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Clinical Science  |   March 2000
Population Pharmacokinetics of Propofol: A Multicenter Study
Author Affiliations & Notes
  • Jürgen Schüttler, M.D.
    *
  • Harald Ihmsen, M.Sc.
  • *Professor of Anesthesiology and Chairman. †Research Assistant.
Article Information
Clinical Science
Clinical Science   |   March 2000
Population Pharmacokinetics of Propofol: A Multicenter Study
Anesthesiology 3 2000, Vol.92, 727-738. doi:
Anesthesiology 3 2000, Vol.92, 727-738. doi:
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.
Materials and Methods
Samples
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 
Table 1. Complete Data Set of the Propofol Population (270 individuals, 4,112 samples)
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Table 1. Complete Data Set of the Propofol Population (270 individuals, 4,112 samples)
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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.
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).
Interindividual and Intraindividual Variability
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
in which θiis the individual value of the parameter θ, θis the mean population value of this parameter, and ηiis 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 cijis the j  th measured concentration of the i  th individual and cpijis the corresponding predicted concentration. Again, εijis 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.
Regression Procedure
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 cijis the j  th measured concentration of the i  th individual and cpijdenotes 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.
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.
Results
The individual estimates revealed an influence of body weight and age on all clearances, V1, and V2; V3was almost constant in all subjects. As an example, figure 1shows the individual estimates of Cl1as 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. V1was almost constant in adults but decreased in children. If divided by the body weight, V1showed a clear relationship with age, which could be described by a power function with negative exponent. The effect of age on Cl1was 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).
Fig. 1. Plot of the individual Bayesian estimates of the elimination clearance (Cl1) versus  body weight for a three-compartment model without any covariates. A power function (line) yielded the best results in regression analysis.
Fig. 1. Plot of the individual Bayesian estimates of the elimination clearance (Cl1) versus 
	body weight for a three-compartment model without any covariates. A power function (line) yielded the best results in regression analysis.
Fig. 1. Plot of the individual Bayesian estimates of the elimination clearance (Cl1) versus  body weight for a three-compartment model without any covariates. A power function (line) yielded the best results in regression analysis.
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Fig. 2. Plot of the propofol concentrations predicted by the individual Bayesian estimates of a three-compartment model without any covariates versus  the measured propofol concentrations of the first subset.
Fig. 2. Plot of the propofol concentrations predicted by the individual Bayesian estimates of a three-compartment model without any covariates versus 
	the measured propofol concentrations of the first subset.
Fig. 2. Plot of the propofol concentrations predicted by the individual Bayesian estimates of a three-compartment model without any covariates versus  the measured propofol concentrations of the first subset.
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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.
Table 2. Results of the Regression Procedure
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Table 2. Results of the Regression Procedure
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Table 3. Pharmacokinetic Parameters for the Final Model
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Table 3. Pharmacokinetic Parameters for the Final Model
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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).
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 2shows 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). V3did 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 V1was 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.
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.
Influence of Mode of Administration
Nearly all parameters with the exception of Cl1and V3were found to be altered with bolus administration compared with infusion data. Whereas V1, V2, and Cl2were larger than after infusion, Cl3was decreased.
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 3shows 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).
Fig. 3. Plot of the propofol concentrations predicted by the final model versus  the measured propofol concentrations of the second subset.
Fig. 3. Plot of the propofol concentrations predicted by the final model versus 
	the measured propofol concentrations of the second subset.
Fig. 3. Plot of the propofol concentrations predicted by the final model versus  the measured propofol concentrations of the second subset.
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Fig. 4. Ratio of measured to predicted propofol concentrations for the second subset as a function of time. Predictions were calculated with the parameters derived form the first subset.
Fig. 4. Ratio of measured to predicted propofol concentrations for the second subset as a function of time. Predictions were calculated with the parameters derived form the first subset.
Fig. 4. Ratio of measured to predicted propofol concentrations for the second subset as a function of time. Predictions were calculated with the parameters derived form the first subset.
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Simulations
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 5shows 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−1for the child, 2.6 mg · kg−1· h−1for the lean adult, 2.3 mg · kg−1· h−1for the average adult, 1.9 mg · kg−1· h−1for the obese adult, and 1.5 mg · kg−1· h−1for 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 7depicts 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 8shows 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.
Table 4. Pharmacokinetic Parameters for Five Typical Individuals, Calculated with the Estimates of the Final Model
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Table 4. Pharmacokinetic Parameters for Five Typical Individuals, Calculated with the Estimates of the Final Model
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Fig. 5. Propofol infusion rates required to maintain a concentration of 1 μg/ml in a child (20 kg body weight, 5 yr of age), a lean adult (50 kg, 30 yr), an adult of average weight (70 kg, 70 yr), an obese adult (110 kg, 30 yr) and an elderly individual (65 kg, 80 yr). The infusion rates were calculated using the parameters of the final model (table 4).
Fig. 5. Propofol infusion rates required to maintain a concentration of 1 μg/ml in a child (20 kg body weight, 5 yr of age), a lean adult (50 kg, 30 yr), an adult of average weight (70 kg, 70 yr), an obese adult (110 kg, 30 yr) and an elderly individual (65 kg, 80 yr). The infusion rates were calculated using the parameters of the final model (table 4).
Fig. 5. Propofol infusion rates required to maintain a concentration of 1 μg/ml in a child (20 kg body weight, 5 yr of age), a lean adult (50 kg, 30 yr), an adult of average weight (70 kg, 70 yr), an obese adult (110 kg, 30 yr) and an elderly individual (65 kg, 80 yr). The infusion rates were calculated using the parameters of the final model (table 4).
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Fig. 6. Time required for a 50% decrease in concentration after continuous infusion of variable length (context-sensitive half-time). Simulations were performed for a child (20 kg body weight, 5 yr of age), a lean adult (50 kg, 30 yr), an adult of average weight (70 kg, 30 yr), an obese adult (110 kg, 30 yr), and an elderly individual (65 kg, 80 yr), based on the final model parameters (table 4).
Fig. 6. Time required for a 50% decrease in concentration after continuous infusion of variable length (context-sensitive half-time). Simulations were performed for a child (20 kg body weight, 5 yr of age), a lean adult (50 kg, 30 yr), an adult of average weight (70 kg, 30 yr), an obese adult (110 kg, 30 yr), and an elderly individual (65 kg, 80 yr), based on the final model parameters (table 4).
Fig. 6. Time required for a 50% decrease in concentration after continuous infusion of variable length (context-sensitive half-time). Simulations were performed for a child (20 kg body weight, 5 yr of age), a lean adult (50 kg, 30 yr), an adult of average weight (70 kg, 30 yr), an obese adult (110 kg, 30 yr), and an elderly individual (65 kg, 80 yr), based on the final model parameters (table 4).
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Fig. 7. Simulations showing the interindividual variability of the context-sensitive half-time for a population of 100 individuals using the pharmacokinetic parameters for an adult of 70 kg aged 30 yr (table 4) and the interindividual variances estimated in the study. The bold line depicts the context-sensitive half-time for the typical individual (70 kg body weight, 30 yr of age).
Fig. 7. Simulations showing the interindividual variability of the context-sensitive half-time for a population of 100 individuals using the pharmacokinetic parameters for an adult of 70 kg aged 30 yr (table 4) and the interindividual variances estimated in the study. The bold line depicts the context-sensitive half-time for the typical individual (70 kg body weight, 30 yr of age).
Fig. 7. Simulations showing the interindividual variability of the context-sensitive half-time for a population of 100 individuals using the pharmacokinetic parameters for an adult of 70 kg aged 30 yr (table 4) and the interindividual variances estimated in the study. The bold line depicts the context-sensitive half-time for the typical individual (70 kg body weight, 30 yr of age).
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Fig. 8. Concentration course after a propofol bolus of 100 mg, calculated with the pharmacokinetic parameters for infusion (thin dashed line) and bolus administration (bold line). The small picture shows the concentration course for the first 10 min.
Fig. 8. Concentration course after a propofol bolus of 100 mg, calculated with the pharmacokinetic parameters for infusion (thin dashed line) and bolus administration (bold line). The small picture shows the concentration course for the first 10 min.
Fig. 8. Concentration course after a propofol bolus of 100 mg, calculated with the pharmacokinetic parameters for infusion (thin dashed line) and bolus administration (bold line). The small picture shows the concentration course for the first 10 min.
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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 Cl3was 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 V1for children. In subjects older than 60 yr, we found a marked linear decrease of the elimination clearance Cl1and a slight decrease of V1, which was best described by a power function with a negative exponent. The reduction of the elimination clearance and V1in 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, Cl1would become almost zero or negative, and V1increases 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.
References
Cockshott ID: Propofol pharmacokinetics and metabolism: An overview. Postgrad Med J 1985; 61:45–50Cockshott, ID
Schüttler J, Schwilden H, Stoeckel H: Total intravenous anaesthesia with propofol and alfentanil by computer-assisted infusion. Anaesthesia 1988; 43(suppl):341–5Schüttler, J Schwilden, H Stoeckel, H
White M, Kenny GN: Intravenous propofol anaesthesia using a computerised infusion system. Anaesthesia 1990; 45:204–9White, M Kenny, GN
Chaudri S, White M, Kenny GN: Induction of anaesthesia with propofol using a target-controlled infusion system. Anaesthesia 1992; 47:551–3Chaudri, S White, M Kenny, GN
Tackley RM, Lewis GT, Prys-Roberts C, Boaden RW, Dixon J, Harvey JT: Computer controlled infusion of propofol. Br J Anaesth 1989; 62:46–53Tackley, RM Lewis, GT Prys-Roberts, C Boaden, RW Dixon, J Harvey, JT
Marsh B, White M, Morton N, Kenny GN: Pharmacokinetic model driven infusion of propofol in children. Br J Anaesth 1991; 67:41–8Marsh, B White, M Morton, N Kenny, GN
Schuttler J, Stoeckel H, Schwilden H: Pharmacokinetic and pharmacodynamic modelling of propofol (“Diprivan”) in volunteers and surgical patients. Postgrad Med J 1985; 61(suppl 3):53–4Schuttler, J Stoeckel, H Schwilden, H
Shafer A, Doze VA, Shafer SL, White PF: Pharmacokinetics and pharmacodynamics of propofol infusions during general anesthesia. A NESTHESIOLOGY 1988; 69 (3):348–56Shafer, A Doze, VA Shafer, SL White, PF
Gepts E, Camu F, Cockshott ID, Douglas EJ: Disposition of propofol administered as constant rate intravenous infusions in humans. Anaesth Analg 1987; 66:1256–63Gepts, E Camu, F Cockshott, ID Douglas, EJ
Gepts E, Jonckheer K, Maes V, Sonck W, Camu F: Disposition kinetics of propofol during alfentanil anaesthesia. Anaesthesia 1988; 43(suppl):8–13Gepts, E Jonckheer, K Maes, V Sonck, W Camu, F
Kay NH, Sear JW, Uppington J, Cockshott ID, Douglas EJ: Disposition of propofol in patients undergoing surgery: A comparison in men and women. Br J Anaesth 1986; 58:1075–9Kay, NH Sear, JW Uppington, J Cockshott, ID Douglas, EJ
Jacobs JR: Intravenous anesthetic drugs: Infusion pharmacology. Int Anaesthesiol Clin 1991; 29:53–71Jacobs, JR
Gin T, Gregory MA, Chan K, Buckley T, Oh TE: Pharmacokinetics of propofol in women undergoing elective caesarean section. Br J Anaesth 1990; 64:148–53Gin, T Gregory, MA Chan, K Buckley, T Oh, TE
Gill SS, Wright EM, Reilly CS: Pharmacokinetic interaction of propofol and fentanyl: Single bolus injection study. Br J Anaesth 1990; 65:760–5Gill, SS Wright, EM Reilly, CS
Cockshott ID, Briggs LP, Douglas EJ, White M: Pharmacokinetics of propofol in female patients: Studies using single bolus injection. Br J Anaesth 1987; 59:1103–10Cockshott, ID Briggs, LP Douglas, EJ White, M
Morgan DJ, Campbell GA, Crankshaw DP: Pharmacokinetics of propofol when given by intravenous infusion. Br J Clin Pharmacol 1990; 30:144–8Morgan, DJ Campbell, GA Crankshaw, DP
Kirkpatrick T, Cockshott ID, Douglas EJ, Nimmo WS: Pharmacokinetics of propofol (diprivan) in elderly patients. Br J Anaesth 1988; 60:146–50Kirkpatrick, T Cockshott, ID Douglas, EJ Nimmo, WS
Saint-Maurice C, Cockshott ID, Douglas EJ, Richard MO: Pharmacokinetics of propofol in young children after a single dose. Br J Anaesth 1989; 63:667–70Saint-Maurice, C Cockshott, ID Douglas, EJ Richard, MO
Vandermeersch E, van Hemelrijck J, Byttebier G, van Aken H: Pharmacokinetics of propofol during continuous infusion for pediatric anesthesia. Acta Anaesthesiol Belg 1989; 40:161–5Vandermeersch, E van Hemelrijck, J Byttebier, G van Aken, H
Jones RD, Chan K, Andrew LJ: Pharmacokinetics of propofol in children. Br J Anaesth 1990; 65:661–7Jones, RD Chan, K Andrew, LJ
Kataria BK, Ved SA, Nicodemus HF, Hoy GR, Lea D, Dubois MY, Mandema JW, Shafer SL: The pharmacokinetics of propofol in children using three different data analysis approaches. A NESTHESIOLOGY 1994; 80:104–22Kataria, BK Ved, SA Nicodemus, HF Hoy, GR Lea, D Dubois, MY Mandema, JW Shafer, SL
Servin F, Desmonts JM, Haberer JP, Cockshott ID, Plummer GF, Farinotti R: Pharmacokinetics and protein binding of propofol in patients with cirrhosis. A NESTHESIOLOGY 1988; 69:887–91Servin, F Desmonts, JM Haberer, JP Cockshott, ID Plummer, GF Farinotti, R
Kirvela M, Olkkola KT, Rosenberg PH, Yli-Hankala A, Salmela K, Lindgren L: Pharmacokinetics of propofol and haemodynamic changes during induction of anaesthesia in uraemic patients. Br J Anaesth 1992; 68:178–82Kirvela, M Olkkola, KT Rosenberg, PH Yli-Hankala, A Salmela, K Lindgren, L
Albanese J, Martin C, Lacarelle B, Saux P, Durand A, Gouin F: Pharmacokinetics of long-term propofol infusion used for sedation in ICU patients. A NESTHESIOLOGY 1990; 73:214–7Albanese, J Martin, C Lacarelle, B Saux, P Durand, A Gouin, F
Bailie GR, Cockshott ID, Douglas EJ, Bowles BJ: Pharmacokinetics of propofol during and after long-term continuous infusion for maintenance of sedation in ICU patients. Br J Anaesth 1992; 68:486–91Bailie, GR Cockshott, ID Douglas, EJ Bowles, BJ
Frenkel C, Schüttler J, Ihmsen H, Heye H, Rommelsheim K: Pharmacokinetics and pharmacodynamics of propofol/alfentanil infusions for sedation in ICU patients. Intensive Care Med 1995; 21:981–8Frenkel, C Schüttler, J Ihmsen, H Heye, H Rommelsheim, K
Smith C, McEwan AI, Jhaveri R, Wilkinson M, Goodman D, Smith LR, Canada AT, Glass PS: The interaction of fentanyl on the Cp50 of propofol for loss of consciousness and skin incision. Anesthesiology 1994; 81:820–8Smith, C McEwan, AI Jhaveri, R Wilkinson, M Goodman, D Smith, LR Canada, AT Glass, PS
Plummer GF: Improved method for the determination of propofol in blood by high performance liquid chromatography with fluorescence detection. J Chromatogr 1987; 421:171–6Plummer, GF
Beal SL, Sheiner LB: NONMEM User’s Guide. San Francisco, University of California San Francisco, 1998
Hughes MA, Glass PS, Jacobs JR: Context-sensitive half-time in multicompartment pharmacokinetic models for intravenous anesthetic drugs. A NESTHESIOLOGY 1992; 76:334–41Hughes, MA Glass, PS Jacobs, JR
Coetzee JF, Glen JB, Wium CA, Boshoff L: Pharmacokinetic model selection for target controlled infusions of propofol. Assessment of three parameter sets. A NESTHESIOLOGY 1995; 82:1328–45Coetzee, JF Glen, JB Wium, CA Boshoff, L
Vuyk J, Engbers FHM, Burm AGL, Vletter AA, Bovill JG: Performance of computer-controlled infusion of propofol: An evaluation of five pharmacokinetic parameter sets. Anesth Analg 1995; 81:1275–82Vuyk, J Engbers, FHM Burm, AGL Vletter, AA Bovill, JG
Fechner J, Albrecht S, Ihmsen H, Knoll R, Schwilden H, Schuttler J: Predictability and precision of “target-controlled infusion” (TCI) of propofol with the “Disoprifusor TCI” system. Anaesthesist 1998; 47:663–8Fechner, J Albrecht, S Ihmsen, H Knoll, R Schwilden, H Schuttler, J
Bailey JM, Mora CT, Shafer SL: Pharmacokinetics of propofol in adult patients undergoing coronary revascularization. A NESTHESIOLOGY 1996; 84:1288–97Bailey, JM Mora, CT Shafer, SL
Schnider TW, Minto CF, Gambus PL, Andresen C, Goodale DB, Shafer SL, Youngs EJ: The influence of method of administration and covariates on the pharmacokinetics of propofol in adult volunteers. A NESTHESIOLOGY 1998; 88:1170–82Schnider, TW Minto, CF Gambus, PL Andresen, C Goodale, DB Shafer, SL Youngs, EJ
Lange H, Stephan H, Rieke H, Kellermann M, Sonntag H, Bircher J: Hepatic and extrahepatic disposition of propofol in patients undergoing coronary bypass surgery. Br J Anaesth 1990; 64:563–70Lange, H Stephan, H Rieke, H Kellermann, M Sonntag, H Bircher, J
Major E, Aun C, Yate PM, Savege M, Verniquet AJW, Adam H, Douglas EJ: Influence of sample site on blood concentrations of ICI 35868. Br J Anaesth 1983; 55:371–5Major, E Aun, C Yate, PM Savege, M Verniquet, AJW Adam, H Douglas, EJ
Fan SZ, Yu HY, Chen YL, Liu CC: Propofol concentration monitoring in plasma or whole blood by gas chromatography and high-performance liquid chromatography. Anest Analg 1995; 81:175–8Fan, SZ Yu, HY Chen, YL Liu, CC
Minto CF, Schnider TW, Egan TD, Youngs E, Lemmens HJM, Gambus PL, Billard V, Hoke, JF, Moore KHP, Hermann DJ, Muir KT, Mandema JW, Shafer SL: Influence of age and gender on the pharmacokinetics and pharmacodynamics of remifentanil. A NESTHESIOLOGY 1997; 86:10–23Minto, CF Schnider, TW Egan, TD Youngs, E Lemmens, HJM Gambus, PL Billard, V Hoke, JF Moore, KHP Hermann, DJ Muir, KT Mandema, JW Shafer, SL
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.
Fig. 1. Plot of the individual Bayesian estimates of the elimination clearance (Cl1) versus  body weight for a three-compartment model without any covariates. A power function (line) yielded the best results in regression analysis.
Fig. 1. Plot of the individual Bayesian estimates of the elimination clearance (Cl1) versus 
	body weight for a three-compartment model without any covariates. A power function (line) yielded the best results in regression analysis.
Fig. 1. Plot of the individual Bayesian estimates of the elimination clearance (Cl1) versus  body weight for a three-compartment model without any covariates. A power function (line) yielded the best results in regression analysis.
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Fig. 2. Plot of the propofol concentrations predicted by the individual Bayesian estimates of a three-compartment model without any covariates versus  the measured propofol concentrations of the first subset.
Fig. 2. Plot of the propofol concentrations predicted by the individual Bayesian estimates of a three-compartment model without any covariates versus 
	the measured propofol concentrations of the first subset.
Fig. 2. Plot of the propofol concentrations predicted by the individual Bayesian estimates of a three-compartment model without any covariates versus  the measured propofol concentrations of the first subset.
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Fig. 3. Plot of the propofol concentrations predicted by the final model versus  the measured propofol concentrations of the second subset.
Fig. 3. Plot of the propofol concentrations predicted by the final model versus 
	the measured propofol concentrations of the second subset.
Fig. 3. Plot of the propofol concentrations predicted by the final model versus  the measured propofol concentrations of the second subset.
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Fig. 4. Ratio of measured to predicted propofol concentrations for the second subset as a function of time. Predictions were calculated with the parameters derived form the first subset.
Fig. 4. Ratio of measured to predicted propofol concentrations for the second subset as a function of time. Predictions were calculated with the parameters derived form the first subset.
Fig. 4. Ratio of measured to predicted propofol concentrations for the second subset as a function of time. Predictions were calculated with the parameters derived form the first subset.
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Fig. 5. Propofol infusion rates required to maintain a concentration of 1 μg/ml in a child (20 kg body weight, 5 yr of age), a lean adult (50 kg, 30 yr), an adult of average weight (70 kg, 70 yr), an obese adult (110 kg, 30 yr) and an elderly individual (65 kg, 80 yr). The infusion rates were calculated using the parameters of the final model (table 4).
Fig. 5. Propofol infusion rates required to maintain a concentration of 1 μg/ml in a child (20 kg body weight, 5 yr of age), a lean adult (50 kg, 30 yr), an adult of average weight (70 kg, 70 yr), an obese adult (110 kg, 30 yr) and an elderly individual (65 kg, 80 yr). The infusion rates were calculated using the parameters of the final model (table 4).
Fig. 5. Propofol infusion rates required to maintain a concentration of 1 μg/ml in a child (20 kg body weight, 5 yr of age), a lean adult (50 kg, 30 yr), an adult of average weight (70 kg, 70 yr), an obese adult (110 kg, 30 yr) and an elderly individual (65 kg, 80 yr). The infusion rates were calculated using the parameters of the final model (table 4).
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Fig. 6. Time required for a 50% decrease in concentration after continuous infusion of variable length (context-sensitive half-time). Simulations were performed for a child (20 kg body weight, 5 yr of age), a lean adult (50 kg, 30 yr), an adult of average weight (70 kg, 30 yr), an obese adult (110 kg, 30 yr), and an elderly individual (65 kg, 80 yr), based on the final model parameters (table 4).
Fig. 6. Time required for a 50% decrease in concentration after continuous infusion of variable length (context-sensitive half-time). Simulations were performed for a child (20 kg body weight, 5 yr of age), a lean adult (50 kg, 30 yr), an adult of average weight (70 kg, 30 yr), an obese adult (110 kg, 30 yr), and an elderly individual (65 kg, 80 yr), based on the final model parameters (table 4).
Fig. 6. Time required for a 50% decrease in concentration after continuous infusion of variable length (context-sensitive half-time). Simulations were performed for a child (20 kg body weight, 5 yr of age), a lean adult (50 kg, 30 yr), an adult of average weight (70 kg, 30 yr), an obese adult (110 kg, 30 yr), and an elderly individual (65 kg, 80 yr), based on the final model parameters (table 4).
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Fig. 7. Simulations showing the interindividual variability of the context-sensitive half-time for a population of 100 individuals using the pharmacokinetic parameters for an adult of 70 kg aged 30 yr (table 4) and the interindividual variances estimated in the study. The bold line depicts the context-sensitive half-time for the typical individual (70 kg body weight, 30 yr of age).
Fig. 7. Simulations showing the interindividual variability of the context-sensitive half-time for a population of 100 individuals using the pharmacokinetic parameters for an adult of 70 kg aged 30 yr (table 4) and the interindividual variances estimated in the study. The bold line depicts the context-sensitive half-time for the typical individual (70 kg body weight, 30 yr of age).
Fig. 7. Simulations showing the interindividual variability of the context-sensitive half-time for a population of 100 individuals using the pharmacokinetic parameters for an adult of 70 kg aged 30 yr (table 4) and the interindividual variances estimated in the study. The bold line depicts the context-sensitive half-time for the typical individual (70 kg body weight, 30 yr of age).
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Fig. 8. Concentration course after a propofol bolus of 100 mg, calculated with the pharmacokinetic parameters for infusion (thin dashed line) and bolus administration (bold line). The small picture shows the concentration course for the first 10 min.
Fig. 8. Concentration course after a propofol bolus of 100 mg, calculated with the pharmacokinetic parameters for infusion (thin dashed line) and bolus administration (bold line). The small picture shows the concentration course for the first 10 min.
Fig. 8. Concentration course after a propofol bolus of 100 mg, calculated with the pharmacokinetic parameters for infusion (thin dashed line) and bolus administration (bold line). The small picture shows the concentration course for the first 10 min.
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Table 1. Complete Data Set of the Propofol Population (270 individuals, 4,112 samples)
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Table 1. Complete Data Set of the Propofol Population (270 individuals, 4,112 samples)
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Table 2. Results of the Regression Procedure
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Table 2. Results of the Regression Procedure
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Table 3. Pharmacokinetic Parameters for the Final Model
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Table 3. Pharmacokinetic Parameters for the Final Model
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Table 4. Pharmacokinetic Parameters for Five Typical Individuals, Calculated with the Estimates of the Final Model
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Table 4. Pharmacokinetic Parameters for Five Typical Individuals, Calculated with the Estimates of the Final Model
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