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.
Received October 9th, 2013; revised November 8th, 2013; accepted November 16th, 2013
Copyright © 2013 Charlene A. Baksh et al. This is an open access article distributed under the Creative Commons Attribution Li-
cense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In
accordance of the Creative Commons Attribution License all Copyrights © 2013 are reserved for SCIRP and the owner of the intel-
lectual property Charlene A. Baksh et al. All Copyright © 2013 are guarded by law and by SCIRP as a guardian.
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-

2  (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 1516
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
0 10203040
0 10203040
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 1518
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
7.
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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1519
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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