Purpose: This study aims to describe a population pharmacokinetic model for docetaxel in Asian breast cancer patients and to evaluate the effects of single-nucleotide polymorphisms (SNP) in the cytochrome P450 3A (CYP3A) gene expression regulators, constitutive androstane receptor (CAR), pregnane X receptor (PXR), and hepatic nuclear factor 4α (HNF4α), on the pharmacokinetics of docetaxel.

Experimental Design: Docetaxel was given as an i.v. infusion of 75 mg/m2 over 1 h to 101 female breast cancer patients. CAR, PXR, and HNF4α were comprehensively sequenced. Docetaxel concentrations were measured using a liquid chromatography/tandem mass spectrometry method and its population pharmacokinetic variables, and the covariate effects of clearance predictors were estimated using a nonlinear mixed effects model.

Results: Final estimates for docetaxel clearance was 47.1 L/h/70 kg/1.75 m. Between subject variability in docetaxel clearance was 22.5%. Covariates that showed significant association with docetaxel clearance included body size, α1 acid glycoprotein and liver function. SNPs identified in the coding regions of CAR and HNF4α and 5′ untranslated region of PXR in this Asian breast cancer cohort did not seem to improve predictability of docetaxel clearance.

Conclusions: SNPs identified in CYP3A gene expression regulators CAR, HNF4α, and PXR in the Asian female breast cancer population do not seem to have any significant effect on the clearance of docetaxel, a CYP3A substrate.

Docetaxel (Taxotere, Sanofi-Aventis), a member of the taxane class of antineoplastic agents, exerts its antitumor effect by binding to and promoting stabilization of the microtubular network. Stabilization of the microtubule bundle causes cell cycle arrest and apoptosis (13). Large variability in docetaxel pharmacokinetics between patients has been reported with important implications for its pharmacodynamic responses (410). This unpredictable pharmacokinetic behavior of docetaxel has been identified as the main factor, limiting its use, and has been postulated to be attributable to its dependence on cytochrome P450 3A4 (CYP3A4)–mediated metabolism for inactivation.

Phenotyping strategies with probes targeted at the CYP3A4 pathway, docetaxel's main route of metabolism, have been tested in multiple studies as a possible tool for individualizing docetaxel dosing (1113). Yet, even with the identification of genetic polymorphisms in CYP3A, the large variation in CYP3A expression and activity has not been explained by these polymorphisms (14, 15).

From a molecular perspective, recent evidence has shown that CYP expression is partly controlled by target genes regulated at the transcriptional level by gene regulators. These include the constitutive androstane receptor (CAR), pregnane X receptor (PXR), and retinoid X receptor from the steroid family of nuclear receptors, as well as transcriptional factors, such as hepatic nuclear factor 4α (HNF4α; ref. 16) and HNF3γ (17). In vitro studies have shown that CAR, PXR, and HNF4α interact and affect CYP2C9 expression (18). Whereas PXR and CAR together are known to modify CYP3A4 gene expression, PXR has been identified as the dominant regulator (19, 20). In the current study, four single-nucleotide polymorphisms (SNP) were identified by sequencing CAR, PXR, and HNF4α in a group of Asian women with breast cancer. One SNP was identified each for CAR and PXR, whereas two 2 SNPs were identified for HNF4α. The hypothesis of this study was that these SNPs, in the coding regions of exon 5 in CAR and exons 1C and 4 of HNF4α and the 5′ untranslated region in exon 1 of PXR, may have a role in regulating CYP3A expression, thus displaying an effect on the clearance of a CYP3A substrate, such as docetaxel. The aims of this study are to establish a population pharmacokinetic model for docetaxel in Asian breast cancer patients and to determine if SNPs in CAR, PXR, and HNF4α can explain between subject variability in docetaxel clearance.

Study design. This is a single-centered, open-labeled, randomized phase II study of two different schedules of sequential docetaxel and Adriamycin chemotherapy given once every 3 weeks in stages II to IV breast cancer patients. To be eligible for this study, all patients must be female; above the age of 18; with histologically or cytologically proved stages II to IV breast cancers that have measurable primary breast tumor(s) of diameter 2.0 cm or larger; have a minimum Karnofsky performance score of 70; estimated life expectancy of at least 12 weeks; adequate hematopoietic, hepatic, and renal functions [defined, respectively, as follows: absolute neutrophil count ≥1.5 × 109/L, WBC count ≥3.5 × 109/L, platelet count ≥100 × 109/L, hemoglobin ≥9 g/dL, total bilirubin ≤1.5 × upper limit of normal, alanine aminotransferase (ALT) or aspartate aminotransferase (AST) ≤2.5 × upper limit of normal (or ≥5× in those with liver metastases), alkaline phosphatase ≤2.5 × upper limit of normal, serum creatinine concentration ≤1.5 × upper limit of normal]; and adequate contraception for women of child-bearing potential.

Patients who were pregnant; received concurrent treatment with other anticancer therapy within 30 days at accrual; received medications known to be CYP3A substrates, inhibitors, or inducers within 1-month study entry were excluded from this study. The study protocol was approved by the institution's review board, and all patients gave written informed consent.

Genotyping procedures. Whole blood was collected from patients, and DNA from peripheral mononuclear cells was extracted for comprehensive sequencing of PXR, CAR, and HNF4α as previously described (21).

Pharmacokinetic analysis. Docetaxel was given as a 75 mg/m2 i.v. infusion over 1 h, and blood samples were taken to determine docetaxel pharmacokinetics during the first dose of docetaxel at baseline, 1, 2, 4, 7, and 24 h after docetaxel infusion. Determination of docetaxel concentrations was done by isocratic liquid chromatography/tandem mass spectrometry method described previously (22). Analytic grade docetaxel reference standard was a gift from Aventis Pharmaceuticals SA.

Pharmacokinetic variables and their variability were estimated using nonlinear mixed effect modeling (NONMEM version V release 1.1, GloboMax LLC). The first-order conditional estimation method with the interaction option was used with a convergence criterion of six significant digits. Relations between docetaxel clearance and that of covariates (age, sex, race, weight, height, body surface area, tumor grade, creatinine clearance, α1 acid glycoprotein, albumin, and liver function) were tested for statistical significance. Creatinine clearance was calculated based on the Cockroft and Gault formula, but with body weight fixed at 70 kg. Effect of patient's weight and height on docetaxel clearance was modeled as a separate size descriptor, called the normal fat weight (NFWT), which was centered on 1 and standardized at the weight of a 70-kg person with height of 175 cm (NFWTSTD). The NFWT descriptor contained estimates of maximum fat free mass in kilograms, 50% of the maximum actual weight (WT50) in kilograms, and a fat fraction (FFAT).

Derivation of normal weight for a standard subject with height of 1.75 m and weight of 70 kg. Lean body mass for standard subject in kilograms,

\[\mathrm{LBM}_{\mathrm{STD}}=\mathrm{LBW}_{\mathrm{MAX}}{\times}1.75^{2}{\times}\frac{70}{(\mathrm{WT}_{50}+70{\times}1.75^{2})}\]

Normal fat weight for standard subject with height of 1.75 m and weight of 70 kg,

\[\mathrm{NFWT}_{\mathrm{STD}}=\mathrm{LBM}_{\mathrm{STD}}+\mathrm{FFAT}{\times}(70{-}\mathrm{LBM}_{\mathrm{STD}})\]

Likewise, the NFWT for any subject can be calculated based on the subject's measured height and weight.

Derivation of the size descriptor, standard normal fat weight, which is centered on 1. Standard normal fat weight (SNFWT),

\[\mathrm{SNFWT}=\frac{\mathrm{NFWT}}{\mathrm{NFWT}_{\mathrm{STD}}}\]

Liver function was categorized as the covariates HEP or HEP1. HEP was defined as having AST and/or ALT above the institutional upper limits of normal at 50 units/L and 70 units/L, respectively, whereas HEP1 was defined having alkaline phosphatase above the institutional upper limit of normal at 130 units/L.

The covariate effects of each of the SNPs from CAR, PXR, and HNF4α were applied to docetaxel clearance and investigated via NONMEM.

\[\mathrm{CL}=\mathrm{CL}_{\mathrm{POP}}{\cdot}(1+{\beta}_{\mathrm{SNP}})\]

for homozygous or heterozygous SNPs, wherein βSNP = 0 for the wild type and is the estimated covariate effect for heterozygous and homozygous SNPs of CAR, PXR, or HNF4α, where applicable. This covariate modeling method was similar to that reported by Henningsson et al. (23). The influence of body size was introduced using allometric scaling,

\[\mathrm{CL}=(\mathrm{CL}_{\mathrm{POP}}){\cdot}\left(\frac{\mathrm{NFWT}}{\mathrm{NFWT}_{\mathrm{STD}}}\right)^{3/4}\]

Finally, based on reported overlapping functions between HNF4α and CAR or PXR in the induction of CYP3A (24) gene expression, a full model with the covariate effects of individual SNPs and combinations of all interaction permutations between HNF4α exon 1C and CAR or PXR SNPs on the nonrenal component of docetaxel clearance were introduced as shown:

\[\mathrm{CL}=\mathrm{CL}_{\mathrm{POP}}{\cdot}(1+{\beta}_{\mathrm{CAR}}){\cdot}(1+{\beta}_{\mathrm{PXR}}){\cdot}(1+{\beta}_{\mathrm{HNF}4{\alpha}\mathrm{Exon}1\mathrm{C}}){\cdot}(1+{\beta}_{\mathrm{CAR}/\mathrm{PXR}}){\cdot}(1+{\beta}_{\mathrm{HNF}4{\alpha}\mathrm{Exon}1\mathrm{C}/\mathrm{PXR}}){\cdot}(1+{\beta}_{\mathrm{HNF}4{\alpha}\mathrm{Exon}1/\mathrm{CAR}})\]

where CLPOP is the population value for the nonrenal component of population clearance; βCAR, βPXR, βHNF4α Exon 1C, βCAR-PXR, βHNF4α Exon 1C–PXR, and βHNF4α Exon 1C–CAR are the covariate effects of CAR, PXR, HNF4α Exon1C variants, and their interaction terms. The covariate effects for wild type(s) were set to 0 as the reference for variants.

The random effects for between subject variability of the pharmacokinetic variables in the model were described by an exponential model for random effects. Model discrimination was based on changes of the NONMEM's objective function value (OBJ). A decrease in OBJ (ΔOBJ) of >3.84 (P < 0.05; degree of freedom, 1) was considered statistically significant. Models were compared based on visual inspection of diagnostic plots. A bootstrap sampling method with replacement was conducted on the full covariate model using 1,000 bootstrap replications. This was used to construct the 95% confidence intervals (95% CI) of the variables. If the 95% CI of any SNP effect overlapped 0, it was interpreted as not having any significant influence on the clearance of docetaxel.

A total of 95 of the 101 patients accrued for this study had both docetaxel concentrations and genotyping data that could be used for this study. In total, 466 docetaxel concentration measurements were available for pharmacokinetic modeling. Although four variants were identified from the three genes studied, Met49Val and Thr130Ile in exons 1C and 4, respectively, of HNF4α, PXR 5′ untranslated region −24381A>C, and CAR exon 5 Pro180Pro, this SNP and that of its interaction terms with SNPs in the other three genes were excluded in the covariate modeling step of this study, because variants in HNF4α exon 4 were very rare, with only two patients (2.1%) being heterozygous for the variant and none with homozygous mutation.

Table 1 summarizes baseline demographic and biochemistry characteristics of the patients. A summary of all the models tested is listed in Table 2 for comparison. In constructing the basic structural model, a two-compartment model showed significant improvement over a one-compartment model, both in terms of objective function improvement and visual inspection of individual patient fit plots. The different size descriptors, including weight, height, body surface area, and SNFWT, sex, race, tumor staging, Karnofsky performance score, and creatinine clearance were tested on the model as a covariate of clearance (CL). The covariates that resulted in a significant change in OBJ between two nested models were SNFWT, α1 acid glycoprotein, and HEP1.

Table 1.

Patient characteristics (n = 95)

CharacteristicValue (mean ± SD)
Demographics  
    Docetaxel dose (mg) 114.5 ± 12.3 
    Age, y 49.3 ± 9.9 
Karnofsky performance score  
    80%/90%/100% 4:12:79 
Race  
    Chinese/Malay/Indian/others 59:26:8:2 
Breast cancer grades  
    2:3:4 6:42:39 
Height (cm) 155 ± 5.8 
Weight (kg) 58.6 ± 14 
Body surface area (m−21.55 ± 0.16 
Baseline biochemistry measurements  
    Bilirubin (μmol/L) 6.1 ± 4.3 
    ALT (units/L) 29.1 ± 17.3 
    AST (units/L) 28.2 ± 16.5 
    Alkaline phosphatase (units/L) 83.6 ± 32.9 
    Lactate dehydrogenase (units/L) 623 ± 336.3 
    α1 acid glycoprotein (g/L) 0.86 ± 0.39 
    Serum creatinine (μmol/L) 65.2 ± 13.8 
    Creatinine clearance, calculated based on a 70-kg person (mL/min) 106.4 ± 26.4 
CharacteristicValue (mean ± SD)
Demographics  
    Docetaxel dose (mg) 114.5 ± 12.3 
    Age, y 49.3 ± 9.9 
Karnofsky performance score  
    80%/90%/100% 4:12:79 
Race  
    Chinese/Malay/Indian/others 59:26:8:2 
Breast cancer grades  
    2:3:4 6:42:39 
Height (cm) 155 ± 5.8 
Weight (kg) 58.6 ± 14 
Body surface area (m−21.55 ± 0.16 
Baseline biochemistry measurements  
    Bilirubin (μmol/L) 6.1 ± 4.3 
    ALT (units/L) 29.1 ± 17.3 
    AST (units/L) 28.2 ± 16.5 
    Alkaline phosphatase (units/L) 83.6 ± 32.9 
    Lactate dehydrogenase (units/L) 623 ± 336.3 
    α1 acid glycoprotein (g/L) 0.86 ± 0.39 
    Serum creatinine (μmol/L) 65.2 ± 13.8 
    Creatinine clearance, calculated based on a 70-kg person (mL/min) 106.4 ± 26.4 
Table 2.

Population pharmacokinetic variables and covariate effect estimates from final pharmacokinetic model

Model no.Model descriptionΔOBJDegrees of freedomP
Base model (one compartment; no covariates) — — — 
Base model (two compartments; no covariates) −758.53 <0.001 
#2 + SNFWT −8.994 <0.05 
#4 + A1AG −12.09 <0.001 
#5 + Hep −0.353 0.55 
#5 + Hep1 −5.555 <0.05 
#7 + albumin — 
#7 + age — 
#7 + race −1.247 0.26 
10 #7 + KPS −1.99 0.16 
11 #7 + tumor grade −1.061 0.3 
12 #7 + HNF4α exon 4 −0.085 0.96 
13 #7 + PXR −0.721 0.7 
14 #7 + HNF4α exon 1C −3.762 0.15 
15 #7 + CAR −3.82 0.15 
16 #7 + HNF4α exon 1C + PXR −9.934 12 0.62 
17 #7 + CAR + PXR −10.038 12 0.61 
18 #7 + HNF4α exon 1C + CAR −12.574 12 0.4 
19 #7 + HNF4α exon 1C + PXR + CAR −19.704 35 >0.9 
20 #20 + covariance block for CL and Q −63.558 <0.0001 
Model no.Model descriptionΔOBJDegrees of freedomP
Base model (one compartment; no covariates) — — — 
Base model (two compartments; no covariates) −758.53 <0.001 
#2 + SNFWT −8.994 <0.05 
#4 + A1AG −12.09 <0.001 
#5 + Hep −0.353 0.55 
#5 + Hep1 −5.555 <0.05 
#7 + albumin — 
#7 + age — 
#7 + race −1.247 0.26 
10 #7 + KPS −1.99 0.16 
11 #7 + tumor grade −1.061 0.3 
12 #7 + HNF4α exon 4 −0.085 0.96 
13 #7 + PXR −0.721 0.7 
14 #7 + HNF4α exon 1C −3.762 0.15 
15 #7 + CAR −3.82 0.15 
16 #7 + HNF4α exon 1C + PXR −9.934 12 0.62 
17 #7 + CAR + PXR −10.038 12 0.61 
18 #7 + HNF4α exon 1C + CAR −12.574 12 0.4 
19 #7 + HNF4α exon 1C + PXR + CAR −19.704 35 >0.9 
20 #20 + covariance block for CL and Q −63.558 <0.0001 

Abbreviations: CL, clearance; A1AG, α1 acid glycoprotein; Hep, AST and/or ALT above upper limit of normal; Hep1, AST and/or ALT and/or lactate dehydrogenase above upper limit of normal; KPS, Karnofsky performance score.

None of the genes alone or in dual combination managed to exert a large enough effect to show a statistically significant ΔOBJ between two nested models. Information on the individual covariate effects of each gene and the interaction terms between any two genes present in this cohort, when compared against the wild types, were available through model 20. The final estimates of the population pharmacokinetic variables, together with their covariate effects, are listed in Table 3. There was a suggestion that the CAR 180 C>T heterozygous mutation has a negative effect on docetaxel clearance, whereas CAR 180 C>T heterozygous mutation–PXR −24381 A>C wild-type mutation and CAR 180 C>T heterozygous mutation–PXR −24381 A>C homozygous mutation had an increased effect on docetaxel clearance when compared with wild type(s). Figures 1 and 2 show the respective goodness-of-fit diagnostic plot and visual predictive check with 90% CI of the final docetaxel model.

Table 3.

Final estimates of docetaxel population pharmacokinetic variables in female Asian breast cancer patients based on model #20 in Table 2 

VariableMedian population estimate (between subject variability)Bootstrap (95% CI)
Fixed effects   
    V1 (L/h/70 kg/1.75 m) 17.0 (20.9%) (15.0 to 19.0) 
    Q (L/h/70 kg/1.75 m) 11.7 (31.9%) (10.0 to 13.7) 
    V2 (L/h/70 kg/1.75 m) 173.0 (0.6%) (150.0 to 200.03) 
    CL (L/h/70 kg/1.75 m) 47.1 (22.5%) (39.1 to 58.4) 
    MRT (h) 4.04 (3.27 to 5.0) 
    A1AG −0.18 (−0.24 to −0.08) 
    FFAT — 
    FHEP1 −0.14 (−0.31 to 0.01) 
Covariate effects of SNPs   
    CAR 180 C>T   
        Wild type (n = 24) — 
        Heterozygous (n = 39) −0.7 (−0.88 to −0.32) 
        Homozygous (n = 32) −0.44 (−0.78 to 0.16) 
    PXR −24381 A>C   
        Wild type (n = 55) — 
        Heterozygous (n = 31) 0.13 (−0.62 to 2.13) 
        Homozygous (n = 9) −0.6 (−0.91 to 0.79) 
    HNF4α 49 A>G (Exon 1C)   
        Wild type (n = 23) — 
        Heterozygous (n = 40) −0.07 (−0.56 to 1.07) 
        Homozygous (n = 32) −0.024 (−0.69 to 0.68) 
    CAR-PXR interaction terms   
        Wild type–wild type (n = 10) — 
        Wild type–heterozygous (n = 10) -0.54 (−0.8 to 0.15) 
        Wild type–homozygous (n = 4) 0.81 (−0.44 to 4.03) 
        Heterozygous–wild type (n = 24) 1.81 (0.4 to 4.1) 
        Heterozygous-heterozygous (n = 12) 0.46 (−0.46 to 3.15) 
        Heterozygous-homozygous (n = 3) 5.77 (1.04 to 16.7) 
        Homozygous–wild type (n = 21) 0.2 (−0.41 to 1.56) 
        Homozygous-heterozygous (n = 9) −0.33 (−0.73 to 1.23) 
        Homozygous-homozygous (n = 2) 1.38 (−0.07 to 5.51) 
    HNF4α exon 1-PXR interaction terms   
        Wild type–wild type (n = 18) — 
        Wild type–heterozygous (n = 5) 0.65 (−0.33 to 2.72) 
        Wild type–homozygous (n = 0) — — 
        Heterozygous–wild type (n = 21) 0.04 (−0.53 to 1.33) 
        Heterozygous-heterozygous (n = 14) 0.74 (−0.27 to 3.28) 
        Heterozygous-homozygous (n = 5) 0.29 (−0.67 to 4.7) 
        Homozygous–wild type (n = 16) 0.27 (−0.36 to 1.75) 
        Homozygous-heterozygous (n = 12) 0.98 (−0.21 to 4.16) 
        Homozygous-homozygous (n = 4) 0.54 (−0.56 to 4.64) 
    HNF4α exon 1-CAR interaction terms   
        Wild type–wild type (n = 5) — 
        Wild type–heterozygous (n = 8) 0.02 (−0.57 to 1.55) 
        Wild type–homozygous (n = 10) 0.29 (−0.45 to 2.09) 
        Heterozygous–wild type (n = 11) 0.004 (−0.61 to 1.28) 
        Heterozygous-heterozygous (n = 20) −0.01 (−0.61 to 1.38) 
        Heterozygous-homozygous (n = 9) 0.39 (−0.41 to 2.74) 
        Homozygous–wild type (n = 8) −0.06 (−0.58 to 1.16) 
        Homozygous-heterozygous (n = 11) 0.23 (−0.53 to 2.35) 
        Homozygous-homozygous (n = 13) 0.54 (−0.34 to 2.82) 
    Residual error   
        Proportional error (%) 30.0 — 
        Additive error (mg/L) — 
VariableMedian population estimate (between subject variability)Bootstrap (95% CI)
Fixed effects   
    V1 (L/h/70 kg/1.75 m) 17.0 (20.9%) (15.0 to 19.0) 
    Q (L/h/70 kg/1.75 m) 11.7 (31.9%) (10.0 to 13.7) 
    V2 (L/h/70 kg/1.75 m) 173.0 (0.6%) (150.0 to 200.03) 
    CL (L/h/70 kg/1.75 m) 47.1 (22.5%) (39.1 to 58.4) 
    MRT (h) 4.04 (3.27 to 5.0) 
    A1AG −0.18 (−0.24 to −0.08) 
    FFAT — 
    FHEP1 −0.14 (−0.31 to 0.01) 
Covariate effects of SNPs   
    CAR 180 C>T   
        Wild type (n = 24) — 
        Heterozygous (n = 39) −0.7 (−0.88 to −0.32) 
        Homozygous (n = 32) −0.44 (−0.78 to 0.16) 
    PXR −24381 A>C   
        Wild type (n = 55) — 
        Heterozygous (n = 31) 0.13 (−0.62 to 2.13) 
        Homozygous (n = 9) −0.6 (−0.91 to 0.79) 
    HNF4α 49 A>G (Exon 1C)   
        Wild type (n = 23) — 
        Heterozygous (n = 40) −0.07 (−0.56 to 1.07) 
        Homozygous (n = 32) −0.024 (−0.69 to 0.68) 
    CAR-PXR interaction terms   
        Wild type–wild type (n = 10) — 
        Wild type–heterozygous (n = 10) -0.54 (−0.8 to 0.15) 
        Wild type–homozygous (n = 4) 0.81 (−0.44 to 4.03) 
        Heterozygous–wild type (n = 24) 1.81 (0.4 to 4.1) 
        Heterozygous-heterozygous (n = 12) 0.46 (−0.46 to 3.15) 
        Heterozygous-homozygous (n = 3) 5.77 (1.04 to 16.7) 
        Homozygous–wild type (n = 21) 0.2 (−0.41 to 1.56) 
        Homozygous-heterozygous (n = 9) −0.33 (−0.73 to 1.23) 
        Homozygous-homozygous (n = 2) 1.38 (−0.07 to 5.51) 
    HNF4α exon 1-PXR interaction terms   
        Wild type–wild type (n = 18) — 
        Wild type–heterozygous (n = 5) 0.65 (−0.33 to 2.72) 
        Wild type–homozygous (n = 0) — — 
        Heterozygous–wild type (n = 21) 0.04 (−0.53 to 1.33) 
        Heterozygous-heterozygous (n = 14) 0.74 (−0.27 to 3.28) 
        Heterozygous-homozygous (n = 5) 0.29 (−0.67 to 4.7) 
        Homozygous–wild type (n = 16) 0.27 (−0.36 to 1.75) 
        Homozygous-heterozygous (n = 12) 0.98 (−0.21 to 4.16) 
        Homozygous-homozygous (n = 4) 0.54 (−0.56 to 4.64) 
    HNF4α exon 1-CAR interaction terms   
        Wild type–wild type (n = 5) — 
        Wild type–heterozygous (n = 8) 0.02 (−0.57 to 1.55) 
        Wild type–homozygous (n = 10) 0.29 (−0.45 to 2.09) 
        Heterozygous–wild type (n = 11) 0.004 (−0.61 to 1.28) 
        Heterozygous-heterozygous (n = 20) −0.01 (−0.61 to 1.38) 
        Heterozygous-homozygous (n = 9) 0.39 (−0.41 to 2.74) 
        Homozygous–wild type (n = 8) −0.06 (−0.58 to 1.16) 
        Homozygous-heterozygous (n = 11) 0.23 (−0.53 to 2.35) 
        Homozygous-homozygous (n = 13) 0.54 (−0.34 to 2.82) 
    Residual error   
        Proportional error (%) 30.0 — 
        Additive error (mg/L) — 

Abbreviations: V1, volume of distribution in the central compartment; Q, intercompartmental clearance between central and peripheral compartments; V2, volume of distribution in peripheral compartment; FHEP1, liver function index when AST and/or ALT were above upper limits of normal.

Fig. 1.

Goodness-of-fit diagnostic plots for model-predicted docetaxel concentrations (mg/L) against actual measured concentration (mg/L). Line of identity (perforated line) and line of best-fit (solid line) are overlapping. Pearson's correlation, R2 = 0.833.

Fig. 1.

Goodness-of-fit diagnostic plots for model-predicted docetaxel concentrations (mg/L) against actual measured concentration (mg/L). Line of identity (perforated line) and line of best-fit (solid line) are overlapping. Pearson's correlation, R2 = 0.833.

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Fig. 2.

Validation of the final pharmacokinetic model through visual predictive check with a simulated dataset of 100 × 95 patients. A, a plot of actual observations of docetaxel concentrations (gray dots) versus the median values (thick line) and 90% prediction intervals (perforated lines) of the simulated dataset over ∼24 h. B, a plot of the median values of observed docetaxel concentrations (thick black line), its 90% observation intervals (perforated black lines) versus the median values of the simulated dataset (thick gray line) and its 90% prediction intervals (thin gray lines).

Fig. 2.

Validation of the final pharmacokinetic model through visual predictive check with a simulated dataset of 100 × 95 patients. A, a plot of actual observations of docetaxel concentrations (gray dots) versus the median values (thick line) and 90% prediction intervals (perforated lines) of the simulated dataset over ∼24 h. B, a plot of the median values of observed docetaxel concentrations (thick black line), its 90% observation intervals (perforated black lines) versus the median values of the simulated dataset (thick gray line) and its 90% prediction intervals (thin gray lines).

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We have developed a population pharmacokinetic model for docetaxel in Asian breast cancer patients. The covariates that showed statistically significant improvement to model fitting included renal function, a weight descriptor SNFWT (derived from height, weight, sex, and a predicted fat content), which did better than body surface area, α1 acid glycoprotein concentrations, and an index for abnormal liver chemistries based on AST and ALT. Because the final estimate for the fat fraction, FFAT, was 0, the size descriptor was essentially similar to lean body mass. These covariates have all been previously identified to be associated with docetaxel clearance (6, 7, 12, 25). Because docetaxel is known to undergo extensive hepatic metabolism with the CYP3A pathway, it is expected that differences in liver function tests will have profound effect on its clearance. Conversely, because <10% of docetaxel is eliminated by urinary excretion (26) and all patients in this study have renal function indices with the reference range, it was not unexpected that creatinine clearance did not exhibit statistical significance on docetaxel clearance.

Covariates that were tested but did not exert an effect significant enough to be retained in our final model, as determined by the magnitude of ΔOBJ, but had previously been reported to have significant independent effect on docetaxel clearance elsewhere were age (5, 25, 27), body surface area (5, 25, 27), and albumin (7, 25). In this study, we also tested sex, tumor grade, and performance status against docetaxel clearance, but these covariates did not improve model fitting significantly.

The genotype model (model 19 versus model 7) was unable to show any statistically significant improvement in model fitting, thereby rejecting the genetic variants as predictors for docetaxel clearance. A covariance block added to capture correlations between docetaxel clearance (CL) and intercompartmental clearance (Q) had significant effect in improving model fit.

Based on the covariate effects and their 95% CIs listed in Table 3, interactions between CAR 180 C>T heterozygous variant and PXR −24381 A>C homozygous variant, CAR 180 C>T homozygous variant, and PXR −24381 A>C homozygous variant seemed to show much larger covariate effects than their respective wild types. However, the sample sizes of patients with combinations of these two genotype variants were very small, that is, three and two, respectively. In addition, the 95% CI of the latter straddled 0. To be able to detect a 20% (7 L/h/70 kg/1.75 m) difference in clearance from the wild type, a sample size of 26 per subgroup will be required, assuming a power of 90% and an α of 0.05. Hence, the sample sizes for these two subgroups were probably too small to detect if a difference in clearance from wild type truly existed.

Further confirmation that the large covariate effects were probably a result of insufficient numbers in those subgroups were done via a one-way ANOVA analysis with Dunnett's t test, using the wild type(s) as control on SPSS for Windows, release 13.0. The P values for CAR heterozygous–PXR homogygous and CAR homozygous–PXR homozygous variants were 0.918 and 0.967, respectively. Closer examination of the mean docetaxel clearances in these two groups also showed that they were not greater than those of other CAR-PXR variants (Table 4).

Table 4.

Mean docetaxel clearance, their SDs for CAR and PXR variants derived from the individual estimates based on model #20 in Table 2, and the P values compared with wild type

CAR-PXR variantsDocetaxel clearance (L/h/70 kg/1.75 m)
MeanSDP
Wild type–wild type (n = 10) 27.44 6.62 1.0 
Wild type–heterozygous (n = 10) 23.89 4.95 0.719 
Wild type–homozygous (n = 4) 25.72 4.03 0.999 
Heterozygous–wild type (n = 24) 25.48 7.14 0.954 
Heterozygous-heterozygous (n = 12) 24.02 7.12 0.714 
Heterozygous-homozygous (n = 3) 31.3 9.64 0.918 
Homozygous–wild type (n = 21) 27.08 4.37 1.0 
Homozygous-heterozygous (n = 9) 30.1 5.74 0.922 
Homozygous-homozygous (n = 2) 31.24 3.33 0.967 
CAR-PXR variantsDocetaxel clearance (L/h/70 kg/1.75 m)
MeanSDP
Wild type–wild type (n = 10) 27.44 6.62 1.0 
Wild type–heterozygous (n = 10) 23.89 4.95 0.719 
Wild type–homozygous (n = 4) 25.72 4.03 0.999 
Heterozygous–wild type (n = 24) 25.48 7.14 0.954 
Heterozygous-heterozygous (n = 12) 24.02 7.12 0.714 
Heterozygous-homozygous (n = 3) 31.3 9.64 0.918 
Homozygous–wild type (n = 21) 27.08 4.37 1.0 
Homozygous-heterozygous (n = 9) 30.1 5.74 0.922 
Homozygous-homozygous (n = 2) 31.24 3.33 0.967 

Allelic frequencies in the coding regions of PXR were found to be relatively low in the Dutch population, and only three linkages were found between PXR gene and the CYP3A gene (28). Hence, it is possible that PXR SNPs in the exonic regions may not play a role important enough in explaining CYP3A expression variability. In an in vitro study by Chen et al., evidences seem to point to possible cross-talk between CAR-PXR sites and HNF4α binding sites in CYP2C9 promoter region (18).

The results of the genotype covariate model in this study showed that the SNPs in CAR, PXR, and HNF4α did not have significant pharmacokinetic implications on the clearance of docetaxel, a CYP3A substrate. It is likely that for these regulator genes to have an effect on CYP3A expression, which in turn has to be large enough to show as having functional implications on the clearance of its substrates, a host of other factors and a more complex regulating mechanism are involved.

Grant support: National Medical Research Council of Singapore grant NMRC/030/2000 and Biomedical Research Council of Singapore grant BMRC 01/1/26/18/060.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

We thank Dr. Yiong Huak Chan (Yong Loo Lin School of Medicine, National University of Singapore) for providing valuable suggestions to the biostatistical methods.

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