429 nonsmokers and 137 smokers) treated with docetaxel or paclitaxel.Results: Smokers treated with docetaxel showed less grade IV neutropenia (35% vs. 52%; P ¼ 0.01) than nonsmokers. Smokers treated with paclitaxel had less grade III-IV leukopenia than nonsmokers (12% vs. 25%; P ¼ 0.03), and the white blood cell (WBC) nadir was lower in nonsmokers (median, 2.7 Â 10 9 /L; range, 0.05 Â 10 9 to 11.6 Â 10 9 /L) than in smokers (median, 3.3 Â 10 9 /L; range 0.8 Â 10 9 to 10.2 Â 10 9 /L; P ¼ 0.02). Of interest, significantly lower WBC counts and absolute neutrophil counts at baseline were seen in nonsmoking patients treated with paclitaxel (P ¼ 0.0001). Pharmacokinetic parameters were similar in smokers and nonsmokers for both taxanes.Conclusion: Cigarette smoking does not alter the pharmacokinetic determinants of docetaxel and paclitaxel. Smokers treated with docetaxel and paclitaxel have less neutropenia and leukopenia, but further research is warranted to elucidate this potential protective effect.

Dextromethorphan exposure after a single administration adequately predicted endoxifen exposure in individual patients with breast cancer taking tamoxifen. This test could contribute to the personalization and optimization of tamoxifen treatment, but it needs additional validation and simplification before being applicable in future dosing strategies.

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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.

Our data show that the POR*28 allele is associated with a lower in vivo CYP3A5 activity, but has no effects on CYP3A4-mediated erythromycin and midazolam metabolism.