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Population pharmacokineticand pharmacogenetic

analysis of 6-mercaptopurinein paediatric patients withacute lymphoblasticleukaemiaAhmed F. Hawwa, Paul S. Collier, Jeff S. Millership,

Anthony McCarthy,1 Sid Dempsey,1 Carole Cairns1 &

James C. McElnay

Clinical and Practice Research Group, School of Pharmacy, Medical Biology Centre, Queens University

Belfast and1Haematology and Oncology Outpatient Department, Royal Belfast Hospital for Sick

Children,The Royal Hospitals, Belfast Health and Social Care Trust,Belfast, UK

CorrespondenceProfessor Paul S. Collier,Clinical and

Practice Research Group, School of

Pharmacy, Medical Biology Centre,

Queens University Belfast, 97 Lisburn

Road,Belfast BT9 7BL, UK.

Tel:+44 28 9097 2009

Fax: +44 28 9024 7794E-mail: [email protected]

----------------------------------------------------------------------

Keywords6-mercaptopurine, acute lymphoblastic

leukaemia, NONMEM, pharmacogenetics,

population pharmacokinetics

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Received10 July 2008

Accepted5 August 2008

PublishedOnlineEarly24 September 2008

WHAT IS ALREADY KNOWN ABOUT

THIS SUBJECT

The cytotoxic effects of 6-mercaptopurine (6-MP)

were found to be due to drug-derived intracellular

metabolites (mainly 6-thioguanine nucleotides and to

some extent 6-methylmercaptopurine nucleotides)

rather than the drug itself.

Current empirical dosing methods for oral 6-MP result

in highly variable drug and metabolite concentrations

and hence variability in treatment outcome.

WHAT THIS STUDY ADDS

The first population pharmacokinetic model has been

developed for 6-MP active metabolites in paediatric

patients with acute lymphoblastic leukaemia and the

potential demographic and genetically controlled

factors that could lead to interpatient

pharmacokinetic variability among this populationhave been assessed.

The model shows a large reduction in interindividual

variability of pharmacokinetic parameters when body

surface area and thiopurine methyltransferase

polymorphism are incorporated into the model as

covariates.

The developed model offers a more rational dosing

approach for 6-MP than the traditional empirical

method (based on body surface area) through

combining it with pharmacogenetically guided

dosing based on thiopurine methyltransferase

genotype.

AIMS

To investigate the population pharmacokinetics of 6-mercaptopurine (6-MP) activemetabolites in paediatric patients with acute lymphoblastic leukaemia (ALL) and

examine the effects of various genetic polymorphisms on the disposition of these

metabolites.

METHODS

Data were collected prospectively from 19 paediatric patients with ALL (n = 75

samples, 150 concentrations) who received 6-MP maintenance chemotherapy

(titrated to a target dose of 75 mg m-2 day-1). All patients were genotyped for

polymorphisms in three enzymes involved in 6-MP metabolism. Population

pharmacokinetic analysis was performed with the nonlinear mixed effects

modelling program (NONMEM) to determine the population mean parameter

estimate of clearance for the active metabolites.

RESULTS

The developed model revealed considerable interindividual variability (IIV) in the

clearance of 6-MP active metabolites [6-thioguanine nucleotides (6-TGNs) and6-methylmercaptopurine nucleotides (6-mMPNs)]. Body surface area explained a

significant part of 6-TGNs clearance IIV when incorporated in the model (IIV

reduced from 69.9 to 29.3%). The most influential covariate examined, however,

was thiopurine methyltransferase (TPMT) genotype, which resulted in the greatest

reduction in the models objective function (P< 0.005) when incorporated as a

covariate affecting the fractional metabolic transformation of 6-MP into 6-TGNs.

The other genetic covariates tested were not statistically significant and therefore

were not included in the final model.

CONCLUSIONS

The developed pharmacokinetic model (if successful at external validation) would

offer a more rational dosing approach for 6-MP than the traditional empirical

method since it combines the current practice of using body surface area in 6-MP

dosing with a pharmacogenetically guided dosing based on TPMT genotype.

British Journal of ClinicalPharmacology

DOI:10.1111/j.1365-2125.2008.03281.x

826 / Br J Clin Pharmacol / 66:6 / 826837 2008 The AuthorsJournal compilation 2008 The British Pharmacological Society
mailto:[email protected]:[email protected]


2/12

Introduction

6-Mercaptopurine(6-MP) is a purine antimetabolite widely

used in the maintenance chemotherapy of childhood

acute lymphoblastic leukaemia (ALL), the most common

cancer in children. 6-MP has been a key component of

almost every successful therapeutic regimen for low- to

mild-risk ALL. However, it was not until the early 1980s that6-MP cytotoxic effects were found to be due to drug-

derived intracellular metabolite concentrations rather

than the plasma level of 6-MP itself [1].

Following oral administration, 6-MP undergoes exten-

sive biotransformation by three enzymes, two of which are

catabolic, xanthine oxidase (XO) and thiopurine S-methyl

transferase (TPMT) and one anabolic, hypoxanthine phos-

phoribosyl transferase (HPRT). XO metabolises 6-MP

to 6-thiouric acid (6-TU),whereasTPMT methylates 6-MP to

6-mMP. HPRT carries out the first anabolic step to produce

6-thioinosine monophosphate (6-TIMP) and subsequently

the active 6-thioguanine nucleotides (6-TGNs). 6-TIMPcan alternatively be methylated by TPMT, yielding

6-methylmercaptopurine nucleotides (6-mMPNs) [2].

Finally, it is hypothesized that 6-TIMP is converted succes-

sively into 6-thioinosine diphosphate (6-TIDP) and triphos-

phate (6-TITP) to form 6-TIMP once again by the action of

the enzyme inosine triphosphatase (ITPA) [3].

Cytotoxic effects of 6-MP are achieved primarily

through the incorporation of 6-TGNs into the DNA of leu-

cocytes, due to their structural similarity to the endog-

enous purine based guanine [2,4]. Moreover, 6-mMPNs are

strong inhibitors of purine de novo synthesis, which is a

well-established protocol to achieve immunosuppression

[5, 6]. Despite these facts, the pharmacokinetics of the

active metabolites of 6-MP remain poorly explored. A

better understanding of the disposition of 6-TGNs and

6-mMPNs would therefore be immensely helpful in

improving the design of dosing regimens for 6-MP.

In this study, the pharmacokinetics of 6-TGNs and

6-mMPNs in paediatric patients with ALL under 6-MP

maintenance chemotherapy were examined prospectively

and, for the first time,a population pharmacokinetic model

for 6-MP active metabolites, 6-TGNs and 6-mMPNs, in pae-

diatric patients with ALL was developed. In developing this

model, potential factors that could lead to variability in

6-MP cytotoxic metabolites that would be particularlyhelpful in improving the dosing guidelines for 6-MP were

assessed.

Methods

Patients and data collectionThe study was approved by the National Health Service

Office for Research Ethics Committees in Northern Ireland.

Informed parental consent was obtained for each child

before enrolment in the study. In addition, verbal assent

was obtained from older children (>10 years) after provi-sion of a verbal description of the study and what it

involved.

Data were collected from 19 paediatric patients attend-

ing the Haematology and Oncology Outpatient Depart-

ment at the Royal Belfast Hospital for Sick Children and

who had been diagnosed to be suffering from ALL. Blood

samples were taken from children who had been oncontinuous/maintenance 6-MP therapy for at least 1

month and who had received constant daily doses for at

least 1 week. Patients who satisfied the above criteria but

who had received intensification therapy or red blood cell

(RBC) transfusion within the previous 2 months were

excluded.

Blood samples were obtained at a phase of treatment

when children had an indwelling cannula for vincristine

therapy and at least 12 h after the preceding 6-MP dose.

(Note,the blood sample was taken prior to the administra-

tion of vincristine.)

Maintenance chemotherapy for ALL patients consistedof daily oral 6-MP and weeklymethotrexate.The 6-MP dose

was titrated to the target protocol dose of 75 mg m-2 day-1,

adjusted for each child according to leucocyte count and

the presence of clinically relevant infections. Additionally, a

monthly dose of intravenous vincristine was given to all

children irrespective of blood count. Chemotherapy was

administered usually for 2 or 3 years.The children had their

full blood counts assessed at each clinic visit (every 2

weeks) for bone marrow toxicity.

The blood samples (1.5 ml) were collected in ethylene-

diamine tetraaceticacid (EDTA) tubes and kept on ice until

centrifuged at 1000gfor 10 min to separate plasma from

RBC. Separated plasma was frozen at-20C in Eppendorftubes while RBC were washed twice with a balanced salt

solution, then suspended at a density of 8 108 RBC per

200 ml and kept frozen at -20C until required for further

processing. These latter samples for the determination of

metabolite content were taken on a maximum of five occa-

sions (one sample per occasion), at monthly intervals, over

the study period.

A sample of blood (1 ml) from each patient, taken on

one occasion only,was collected in an EDTA tube and kept

at -20C without centrifugation for genotyping the

enzymes of interest (200 ml of whole blood was sufficient

for this purpose).In addition to information on dosing and times of sam-

pling, the following data were collected for each child:age,

weight, surface area, gender, ongoing pathology (e.g.renal

and/or hepatic impairment), concomitant drug therapy,

lab test results andrecords of any side-effects experienced.

The demographic and clinical characteristics of the study

participants are shown in Table 1.

Assay of 6-MP metabolitesRBC concentrations of 6-MP active metabolites, 6-TGNs

and 6-mMPNs, were measured by a reversed high-

6-Mercaptopurine pharmacokinetics and pharmacogenetics

Br J Clin Pharmacol / 66:6 / 827


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performance liquid chromatography methodology that

was developed earlier [7]. Intraday and interday coeffi-

cients of variation (CV) were 3.1 and 4.3%, and the limitsof quantification were 13 and 95 pmol per 8 108 RBC,

respectively.

Genotyping of TMPT, ITPA and XOAll patients were screened for seven common polymor-

phisms in three enzymes involved in 6-MP metabolism

(XO,TPMT and ITPA) that are potentially linked to the phar-

macodynamics, toxicity and treatment outcome of 6-MP;

two polymorphisms in XO (A1936G and A2107G) andITPA (C94A and IVS2+21AC) and three polymorphismsin TPMT (TPMT*3A, TPMT*3B and TPMT*3C).

Detection of the various single nucleotide polymor-phisms (SNPs) in the enzymes genetic loci was based on

TaqMan genotyping assays (ABI, Foster City, CA, USA)

using somatic cell DNA extracted from patient blood

samples (QIAmp DNA Blood Mini kit; Qiagen, Hilden,

Germany).The conditions used for polymerase chain reac-

tion and subsequent detection of the genotypes were as

described in the manufacturers instructions.

Population pharmacokinetic modellingThe pharmacokinetics of 6-MP active metabolites, 6-TGNs

and 6-mMPNs, were determined using a population

approach in which concentrations from ALL patients were

analysed simultaneously to produce estimates of the phar-

macokinetic parameters. RBC concentrationtime profiles

of 6-TGNs and 6-mMPNs were used for nonlinear mixed

effect modelling by extended least squares regression

usingNONMEM(version VI, level 1.1 with double precision)

[8] installed on a personal computer in conjunction with

DIGITAL Visual Fortran compiler (version 5.0.A). Patientswere assigned randomly to either an index group (n = 15)

for the development of the pharmacokinetic model, or to

the validation group (n = 4) for the purpose of assessing

the predictive performance of the derived model.The first-

order conditional estimation (FOCE) method with interac-

tion was used to estimate population mean parameters,

interindividual variability (IIV) in these parameters and

residual variability between measured and predicted

metabolite concentrations.

The concentrationtime courses of 6-MP metabolites

were described by using a one-compartment model with

first-order absorption and elimination.The absorption rateconstant (ka) and the bioavailability factor (F) of the model

were fixed at 1.3 h-1 and 22%,respectively,according to the

literature [1,9].The pharmacokinetic parameters estimated

from this model (implemented using PREDPP subroutine

ADVAN6) were clearance (CL) for 6-TGNs and 6-mMPNs

and the fractional metabolic transformation of 6-MP into

6-TGNs.

The relationship between the parent drug and its

metabolites was defined according to the following differ-

ential equations:

d A 6-MP

dtF A 6-MP

Guta Gut

( )= ( )k

d A 6-MP

dtF A 6-MP A 6-MP

Centrala Gut Central

( )= ( ) ( )k k20

d A 6-TGNs

dtFM A 6-MP

CL C 6-TG

CentralCentral

-TGNs

( )= ( )

3

6

kme

NNs Central( )

d A 6-mMPNs

dtFM A 6-MP

CL C 6-

CentralCentral

-mMPNs

( )= ( )

4

6

kme

mmMPNs Central( )

where ka is the absorption rate constant of the parent drug,k20is the elimination rate constant of 6-MP (k20 = 0.53 h-

1

based on literature [1, 9]), kmeis the metabolic transforma-

tion rate constant of the parent drug into either 6-TGNs or

6-MPNs.FMiis the fractional metabolic transformationinto

the metabolite i (6-TGNs are designated by the number

3 and 6-mMPNs are designated by the number 4, FM4 =

1 FM3), and A, C are the amount/concentration of the

drug or metabolite at the time t.k20 = kme + kother (kother is

the elimination rate constant of bioavailable 6-MP trans-

formed by other metabolic processes, kother = 0.22 k20 =

0.1166 h-1 based on the literature [9]).

Table 1

The demographic and clinical characteristics of the ALL population

Parameter n= 19

Gender (F : M) 6 : 13 (32% : 68%)

Age [median (range) years] 10 (317)

Weight [median (range) kg] 33.4 (13.277.5)Body surface area [median (range) m2] 1.14 (0.592)

6-MP daily dose [median (range) mg] 50 (10100)

6-MP daily dose [median (range) mg kg-1] 1.42 (0.163.46)

6-MP daily dose [median (range) mg m-2] 40 (5.8876.47)

Co-medication during 6-MP chemotherapy

Methotrexate weekly dose [median (range) mg] 15 (525)

Cotrimoxazole (b.d. twice weekly) [median

(range) mg]

360 (120480)

Haematological parameters

Hb [median (range) g dl-1] 12.9 (10.916.5)

WBC [median (range) 109 l-1] 3.3 (1.29.1)

PLT [median (range) 109 l-1] 282 (66648)

ANC [median (range) 109 l-1] 1.68 (0.38.3)

XO, TPMT, and ITPA genotypes

XO A1936,

G (heterozygotes/homozygotes) 1/0XO A2107,G 1/0

TPMT*3A 1/0

TPMT*3B

TPMT*3C 2/0

ITPA C94,A 3/0

ITPA IVS2+21A,C 2/1

ALL, acute lymphoblastic leukaemia; 6-MP, 6-mercaptopurine; WBC, white blood

cells; PLT, platelets; ANC, absolute neutrophil count; XO, xanthine oxidase; TPMT,

thiopurine methyltransferase; ITPA, inosine triphosphatase.

A. F. Hawwa et al.

828 / 66:6 / Br J Clin Pharmacol


4/12

An exponential error model was used to describe the

deviations of the individuals clearance from the true (but

unknown) population mean values:

CL TVCL ei i CL= ,

where CLiis the CL of the ith individual,TVCL is the typical

population estimate for CL and h i,CLis a random variable

that distinguish the ith individuals parameter from the

population mean values as predicted by the regression

model and is assumed to be normally distributed in the log

domain with a geometric mean of zero and a variance ofCL

2 . An exponential model was chosen for the IIV, since

pharmacokinetic parameters are usually log-normally dis-

tributed. In modelling this variability, the square root of the

variance was interpreted as the CV.

Residual variability, which describes the residual error

between the measured and the predicted metabolite con-

centrations,was modelled using additive, proportional and

combined error structures. The additive error model,

however, best described the residual variability. This vari-ability could arise from intra-individual variability in the

pharmacokinetic parameters, inaccuracy in the timing of

sample collection and dosage administration, assay error,

model misspecification, or non-adherence to therapy.

C Cij pred ij ij= +,

Cij is the measured and Cpred,ij is the model predicted

metabolite concentration of the ith individual at the jth sam-

pling time and eij is the residual error term, which is a

random variable with zero mean and variance ofs2. The

magnitude of this residual error or variability was

expressed as a standard deviation (SD).

Regression modelThe initial analysis for the population pharmacokinetics of

6-MP metabolites, 6-TGNs and 6-mMPNs, was conducted

without including any patient covariates in the model

(BASE model). The conditional estimates of hi,CL were

obtained from this BASE model and then plotted against

the following covariates:age, gender, weight, body surface

area (BSA),TPMT, ITPA and XO genotypes in order to iden-

tify any potential relationship between CL and the covari-

ates. The influence of the identified covariates on CL was

individually assessed by incorporating them in the BASEmodel (univariate analysis).The regression relationship for

CL was modelled in a linear or nonlinear way for continu-

ous covariates:

Proportional: CL = qCL (1 + qCOV COV)

Power: CL = qCL COVqCOV

Exponential: qCL eqCOV COV

where COV is a general continuous covariate and qCLand

qCOV are the regression coefficients to be estimated by

NONMEM.

Categorical covariates (1 or 0) were examined using a

multiplicative model:

CL CL COVCOV=

where qCL is the population value in the absence of the

covariate (COV = 0) andqCOVis the fractional change when

the covariate is present (COV = 1). Similar models wereused to investigate the effect of covariates on FM3.

For each model,the improvement in the fit obtained on

addition of a fixed effect variable (covariate) into the

model was assessed using a likelihood ratio test. The

change in the objective function value (DOBJF) produced

by the inclusion of a covariate represents a statistic that is

proportional to minus twice the log-likelihood of the data

and approximates a c2 distribution with degrees of

freedom equal to the difference in the number of struc-

tural parameters (qs) between two models. A decrease in

the OBJF 6.63 was considered statistically significant

(P< 0.01, 1 degree of freedom) for the addition of one fixedeffect.The goodness of fit for each model was also assessed

by examining the precision of parameter estimates (i.e.

standard errors of the mean), the decrease in interindi-

vidual and residual variability, and graphs of residuals

(RES), weighted residuals (WRES) and measured metabo-

lite concentrations plotted separately against predicted

concentrations.

As a result of the univariate analysis, each model with

significant effect was ranked according to its DOBJF com-

pared with the BASE model. The model with the largest

DOBJF was designated as the INTERMEDIATE model and

multiple regression analysis with forward selection wasperformed where covariates were incorporated into the

INTERMEDIATE model one by one along the rank order

established by univariate analysis until all significant cova-

riates were included and no further statistically significant

reduction in OBJF was obtained (FULL model). The FULL

model was then subjected to stepwise, backward elimina-

tion to obtain the FINAL model. An increase in the OBJF

6.63 (P< 0.01, 1 degree of freedom) was required toretain the covariate in the FINAL model.

Model evaluation

Since external validation using a new dataset from anotherstudy is extremely difficult in paediatric studies, internal

validation using data splitting or resampling techniques is

an appropriate alternative [10]. In the present study, inter-

nal validation using the data-splitting technique was per-

formed to verify the predictive value of the population

model.

The predictive performance of the model was assessed

in terms of bias (mean prediction error) and precision (root

mean square predictionerror) by comparing the measured

concentrations in the validation group (n = 4) with the cor-

responding predicted values by the population model




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usingpost hocBayesian forecasting. This was achieved by

fixing the structural and variance model parameters to the

values estimated in the final population model.

The mean prediction error (ME) and root mean square

prediction error (RMSE) were determined according to the

following formulae [11]:

MEN

peii

N

==

11

MSEN

peii

N

==

1 2

1

RMSE MSE =

The prediction error (pei) was calculated as:

pei= C Cobs pred

whereCobsandCpredrepresent the observed and predictedconcentrations, respectively.

In addition,WRES and measured metabolite concentra-

tions were plotted separately against the predicted con-

centrations to assess visually the deviations of model

predicted from observed metabolite concentrations in the

validation group.

In order to evaluatethe performance of the final model,

obtained by fitting the full dataset (comprising index and

validation groups combined together), a posterior visual

predictive check was performed by simulating from

the final estimates and comparing the distribution of

the observations with the simulated distribution. The

adequacy of the model was demonstrated by plotting thetime course of the observations along with the prediction

interval for the simulated values.

Results

On oral administration of 6-MP, large interindividual differ-

ences were observed as regards metabolite concentra-

tions and the course of elimination as shown in Figure 1. It

is apparent from the graph that patients with TPMT muta-

tions had higher 6-TGN concentrations but compared withother patients they had relatively low 6-mMPN concentra-

tions in erythrocytes.

At certain time points after treatment with the drug,

6-TU could also be identified in addition to the parent

drug, 6-MP. However, their low levels (due to the sampling

time chosen in this study as stated above) did not qualify

them for incorporation into NONMEM analysis. NONMEM

analysis in this pharmacokinetic study, therefore, was per-

formed using the measured erythrocyte levels of 6-TGNs

and 6-mMPNs in all samples (n = 75 samples, 150 concen-

trations) obtained from 19 paediatric patients with ALL

receiving 6-MP maintenance chemotherapy (a maximum

of five samples was obtained per patient, one sample per

occasion).

Pharmacokinetic modellingIn the initial model, the data were described with a one-

compartment model with first-order absorption and elimi-

nation since there were no points to enable the accurate

evaluation of the distribution phase. In addition,since mostof the kinetic data were collected in the post-absorption

phase,ka and F(the bioavailability factor) could not be

reliably estimated.Hence, their values were fixedaccording

to the literature (1.3 h-1 and 22%, respectively) throughout

the analysis [1, 9]. An additive error model best described

the residual variability and was used in the basic pharma-

cokinetic model (BASE) in order to be used for further

analysis.

The population estimates from the BASE model for FM3(the fractional metabolic transformation of 6-MP into

6-TGNs), CL6-TGNs(6-TGN clearance) and CL6-mMPNs(6-mMPN

0

5000

10000

15000

20000

25000

30000

4321

Time (in months)

6-mMPNs(pmo

l/8108R

BCs)

0

200

400

600

800

1000

1200

4321Time (in months)

6-TGNs(pmol/8108R

BCs)A

B

Figure 1Individual 6-thioguanine nucleotide (6-TGN) (A) and

6-methylmercaptopurine nucleotide (6-mMPN) (B) concentration plots.

Patients having any mutation are highlighted and their corresponding

types are displayed.The therapeutic lower and upper limits suggested in

literature areindicated by thedashedlines.TPMT mutant();ITPAmutant

(); TPMT and ITPA mutant ()

A. F. Hawwa et al.



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clearance) were 0.028, 0.0125 l h-1 and 0.0231 l h-1 with

an interindividual variability (%CV) of 69.9 and 38.1% for

CL6-TGNsand CL6-mMPNs, respectively. The residual variability

corresponded to a SD of 0.18 and 9.04 mg l-1 of packed

RBC for 6-TGN and 6-mMPN metabolite levels,respectively.

Regression modelsPlotting of the conditional estimates of hi,CL for the

metabolites 6-TGNs and 6-mMPNs in paediatric patients

with ALL from the BASE model vs. covariates showed some

potential relationships. An example is shown in Figure 2.

Univariate analyses showing covariates with significant

effects on FM3, CL6-TGNs and CL6-mMPNs are presented in

Table 2. The addition of several covariates resulted in a

reduction of the OBJF of>6.63 (P< 0.01). These covariateswere TPMT mutations affecting FM3, CL6-TGNsand CL6-mMPNsparameters, along with weight (WT) and BSA, which

affected CL6-TGNs.

In the model that incorporated weight as a covariateaffecting CL6-TGNs, clearance was standardized using an allo-

metric model that is based on theoretical and empirical

evidence that CL is proportional to the 3/4power of weight

[12].

The covariate that caused the largest reduction in the

OBJF was the dichotomous covariate model incorporating

the effect of TPMT mutations on FM3. Therefore, it was

declared as an INTERMEDIATE model, subsequent addition

to which of the significant covariates was analysed. Multi-

variate analysis incorporating both BSA and TPMT muta-

tions as covariates showed that in the presence of the

effect of TPMT mutations on FM3, the effect of BSA on

CL6-TGNs remained significant. The results of multivariate

analysis together with the various steps in building the

FULL model are presented in Table 3.Backward elimination

of any covariate from the FULL model increased the OBJF

by>6.63.Therefore, both factors TPMT mutations and BSAwere retained in the FINAL model.

Adequate correlations between predicted andobserved RBC concentrations of 6-TGNs and 6-mMPNs

were observed in the FINAL model, as shown in Figures 3

and 4.The scatterplots show that the population predicted

and individual predicted concentrations of 6-TGNs and

6-mMPNs were in reasonable agreement with the mea-

sured concentrations around the line of identity, although

some underprediction could be observed, particularly at

higher concentrations.

Model evaluationBias (ME) and precision (RMSE) of the predictive perfor-

mance of the FINAL model were tested in the validationgroup (n = 4) between the measured and predicted

metabolite concentrations. ME computed values for

6-TGNs and 6-mMPNs were -0.047 and 1.67 mg l-1,respec-

tively. The corresponding RMSE values were 0.16 and

4.84 mg l-1, respectively. Both were less than the residual

unexplained variability of the index group, which corre-

sponded to a SD of 0.188 and 9.04 mg l-1 for 6-TGNs and

6-mMPNs, respectively.

The scatter plots of weighted residuals vs. model

predicted RBC concentrations of 6-TGNs and 6-mMPNs

(Figure 5) showed that both were randomly distributed

and the weighted residuals lay within 2 units of the nullordinate of perfect agreement. Specific examples of the

predictive capability of the final model are shown for two

representative individual patients in the validation group

in Figure 6, which shows the time course of measured and

post hoc predicted RBC concentrations of 6-TGNs and

6-mMPNs.

Final population pharmacokinetic modelThe computed population structural parameter estimates,

the interpatient variability (CV%) and the residual variabil-

ity obtained by fitting the full dataset to FINAL model are

presented in Table 4.The final population model for 6-TGNs and 6-mMPNs in

RBC was:

FM TPMT

3 0 019 2 56= ( ). .

CL h BSA6-TGNs1.16

1 0 009141( ) = ( ).

CL h6-mMPNs 1 0 02281( ) = .

where BSA is body surface area in m2 andTPMT = 1 in case

of mutation, otherwise = 0.

1.50

1.00

0.50

0.00

0.50

1.00

1.50

2.001.00 1.50

Surface area (m2)

hi,CL6-TGNs

2.00

Figure 2Plots of the conditional estimates ofh i,CL6-TGNsvs. body surface area.The

solid line indicates the Lowess smooth line




7/12

For a hypothetical individual with population median

value of BSA (i.e.1.14 m2),the model-predicted FM3, CL6-TGNs

and CL6-mMPNs would be 0.019, 0.011 l h-1 and 0.0288 l h-1,respectively. The FM3would increase by 256% to 0.049 if

the patient had a TPMT mutation. The model for CL 6-TGNsfound that CL6-TGNswas proportional to the 1.16 power of

BSA, resulting in an estimated range of 0.00490.02 l h-1

across the BSA range of 0.592.0 m2 among the study

group.

The median individual Bayesian estimates for FM3,

CL6-TGNs, CL6-mMPNs and BSA normalized CL6-TGNs values

obtained by fitting the full dataset from the study popula-

tion to the FINAL model are presented in Table 5. These

estimates are determined as a postprocessing step using

the measured concentrations, in contrast to the populationparameter estimates, which are derived from covariate

information. Since the individual Bayesian estimates are

drawn from a distribution where the population estimates

reflect the posterior mode of the marginal likelihood dis-

tribution for that parameter, a possibility for differences in

this summary parameter could be introduced. In order to

evaluate the predictive performance of the final model

obtained from the full dataset,a posterior predictive check

was performed (Figure 7).

The interpatient variability (CV%) for the population

pharmacokinetic parameters of CL6-TGNsand CL6-mMPNswere

33.6 and 33.2%, respectively. The interpatient variability of

CL6-TGNswas reduced from 65.6 to 33.6% by the inclusion of

BSA covariate, which explained most of the variability in

CL6-TGNs between individuals.The residual unexplained vari-

ability (SD) was 0.177 and 8.42 mg l-1 for 6-TGNs and

6-mMPNs, respectively, which translates to a CV% of 24.3

Table 2

Summary of univariate analysis showing covariate models with significant effects on FM3, CL6-TGNsor CL6-mMPNsof 6-TGN and 6-mMPN metabolites

Effect on Model DOBJF* Parameter estimate P-value

FM3 FM TPMT 3 TPMT

-8.69 FM3 = 0 129.


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and 57.6% at the mean RBC concentrations of 6-TGNs and

6-mMPNs measured in the full dataset (0.729 and

14.61 mg l-1, respectively).

Discussion

Current empirical dosing methods for oral 6-MP result in

highly variable drug and metabolite concentrations, asdemonstrated by the present study and other investiga-

tors [13, 14]. The variability presumably arises in part from

individual differences in bioavailability. Other factors,

however, that could contribute to this variability were

evaluated in the present study for paediatric patients with

ALL using a population pharmacokinetic modelling

approach. The different factors studied were age, gender,

WT and BSA, along with various genetic factors such as

polymorphisms in XO,TPMT and ITPA enzymes.

Quantitativeevaluation of pharmacokineticparameters

of 6-MP seems especially attractive and is a potentially

important prognostic factor in cancer chemotherapy [15].

In addition, the study of drug distribution is of particular

relevance for paediatric patients, given the effect of matu-

rational changes on organ function and body composition

that can affect drug disposition [16]. For these reasons, we

developed in this study, for the first time, a population

pharmacokinetic model for 6-MP and its metabolites for

paediatric patients with ALL.

The pharmacokinetic model developed revealed con-

siderable IIV in the clearance of both 6-MP metabolites

investigated, 6-TGNs and 6-mMPNs. This variability,

however, coincides with the highly variable RBC concen-trations of 6-TGNs and 6-mMPNs reported previously in

children taking identical doses of 6-MP [13, 14]. The esti-

mated IIV (CV%) of clearance in the BASE model, fitted to

the index group, was 69.9 and 38.1% for 6-TGNs and

6-mMPNs, respectively. The model, however, showed large

reduction in the IIV of 6-TGN clearance (69.929.3%) and

a significant reduction in the OBJF (7.95, P< 0.005) whenBSA alone was incorporated into the model as a covariate

affecting 6-TGN clearance.This indicated that large part of

the IIV in 6-TGN clearance was explained by differences in

BSA.

0 706050403020100

10

20

30

40

50

60

70

Population predicted 6-mMPN

concentrations (mg/L)

0 70605040302010

Individual predicted 6-mMPN


Obse

rved6-mMPN

concen

trations(mg/L)s

0

10

20

30

40

50

60

70

Observed6-mMPN

concentra

tions(mg/L)

A

B

Figure 4Scatter plots of observedvs.population predicted (A) and individual pre-

dicted(B) red blood cell (RBC)concentrations of 6-methylmercaptopurine

nucleotides (6-mMPNs) in the index group of the FINAL model. The solid

line represents the line of identity

0 0.80.70.60.50.40.30.20.1

0 252015105

1.5

1

0.5

0

0.5

1

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Model predicted 6-TGN


Weig

htedresiduals

0.6

0.4

0.2

0

0.2

0.4

0.6

0.8

1

Model predicted 6-mMPN


Weightedresiduals

A

B

Figure 5Scatter plots of weighted residuals vs. predicted concentrations of

6-thioguanine nucleotides (6-TGNs) (A) and 6-methylmercaptopurine

nucleotides (6-mMPNs) (B) in red blood cells (RBC)




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Both the WT and BSA had significant effects on 6-TGN

clearance in this study. BSA, however, was found to be a

better predictor of clearance than WT. The final model

obtained by fitting the full dataset indicated that CL6-TGNsincreased by 116% per 1-m2 increase in the BSA resulting

in an estimated range of 0.00490.02 l h-1 across the BSA

range of 0.592.0 m2 among the study group.

Clinical experience also confirms the effect of impaired

renal function on the concentration of 6-MP metabolites

and its clinical outcome. It has been shown that patients

with impaired renal function have increased susceptibility

to 6-MP side-effects. In the present study, none of the

patients had clinically significant renal impairment and

hence renal function was mainly proportional to patients

BSA.

The rationale behind the use of BSA as a criterion for

dosing in anticancer chemotherapy was outlined about

50 years ago [17], giving rise to the practice of using BSA

for dosing anticancer therapy. The current approach for

dosing anticancer drugs is to administer a standard dose,

which is normalized to BSA,and then to adjust or individu-

alize subsequent doses based on the severity of drug tox-

icity. Even though this clearly has practical and economicimplications, its clinical value has, in recent years, been

questioned [18]. One objection to the use of BSA in mea-

suring drug dosage was the difficulty in measuring BSA.

Apart from the inaccuracies inherent in methods for BSA

calculation [19], they are dependent on the accuracy of

weight and height measurements used in BSA calculation.

Another alternative was the use of normograms to avoid

inaccurate determination of BSA.However, the reliability of

these normograms tends to differ [20].

Another objection was that pharmacokinetic studies of

anticancer drugs revealed substantial interpatient variabil-

ity in plasma drug concentrations when the dose wasbased on BSA [13, 21].The potential consequences of this

variability in systemic drug exposure are life-threatening

toxic effects in patients who are exposed to excessive drug

concentrations and tumour progression in patients who

achieve subtherapeutic drug concentrations. The identifi-

cation of specific factors that account for variability in drug

disposition among patients can, therefore, lead to more

rational dosing methods.

In the present study, the most influential covariate

examined was TPMT genotype on the fraction of meta-

bolic transformation, FM3. Its inclusion in the model

resulted in the greatest reduction in OBJF (DOBJF = -8.69,

P< 0.005). Hence, it explained part of the variability in FM3between individuals. In addition,the final model predicted

an increase in FM3 of 256% if the patient had a TPMT muta-

tion. This would mean preferential 6-TGN production and

6-mMPN underproduction if the patient had a TPMT muta-

tion. This is in perfect agreement with previous studies,

which demonstrated an inverse correlation between TPMT

activity and the levels of 6-TGNs in RBC, suggesting that

an inherited decrease in the methylation step (due to the

TPMT mutation) results in shunting 6-MP metabolism

away from 6-mMPNs towards overproduction of 6-TGNs

[22, 23]. More importantly,the optimal dose of 6-MP, which

wasdetermined in the standard empirical fashion,was alsorelated to TPMT genotype [22, 24].

No prior studies, however, have provided specific 6-MP

dosing guidelines based on TPMT genotype.In the present

study, for the first time, the potential utility of TPMT geno-

type in prospectively defining the percentage of 6-MP con-

verted to 6-TGNs or 6-mMPNs as predicted by FM3 was

illustrated.This would identify patients who are less toler-

ant of 6-MP standard doses based on their capacity to

metabolize the drug. One limitation of the developed

model, however, was its inability to account for variation in

TPMT activity among patients having the wild-type. This

4500400035003000200015001000500 25000.0

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0.4

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Concentr

ation(mg/L)

Time (hrs)

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0.00.10.20.30.40.50.6

6

8

10

12

14

16

18

20

Concentration(m

g/L)

A

B

Figure 6Longitudinal assessment of the predictive performance of the FINAL

model in two representative patients from the validation dataset: (A)

12-year-old boy and (B) 6-year-old girl. () Observed and () model-

predicted 6-thioguanine nucleotide (6-TGN) concentrations. ()

Observed and () predicted 6-methylmercaptopurine nucleotide

(6-mMPN) concentrations

A. F. Hawwa et al.



10/12

could be the reason for underprediction observed in the

model for 6-mMPN concentrations at higher levels. Higher

concentrations of 6-mMPNs are probably due to high

TPMT activity that could not be accounted for by the

model. Phenotyping patients, however, by measuringTPMT enzymatic activity could resolve this limitation and

allow for individualization of drug doses among the whole

population.

The other genetic covariates tested in this study (ITPA,

XO mutations) were not statistically significant and there-

fore not included in the FINAL model. It should be noted,

however, that the IIV not explained by the model might

have resulted, in part, from these covariates. It is also pos-

sible that the small number of patients studied (n = 19)

was not enough to identify more potential sources of IIV.

Therefore, the developed population model will need to

be tested and refined by larger prospective studies that

have the potential to encounter and quantify more

covariates.

The reasonably large residual (unexplained) variability

in the model might be due to large IIV in the pharmaco-

kinetic parameters, interoccasional variability, assay errors,

or model misspecification. Since the data in most patients

were collected for a period of several months, this mighthave added to the variability within the same patient.

Moreover, the significant IIV in drug absorption may

explain some of the variability obtained with the orally

administered 6-MP [25]. Non-adherence to therapy could

also have contributed to this variability. Children with ALL

who have reached remission always remain under a pro-

longed and complex course of chemotherapy in spite of

being practically asymptomatic. These factors are likely

to contribute to missing some doses given the home

therapy of 6-MP and the absence of obvious conse-

quences at the time. Therefore, non-adherence is

somehow expected in this group of patients and shouldbe considered.

Although the robustness of the pharmacokinetic

model developed in this study was validated internally

using the data-splitting technique, it is highly recom-

mended that the model be subjected to external valida-

tion before using it to guide dosage adjustments. If

successful, this would offer a more rational dosing

approach than the traditional empirical method, since it

would combine the current practice of using BSA in 6-MP

dosing with a pharmacogenetically guided dosing based

on TPMT genotype.

Table 4

Population pharmacokintics of 6-TGNs and 6-mMPNs, obtained by fitting the full dataset (19 children) to the BASE and FINAL models

Parameter Symbol

Base model Final model

Estimate w(CV%) Estimate w(CV%) SE (%)

FM3 FM3 0.0295 0.0191 64.1

CL6-TGNs(l h-1

) CL6-TGNs

0.0138 65.7 0.00914 33.6 56.8CL6-mMPNs(l h-1) CL6-mMPNs 0.0227 33.9 0.0228 33.1 14.2

TPMT qTPMT 2.56 35.9

BSA qBSA 1.16 49.0

wCL6-TGNs 0.431 0.113 81.2

wCL6-mMPNs 0.115 0.11 82.6

s6-TGNs(mg l-1) 0.171 0.177 17.5

s6-mMPNs(mg l-1) 9.04 8.42 19.3

Structural models:

TVFM FMTPMT

3 3= TPMT

TVCL BSA-TGNs -TGNs6 6= ( )

CL

BSA

TVCL -mMPNs -mMPNs6 6= CL

Random effects models:

CL TVCL e-TGNsi 6-TGNsi CL6-TGNs

6 = ,

CL TVCL e- mMPN si 6- mMPN s i CL6-mMPNs6 = ,

where FM3is the fractional transformation of 6-MP into 6-TGNs; TPMT, 1 if the patient had TPMT mutation and 0 otherwise; CL 6-TGNsis 6-TGN clearance; BSA is body surface area

in m2; CL6-mMPNs is 6-mMPN clearance; w is the interindividual variability; h is squared w; s6-TGNs and s6-mMPNs are the residual variabilities of 6-TGNs and 6-mMPNs. 6-TGN,

6-thioguanine nucleotide; 6-mMPN, 6-methylmercaptopurine nucleotide; TPMT, thiopurine methyltransferase.

Table 5

IndividualBayesianestimatesobtained from theFINAL populationmodel

Parameter Median (P5, P95)

FM3 (no TPMT mutation) 0.0191

FM3 (with TPMT mutation) 0.0491

CL6-TGNs(l h-

1) 0.0112 (0.0046, 0.0235)CL6-mMPNs(l h-1) 0.0226 (0.0153, 0.0310)

CL6-TGNs(l h-1 m-2)* 0.0082 (0.0059, 0.0151)

P5, 5th percentile; P95, 95th percentile. *Calculated using the body surface area

of each individual. 6-TGN, 6-thioguanine nucleotide; 6-mMPN, 6-

methylmercaptopurine nucleotide; TPMT, thiopurine methyltransferase.




11/12

Competing interests

None declared.

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3

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5

6-TGNconce

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10

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70

90

6-mMPNconcentrations(mg/L)

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B

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patients). A plot of the time course of the observed concentrations of6-thioguanine nucleotides (6-TGNs) (A) and 6-methylmercaptopurine

nucleotides (6-mMPNs) (B) along with the median and 90% prediction

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