DMD # 35394 Influence of Cremophor EL and Genetic ...dmd.aspetjournals.org/content/dmd/early/2010/11/05/dmd...2010/11/05 · The formulation vehicle Cremophor EL has previously been

DMD # 35394

1

Influence of Cremophor EL and Genetic Polymorphisms on the Pharmacokinetics of Paclitaxel

and its Metabolites using a Mechanism-Based Model

Martin N. Fransson, Henrik Gréen, Jan-Eric Litton, and Lena E. Friberg

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm,

Sweden; MF, JEL

Division of Drug Research/Clinical Pharmacology, Department of Medical and Health Sciences,

Faculty of Health Sciences, Linköping University, Linköping, Sweden; HG

Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden; LF

DMD Fast Forward. Published on November 5, 2010 as doi:10.1124/dmd.110.035394

Copyright 2010 by the American Society for Pharmacology and Experimental Therapeutics.

This article has not been copyedited and formatted. The final version may differ from this version.DMD Fast Forward. Published on November 5, 2010 as DOI: 10.1124/dmd.110.035394

at ASPE

T Journals on A

ugust 25, 2021dm

d.aspetjournals.orgD

ownloaded from

http://dmd.aspetjournals.org/

DMD # 35394

2

Running title page

a) Pharmacokinetics of Paclitaxel and its Metabolites

b) Martin Fransson, M.Sc. Department of Medical Epidemiology and Biostatistics PO Box 281/Nobels väg 12 A SE-171 77 Stockholm, Sweden Phone: +46 (0)8-524 839 74 Fax: +46 (0) 8-31 49 75 E-mail: [email protected]

c) Number of text Pages: 42 Number of Tables: 5 Number of Figures: 4 Number of References: 40 Number of words in Abstract: 239 Number of words in Introduction: 743 Number of words in Discussion: 1277

d) 6αOH-pac, 6α-hydroxypaclitaxel; p3OH-pac, p-3’-hydroxypaclitaxel; diOH-pac, 6α-, p-3’-dihydroxypaclitaxel; CrEL, Cremophor EL; OFV, Objective Function Value; SE, Standard Error; RSE, Relative Standard Error; IIV, Interindividual Variability; LLP, Log Likelihood Profiling; CV, Coefficient of Variation; d.f., degrees of freedom


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

3

Abstract

The formulation vehicle Cremophor EL has previously been shown to affect paclitaxel kinetics,

but it is not known whether it also affects the kinetics of paclitaxel metabolites. This may be

important for understanding paclitaxel metabolism in vivo, and when investigating the role of

genetic polymorphisms in the metabolizing enzymes CYP2C8 and CYP3A4/CYP3A5, and the

ABCB1 transporter. This study used the population pharmacokinetic approach to explore the

influence of predicted Cremophor EL concentrations on paclitaxel (Taxol) metabolites. In

addition, correlations between genetic polymorphisms and enzyme activity to clearance of

paclitaxel, its two primary metabolites, 6α-hydroxypaclitaxel and p-3’-hydroxypaclitaxel, and its

secondary metabolite, 6α-, p-3’-dihydroxypaclitaxel were investigated. Model building was based

on 1156 samples from a study with 33 women undergoing paclitaxel treatment for gynaecological

cancer. Total concentrations of paclitaxel were fitted to a model previously described. One-

compartment models characterized unbound metabolite concentrations. Total concentrations of

6α-hydroxypaclitaxel and p-3’-hydroxypaclitaxel were strongly dependent on predicted

Cremophor EL concentrations, but this association was not found for 6α-, p-3’-

dihydroxypaclitaxel. Clearance of 6α-hydroxypaclitaxel (fraction metabolized) was significantly

correlated (*p < 0.05) to the ABCB1 allele G2677T/A. Individuals carrying the polymorphisms

G/A (n = 3) or G/G (n= 5) showed a 30% increase while individuals with polymorphism T/T (n =

8) showed a 27% decrease relative the polymorphism G/T (n = 17). The correlation of G2677T/A

to 6α-hydroxypaclitaxel has not been described previously, but supports other findings of the

ABCB1 transporter playing a part in paclitaxel metabolism.


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

4

Introduction

Paclitaxel is a natural substance from the Pacific Yew tree and was recognized for its property to

cause apoptosis by promoting microtubule stabilization (Schiff et al., 1979), and is today used for

the treatment of a number of different cancer types. Due to the large interindividual differences

observed in the pharmacokinetics of the drug, identification of patient factors that can be used to

individualize the dose in clinical practise are desirable. Paclitaxel is dosed by body surface area

but the measure of body size explains only a small part of the variability (Mathijssen et al., 2007).

Other demographic characteristics previously proposed to be attributable to population variability

include gender, age, body weight and bilirubin (Henningsson et al., 2003; Joerger et al., 2006).

Several of the genes involved in the pharmacokinetics of paclitaxel contain single nucleotide

polymorphisms (SNP) that may result in interindividual variability of paclitaxel clearance.

Previously considered polymorphisms for paclitaxel clearance or exposure include SNPs for the

metabolizing enzymes CYP2C8, CYP3A4 and CYP3A5, but also for the ABCB1 transporter

gene (Henningsson et al., 2005a; Nakajima et al., 2005; Sissung et al., 2006; Yamaguchi et al.,

2006; Green et al., 2009; Bergmann et al., 2010). So far, results are ambiguous, with a few

positive findings on SNPs affecting clearance of paclitaxel (Yamaguchi et al., 2006; Green et al.,

2009; Bergmann et al., 2010). Although paclitaxel metabolites are considered not to be active

against the tumours and have been shown to be up to 30-fold less cytotoxic than the parent

compound (Harris et al., 1994a; Sparreboom et al., 1995), the particular metabolic pattern for

paclitaxel may be of interest for identifying correlations between individual clearance and SNP

variants for participating enzymes. The metabolites may also be involved in the neuropathy seen

after paclitaxel-containing chemotherapy (Leskela et al., 2010).


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

5

Elimination of paclitaxel occurs mainly as hepatic metabolism (Walle et al., 1995), carried out

by cytochrome P-450 monooxygenases as a one- or two-step hydroxylation process in either of

two metabolic paths (Monsarrat et al., 1993). In the first step, paclitaxel is oxidised by CYP2C8

to 6α-hydroxypaclitaxel (6αOH-pac) or by CYP3A to p-3’-hydroxypaclitaxel (p3OH-pac)

(Cresteil et al., 1994; Harris et al., 1994b; Rahman et al., 1994). 6αOH-pac is the predominate

metabolite in most cases (Kumar et al., 1994; Harris et al., 1994a; Sonnichsen et al., 1995), with

an estimated overall formation in vitro of 63% (Cresteil et al., 2002). In the second step, 6αOH-

pac and p3OH-pac may be further oxidised to the secondary metabolite 6α-, p-3’-

dihydroxypaclitaxel (diOH-pac) by CYP3A and CYP2C8, respectively (Sonnichsen et al., 1995;

Monsarrat et al., 1998). Together, paclitaxel and the three metabolites constitute a diamond-

shaped pathway with each P-450 monooxygenase in parallel on the two arms (Figure 1).

Distribution and/or elimination of total concentrations of paclitaxel (Taxol) have earlier been

suggested to be saturable (Sonnichsen et al., 1994; Gianni et al., 1995; Karlsson et al., 1999). The

observed non-linear kinetics of total concentrations, when administered as Taxol, have been

attributed primarily to micelle encapsulation by the formulation vehicle Cremophor EL (CrEL)

(Sparreboom et al., 1996). When binding of paclitaxel to CrEL and plasma proteins is taken into

account, unbound concentrations of paclitaxel will fit a model with linear kinetics (Henningsson

et al., 2001). The use of this more mechanism-based model ideally requires measurements of

unbound concentrations of paclitaxel and concentrations of CrEL in addition to total plasma

concentrations. However, the model has been shown to be at least as good as using a more

empirical explanation of the non-linear behaviour (Gianni et al., 1995) when only total paclitaxel

concentrations are available for analysis (Fransson and Green, 2008). CrEL concentrations were

predicted from a previously developed model (Henningsson et al., 2005b) and a similar approach


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

6

can be taken to investigate if and how CrEL also is influencing the metabolite kinetics of

paclitaxel.

The purpose of the present study was to investigate the influence of CrEL on paclitaxel

(Taxol) metabolite kinetics, and to investigate correlations of paclitaxel and metabolites’

clearances with enzyme activity and SNP variants of the metabolizing enzymes CYP2C8,

CYP3A4, CYP3A5 and the transporter protein ABCB1. In particular, the metabolic pattern

suggests that a correlation may exist for the proportion of clearance of paclitaxel to 6αOH-pac

and p3OH-pac, and clearances of these metabolites to diOH-pac. Using information on both

parent and metabolite concentrations, in addition to predictions of CrEL concentrations, in a

population pharmacokinetic model, can provide a better understanding for the elimination of

paclitaxel, and potential correlations may be easier to elucidate.


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

7

Materials and Methods

Patients and study design

Thirty-three Caucasian women, 36 to 75 years of age (median 62), with different types of

gynaecological cancers: epithelial ovarian cancer (n = 26), peritoneal cancer (n = 4), ovarian or

peritoneal (n = 1), carcinoma in corpus uteri (n = 1) and in cervix uteri (n = 1), were included in

the study as previously described (Green et al., 2009). The patients received a combination

therapy of paclitaxel (Taxol) (Bristol-Myers Squibb, Wallingford, CT, USA), administrated as an

intravenous infusion with a target time of three hours and dosing at 175 mg/m2 (n = 30) or 135

mg/m2 (n = 3, due to poor general condition) and carboplatin, with AUC 5 or 6 mg*min/mL

according to Calvert’s formula (Calvert et al., 1989). For thirty-one patients treatment consisted

of at least six cycles of chemotherapy. One patient received only a single cycle due to

septicaemia and another patient was withdrawn after four cycles due to severe neurotoxicity.

Blood samples for pharmacokinetic analysis were obtained during one cycle per patient from the

1st to 8th cycle (median cycle number 3), and collected in EDTA tubes. Target times for sampling

were immediately before infusion, 30 min and 1 h after start of infusion, immediately before stop

of infusion, at 5, 15, 30 min and 1, 2, 4, 8 and 24 h after stop of infusion. Centrifuged plasma

samples were stored in −80° C until analysis. The regional ethics committees approved the study

and written informed consent was obtained from each patient.

Assays and samples

Total concentrations of paclitaxel, 6αOH-pac and p3OH-pac from all patients, and diOH-pac,

from fifteen patients, were analysed in plasma samples using solid phase extraction, reverse-


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

8

phase high-performance liquid chromatography and an ion trap mass spectrometer with a sonic

spray ionization interphase as described elsewhere (Green et al., 2006b). For paclitaxel, 6αOH-

pac and p3OH-pac, docetaxel (Aventis Pharma, Vitry Alforville, France) was used as an internal

standard, and for diOH-pac, paclitaxel was used as reference. The lower limit of quantification

was 0.5 ng/mL for paclitaxel and 2 ng/mL for hydroxymetabolites (Green et al., 2006b), but

observations below the lower limit of quantification were included in the analysis if detected.

Twenty-seven observations were considered non-detectable. For one patient, 15 observations of

paclitaxel (n = 5), 6αOH-pac (n = 4), p3OH-pac (n = 3) and diOH-pac (n = 3) were removed

because of a large (> 30%) within-sample variability for paclitaxel. In total, 1156 observations of

paclitaxel (n = 345), 6αOH-pac (n = 332), p3OH-pac (n = 336) and diOH-pac (n = 143) remained

for pharmacokinetic analysis.

Enzyme activity and genotyping

The selection of CYP3A4 phenotyping method was based on finding an non-radioactive assay

without any risk of sedation and with an reaction similiar to that of paclitaxel, i.e., an

hydroxylation. We finally settled on the use of oral quinine tablets followed by a simple HPLC

method for the quantification of the ratio in plasma. Enzyme activity for CYP3A4 in vivo was

determined by administration of a 250 mg quinine tablet to the patients 24 to 48 h prior to start of

chemotherapy followed by blood sampling 16 h later (Mirghani et al., 1999; Mirghani et al.,

2001). The CYP3A4 activity was expressed as metabolic ratio of quinine (concentration of

quinine / concentration of hydroxylquinine) and was in the range of 2.2 to 41.3 (median 10.0),

where a low value indicates a high metabolic ratio. For two patients CYP3A4 activity could not


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

9

be determined. For these cases, the missing values were imputed as the median of the remaining

patient group.

The individual patients’ genotypes were determined using Pyrosequencing according to the

manufacturers’ protocol and as previously described (Green et al., 2006a; Green et al., 2009). The

genotype frequencies of the study group are shown in Table 1 and have previously been

published (Green et al., 2009), except CYP2C8 Haplotype C and CYP3A5*3.

Population Pharmacokinetic Analysis

For population pharmacokinetic analysis the NONMEM software (Beal, Sheiner and

Boeckmann, version VI, level 2.0, Icon Development Solutions, Ellicott City, Maryland, USA,

2006) was used in combination with PsN (version 3.1.0, http://psn.sourceforge.net). Additional

calculations of NONMEM output and graphical analysis was performed in Xpose (version 4.1.0,

http://xpose.sourceforge.net) for R (version 2.10.0, http://www.r-project.org, R Foundation for

Statistical Computing, Vienna, Austria, 2009). Fortran files were compiled using the G77

compiler.

The mechanism-based model (Henningsson et al., 2001) was used to fit total concentrations of

paclitaxel. The mechanism-based model consists of a linear three-compartment model for

unbound paclitaxel and the total paclitaxel concentrations were described as:

[ ] [ ] [ ] [ ] [ ] [ ][ ]um

umaxuCrELulinut pacK

pac*Bpac*CrEL*Bpac*Bpacpac

++++=

Eq. 1


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

10

where [pac]t is the plasma concentration of total paclitaxel, [pac]u is the corresponding unbound

concentration, Blin is the linear binding to plasma components, BCrEL describes the binding

directly proportional to the CrEL concentration [CrEL], Bmax is the maximum non-linear binding

to plasma components and Km is the concentration at half Bmax.

Only total concentrations of paclitaxel and metabolites were available and unbound

concentrations were estimated from total concentrations. In case of unbound concentrations of

paclitaxel, Eq. 1 was used, while systematic testing of various models was applied to find the best

structures in terms of fit (see below) for prediction of unbound metabolite concentrations. It has

been shown that the mechanism-based model structure is not identifiable (with high probability)

without information on unbound concentrations of paclitaxel (Fransson and Green, 2008).

Therefore, the approach with frequentist priors (Gisleskog et al., 2002) was used in the current

study to stabilize the analysis. The priors (mean and standard error) used in the final model are

presented in Table 2. Since CrEL concentrations were not available from the study, a model

describing the population pharmacokinetics of CrEL, with three compartments and saturated

elimination, was implemented and used to predict CrEL concentrations at any time point

(Henningsson et al., 2005b). Model parameters for CrEL were tried as priors or as fixed

estimates. Earlier identified covariate relations were not used in the CrEL model. The paclitaxel

model and the CrEL model were specified using the ADVAN 6 subroutine in NONMEM VI, and

all estimations were performed with the First Order Conditional Estimation method with

Interaction.

Interindividual variability (IIV) for an individual i was in general modelled exponentially:


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

11

)exp(η*θθ ipopi =

Eq. 2

where θpop is the parameter for the typical value for the population and η ~ N(0, ω2). Residual

errors for observed concentrations (cobs) vs. predicted concentrations (cpred) were modelled using

a proportional error in combination, if needed, with an additive error according to:

22

21predpredobs ε)ε*(ccc ++=

Eq. 3

where ε1 ~ N(0, σ12) and ε2 ~ N(0, σ2

2).

The combined paclitaxel-CrEL model was first fitted to total paclitaxel concentrations only.

Priors were used on all parameters previously reported at the population level, and for IIV the

prior for clearance of unbound paclitaxel was included (Henningsson et al., 2001). Significance

of IIV in remaining parameters was tested one at a time without additional priors. The

mechanism-based model structure of paclitaxel was extended to also include the metabolites.

Initially the paclitaxel parameters were fixed to the estimates obtained in the first step. Total

metabolite concentrations were fitted sequentially in the following order: only 6αOH-pac, only

p3OH-pac, 6αOH-pac in combination with p3OH-pac, and 6αOH-pac and p3OH-pac in

combination with diOH-pac. For metabolites, the issue of identifiability caused by not knowing

the unbound fraction implies that the size of any linear terms cannot be estimated in an equation


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

12

connecting unbound and total concentrations. Hence, a parameter corresponding to Blin for

metabolites cannot be estimated. All metabolite parameters will therefore be scaled by the

(unknown) fraction that is linearly bound.

The NONMEM objective function value (OFV), which is proportional to −2xlog likelihood of

the data, was used to guide model development. For nested models, the OFV is χ2 distributed and

a new model was selected if the addition of one parameter caused a drop in OFV by at least

10.83, which corresponds to a significance level of ***p < 0.001. In addition, the new parameter

should not cause problems with identifiability. Individual plots and plots of conditional weighted

residuals versus time (Hooker et al., 2007) were also used to determine the importance of the new

parameter.

For parameter estimates not supported by priors, the standard errors provided by NONMEM

were complemented by log-likelihood profiling (LLP). By fixing each parameter at a time in the

original model, log-likelihood profiling will calculate the parameter value that results in an

increase in OFV for a pre-specified significance level. Since a model with a fixed parameter is

nested relative the original model, the lower and higher parameter value that each cause an

increase in OFV by 3.84, will constitute a 95% confidence interval for the specific parameter. As

the search is performed on both sides of the final estimate no symmetry assumptions are made.

For parameter estimates supported by priors, the informativeness of the study specific data was

investigated by calculating the quota of prior relative standard errors (RSE) to model relative

standard errors. The predictive performance of the final model was evaluated using visual

predictive checks based on 1000 simulations.

Sensitivity analysis was also performed to determine the influence of frequentist priors on

parameter estimates and significance of covariates by (1) increasing the standard error for each


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

13

prior by 50%, (2) decreasing and (3) increasing the estimate for one of the priors with − / +2x the

standard error, and (4) using a different set of priors (Henningsson et al., 2005a).

Covariate testing

Shrinkage in the random effects was calculated to evaluate the appropriateness of using the

Empirical Bayes estimates for investigation of potential covariate relationships (Savic and

Karlsson, 2009). Testing for significant covariates was performed directly in NONMEM, using

step-wise covariate modelling in PsN with forward selection and backward elimination. Due to

the relatively small population (n = 33) covariate testing was considered to be the secondary aim

of the work, and was hence performed only as a univariate analysis. Covariates were to be

included in the final model only if they were significant in both the forward-selection (**p <

0.01) and backward-elimination step (***p < 0.001). Inclusion of priors was not compatible with

step-wise covariate modelling as implemented in PsN (version 3.1.0) and therefore parameters

supported by priors (i.e., all structural paclitaxel parameters except clearance) were fixed to the

estimates of the final model. In addition, significance testing was performed in the R software,

using one-way ANOVA on Empirical Bayes estimates derived from the POSTHOC step in

NONMEM, since it has been reported earlier that ANOVA tests in some cases may be more

reliable for dealing with type-1 errors than the likelihood ratio test obtained by NONMEM OFV

(Bertrand et al., 2009). All IIV parameters, except those that were fixed, were tested against age.

In addition, paclitaxel and metabolites’ clearances and the relative fraction parameter, fp3OH/fmpac,

were tested for relationships to SNP variants and CYP3A4 enzyme activity.


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

14

Results

Model structure for paclitaxel metabolites

Observed total concentrations of paclitaxel and its three metabolites are shown in Figure 2. The

previously developed mechanism-based-CrEL model adequately described total paclitaxel

concentrations, using IIV in clearance for the three-compartment model structure of unbound

paclitaxel. Inclusion of IIV in Blin (Eq. 1) further improved the fit (ΔOFV = −85), and after

inclusion IIV in other population parameters was not significant (***p < 0.001).

Using frequentist priors instead of fixed parameter values in the CrEL model had little effect

on estimates in the combined mechanism-based-CrEL model, and the CrEL parameters were

therefore fixed to stabilize the analysis.

The final parameter estimates are presented in Table 3. The ratio of relative standard errors for

estimates and priors was less than 1 for all population parameters, confirming that the current

data set on paclitaxel contained new information and was in line with the data the priors were

derived from. The priors for clearance of unbound paclitaxel and the related IIV parameter were

omitted in the final model not to interfere with covariate testing.

The mechanism-based model was extended with compartments to fit metabolite

concentrations. The fraction eliminated by different elimination routes cannot be estimated unless

the metabolites are administered. To keep the model structure identifiable, it was therefore

assumed that all parent drug and primary metabolites were converted into the here observed

metabolites.

A one-compartment model with linear elimination and binding of predicted unbound

concentrations of 6αOH-pac to predicted CrEL concentrations resulted in a drop in OFV of −230


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

15

units and improved CWRES plots. A nonlinear binding component, similar to Bmax and Km in Eq.

1, was found to improve the OFV further (−61) but could be simplified to a single non-specific

binding parameter (Bnsp,6αOH) without increase in OFV. A small decrease (−4) in OFV was found

by multiplying the constant with CrEL concentrations. To explain the steep increase in total

6αOH-pac concentrations (Figure 2), a Hill factor was applied in the Cremophor EL binding

mechanism and was found significant (ΔOFV = −123). IIV in the Hill coefficient decreased OFV

further with 55 units. The final model for the relationship between unbound and total

concentrations of 6αOH-pac is presented below:

[ ] [ ] [ ][ ] [ ] [ ]CrEL*BOH6*CrEL)(CrEL

CrEL*BOH6OH6 OHnsp,6uHillHill

50

HillOHCrEL,6

utCrELCrEL

CrEL

αα ααα +

++=

Eq. 4

where BCrEL,6αOH is the maximal binding rate to CrEL, Bnsp,6αOH is the non-specific binding

parameter to CrEL, CrEL50 is the CrEL concentration at half maximal binding rate and HillCrEL is

the Hill coefficient for CrEL concentration. Allowing an additive error at the time of the first

sample improved the stability of the model and lowered the OFV with 15 units.

The final model structure for unbound p3OH-pac was also described with a one-compartment

model with linear elimination and the relationship between the total and unbound metabolite and

CrEL concentration was identical to Eq. 4. The parameters HillCrEL and CrEL50 in Eq. 4 were set

to be the same for both primary metabolites. There was no need for an additive error on first

samples for p3OH-pac.


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

16

The distribution of clearance of unbound paclitaxel to the formation of the primary

metabolites was modelled using a relative fraction parameter. Preliminary modelling attempts

indicated that this parameter was not identifiable at a population level and was therefore fixed to

a corresponding literature value describing in vitro formation of p3OH-pac, which has been

estimated to 37% of the paclitaxel metabolized to 6αOH-pac and p3OH-pac (Cresteil et al.,

2002). To allow IIV for the relative fraction parameter, fp3OH/fmpac, but to keep individual

estimates in the range of 0 to 1 logit transformation was used, so that:

( )( )βexp1

βexp/fmf pacp3OH +

=

Eq. 5

where IIV was described additively:

ηββ pop +=

Eq. 6

and βpop is the population value corresponding to fp3OH/fmpac = 0.37, and η ~ N(0, ω2). Using a

block structure with three additional covariance parameters for IIV in CL6αOH/fmpac, CLp3OH/fmpac

and β in Eq. 6 did not convey a significant change (*p < 0.05) in OFV. When fitting the two

primary metabolites without using a block structure IIV in CLp3OH/fmpac was not significant (*p <

0.05), and was consequently removed.


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

17

Total concentrations of the secondary metabolite diOH-pac was included using a one-

compartment model with linear elimination. No association between total diOH-pac and CrEL

was found. An additive error, on top of a proportional, reduced the OFV with −71 units.

For the full model, the log-likelihood profiling for each parameter not supported by a prior is

presented in Table 3. The boundaries of the 95% confidence intervals are in agreement with the

symmetric 95% confidence intervals calculated from the standard errors reported by NONMEM,

but showed a trend of being right-skewed. Observed concentrations versus population

predictions, stratified on parent drug and the different metabolites, are shown in Figure 3 and

visual predictive checks are shown in Figure 4.

Covariate analysis

Rare SNP variants (Table 1) were pooled with larger groups before covariate analysis; C3435T

C/C was pooled with T/C, G2677T/A G/A was pooled with G/G, CYP2C8*1B A/A was pooled

with C/A and CYP2C8 Haplotype C G/G was pooled with C/G.

Shrinkage for non-fixed random effects ranged from 6.2-34%. For compatibility reasons, all

parameters using priors had to be fixed to their respective estimate from the final model (Table 3)

when applying the automatic step-wise covariate model building procedure in PsN. All

covariates significant on p < 0.1, corresponding to a drop in OFV with at least −2.71 units for one

additional degree of freedom, were tested using one-way ANOVA. The results from the covariate

analysis are presented in Table 4. No parameters were significantly correlated to the investigated

covariates for **p < 0.01, which was considered to be the required level for inclusion in the final

model during the forward-selection step. However, CL6αOH/fmpac, which was significantly (*p <

0.05) affected by GM2677T/A was also tested by inclusion in the final model using frequentist


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

18

priors instead of fixed estimates. Individuals with SNP variant G/A or G/G (pooled) showed a

30% (2.8-62%, 80% confidence interval) increase as compared to the reference group constituted

by the G/T heterozygous patients, while individuals with variant T/T showed a 27% (9.4-42%,

80% confidence interval) decrease in CL6αOH/fmpac relative the reference group G/T. Patients with

the wild type G/G or G/A genotype had the highest CL6αOH/fmpac, while G/T individuals were

intermediate and T/T homozygous patients in position 2677 had the lowest CL6αOH/fmpac.

Inclusion of the covariate decreased the IIV (CV%) in CL6αOH/fmpac from 36.6% to 30.5%. CLpac

also showed a significant (*p < 0.05) correlation to CYP3A5*3, and was tested separately in the

final model. Individuals with SNP variant G/A showed a 20% (4.7-32%, 95% confidence

interval) decrease relative the reference group G/G. Inclusion of the covariate decreased the IIV

(CV%) in CLpac from 15.3% to 12.7%. In addition, there was a tendency that CLpac and

CLdiOH/fmmet were correlated (p < 0.1) to CYP2C8*1C and CYP3A4*1B, respectively.

Since inclusion of the covariates only resulted in a minor reduction in OFV, two alternative

models that included IIV in V1 and V2 were evaluated to investigate the consistency in covariate

effects. In Alternative model 2 also the IIV in the linear binding of paclitaxel to plasma proteins

(Blin) was excluded to exactly mimic the parameterization in the original model (Henningsson et

al., 2001). Only GM2677T/A showed a significant effect for all three models (*p < 0.05 for

ANOVA and p < 0.1 based on drop in OFV).

The results from the sensitivity analysis are presented in Table 5. CL6αOH/fmpac was

significantly (*p < 0.05) correlated to GM2677T/A regardless of the tested sets of prior estimates.

The parameter CL6αOH/fmpac, without inclusion of covariate, is presented to show the effect on a

parameter estimate when using different sets of frequentist priors.


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

19

Discussion

To our knowledge, this is the first time the pharmacokinetics of both paclitaxel and its most

acknowledged metabolites have been characterized using a population approach with nonlinear

mixed effects modelling. Previously, paclitaxel and 6αOH-pac have been modelled using

nonlinear kinetics for both parent drug and metabolite (Gianni et al., 1995), without considering

the formulation vehicle CrEL. It has later been shown that the nonlinear kinetics for paclitaxel

most likely is a result of binding to CrEL and plasma components, and that unbound

concentrations follow linear kinetics (Henningsson et al., 2001). In this work, we have presented

strong indications that the same may be true for the two primary metabolites, since total

concentrations of 6αOH-pac and p3OH-pac both seemed to be dependent on the predicted time-

course of CrEL and by extensive and non-linear binding, while no such association was apparent

for the secondary metabolite diOH-pac.

A Hill equation has earlier been used to describe micelle kinetics (Piszkiewicz, 1977),

although for catalytic micelles rather than for substrate binding only. Still, the estimate of 2.71 in

Table 3 for the Hill coefficient is well within the range of the previously derived coefficients for a

number of combinations of detergents and substrates, which has been reported to range from

approximately 1 to 6 with the majority below 3 (Piszkiewicz, 1977).

The visual predictive checks in Figure 4 show good adequacy of the models, although for total

diOH-pac concentration the lower percentile was over predicted. It is plausible that the low

number of patients (n = 15) contributing with observations to diOH-pac, in combination with

relatively noisy data, contributed to the relatively poor visual predictive check for diOH-pac.


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

20

The maximum binding rate to CrEL in the Hill term was five times higher for 6αOH-pac than

for p3OH-pac (Table 3), explaining the more steep increase in total 6αOH-pac concentrations

than for total p3OH-pac concentrations during the infusion (Figure 2).

No significant correlation between the pathways of paclitaxel to p3OH-pac and 6αOH-pac to

diOH-pac or paclitaxel to 6αOH-pac and p3OH-pac to diOH-pac could be detected. In addition,

the first two pathways were not significantly correlated to the CYP3A4 phenotype. The absence

of these associations is noteworthy but may depend on the relatively small study group. For

parameters where shrinkage in random effects were relatively high (CLpac, 21%; CLdiOH/fmmet,

34%), the approach with ANOVA may not help in detecting false negatives (Bertrand et al.,

2009). It has also been shown earlier that study groups with less than 50-100 individuals may

cause problems in characterizing covariate effects (Ribbing and Jonsson, 2004). This issue should

also be considered for the finding in this work of reduced clearance (fraction metabolized) of

6αOH-pac/fmpac for carriers of the ABCB1 G2677T/A G/T and T/T polymorphisms. The study

group under consideration here has previously been used to show (non-parametrically) a

significantly higher clearance for total paclitaxel concentrations in individuals carrying the G/A

variant of G2677T/A (Green et al., 2009), but this is the first time G2677T/A has been

significantly correlated to the primary metabolite 6αOH-pac. Since 6αOH-pac is the major

metabolite for most patients, it may also reflect paclitaxel clearance, which makes it plausible

that the finding in this model-based analysis is related to the same source as the earlier finding for

the parent drug. Previously, Yamaguchi et al. found a positive correlation of the ABCB1 total

mutant allele number to the clearance of paclitaxel in 13 Japanese ovarian cancer patients

(Yamaguchi et al., 2006). Nakajima et al. did not present a similar finding in 23 ovarian cancer

patients, although they found that carriers of the ABCB1 genotype C3435T had a significantly


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

21

higher AUC of p3OH-pac (Nakajima et al., 2005). Sissung et al. did not either find a correlation

between the ABCB1 genotypes C3435T and G2677T/A and paclitaxel pharmacokinetics

(Sissung et al., 2006), although that study was also small (n = 26). Correlations between kinetics

for unbound paclitaxel and the genotypes CYP2C8*2, CYP2C8*3, CYP2C8*4, CYP3A4*3 and

the C3435T genotype were not found in a large study with 97 individuals (Henningsson et al.,

2005a). However, that study was based on a less homogenous group of patients than the current

study, since it included a large number of dosing schedules, infusion times, and both males and

females being treated for different kinds of tumours (Henningsson et al., 2005a). More recently,

in a relatively large (n = 93) and more homogenous study a significant correlation between

CYP2C8*3 and clearance of unbound paclitaxel was reported (Bergmann et al., 2010), but no

correlation between ABCB1 genotypes and clearance was found. In the present study, the

CYP2C8*3 correlation could not be detected, which may be caused by too few individuals (n =

6) carrying the specific genotype.

All correlations significant at p < 0.1 from the step-wise covariate modelling have been

summarised in Table 4, together with their corresponding p-value from ANOVA using Empirical

Bayes estimates. Although effects can be considered small, they are consistent between

NONMEM OFV and ANOVA, which would reduce the probability of a type-1 error (Bertrand et

al., 2009). The significant correlation of CLpac to CYP3A5*3 may be in correspondence with a

recent finding (Leskela et al., 2010), where the allele was shown to have a protective effect

against neurotoxicity. In the current study, carriers of CYP3A5*3 G/A showed a 20% decrease in

clearance of unbound paclitaxel relative the reference group G/G. This is somewhat surprising,

since the G/G group is carrying an inactive allele and would thus be expected to have lower

clearance. However, it should be pointed out that only five individuals were carriers of the G/A


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

22

genotype and that a larger population would be desirable to further clarify this outcome, and, for

the two alternative models, the genotype was not statistically significant (Table 4).

In contrast to the finding for CYP3A5*3, the significant correlation of CL6αOH-pac/fmpac of

G2677T/A was significant over all models, and also consistent over different sets of frequentist

priors as presented in Table 5. The latter implies that the significant correlation is not an artefact

of a particular set of priors. The estimated covariate effects for G2677T/A on clearance of 6αOH-

pac/fmpac were relatively large, with an increase and decrease of 30 and 27% for G/G pooled with

G/A and T/T respectively, compared with the effect of CYP2C8*3 on clearance of unbound

paclitaxel reported by Bergmann et al, which was only a decrease of 11% (Bergmann et al.,

2010). However, the effect estimates of G2677T/A in this study were associated with a high

uncertainty, and the 95% and 90% CIs (but not the 80% CI) for the estimates included zero.

The correlation of clearance of diOH-pac/fmmet could very well be an artefact, since only three

individuals carried the specific genotype. To our knowledge, there is no information in literature

concerning the role of CYP3A4 in clearance of diOH-pac. The correlation of CYP2C8*1C to

clearance of unbound paclitaxel was not found in Bergmann et al., and was not consistent for the

Alternative models.

In conclusion, our study supports the previous findings of the ABCB1 transporter playing a

part in paclitaxel metabolism (Nakajima et al., 2005; Yamaguchi et al., 2006) or paclitaxel

treatment outcome (Green et al., 2006a). For the first time, we have also presented strong

indications that the formulation vehicle Cremophor EL, similar to the parent drug, affects the

pharmacokinetics of the two primary metabolites. Finally, the finding by Leskela et al. that alleles

associated with decreased paclitaxel metabolism seem to contribute to a lower risk for

neurotoxicity (Leskela et al., 2010) could imply that paclitaxel metabolites play a role in adverse


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

23

effects in paclitaxel treatment. If so, paclitaxel metabolites could be clinically relevant, in which

case the description of metabolites kinetics developed in this work may be important.


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

24

Acknowledgments

We thank Sven Sandin for support in statistics and helpful discussions throughout the project.


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

25

Authorship Contributions

Participated in research design: Fransson, Gréen, and Friberg.

Conducted experiments: Gréen.

Contributed new reagents or analytic tools: .

Performed data analysis: Fransson, Gréen, and Friberg.

Wrote or contributed to the writing of the manuscript: Fransson, Gréen, Litton, and Friberg.

Other: Fransson, Gréen, Litton, and Friberg acquired funding for the research.


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

26

References

Bergmann TK, Brasch-Andersen C, Green H, Mirza M, Pedersen RS, Nielsen F, Skougaard K,

Wihl J, Keldsen N, Damkier P, Friberg LE, Peterson C, Vach W, Karlsson MO, and Brosen K

(2010) Impact of CYP2C8*3 on paclitaxel clearance: a population pharmacokinetic and

pharmacogenomic study in 93 patients with ovarian cancer. Pharmacogenomics J

Bertrand J, Comets E, Laffont CM, Chenel M, and Mentré F (2009) Model-based tests to detect

gene effect in pharmacokinetic studies. PAGE 18 (2009) Abstr 1464.

Calvert AH, Newell DR, Gumbrell LA, O'reilly S, Burnell M, Boxall FE, Siddik ZH, Judson IR,

Gore ME, and Wiltshaw E (1989) Carboplatin dosage: prospective evaluation of a simple

formula based on renal function. J Clin Oncol 7: 1748-1756.

Cresteil T, Monsarrat B, Alvinerie P, Treluyer JM, Vieira I, and Wright M (1994) Taxol

metabolism by human liver microsomes: identification of cytochrome P450 isozymes involved

in its biotransformation. Cancer Res 54: 386-392.

Cresteil T, Monsarrat B, Dubois J, Sonnier M, Alvinerie P, and Gueritte F (2002) Regioselective

metabolism of taxoids by human CYP3A4 and 2C8: structure-activity relationship. Drug Metab

Dispos 30: 438-445.

Fransson M, and Green H (2008) Comparison of two types of population pharmacokinetic model

structures of paclitaxel. Eur J Pharm Sci 33: 128-137.

Gianni L, Kearns CM, Giani A, Capri G, Vigano L, Lacatelli A, Bonadonna G, and Egorin MJ

(1995) Nonlinear pharmacokinetics and metabolism of paclitaxel and its

pharmacokinetic/pharmacodynamic relationships in humans. J Clin Oncol 13: 180-190.


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

27

Gisleskog PO, Karlsson MO, and Beal SL (2002) Use of prior information to stabilize a

population data analysis. J Pharmacokinet Pharmacodyn 29: 473-505.

Green H, Soderkvist P, Rosenberg P, Horvath G, and Peterson C (2006a) mdr-1 single nucleotide

polymorphisms in ovarian cancer tissue: G2677T/A correlates with response to paclitaxel

chemotherapy. Clin Cancer Res 12: 854-859.

Green H, Soderkvist P, Rosenberg P, Mirghani RA, Rymark P, Lundqvist EA, and Peterson C

(2009) Pharmacogenetic studies of Paclitaxel in the treatment of ovarian cancer. Basic Clin

Pharmacol Toxicol 104: 130-137.

Green H, Vretenbrant K, Norlander B, and Peterson C (2006b) Measurement of paclitaxel and its

metabolites in human plasma using liquid chromatography/ion trap mass spectrometry with a

sonic spray ionization interface. Rapid Commun Mass Spectrom 20: 2183-2189.

Harris JW, Katki A, Anderson LW, Chmurny GN, Paukstelis JV, and Collins JM (1994a)

Isolation, structural determination, and biological activity of 6 alpha-hydroxytaxol, the principal

human metabolite of taxol. J Med Chem 37: 706-709.

Harris JW, Rahman A, Kim BR, Guengerich FP, and Collins JM (1994b) Metabolism of taxol by

human hepatic microsomes and liver slices: participation of cytochrome P450 3A4 and an

unknown P450 enzyme. Cancer Res 54: 4026-4035.

Henningsson A, Karlsson MO, Vigano L, Gianni L, Verweij J, and Sparreboom A (2001)

Mechanism-based pharmacokinetic model for paclitaxel. J Clin Oncol 19: 4065-4073.

Henningsson A, Marsh S, Loos WJ, Karlsson MO, Garsa A, Mross K, Mielke S, Vigano L,

Locatelli A, Verweij J, Sparreboom A, and Mcleod HL (2005a) Association of CYP2C8,

CYP3A4, CYP3A5, and ABCB1 polymorphisms with the pharmacokinetics of paclitaxel. Clin

Cancer Res 11: 8097-8104.


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

28

Henningsson A, Sparreboom A, Loos WJ, Verweij J, Silvander M, and Karlsson MO (2005b)

Population Pharmacokinetic Model for Cremophor EL. PAGE 14 (2005) Abstr 770.

Henningsson A, Sparreboom A, Sandstrom M, Freijs A, Larsson R, Bergh J, Nygren P, and

Karlsson MO (2003) Population pharmacokinetic modelling of unbound and total plasma

concentrations of paclitaxel in cancer patients. Eur J Cancer 39: 1105-1114.

Hooker AC, Staatz CE, and Karlsson MO (2007) Conditional weighted residuals (CWRES): a

model diagnostic for the FOCE method. Pharm Res 24: 2187-2197.

Joerger M, Huitema AD, Van Den Bongard DH, Schellens JH, and Beijnen JH (2006)

Quantitative effect of gender, age, liver function, and body size on the population

pharmacokinetics of Paclitaxel in patients with solid tumors. Clin Cancer Res 12: 2150-2157.

Karlsson MO, Molnar V, Freijs A, Nygren P, Bergh J, and Larsson R (1999) Pharmacokinetic

models for the saturable distribution of paclitaxel. Drug Metab Dispos 27: 1220-1223.

Kumar GN, Oatis JE, Jr., Thornburg KR, Heldrich FJ, Hazard ES, 3rd, and Walle T (1994) 6

alpha-Hydroxytaxol: isolation and identification of the major metabolite of taxol in human liver

microsomes. Drug Metab Dispos 22: 177-179.

Leskela S, Jara C, Leandro-Garcia LJ, Martinez A, Garcia-Donas J, Hernando S, Hurtado A,

Vicario JC, Montero-Conde C, Landa I, Lopez-Jimenez E, Cascon A, Milne RL, Robledo M,

and Rodriguez-Antona C (2010) Polymorphisms in cytochromes P450 2C8 and 3A5 are

associated with paclitaxel neurotoxicity. Pharmacogenomics J

Mathijssen RH, De Jong FA, Loos WJ, Van Der Bol JM, Verweij J, and Sparreboom A (2007)

Flat-fixed dosing versus body surface area based dosing of anticancer drugs in adults: does it

make a difference? Oncologist 12: 913-923.


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

29

Mirghani RA, Ericsson O, Cook J, Yu P, and Gustafsson LL (2001) Simultaneous determination

of quinine and four metabolites in plasma and urine by high-performance liquid

chromatography. J Chromatogr B Biomed Sci Appl 754: 57-64.

Mirghani RA, Hellgren U, Westerberg PA, Ericsson O, Bertilsson L, and Gustafsson LL (1999)

The roles of cytochrome P450 3A4 and 1A2 in the 3-hydroxylation of quinine in vivo. Clin

Pharmacol Ther 66: 454-460.

Monsarrat B, Alvinerie P, Wright M, Dubois J, Gueritte-Voegelein F, Guenard D, Donehower

RC, and Rowinsky EK (1993) Hepatic metabolism and biliary excretion of Taxol in rats and

humans. J Natl Cancer Inst Monogr 39-46.

Monsarrat B, Chatelut E, Royer I, Alvinerie P, Dubois J, Dezeuse A, Roche H, Cros S, Wright

M, and Canal P (1998) Modification of paclitaxel metabolism in a cancer patient by induction

of cytochrome P450 3A4. Drug Metab Dispos 26: 229-233.

Nakajima M, Fujiki Y, Kyo S, Kanaya T, Nakamura M, Maida Y, Tanaka M, Inoue M, and

Yokoi T (2005) Pharmacokinetics of paclitaxel in ovarian cancer patients and genetic

polymorphisms of CYP2C8, CYP3A4, and MDR1. J Clin Pharmacol 45: 674-682.

Piszkiewicz D (1977) Positive cooperativity in micelle-catalyzed reactions. J Am Chem Soc 99:

1550-1557.

Rahman A, Korzekwa KR, Grogan J, Gonzalez FJ, and Harris JW (1994) Selective

biotransformation of taxol to 6 alpha-hydroxytaxol by human cytochrome P450 2C8. Cancer

Res 54: 5543-5546.

Ribbing J, and Jonsson EN (2004) Power, selection bias and predictive performance of the

Population Pharmacokinetic Covariate Model. J Pharmacokinet Pharmacodyn 31: 109-134.


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

30

Savic RM, and Karlsson MO (2009) Importance of shrinkage in empirical bayes estimates for

diagnostics: problems and solutions. AAPS J 11: 558-569.

Schiff PB, Fant J, and Horwitz SB (1979) Promotion of microtubule assembly in vitro by taxol.

Nature 277: 665-667.

Sissung TM, Mross K, Steinberg SM, Behringer D, Figg WD, Sparreboom A, and Mielke S

(2006) Association of ABCB1 genotypes with paclitaxel-mediated peripheral neuropathy and

neutropenia. Eur J Cancer 42: 2893-2896.

Sonnichsen DS, Hurwitz CA, Pratt CB, Shuster JJ, and Relling MV (1994) Saturable

pharmacokinetics and paclitaxel pharmacodynamics in children with solid tumors. J Clin Oncol

12: 532-538.

Sonnichsen DS, Liu Q, Schuetz EG, Schuetz JD, Pappo A, and Relling MV (1995) Variability in

human cytochrome P450 paclitaxel metabolism. J Pharmacol Exp Ther 275: 566-575.

Sparreboom A, Huizing MT, Boesen JJ, Nooijen WJ, Van Tellingen O, and Beijnen JH (1995)

Isolation, purification, and biological activity of mono- and dihydroxylated paclitaxel

metabolites from human feces. Cancer Chemother Pharmacol 36: 299-304.

Sparreboom A, Van Tellingen O, Nooijen WJ, and Beijnen JH (1996) Nonlinear

pharmacokinetics of paclitaxel in mice results from the pharmaceutical vehicle Cremophor EL.

Cancer Res 56: 2112-2115.

Walle T, Walle UK, Kumar GN, and Bhalla KN (1995) Taxol metabolism and disposition in

cancer patients. Drug Metab Dispos 23: 506-512.

Yamaguchi H, Hishinuma T, Endo N, Tsukamoto H, Kishikawa Y, Sato M, Murai Y, Hiratsuka

M, Ito K, Okamura C, Yaegashi N, Suzuki N, Tomioka Y, and Goto J (2006) Genetic variation


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

31

in ABCB1 influences paclitaxel pharmacokinetics in Japanese patients with ovarian cancer. Int

J Gynecol Cancer 16: 979-985.


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

32

Footnotes

a)

This work has been supported by the Swedish Knowledge Foundation through the Industrial

Ph.D. programme in Medical Bioinformatics at the Strategy and Development Office (SDO) at

Karolinska Institutet; the Swedish Cancer Society; Gunnar Nilsson’s Cancer Foundation; and the

County Council in Östergötland.

b)

Previously presented at PAGE 19 (2010) Abstr 1912 [www.page-meeting.org/?abstract=1912]

c)

Martin Fransson, M.Sc.

Department of Medical Epidemiology and Biostatistics

PO Box 281/Nobels väg 12 A

SE-171 77 Stockholm, Sweden

Phone: +46 (0)8-524 839 74

Fax: +46 (0) 8-31 49 75

E-mail: [email protected]


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

33

Legends for figures

Figure 1. Metabolic pathways for paclitaxel by CYP2C8 and CYP3A.

Figure 2. Observed total plasma concentrations of paclitaxel (n = 345), 6α-hydroxypaclitaxel (n =

332), p-3’-hydroxypaclitaxel (n = 336) and 6α-, p-3’-dihydroxypaclitaxel (n = 143). Paclitaxel,

6α-hydroxypaclitaxel and p-3’-hydroxypaclitaxel were observed in 33 individuals and 6α-, p-3’-

dihydroxypaclitaxel was observed in 15 individuals.

Figure 3. Observed concentrations versus population predictions of paclitaxel, 6α-

hydroxypaclitaxel, p-3’-hydroxypaclitaxel and 6α-, p-3’-dihydroxypaclitaxel for the final model.

Figure 4. Visual predictive checks based on 1000 simulations. 95% CI of 5th, 50th and 95th

percentiles of simulated data (dashed lines). Solid lines are 5th, 50th and 95th percentiles of

observed data.


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

34

Tables

Table 1

Genotype frequencies in the study group

Gene Allele Polymorphism n

ABCB1 C3435T C/C 4

T/C 19

T/T 10

G2677T/A G/G 3

G/A 5

A/A 0

G/T 17

T/A 0

T/T 8

CYP2C8 *1B C/C 23

C/A 9

A/A 1

*1C T/T 21

T/G 12

G/G 0


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

35

*3 A/A 27

A/G 6

G/G 0

*4 C/C 29

G/C 4

G/G 0

Haplotype C C/C 20

C/G 11

G/G 2

CYP3A4 *1B A/A 30

A/G 3

G/G 0

CYP3A5 *3 G/G 28

G/A 5

A/A 0


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

36

Table 2

Priors used for the mechanism-based model of paclitaxel (Henningsson et al., 2001)

Parametera Prior Estimate Prior RSEb (%)

V1 (L) 229 17

V2 (L) 856 15

V3 (L) 303 21

Q2 (L/h) 134 22

Q3 (L/h) 213 23

Blin 7.59 12

BCrEL 3.78 10

Bmax (µmol/L) 0.0245 27

Km (µmol/L) 0.000106 43

a V1, V2, V3, volumes of the central, first- and second peripheral compartment for unbound

concentrations of paclitaxel; Q2, Q3, intercompartmental clearances between the central and

peripheral compartments for unbound concentrations of paclitaxel; Blin, linear binding to plasma

components; BCrEL, binding directly to proportional to CrEL concentration; Bmax, maximal

binding to plasma components; Km, concentration at half of the maximal binding to plasma

components.

b Relative Standard Error.


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

37

Table 3

Parameter estimates for the final model without covariate relations

Parametera Estimate RSE (%) LLP 95% CI RSE/RSE Prior

Lower Upper

CLpac (L/h) 378 8.4 322 441

V1 (L) 188 11 0.66

V2 (L) 1100 8.2 0.54

V3 (L) 222 14 0.68

Q2 (L/h) 123 10 0.45

Q3 (L/h) 181 13 0.57

Blin 7.89 10 0.84

BCrEL 3.62 9.1 0.91

Bmax (µmol/L) 0.0207 22 0.81

Km (µmol/L) 0.000123 36 0.83

ε1,pac (%) 24.4 4.7 22.3 26.8

IIVCLpac (CV%)b 15.3 39c 10.0 22.3

IIVBlin (CV%)b 101 34c 67.3 176

CL6αOH/fmpac (L/h) 2830 26 1850 4750

V6αOH /fmpac (L) 1390 24 945 2230

BCrEL,6αOH 43.0 40 24.4 99

Bnsp,6αOH (µmol/L) 0.000754 12 0.000588 0.000947


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

38

ε1, 6αOH (%) 25.4 5.6 22.9 28.4

ε2, 6αOH, i=1 (µmol/L) 0.00447 30 0.00250 0.00772

IIVCL6αOH/fmpac (CV%)b 36.6 36c 25.1 53.8

CLp3OH/fmpac (L/h) 1470 16 1090 2020

Vp3OH /fmpac (L/h) 1140 13 883 1490

BCrEL,p3OH 8.60 32 5.24 16.1

Bnsp,p3OH (µmol/L) 0.00124 10 0.00103 0.00149

ε1, p3OH (%) 37.7 4.8 34.5 41.6

IIVfp3OH/fmpac 0.425d 0.29c 0.245d 0.769d

CrEL50 (mL/L) 4.48 18 3.55 6.21

HillCrEL 2.71 13 2.20 3.39

IIVHillCrEL (CV%)b 40.1 38c 26.8 58.9

CLdiOH/fmmet (L/h) 831 15 606 1140

VdiOH /fmmet (L) 167 23 97.1 246

ε1, diOH (%) 49.8 8.4 42.6 59.3

ε2, diOH (µmol/L) 0.00197 22 0.00134 0.00315

IIVCLdiOH/fmmet (CV%)b 60.7 39c 40.8 104

a CLpac, clearance of unbound concentrations of paclitaxel; V1, V2, V3, volumes of the central,

first- and second peripheral compartment for unbound concentrations of paclitaxel; Q2, Q3,

intercompartmental clearances between the central and peripheral compartments for unbound


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

39

concentrations of paclitaxel; Blin, linear binding to plasma components; BCrEL, binding directly to

proportional to CrEL concentration; Bmax, maximal binding to plasma components; Km,

concentration at half of the maximal binding to plasma components; CL6αOH/fmpac, CLp3OH/fmpac,

clearance of unbound concentrations of 6αOH-pac and p3OH-pac over fraction metabolized

unbound paclitaxel; V6αOH /fmpac, Vp3OH /fmpac, volume of distribution of unbound concentrations

of 6αOH-pac and p3OH-pac over fraction metabolized unbound paclitaxel; BCrEL,6αOH, BCrEL,p3OH,

maximal binding rate to CrEL of 6αOH-pac and p3OH-pac; Bnsp,6αOH, Bnsp,p3OH, non-specific

CrEL component for 6αOH-pac and p3OH-pac; CrEL50, CrEL concentration at half maximal

binding rate; HillCrEL, Hill coefficient for CrEL concentration; CLdiOH/fmmet, clearance of unbound

concentrations of diOH-pac over fraction metabolized unbound primary metabolite; VdiOH /fmmet,

volume of distribution of unbound concentrations of diOH-pac over fraction metabolized

unbound primary metabolite; ε1, proportional residual error for total concentrations; ε2, 6αOH, i=1,

additive error for first sample for each individual of total concentration of 6αOH-pac; ε2,diOH,

additive error for total concentration of diOH-pac; IIVCLpac, IIVBlin, IIVCL6αOH/fmpac, IIVfp3OH/fmpac,

IIVHillCrEL, IIVCLdiOH/fmmet, interindividual variability in the designated parameter.

b Coefficient of Variation, calculated as (exp(ω2) – 1)0.5.

c With respect to the variance term, ω2.

d Estimate given for the corresponding variance term, ω2, of β in Eq. 5 and Eq. 6.


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

40

Table 4

Step-wise covariate modelling and ANOVA

Parameter Covariatea Δd.f. ΔOFVb p (ΔOFV)c p (ANOVA)d

CL6αOH/fmpac (L/h) G2677T/A 2

Final model −7.38 0.025 0.012

Alternative model 1e −4.79 0.091 0.048

Alternative model 2f −5.86 0.054 0.033

CLpac (L/h) CYP3A5*3 1

Final model −5.91 0.015 0.024

Alternative model 1 −2.96 0.085 0.12


CLdiOH/fmmet (L/h) CYP3A4*1B 1

Final model −3.52 0.061 0.13



CLpac (L/h) CYP2C8*1C 1

Final model −3.42 0.064 0.070



aCovariates significant on p < 0.1 in the final model using step-wise covariate modelling.


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

41

b Relative a model with fixed estimates instead of priors for paclitaxel parameters.

c p-value corresponding to the drop in OFV.

d Using Empirical Bayes estimates from the corresponding model supported by priors.

e Identical to the final model but also using IIV, supported by priors (Henningsson et al., 2001),

on the distribution volumes V1 (CV 46%, d.f. 28) and V2 (CV 40%, d.f. 13) for unbound

paclitaxel.

f Identical to Alternative model 1 but without IIV on the parameter Blin for plasma protein

binding. The model structure is hierarchical to Alternative model 1 but not to the Final model.


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


DMD # 35394

42

Table 5

Sensitivity analysis for significant ABCB1 allele G2677T/A using Empirical Bayes estimates

Modela p (ANOVA)

G2677T/A on CL6αOH/fmpac

CL6αOH/fmpac (L/h)

no covariate

Final model 0.012 2830

Test 1 0.012 2790

Test 2 0.010 2800

Test 3 0.014 2870

Test 4 0.011 2290

a Estimates for the final model, as presented in Table 3; Test 1, increased standard error for each

prior by 50%; Test 2, prior for BCrEL set to prior estimate – 2 x standard error; Test 3, prior for

BCrEL set to prior estimate + 2 x standard error; Test 4, prior estimates from Henningsson et al.,

(2005a) for unbound paclitaxel parameters only (V1, V2, V3, Q2 and Q3).


at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from



at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from



at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from



at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from



at ASPE

T Journals on A

ugust 25, 2021dm


ownloaded from


Documents

DMD # 35394 Influence of Cremophor EL and Genetic ...dmd.aspetjournals.org/content/dmd/early/2010/11/05/dmd...2010/11/05 · The formulation vehicle Cremophor EL has previously been