Population pharmacokinetics of raltitrexed in patients with advanced solid tumours

Transcription

1 et al. British Journal of Clinical Pharmacology DOI: /j x Population pharmacokinetics of raltitrexed in patients with advanced solid tumours Elaine Y. L. Blair, Laurent P. Rivory, 1,2 Stephen J. Clarke 2 & Andrew J. McLachlan Faculty of Pharmacy, and 1 Department of Pharmacology, The University of Sydney, NSW 2006, Australia, and 2 Medical Oncology, Sydney Cancer Centre, Missenden Road, Camperdown, NSW 2050, Australia Correspondence Andrew J. McLachlan, Faculty of Pharmacy, Pharmacy Building A15, Science Road, The University of Sydney, NSW 2006, Australia. Tel: Fax: andrewm@pharm.usyd.edu.au L.P.R. present address: Johnson and Johnson Research, 1 Central Avenue, Eveleigh, NSW 1430, Australia. Keywords raltitrexed, cancer, population pharmacokinetics Received 21 March 2003 Accepted 30 October 2003 Aims To investigate the population pharmacokinetics of raltitrexed in patients with advanced solid tumours and to identify patient covariates contributing to the interpatient variability in the pharmacokinetics of raltitrexed. Methods Patient covariate and concentration time data were collected from patients receiving mg m -2 raltitrexed during the early clinical trials of raltitrexed. Data were fitted using nonlinear mixed effects modelling to generate population mean estimates for clearance (CL) and central volume of distribution (V). The relationship between individual estimates of the pharmacokinetic parameters and patient covariates was examined and the influence of significant covariates on the population parameter estimates and their variance was investigated using stepwise multiple linear regression. The performance of the developed model was tested using an independent validation dataset. All patient data were pooled in the total cohort to refine the population pharmacokinetic model for raltitrexed. Results A three-compartment pharmacokinetic model was used to fit the concentration time data of raltitrexed. Estimated creatinine clearance (CL CR ) was found to influence significantly the CL of raltitrexed and explained 35% of variability in this parameter, whilst body weight (WT) and serum albumin concentrations (ALB) accounted for 56% of the variability in V. Satisfactory prediction (mean prediction error 0.17 mg l -1 and root mean square prediction error 4.99 mg l -1 ) of the observed raltitrexed concentrations was obtained in the model validation step. The final population mean estimates were 2.17 l h -1 [95% confidence interval (CI) 2.06, 2.28] and 6.36 l (95% CI 6.02, 6.70) for CL and V, respectively. Interpatient variability in the pharmacokinetic parameters was reduced (CL 28%, V 25%) when influential covariates were included in the final model. The following covariate relationships with raltitrexed parameters were described by the final population model: CL (l h -1 ) = CL CR (ml min -1 ) and V (l) = WT (kg) ALB (g l -1 ). Conclusions A population pharmacokinetic model has been developed for raltitrexed in patients with advanced cancer. Pharmacokinetic parameters of raltitrexed are markedly influenced by the patient s renal function, body weight and serum albumin levels, which may be taken into account in dose individualization. The use of influential covariates to guide anticancer dosage selection may result in less variability in drug exposure and potentially a better clinical outcome. Br J Clin Pharmacol 57: Blackwell Publishing Ltd

2 Population pharmacokinetics of raltitrexed Introduction Raltitrexed (ZD1694, Tomudex ; AstraZeneca) is an anticancer agent which has been shown to be effective in treating many tumour types such as colorectal [1, 2] and breast [3] cancers both as a single agent and in combination with other compounds. It is a folate analogue which selectively inhibits thymidylate synthase to interrupt the de novo synthesis of thymidine triphosphate resulting in DNA fragmentation and cell death [4, 5]. Its potency is enhanced by rapid cellular uptake and extensive intracellular polyglutamation [4, 5]. The clinical pharmacokinetics of raltitrexed have been studied in patients with cancer using conventional pharmacokinetic analyses [6 9]. Plasma concentrations of raltitrexed reach a peak during or shortly after infusion (t max = min) and then decline triexponentially [6, 8] with mean half-lives t 1/2a, t 1/2b and t 1/2g of 12 min, 103 min and 257 h, respectively [8]. Raltitrexed has been shown to be highly protein bound in plasma (> 90%) [10, 11] and yet is extensively distributed (V ss of 7.90 l kg -1 ) [11]. The terminal elimination phase accounts for approximately two-thirds of the total area under the concentration time curve of raltitrexed [8]. The long elimination half-life and large V ss probably reflect the considerable tissue sequestration of the active polyglutamates and the slow redistribution of raltitrexed from tissues into the blood circulation [5, 8, 12]. This resembles the disposition characteristics of other polyglutamated antifolates such as methotrexate [13]. Apart from the intracellular polyglutamation, raltitrexed is not extensively metabolized [8]. It mainly undergoes renal elimination with 40 50% of the dose excreted unchanged in the urine and approximately 15% of the dose excreted unchanged in the faeces [8, 11]. Renal function has been reported to influence the pharmacokinetics of raltitrexed, with the mean raltitrexed clearance approximately halved in patients with mild to moderate renal impairment (mean clearance reduced from 66.7 to 32.3 ml min -1 in patients with normal and impaired renal function, respectively) [9]. The effect of hepatic impairment on the pharmacokinetics of raltitrexed has not been fully defined. In a review of early clinical phase studies Judson [14] reported patients with mild to moderate hepatic impairment had an approximately 25% reduction in the raltitrexed clearance, but these patients did not display clear evidence of a difference in raltitrexed tolerance. In a recent large randomized study comparing raltitrexed to two infusion schedules of 5-fluorouracil plus folinic acid, a 6% death rate was reported in patients receiving raltitrexed compared with <1% in patients treated with the 5-fluorouracil regimens. In addition, patients often developed severe toxicity, predominantly neutropenia and diarrhoea [15]. The dose selection of raltitrexed is currently individualized based on body surface area, but significant interpatient variability remains in its pharmacokinetics, treatment response and toxicity [6, 11, 15]. The considerable interpatient pharmacokinetic variability of anticancer drugs is well known, and given the narrow therapeutic window associated with cancer chemotherapy in general [16], population pharmacokinetic studies could assist in evaluating the influence of significant patient factors on pharmacokinetic parameters to account for some of the variability [17, 18]. This approach has been employed for other anticancer agents including docetaxel [19], etoposide [20] and topotecan [21] and has been used to guide rational dose selection in some patient groups. The aims of this study were to investigate the population pharmacokinetics of raltitrexed in patients with advanced solid tumours and to describe the influence of patient covariates on the interpatient variability in raltitrexed pharmacokinetics. Methods Study design and treatment schedule Data from cancer patients who participated in Phase I and pharmacokinetic studies during the clinical development of raltitrexed were used in the present study. The patients were treated with raltitrexed for a variety of solid tumour types including colorectal, breast and ovarian cancers, with colorectal cancer being the most prevalent. The clinical studies included a European [6] and a US [7] Phase I dose-finding studies, a radiolabel massbalance study [8], and an open-label study evaluating the effect of renal function on the pharmacokinetics of raltitrexed [9]. Raltitrexed was administered intravenously as a short infusion at intervals of 3 weeks. Escalating doses of raltitrexed ranging from mg m -2 and mg m -2 were given in the two Phase I clinical trials. Patients in the disposition study were given a single dose of 3.0 mg m -2 (7.5 mci m -2 ) 14 C- raltitrexed and those enrolled in the renal function study received a single dose of 3.0 mg m -2 raltitrexed as the first dose of a treatment plan which commenced if patients showed clinical benefit after the first dose. Blood sample collection and assay Extensive sampling was taken during and after the raltitrexed infusion. Blood samples were collected over h in the Phase I studies and up to 29 days in the studies where the radiolabelled drug was administered Br J Clin Pharmacol 57:4 417

3 E. Y. L. Blair et al. and where the effect of renal function was investigated. Raltitrexed plasma concentrations were determined using a sensitive radioimmunoassay which uses sheep antiserum raised against raltitrexed [6, 22]. Assay performance was monitored using control samples. The intra- and interassay coefficient of variation for plasma measurements was 10.7% and 12.9%, respectively, and the reported lower limit of detection of the assay was 0.2 ng ml -1 [22]. Clinical and pharmacokinetic data Demographic, clinical and pharmacokinetic data were collected from 112 adult patients who received raltitrexed treatment. Additional pharmacokinetic and clinical data were available from 23 of the patients who received a second treatment course. Treatment course of raltitrexed was considered as a separate individual in the population pharmacokinetic analysis [23]. Each course was separated by at least 3 weeks in which there were changes in patient characteristics including weight and creatinine levels. There is no clinically significant accumulation of raltitrexed plasma levels following repeated administration [8, 11]. Pharmacokinetic data collected included the dose of raltitrexed, actual time and length of infusion, actual time of sample collection and plasma concentration of raltitrexed. The actual duration of infusion varied from 15 to 30 min in these studies. Patient-specific covariate data included in this study were patients sex, age, weight, body surface area, estimated creatinine clearance, serum albumin levels and hepatic function markers such as serum alanine aminotransferase, aspartate aminotransferase and total bilirubin levels. Estimated creatinine clearance was calculated using the Cockcroft Gault formula [24]. Missing creatinine clearance (n = 10) and albumin (n = 27) data were assigned with median values of these covariates calculated from the total data cohort and this did not significantly change the distributional characteristics (mean and variance) of these covariates. The method of data-splitting was used to validate the developed population model [25, 26], through which the patients in the total data cohort were randomly allocated at a ratio of 2 : 1 into a model development dataset consisting of 90 patients, including 23 who had samples taken up to 29 days, and a model validation dataset consisting of 45 patients, including 12 who had data of up to 29 days. ANOVA was performed to assess any significant differences (P < 0.05) in the clinical and pharmacokinetic data among the model development, model validation and total datasets (SPSS for Windows; SPSS Inc., Chicago, IL, USA). Population pharmacokinetic analysis The pharmacokinetic and covariate data were analyzed using the nonlinear mixed effects modelling approach implemented in the population analysis software, P- Pharm version (InnaPhase, Champs-sur-Marne France). A three-step data analysis approach as described previously in the literature [23, 27, 28] was used in the present study, in which (i) an initial population pharmacokinetic model (the basic model) was developed to estimate population parameters and their variance under the assumption that no dependency exists between the pharmacokinetic parameters and covariates, then (ii) the correlation between the posterior individual estimates of parameters and patient covariates was investigated by exploratory data analysis [29], and (iii) population pharmacokinetic parameters were re-estimated taking into account the relationship found in step ii (the covariate model) and the results were compared with those obtained in step i when the influence of covariates was not considered. The choice of the population basic model was initially investigated using a subgroup of the 35 patients who had concentration time data collected up to 29 days to establish the complete pharmacokinetic profile of raltitrexed. The model development dataset was then used to develop the population basic model. The starting parameter estimates were chosen from the pharmacokinetic parameter values reported in the literature [8, 11]. The pharmacokinetic data were fitted with twocompartment and three-compartment pharmacokinetic models with zero-order input and first-order elimination, and a number of pharmacostatistical models including heteroscedastic and homoscedastic error variance and normal or lognormal distribution of the error terms to determine the best fit. Selection of an appropriate model was based on the criteria described by Nath et al. [23] including low estimates of interpatient variability in pharmacokinetic parameters, residual random variability (s) and Akaike Information Criterion (AIC) [30], combined with a high estimate of the maximum likelihood (ML) function value, examination of the goodness of fit between observed and model-predicted concentrations and the residual distribution of which should be not significantly different from a standard normal distribution using Student s t-test and a Kolmogorov Smirnov test. Scatterplots of individual Bayesian parameter estimates vs. each patient covariate were generated using Microsoft Excel. The covariate parameter relationships were screened graphically [31] and by regression analysis (SPSS for Windows). The data in the total dataset were used in order to maximize the exploration :4 Br J Clin Pharmacol

4 Population pharmacokinetics of raltitrexed of possible relationships. Data transformation including natural logarithm and the inverse of the covariates was also tested as additional covariates. The significance of influential covariates on pharmacokinetic parameters was determined using stepwise multiple linear regression in P-Pharm to correlate the parameters to multiple covariates considering the intercorrelation between the covariates. The partial F criterion for each covariate in the regression was compared with a preselected F statistic threshold of 10 to include or remove covariates from the regression [32, 33]. The tests were considered statistically significant at the level of P < The influence of the selected covariates was taken into account in further population analyses to regenerate population mean parameter estimates and their variance. Improvement to the model was assessed by examination of improvement in the statistical and graphical evaluation criteria including reduction in s, AIC and interpatient variability, improvement in the agreement between the observed and predicted concentrations, examination of residual plots and uniformity of the distribution of the residuals vs. the predicted concentrations about the line of unity. Investigation of volume of distribution at steady-state of raltitrexed The population pharmacokinetic model was reparameterized to estimate volume of distribution at steady-state (V ss ) of raltitrexed using the equation V ss = V(1 + k 12 /k 21 + k 13 /k 31 ). The interpatient variability in V ss and its influential covariates were determined using the steps described above. Validation and predictive performance of the population model for raltitrexed The predictive performance of the developed model was assessed using the posterior Bayesian estimates of concentrations in the model validation dataset. The bias and precision of the prediction error were evaluated by calculating the mean prediction error and the root mean square prediction error and their 95% confidence interval [25, 34] and by examining the plots of observed vs. predicted concentrations by the model. The distribution of the residuals was compared with the standard normal distribution [N(0,1)] using Student s t-test and a Kolmogorov Smirnov test [33]. Model refinement and alternative parameterization The developed model was run using the total dataset (model development plus validation sets) to refine the model and to generate the final population parameter estimates for raltitrexed. Re-parameterization of the covariate model with respective coefficients between the pharmacokinetic parameters and the covariates was carried out to refine the regression coefficient (q n ) for each covariate included in the final model. The regression equations for the clearance (CL) and central volume of distribution (V) were CL = q 1 + q 2 CL CR and V = q 3 + q 4 WT + q 5 ALB, respectively. Results Population characteristics and datasets Table 1 summarizes the patient demographic, clinical and concentration time data characteristics in the model development, model validation and total datasets. There were no significant differences among the three datasets. Overall, 1374 (3 22 per patient), 731 (7 23 per patient) and 2105 (3 23 per patient) observations were available in the three datasets, respectively. Selection of the basic population pharmacokinetic model Both analyses using the concentration time data in the subgroup of 35 patients and the model development dataset showed that a three-compartment pharmacokinetic model with heteroscedastic residual error (with a weighing factor of 1/concentration 2 ) and a normal distribution of interpatient variability in the clearance, volume of distribution and the distributional first-order rate constants provided a better description of the data when compared with a two-compartment model and other candidate pharmacostatistical model combinations. The three-compartment basic model showed lower AIC (4.42) and s (0.062) estimates, a higher estimate of ML value (- 5693), a more favourable distribution of residuals and prediction of the observed concentrations than the two-compartment model (AIC = 4.65, s= 0.132, ML = ) with the same pharmacostatistical models using the model development dataset. Covariate screening Weight, body surface area, estimated creatinine clearance and serum albumin concentrations showed a linear graphical correlation with the individual posterior Bayesian estimates of clearance and volumes of distribution (Figure 1). No significant correlation was identified between the parameters and patient age, sex, alanine aminotransferase, aspartate aminotransferase and total bilirubin levels. Data transformation of covariates did not improve the correlations of the covariates with these parameters. Br J Clin Pharmacol 57:4 419

5 E. Y. L. Blair et al. Table 1 Patient characteristics and pharmacokinetic data in the model development, model validation and total datasets [results presented as number, or mean ± SD (range)] Model development Model validation Total cohort Significance Number of patients Number of courses Number of observations Number of observations per patient 15 ± 3 (3 22) 16 ± 3 (7 23) 16 ± 3 (3 23) NS (P = 0.237) Dose administered (mg) 3.9 ± 2.3 ( ) 4.9 ± 1.9 ( ) 4.3 ± 2.2 ( ) NS (P = 0.410) Sex (male : female) 51 : : : 61 Age (years) 56.1 ± 11.0 (21 73) 54.1 ± 11.9 (24 74) 55.5 ± 11.3 (21 74) NS (P = 0.602) Weight (kg) 72.0 ± 16.7 ( ) 74.5 ± 17.4 ( ) 72.8 ± 16.9 ( ) NS (P = 0.712) Body surface area (m 2 ) 1.8 ± 0.2 ( ) 1.8 ± 0.2 ( ) 1.8 ± 0.2 ( ) NS (P = 0.979) Estimated creatinine clearance 84.5 ± 30.8 ( ) 90.5 ± 28.8 ( ) 86.5 ± 30.2 ( ) NS (P = 0.583) (ml min -1 ) Albumin (g l -1 ) 37.3 ± 5.1 ( ) 36.0 ± 6.2 ( ) 36.9 ± 5.4 ( ) NS (P = 0.545) Alanine aminotransferase (U l -1 ) 14.1 ± 10.4 ( ) 16.1 ± 10.8 ( ) 14.7 ± 10.5 ( ) NS (P = 0.156) Aspartate aminotransferase (U l -1 ) 20.9 ± 16.5 ( ) 19.9 ± 11.6 ( ) 20.5 ± 14.9 ( ) NS (P = 0.214) Total bilirubin (mmol l -1 ) 10.4 ± 4.3 ( ) 9.6 ± 4.0 ( ) 10.1 ± 4.2 ( ) NS (P = 0.946) Effects of covariates on clearance Initial screening showed a significant linear correlation between the individual posterior Bayesian estimates of clearance and estimated creatinine clearance (r 2 = 0.095, P < 0.05) and serum albumin concentrations (r 2 = 0.041, P < 0.05). Stepwise multiple linear regression analysis identified these two covariates as influential factors on raltitrexed clearance using the model development dataset. However, when the population analysis was conducted using the total data cohort for model refinement, only the estimated creatinine clearance was selected as an influential covariate. Adding the serum albumin concentrations as a second influential covariate in the model did not improve the population fitting of the data. Estimated creatinine clearance was found to account for 35% (P < 0.001) of the variability in raltitrexed clearance and the following relationship between raltitrexed clearance and estimated creatinine clearance was identified: CL (l h -1 ) = CL CR (ml min -1 ). By including estimated creatinine clearance in the population model, the interpatient variability in raltitrexed clearance was reduced from 33% to 29%. Effects of covariates on volume of distribution Weight, body surface area, estimated creatinine clearance and serum albumin concentrations showed significant linear graphical correlations with the individual posterior Bayesian estimates of the volume of distribution (r 2 = 0.243, 0.206, 0.122, 0.114, respectively; P < 0.001). No significant correlation was identified with other patient covariates or transformed covariate data. Using multiple stepwise linear regression, body weight and serum albumin concentrations were found to influence markedly the volume of distribution of raltitrexed and accounted for 56% (P < 0.001) of the variability in this parameter and reduced the interpatient variability from 36% to 27%. The relationship was described by the following linear regression equation: V (l) = WT (kg) ALB (g l -1 ). Population pharmacokinetic parameters of raltitrexed The population mean pharmacokinetic parameters estimated by the basic and covariate models using the model development dataset are given in Table 2. Similar parameter estimates were obtained with the two models. The population mean estimates for CL and V generated by the basic model are 2.06 l h -1 and 6.65 l compared with 2.11 l h -1 and 6.68 l generated by the covariate model, respectively. The covariate model showed a lower AIC and a higher ML estimate than those with the basic model. There was a reduction in the interpatient variability in the pharmacokinetic parameters of raltitrexed with the covariate model. Figure 2 shows the population fitting for the observed concentration time data using the covariate population pharmacokinetic model. Volume of distribution at steady-state of raltitrexed Re-parameterization of the three-compartment pharmacokinetic model allowed estimation of V ss of raltitrexed :4 Br J Clin Pharmacol

6 Population pharmacokinetics of raltitrexed 4 a 12 b 3 9 CL(l h 1 ) 2 V (l) Creatinine clearance (ml min 1 ) Weight (kg) 12 c 630 d V(l) 6 V ss (l ) Albumin concentration (g l 1 ) Albumin concentration (g l 1 ) e 0.16 f CL (l h 1 kg 1 ) V (l kg 1 ) Creatinine clearance (ml min 1 ) Albumin concentration (g l 1 ) Figure 1 Scatterplots showing relationships between CL, V and V ss against selected covariates: (a) inividual CL vs. estimated creatinine clearance, (b) individual V vs. weight, (c) individual V vs. serum albumin concentration, (d) individual V ss vs. serum albumin concentration, (e) individual CL adjusted for weight vs. estimated creatinine clearance, (f) individual V adjusted for weight vs. serum albumin concentration Br J Clin Pharmacol 57:4 421

7 E. Y. L. Blair et al. Parameter Basic model Covariate model Mean % CV 95% CI Mean % CV 95% CI CL (l h -1 ) , , 2.25 V (l) , , 7.04 k 12 (h -1 ) , , 1.03 k 21 (h -1 ) , , 1.00 k 13 (h -1 ) , , 1.02 k 31 (h -1 ) , , 0.01 Sigma ML AIC Table 2 Population pharmacokinetic parameters of raltitrexed by the basic and covariate models with the model development dataset during model development step CL, Raltitrexed clearance; V, raltitrexed volume of distribution of the central compartment; k 12, k 21, k 13, k 31, intercompartment distributional rate constants; Sigma, residual error; ML, Maximum Likelihood function value; AIC, Akaike Information Criterion; % CV, interindividual variability expressed as percent coefficient of variation; 95% CI, lower and upper limits of the 95% confidence interval. Plasma raltitrexed concentration (mg l 1 ) Time (h) Figure 2 Plasma concentration vs. time graph of raltitrexed data. The line represents the fitting of the covariate population model A linear graphical relationship was identified between the individual posterior Bayesian estimates of V ss and serum albumin concentrations (r 2 = 0.126, P < 0.05). Serum albumin concentration was found to account for 61% (P < 0.001) of the variability in V ss. By taking into account albumin concentrations in the model, the large interpatient variability in V ss was reduced from 39% to 29%. The population mean estimate for V ss generated by this model was l. Performance of the population pharmacokinetic model The population mean parameter estimates from using the model validation set (CL = 2.14 l h -1, V = 6.66 l) are very similar to those generated from the model development set. The developed population model showed a satisfactory predictive performance for which there was a close agreement between the observed and modelpredicted concentration data using the independent validation dataset (Figure 3). The slope of the regression line was close to one and there was a uniform distribution of the data about the line of unity. The mean prediction error of the model was close to zero [0.17 mg l -1, 95% confidence interval (CI) -0.52, 0.86] and the root mean square prediction error was low (4.99 mg l -1, 95% CI 2.53, 7.45). The distribution of the residuals was not statistically different from a standard normal distribution. Figure 4 shows the pharmacokinetic profile of raltitrexed predicted by Bayesian estimation in a patient with impaired renal function and a patient with normal renal function using the developed population pharmacokinetic model. Model refinement and alternative parameterization An analysis using the developed population pharmacokinetic model and the total dataset generated parameter estimates which were in close agreement with those using the model development dataset. The population mean estimates for the clearance and volume of distribution of raltitrexed were 2.01 l h -1 and 6.39 l, with interpatient variability of 36% and 33%, respectively. The inclusion of influential covariates resulted in similar estimates for the clearance and volume of distribution of 2.17 l h -1 and 6.36 l, respectively, and reduced their :4 Br J Clin Pharmacol

8 Population pharmacokinetics of raltitrexed Observed raltitrexed concentration (mg l 1 ) Figure 3 Scatterplot of observed vs. predicted raltitrexed concentrations in the model validation dataset using the covariate model. The solid line is the line of unity, and the broken line and the equation are the linear regression of observed vs. predicted concentrations. Line of unity ( ), regression line ( ) 200 y = 0.971x r 2 = Predicted raltitrexed concentration (mg l 1 ) Raltitrexed concentration (mg l 1 ) CL CR CL (ml min 1 ) (l h 1 ) Time(h) Figure 4 Raltitrexed pharmacokinetic profile in a patient with impaired renal function ( ) and a patient with normal renal function ( ). The lines represent model predictions after Bayesian estimation using the covariate model. Raltitrexed clearance was calculated using CL (l h -1 ) = CL CR (ml min -1 ). CL CR represents the estimated creatinine clearance and CL represents the raltitrexed clearance Table 3 Final population estimates of raltitrexed pharmacokinetic parameters using the total cohort, and regression models describing the relationships between the pharmacokinetic parameters of raltitrexed and influential patient covariates Parameter Mean % CV 95% CI Regression model Regression coefficients CL (l h -1 ) , 2.28 CL = q 1 + q 2 CL CR q 1 = 0.54 ± 0.12 q 2 = 0.02 ± V (l) , 6.70 V = q 3 + q 4 WT + q 5 ALB q 3 = 6.64 ± 1.26 q 4 = 0.08 ± 0.02 q 5 = ± 0.03 CL CR, Estimated creatinine clearance (ml min -1 ); WT, weight (kg); ALB, serum albumin concentration (g l -1 ); q n, regression coefficient (mean ± SD). interpatient variability to 28% and 25%, respectively (Table 3). Re-parameterization of the population pharmacokinetic model incorporating covariates refined the regression coefficients for the covariates, and the regression relationship with the parameter estimates in the final model are given in Table 3. Discussion The present study investigated the pharmacokinetics of raltitrexed in patients with advanced solid tumours using a population pharmacokinetic approach. The findings of the pharmacokinetic properties of raltitrexed in the study were in agreement with those obtained using a conventional pharmacokinetic analysis approach [6 9]. The concentration time profile of raltitrexed was best described by a three-compartment pharmacokinetic model and the clearance and volume of distribution of raltitrexed were found to be in close agreement with those previously reported [8, 11]. Considerable interpatient variability was also observed for the pharmacokinetic parameters of raltitrexed in this patient population. The pharmacokinetics and clinical toxicity of raltitrexed have previously been evaluated in patients with renal impairment [9]. Although a direct relationship has not been defined between creatinine clearance and the degree of clinical toxicity of raltitrexed, it was reported that the raltitrexed clearance is approximately halved in patients with mild to moderate renal function impairment [9]. These patients exhibit a significantly prolonged half-life and a greater AUC than patients with normal renal function [9]. Currently, the Prescribing Br J Clin Pharmacol 57:4 423

9 E. Y. L. Blair et al. Information for Tomudex recommends 50 75% of the usual dose (3 mg m -2 ) every 4 weeks in patients with a creatinine clearance of between 25 and 65 ml min -1 and raltitrexed not to be used in patients with a creatinine clearance of <25 ml min -1. The results of the present study confirm that the estimated creatinine clearance is an influential factor in the clearance of raltitrexed. The dramatic effect of renal function on raltitrexed exposure is demonstrated in Figure 4, in which an estimated 62% decrease in raltitrexed clearance was found in a patient with low creatinine clearance compared with that in a patient with normal renal function. The relationship between creatinine clearance and raltitrexed clearance was previously described as CL (l h -1 ) = CL CR (ml min -1 ) [11]; however, this regression equation was derived with data collected from a relatively small number of patients. In contrast, the present study showed that CL (l h -1 ) = CL CR (ml min -1 ) using data from over 100 patients and population data analysis taking into account the inter- and intrapatient variability and other sources of residual error in the patient population. Approximately 35% of the variation in raltitrexed clearance was explained by creatinine clearance and the inclusion of creatinine clearance in the covariate model reduced the interpatient variability in raltitrexed clearance from 36% to 28%. However, considerable interpatient pharmacokinetic variability remains unaccounted for by any of the patient covariates that we re-investigated in this study. This may be explained by the fact that renal elimination accounts for approximately only 50% of total raltitrexed elimination [7, 11]. Raltitrexed clearance was reported to be reduced by approximately 25% in patients with mild to moderate hepatic impairment, but the difference in patient tolerance to raltitrexed was considered insignificant for dose reduction in this patient group [14]. However, it is not possible to make a full assessment of these limited data in the published literature. In the present study, no significant correlation between raltitrexed clearance and liver function test values (alanine aminotransferase, aspartate aminotransferase and total bilirubin levels) was found. Whilst the findings support the current recommendation that no dosage adjustment be undertaken in patients with hepatic impairment, hepatic enzymes and/or serum bilirubin levels have been suggested to be poor indicators of metabolizing activity and poor correlations between various biochemical indices of liver impairment and clearance of anticancer drugs have been reported [35]. Therefore, careful assessment of the clinical effects of raltitrexed in patients with mild to moderate liver impairment is recommended. Raltitrexed is more than 90% protein bound and higher unbound plasma concentrations of raltitrexed have been reported in patients with lower serum albumin concentrations [11]. Population modelling using the model development dataset suggested a possible influence of albumin concentrations on raltitrexed clearance, where high albumin concentrations may result in a low clearance of raltitrexed, possibly resulting from higher protein binding. However, this correlation was not supported by the analyses of the total data cohort and the inclusion of albumin concentrations as an additional covariate in the final model did not improve the fit of the data. The influence of albumin concentrations is likely to be related to the unbound fraction (fu) of raltitrexed in the body. A relationship between these two covariates was previously defined by Clarke [36] where fu = 1/{ [albumin (g l -1 )]}. Patients with low albumin concentrations could potentially be at higher risk of toxicities because of the effects of protein binding on the pharmacokinetic behaviour of the drug. The impact of albumin concentrations was not definitive in the present study. Nevertheless, this finding proposes a possible factor worthy of further investigation. A large population estimate of V ss was found in this study which indicates extensive tissue distribution of raltitrexed. Beale et al. [8] reported that about half of the radiolabelled dose of raltitrexed was not recovered within a period of 29 days, suggesting retention within tissues, possibly in the polyglutamated form. In addition, it was proposed that the long terminal half-life of raltitrexed is controlled by slow drug redistribution from tissues or binding sites [9]. Because of the highly polyglutamatable nature of raltitrexed, total concentrations of drug (parent compound and polyglutamates) in tissues and tumours could better correlate with clinical outcome than plasma drug levels. The relationship between the drug disposition and the extent of tissue sequestration of raltitrexed requires further investigation. The current practice for dosage selection for most anticancer agents, including raltitrexed, is based on body surface area. However, body surface area was not selected in the present study as a significant influential covariate affecting either the clearance or volume of distribution of raltitrexed. Pharmacokinetically guided dose selection, such as using area under the plasma concentration time curve, has been studied and employed for many anticancer drugs and is suggested to result in less drug toxicity [37]. A recent study by Baker et al. [38] investigated the pharmacokinetics of 33 anticancer agents in Phase I trials as a function of body surface area and reported that body surface area-based :4 Br J Clin Pharmacol

10 Population pharmacokinetics of raltitrexed dosing was significantly associated with drug clearance for five agents only. These findings suggest that the utility of dose selection on the basis of body surface area requires further assessment. Interoccasion variability was an important issue when developing the population pharmacokinetic model, as the P-Pharm software employed in this analysis cannot independently partition sources of error, other than residual and interindividual variability. Using P-Pharm, interoccasion variability is lumped with residual variability which provides a possibility of an overestimate of the residual error [39]. Twenty-three out of 112 patients in the study had pharmacokinetic data available from a second course with the samples taken over a short time period (2 48 h). In this study we chose to analyze data from the second courses as separate individuals [23]. A preliminary analysis of the data showed that the estimates of pharmacokinetic parameters and the residual error when treating each course as a separate individual were in close agreement with those when treated as repeated doses. The limited and relatively short time second course data were not considered to contribute significantly to the prolonged elimination phase of raltitrexed. Large interpatient variability in pharmacokinetics may result in variability in drug exposure, treatment response and toxicity. Clinical application of population pharmacokinetic modelling could potentially offer alternative dosing strategies, such as Bayesian estimation of target concentrations [40], using patient-specific covariate information to reduce the interpatient variability of raltitrexed and improve treatment outcome. In the case of raltitrexed, further research is needed to define the role of polyglutamation and tissue sequestration drug in influencing the efficacy and toxicity of this drug. The present study has described the population pharmacokinetics of raltitrexed in patients with advanced solid tumours and found that interpatient variability can be accounted for by a patient s renal function, body weight and serum albumin levels. Unfortunately comprehensive pharmacodynamic data for raltitrexed in this patient population are lacking [11]. This prevents an unambiguous assessment of the clinical implications of the effect of patient covariates on the dosing requirements for raltitrexed. Grem et al. [7] provided some preliminary evidence that raltitrexed C max, AUC 0-24 h and dose could be linked to changes in granulocyte count using a sigmoidal maximum effect (E max ) pharmacodynamic model. 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