Introduction

Doxorubicin is the mainstay in the treatment of cancer. A disadvantage of doxorubicin is its association with cardio toxicity, which is associated with elevated trough levels and sustained elevated peak levels. It exerts its cytotoxic activity by prefer-entially intercalating with nucleotides (1–4). Dosage adjustment based on individual pharmacokinetic parameters is of considerable importance for effec-tive and safe use of drugs (especially anti-cancer agents) (5).

Although drug-induced toxicity is dose- dependent, the individual susceptibility to side-

effects varies considerably. As other anti-cancer agents, the administered dose of doxorubicin is normalized by a patient’s body surface area (BSA) (6–8). However, for most anti-cancer agents, clear-ance is poorly correlated to body-size measures and, hence, the routine use of BSA as the only independent variable considered in drug dosing is questionable. Previous studies have revealed sig-nificant correlations between inter-individual vari-ation in doxorubicin clearance and the likelihood of tumor response and or toxicity (9). However, the factors contributing to pharmacokinetic vari-ability for these agents are largely unknown and unstudied. In an attempt to further optimize use

Clinical Research and Regulatory Affairs, 2009; 26(4): 93–100

R E S E A R C H A R T I C L E

Population pharmacokinetics of doxorubicin in Indian cancer patients using NONMEM

Vijay S. Kumar1, Harish K. Kaushik1, Satish B. Kumar1, Narsimha Y. Reddy1, Narasimha Reddy2, T. Kumaraswamy3, Praneeth S. Kumar4 and Krishna R. Devarakonda5

1Drug Metabolism and Clinical Pharmacokinetics Lab, University College of Pharmaceutical Sciences, Kakatiya University, Warangal, India, 2Bibi Cancer Hospital and Research Institute, Hyderabad, India, 3CRC, Apollo Hospitals, Hyderabad, India, 4St.Peter’s College of Pharmacy, Warangal, India and 5Clinical Research Division, Covidien Inc., Hazelwood, Missouri, USA

AbstractThe population pharmacokinetics of doxorubicin were evaluated based on a mixed-effect model using the NONMEM (VI) program. Doxorubicin in plasma was measured using high-performance liquid chroma-tography. Plasma concentration measurements (85 plasma samples) of doxorubicin from 28 patients with cancer receiving doxorubicin (with other co-medication) ranging from 20–120 mg by infusion over 1–2 h were analyzed according to a two-compartment model both in FO and FOCE methods. Additive propor-tional error model was used to describe inter-individual and residual variability. The influence of covariates such as age, body surface area, gender, and clinical laboratory values (SGOT, SGPT) on total body clearance (CL) and volume of distribution (Vd) were examined. No covariate was found to affect the CL and Vd of unchanged doxorubicin. The CL and Vd estimated by FO method were 1.42 L/h and 51.1 L, respectively, and FOCE method are 1.43 L/h and 51.4 L, respectively. The inter-individual variability for CL and Vd and residual variability were 45.8%, 36%, and 12.6%, respectively. The population means and inter-individual and residual variability of pharmacokinetics of doxorubicin were evaluated using the NONMEM program. The results of this study show that the population pharmacokinetic approach could be useful to manage doxorubicin cardio toxicity using sparse data in a clinical setting.

Keywords: Population pharmacokinetics; doxorubicin

Address for Correspondence: Krishna R. Devarakonda, M. Pharm, PhD, FCP, Clinical Research Division, Covidien Inc., 675 McDonnell BlV, Hazelwood, MO 63042, USA. E-mail: krishna.devarakonda@covidien.com

(Received 22 May 2009; revised 24 July 2009; accepted 28 July 2009)

ISSN 1060-1333 print/ISSN 1532-2521 online © 2009 Informa UK LtdDOI: 10.3109/10601330903252214 http://www.informahealthcare.com/crr

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of these agents, we have now characterized the population pharmacokinetics of doxorubicin in an Asian patient population under general clinical conditions, and explored demographic sub-popula-tions or drug conditions for which dose adjustment may be needed.

High variations in PK parameters have been described for all major anti-cancer drugs; 3–10-fold inter-individual variation in systemic exposure have been reported, even in patients without renal or liver failure or other metabolic dysfunction (10). Pharmacokinetic studies appear to be the first step in the assessment of PK-PD relationships, necessary for therapeutic drug monitoring. In this first step, plasma concentrations are measured in a population of patients for whom the PK parameters (mainly CL, V

d) are calculated. Developments in population phar-

macokinetic software using Bayesian methodology allow the investigator to estimate PK parameters for some drugs with good precision, with limited sam-pling strategies (11, 12) Inter-individual and intra-individual course-to-course variability as well as the influence of clinical and biological covariates (e.g. weight, body-surface area, serum creatinine) on PK parameters can easily be assessed.

Methods

Patients

The patient group was selected from cancer patients who visited Bibi cancer hospital (Hyderabad, India); Mahatma Gandhi Memorial Hospital (Warangal, India), and St. Ann’s cancer Hospital (Kazipet, India). Informed consent was taken from the patients who were willing to participate in the study. Permission was taken from the concerned departments in the hospitals prior to the study. Institutional ethics com-mittee approval was taken before starting the study (Letter No: UCPSc/PK/2007-06). Demographic data of all the patients was collected which includes name, age, sex, weight, height, disease status, smoking sta-tus, alcoholic status, family history, concomitant diseases (C.V.S, C.N.S, hepatic, and Renal diseases), and concomitant medications taken along with doxorubicin.

Demographic data is given in Table 1. The study included 28 South Indian patients (11 were males and 17 females) suffering from various types of (majority breast cancers) cancers, who were on long-term ther-apy with doxorubicin. The medical oncologist fixed the dosage regimen. After completing the infusion, blood samples (1–4) were collected from each patient at different time points. A wide range (like 0 min, +

10 min, + 1 h 45, + 2 h 45, + 3 h 45, + 4 h 15, + 4 h 45, + 5 h 45, + 9 h 45, + 20 h 45, + 24 h 45, + 26 h 45, + 27 h 45, + 28 h 45, + 33 h 45, + 41 h 15, + 43 h 45, and + 45 h 45 in the extensive sampling strategy at the end of the infusion of sampling schedules were randomly allocated) of time points was selected in all patients to obtain better results. The collected samples were stored at −70°C until further analysis was carried out. The age of the patient population was between 6–78 years.

Assays

Plasma concentrations of doxorubicin were assayed according to an HPLC method as reported (13). To each 1 ml of plasma sample, internal standard (10 µl of 500 µg/ml epirubicin) was added. Liquid–liquid extraction was done by using dichloromethane. After vortex mixing (for 8 min), the samples were centri-fuged at 13,000 g (room temperature) for 10 min. Then the organic layer was evaporated near to dryness. The dried extracts were reconstituted in mobile phase (100 µl), vortexed for 1 min and 20 µl was injected onto HPLC. The flow rate of the mobile phase con-sisting of Water/Acetonitrile (75:25) was1 mL/min.The peaks were detected at 254 nm. The lower limit of quantification and upper limit of quantification of the method were 0.05 μg/mL and 50 μg/mL, respectively. A reverse phase C18 column (Synergi 4u Fusion-RP 80A, 5 μm, 250 × 4.6 mm, Phenomenex, USA) was used.

NONMEM analysis

Our population pharmacokinetic and statistical method of analysis follows the FDA Guidance for Industry on Population Pharmacokinetics. Age, gen-der, race, body weight, height, and body mass index were considered as possible covariates. Baseline values were included in the NONMEM dataset. The data were analyzed in a 4-step process (14). First, an exploratory graphical analysis of the data was performed (SPSS). Second, structural models were fit to the data and assessed using the NONMEM program (Version VI, UCSF, San Francisco, CA). Based on graphical evalu-ation of Doxorubicin plasma concentration vs time

Table 1. Range and mean ± SD values for patients under study.Parameter Range Mean ± SDAge (years) 6–73 39.05 ± 15.57Body weight (Kg) 15–86 52.19 ± 12.24Dose (mg) 20–120 62.41 ± 24.33

Serum level (µg/ml) 0–2.02 0.61 ± 0.506

Sampling time (h) 0–46.83 7.35 ± 11.34

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profiles overall and by dose, various structural model features were explored. Inter-subject variability terms were added to the pharmacokinetic parameters, and the form of the residual error model was evaluated during the model development process. Inter-subject variability was modeled for all pharmacokinetic parameters as follows:

θ = θi T i⋅ exp ( )h (1)

where θi is the parameter for the ith participant, θ

T is

the typical value of the parameter in the population, and η

i is a random inter-subject effect with mean 0

and variance ω2.Two residual error models were tested. In the first,

residual variability was assumed to be log normally distributed, as follows:

(2)

In the final population model, residual variability was assumed to be proportional to the prediction and was modeled as follows:

(3)

For equations (2) and (3), yij and ŷ

ij represent the jth

observed and predicted concentration, respectively, for the ith participant, and ε is the random residual effect, which is log normally distributed (equation 2) or normally distributed (equation 3) with mean 0 and variance σ2. The process was guided by examination of diagnostic plots to assess the goodness of fit of the model to the data. Third, distributions of individual parameter estimates were obtained using the post-hoc feature in NONMEM. Symmetry of the individual parameters about the estimated median parameter was assessed graphically.

In the final step, sources of variability in doxoru-bicin pharmacokinetics were identified. Initially, parameter covariate relationships were explored graphically and using generalized additive models in SPSS®. Significant parameter–covariate relationships thus selected were included in a full tentative phar-macokinetic model in NONMEM. Continuous covari-ates (centered about standard values) were included in the model as follows:

θ = θ /i T iK⋅(Cov Cov )med

cov

where Covi is the value of the covariate for the ith par-

ticipant, Covmed

is the median value of the covariate

and Kcov

represents the influence of the covariate on the parameter, θ

T. Categorical covariates were intro-

duced in the model as follows:

θ = θi T i K⋅ ⋅exp (Cov )cov

where Covi is a binary variable. In this way, indicator

variables were used to evaluate the effects of gen-der, race, and apparent clearance. Covariates were sequentially excluded from the model using a step-wise deletion method in which the statistical signifi-cance of each parameter–covariate relationship was tested using a likelihood ratio test.5 for the stepwise deletion method; a p < 0.01 level of significance was used (15). Other covariates of interest were added by stepwise additions to the tentative model. Final covariate selection was based on the likelihood ratio test in NONMEM, using a significance level of p < 0.01. Parameter estimates were examined to ensure that they were well estimated and plausi-ble. Confidence intervals were estimated for each population parameter as θ ± Z (SE), where θ is a population parameter estimate, SE is its associated standard error, and Z is the interval coefficient for a standardized normal distribution (Z = 1.96 for a 95% confidence interval). At each stage of the analysis, the model was evaluated graphically and refined as necessary. When this process was complete, the resultant model was considered the final popula-tion pharmacokinetic model for Doxorubicin. Final parameter estimates were obtained using the first-order conditional estimation (FOCE) method in NONMEM.

Population pharmacokinetic model development

Data files were constructed using Excel and Notepad. All the demographic data (patient id, age, sex, weight, height, dose, etc.) and concentration of doxorubicin obtained at different time intervals was used in com-pilation of the data sheet. The base model for analy-sis was prepared using the two compartment linear model using the sub-routine ADVAN3 in PREDPP module in FO and FOCE methods.

Additive proportional error model was used to describe inter-individual and residual variability. The influence of covariates such as age, body surface area, gender, and clinical laboratory values (SGOT, SGPT) on total body clearance (CL) and volume of distribu-tion (V

d) were examined.

Each covariate was added to the basic pharma-cokinetic model and the objective function value was noted. An analysis was then performed by a forward, stepwise technique where each covariate

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which individually caused a decrease in objec-tive function value was added cumulatively to the model. This process was continued until no further reduction in the objective function value resulted. Finally, a backwards elimination step was per-formed by setting the coefficient of each covariate, in turn, to zero, noting the change in the objective function value, diagnostic plots, etc., and the final model was derived.

FO method: The final structural model (ADVAN3):

CL EXP ( 1)

V EXP ( 2)

=1.42∗= 51.1∗

h

h

The final scatter plots are given in Figure 1 and Figure 2 for FO and FOCE models respectively. This model resulted in a decrease in residual variability. The parameter estimates obtained by using the final model are given in Table 2. Mean population phar-macokinetic parameters are given in Table 3. The population parameters are given in Table 4. Mean population pharmacokinetic parameters are given in Table 5.

Discussion

During the last 10 years the concept of therapeu-tic drug monitoring has emerged, supported by advances in population pharmacokinetics. As a first step, descriptive PK studies allow individual PK parameters as well as their variability to be esti-mated. Population pharmacokinetics and Bayesian methodology enables the investigator to estimate these parameters with acceptable precision, even with a limited sampling strategy, considerably reducing patient discomfort and labor intensity and therefore making PK studies easier to perform. Population pharmacokinetics of doxorubicin in cancer patients using NONMEM was undertaken for the first time in India. The study population was representative of the cancer patient population in India. Hence the population parameters obtained in the present study can be used in optimizing the dos-age of doxorubicin for individual patients in India. This will not only reduce the incidence of adverse drug effects, but also aid in cost-effective long-term drug therapy.

The PK of doxorubicin has been extensively inves-tigated and conflicting data have been published regarding the most appropriate pharmacokinetic model. In the clinical setting, one compartment

model has usually been employed, although one study reported that the pharmacokinetics of doxoru-bicin is better characterized by a three-compartment model.

In the present study we found that the two- compartment model better describes the pharma-cokinetics of doxorubicin. Quite a few studies were conducted to understand the population pharma-cokinetics of doxorubicin.

Clearance obtained in our study was lower com-pared to previous studies conducted in various other populations (16) which reported a higher CL for doxorubicin (52 L/h). We observed that none of the covariates had any influence either CL or V, similar to Gehl et al. (5), who reported CL and V to be 32 L/h and 9.3 L, respectively (inter-individual variability 17.2% and 19.2%), they questioned the use of body surface area as key parameter for dose calculation. We did not study any polymorphism in genes as conducted in a population PK study (17) on doxorubicin and doxorubicinol in Asian breast cancer patients and they reported CL and V to be 34.7 L/h (CV: 22%) and 8.24 L (CV: 32.4%), respectively, which observed no covariate had a significant impact on doxorubicin pharmacokinetic variability which also led to the suspicion that the pharmacokinetic variability is due to the influ-ence of polymorphisms in genes encoding vari-ous proteins across the doxorubicin biochemical pathway. Contrary to our study, Fisher et al. (6) reported that BSA was positively correlated with doxorubicin CL. AST and patient age were nega-tively correlated.

In the present study no covariate influenced either CL and V. This is in agreement with most of the other reported population PK studies. The CL (1.43 L/h) is very low and the volume of distribution (53 L) is higher compared with the other studies except for one study (16) conducted, where the V is 2360 L. These variations are probably due to variation in ethnicity. The range of concentrations obtained in different patients was 0.23–2.02 µg/ml. These values are somewhat higher than those reported in the literature.

Conclusions

We studied the population pharmacokinetics of doxorubicin in an Indian population using plasma concentration data. The results of our present investi-gation can have a positive impact on management of doxorubicin therapy in the study population.

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Figure 1. Scatter plots for FO method plotted between PRED vs DV, TIME vs WRES, TIME vs RES, PRED vs RES, IPRED vs PRED, ID vs CL, ID vs RES, ID vs VD, and TIME vs DV and PRED.

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Figure 2. Scatter plots for FOCE method plotted between PRED vs DV, TIME vs WRES, TIME vs RES, PRED vs RES, IPRED vs PRED, ID vs CL, ID vs RES, ID vs VD, and TIME vs DV and PRED.

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Acknowledgement

A part of this work was presented as a poster at, 37th ACCP Annual Meeting and Exposition, held at PHILADELPHIA, USA, in 2008.

Declaration of interest: This study was funded by All India Commitee for Technical Education, New Delhi, India.

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Table 4. FOCE method population pharmacokinetic parameters.Parameter Meaning Estimation

θ1

Coefficient (CL) 1.43

θ2

Coefficient (V) 51.4

θ3

Absorption rate constant 0.271

θ4

Elimination rate constant 0.689

ω1

Inter-patient variability (CL) 0.215

ω2

Inter-patient variability (V) 0.131

Є2

Residual Error 0.131

Table 5. Parameter estimates obtained by using the final (FOCE) model.

Model OFV

Population estimate

(%SE)

Between subject variability

(%SE)Base model −137.01 IV 1 compartment

CL = CLpop* η CL

V = Vpop* η VFinal model −137.01IV 1 compartment

CL = CLpop* η CL

V = Vpop* η VCL (L/h) 1.43 1.43 (14.54) 46.5 (43.9)V (L) 51.4 51.4 (8.15) 36.1 (55.1)K12 0.271 0.271(27.6) K21 0.689 0.689 (32.5)Residual variabilityProportional error 12.6 (79.3)Additive error 0.006,64 μg/mL (45.79)

Table 3. FO method population pharmacokinetic parameters.Parameter Meaning Estimation

θ1

Coefficient (CL) 1.42

θ2

Coefficient (V) 51.1

ω1

Inter-patient variability (CL) 0.285

ω2

Inter-patient variability (V) 0.266E

Є1

Residual error 0.0453

Table 2. Parameter estimates obtained by using the final (FO) model.

Model OFV

Population estimate

(%SE)

Between subject variability

(%SE)Base model −115.834 IV 1 compartment

CL = CLpop* η CL

V = Vpop* η VFinal model −115.834IV 1 compartment

CL = CLpop* η CL

V = Vpop* η VCL (L/h) 1.42 1.42 (15.77) 53.38 (76.4)V (L) 51.1 51.1 (6.63) 51.58 (81.2)K12 0.290 0.290 (53.1) K21 1.04 1.04 (64.51)Residual variabilityProportional error 21.28 (23.3)

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