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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.

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.

OBJECTIVE: P450 oxidoreductase (POR) is essential for cytochrome P450 (CYP) activity in humans. The POR*28 allele (A503V) has been shown to impact on in-vitro CYP-mediated metabolism, including CYP3A isoenzymes. The aim of the present study was to determine the in vivo impact of the POR*28 allele on the pharmacokinetics of the classic CYP3A phenotyping probes midazolam and erythromycin. Whereas midazolam is metabolized by both CYP3A4 and CYP3A5, erythromycin is exclusively oxidized by CYP3A4. MATERIALS AND METHODS: To assess CYP3A activity, 108 cancer patients received midazolam and 45 others underwent the erythromycin breath test. Patients were genotyped for POR*28, CYP3A4*22 and CYP3A5*3. RESULTS: In patients expressing CYP3A5, POR*28 carriers showed 45% lower midazolam metabolic ratios compared with POR*1/*1 patients (P<0.001). This is in line with a lower CYP3A5 activity toward midazolam for POR*28 carriers. In CYP3A5 nonexpressers, POR*28 had no influence on midazolam pharmacokinetics. For erythromycin, POR*28 carriership did not influence its metabolism. CONCLUSION: 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.

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.

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.

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 = 0.01) than nonsmokers. Smokers treated with paclitaxel had less grade III-IV leukopenia than nonsmokers (12% vs. 25%; P = 0.03), and the white blood cell (WBC) nadir was lower in nonsmokers (median, 2.7 x 10(9)/L; range, 0.05 x 10(9) to 11.6 x 10(9)/L) than in smokers (median, 3.3 x 10(9)/L; range 0.8 x 10(9) to 10.2 x 10(9)/L; P = 0.02). Of interest, significantly lower WBC counts and absolute neutrophil counts at baseline were seen in nonsmoking patients treated with paclitaxel (P = 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.

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) inherited genetic defects in this process may impair systemic drug elimination. Experimental Design: Transport of docetaxel was studied in vitro using various cell lines stably transfected with OATP1B1*1A (wild-type), OATP1B1*5 [c.521T>C (V174A); rs4149056], OATP1B3, or the mouse transporter Oatp1b2. Docetaxel clearance was evaluated in wild-type and Oatp1b2-knockout mice as well as in two cohorts of patients with multiple variant transporter genotypes (n = 213). 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 caution is warranted if docetaxel has to be administered together with agents that potently inhibit both OATP1B1 and OATP1B3.