• bioelectrical impedance analysis;
  • fluorouracil;
  • pharmacokinetics


  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. References

Aims  To verify whether fluorouracil (FU) clearance (CL) and volume of distribution (Vss) are better correlated with specific body compartments, such as body cell mass (BCM), total body water (TBW) or fat free mass (FFM), rather than with body surface area (BSA) or total body weight (BW).

Methods  Thirty-four patients (13 females and 21 males) affected by colorectal cancer and receiving FU as adjuvant therapy entered the study. CL and Vss were determined after a 2 min i.v. injection of FU (425 mg m−2) and leucovorin (20 mg m−2). Body composition, in terms of BCM, TBW and FFM, was evaluated non-invasively by bioelectrical impedance analysis (BIA).

Results  Significant but poor correlations were found between CL or Vss and most anthropometric parameters, including BIA-derived measures (r2 range=0.10–0.21). However, when multiple regression analysis was performed with sex, TBW and FFM as independent variables, the correlations improved greatly. The best correlation was obtained between CL and sex (r2=0.44) and between Vss and sex (r2=0.36). FFM-normalized CL was significantly higher in women than in men (0.030±0.008 vs 0.022±0.005 l min−1 kg−1; 95% CI of difference 0.012, 0.003; P=0.003), suggesting that FU metabolism is more rapid in females. Surprisingly, Vss was highly correlated with CL (r2=0.67; CL=0.52+Vss×0.040). This finding may either be explained by extensive drug metabolism in extra-hepatic organs or by variable inactivation on first-pass through the lung. Both these hypotheses need experimental validation.

Conclusions  The pharmacokinetics of FU are better predicted by FFM and TBW than by standard anthropometric parameters and predictions are sex-dependent. The use of BIA may lead to improved dosing with FU.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. References

Fluorouracil (FU) is still used to treat a variety of solid tumours and in particular, in combination with leucovorin, is the standard treatment for adjuvant chemotherapy of colorectal cancer [1]. One major hindrance to optimizing treatment with FU is its considerable interpatient pharmacokinetic variability, which leads to unpredictable plasma drug concentrations [2, 3]. In humans, more than 80% of an i.v. dose of FU is eliminated from the body via reductive metabolism by dihydropyrimidine dehydrogenase (DPD) [4]. Plasma drug clearance is highly variable and depends on dose, exhibiting saturation kinetics [5], time of day [6] and administration schedule (i.v. bolus or prolonged infusion) [7]. Even when the drug is given under standardized conditions, namely at the same dose, infusion duration, time of day, a large interindividual variability in clearance has been reported [8]. Pharmacokinetic-pharmacodynamic correlation studies have shown that, following continuous 5FU i.v. infusion, clinical response and/or toxicity are related to the area under the plasma concentration vs time curve (AUC) [3] and that individual FU dose adjustment with pharmacokinetic monitoring provides high response and survival rates associated with good tolerability [9]. Although the usefulness of monitoring FU AUC following i.v. bolus administration has not yet been established, preliminary findings from our laboratory [10] suggest that a correlation between AUC and FU toxicity does exist.

Factors, such as sex, age and DPD activity in peripheral blood mononuclear cells, have been shown to be correlated with FU clearance, but these relationships are generally weak, such that most variability in clearance remains unexplained [3]. Although FU is a hydrophilic drug (octanol/water partition coefficient=0.1) [11], it passes across biological membranes easily by saturable active transport [12], thus reaching its sites of action and elimination. Since FU plasma clearance after prolonged i.v. infusion largely exceeds liver blood flow and even cardiac output, its metabolism must also occur in extra-hepatic tissues [13].

On the basis of these considerations, FU clearance and volume of distribution are probably related to body composition, particularly body cell mass (BCM), fat free (lean) mass (FFM) and total body water (TBW). BCM, FFM and TBW can be estimated by bioelectrical impedance analysis (BIA), a noninvasive technique which measures the resistance (R) and reactance (Xc) of the body to the flow of a low-voltage alternating current [14, 15].

The aims of our study were to assess whether body composition parameters as assessed by BIA are correlated with FU clearance and volume of distribution, and whether this correlation is strong enough to permit a better prediction of FU pharmacokinetic parameters than that obtained with standard anthropometric measures, namely body weight and body surface area.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. References


Thirty-four patients (21 males, 13 females), aged between 45 and 80 years and diagnosed with colorectal cancer (Dukes class: B2-C) took part in the study, after giving their written informed consent. All of them had a normal hydration status. After radical surgery, they started adjuvant chemotherapy in which FU (425 mg m−2) and leucovorin (20 mg m−2) were administered by rapid (2 min) intravenous injection daily for 5 days, for six consecutive cycles every 4–5 weeks [16]. The study was performed on the 2nd day of the 1st therapy cycle, between 14.00 and 15.00 h. The main demographic characteristics of each patient (age, sex, body weight, surface area) were recorded. Body surface area (BSA) was calculated by means of the formula of Haycock [17]: BSA=body weight0.5378×height0.3964×0.024265.

The study procedure was approved by the Ethics Committee of the Hospital of Rovigo.

BIA measurements

BIA measurements were performed 15 min before FU administration. After cleaning the skin with alcohol, two pre-gelled electrodes were cut in half and placed on the dorsal surface of the right hand and right foot. Two were current-introducing electrodes which were placed 5–6 cm distal to two voltage-sensing electrodes. Low-intensity (800 µA), single-frequency alternating current (50 KHz) was delivered through a BIA 101 analyser (Akern Srl, Firenze, Italy, on licence from RJL System, Detroit, USA) and the two primary impedance components, resistance (R) and reactance (Xc), were measured. Total body water (TBW), fat free mass (FFM) and body cell mass (BCM) were then calculated from body weight, height, age, sex, R and Xc, using software provided by Akern Srl (BODYGRAM Y2K, version 2000 [18]. This method of estimating body composition parameters has been validated by Kotler et al.[15] for middle-aged people and by Deurenberg et al.[19] for elderly subjects (>60 years).

H.p.l.c. analysis of FU

The drug was extracted from 0.5 ml plasma with ethylacetate (8 ml) after adding 0.5 ml of a Na2SO4 solution (200 g l−1) and 50 µl of Na acetate buffer (pH 4.7), using 5-bromouracil as internal standard. The dried residuum was reconstituted with 300 µl water, and 50 µl were injected onto a LiChroCART 125–4® column (Merk) packed with Superspher 60 PR-8 and kept at 50° C, using freshly distilled water as the mobile phase (flow rate: 1 ml min−1; Waters 515 HPLC pump). Analytes were detected at 254 nm (Waters, 2487 dual λ absorbance u.v. detector). The lowest detection limit was 20 ng ml−1 and intraday and interday coefficients of variation were 4.5% and 6.1%, respectively.

Pharmacokinetic measurements

FU plasma concentrations were determined at 0, 2.5, 5, 10, 15, 20, 30, 45, 60 min after drug administration. Plasma concentration-time curves were analysed according to one and two compartment, first order pharmacokinetic models. The models were fitted to the concentration-time data (weighting the values by 1/Y2) using nonlinear regression analysis software in GraphPad PRISM (San Diego, CA). The optimal model was chosen by calculating the Akaike Information Criterion [20]: AIC=N×ln(SSQ)+2p, where N is the number of samples, ln the natural logarithm, SSQ the sum of the squared residues, and p the number of model parameters. The model yielding the lower AIC was chosen.

The following pharmacokinetic parameters were then calculated:

  • image
  • Clearance (CL)=Dose/AUC(0,∞)
  • image
  • Dose (A/λ12+B/λz2)/(A/λ1+B/λz)2
  • (2 compartment model)
  • image
  • (only for the 2 compartment model)
  • image
  • image

where A and B are y intercepts and λ1 and λz are rate constants.

Statistical analysis

Data were expressed as mean±s.d. Comparisons between mean values obtained in females and males were made using the Student's t-test for unpaired data. 95% confidence intervals (CI) on differences were also calculated. The accepted significance level was P<0.05. Correlations and regression lines between anthropometric measures (body weight, body surface area, TBW, FFM and BCM) or age and pharmacokinetic parameters (CL, Vss) were performed by means of the statistical package included in the GraphPad PRISM software (San Diego, CA). Goodness of fit was assessed by the coefficient of determination (r2). Slopes and intercepts of regression lines obtained in males and females were compared using the the GraphPad PRISM statistical software.

Forward stepwise multiple regression analysis was carried out, using Statistica software (StatSoft Inc, Tulsa, OK), with CL or Vss as dependent variables, and BIA measures and sex as independent variables, in order to obtain the model most predictive of the pharmacokinetic parameters. In this analysis, both r2 and adjusted r2 were calculated.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. References

Table 1 shows the main demographic and BIA parameters found in males, females, and in the whole study population. Females had significantly lower body weight (BW), body surface area (BSA), TBW and FFM when compared with males. Mean ages and BCM were not significantly different between the two groups. TBW and FFM but not BCM represented a significantly smaller percentage of BW in females than in males (TBW=51.1±4.5% vs 57.7±3.9%, 95% CI on the difference 4.0, 10.8, P<0.0001; FFM=65.2±5.6% vs 74.1±6.8%, 95% CI 4.1, 14.9, P=0.0004; BCM= 30.8±5.8% vs 30.4±5.2%).

Table 1.  Patients' characterisitics (mean±s.d.).
 Age (years)BW (kg)BSA (m2)R (Ω)Xc (Ω)TBW (kg)FFM (kg)BCM (kg)
  • BW=body weight; BSA=body surface area; R=resistance; Xc=reactance; TBW=total body water; FFM=fat free mass; BCM=body cell mass.

  • *

    Significant difference between males and females.

Males (n=21)64.6±9.672.3±7.91.78±0.12493±6441.7±8.641.6±4.653.4±5.721.8±3.3
Females (n=13)66.8±9.662.2±11.4*1.58±0.16*572±74*48.3±9.1*31.5±4.1*40.2±5.8*19.1±4.8

FU plasma concentration-time curves were better described by a 2 compartment than a 1 compartment linear model in 22 out of 34 patients. In the 12 remaining subjects, a 1 compartment model was more appropriate. In all cases, r2 was>0.96 (mean±s.d.: 0.99±0.01). Table 2 summarizes the mean values for dose, AUC, CL, CL/kg, Vss, Vss/kg, Vc, initial and terminal half-life in males, females, and all patients. None of the pharmacokinetic parameters differed between males and females (apart from the absolute dose, which was lower in females) or was related to age.

Table 2.  Pharmacokinetic parameters of FU (mean±s.d.).
 Dose (mg)AUC (mg l−1 min−1)CL (l min−1)CL/kg (l min−1 kg−1)Vss (l)Vss/kg (l kg−1)Vc § (l)Final t½ (min)Initial t½§ (min)
  • §

    Parameter calculated only in the group of patients (n=22) with bi-exponential FU plasma concentration decay.

  • *

    Significant difference between males and females.

Males (n=21)756±51680±2091.24±0.360.017±0.00517.5±8.40.24±0.1112.3±7.311.1±3.12.2±1.2
Females (n=13)667±66*587±1461.23±0.440.019±0.00417.3±6.60.27±0.078.7±2.110.1±2.40.7±0.4

Significant but poor correlations were found over the whole population between BW, BSA, TBW, FFM, BCM and CL, and between BW, TBW, FFM and Vss (Table 3). BSA and BMC were not significantly correlated with Vss.

Table 3.  Correlations between FU pharmacokinetic and anthropometric parameters in the whole population, as expressed by r2 and P values (brackets).
  1. Abbreviations as in Table 1.

CL vs0.
(0.013)(0.071, NS)(0.018)(0.014)(0.052, NS)

Whereas no significant differences between sexes were found in correlations between BW, BSA and BCM, and CL and V (Figure 1), the regression lines for TBW and FFM had significantly greater Y-intercepts in females than in males, but without differences in slope (Figure 2). Furthermore, coefficients of determination (r2) obtained separately in men and women were substantially higher than those found in the whole population (Tables 3 and 4).


Figure 1. Relationships between BIA measures (BW, BSA and BCM) and FU pharmacokinetic parameters (CL and Vss). Since all regression lines did not significantly differ between sexes, only lines referring to the combined data are shown. ○: females; •: males.

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Figure 2. Relationships between BIA measures (TBW and FFM) and FU pharmacokinetic parameters (CL and Vss). Regression lines were significantly different between females (○) and males (•). P values refer to differences in intercepts.

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Table 4.  Correlations (r2) between FU pharmacokinetic parameters and selected BIA measures (TBW and FFM), in males and females as expressed by r2 and P values.
MalesCL vs0.380.48
FemalesCL vs0.460.40

According to the stepwise multiple regression analysis performed with TBW and FFM as continuous variables, and sex as a categorical variable (females=0; males=1), FFM and sex were significantly related to CL, whereas TBW and sex were related to Vss. Thus, since FFM and TBW were highly correlated with each other (r2=0.95), they explained much of the variance in CL and Vss. The corresponding regression models were as follows:

  • CL=-0.56+FFM (0.044-SEX×0.61; r2=0.44; adjusted r2=0.41; P<0.00011)
  • Vss=-15.76+TBW×1.05-SEX×10.50; r2=0.36; adjusted r2=0.32; P<0.0010

A multiple regression analysis including sex as a categorical variable was also performed for BW, given this was the non-BIA parameter best correlated with CL and Vss. The following equations were obtained:

  • CL=-0.152+BW×0.022-SEX×0.25; r2=0.29; adjusted r2=0.25; P<0.0044
  • Vss=-9.74+BW×0.42-SEX×3.10; r2=0.23; adjusted r2=0.18; P<0.017

It appears that, like with FFM and TBW, the correlation between BW and CL and Vss improves by including sex as an additional variable.

Since FFM and TBW were the parameters best correlated with CL and Vss, respectively, we normalized CL by FFM and Vss to TBW in males and females. Both FFM-normalized CL (CL/FFM) and TBW-normalized Vss (Vss/TBW) were significantly higher in women than in men (CL/FFM: 0.030±0.008 vs 0.022± 0.005 l min−1 kg−1, 95% CI 0.003, 0.012, P=0.003; Vss/TBW: 0.54±0.15 vs 0.41±0.16 l kg−1, 95% CI 0.057, 0.128, P=0.032).

The finding that both CL and Vss correlated with hydrophilic body compartments (FFM and TWW) prompted us to test whether Vss and CL were also correlated with each other. A very good relationship was found between these two parameters in the population as a whole (r2=0.67; CL=0.52+Vss×0.040) but, at variance with the FFM and TBW correlations, no difference emerged between the regression lines of males and females (Figure 3). The relationship was still highly significant when Vss/kg and CL/kg were used instead of Vss and CL (r2=0.55), indicating that body size played a minor role in explaining the correlation between CL and Vss.


Figure 3. Relationship between Vss and CL. ○: females; •: males. Asterisks and crosses: data (not included in regression analysis) taken from references 26 and 27, respectively.

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In summary, FFM and TBW are better predictors of CL and Vss, respectively, than BW and BSA when data from males and females are analysed separately, and Vss appears to be the best predictor of FU CL, independent of sex.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. References

The results of this study provide information on 1) the prediction of the CL and Vss of FU from measures of BIA, and 2) new insights into FU pharmacokinetics. These two aspects will be discussed separately.

Correlation between BIA parameters and FU pharmacokinetics

Our findings indicate that FU CL and Vss are weakly correlated in the whole population studied, with both standard anthropometric parameters (BW and BSA) and BIA-derived measures (BCM, TBW and FFM). However, when correlations were separately sought for men and women, significantly different regression lines were found only for TBW and FFM, and r2 values were considerably higher than those found in the combined group. Stepwise multiple regression analysis established that CL was significantly correlated with sex and FFM, whereas Vss was correlated with sex and TBW. The models derived from multiple regression accounted for 44% and 36% of the variance in CL and Vss, respectively. These values are clearly higher than those found for parameters currently used for adjusting FU doses, namely BSA and BW, suggesting that the combination of FFM/TBW and sex in the model may substantially improve the accuracy of the prediction of clearance, thus improving the dose. In this respect, it is noteworthy that model r2 values were more than three times higher than those obtained for BSA with simple linear regression (CL: 0.44 vs 0.12; Vss: 0.36 vs 0.10). Nevertheless, it should be stressed that, because of the dose- and schedule-dependence of FU kinetics, the clinical applicability of the model would be restricted to patients treated with the dosing schedule employed here (425 mg m−2 as a 2 min i.v. bolus). Furthermore, since 56% and 64% of CL and Vss variance, respectively, was not accounted by FFM or TBW, a substantial fraction of pharmacokinetic variability still remains unexplained.

A corollary of the different FFM-CL relationship between sexes is that FFM-normalized CL was (35% higher in females than in males, suggesting that FU metabolism per kg of lean body mass is more rapid in women than in men. This hypothesis is also supported by the data of Lu et al.[21], who found that liver DPD activity was (25% higher in women than in men. By contrast, other authors [22, 23], who measured DPD activity in peripheral blood mononuclear cells (PBMC), found it either unchanged or slightly decreased in women. However, DPD activity in PBMC may not reflect FU metabolism in the body, since it is known that DPD in peripheral blood lymphocytes differs in kinetic characteristics from that in the liver [24], and the existence of two different isoforms has been postulated. Moreover, only a weak correlation has been found in man between PBMC-DPD activity and FU plasma clearance [25].

FU pharmacokinetics

The values of CL, Vss and final t½ obtained here are comparable with those formerly reported after administration of similar FU doses by i.v. bolus injection [2]. The use of one- or two-compartment linear models to describe FU plasma concentration decay is also consistent with previous studies in which both models have been alternatively employed [2]. A paradox of FU kinetics is that, in spite of dose-dependent elimination, plasma concentration decay after i.v. bolus does not show the characteristic convex shape expected of a compound eliminated by a saturable process but exhibits the linear decay typical of first order elimination kinetics. Two explanations have been proposed to reconcile this apparent discrepancy. Schaaf et al.[5] suggest that slowly eliminated FU metabolites (possibly dihydrofluorouracil, for example) progressively inhibit DPD activity. Alternatively, Collins et al.[13], using computer simulations based on FU pharmacokinetic data, demonstrated that a two-compartment nonlinear model can generate plasma concentration decay curves mimicking a one- or a two-compartment linear model. Both explanations, although conceivable, lack direct experimental confirmation.

The most intriguing result of our study was the unexpected correlation found between Vss and CL (r2=0.67). Indeed, following FU rapid bolus i.v. injection, other authors [26, 27] reported data which are in agreement with our findings, although a correlation between the two parameters was not formally performed. Nowakowska-Dulawa [26], studying the circadian rhythm of FU pharmacokinetics in patients with gastrointestinal cancer, reported that both CL and Vss mean values were higher in subjects with liver metastases (CL=1.05 l min−1; Vss=18.4 l) than in those without (CL=0.86 l min−1; Vss=14.9). More recently, Bocci et al.[27] found parallel, dose-dependent variations in CL and Vss in two groups of patients with colorectal cancer treated with FU doses of 250 and 370 mg m−2 (CL=54.6 l h−1 m−2 and 25.4 l h−1 m−2; Vss=9.1 l m−2 and 4.1 l m−2, respectively). Furthermore, on plotting these data pairs with our own (setting BSA equal to 1.73 m2, the value that Bocci used), we observe a substantial overlap (Figure 3).

In principle, Vss and CL are not expected to be closely related unless drug clearance is restricted by plasma protein binding, i.e. the drug has a low liver extraction ratio [28]. In this case, since plasma protein binding affects Vss as well, interindividual variations in the protein-bound fraction may influence CL and Vss in the same subject to a similar extent and generate a correlation between Vss and CL in the population. This explanation, however, cannot apply to FU pharmacokinetics, since FU plasma protein binding is negligible (10%) [11] and large changes are unrealistic.

Another possibility is that FU metabolism takes place in many body compartments and not only in the liver, so that drug distribution and elimination are linked to some extent. Several pieces of evidence suggest that this may be the case. First, DPD activity has been detected in several tissues besides liver, in man [24] and other mammals [29]. Second, FU hepatic clearance measured directly in cancer patients following i.v. infusion appears to be lower (0.24–0.45 l min−1) than systemic clearance [30]. Therefore, it is likely that FU metabolism occurs in other (possibly most) body tissues.

A third possibility is that, following i.v. administration, FU undergoes substantial and variable metabolic inactivation on first-pass through the lung. As a consequence, despite intravenous drug administration, systemic bioavailability unpredictably varies among patients and the calculation of CL and Vss values is affected in a similar way, thus creating a spurious correlation between the two parameters. This hypothesis is supported by both theoretical and experimental evidence. Based on the observation that FU CL following low rate i.v. infusion can exceed cardiac output (values of 18.4 l min−1 have been reported), Collins et al.[13] developed a mathematical model to study the relationship between pulmonary drug extraction and FU total body CL, and showed that the latter exceeds cardiac output whenever pulmonary extraction is greater than 0.50, even if the lung is the sole organ of elimination. Furthermore, Naguib et al.[24] measured DPD activity in various human tissues and found that the lung has about one quarter of that of the liver. Since lung blood flow is about four times higher than that of the liver, the first-pass FU metabolism of the two organs should be comparable. Lastly, analysing the data of Ensminger et al.[30], who measured FU serum concentrations in the hepatic vein of three cancer patients following i.v. drug infusion into the hepatic artery or a peripheral vein, that the hepatic venous concentration of FU was about 50% higher when the drug was infused into the hepatic artery than into a peripheral vein. This concentration gradient is easily explained by involving substantial elimination of the drug by the lung.

The major consequence of a first-pass pulmonary metabolism is that a considerable proportion of variability in FU CL after i.v. administration can be attributed to presystemic lung inactivation as well as systemic clearance. Another implication of pulmonary metabolism is that the published values of CL and Vss may be an overestimate of the true values to varying extents.

Accordingly, it would be useful to investigate the extraction of FU by the lung using appropriate pharmacokinetic techniques.

This work was supported by grants from the Regione Veneto (ricerca finalizzata n°934/02/99) and University of Padova (fondi ex-60%, area del farmaco).


  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. References
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