Clinical Cancer Research Joint Metastasis Research Society-AACR Conference on Metastasis Translational Cancer Medicine 2008: Cancer Clinical Trials and Personalized Medicine
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Cancer Research Clinical Cancer Research
Cancer Epidemiology Biomarkers & Prevention Molecular Cancer Therapeutics
Molecular Cancer Research Cancer Prevention Research
Cancer Prevention Journals Portal Cancer Reviews Online
Annual Meeting Education Book Cell Growth & Differentiation
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Baker, S. D.
Right arrow Articles by Sparreboom, A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Baker, S. D.
Right arrow Articles by Sparreboom, A.
Clinical Cancer Research Vol. 10, 8341-8350, December 15, 2004
© 2004 American Association for Cancer Research

Clinical Trials

Factors Affecting Cytochrome P-450 3A Activity in Cancer Patients

Sharyn D. Baker1, Ron H. N. van Schaik2, Laurent P. Rivory5, Albert J. ten Tije4, Kimberly Dinh1, Wilfried J. Graveland3, Paul W. Schenk2, Kellie A. Charles5, Stephen J. Clarke6, Michael A. Carducci1, William P. McGuire7, Fitzroy Dawkins8, Hans Gelderblom9, Jaap Verweij4 and Alex Sparreboom10

1 The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, Maryland; Departments of 2 Clinical Chemistry, 3 Biostatistics, and 4 Medical Oncology, Erasmus University Medical Center, Rotterdam, the Netherlands; Departments of 5 Pharmacology and 6 Medical Oncology, University of Sydney, Sydney, Australia; 7 Franklin Square Hospital Center, Baltimore, Maryland; 8 Howard University Cancer Center, Washington, DC; 9 Leiden University Medical Center, Leiden, the Netherlands; and 10 Clinical Pharmacology Research Core, National Cancer Institute, Bethesda, Maryland


   ABSTRACT
Top
 ABSTRACT
INTRODUCTION
 PATIENTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Purpose: The purpose is to identify the demographic, physiologic, and inheritable factors that influence CYP3A activity in cancer patients

Experimental Design: A total of 134 patients (62 females; age range, 26 to 83 years) underwent the erythromycin breath test as a phenotyping probe of CYP3A. Genomic DNA was screened for six variants of suspected functional relevance in CYP3A4 (CYP3A4*1B, CYP3A4*6, CYP3A4*17, and CYP3A4*18) and CYP3A5 (CYP3A5*3C and CYP3A5*6).

Results: CYP3A activity (AUC0–40min) varied up to 14-fold in this population. No variants in the CYP3A4 and CYP3A5 genes were a significant predictor of CYP3A activity (P > 0.2954). CYP3A activity was reduced by ~50% in patients with concurrent elevations in liver transaminases and alkaline phosphatase or elevated total bilirubin (P < 0.001). In a multivariate analysis, CYP3A activity was not significantly influenced by age, sex, and body size measures (P > 0.05), but liver function combined with the concentration of the acute-phase reactant, {alpha}-1 acid glycoprotein, explained ~18% of overall variation in CYP3A activity (P < 0.001).

Conclusions: These data suggest that baseline demographic, physiologic, and chosen genetic polymorphisms have a minor impact on phenotypic CYP3A activity in patients with cancer. Consideration of additional factors, including the inflammation marker C-reactive protein, as well as concomitant use of other drugs, food constituents, and complementary and alternative medicine with inhibitory and inducible effects on CYP3A, is needed to reduce variation in CYP3A and treatment outcome to anticancer therapy.


   INTRODUCTION
Top
 ABSTRACT
 INTRODUCTION
PATIENTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Cancer chemotherapy is characterized by a wide variation in response among patients. This is due, in part, to pharmacokinetic variability. The most widely used strategy to decrease pharmacokinetic variability is to normalize a drug dose to the patient’s body surface area. Because body surface area-based dosing strategies do not reduce interindividual variability in anticancer drug pharmacokinetics (1) , other measures to predict drug disposition and effects are needed. As cytochrome P-450 3A (CYP3A) is involved in the metabolism of ~50% of all prescribed drugs (2) , including many anticancer agents, phenotyping strategies to predict an individual’s CYP3A activity before cytotoxic chemotherapy treatment is one approach for dose individualization. Various noninvasive in vivo probes for evaluating CYP3A activity have been described and several have been shown to correlate with drug clearance (3) . The most widely tested and accepted CYP3A probes are midazolam and erythromycin, although selection of the ideal CYP3A-phenotyping probe remains controversial (4 , 5) .

Little is known regarding factors affecting CYP3A activity in cancer patients. Rivory et al. (6) noted an association between the inflammatory response and CYP3A activity, which was assessed with the erythromycin breath test, in 40 patients with advanced cancer. CYP3A activity was inversely correlated with both inflammatory markers C-reactive protein and {alpha}-1 acid glycoprotein, with the former accounting for 44% of interpatient variation. In the current study, the influence of patient characteristics, including age, body size, liver function and sex, the acute phase reactant {alpha}-1 acid glycoprotein, and CYP3A4 and CYP3A5 genotypes on CYP3A activity, as assessed with the erythromycin breath test, was explored in 134 patients with advanced cancer.


   PATIENTS AND METHODS
Top
 ABSTRACT
 INTRODUCTION
 PATIENTS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Patients.
Patients were enrolled to one of two clinical protocols where the erythromycin breath test was performed at baseline (see below) before initiation of chemotherapy treatment. Patients had advanced solid tumors (histologically or cytologically confirmed). Criteria for inclusion of patients into this study were as follows: (a) age ≥ 18 years; (b) performance score ≤ 2 according to the Eastern Cooperative Oncology Group criteria; and (c) serum creatinine ≤ 2.0x the institutional upper limit of normal. Patients with varying degrees of liver impairment were included in this study and grouped according to the following: liver function group 1, total bilirubin < 1.5x upper limit of normal and no elevations in aspartate aminotransferase (AST), alanine aminotransferase (ALT), or alkaline phosphatase as described for groups 2, 3A, and 3B; liver function group 2, total bilirubin <1.5x upper limit of normal, elevations in AST and/or ALT > 1.0x upper limit of normal concurrent with alkaline phosphatase ≥ 2.5x upper limit of normal, or AST and/or ALT ≥ 1.5x upper limit of normal concurrent with alkaline phosphatase > 1.0x upper limit of normal, or isolated elevations of AST and/or ALT or alkaline phosphatase ≥ 5.0x upper limit of normal; liver function group 3A, total bilirubin < 1.5x upper limit of normal, concurrent elevations in AST and/or ALT ≥ 1.5x upper limit of normal concurrent with alkaline phosphatase ≥ 2.5x upper limit of normal; and group 3B, total bilirubin ≥ 1.5x upper limit of normal with any elevations in liver transaminases or alkaline phosphatase.

Patients were not eligible for the clinical trial conducted in Baltimore, Maryland, Washington, DC, and Rotterdam and Leiden, the Netherlands (study protocol 1) if they were concurrently taking phenytoin, carbamazepine, barbiturates, rifampin, phenobarbital, St. John’s wort, and/or ketoconazole. Patients enrolled to the clinical trial in Sydney, Australia (study protocol 2) were not eligible if they were concurrently taking medications that were known to induce or inhibit CYP3A activity. The dose, frequency, and duration of all concomitant drugs were recorded. The clinical protocols were approved by the local institutional review boards (Baltimore, Maryland, Washington, DC, Rotterdam and Leiden, the Netherlands, and Sydney, Australia), and all patients provided written informed consent before enrollment.

Pretreatment evaluations included in this study were performance status, height, weight, and the following serum chemistries: serum creatinine, alkaline phosphatase, AST, ALT, total bilirubin, and {alpha}-1 acid glycoprotein. Body surface area was calculated with Mosteller’s formula: body surface area = [height (cm) x weight (kg) ÷ 3600]0.5 (7) . Body mass index (BMI) was calculated with the formula: BMI = [weight (kg)/(height/100)2].

Erythromycin Breath Test.
For study protocol 1, the erythromycin breath test dose and collection balloons were obtained from Metabolic Solutions (Nashua, NH). The dose consisted of 0.04 mg of [14C-N-methyl]-erythromycin, containing 3 µCi of radioactivity, dissolved in 4.5 mL of 5% dextrose solution. The dose was administered as an i.v. injection over ~1 minute. Breath samples were collected in balloons postinjection at 5, 10, 15, 20, 25, 30, and 40 minutes. Samples were shipped to Metabolic Solutions for measurement of breath carbon dioxide. The data were reported as the flux of 14CO2, expressed as a percentage of dose exhaled per minute at each collection time point assuming a CO2 output of 5 mmol/min/m2 body surface area (8) .

For study protocol 2, the erythromycin breath test was performed as described previously (9) . Briefly, 4 µCi of 14C-erythromycin (55 mCi/mmol N-methyl-14C, NEN Life Science Products, Inc., Boston, MA) was administered as an i.v. injection, and breath samples were collected into gas-tight balloons (Pytest, Ballard Medical Products, UT) postinjection at 5, 10, 15, 20, 25, 30 and 40 minutes. Breath samples were processed by bubbling the collected gas through a capture solution consisting of hyamine hydroxide 10x (Packard, Sydney, New South Wales, Australia) in 50:50 (v/v) methanol:etomidate to which a trace of phenolphthalein was added. After the addition of scintillation solution (Ultima Gold, Packard) and counting, the data were reported as the flux of 14CO2, expressed as a percentage of dose exhaled per minute, at each collection time point assuming a CO2 output of 5 mmol/min/m2 body surface area (8) .

The conventional erythromycin breath test parameter, the flux at 20 minutes (C20min) was the observed value. The area under the flux curve from time zero to 40 minutes (AUC0–40min) was determined with the linear trapezoidal method. The erythromycin breath test parameter, 1/Tmax, was determined as described previously (9 , 10) . A monoexponential equation was fitted to the percent dose 14C exhaled/min-time data, and the time of the maximum percent dose 14C exhaled/min (Tmax) was estimated. For some patients, the profiles had not reached a maximum and were still increasing at 40 minutes. In these cases, Tmax was set at 50 minutes, as recommended previously (6) .

Genotyping Procedures.
DNA was isolated from whole blood with a QIAamp DNA Blood Midikit or from plasma with a QIAamp UltraSens Virus kit (Qiagen, Valencia, CA). DNA was amplified with PCR-based techniques. RFLP analysis was used to identify variations in the CYP3A4 (CYP3A4*1B, CYP3A4*6, CYP3A4*17, and CYP3A4*18) and CYP3A5 (CYP3A5*3C and CYP3A5*6) genes as described previously (11 , 12) . For the CYP3A5*6 assay, samples were first analyzed for the CYP3A5*3C variant. Subsequently, only samples with at least one wild-type allele (*1/*1 and *1/*3 genotypes) were then analyzed for the CYP3A5*6 variant. All variant alleles found were confirmed by PCR-RFLP.

Statistical Considerations.
Erythromycin breath test parameters were summarized as the mean, 95% confidence level, and range. Values for age were grouped as <70 and ≥70 (elderly) years. Values for body surface area and {alpha}-1 acid glycoprotein were grouped as follows: lower quartile, interquartile range, and upper quartile. Values for BMI were grouped as follows: <25 (normal weight), 25 to 29.9 (overweight), and ≥30 (obese). For continuous variables, nonparametric tests were used to compare mean values between different groups. When three or more groups were compared, a trend test was used (13) . Univariate correlation analysis was performed with the software program JMP version 3.2.6 (SAS Institute, Carey, NC). Although this analysis was mainly exploratory in intent, an adjustment was used to evaluate the significance of the multiple comparisons (five demographic characteristics and two genotypes). P values (two-sided) of <0.007 were regarded as statistically significant, and those <0.05 were considered a trend.

Multiple linear regression models were then used to assess the influence of age, body size (body surface area and BMI), liver function group, sex, {alpha}-1 acid glycoprotein, and CYP3A4 and CYP3A5 genotypes (predictor variables) on erythromycin breath test parameters (outcome variables). Age, body size, and {alpha}-1 acid glycoprotein were included as continuous variables; liver function group, sex, and CYP3A4 and CYP3A5 genotypes were included as categorical variables. Regression coefficients, 95% confidence intervals, and the associated P values were determined from the multiple linear regression modeling. Stepwise backward elimination was performed to systematically exclude the least significant factors until the P value was <0.05. Multiple linear regression modeling was performed with the software program Stata, version 8.2 (Stata Corp., College Station, TX). For this analysis, the a priori level of significance was set at P < 0.05.

To determine the power to detect differences in CYP3A activity between different genotypes, a mean CYP3A activity (C20min) of 0.050% dose/min was used; this value was estimated from the presently studied group of cancer patients. In this group of patients, the SD of the expected differences of the two measurements was estimated to be 0.023% dose/min. The calculation was designed to detect an effect size of 0.025/0.023, where 0.025 is 50% of the mean CYP3A activity, and was based on a two-sided analysis, a sample size of 118 for CYP3A4*1B and 121 for CYP3A5*3C, and a significance level ({alpha}) of 0.05 (5%). This statistical analysis was performed in the SISA-Binomial program (D. G. Uitenbroek, Hilversum, the Netherlands, 1997).11


   RESULTS
Top
 ABSTRACT
 INTRODUCTION
 PATIENTS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Patients.
A total of 134 patients with cancer (62 females and 72 males) was included in this study (Table 1)Citation . The median age was 61 years (range, 26 to 83 years), and 35 patients were ≥70 years. The majority of the population was white (European and European American) and seven patients were black (African American). Twenty patients were obese with a BMI ≥ 30 kg/m2, and 32 patients had elevated liver function tests as defined for liver function group 2 (n = 14), liver function group 3A (n = 12), and liver function group 3B (n = 6).


View this table:
[in this window]
[in a new window]
  		Table 1 Patient characteristics

 
CYP3A Phenotype.
The erythromycin breath test parameters were similar between the two study protocols: C20min, 0.047 versus 0.048% dose/min (P = 0.5615); AUC0–40min, 1.57 versus 1.69% dose/min (P = 0.3294); and 1/Tmax, 0.062 versus 0.061 min-1 (P = 0.2036); the central tendency and variance for these parameters were similar for data obtained from study protocol 1 (n = 81) and study protocol 2 (n = 53; Fig. 1Citation ). C20min and AUC0–40min were highly correlated (R2 = 0.9657, P < 0.0001), whereas C20min and 1/Tmax were less strongly correlated (R2 = 0.2578, P < 0.0001). In this population of cancer patients, interpatient variation in CYP3A activity was 50- and 10-fold as determined by values for AUC0–40min and 1/Tmax, respectively (Fig. 1Citation and Table 2Citation ); this extent of interpatient variation was observed in patients with normal liver function as defined for liver function group 1. In consideration of patients with ERMBT parameter values at the extreme of the population, values for AUC0–40min and 1/Tmax for 95% of the population varied 14-fold (0.240–3.30% dose/min) and 5-fold (0.020 to 0.10 min–1), respectively.



View larger version (41K):
[in this window]
[in a new window]
  		Fig. 1. Distribution of erythromycin breath test parameters in 134 cancer patients: C20min (A); AUC0–40min (B); 1/Tmax (C); in 81 patients from study protocol 1 (D–F); and in 53 patients from study protocol 2 (G–I).

 

View this table:
[in this window]
[in a new window]
  		Table 2 Liver function tests and ERMBT parameters

 
CYP3A Genotype.
Genotype and allele frequencies for four variants of CYP3A4 and two variants of CYP3A5 are summarized in Table 3Citation . All patients were wild-type for CYP3A4*6 (n = 120), CYP3A4*17 (n = 116), and CYP3A4*18 (n = 122). For CYP3A4*1B, 106 patients were wild-type, 12 patients were heterozygous variant, and no patients were homozygous variant; 4 of 6 African Americans were heterozygous variant. For CYP3A5*3C, 2 patients were wild-type (*1/*1 genotype), 19 patients were heterozygous variant (*1/*3 genotype), and 100 patients were homozygous variant (*3/*3 genotype); 6 of 6 African Americans and 15 of 115 white subjects carried at least one *1 allele, respectively. The allele frequencies for CYP3A4*1B and CYP3A5*3C were in Hardy-Weinberg equilibrium. For CYP3A5*6, no patients were homozygous variant, and two were heterozygous variant (both heterozygotes were African American). Because of the low frequency of variants, genotypes for CYP3A4*6, CYP3A4*17, CYP3A4*18, and CYP3A5*6 were not included for genotype-phenotype association analysis as shown below.


View this table:
[in this window]
[in a new window]
  		Table 3 Genotype and allele frequencies for CYP3A4/5 genes

 
Predictors of CYP3A Activity.
CYP3A activity was reduced by ~50% in patients in liver function groups 3A and 3B (P < 0.001 for trend; Table 2Citation ). Consequently, for univariate association analysis between patient characteristics, CYP3A4/5 genotype, and CYP3A activity, patients in liver function groups 3A and 3B were excluded (Table 4)Citation . CYP3A activity was similar in patients ages ≥ 70 years compared with those < 70 years of age (P > 0.3726) at the upper extreme of BSA values (P > 0.092 for trend) and in obese patients (P > 0.162 for trend). CYP3A, as determined from the parameter AUC0–40min, was higher in females compared with male patients (1.92 versus 1.56% dose/min; P = 0.0063); the erythromycin breath test parameters C20min and 1/Tmax showed a trend for difference according to sex (P = 0.0120 and 0.0491, respectively). Analysis of {alpha}-1 acid glycoprotein values in the three-quartile groups showed that only the erythromycin breath test parameter 1/Tmax was associated with {alpha}-1 acid glycoprotein (lower quartile, mean = 0.077 min-1; interquartile range, mean = 0.065 min-1; upper quartile, mean = 0.055 min-1; P = 0.001 for trend). Linear correlation between 1/Tmax and {alpha}-1 acid glycoprotein concentration showed a weak but significant association (R2 = 0.08945, P = 0.0015), with lower 1/Tmax values at higher {alpha}-1 acid glycoprotein concentrations. There was a trend for lower CYP3A activity in patients with {alpha}-1 acid glycoprotein concentrations in the upper quartile compared with patients with {alpha}-1 acid glycoprotein values below the upper quartile (75% quartile; C20min, 0.053 versus 0.042% dose/min, P = 0.0481; AUC0–40min, 1.82 versus 1.43% dose/min, P = 0.0560; 1/Tmax, 0.067 versus 0.055 min-1, P = 0.0057). CYP3A activity was not associated with CYP3A5*3C genotype (P > 0.2954; power = 94%), as illustrated in Fig. 2Citation or CYP3A4*1B genotype (P > 0.2719; power = 69%).


View this table:
[in this window]
[in a new window]
  		Table 4 CYP3A activity as a function of patient characteristics and CYP3A4/5 genotype frequencies

 


View larger version (12K):
[in this window]
[in a new window]
  		Fig. 2. Genotype-phenotype associations between CYP3A5*3C genotype and the erythromycin breath test parameters C20min (A), AUC0–40min (B), and 1/Tmax (C) in 103 cancer patients with normal liver function (groups 1 and 2). Lines are mean values.

 
Because body surface area and BMI were highly correlated (R2 = 0.3794, P < 0.0001), only body surface area was included as a body size indicator in the multivariate models. In addition, because of the small number of observations and similar values for CYP3A activity in liver function groups 3A and 3B, these two groups were combined for multiple linear regression analysis. Observations for body surface area, sex, liver function group, and {alpha}-1 acid glycoprotein concentration were available for 126 patients. When CYP3A4 and CYP3A5 genotypes were added to the model, observations were available for 115 individuals; because no association was noted between CYP3A4*1B or CYP3A5*3C genotypes and erythromycin breath test parameters values (P > 0.2719; Table 4Citation ), CYP3A4 and CYP3A5 genotypes were not included in the multiple linear regression analysis. After stepwise backward deletion, age, body surface area, and sex were removed from the final model for the three erythromycin breath test outcome variables (C20min, AUC0–40min, and 1/Tmax). {alpha}-1 Acid glycoprotein and liver function group 3A/B were predictors of CYP3A activity, where the multivariate model explained ~18% of overall variation in CYP3A activity (Table 5)Citation . Both {alpha}-1 acid glycoprotein and liver dysfunction were negatively correlated with CYP3A activity. Liver impairment had the most profound effect on CYP3A activity (coefficient = –0.798; P < 0.001); assuming an average AUC0–40min value of 1.61% dose/min, this represents a 50% reduction in CYP3A activity, which is consistent with that observed from the univariate association analysis (Table 2)Citation .


View this table:
[in this window]
[in a new window]
  		Table 5 Multiple regression models for ERMBT parameters

 

   DISCUSSION
Top
 ABSTRACT
 INTRODUCTION
 PATIENTS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
In adults, CYP3A4 and CYP3A5 are the most important among the four CYP3A subfamily members for CYP3A-mediated drug metabolism (14, 15, 16) , and because of the genetic diversity in the genes encoding these proteins (17) , genotyping for CYP3A4 and CYP3A5 variants may be useful for prediction of total hepatic CYP3A activity. The CYP3A5 protein isoform is known to be expressed in only 10 to 30% of white subjects because of a splice variant in intron 3 of the CYP3A5 gene at nucleotide position 6986 (CYP3A5*3C; refs. 18 , 19 ). Approximately 85 to 95% of white subjects and 35 to 45% black subjects are homozygous for CYP3A5*3C and thus deficient in CYP3A5 (18, 19, 20) . Another splice variant (CYP3A5*6), which is observed in black populations also, results in lack of CYP3A5 expression (18 , 19) . Genetic differences may also explain 60 to 90% of the observed variation in CYP3A4-mediated drug-metabolizing capacity between patients (17) . Over 30 single nucleotide polymorphisms in CYP3A4 have been published, representing alleles CYP3A4*1 to CYP3A4*19, most of which are very rare and unlikely to impact on CYP3A4 activity in vivo. The best characterized variant, a promoter variant with an A to G transition at nucleotide –392 (CYP3A4*1B), was shown in vitro to have increased transcriptional activity (17 , 21) This variant exhibits interethnic variation with allele frequencies of 2 to 10% in white subjects and 35 to 84% in black subjects (18 , 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38) . Three other CYP3A4 polymorphisms (CYP3A4*6, CYP3A4*17, and CYP3A4*18) have been shown in vitro to result in functional changes in CYP3A activity (39 , 40) .

In the present study, no association was noted between the CYP3A5*3C variant and CYP3A activity. This is similar to findings in healthy subjects with the CYP3A phenotyping probes midazolam (30 , 35 , 41 , 42) , erythromycin (35 , 36) , and nifedipine (36 , 43) . One recent study involving a predominantly white population of 67 cancer patients observed 1.7-fold higher midazolam clearance in 9 patients with the *1/*3 genotype at the CYP3A5*3C allele compared with 58 patients with the *3/*3 genotype (44) . These data are not consistent with the present study with the erythromycin breath test involving 18 patients with the *1/*1 or *1/*3 genotype and 85 patients with the *3/*3 genotype (Table 4)Citation . The contradictory results could be explained by consideration of the probe drug used to assess CYP3A activity: erythromycin is reported to be a CYP3A4 substrate but a poor substrate for CYP3A5, whereas midazolam is a substrate for both CYP3A4 and CYP3A5 (3) . However, studies in healthy subjects involving populations of similar sample size and diverse race/ethnicity, including African Americans (35) , Asians (41 , 45) , and whites (35) , showed no association between CYP3A5*3C genotype and midazolam clearance. One additional factor to consider regarding the lack of observed genotype-phenotype relationships for CYP3A is that these studies may have involved the administration of probe drug doses that produced plasma concentrations well below CYP3A4 enzyme saturation, as is the case for erythromycin dose used in the erythromycin breath test. A recent study showed a dose-dependent association between CYP3A5*3C genotype and plasma concentrations of ABT-773, where drug exposure was higher in CYP3A5-negative individuals (those that were homozygous variant [*3/*3 genotype]) only at the highest dose administered (450 versus 150 or 300 mg; ref. 46 ). When CYP3A4 drug-metabolizing capability has become saturated, individuals that express CYP3A5 may metabolize the compound more quickly because of the additional activity of a second major CYP3A enzyme.

No association was observed between the CYP3A4*1B variant and CYP3A activity, which was also consistent to that observed in healthy subjects with the CYP3A-phenotyping probes midazolam (30 , 35 , 42) , erythromycin (35) , and nifedipine (36) . Because of the lack of CYP3A4*6, CYP3A4*17, and CYP3A4*18 variants in the present study, these polymorphisms most likely have no relevance to CYP3A activity in white populations. The data presented on the association between CYP3A4 and CYP3A5 genotype and CYP3A activity adds to the list of evidence that the chosen single nucleotide polymorphisms are not causative for altered in vivo activity.

In the current study, CYP3A activity was unaltered in patients with mild elevations in liver function tests (group 2) but was reduced by ~50% in patients with moderate to severe liver impairment as defined for liver function groups 3A and 3B. Interestingly, categorization of liver function tests for group 3A was described by Bruno et al. (47 , 48) for prediction of docetaxel clearance; patients with total bilirubin < 1.5x upper limit of normal but elevations in liver transaminases (≥1.5x upper limit of normal) concurrent with elevated alkaline phosphatase (≥2.5x upper limit of normal) were shown to have reduced docetaxel clearance by 25%. The same categorization of liver function tests to describe liver impairment was also associated with reduced CYP3A activity in the present study. However, liver function tests are not accurate in prediction of CYP3A activity as patients with the lowest CYP3A activity had normal liver function (group 1). The use of the erythromycin breath test as a phenotypic probe has been questioned as conflicting reports on its ability to predict the total body clearance of probe drugs have been reported (4 , 49) , although a study in 20 patients with sarcoma showed that the erythromycin breath test predicted docetaxel clearance in those with greatly reduced drug clearance (50) . Nevertheless, despite the limitations of the erythromycin breath test, results of the present study demonstrate that patients with low breath levels of 14CO2 have low CYP3A function and are likely to have reduced CYP3A-mediated drug clearance.

Because of large variations in concentrations of the acute phase reactant protein {alpha}-1 acid glycoprotein in cancer patients (7-fold), it has been hypothesized that decreased hepatic clearance by CYP3A in some individuals might be a consequence of an inflammatory response (51) . In the present study, {alpha}-1 acid glycoprotein was associated with CYP3A activity in both univariate and multiple regression analysis. However, combined with liver function, only 18% of CYP3A variation was explained by these two variables. Previously, a better correlation was observed between C-reactive protein and CYP3A activity compared with {alpha}-1 acid glycoprotein (6) . Assessment of C-reactive protein, a more specific marker of inflammation, was not measured in this study, but it may have accounted for more of the variation in CYP3A activity in this population of cancer patients.

In the present study, body surface area and BMI were not correlated with CYP3A activity, consistent with poor correlations between body size parameters and the clearance of drugs metabolized by CYP3A (1 , 52) . However, alterations in anticancer drug clearance have been noted in obese patients and those at the upper extreme of body size, such as for doxorubicin and docetaxel (52) . Other factors in addition to CYP3A activity, such as changes in volume of distribution, may contribute to altered drug disposition in obese patients (53) .

The influence of age on the expression and activity of drug-metabolizing enzymes remains controversial with reports describing either a decline in activity or no change in activity in elderly patients (54, 55, 56) . In the current study, CYP3A activity was shown to be similar in patients ages < 70 years (n = 99) and ≥ 70 years (n = 35). Prior in vitro studies have suggested an age related decline in CYP3A activity (57) . However, our results are consistent with an in vivo study that applied the erythromycin breath test as a phenotyping probe of CYP3A-mediated drug clearance, where no decrease in CYP3A activity was observed as a function of age in 39 older hypertensive men (56) .

Many drugs that are substrates of CYP3A show higher clearance in women than in men (58, 59, 60) . In concordance with this observation, previous studies have shown ~20 to 25% higher CYP3A activity in females than males with the erythromycin breath test (8 , 35 , 54) . However, it has been suggested that this observation is due to one limitation of the erythromycin breath test, the assumption that individuals (both females and males) produce 5 mmol CO2/min/m2 at rest (10) . Reanalysis of previously published data evaluating the calculation of CO2 output in different populations revealed a ~20% lower rate of CO2 production in females, which is consistent with the 20 to 25% difference observed in erythromycin breath test results between the two sexes (10) . In the present study, females were found to have ~15 to 20% higher CYP3A activity than males in univariate analysis, but the association was not significant in multiple regression analysis. A recent study of 94 surgical liver samples found 2-fold higher CYP3A4 protein content and higher expression of CYP3A4 mRNA transcripts in female compared with male samples (61) . In contrast, other studies in human livers observed no sex differences in CYP3A (62 , 63) . Regardless of apparent sex-related differences in CYP3A activity, the same range of wide interpatient variation in CYP3A activity was observed in both female and male cancer patients (Table 4)Citation , indicating that dosing strategies for drugs cleared by CYP3A should focus on the individual and not necessarily sex.

One additional factor to consider is the influence of drug interactions on interpatient variation in CYP3A activity. There is considerable motivation for understanding adverse drug interactions with anticancer agents because of their narrow therapeutic index. Usually, such interactions arise as a result of altered pharmacokinetics of the drugs involved. Careful examination of the concomitant drug profiles for drugs that are substrates, inhibitors, or inducers of CYP3A did not reveal an association with results of the erythromycin breath test in the entire population or upon analysis of values in the lower and upper quartiles. In addition, many botanical dietary supplements contain pharmacologically active phytochemicals, and to date, numerous herb-drug interactions have been described in the medical literature (64 , 65) and are most commonly pharmacokinetic in nature. Availability of this type of information is particularly relevant to the treatment of cancer patients who are known to take a wide variety of complementary and alternative medicine concomitantly with their chemotherapeutic regimen (66) . However, little information is available from prospective evaluation of drug interactions between complementary and alternative medicine and anticancer agents to help guide the use of complementary and alternative medicine in cancer patients receiving chemotherapy (67) . To complicate matters additionally, the current study protocol conducted in the United States and the Netherlands included a list of complementary and alternative medicine to review with each patient before study enrollment; however, of 81 patients enrolled on this protocol, no patients were documented to be taking complementary and alternative medicine. Because various studies have documented the extensive use of complementary and alternative medicine in cancer patients and that <40% of patients disclose their herbal supplement usage to healthcare providers (68) , ways to ensure accurate and complete documentation of complementary and alternative medicine use by patients enrolled to clinical trials is urgently needed.

In conclusion, the current investigation has identified liver function and {alpha}-1 acid glycoprotein concentration in plasma as significant predictors of CYP3A activity in patients with cancer. However, these two factors combined explained only 18% of overall variation in CYP3A activity. Consideration of additional factors, including C-reactive protein, as well as the concomitant use of other drugs, food constituents, and complimentary and alternative medicine with inhibitory and inducible effects on CYP3A, may account for variation in CYP3A activity in cancer patients. In light of the present results noting no significant genotype-phenotype associations between the chosen single nucleotide polymorphisms and CYP3A activity with the erythromycin breath test, CYP3A phenotyping strategies should provide the most clinically relevant information reflecting the combined effects of genetic, environmental, and endogenous/physiologic factors on drug disposition and effects.


   ACKNOWLEDGMENTS
 
We thank the following people for their contribution to this work: Judy Weber, Yelena Zabelina, and Karen Oleszewski (Baltimore, MD); Kim Utley and Frederic Lombardo (Washington, DC); Marian Verbruggen, Hans van der Meulen, Tatjana Pronk, Aysun Komurco, Frederike Engels, Marloes van der Werf, and Ilse P. van der Heiden (Rotterdam, the Netherlands); and Jan Ouwerkerk and Jan Camps (Leiden, the Netherlands). This article is dedicated to the memory of our dear friend and colleague Theresa Rogers, who passed away in April 2004.


   FOOTNOTES
 
Grant support: Aventis Pharmaceuticals (Bridgewater, New Jersey) Grant GIA 19075 and New South Wales Cancer Council and National Health and Medical Research Council of Australia.

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.

Requests for reprints: Sharyn D. Baker, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Bunting-Blaustein Cancer Research Building, 1650 Orleans Street, Room 1M87, Baltimore, MD 21231-1000. Phone: (410) 502-7149; Fax: (410) 614-9006; E-mail: sdbaker{at}jhmi.edu

11 Internet address: http://home.clara.net/sisa/samsize.htm. Back

Received 7/13/04; revised 9/15/04; accepted 9/22/04.


   REFERENCES
Top
 ABSTRACT
 INTRODUCTION
 PATIENTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Baker SD, Verweij J, Rowinsky EK, et al Role of body surface area in dosing of investigational anticancer agents in adults, 1991–2001. J Natl Cancer Inst (Bethesda) 2002;94:1883-8.[Abstract/Free Full Text]
  2. Wilkinson GR. Pharmacokinetics: the dynamics of drug absorption, distribution, and elimination Hardman JG Limbird LE eds. . Goodman & Gilmans’s the pharmacological basis of therapeutics 10th ed. 20013-29. McGraw-Hill Medical Publishing Division New York
  3. Streetman DS, Bertino JS, Jr, Nafziger AN. Phenotyping of drug-metabolizing enzymes in adults: a review of in-vivo cytochrome P450 phenotyping probes. Pharmacogenetics 2000;10:187-216.[CrossRef][Medline]
  4. Chiou WL, Jeong HY, Wu TC, Ma C. Use of the erythromycin breath test for in vivo assessments of cytochrome P4503A activity and dosage individualization. Clin Pharmacol Ther 2001;70:305-10.[CrossRef][Medline]
  5. Rogers JF, Rocci ML, Jr, Haughey DB, Bertino JS, Jr. An evaluation of the suitability of intravenous midazolam as an in vivo marker for hepatic cytochrome P4503A activity. Clin Pharmacol Ther 2003;73:153-8.[CrossRef][Medline]
  6. Rivory LP, Slaviero KA, Clarke SJ. Hepatic cytochrome P450 3A drug metabolism is reduced in cancer patients who have an acute-phase response. Br J Cancer 2002;87:277-80.[CrossRef][Medline]
  7. Mosteller RD. Simplified calculation of body-surface area. N. Engl J Med 1987;317:1098[Medline]
  8. Watkins PB, Murray SA, Winkelman LG, Heuman DM, Wrighton SA, Guzelian PS. Erythromycin breath test as an assay of glucocorticoid-inducible liver cytochromes P-450. Studies in rats and patients. J Clin Investig 1989;83:688-97.
  9. Rivory LP, Slaviero K, Seale JP, et al Optimizing the erythromycin breath test for use in cancer patients. Clin Cancer Res 2000;6:3480-5.[Abstract/Free Full Text]
  10. Rivory LP, Slaviero KA, Hoskins JM, Clarke SJ. The erythromycin breath test for the prediction of drug clearance. Clin Pharmacokinet 2001;40:151-8.[CrossRef][Medline]
  11. van Schaik RH, de Wildt SN, van Iperen NM, Uitterlinden AG, van den Anker JN, Lindemans J. CYP3A4-V polymorphism detection by PCR-restriction fragment length polymorphism analysis and its allelic frequency among 199 Dutch Caucasians. Clin Chem 2000;46:1834-6.[Free Full Text]
  12. Hesselink DA, van Schaik RH, van der Heiden IP, et al Genetic polymorphisms of the CYP3A4, CYP3A5, and MDR-1 genes and pharmacokinetics of the calcineurin inhibitors cyclosporine and tacrolimus. Clin Pharmacol Ther 2003;74:245-54.[CrossRef][Medline]
  13. Cuzick J. A Wilcoxon-type test for trend. Stat Med 1985;14:445-6.
  14. de Wildt SN, Kearns GL, Leeder JS, van den Anker JN. Cytochrome P450 3A: ontogeny and drug disposition. Clin Pharmacokinet 1999;37:485-505.[CrossRef][Medline]
  15. Koch I, Weil R, Wolbold R, et al Interindividual variability and tissue-specificity in the expression of cytochrome P450 3A mRNA. Drug Metab Dispos 2002;30:1108-14.[Abstract/Free Full Text]
  16. Williams JA, Ring BJ, Cantrell VE, et al Comparative metabolic capabilities of CYP3A4, CYP3A5, and CYP3A7. Drug Metab Dispos 2002;30:883-91.[Abstract/Free Full Text]
  17. Lamba JK, Lin YS, Schuetz EG, Thummel KE. Genetic contribution to variable human CYP3A-mediated metabolism. Adv Drug Deliv Rev 2002;54:1271-94.[CrossRef][Medline]
  18. Kuehl P, Zhang J, Lin Y, et al Sequence diversity in CYP3A promoters and characterization of the genetic basis of polymorphic CYP3A5 expression. Nat Genet 2001;27:383-91.[CrossRef][Medline]
  19. Hustert E, Haberl M, Burk O, et al The genetic determinants of the CYP3A5 polymorphism. Pharmacogenetics 2001;11:773-9.[CrossRef][Medline]
  20. Lee SJ, Usmani KA, Chanas B, et al Genetic findings and functional studies of human CYP3A5 single nucleotide polymorphisms in different ethnic groups. Pharmacogenetics 2003;13:461-72.[CrossRef][Medline]
  21. Amirimani B, Walker AH, Weber BL, Rebbeck TR. RESPONSE: re: modification of clinical presentation of prostate tumors by a novel genetic variant in CYP3A4. J Natl Cancer Inst (Bethesda) 1999;91:1588-90.[Free Full Text]
  22. Cavaco I, Gil JP, Gil-Berglund E, Ribeiro V. CYP3A4 and MDR1 alleles in a Portuguese population. Clin Chem Lab Med 2003;41:1345-50.[CrossRef][Medline]
  23. Westlind A, Lofberg L, Tindberg N, Andersson TB, Ingelman-Sundberg M. Interindividual differences in hepatic expression of CYP3A4: relationship to genetic polymorphism in the 5'-upstream regulatory region. Biochem Biophys Res Commun 1999;259:201-5.[CrossRef][Medline]
  24. von Ahsen N, Richter M, Grupp C, Ringe B, Oellerich M, Armstrong VW. No influence of the MDR-1 C3435T polymorphism or a CYP3A4 promoter polymorphism (CYP3A4-V allele) on dose-adjusted cyclosporin A trough concentrations or rejection incidence in stable renal transplant recipients. Clin Chem 2001;47:1048-52.[Abstract/Free Full Text]
  25. Tayeb MT, Clark C, Ameyaw MM, et al CYP3A4 promoter variant in Saudi, Ghanaian and Scottish Caucasian populations. Pharmacogenetics 2000;10:753-6.[CrossRef][Medline]
  26. Garcia-Martin E, Martinez C, Pizarro RM, et al CYP3A4 variant alleles in white individuals with low CYP3A4 enzyme activity. Clin Pharmacol Ther 2002;71:196-204.[CrossRef][Medline]
  27. Hamzeiy H, Vahdati-Mashhadian N, Edwards HJ, Goldfarb PS. Mutation analysis of the human CYP3A4 gene 5'-regulatory region: population screening using non-radioactive SSCP. Mutat Res 2002;500:103-10.[Medline]
  28. Paris PL, Kupelian PA, Hall JM, et al Association between a CYP3A4 genetic variant and clinical presentation in African-American prostate cancer patients. Cancer Epidemiol Biomark Prev 1999;8:901-5.[Abstract/Free Full Text]
  29. Sata F, Sapone A, Elizondo G, et al CYP3A4 allelic variants with amino acid substitutions in exons 7 and 12: evidence for an allelic variant with altered catalytic activity. Clin Pharmacol Ther 2000;67:48-56.[CrossRef][Medline]
  30. Wandel C, Witte JS, Hall JM, Stein CM, Wood AJ, Wilkinson GR. CYP3A activity in African-American and European-American men: population differences and functional effect of the CYP3A4*1B 5'-promoter region polymorphism. Clin Pharmacol Ther 2000;68:82-91.[CrossRef][Medline]
  31. Zeigler-Johnson CM, Walker AH, Mancke B, et al Ethnic differences in the frequency of prostate cancer susceptibility alleles at SRD5A2 and CYP3A4. Hum Hered 2002;54:13-21.[CrossRef][Medline]
  32. Felix CA, Walker AH, Lange BJ, et al Association of CYP3A4 genotype with treatment-related leukemia. Proc Natl Acad Sci USA 1998;95:13176-81.[Abstract/Free Full Text]
  33. Walker AH, Jaffe JM, Gunasegaram S, et al Characterization of an allelic variant in the nifedipine-specific element of CYP3A4: ethnic distribution and implications for prostate cancer risk. Mutations in brief no. 191. Online. Hum Mutat 1998;12:289[Medline]
  34. Lamba JK, Lin YS, Thummel K, et al Common allelic variants of cytochrome P4503A4 and their prevalence in different populations. Pharmacogenetics 2002;12:121-32.[CrossRef][Medline]
  35. Floyd MD, Gervasini G, Masica AL, et al Genotype-phenotype associations for common CYP3A4 and CYP3A5 variants in the basal and induced metabolism of midazolam in European- and African-American men and women. Pharmacogenetics 2003;13:595-606.[CrossRef][Medline]
  36. Ball SE, Scatina J, Kao J, et al Population distribution and effects on drug metabolism of a genetic variant in the 5'-promoter region of CYP3A4. Clin Pharmacol Ther 1999;66:288-94.[CrossRef][Medline]
  37. Cavaco I, Reis R, Gil JP, Ribeiro V. CYP3A4*1B and NAT2*14 alleles in a native African population. Clin Chem Lab Med 2003;41:606-9.[CrossRef][Medline]
  38. Chelule PK, Gordon M, Palanee T, et al MDR1 and CYP3A4 polymorphisms among African, Indian, and white populations in KwaZulu-Natal, South Africa. Clin Pharmacol Ther 2003;74:195-6.
  39. Dai D, Tang J, Rose R, et al Identification of variants of CYP3A4 and characterization of their abilities to metabolize testosterone and chlorpyrifos. J Pharmacol Exp Ther 2001;299:825-31.[Abstract/Free Full Text]
  40. Hsieh KP, Lin YY, Cheng CL, et al Novel mutations of CYP3A4 in Chinese. Drug Metab Dispos 2001;29:268-73.[Abstract/Free Full Text]
  41. Shih PS, Huang JD. Pharmacokinetics of midazolam and 1'-hydroxymidazolam in Chinese with different CYP3A5 genotypes. Drug Metab Dispos 2002;30:1491-6.[Abstract/Free Full Text]
  42. Eap CB, Buclin T, Hustert E, et al Pharmacokinetics of midazolam in CYP3A4- and CYP3A5-genotyped subjects. Eur J Clin Pharmacol 2004;60:231-6.[Medline]
  43. Fukuda T, Onishi S, Fukuen S, et al CYP3A5 genotype did not impact on nifedipine disposition in healthy volunteers. Pharmacogenomics J 2004;4:34-9.[CrossRef][Medline]
  44. Wong M, Balleine RL, Collins M, Liddle C, Clarke CL, Gurney H. CYP3A5 genotype and midazolam clearance in Australian patients receiving chemotherapy. Clin Pharmacol Ther 2004;75:529-38.[CrossRef][Medline]
  45. Goh BC, Lee SC, Wang LZ, et al Explaining interindividual variability of docetaxel pharmacokinetics and pharmacodynamics in Asians through phenotyping and genotyping strategies. J Clin Oncol 2002;20:3683-90.[Abstract/Free Full Text]
  46. Katz DA, Grimm DR, Cassar SC, et al CYP3A5 genotype has a dose-dependent effect on ABT-773 plasma levels. Clin Pharmacol Ther 2004;75:516-28.[CrossRef][Medline]
  47. Bruno R, Vivler N, Vergniol JC, De Phillips SL, Montay G, Sheiner LB. A population pharmacokinetic model for docetaxel (Taxotere): model building and validation. J Pharmacokinet Biopharm 1996;24:153-72.[CrossRef][Medline]
  48. Bruno R, Hille D, Riva A, et al Population pharmacokinetics/pharmacodynamics of docetaxel in phase II studies in patients with cancer. J Clin Oncol 1998;16:187-96.[Abstract/Free Full Text]
  49. Rivory LP, Watkins PB. Erythromycin breath test. Clin Pharmacol Ther 2001;70:395-9.[Medline]
  50. Hirth J, Watkins PB, Strawderman M, Schott A, Bruno R, Baker LH. The effect of an individual’s cytochrome CYP3A4 activity on docetaxel clearance. Clin Cancer Res 2000;6:1255-8.[Abstract/Free Full Text]
  51. Slaviero KA, Clarke SJ, Rivory LP. Inflammatory response: an unrecognised source of variability in the pharmacokinetics and pharmacodynamics of cancer chemotherapy. Lancet Oncol 2003;4:224-32.[CrossRef][Medline]
  52. Rudek MA, Sparreboom A, Garrett-Mayer ES, et al Factors affecting pharmacokinetic variability following doxorubicin and docetaxel-based therapy. Eur J Cancer 2004;40:1170-8.
  53. Baker SD, Grochow LB, Donehower RC. Should anticancer drug doses be adjusted in the obese patient?. J Natl Cancer Inst (Bethesda) 1995;87:333-4.[Free Full Text]
  54. Hunt CM, Westerkam WR, Stave GM. Effect of age and gender on the activity of human hepatic CYP3A. Biochem Pharmacol 1992;44:275-83.[CrossRef][Medline]
  55. Tanaka E. In vivo age-related changes in hepatic drug-oxidizing capacity in humans. J Clin Pharm Ther 1998;23:247-55.[CrossRef][Medline]
  56. Schwartz JB. Race but not age affects erythromycin breath test results in older hypertensive men. J Clin Pharmacol 2001;41:324-9.[Abstract]
  57. Schmucker DL. Liver function and phase I drug metabolism in the elderly: a paradox. Drugs Aging 2001;18:837-51.[CrossRef][Medline]
  58. Harris RZ, Benet LZ, Schwartz JB. Gender effects in pharmacokinetics and pharmacodynamics. Drugs 1995;50:222-39.[Medline]
  59. Meibohm B, Beierle I, Derendorf H. How important are gender differences in pharmacokinetics?. Clin. Pharmacokinet 2002;41:329-42.[CrossRef][Medline]
  60. Cummins CL, Wu CY, Benet LZ. Sex-related differences in the clearance of cytochrome P450 3A4 substrates may be caused by P-glycoprotein. Clin Pharmacol Ther 2002;72:474-89.[CrossRef][Medline]
  61. Wolbold R, Klein K, Burk O, et al Sex is a major determinant of CYP3A4 expression in human liver. Hepatology 2003;38:978-88.[CrossRef][Medline]
  62. Shimada T, Yamazaki H, Mimura M, Inui Y, Guengerich FP. Interindividual variations in human liver cytochrome P-450 enzymes involved in the oxidation of drugs, carcinogens and toxic chemicals: studies with liver microsomes of 30 Japanese and 30 Caucasians. J Pharmacol Exp Ther 1994;270:414-23.[Abstract/Free Full Text]
  63. Schmucker DL, Woodhouse KW, Wang RK, et al Effects of age and gender on in vitro properties of human liver microsomal monooxygenases. Clin Pharmacol Ther 1990;48:365-74.[Medline]
  64. Ioannides C. Pharmacokinetic interactions between herbal remedies and medicinal drugs. Xenobiotica 2002;32:451-78.[CrossRef][Medline]
  65. Zhou S, Gao Y, Jiang W, Huang M, Xu A, Paxton JW. Interactions of herbs with cytochrome P450. Drug Metab Rev 2003;35:35-98.[CrossRef][Medline]
  66. Richardson MA, Sanders T, Palmer JL, Greisinger A, Singletary SE. Complementary/alternative medicine use in a comprehensive cancer center and the implications for oncology. J Clin Oncol 2000;18:2505-14.[Abstract/Free Full Text]
  67. Mathijssen RH, Verweij J, de Bruijn P, Loos WJ, Sparreboom A. Effects of St. John’s wort on irinotecan metabolism. J Natl Cancer Inst (Bethesda) 2002;94:1247-9.[Abstract/Free Full Text]
  68. Klepser T, Doucette W, Horton H, Buys M, Ernst M, Ford J. Assessment of a patient’s perceptions and beliefs regarding herbal therapies. Pharmacotherapy 2000;20:83-7.[CrossRef][Medline]



This article has been cited by other articles:


Home page
 JCOHome page
D. Rathkopf, M. A. Carducci, M. J. Morris, S. F. Slovin, M. A. Eisenberger, R. Pili, S. R. Denmeade, M. Kelsen, T. Curley, M. Halter, et al.
Phase II Trial of Docetaxel With Rapid Androgen Cycling for Progressive Noncastrate Prostate Cancer
J. Clin. Oncol., June 20, 2008; 26(18): 2959 - 2965.
[Abstract] [Full Text] [PDF]

Home page
 Drug Metab. Dispos.Home page
E. T. Morgan, K. B. Goralski, M. Piquette-Miller, K. W. Renton, G. R. Robertson, M. R. Chaluvadi, K. A. Charles, S. J. Clarke, M. Kacevska, C. Liddle, et al.
Regulation of Drug-Metabolizing Enzymes and Transporters in Infection, Inflammation, and Cancer
Drug Metab. Dispos., February 1, 2008; 36(2): 205 - 216.
[Abstract] [Full Text] [PDF]

Home page
 Clin. Cancer Res.Home page
L.-S. Tham, N. H.G. Holford, S.-Y. Hor, T. Tan, L. Wang, R.-C. Lim, H.-S. Lee, S.-C. Lee, and B.-C. Goh
Lack of Association of Single-Nucleotide Polymorphisms in Pregnane X Receptor, Hepatic Nuclear Factor 4{alpha}, and Constitutive Androstane Receptor with Docetaxel Pharmacokinetics
Clin. Cancer Res., December 1, 2007; 13(23): 7126 - 7132.
[Abstract] [Full Text] [PDF]

Home page
 JCOHome page
A. Sparreboom, A. C. Wolff, R. H.J. Mathijssen, E. Chatelut, E. K. Rowinsky, J. Verweij, and S. D. Baker
Evaluation of Alternate Size Descriptors for Dose Calculation of Anticancer Drugs in the Obese
J. Clin. Oncol., October 20, 2007; 25(30): 4707 - 4713.
[Abstract] [Full Text] [PDF]

Home page
 Clin. Cancer Res.Home page
E. L. Bradshaw-Pierce, S. G. Eckhardt, and D. L. Gustafson
A Physiologically Based Pharmacokinetic Model of Docetaxel Disposition: from Mouse to Man
Clin. Cancer Res., May 1, 2007; 13(9): 2768 - 2776.
[Abstract] [Full Text] [PDF]

Home page
 Clin. Cancer Res.Home page
K. A. Charles, L. P. Rivory, S. L. Brown, C. Liddle, S. J. Clarke, and G. R. Robertson
Transcriptional Repression of Hepatic Cytochrome P450 3A4 Gene in the Presence of Cancer
Clin. Cancer Res., December 15, 2006; 12(24): 7492 - 7497.
[Abstract] [Full Text] [PDF]

Home page
 Clin. Cancer Res.Home page
A. Hurria, M. T. Fleming, S. D. Baker, Wm. K. Kelly, K. Cutchall, K. Panageas, J. Caravelli, H. Yeung, M. G. Kris, J. Gomez, et al.
Pharmacokinetics and toxicity of weekly docetaxel in older patients.
Clin. Cancer Res., October 15, 2006; 12(20): 6100 - 6105.
[Abstract] [Full Text] [PDF]

Home page
 JCOHome page
M. Michael, M. Thompson, R. J. Hicks, P. L. Mitchell, A. Ellis, A. D. Milner, J. Di Iulio, A. M. Scott, V. Gurtler, J. M. Hoskins, et al.
Relationship of Hepatic Functional Imaging to Irinotecan Pharmacokinetics and Genetic Parameters of Drug Elimination
J. Clin. Oncol., September 10, 2006; 24(26): 4228 - 4235.
[Abstract] [Full Text] [PDF]

Home page
 JCOHome page
M. A. Carducci, L. Musib, M. S. Kies, R. Pili, M. Truong, J. R. Brahmer, P. Cole, R. Sullivan, J. Riddle, J. Schmidt, et al.
Phase I Dose Escalation and Pharmacokinetic Study of Enzastaurin, an Oral Protein Kinase C Beta Inhibitor, in Patients With Advanced Cancer
J. Clin. Oncol., September 1, 2006; 24(25): 4092 - 4099.
[Abstract] [Full Text] [PDF]

Home page
 JCOHome page
S. D. Baker and A. Sparreboom
Predicting Vinorelbine Dispositio