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Anesthesiology:
Clinical Investigations

Population Pharmacokinetics of Propofol: A Multicenter Study

Schüttler, Jürgen M.D.*; Ihmsen, Harald M.Sc.†

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Abstract

Background: Target-controlled infusion is an increasingly common type of administration for propofol. This method requires accurate knowledge of pharmacokinetics, including the effects of age and weight. The authors performed a multicenter population analysis to quantitate the effects of covariates.
Methods: The authors analyzed 4,112 samples of 270 individuals (150 men, 120 women, aged 2–88 yr, weighing 12–100 kg). Population pharmacokinetic modeling was performed using NONMEM (NONMEM Project Group, University of California, San Francisco, CA). Inter- and intraindividual variability was estimated for clearances and volumes. The effects of age, weight, type of administration and sampling site were investigated.
Results: The pharmacokinetics of propofol were best described by a three-compartment model. Weight was found to be a significant covariate for elimination clearance, the two intercompartmental clearances, and the volumes of the central compartment, the shallow peripheral compartment, and the deep peripheral compartment; power functions with exponents smaller than 1 yielded the best results. The estimates of these parameters for a 70-kg adult were 1.44 l/min, 2.25 l/min, 0.92 l/min, 9.3 l, 44.2 l, and 266 l, respectively. For patients older than 60 yr the elimination clearance decreased linearly. The volume of the central compartment decreased with age. For children, all parameters were increased when normalized to body weight. Venous data showed a decreased elimination clearance; bolus data were characterized by increases in the volumes of the central and shallow peripheral compartments and in the rapid distribution clearance (Cl2) and a decrease in the slow distribution clearance (Cl3).
Conclusions: Pharmacokinetics of propofol can be well described by a three-compartment model. Inclusion of age and weight as covariates significantly improved the model. Adjusting pharmacokinetics to the individual patient should improve the precision of target-controlled infusion and may help to broaden the field of application for target-controlled infusion systems.
PROPOFOL is widely used for both induction and maintenance of general anesthesia. Its tremendous body uptake as well as the rapid elimination caused by a huge apparent volume of distribution and a high clearance make propofol the best controllable intravenous hypnotic from a pharmacokinetic point of view. 1
Based on the pharmacokinetic properties of propofol, drug-administration schemes have been developed that allow a defined concentration to be rapidly achieved and held constant. Target-controlled infusion was introduced for research purpose years ago, with computer-driven infusion pumps using two- or three-compartment models. 2–6 A commercial target-controlled infusion system for propofol is now available (Diprifusor-TCI, Zeneca Pharmaceuticals, Macclesfield, UK). Such systems require appropriate pharmacokinetic data to ensure that the desired concentration is achieved. In the past 15 yr, several studies on the pharmacokinetics of propofol have been performed to derive pharmacokinetic parameters not only for adult patients 7–16 but also for elderly patients, 17 children, 6,18–21 and patients with diseases influencing propofol metabolism. 22,23 Propofol has also been used for sedation of patients in intensive care medicine. 24–26
In this study we performed a population pharmacokinetic analysis with data from five research groups (J. Schüttler, 7 University of Erlangen-Nuremberg, Germany; I. Cockshott, 17,18 Zeneca Pharmaceuticals, UK; P. Glass, 27 Duke University, Durham, NC; M. White, 3,6 Academisch Ziekenhuis Leiden, The Netherlands; and S. Shafer, 21 Stanford University, Palo Alto, CA). Population analysis allows us to quantitate the variability of the parameters between individuals (interindividual) as well as within any patient (intraindividual) and to investigate the influence of covariates. The aims of this study were to estimate the pharmacokinetics of propofol with special respect to the covariates age, body weight, and gender and to evaluate the inter- and intraindividual variability.
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Materials and Methods

Samples
Table 1
Table 1
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We analyzed 4,112 concentrations of 270 individuals. Two-hundred fifty-six were patients undergoing surgery; 14 were volunteers (table 1). Propofol (Disoprivan or Diprivan; Zeneca Pharmaceuticals, Macclesfield, United Kingdom) was administered as a bolus or using continuous infusion, with the infusions computer-controlled in groups 2 and 4–8. The sampling period was in the range of 0.25–24 h. Propofol concentrations were measured from arterial (groups 1, 2, 3, and 6) and venous samples (groups 4, 5, and 7–9). Propofol concentrations were measured in whole blood (groups 1–8) and plasma (group 9) using high-pressure liquid chromatography (groups 1–6 and 9) and gas–liquid chromatography (groups 7 and 8) with fluoreometric or electrochemical detection. 28
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Pharmacokinetic Analysis
The propofol concentration data were analyzed with NONMEM (version V, double precision). 29 NONMEM allows multiple nonlinear regression of population data simultaneously, which means that not only the mean kinetic parameters but also inter- and intraindividual variability can be estimated. In addition, it is possible to quantitate the influence of covariates such as body weight, age, and gender. Another advantage of the population approach is that the number of observations per individual can be kept relatively small.
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Pharmacokinetic Model
Pharmacokinetics were assumed to be linear with two- or three-compartments and elimination from the central compartment. The elimination clearance (Cl1), the intercompartmental clearances (Cl2, Cl3), and the volumes of the central compartment (V1), the shallow peripheral compartment (V2), and the deep peripheral compartment (V3) were chosen as pharmacokinetic parameters to be estimated. To investigate the effect of covariates, additional parameters were successively included in the model (see Regression Procedure).
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Interindividual and Intraindividual Variability
Equation 1
Equation 1
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One major advantage of NONMEM is that interindividual and intraindividual variability can be quantified. The interindividual variability describes the variance of a pharmacokinetic parameter among different subjects. We estimated the variability of all clearances and volumes using a log-normal model. This means for the i th individual MATH 1
Equation 2
Equation 2
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in which θi is the individual value of the parameter θ, θ is the mean population value of this parameter, and ηi is a random variable with mean zero and variance ωη2. For the intraindividual variability that describes the residual errors resulting from assay errors, time-recording inaccuracy, model misspecification, and so forth, we used a constant coefficient of variation model:MATH 2
in which cij is the j th measured concentration of the i th individual and cpij is the corresponding predicted concentration. Again, εij is a random variable with mean zero and variance ςε2. NONMEM estimates the mean pharmacokinetic parameters of the population, the interindividual variances ωη2, and the intraindividual variances ςε2, including estimates of the standard errors and correlation coefficients for all parameters.
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Regression Procedure
Equation 3
Equation 3
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The complete data set was randomly divided into two subsets containing 135 individuals each. The subjects of each subset were comparable with respect to age, weight, gender, sampling site, and administration mode. The first subset was used for the development of the model as described subsequently. The predictive accuracy of the model was then tested with the second subset. In a first step, individual Bayesian estimates of the pharmacokinetic parameters of each individual were obtained using a three-compartment model without any covariates. The estimated parameters were plotted independently against body weight, age, and gender to identify the influence of the covariates and the shape of the parameter–covariate relationships. Subsequently, we performed a population analysis of all data, beginning with a simple model without any covariates and successively incorporating additional parameters. The effects of covariates were tested for statistical significance using the NONMEM objective function (which is −2 · log-likelihood) and the standard errors of the additional parameters. An additional parameter was included in the model if the decrease of the objective function was at least 7.8 (P < 0.005) and the 95% confidence interval of the additional parameter (mean ± 2 · SE) did not include zero (null hypothesis value). In addition, the inter- and intraindividual variabilities should decrease as an additional covariate parameter explains the difference between individuals. To exclude covariate correlations, we tested whether deletion of any additional parameter from the full model resulted in a decreased goodness of fit. To estimate the accuracy of the model we calculated the weighted residual (WR) and the absolute weightedresidual (AWR) for each sample:MATH 3
in which cij is the j th measured concentration of the i th individual and cpij denotes the corresponding predicted value. The median population values of WR (median weighted residual, MWR) and AWR (median absolute weighted residual, MAWR) were used as overall measures for goodness of fit.
Finally, we calculated MWR and MAWR for the remaining subset of individuals who were not included during model development, using the estimated parameters of the full model. This gives additional information about the ability of the final model to predict propofol concentrations.
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Simulations
To illustrate the pharmacokinetic findings, various simulations were carried out. Using the estimated parameters we calculated the time for a 50% decrease in concentration after continuous infusion (context-sensitive half-time). 30 To show the effect of age on dosing we computed the infusion rates necessary to maintain a defined concentration. The interindividual variability was illustrated by calculating the context-sensitive half-time for a population of 100 subjects whose pharmacokinetic parameters were log-normally distributed with means and variances as estimated for the full model. To demonstrate the influence of the administration mode (bolus vs. infusion) we simulated the concentration course after a bolus dose using the kinetic parameters for bolus and infusion, respectively. All simulations were performed with software written by the authors.
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Results

Fig. 1
Fig. 1
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Fig. 2
Fig. 2
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The individual estimates revealed an influence of body weight and age on all clearances, V1, and V2; V3 was almost constant in all subjects. As an example, figure 1 shows the individual estimates of Cl1 as a function of body weight. The shapes of the relationships suggested that body weight should not be incorporated into the model in a linear fashion but as a power function with a positive exponent smaller than one. V1 was almost constant in adults but decreased in children. If divided by the body weight, V1 showed a clear relationship with age, which could be described by a power function with negative exponent. The effect of age on Cl1 was a linear decrease for patients older than 60 yr. The predictive accuracy of the individual Bayesian estimates was quite high (MWR = −1.5%, MAWR = 9.6%;fig. 2).
Table 2
Table 2
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Table 3
Table 3
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In the subsequent population analysis, these effects were modeled by incorporating additional parameters. The results of this procedure are shown in table 2, in which −2LL denotes the value of the objective function (−2 log-likelihood), describing the goodness of fit. As mentioned previously, a decrease of −2 log-likelihoods means an improvement of fit. Significant effects were retained in the subsequent regressions. The pharmacokinetic parameters of the final model are shown in table 3.
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Number of Compartments
Initially, a simple two-compartment model was assumed, but the resulting fit was quite poor. A three-compartment model markedly improved the fit, because of the long sampling period in some groups (1, 4, and 5).
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Influence of Covariates
As suggested from the individual estimates, we found effects of body weight on Cl1, Cl2, Cl3, V1, and V2. The influence was best modeled by a power function with an exponent smaller than 1. As an example, table 2 shows the results for a simple weight normalization of Cl1 (model 3) and the power function (model 4). This means that the weight-normalized parameter (parameter divided by body weight) increases with decreasing weight (e.g., for children). V3 did not vary with age and body weight. The elimination clearance decreased linearly in individuals older than 60 yr. The volume of the central compartment decreased with age if divided by body weight. This led to a worse fit if V1 was modeled weight-proportionally (model 9, table 2). Inclusion of age and weight as a power function, however, improved the model (model 10, table 2). No influence of gender could be found for the analyzed subjects.
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Influence of Sampling Site
An influence of different sampling sites was found only for Cl2, which was smaller for venous samples; no other parameter was altered significantly.
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Influence of Mode of Administration
Nearly all parameters with the exception of Cl1 and V3 were found to be altered with bolus administration compared with infusion data. Whereas V1, V2, and Cl2 were larger than after infusion, Cl3 was decreased.
Fig. 3
Fig. 3
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Fig. 4
Fig. 4
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The estimates of all parameters and their standard errors are summarized in table 3. Fixing of any additional parameter to zero led to a significant decrease in goodness of fit (increase of −2 log-likelihood), indicating that all additional parameters were required. Calculation of the weighted residuals revealed median values of −3.4% (MWR) and 24.9% (MAWR) for the first subset. The prediction errors for the second subset calculated with the estimated kinetic parameters were similar (MWR = −0.4%, MAWR = 25.5%). Figure 3 shows the predicted concentrations, as calculated with the parameters derived from the first subset, plotted against the measured concentrations of the second subset. The ratio of measured:predicted concentration was calculated for each sample and plotted against time for each individual (fig. 4).
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Simulations
Table 4
Table 4
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Fig. 5
Fig. 5
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Fig. 6
Fig. 6
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Fig. 7
Fig. 7
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Fig. 8
Fig. 8
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We performed several simulations for five typical individuals (child, lean adult, average adult, obese adult, and elderly) using the estimated pharmacokinetic parameters of the full model (table 4). Figure 5 shows that the propofol infusion rate maintains a propofol concentration of 1 μg/ml for 2 h. The total doses, including the loading dose, were 3.7 mg · kg−1 · h−1 for the child, 2.6 mg · kg−1 · h−1 for the lean adult, 2.3 mg · kg−1 · h−1 for the average adult, 1.9 mg · kg−1 · h−1 for the obese adult, and 1.5 mg · kg−1 · h−1 for the elderly individual. The context-sensitive half-times for these five individuals are depicted in figure 6. The half-times are nearly the same for the child and the adults but markedly increased for the 80-yr-old subject. Figure 7 depicts the context-sensitive half-times for 100 individuals, calculated with kinetic parameters that were log-normally distributed with the mean values of a 30-yr-old adult with average weight (table 4) and variances as estimated for the full model (table 3). Figure 8 shows the effect of the administration mode. The concentration course after a bolus dose of 100 mg propofol was calculated using the kinetic parameters obtained from bolus and infusion data, respectively. Use of the infusion kinetics leads to an overestimation of the concentration during the first 10 min and an underprediction during the following 4 h.
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Discussion

We analyzed a quite unhomogeneous population with different modes of administration (bolus dose and continuous infusion), different sampling sites (venous and arterial), and a wide range of ages and weights. Therefore we had to incorporate additional parameters to describe all effects of covariates on the pharmacokinetics of propofol. The final model was able to describe the pharmacokinetics of the population with sufficient precision, as indicated by the values of MWR and MAWR for both data subsets. A precision of about 25% is typical for pharmacokinetic models. 31–33 The plot of predicted versus measured concentrations (fig. 3), however, shows a good correlation for concentrations up to 8 μg/ml, but a lack of fit for higher concentrations in which the model underestimates the measured concentration. Consideration should be given to the fact that the model for intraindividual variability assumes that the error increases with increasing concentrations, but this error should be centered around zero, whereas we observed only a positive deviation (measured > predicted). This underestimation may indicate nonlinear pharmacokinetics of propofol, in the sense that the total body clearance decreases with increasing concentration. Nonlinear pharmacokinetics of propofol have been investigated previously, with controversial results. Coetzee et al.31 and Vuyk et al.32 suggested that propofol may have nonlinear kinetics; Bailey et al.34 and Schnider et al.35 did not find any indication of nonlinearity. It is known that propofol reduces liver blood flow, particularly at high concentrations such as are found shortly after bolus administration. 36 The very high concentrations in our data were achieved with continuous infusion, which also may cause a reduced blood flow and therefore a reduction of clearance. One general problem with compartment models is that they assume instantaneous mixing in the central compartment, which is obviously a simplification. Major et al. found considerable differences between venous and arterial samples during the first 60 s after administration of a propofol bolus dose, indicating that instantaneous mixing does not occur. 37 Therefore, it is possible that incomplete mixing in case of high infusion rates causes unexpectedly high propofol concentrations.
Whereas there was only a slight effect of the sampling site, the mode of administration (bolus vs. infusion) did significantly affect the pharmacokinetics (fig. 8). Similarly, Schnider et al. reported an overestimation of the early concentrations after bolus administration, followed by an underprediction using kinetic parameters evaluated from infusion data. 35 A possible reason for this phenomenon may be model misspecification, because the conventional compartment model assumes instantaneous mixing and does not consider recirculation effects. On the other hand, it must be taken into account that the sampling in bolus studies is quite different from that used in infusion studies, in which there are not so many samples in the very early time and therefore less information about the initial distribution process. At least the bolus data that were included in our analysis were characterized by a very long sampling time. Therefore, it is quite reasonable that Cl3 was reduced compared with infusion pharmacokinetics, which means a slowed transfer back from the deep third compartment and in consequence higher concentrations in the terminal phase as shown in figure 8.
In addition, it should be considered that differences in the propofol assays may be a source of intraindividual error. All propofol concentrations were measured with assays based on the same method, 28 but with some modifications. In groups 7 and 8, propofol was analyzed with gas–liquid chromatography rather than high-pressure liquid chromatography, which was used for all other data. Analysis of group 9 used propofol plasma concentrations; the propofol concentrations of the remaining groups were measured in whole blood. Fan et al. did not find significant differences between propofol concentrations measured with high-pressure liquid chromatography and gas chromatography, respectively, but the plasma concentrations during infusions were higher than those from whole blood. 38
The estimates of the pharmacokinetic parameters for an adult as revealed in this study are similar to those found by other investigators, 5,8–11,15 with the exception of V1, which was smaller in our analysis. The central volume is more difficult to determine than the clearance, because it depends on the mode of administration, the sampling site, and the number of compartments. The very first concentration values after the start of administration are essential for the consistent estimation of the central volume. Because we had only few data from the first minutes, and because these were mainly from bolus administration, there might be a model misspecification.
One major aim of the present study was to quantitate the effect of covariates on the pharmacokinetics of propofol. Body weight was obviously the covariate that influenced all parameters with the exception of V3. Interestingly, the influence of weight was best described by a power function; in most previously published models the pharmacokinetic parameters were weight-proportional. 5,6,21 The combination of children and adults in our data may explain these results, because other authors analyzed more homogenous groups of only adults or children. The power function for weight may therefore describe not only the influence of body weight but also partly the effect of age. This is supported by the fact that for nearly all parameters for which the influence of body weight was modeled as a power function (Cl1, Cl2, Cl3, and V2), no additional parameters for children were necessary. All estimated exponents of the power functions were smaller than 1, which means that the corresponding parameters were higher for children if normalized for body weight, as reported by other authors. 18,20,21 For the central volume of distribution (V1), a power function for weight and age revealed the best results. This also means an increase of the weight-normalized V1 for children. In subjects older than 60 yr, we found a marked linear decrease of the elimination clearance Cl1 and a slight decrease of V1, which was best described by a power function with a negative exponent. The reduction of the elimination clearance and V1 in elderly have also been observed in other studies. 17 Although weight and age are correlated, inclusion of both covariates improved the fit significantly compared with inclusion of only weight or age. Particularly for V1, simple weight normalization led to a worse fit, but the inclusion of weight and age improved the fit. In several studies on population pharmacokinetics, lean body mass was found to be a significant covariate for pharmacokinetics. 35,39 Unfortunately, we could not model the effect of lean body mass, because we had not the heights of all subjects. Furthermore, a formula for lean body mass of children is not available. For those adults whose heights were known, the individual pharmacokinetic parameters did not correlate better with lean body mass than with body weight. The power function of weight, however, may reflect in part the influence of height, because the absolute dose for an obese adult, for example, is increased compared with an adult of average weight, but the weight-normalized dose is smaller than for an average adult (table 4). Generally, one has to consider that the estimated effects of age are valid only for that range investigated in our study (2–88 yr). For patients older than 90 yr, Cl1 would become almost zero or negative, and V1 increases toward infinity the younger the patient is (the effect of body weight on V1, however, may compensate for this).
For clinical practice, the effects of body weight and age allow the dosing to be adjusted to the individual patient. The different infusion schemes necessary to maintain a propofol concentration of 1 μg/ml in three adults who are thin, of average weight, and obese, an 80-yr-old patient, and a 5-yr-old child of 20 kg body weight are plotted in figure 5. If normalized to weight, the total doses required for a period of 120 min are quite higher for children and smaller for elderly individuals. Because an obese adult needs less than an average adult, simple weight-normalization of the dose (as it is used for example in the common target-controlled infusion pumps) would lead to overdosing for such a patient. To evaluate the effect of covariates on recovery, we estimated the time required for a 50% decrease in propofol concentration after a constant infusion of variable length (fig. 6). This context-sensitive half-time is clearly prolonged in elderly individuals and nearly identical for children and the adults of different weights. It should be emphasized that this prolonged half-time for elderly individuals does occur, although the kinetic parameters were adjusted for age and weight. This means that the adjustment of pharmacokinetics can help to avoid misdosing, but differences with respect to the recovery cannot be overcome.
Even with inclusion of covariates, the interindividual variabilities remained relatively large, indicating a large variance of pharmacokinetics among patients. This leads to a broad range of context-sensitive half-times (fig. 7), which masks the small differences between children and adults but not the differences between adults and elderly. The relatively large interindividual error may be considered a limiting factor for target-controlled infusion and open-loop control of anesthesia, which are based on pharmacokinetic models. Clinical practice, however, has shown that effective and safe anesthesia can be achieved with infusion schemes based on pharmacokinetic models, because titration of the target concentration may help to overcome the problem of interindividual variability of pharmacokinetics and pharmacodynamics. 2–6 The use of population-based pharmacokinetic parameters is likely to further improve the accuracy of target-controlled drug-delivery systems. Moreover, the field for target-controlled infusion may be broadened using our results for application in children and elderly patients.
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Appendix
Participants of the multicenter study: Ian D. Cockshott, Ph.D., Zeneca Pharmaceuticals, Macclesfield, United Kingdom; Peter S. Glass, M.D., Professor, Department of Anesthesiology, Duke University Medical Center, Durham, North Carolina; Jürgen Schüttler, M.D., Professor and Chairman, Department of Anesthesiology, Friedrich-Alexander-University of Erlangen-Nuremberg, Erlangen, Germany; Steven L. Shafer, M.D., Professor, Department of Anesthesiology, Stanford University School of Medicine, Palo Alto, California; and Martin White, Ph.D., Staff Anesthesiologist, Department of Anaesthesiology, Academisch Ziekenhuis Leiden, Leiden, The Netherlands.

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Choong, E; Loryan, I; Lindqvist, M; Nordling, A; el Bouazzaoui, S; van Schaik, RH; Johansson, I; Jakobsson, J; Ingelman-Sundberg, M
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British Journal of Anaesthesia
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British Journal of Anaesthesia
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2008 Cairo International Biomedical Engineering Conference
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Anesthesia and Analgesia
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International Journal of Urology
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Clinical Pharmacology & Therapeutics
Population pharmacokinetic and pharmacodynamic modeling of propofol for long-term sedation in critically ill patients: A comparison between propofol 6% and propofol 1%
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Annales Francaises D Anesthesie Et De Reanimation
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European Journal of Control
Introduction to automated drug delivery in clinical anesthesia
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Acta Pharmacologica Sinica
Influence of age and sex on pharmacodynamics of propofol in neurosurgical patients: model development
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Annual Review of Pharmacology and Toxicology
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Ieee Embs Apbme 2003
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British Journal of Anaesthesia
Changes in apparent systemic clearance of propofol during transplantation of living related donor liver
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Target-controlled infusion Clinical relevance and special features when using pharmacokinetic models
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Advances in Modelling and Clinical Application of Intravenous Anaesthesia
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Pediatric Anesthesia
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Acta Pharmacologica Sinica
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Regional Anesthesia and Pain Medicine
Diaphragmatic excursion and respiratory function after the modified Raj technique of the infraclavicular plexus block
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Acta Anaesthesiologica Scandinavica
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Clinical adaptation of a pharmacokinetic model of Propofol plasma concentrations in children
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Acta Anaesthesiologica Scandinavica
Predictive ability of propofol effect-site concentrations during fast and slow infusion rates
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British Journal of Anaesthesia
Study of the time course of the clinical effect of propofol compared with the time course of the predicted effect-site concentration: performance of three pharmacokinetic-dynamic models
Coppens, M; Van Limmen, JGM; Schnider, T; Wyler, B; Bonte, S; Dewaele, F; Struys, MMRF; Vereecke, HEM
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Anaesthesia and Intensive Care
A comparison of parametric and non-parametric approaches to target-controlled infusion of propofol
Crankshaw, DP; Brown, MJ; Bjorksten, AR
Anaesthesia and Intensive Care, 38(3): 437-444.

Current Therapeutic Research-Clinical and Experimental
Use of propofol to facilitate extubation in mechanically ventilated children: A case for pediatric trials
Glover, ML; Blumer, JL; Reed, MD
Current Therapeutic Research-Clinical and Experimental, 63(1): 77-90.

Annales Francaises D Anesthesie Et De Reanimation
Which propofol target concentration for ASA III elderly patients for conscious sedation combined with regional anaesthesia?
Villeret, I; Laffon, M; Ferrandiere, M; Delerue, D; Fusciardi, J
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British Journal of Anaesthesia
Evaluation of the predictive performance of four pharmacokinetic models for propofol
Glen, JB; Servin, F
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Pediatric Anesthesia
Which model for propofol TCI in children
Constant, I; Rigouzzo, A
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Anaesthesia
Effect site concentration during propofol TCI sedation: a comparison of sedation score with two pharmacokinetic models
Barakat, AR; Sutcliffe, N; Schwab, M
Anaesthesia, 62(7): 661-666.
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Clinical Pharmacokinetics
Prediction of Propofol Clearance in Children from an Allometric Model Developed in Rats, Children and Adults versus a 0.75 Fixed-Exponent Allometric Model
Peeters, MYM; Allegaert, K; van Oud-Alblas, HJB; Cella, M; Tibboel, D; Danhof, M; Knibbe, CAJ
Clinical Pharmacokinetics, 49(4): 269-275.

Aaps Journal
Population pharmacokinetics/pharmacodynamics of anesthetics
Olofsen, E; Dahan, A
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Paediatric Anaesthesia
Target concentration of propofol required to insert the laryngeal mask airway in children
Viviand, X; Berdugo, L; De La Noe, CA; Lando, A; Martin, C
Paediatric Anaesthesia, 13(3): 217-222.

International Journal of Adaptive Control and Signal Processing
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Journal of Pharmaceutical Sciences
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Mahmood, I
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Clinical Pharmacology & Therapeutics
Stereoselective pharmacokinetics of ketamine: R(-)-ketamine inhibits the elimination of S(+)-ketamine
Ihmsen, H; Geisslinger, G; Schuttler, J
Clinical Pharmacology & Therapeutics, 70(5): 431-438.
10.1067/mcp.2001.119722
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American Journal of Veterinary Research
Effect of variable-dose propofol alone and in combination with two fixed doses of ketamine for total intravenous anesthesia in cats
Ilkiw, JE; Pascoe, PJ; Tripp, LD
American Journal of Veterinary Research, 64(7): 907-912.

Clinical Pharmacokinetics
Strategies to optimise propofol-opioid anaesthesia
Lichtenbelt, BJ; Mertens, M; Vuyk, J
Clinical Pharmacokinetics, 43(9): 577-593.

British Journal of Clinical Pharmacology
Kidneys contribute to the extrahepatic clearance of propofol in humans, but not lungs and brain
Hiraoka, H; Yamamoto, K; Miyoshi, S; Morita, T; Nakamura, K; Kadoi, Y; Kunimoto, F; Horiuchi, R
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Ieee Transactions on Biomedical Engineering
A model-predictive hypnosis control system under total intravenous anesthesia
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British Journal of Anaesthesia
Pharmacodynamic modelling of the bispectral index response to propofol-based anaesthesia during general surgery in children
Jeleazcov, C; Ihmsen, H; Schmidt, J; Ammon, C; Schwilden, H; Schuttler, J; Fechner, J
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Anesthesia and Analgesia
Priorities in perioperative geriatrics
Cook, DJ; Rooke, GA
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Anaesthesist
Accuracy of target-controlled infusion (TCI) with 2 different propofol formulations
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Optimal Dose Identification
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British Journal of Anaesthesia
Bispectral index, serum drug concentrations and emergence associated with individually adjusted target-controlled infusions of remifentanil and propofol for laparoscopic surgery
Hoymork, SC; Raeder, J; Grimsmo, B; Steen, PA
British Journal of Anaesthesia, 91(6): 773-780.
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Anasthesiologie & Intensivmedizin
Target-controlled infusion (TCI): Administration of intravenous anaesthetics by means of computer-controlled infusion pumps
Bruhn, J; Ropcke, H; Bouillon, T
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Clinical Pharmacology & Therapeutics
Changes in drug plasma concentrations of an extensively bound and highly extracted drug, propofol, in response to altered plasma binding
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Clinical Pharmacology & Therapeutics, 75(4): 324-330.
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British Journal of Anaesthesia
Recent advances in intravenous anaesthesia
Sneyd, JR
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Gastrointestinal Endoscopy
Efficiency of propofol versus midazolam and fentanyl sedation at a pediatric teaching hospital: a prospective study
Lightdale, JR; Valim, C; Newburg, AR; Mahoney, LB; Zgleszewski, S; Fox, VL
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Journal of Pharmacokinetics and Pharmacodynamics
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Bienert, A; Kusza, K; Wawrzyniak, K; Grzeskowiak, E; Kokot, ZJ; Matysiak, J; Grabowski, T; Wolc, A; Wiczling, P; Regulski, M
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Paediatric Anaesthesia
Scaling for size: some implications for paediatric anaesthesia dosing
Anderson, BJ; Meakin, GH
Paediatric Anaesthesia, 12(3): 205-219.

Biomedical Chromatography
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Dawidowicz, AL; Kalitynski, R
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Anaesthesia
Cardiovascular changes after achieving constant effect site concentration of propofol
Jack, ES; Shaw, M; Harten, JM; Anderson, K; Kinsella, J
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Revue Neurologique
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American Journal of Health-System Pharmacy
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Drugs & Aging
General Anaesthesia in Elderly Patients with Cardiovascular Disorders Choice of Anaesthetic Agent
Das, S; Forrest, K; Howell, S
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Periodicum Biologorum
EEG monitoring during midazolam/propofol anesthesia in children
Popovic, L; Domijan, M; Batinica, S; Zupancic, B; Antabak, A; Luetic, T; Barisic, N
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Advances in Modelling and Clinical Application of Intravenous Anaesthesia
Clinical application of pharmacokinetic and pharmacodynamic models
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British Journal of Anaesthesia
Accuracy of the 'Paedfusor' in children undergoing cardiac surgery or catheterization
Absalom, A; Amutike, D; Lal, A; White, M; Kenny, GNC
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Journal of Pharmacy and Pharmacology
Altered dose-to-effect of propofol due to pharmacokinetics in rats with experimental diabetes mellitus
Leal, N; Calvo, R; Agrad, FZ; Lukas, JC; de La Fuente, L; Suarez, E
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2005 Ieee International Conference on Control Applications (Cca), Vols 1And 2
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2005 Ieee International Conference on Control Applications (Cca), Vols 1And 2, (): 154-159.

Pediatric Anesthesia
Dexmedetomidine pharmacokinetics in pediatric intensive care - a pooled analysis
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British Journal of Anaesthesia
Gender differences in the pharmacokinetics of propofol in elderly patients during and after continuous infusion
Vuyk, J; Oostwouder, CJ; Vletter, AA; Burm, AGL; Bovill, JG
British Journal of Anaesthesia, 86(2): 183-188.

British Journal of Anaesthesia
Predictive performance of 'Servin's formula' during BIS (R)-guided propofol-remifentanil target-controlled infusion in morbidly obese patients
Albertin, A; Poli, D; La Colla, L; Gonfalini, M; Turi, S; Pasculli, N; La Colla, G; Bergonzi, PC; Dedola, E; Fermo, I
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Respiratory Medicine
Influence of respiratory efforts on b2-agonist induced bronchodilation in mechanically ventilated COPD patients: A prospective clinical study
Malliotakis, P; Mouloudi, E; Prinianakis, G; Kondili, E; Georgopoulos, D
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European Journal of Clinical Pharmacology
Population pharmacokinetics of buprenorphine following a two-stage intravenous infusion in healthy volunteers
Jensen, ML; Foster, DJR; Upton, RN; Kristensen, K; Hansen, SH; Jensen, NH; Nielsen, BN; Skram, U; Villesen, HH; Christrup, L
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Journal of Chromatography B-Analytical Technologies in the Biomedical and Life Sciences
Quantitative analysis of propofol in whole blood using capillary electrophoresis
Hui, Y; Raedschelders, K; Zhang, H; Ansley, DM; Chen, DDY
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Pediatric Anesthesia
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Propofol/remifentanil versus sevoflurane/remifentanil in paediatric anaesthesia
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Anesthesiology
Comparison of Predicted Induction Dose with Predetermined Physiologic Characteristics of Patients and With Pharmacokinetic Models Incorporating those Characteristics as Covariates
Kazama, T; Morita, K; Ikeda, T; Kurita, T; Sato, S
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Anesthesiology
Pharmacokinetics of Propofol Administered by Target-controlled Infusion to Alcoholic Patients
Servin, FS; Bougeois, B; Gomeni, R; Mentré, F; Farinotti, R; Desmonts, J
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Anesthesiology
Using Front-end Kinetics to Optimize Target-controlled Drug Infusions
Avram, MJ; Krejcie, TC
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Anesthesiology
Pharmacokinetics of Propofol Infusions in Critically Ill Neonates, Infants, and Children in an Intensive Care Unit
Rigby-Jones, AE; Nolan, JA; Priston, MJ; C. Wright, PM; Robert Sneyd, J; Wolf, AR
Anesthesiology, 97(6): 1393-1400.

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Current Opinion in Anesthesiology
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European Journal of Anaesthesiology (EJA)
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European Journal of Anaesthesiology (EJA), 19(3): 177-188.

Anesthesiology
Pharmacokinetics of a Single Bolus of Propofol in Chinese Children of Different Ages
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Anesthesiology, 104(1): 27-32.

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Anesthesiology
Human Kidneys Play an Important Role in the Elimination of Propofol
Takizawa, D; Hiraoka, H; Goto, F; Yamamoto, K; Horiuchi, R
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Anesthesiology
Propofol Pharmacokinetics and Pharmacodynamics for Depth of Sedation in Nonventilated Infants after Major Craniofacial Surgery
Peeters, MY; Prins, SA; Knibbe, CA; DeJongh, J; van Schaik, RH; van Dijk, M; van der Heiden, IP; Tibboel, D; Danhof, M
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Anesthesiology
Mixed-effects Modeling of the Influence of Alfentanil on Propofol Pharmacokinetics
Vuyk, J; Mertens, MJ; Olofsen, E; Burm, AG; Bovill, JG
Anesthesiology, 100(4): 795-805.

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Anesthesiology
Pharmacokinetics and Pharmacodynamics of Propofol Microemulsion and Lipid Emulsion after an Intravenous Bolus and Variable Rate Infusion
Kim, K; Choi, B; Park, S; Lee, S; Christensen, LV; Zhou, J; Yoo, B; Shin, H; Bae, K; Kern, SE; Kang, S; Noh, G
Anesthesiology, 106(5): 924-934.
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Anesthesiology
Pharmacokinetics and Clinical Pharmacodynamics of the New Propofol Prodrug GPI 15715 in Volunteers: Retracted
Fechner, J; Ihmsen, H; Hatterscheid, D; Schiessl, C; Vornov, JJ; Burak, E; Schwilden, H; Schüttler, J
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Anesthesiology
Early Phase Pharmacokinetics but Not Pharmacodynamics Are Influenced by Propofol Infusion Rate
Masui, K; Kira, M; Kazama, T; Hagihira, S; Mortier, EP; Struys, MM
Anesthesiology, 111(4): 805-817.
10.1097/ALN.0b013e3181b799c1
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Anesthesiology
Population Pharmacokinetics of Propofol for Target-controlled Infusion (TCI) in the Elderly
Vuyk, J; Schnider, T; Engbers, F
Anesthesiology, 93(6): 1557-1558.

Anesthesiology
Population Pharmacokinetics of Propofol for Target-controlled Infusion (TCI) in the Elderly
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Anesthesiology, 93(6): 1558-1560.

Anesthesiology
Non–steady State Analysis of the Pharmacokinetic Interaction between Propofol and Remifentanil
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Anesthesiology, 97(6): 1350-1362.

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European Journal of Anaesthesiology (EJA)
Impact of patient age on propofol consumption during propofol‐remifentanil anaesthesia
Kreuer, S; Schreiber, JU; Bruhn, J; Wilhelm, W
European Journal of Anaesthesiology (EJA), 22(2): 123-128.
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European Journal of Anaesthesiology (EJA)
Modelling the pharmacodynamic interaction between remifentanil and propofol by EEG‐controlled dosing
Albrecht, S; Fechner, J; Hering, W; Ihmsen, H; Palmaers, T; Schiittler, J
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Pediatric Emergency Care
Propofol for Pediatric Sedation
Barnett, P
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Keywords:
Age; body weight; children; elderly; NONMEM.

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