Population Pharmacokinetics of UCN-01

doi:10.4236/jct.2013.410183

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Journal of Cancer Therapy, 2013, 4, 1513-1519

Published Online December 2013 (http://www.scirp.org/journal/jct)

http://dx.doi.org/10.4236/jct.2013.410183

Open Access JCT

1513

Population Pharmacokinetics of UCN-01

Charlene A. Baksh1, Martin J. Edelman2, Edward A. Sausville2, William D. Figg3, Hao Zhu4,

Kenneth S. Bauer1

1Department of Pharmacy Practice and Science, University of Maryland Baltimore, School of Pharmacy, Baltimore, USA; 2University

of Maryland Greenbaum Cancer Center, Baltimore, USA; 3Clinical Pharmacology Research Core, National Cancer Institute, National

Institutes of Health, Bethesda, USA; 4Office of Clinical Pharmacology, Food and Drug Administration, Silver Spring, USA.

Email: [email protected]

Received October 9th, 2013; revised November 8th, 2013; accepted November 16th, 2013

cense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In

ABSTRACT

UCN-01 (7-Hydroxystaurosporine) is an investigational anticancer agent that is currently being evaluated as targeted

therapy in phase II clinical studies. The aims of this work were to describe the population pharmacokinetics of UCN-01

in patients with advanced solid tumors, and to identify covariates in patients with advanced solid tumors that affected

the pharmacokinetic parameters of UCN-01. The utility of performing this research is to provide optimization of treat-

ment and individualized dose therapy for minimization of toxicity. So, in addition to elucidating the population phar-

macokinetic parameter estimates from a Phase I trial where UCN-01 was given in combination with carboplatin in pa-

tients with advanced solid tumors, and a trial where the drug was given alone as a 72-hour infusion in the same type of

population, a covariate analysis was performed in order to identify pharmacokinetic determinants of UCN-01. Using

NONMEM to perform nonlinear mixed-effects modeling, a linear two-compartment model was found to provide the

best fit for UCN-01 data. A meta-analysis was performed, which included pooled 3-hour and 72-hour infusion data, and

provided population pharmacokinetic estimates for CL (0.0157 L/hr [6.1%RSE]), V1 (2.51 L [10.0% RSE]), Q (4.05

L/hr [14.3% RSE]), and V2 (8.39 L [6.6% RSE]). Inter-individual variability was found for each of the main pharma-

cokinetic parameters to be ETACL (44.9% [20.8% RSE]), ETAV1 (43.9% [39.8% RSE]), ETAQ (6.09% [62.5% RSE]),

and ETAV2 (4.17% [30.0% RSE]). Body surface area was found to be a statistically-significant variable from one of

the individual study analyses (3-hour infusion). Population PK modeling has contributed to a better understanding of the

clinical pharmacology of UCN-01. Dose individualization may improve treatment with UCN-01. Further clinical de-

velopment may be supported by optimization of combination chemotherapy.

Keywords: Pharmacokinetics; UCN-01; 7-Hydroxystaurosporine; Pharmacometrics; Population Modeling; Phase I;

Clinical Pharmacology

1. Introduction

7-Hydroxystaurosporine (UCN-01) is a protein kinase

inhibitor, which has cellular targets of chk1 and chk2

DNA damage-dependent checkpoint kinases, phosphati-

dylinositol-dependent kinase I (PDK1), and pathways

leading to cyclin-dependent kinase activation [1]. The

resultant effects are cell-cycle arrest and the induction of

apoptosis. UCN-01 also promotes the sensitization of

DNA-damaging agents such as carboplatin.

UCN-01 has an extremely high affinity for α1-Acid

Glycoprotein (AAG) [2]. AAG is an acute phase reactant

protein, whereby the plasma concentration of AAG may

change under various physiological and pathological

conditions, including cancer, resulting in an alteration of

the binding of various drugs.

UCN-01 is currently being investigated for use in pa-

tients with advanced solid tumors. During the process of

therapeutic development, there are many aspects of drug

disposition which are reviewed in order to ensure patients’

safety, as well as therapeutic efficacy. This work focuses

on the hypothesis that the pharmacokinetic parameters of

UCN-01 in patients with advanced solid tumors are in-

fluenced by measureable covariates. This hypothesis was

posed in order to answer the research question of what

Population Pharmacokinetics of UCN-01

1514

covariates affect the population pharmacokinetic para-

meters of UCN-01 in patients with advanced solid tu-

mors. Two main study objectives met by conducting this

research—first, to describe the population pharmacoki-

netics of UCN-01 in patients with advanced solid tumors,

and secondly, to identify covariates in patients with ad-

vanced solid tumors that affect the pharmacokinetic pa-

rameters of UCN-01. The effort to answer the proposed

research question is worthwhile in order to provide

treatment optimization, and perhaps individualized dose

therapy, for minimization of toxicity.

2. Methods

2.1. Pharmacokinetic Analysis

Model development was performed using nonlinear

mixed-effect modeling within the program NONMEM

(version VI, level 1.0, Globomax; Hanover, Maryland)

using the WINGS for NONMEM interface (version 614,

University of Auckland, Auckland, New Zealand). Either

the first order (FO) or the first-order conditional estima-

tion (FOCE) method was used for parameter estimation.

The NONMEM data file was prepared using Microsoft

Excel 2007 (Microsoft Corporation, Redmond, Wash-

ington), and NONMEM outputs (i.e. diagnostic graphics)

were processed using S-PLUS version 8.0 (Insightful

Corporation, Seattle, Washington). The hardware plat-

form included 2.0GHz AMD Turion 64X2 TL-60 pro-

cessors with 2.93GB RAM running Microsoft Windows

XP. A two-compartment model was found to fit the UCN-01

concentration-time profile in preliminary analyses. The

fundamental pharmacokinetic parameters used to char-

acterize the two-compartment population model were

clearance (CL), volume of distribution in compartment 1

(V1), intercompartmental clearance (Q), and volume of

distribution in compartment 2 (V2). Unexplained inter-

individual variability (IIV) in pharmacokinetic model

parameters was estimated using the following model with

the random effect ηj:



expPj TVPj



 (1)

where TVP is the typical value of the pharmacokinetic

parameter in the population, Pj is the individual value for

P in the jth individual, and ηj is a random variable with

the mean of zero and variance of ωp2. This model as-

sumes a log-normal distribution for the Pj values. Esti-

mates of IIV in P are presented as the square root of ωp2,

which is an approximation of the coefficient of variation

of P for a log-normally distributed quantity. The TVP

may be further modeled as a function of covariates as

follows:



12 expTVP PPCG







(2)



1medianexpTVPPCT CTP





where θPj (j = 1, 2,…) represents elements of a vector

for population fixed-effect parameters, CT is the con-

tinuous covariate value of the patient, CT median is the

median covariate value along the studied patient popula-

tion, and CG is the categorical covariate coded as 0 or 1

in the data set.

Random residual variability of the predictions was

modeled according to a combined proportional and addi-

tive error model:



11 2CijC ijijij



  (4)

where Cij is the amount of the ith plasma concentration

measured in the jth individual; C * ij is the respective

model-predicted concentration; and εij is the symmetri-

cally-distributed random variable with expectation zero

and variance σ2. Assay error or incorrect dose and/or

sample records were considered potential sources of re-

sidual error [3].

2.2. Statistical Analysis

First, analysis was performed in order to find population

estimates of each of the pharmacokinetic parameters CL,

V1, Q, and V2 for UCN-01. Second, a stepwise proce-

dure was executed in order to reveal any statistically-

significant covariates. Figure 1 shows the process of

forward selection/backward elimination for covariate

selection. Effects selected during the first analysis

(nominal p value of 0.05, log-likelihood ratio test) were

sequentially included in the model, taking the pair (cate-

gorical covariate, continuous covariate/pharmacokinetic

parameter) with the largest drop in NONMEM objective

function value first, until no further pair with an associ-

ated nominal p value of 0.05 could be included. A se-

quential elimination step followed, deleting the pair with

smallest increase in NONMEM objective function first,

until no further pair with an associated nominal p value

of 0.01 could be excluded. The final pharmacokinetic

population model was based on FOCE.

2.3. 3-hr Infusio n

Data from a total of 20 subjects, who received various

dosages of intravenous UCN-01 in either single- or re-

peated 3-hr infusion regimens, and for whom full pharma-





(3)

Figure 1. Stepwise procedure depicting model development.

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Population Pharmacokinetics of UCN-01 1515

cokinetic data sets were available, were used to develop

the UCN-01 population PK model.

This study was described in detail by Edelman, et al.

in a previous publication and was a single-center, open-

label trial where patients received doses according to a

pre-specified schema (Table 1) [4]. Patients received

doses every 21 days, up to 6 cycles. The number of

pharmacokinetic samples per subject ranged from 6 to 68

(minimum to maximum), mean patient age was 60 years,

and mean patient weight was 73 kg. Approval was re-

ceived from the Institutional Review Board of the Uni-

versity of Maryland, and each patient was provided writ-

ten informed consent.

Plasma concentrations were determined using a spe-

cific high-performance liquid chromatography method

(HPLC) [5]. The assay method was sensitive, with inter-

and intra-assay coefficients of variation (cv%) of preci-

sion of the quality control samples (0.300 - 7.50 μg/mL)

ranging from 0.830% - 0.900%. Standard curves covered

a range of 0.100 - 20 μg/mL. Linearity was evaluated

using least-squares regression analysis to plot the peak

height ratio of UCN-01 to internal standard against UCN-01

concentration. Sample analysis was performed at the

University of Maryland, School of Pharmacy (Baltimore,

Maryland, USA).

2.4. 72-hr Infusio n

Data from a total of 28 subjects, who received various

dosages of intravenous UCN-01 in single-dose regimens

in one study, and for whom full pharmacokinetic data

sets were available, were used to develop the UCN-01

population PK model. The study was described in detail

by Sausville, et al. in a previous publication.[6] This was

a single-center, open-label trial where patients who were

treated on the first three dose levels (1.8, 3.6, and 6

mg/ m2/d for 3 days) received all courses as a 72-hour

infusion, with second and subsequent courses adminis-

tered at 2-week intervals. At doses ≥12 mg/m2/d for 3

days, second and subsequent courses were administered

for only 36 hours at the same concentration and infusion

rate, which effectively reduced the administered dose by

Table 1. UCN-01 as a 3-hour infusion.

Dose level UCN-01

(cycle 1) mg/m2

UCN-01

(cycle 2+) mg/m2

Carboplatin AUC

(mg·min/mL)

1 50 25 3

2 50 25 3

3 70 35 3

4 90 45 4

5 90 45 5

6 90 45 5

50% for the second and subsequent courses. In addition,

the time between courses was lengthened to 4 weeks [6].

The number of pharmacokinetic samples per subject

ranged from 8 to 28 (minimum to maximum), mean pa-

tient age was 55 years. Approval was received from the

National Cancer Institute institutional review board, and

each patient was provided written informed consent.

Plasma concentrations were determined using a spe-

cific high-performance liquid chromatography method

(HPLC) [5]. Linearity was evaluated using least-squares

regression analysis to plot the peak height ratio of UCN-

01 to internal standard against UCN-01 concentration.

Sample analysis was performed at the National Cancer

Institute (Bethesda, Maryland, USA).

2.5. Meta-Analysis

Data from a total of 48 subjects, who received various

dosages of intravenous UCN-01 in single- and multi-

ple-dose regimens in two studies, and for whom full

pharmacokinetic data sets were available, were used to

develop the UCN-01 population PK model. The studies

were described in detail by Edelman et al. and Sausville,

et al. in previous publications [7]. These were both sin-

gle-center, open-label trials where patients received

doses according to those described in the respective sec-

tions above. The number of pharmacokinetic samples per

subject ranged from 6 to 68 (minimum to maximum),

and mean patient age was 58 years. Approval was re-

ceived from the Institutional Review Board of the Uni-

versity of Maryland, or the National Cancer Institute in-

stitutional review board, whichever was applicable to the

respective trial. Each patient was provided written in-

formed consent.

Plasma concentrations were determined using a spe-

cific high-performance liquid chromatography method

(HPLC) [6]. Linearity was evaluated using least-squares

regression analysis to plot the peak height ratio of UCN-

01 to internal standard against UCN-01 concentration.

Sample analysis was performed at either University of

Maryland, School of Pharmacy (Baltimore, Maryland,

USA), or the National Cancer Institute (Bethesda, Mary-

land, USA), whichever was applicable to the respective

study.

3. Results

3.1. 3-hr Infusio n

The results obtained from the tested models for the 3-hr

infusion study are displayed in Table 2. A linear two-

compartment model was found to best fit the data, which

described CL, V1, Q, and V2, with intravenous admini-

stration and first-order elimination (ADVAN 3 TRANS

4). Inter-individual variability was incorporated on all

fixed-effect parameters. A combined proportional and

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Population Pharmacokinetics of UCN-01

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Table 2. Results from 3-hr infusion study.

Model Pharmacokinetic model OFV −ΔOFV

291 One-compartment model first-order

elimination 2014.675-

294 Two-compartment model first-order

elimination (base model) 1632.389-

314 Model 294 + BSA on V1 1623.1300.259*

342 Model 314 + Albumin on Q 1618.6380.492*

395 Model 342 + BSA on V2 1613.7920.486*

additive error model best described the residual variabil-

ity (IAV). Inter-occasion variability (IOV) was not

needed to be accounted for on any fixed-effect parameter

(Figure 2).

All covariates were tested separately for their effect on

the pharmacokinetic parameters before being included in

the model. Combinations of covariates were evaluated.

The results following the backward elimination step

showed that only BSA significantly influenced UCN-01

V1, whereas all other covariates tested on all fixed-effect

parameters did not (i.e. AAG, albumin, bilirubin, Scr,

age, height, weight, race, and sex; not shown in Table 2).

Figure 3 depicts the graphical relationship between BSA

and IIV on V1. By including BSA on V1, there was a

reduction of 46% in unexplained IIV for V1. The esti-

mated pharmacokinetic parameters are shown in Table 3 .

A comparison between the base model and final model

for the UCN-01 population PK is shown in Figure 4.

3.2. 72-hr Infusio n

The results obtained from the tested models for the 72-hr

infusion study are displayed in Table 4. A linear two-

compartment model was found to best fit the data, which

described CL, V1, Q, and V2, with intravenous admini-

stration and first-order elimination (ADVAN 3 TRANS

4). IIV was incorporated on all fixed-effect parameters.

A combined proportional and additive error model best

described the IAV.

All covariates were tested separately for their effect on

the pharmacokinetic parameters before being included in

the model, and combinations of covariates were evalu-

ated. The results following the backward elimination step

showed that no covariates tested on any fixed-effect pa-

rameters had a statistically-significant effect (i.e. AAG,

albumin, bilirubin, BSA, Scr, age, and sex). Therefore,

the base model was chosen to best represent this data.

The estimated pharmacokinetic parameters are shown in

Table 5. Figure 5 is a graphical depiction of the final

population PK model for UCN-01 72-hr infusion.

Figure 2. Graphical evaluation of inter-occasion variability

(IOV).

Figure 3. Relationship between BSA and IIV on V1.

Figure 4. Comparison of the base model (left) and final

population PK (right) model for UCN-01 3-hr infusion.

3.3. Meta-Analysis

The results obtained from the various models tested for

the meta-analysis are displayed in Table 6. A linear two-

compartment model was found to best fit the data, which

described CL, V1, Q, and V2, with intravenous admini-

stration and first-order elimination (ADVAN 3 TRANS

4). IIV was incorporated on all fixed-effect parameters.

A combined proportional and additive error model best

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Population Pharmacokinetics of UCN-01 1517

0 10203040

Observed vs predicted concentrations

Observed concentrations, ug/mL

Predicted concentration, ug/mL

Population predictions (PRED)

Individual predictions (IPRED)

Figure 5. Final population PK model for UCN-01 72-hr

infusion.

Table 3. Estimated pharmacokinetic parameters from

UCN-01 3-hr infusion study.

Model OFV

Population

estimate

(%SE)

Inter-patient

variability

(%SE)

Base model 1632.389

iv 2 cmt

CL = CLpop·ηCL

V1 = V1pop·ηV1

Q = Qpop·ηQ

V2 = V2pop·ηV2

Final model 1623.130

iv 2 cmt with covariates

CL = CLpop·ηCL

V1 = V1pop·(BSA/1.9)θ2·ηV1

Q = Qpop·ηQ

V2 = V2pop·ηV2

CL (L/hr) 0.0177 (10.1) 34.7% (37.0)

V1 (L) 2.43 (20.2) 55.0% (45.2)

Q (L/hr) 4.19 (17.9) 43.6% (43.7)

V2 (L) 9.83 (14.0) 50.0% (36.6)

Residual Variability

Proportional error 18.0% (24.7)

Additive error 1.67 μg/mL (36.0)

Table 4. Results from 72-hr infusion study.

ModelPharmacokinetic model OFV Δ OFV

004 One-compartment model first-order

elimination 1125.865--

042 Two-compartment model first-order

elimination (base model) 630.030--

050Model 042 + BSA on V1 628.827−1.203

053Model 042 + AAG on CL 630.0370.007

058Model 042 + Albumin on CL 625.735−4.295*

Table 5. Estimated pharmacokinetic parameters from UCN-

01 72-hr infusion study.

Model OFV

Population

estimate (%SE) Inter-individual

Variability (%SE)

Base/Final model630.030

iv 2 cmt

CL = CLpop·ηCL

V1 = V1pop·ηV1

Q = Qpop·ηQ

V2 = V2pop·ηV2

CL (L/hr) 0.0141 (9.6) 45.5% (26.7)

V1 (L) 2.50 (9.3) 30.0% (56.6)

Q (L/hr) 0.267 (15.4) 71.6% (57.6)

V2 (L) 7.40 (6.3) 37.2% (26.6)

Residual Variability

Proportional error12.8% (28.0)

Additive error 0.36 μg/mL (37.0)

Table 6. Results from meta-analysis.

ModelPharmacokinetic model OFV Δ OFV

047 One-compartment model first-order

elimination 3239.984--

048 Two-compartment model first-order

elimination (base model) 2448.631--

105Model 048 + Study on Q 2379.524−69.107*

116Model 105 + Bilirubin on V1 2372.243−7.281*

described the IAV.

All covariates were tested separately for their effect on

the pharmacokinetic parameters before being included in

the model. Second, combinations of covariates were

evaluated. The results following the backward elimina-

tion step showed that only the variable Study on Q had a

statistically-significant effect on a fixed-effect parameter,

but none of the covariates tested provided this effect (i.e.

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Population Pharmacokinetics of UCN-01

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AAG, albumin, bilirubin, BSA, Scr, age, and sex). Fig-

ure 6 depicts the graphical relationship between Study

and IIV on Q. By including Study on Q, there was a re-

duction of 82.5% in unexplained IIV for Q. The esti-

mated pharmacokinetic parameters are shown in Table 7 .

A comparison between the base model and final model

for the UCN-01 population PK model is shown in Figure

4. Discussion

Patients data from both single-drug regimen and multiple-

drug regimens for UCN-01 were used in order to esti-

mate pharmacokinetic parameters. Sources of variability

in patients with refractory neoplasms and advanced solid

tumors were also estimated. Table 8 provides a summary

of parameter estimates obtained from the three analyses

which were conducted. Table 9 shows a summary of the

IIV for each fixed-effect parameter produced by each of

the analyses. The comparison between fixed-effect pa-

rameter estimates shows that the major difference be-

tween estimates is for parameter Q.

Covariate analysis provided more insight into the rea-

sons for IIV. The results of the 3-hr infusion analysis

suggest that BSA should be taken into account in order to

Figure 6. Relationship between Study and IIV on Q for meta-

analysis of UCN-01 data.

Figure 7. Comparison of the base model (left) and final po-

pulation PK (right) model for UCN-01 meta-analysis.

Table 7. Estimated pharmacokinetic parameters from meta-

analysis.

Model OFV

Population

estimate

(%SE)

Inter-patient

variability

(%SE)

Base model 2448.631

iv 2 cmt

CL = CLpop·ηCL

V1 = V1pop·ηV1

Q = Qpop·ηQ

V2 = V2pop·ηV2

Final model 2422.931

iv 2 cmt with covariates

CL = CLpop·ηCL

V1 = V1pop·ηV1

Q = Qpop·θ2STUDY·ηQ

V2 = V2pop·ηV2

CL (L/hr) 0.0157 (6.1) 44.9% (20.8)

V1 (L) 2.51 (10.0) 43.9% (39.8)

Q (L/hr) 4.05 (14.3) 6.09% (62.5)

V2 (L) 8.39 (6.6) 4.17% (30.0)

Residual Variability

Proportional error 2.14% (24.7)

Additive error 0.22 μg/mL

Table 8. Summary of parameter estimates for UCN-01.

UCN-01 Population PK AnalysisCL (L/hr) V1 (L) Q (L/hr)V2 (L)

3-hr infusion 0.0177 2.43 4.199.83

72-hr infusion 0.0141 2.50 0.2677.40

Meta-analysis 0.0157 2.51 4.058.39

Table 9. Summary of the inter-individual variability for

each fixed-effect parameter.

UCN-01 Population PK AnalysisηCL (%) ηV1 (%) ηQ (%)ηV2 (%)

3-hr infusion 34.7 55.0 43.650.0

72-hr infusion 45.5 30.0 71.637.2

Meta-analysis 44.9 43.9 6.094.17

ensure the appropriate dose is utilized. This is in agree-

ment with previous studies with UCN-01, and the current

practice of most chemotherapeutic agents being dosed

based on patient BSA [8].

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Population Pharmacokinetics of UCN-01

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The results of the 72-hr infusion study suggest that

none of the covariates assessed are able to explain statis-

tically-significant patient variability in this population. In

contrast with the 3-hr infusion study, BSA was not found

to be significant on any fixed-effect parameter in this

extended-infusion analysis. Perhaps the small number of

patients in this study may have influenced the inability to

find BSA, a statistically-significant covariate.

The results of the meta-analysis suggest that the cate-

gorical variable Study should be taken into account in

order to explain IIV on the fixed-effect parameter Q.

This is in agreement with the difference between esti-

mates of Q found for the individual study analyses from

the 3-hr and 72-hr infusion studies, and gives an account

of the magnitude of difference between the extended-

infusion versus shorter infusion of this drug. Additionally,

the results of the meta-analysis suggest that none of the

other covariates assessed are able to explain statistically-

significant patient variability in this population. This

seems to be counterintuitive because, again, usually it is

seen that chemotherapeutic agents are dosed based on

BSA. Perhaps because there were more patients and

more data points available from the 72-hr infusion study,

the significance of BSA from the 3-hr infusion study was

overshadowed due to lack of statistical power after pool-

ing the data.

A relationship between AAG and fixed-effect parame-

ters was sought because of previous knowledge of the

increased binding affinity of UCN-01 to AAG, but AAG

was not able to be found as a statistically-significant co-

variate [2]. This is likely due to the small sample size

used in this population PK analysis. A way in which to

increase the sample size of the analyzed data would be to

pool the data set from this study with that of other studies

utilizing UCN-01 as a therapeutic agent, in order to in-

crease the statistical power, and perhaps reveal AAG or

any other variable considered as a covariate. However,

this study was able to confirm the findings of pharma-

cokinetic parameter estimates from previous studies, and

confirmed that UCN-01 follows two-compartment linear

pharmacokinetics.

5. Acknowledgements

Funding was provided by the Department of Pharmacy

Practice and Science, University of Maryland Baltimore,

School of Pharmacy, Baltimore, Maryland, USA, and

American Foundation for Pharmaceutical Education Pre-

doctoral Fellowship.

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