Pharmacokinetics and pharmacodynamics of propofol in patients

Pharmacokinetics and pharmacodynamics of propofol in patients

Pharmacological Reports
Copyright © 2012
2012, 64, 113–122
by Institute of Pharmacology
ISSN 1734-1140
Polish Academy of Sciences
Pharmacokinetics and pharmacodynamics
of propofol in patients undergoing abdominal
aortic surgery
Pawe³ Wiczling1, Agnieszka Bienert2, Pawe³ Sobczyñski3, Roma
Hartmann-Sobczyñska4, Krzysztof Bieda3, Aleksandra Marcinkowska2,
Maria Malatyñska2, Roman Kaliszan1, Edmund Grzeœkowiak2
1
Department of Biopharmaceutics and Pharmacokinetics, Medical University of Gdansk, Hallera 107,
PL 80-416, Gdañsk, Poland
2
Department
4
3
of Clinical Pharmacy and Biopharmacy, Department of Anaesthesiology and Intensive Therapy,
Department of Experimental Anaesthesiology, Poznan University of Medical Sciences, œw. Marii Magdaleny 14,
PL 61-861 Poznañ, Poland
Correspondence: Agnieszka Bienert, e-mail: [email protected]
Abstract:
Available propofol pharmacokinetic protocols for target-controlled infusion (TCI) were obtained from healthy individuals. However, the disposition as well as the response to a given drug may be altered in clinical conditions. The aim of the study was to examine
population pharmacokinetics (PK) and pharmacodynamics (PD) of propofol during total intravenous anesthesia (propofol/fentanyl)
monitored by bispectral index (BIS) in patients scheduled for abdominal aortic surgery. Population nonlinear mixed-effect modeling
was done with Nonmem. Data were obtained from ten male patients. The TCI system (Diprifusor) was used to administer propofol.
The BIS index served to monitor the depth of anesthesia. The propofol dosing was adjusted to keep BIS level between 40 and 60.
A two-compartment model was used to describe propofol PK. The typical values of the central and peripheral volume of distribution,
and the metabolic and inter-compartmental clearance were VC = 24.7 l, VT = 112 l, Cl = 2.64 l/min and Q = 0.989 l/min. Delay of the
anesthetic effect, with respect to plasma concentrations, was described by the effect compartment with the rate constant for the distribution to the effector compartment equal to 0.240 min–1. The BIS index was linked to the effect site concentrations through a sigmoidal Emax model with EC50 = 2.19 mg/l. The body weight, age, blood pressure and gender were not identified as statistically
significant covariates for all PK/PD parameters. The population PK/PD model was successfully developed to describe the time
course and variability of propofol concentration and BIS index in patients undergoing surgery.
Key words:
propofol, aortic surgery, pharmacokinetics and pharmacodynamics
Introduction
Propofol total intravenous anesthesia (TIVA) is
a widely used technique to induce unconsciousness in
patients undergoing major aortic surgery. Vascular
surgery patients present a formidable challenge to the
practising intensivist. These patients are often at ad-
vanced age and carry significant cardiac, respiratory,
and renal co-morbidities [26]. Among different types
of non-cardiac surgery, peripheral vascular surgery is
likely to have the highest cardiac morbidity and overall mortality. The economic burden associated with
this complication is estimated to be $20 billion annually [26]. As the patients undergoing this type of sur-
Pharmacological Reports, 2012, 64,
113–122
113
gery are at higher risk during general anesthesia [23,
26], optimal dosing of administered drugs is of great
importance.
Propofol is a drug with narrow therapeutic index,
which may cause severe hypotension and hemodynamic instability during induction of anesthesia, especially if it is given in too large doses. Available propofol pharmacokinetic protocols, which are used for
target controlled infusions (TCI), were derived on the
basis of a relatively small number of healthy individuals [1, 19, 20]. However, the disposition as well as
the response to any given drug may be altered by
clinical conditions. During abdominal aortic surgery
many factors may potentially alter the pharmacokinetics (PK) and pharmacodynamics (PD) of propofol.
For example, the aortic cross-clamping affects the renal artery blood flow, either through direct interruption of the flow or through thromboembolic events.
The aortic cross-clamping affects the cardiac output
and organ flow during surgery [3, 10, 11, 23], thus vasopressors are given intraoperatively to prevent hypotension. In consequence, this may modify the clearance of drugs with a high hepatic extraction ratio, like
propofol. Propofol is a highly extracted drug. Therefore, its clearance is flow-dependent. For such drugs
changes in the cardiac output are expected to modify
the concentration and effects. It has been demonstrated
that propofol clearance in critically ill patients is decreased by 38% in patients with heart failure [16].
Propofol is a highly lipophilic drug, which tightly
binds to various plasma components, i.e., erythrocytes, hemoglobin and serum albumin [14]. During
abdominal aortic surgery patients are continuously infused with crystalloid and colloid fluids, which may
dilute the blood, have influence on the hematocrit and
in consequence on the free fraction of the drug. In the
study by Knibbe et al. [12] the body temperature and
serum triglycerides were found to influence the metabolic clearance of propofol. Hyper- and hypothermia,
metabolic changes and blood loss are common events
and complications during aortic surgery [23]. Thus, the
available propofol TCI systems, which are based on
data from healthy subjects, may fail in this group of patients. In literature there are limited data on PK/PD parameters of propofol during this type of surgery.
The aim of our study was to develop the PK/PD
model for propofol in patients undergoing abdominal
aortic surgery and to compare it to the models available in TCI systems as well as to the literature models
for healthy and ill subjects.
114
Pharmacological Reports, 2012, 64,
113–122
Subjects and Methods
After approval by the Research Ethics Committee at
the University of Medical Sciences (Poznañ, Poland)
and informed, written consent, ten male ASA physical
status III patients (50–75 years, 50–92 kg weight)
scheduled for elective abdominal aortic surgery were
included. The surgery was conducted under propofolfentanyl TIVA. The TCI system Diprifusor® (Astra
Zeneca, UK) was used to administer propofol. Pancuronium 0.1 mg/kg was administered to facilitate intubation and then as required. Fentanyl, 2–3 µg/kg, was
administered whenever inadequate analgesia was assessed throughout the surgery. The bispectral index
(BIS; A-2000, Aspect Medical System, Newton, MA,
USA) was used to measure the depth of anesthesia
and propofol dosing was adjusted to keep the BIS
level between 40 and 60. During the surgery, crystalloid and colloid fluids were infused according to the
standard protocol. After the surgery, the patients were
mechanically ventilated in intensive care unit (ICU)
settings until full recovery and propofol infusion was
maintained until extubation. Blood samples for measuring the plasma propofol concentration were drawn
from forearm veins before the propofol infusion, 1, 3,
5, 10, 15 and 30 min after the beginning of the infusion, then every 30 min until the end of anesthesia and
also after 1, 3, 5, 10, 15, 30, 60 90, 120 and 240 min
after the termination of propofol infusion. The blood
samples were transferred into heparinized tubes and
they were centrifuged immediately after collection.
Plasma was stored at 4°C. Propofol concentration in
the plasma was measured within 8 weeks by highperformance liquid chromatography with a fluorescence detector [7, 8]. The limit of quantification was
estimated at 50 ng/ml. The within-day coefficients of
variation were less than 10%.
PK/PD model
Population non-linear mixed-effect modeling was
done with Nonmem (Version 7.1.0, Icon Development Solutions, Ellicott City, MD, USA) and the Intel
Fortran Compiler 9.0. Nonmem runs were executed
with Wings for Nonmem (WFN711, http://wfn.
sourceforge.net). The first-order conditional estimation with interaction (FOCE) method was used. The
minimum value of the Nonmem objective function
(MOF), typical goodness of fit diagnostic plots, and
PK/PD of propofol in aortic surgery patients
Pawe³ Wiczling et al.
Fig. 1. Diagram of PK/PD model of propofol
evaluation of the precision of PK parameter and variability estimates were used to discriminate between
various models during the model-building process.
The Nonmem data processing and plots were done in
Matlab® Software version 7.0 (The MathWorks, Inc.,
Natick, MA, USA).
The schematic representation of the PK and PD
model is given in Figure 1. The free plasma propofol
concentration was described with a two-compartment
model (ADVAN3 TRANS4):
where BIS0 denotes the baseline BIS score (fully
awake). The Emax is the maximal effect fixed to 1 and
EC50 is the concentration for 50% decrease in BIS
score. The Ce denotes the effect compartment (biophase) concentration:
(3)
where keo denotes biophase distribution rate constant.
Inter-individual variability (IIV) for the PK/PD parameters was modelled assuming log normal distribution:
(1)
(4)
where CP and CT are the propofol concentrations in
the central and peripheral compartment, VC and VT denote the volume of the central and peripheral compartment, and Cl and Q denote the metabolic and
inter-compartmental clearance. The Input(t) reflects
the infusion rate and all extra boluses that were given
to individual patients
In the PD analysis the fixed individual PK parameters were used to generate drug concentration for
a given time point. Delay of the anesthetic effect, with
respect to plasma concentrations, was described with
the effect compartment. The time course of electroencephalographic (EEG) spectral frequency index (BIS)
was linked to the effect (biophase) site concentrations
through a sigmoidal Emax model:
(2)
where P is the individual parameter, qP is the typical
value of this parameter in the population, and hP is
a random effect for that parameter with the mean 0
and variance wP2. The observed concentration of propofol and BIS score were defined by the following
equations:
(5)
(6)
where CP and BIS are defined by the Eq. (1) and (2) of
the basic structural population model, eprop,C represents the proportional residual random errors of propofol concentrations and eprop,BIS represents the additive residual random of BIS scores. It was assumed
that e is normally distributed with the mean of 0 and
variances denoted by s2prop,C and s2add,BIS.
Pharmacological Reports, 2012, 64,
113–122
115
Covariates selection
Possible relationships between patient-specific covariates (body size, age, blood pressure and gender)
and the individual pharmacokinetic parameter estimates were explored by graphical analysis. During
the covariate search, the effect of each covariate was
examined by adding appropriate relationship into the
base model. The likelihood ratio was determined as
the difference in the objective function (MOF) of the
full model (with covariate) and reduced model (without covariate) after refitting the data. Since the difference in the minimum of the NONMEM objective
function (MOF) obtained for the two hierarchical
models (likelihood ratio) is approximately c2 distributed, the difference in MOF between models of 7.9
for one degree of freedom was considered to be statistically significant at p < 0.005 for the factor to be included in the base model.
Simulations
In literature there is a great variety of models used to
describe the PK/PD of propofol in healthy and ill pa-
2
ID: 1
ID: 2
ID: 3
ID: 4
ID: 5
ID: 6
ID: 7
ID: 8
ID: 9
ID: 10
tients. It makes the direct comparison of model parameters difficult, as the potential difference might be
a consequence of different model structure. In this
study, the final model has been compared with literature PK/PD models of propofol by means of simulations. We selected the following models: Schnider
[19], Bjornsson [5], Schuttler [21], Marsh [13] and
Peeters [17] and simulated concentrations and BIS index for a typical 40-year-old male of 70 kg and 170 cm
To standardize the comparison of the models, the
same infusion was used with the initial dose 2 mg/kg,
rate of infusion 0.2 mg/min/kg and duration of infusion 200 min.
The performance error (MDAPE)
The propofol concentration calculated with the TCI
system (target concentration) was recorded every time
a sample was taken. At each time-point the percentage performance error was calculated as: PE = 100 ·
(measured concentration-target concentration)/(target
concentration). For each subject a median absolute
performance error (MDAPE) was calculated as the
median of the absolute PEs. Similarly, a median per-
Fig. 2. Plot of observed (open circle),
population predicted (dotted line) and
individual predicted (solid line) propofol concentrations vs. time for the final
PK model
10
0
10
−2
10
2
10
0
Propofol Concentration, mg/l
10
−2
10
2
10
0
10
−2
10
2
10
0
10
−2
10
2
10
0
10
−2
10
0
5
10
15
20 0
Time, h
116
Pharmacological Reports, 2012, 64,
113–122
5
10
15
20
PK/PD of propofol in aortic surgery patients
Pawe³ Wiczling et al.
Tab. 1. Summary of the final population PK parameters and inter-subject and residual error variance estimates of propofol
Parameter, unit
Description
Estimate (% CV)
Fixed effect
qV, (l)
Volume of central compartment
24.7 (16.2)
qCl (l/min)
Elimination clearance
2.64 (48.2)
qVT (l)
Volume of peripheral compartment
112 (24.6)
qQ (l/min)
Distribution clearance
0.989 (11.2)
w2VC (%)
Inter-individual variability of VC
40.2 (32.8)
w2CL (%)
w2VT (%)
w2Q (%)
Inter-individual variability of CL
38.2 (48.2)
Inter-individual variability of VT
90 (25.2)
Inter-individual variability of Q
0 Fixed
Proportional residual error variability
39.5 (6.8)
Inter-individual variability
Residual variability
s2prop,Cp (%)
formance error (MDPE) was calculated as the median
of PEs. MDAPE indicates the inaccuracy of TCI
while the MDPE reflects the bias in achieving the target concentration [6, 24].
Results
The pharmacokinetic model of propofol in patients
scheduled for abdominal aortic surgery was based on
170 samples from 10 surgery patients (10–21 propofol concentration measurements per patient). A twocompartment pharmacokinetic model without any covariates best described the observations. The use of
a two-compartment model is in agreement with the
literature models of critically ill patients undergoing
long term sedation [4, 12, 17]. On the contrary, for
healthy subjects and very rich data sets even three- or
four-compartment models have been identified [5, 21,
22]. In this study, it was impossible to distinguish all
the fast disposition processes due to limited sampling,
especially within the first 3 min after the beginning
and end of the infusion.
The propofol PK data along with model predictions
is presented in Figure 2. This plot shows that the final
PK model describes the measured concentrations
well. The parameter estimates of the proposed PK
model are listed in Table 1. All parameters were estimated with high precision (coefficient of variations
lower than 50%). The typical value of the volume of
the central and peripheral compartment was 24.7 l and
112 l. The metabolic and distribution clearances were
estimated at 2.64 l/min and 0.989 l/min. Except Q, the
inter-individual variability of all parameters was high,
ranging from 40% to 90%. The body weight, age,
blood pressure and gender were not identified as significant covariates during the model building process.
It was most likely the consequence of a small number
of patients with similar demographic characteristics.
The literature metabolic clearances for a typical adult
male patient of 70 kg ranged from 1.23 to 2.05 l/min
[5, 13, 17, 19, 21,], and were much lower than the
value of clearance estimated in this study. The simulations presented in Figure 3 show that a higher clearance led to lower concentrations than those predicted
on the basis of literature models. It has a big consequence for the TCI system, where the dosing of the
patients is based on the model predicted concentrations. In our study, the TCI inaccuracy (MDAPE) was
estimated at 30.5% (14.9 – 57.9%), whereas the mean
bias (MDPE) value was –11.0% (–37.0 – 19.6%).
The pharmacodynamic part of the model was based
on 363 samples from 10 surgery patients (30–47 BIS
index values per patient). A sigmoidal Emax model
linking the BIS index with biophase concentration
was used to describe the experimental data. This type
of model has typically been used in literature to describe various indexes used to monitor the depth of
anesthesia [4, 5, 17].
Pharmacological Reports, 2012, 64,
113–122
117
2
10
Our Model
Bjornsson
0
10
0
50
100
150
200
250
300
2
350
400
Our Model
Peeters
10
0
Propofol Concentration, mg/l
10
0
50
100
150
200
250
300
2
10
350
Fig. 3. The comparison of our model
and the literature pharmacokinetic
models of propofol (Schnider [19],
Bjornsson [5], Schuttler [21], Marsh
[13] and Peeters [17]) for a 40-year-old
male of 70 kg and 170 cm. The following infusion was used for simulations:
initial dose 2 mg/kg, rate of infusion
0.2 mg/min/kg and duration of infusion
200 min
400
Our Model
Schnider
0
10
0
50
100
150
200
250
300
2
350
400
Our Model
Schuttler
10
0
10
0
50
100
150
200
250
300
350
400
2
10
Our Model
Marsch
0
10
0
50
100
150
200
Time, min
250
300
ID: 1
ID: 2
ID: 3
ID: 4
ID: 5
ID: 6
ID: 7
ID: 8
ID: 9
ID: 10
350
400
Fig. 4. Plot of observed (open circle),
population predicted (dotted line) and
individual predicted (solid line) propofol concentrations vs. time for the final
PD model
100
50
0
100
Bispectral Index, BIS
50
0
100
50
0
100
50
0
100
50
0
0
5
10
15
20 0
Time, h
118
Pharmacological Reports, 2012, 64,
113–122
5
10
15
20
PK/PD of propofol in aortic surgery patients
Pawe³ Wiczling et al.
Tab. 2. Summary of the final population PD parameters and inter-subject and residual error variance estimates of propofol
Parameter, unit
Description
Estimate (% CV)
qBaseline (–)
Baseline BIS score
97 (3.4)
qEmax (–)
Maximum decrease of BIS from baseline
1 Fixed
Rate constant for distribution from effect compartment
0.240 (36.7)
qg (–)
Shape factor
1 Fixed
qC50 (mg/l)
Effect site concentration needed to reach 50% of Emax
2.19 (16.8)
Inter-individual variability of EC50
41.7 (37.4)
Additive residual error variability
14 (6.8)
Fixed effect
qkeo
(min-1)
Inter-individual variability
w2EC50 (%)
Residual variability
s2add,BIS (%)
The propofol BIS index along with model predictions is presented in Figure 4. This plot shows that the
final PD model describes the experimental data well.
All the estimated PD parameters are listed in Table 2.
This baseline BIS value is 97, which corresponds to
a fully awake patient. The high biophase drug concentrations were assumed to produce BIS index zero
(maximal anesthetic effect). The biophase distribution
rare constant equalled 0.240 min–1. The estimated
EC50 (2.19 mg/l) was in agreement with the literature
values of 1.98 mg/l for ICU patients [4] and 1.84 mg/l
for the patients with Sequential Organ Failure Assessment (SOFA) score equal to 15 [17]. For healthy subjects the EC50 was found to be higher (2.55 mg/l) [5].
The EC50 was the only PD parameter for which it was
possible to estimate inter-individual variability (42%).
The predicted BIS index changes after standard infusion protocol are given in Figure 5. Interestingly,
the BIS index profiles are considerably different for
all the presented simulations. The Björnsson model
(healthy patients) predicts the lowest, whereas Peeters
model the highest BIS values for the same infusion
protocol. The BIS values of this study are between the
literature model predictions. Taking into consideration the dependence of EC50 on the SOFA physical
status in the Peeters model, the worsening of ICU
patient conditions from the SOFA index of 3 to 15
changes the EC50 from 4.48 mg/l to 1.84 mg/l. It explains our low EC50 value very well, as the patients in
this study were in serious conditions (ASA III).
Discussion
In this study we demonstrated a population PK and
PD model of propofol in patients scheduled for abdominal aortic surgery. This model was compared
with existing models, especially those used in TCI devices (Schnider and Marsh model). Since the group of
patients is severely ill (ASA III), it is desirable to
compare the TCI model predictions, which were based
on healthy individuals with the actual experimental
data.
Intuitively, the propofol models should be different
for healthy and ill patients, especially for patients undergoing serious surgical procedures. The literature
shows that in ICU, for patients suffering from reduced
myocardial contractility, the clearance was slower by
as much as 38% in comparison with critically ill patients without heart failure. Simultaneously, the pharmacodynamics of propofol depended on the severity
of the patient’s state expressed according to the SOFA
score [17]. In the case of our study, elimination clearance is higher than the literature data in healthy and ill
patients. These results are difficult to explain and
might be attributed to the use of an opioid and other
co-administered drugs, high blood loss, administration of fluids, etc. Rapid fluid infusion therapy to treat
hypovolemia in anesthetized patients is a common
practical regimen in daily clinical settings. Adachi
et al. [2] studied the effects of rapid fluid infusion
Pharmacological Reports, 2012, 64,
113–122
119
Fig. 5. The comparison of our model
and the literature pharmacodynamic
models of propofol (Bjornsson [5] and
Peeters [17]) for a 40-year-old male of
70 kg and 170 cm. The following infusion
was used for simulations: initial dose
2 mg/kg, rate of infusion 0.2 mg/
min/kg and duration of infusion
200 min. For the Peeters et al. model,
profiles of patient with two different Sequential Organ Failure Assessment
(SOFA) scores are presented
100
Our Model
Bjornsson
80
60
40
20
0
50
100
150
200
250
300
350
400
200
Time, min
250
300
350
400
BIS
0
100
Our Model
Peeters (SOFA = 15)
Peeters (SOFA = 3)
80
60
40
20
0
0
50
100
150
therapy on propofol concentrations and BIS value
during general anesthesia in ASA I and II patients and
they found that the rapid infusion therapy significantly decreases propofol concentrations without any
changes in the BIS value. Thus, the authors concluded
that this phenomenon may be of little clinical importance. However, in contrast to our study, they examined relatively healthy subjects. We assessed the PK
and PD of propofol in ASA III patients undergoing
abdominal aortic surgery, where large volumes of
crystalloid and colloid fluids are continuously infused
to the patients. Therefore, these changes may be exacerbated. Adachi et al. [2] found that after fluid infusion therapy both the cardiac output and cardiac index
increased. It is known that propofol clearance is flow
dependent [16]. Therefore, any increase in the cardiac
output may lead to an enhanced elimination process
resulting in lower plasma concentrations of this drug.
Unfortunately, we did not record the cardiac output in
our study, so we are not able to confirm this hypothesis. On the other hand, in the case of pharmacodynam-
120
Pharmacological Reports, 2012, 64,
113–122
ics, the obtained EC50 corresponds very well to the literature values of seriously ill patients and is lower
than the one observed in healthy subjects. Thus, lower
propofol concentrations, simultaneously with the
greater CNS sensitivity to propofol observed in our
patients, may compensate each other, leading to no
dosing changes required in this group of patients. Our
results may therefore partly explain the ones obtained
by Adachi et al. [2].
The obtained results suggest that the actual plasma
concentrations of propofol are lower than predicted
from the Schnider or Marsh model. We used the
Diprifusor TCI system in our study, which is based on
the Marsh model [13]. The tendency of the TCI system to overestimate the propofol concentrations in
plasma was also demonstrated, as shown by the negative bias value (MDPE) obtained in our study. The
mean inaccuracy of the TCI system (MDAPE) was
estimated at 30.5%. Swinhoe et al. [24] evaluated the
MDAPE of the TCI Dipriphusor system in ASA I-III
(mainly ASA II) status patients and obtained the value
of 24.1%. Thus, the higher error obtained in our study
PK/PD of propofol in aortic surgery patients
Pawe³ Wiczling et al.
may be connected with the disease severity of our patients (all were ASA III status). Another factor which
should be taken into account as the cause of high
interindividual variability in propofol pharmacokinetics is the polymorphism in genes coding for metabolizing enzymes. Propofol is mainly metabolized by
glucuronosyltransferase UGT1A9. The secondary
metabolic cascade mediated mainly by CYP2B6 and
to lesser extent by CYP2C9 leads to the production of
4-hydroxypropofol that is further metabolized by conjugation. Over 28 variant alleles and several subvariants have been described for CYP2B6 and it appears
to be one of the most polymorphic cytochrome P-450
genes in humans. Also, for UGT1A9 gene several
polymorphisms including mutations in the gene promoter have been described [15, 18]. Recently it has
been demonstrated that D256N mutation lowers
UGT1A9 activity for propofol, suggesting that carriers of D256N may be at risk of suffering adverse effects of propofol [25]. Propofol acts by activation of
GABAA receptor. Also mutations in the coding genes
may potentially be of some importance as far as the
inter-patient variability in the pharmacodynamics of
propofol is concerned. However, the data in this field
are limited. Until now the influence of polymorphism
in genes for GABAA on the propofol anesthesia has
not been confirmed [9].
The small number of patients used in our study to
build a PK model needs to be increased in order to obtain results applicable to a broader population of patients encountered in the clinic. A larger number of
patients would give a possibility to build a more comprehensive PK/PD model with various covariates,
such as weight, age, gender, genomic data, or health
status. A model developed in this way could be used
to control the TCI instruments for precise dosage of
propofol.
To conclude, a population PK/PD model was successfully developed to describe the time course and
variability of propofol concentration and BIS index in
ASA III patients undergoing aortic surgery. The results suggest that the models currently used in the TCI
system should be used with caution, as the real concentration and BIS index might be different than predicted by currently used models. Additional experiments are needed to fully understand the interplay between all factors affecting propofol PK/PD, especially
the administration of fluids, the co-administration of
other drugs and surgical procedure affecting blood
flow through eliminating organs.
Acknowledgment:
One of the authors (Pawe³ Wiczling) was supported by a grant
from Iceland, Liechtenstein, and Norway through the EEA Financial
Mechanism via Homing Program from the Foundation for Polish
Science.
References:
1. Absalom AR, Mani V, De Smet T, Struys MM: Pharmacokinetic models for propofol-defining and illuminating
the devil in the detail. Br J Anaesth, 2009, 103, 26–37.
2. Adachi YU, Satomoto M, Higuchi H, Watanabe K:
Rapid fluid infusion therapy decreases the plasma concentration of continuously infused propofol. Acta Anaesthesiol Scand, 2005, 49, 331–336.
3. Beattie C, Moores C, Thomson AJ, Nimmo AF: The effect of anesthesia and aortic clamping on cardiac output
measurement using arterial pulse power analysis during
aortic aneurysm repair. Anesthesia, 2010, 65,
1194–1199.
4. Bienert A, Kusza K, Wawrzyniak K, Grzeskowiak E,
Kokot ZJ, Matysiak J, Grabowski T et al.: Assessing circadian rhythms in propofol PK and PD during prolonged
infusion in ICU patients. J Pharmacokinet Pharmacodyn,
2010, 37, 289–304.
5. Bjornsson MA, Norberg A, Kalman S, Karlsson MO,
Simonsson US: A two-compartment effect site model
describes the bispectral index after different rates of propofol infusion. J Pharmacokinet Pharmacodyn, 2010, 37,
243–255.
6. Cavaliere F, Conti G, Moscato U, Meo F, Pennisi MA,
Costa R, Proietti R: Hypoalbuminaemia does not impair
Diprifusor performance during sedation with propofol.
Br J Anaesth, 2005, 94, 453–458.
7. Dawidowicz AL, Fornal E, Fijalkowska A: Determining
the influence of storage time on the level of propofol in
blood samples by means of chromatography. Biomed
Chromatogr, 2000, 14, 249–255.
8. Dawidowicz AL, Kalitynski R: HPLC investigation of
free and bound propofol in human plasma and cerebrospinal fluid. Biomed Chromatogr, 2003, 17, 447–452.
9. Iohom G, Ni Chonghaile M, O’Brien JK, Cunningham
AJ, Fitzgerald DF, Shields DC: An investigation of potential genetic determinants of propofol requirements
and recovery from anesthesia. Eur J Anaesthesiol, 2007,
24, 912–919.
10. Kakinohana M, Nakamura S, Fuchigami T, Miyata Y,
Sugahara K: Influence of the descending thoracic aortic
cross clamping on bispectral index value and plasma
propofol concentration in humans. Anesthesiology, 2006,
104, 939–943.
11. Kim GS, Ahn HJ, Kim WH, Kim MJ, Lee SH: Risk factors for postoperative complications after open infrarenal
abdominal aortic aneurysm repair in Koreans. Yonsei
Med J, 2011, 52, 339–346.
12. Knibbe CA, Zuideveld KP, DeJongh J, Kuks PF, Aarts
LP, Danhof M: Population pharmacokinetic and pharmaPharmacological Reports, 2012, 64,
113–122
121
13.
14.
15.
16.
17.
18.
19.
122
codynamic modeling of propofol for long-term sedation
in critically ill patients: a comparison between propofol
6% and propofol 1%. Clin Pharmacol Ther, 2002, 72,
670–684.
Marsh B, White M, Morton N, Kenny GN: Pharmacokinetic model driven infusion of propofol in children.
Br J Anaesth, 1991, 67, 41–48.
Mazoit JX, Samii K: Binding of propofol to blood components: implications for pharmacokinetics and for pharmacodynamics. Br J Clin Pharmacol, 1999, 47, 35–42.
Mo SL, Liu YH, Duan W, Wei MQ, Kanwar JR, Zhou
SF: Substrate specificity, regulation, and polymorphism
of human cytochrome P450 2B6. Curr Drug Metab,
2009, 10, 730–753.
Peeters MY, Aarts LP, Boom FA, Bras LJ, Tibboel D,
Danhof M, Knibbe CA: Pilot study on the influence of
liver blood flow and cardiac output on the clearance of
propofol in critically ill patients. Eur J Clin Pharmacol,
2008, 64, 329–334.
Peeters MY, Bras LJ, DeJongh J, Wesselink RM, Aarts
LP, Danhof M, Knibbe CA: Disease severity is a major
determinant for the pharmacodynamics of propofol in
critically ill patients. Clin Pharmacol Ther, 2008, 83,
443–451.
Restrepo JG, Garcia-Martin E, Martinez C, Agundez JA:
Polymorphic drug metabolism in anesthesia. Curr Drug
Metab, 2009, 10, 236–246.
Schnider TW, Minto CF, Gambus PL, Andresen C,
Goodale DB, Shafer SL, Youngs EJ: The influence of
method of administration and covariates on the pharma-
Pharmacological Reports, 2012, 64,
113–122
20.
21.
22.
23.
24.
25.
26.
cokinetics of propofol in adult volunteers. Anesthesiology, 1998, 88, 1170–1182.
Schnider TW, Minto CF, Shafer SL, Gambus PL,
Andresen C, Goodale DB, Youngs EJ: The influence
of age on propofol pharmacodynamics. Anesthesiology,
1999, 90, 1502–1516.
Schuttler J, Ihmsen H: Population pharmacokinetics of
propofol: a multicenter study. Anesthesiology, 2000, 92,
727–738.
Shafer SL: Advances in propofol pharmacokinetics and
pharmacodynamics. J Clin Anesth, 1993, 5, 14S–21S.
Shine TS, Murray MJ: Intraoperative management of
aortic aneurysm surgery. Anesthesiol Clin North
America, 2004, 22, 289–305, vii.
Swinhoe CF, Peacock JE, Glen JB, Reilly CS: Evaluation of the predictive performance of a ‘Diprifusor’ TCI
system. Anesthesia, 1998, 53 Suppl 1, 61–67.
Takahashi H, Maruo Y, Mori A, Iwai M, Sato H,
Takeuchi Y: Effect of D256N and Y483D on propofol
glucuronidation by human uridine 5’-diphosphate glucuronosyltransferase (UGT1A9). Basic Clin Pharmacol
Toxicol, 2008, 103, 131–136.
Venkataraman R: Vascular surgery critical care: perioperative cardiac optimization to improve survival.
Crit Care Med, 2006, 34, S200–207.
Received: August 2, 2011; in the revised form: October 20, 2011;
accepted: November 4, 2011.