|Year : 2012 | Volume
| Issue : 3 | Page : 393-397
Propofol pharmacokinetics in China: A multicentric study
Hong-bo Ye1, Jin-heng Li1, Jian-zhong Rui1, Hong Zheng2, Xin-an Zhang3, Xin-jin Chi4, Wen-ying Chen5, Jian-guo Xu6
1 Department of Pharmacology, Jinling Hospital, Nanjing city, Jiangsu, China
2 Department of Anesthesiology, First Affiliated Hospital of Xinjiang Medical University, Urlumgi city, Xinjiang, China
3 Department of Anesthesiology, Liuhuaqiao Hospital, Guangzhou city, Guangdong, China
4 Department of Anesthesiology, the Third affiliated Hospital, SUN-Yat-sen University, Guangzhou city, Guangdong, China
5 Department of Pharmacy, The first Affiliated Hospital of Guangzhou Medical College, Guangzhou city, Guandong, China
6 Department of Anesthesiology, Jinling Hospital, Nanjing city, Jiangsu, China
|Date of Submission||08-Apr-2010|
|Date of Decision||20-Feb-2012|
|Date of Acceptance||28-Feb-2012|
|Date of Web Publication||17-May-2012|
Department of Pharmacology, Jinling Hospital, Nanjing city, Jiangsu
Source of Support: None, Conflict of Interest: None
Objective : A multicenter population pharmacokinetics study of propofol was performed to establish a new population model.
Materials and Methods : Three thousand two hundred and fifty-nine blood samples of 220 participants were measured by HPLC-UV or HPLC-FLU or GC-MS. Target-controlled infusion after single bolus or continuous infusion was applied for propofol anesthesia. The samples were taken from 2 to 1500 min. The concentration-time profiles were analyzed by nonlinear mixed effect model (NONMEM) with first order estimation method. The inter-individual variability and the residual variability were described by exponential model and constant coefficient variation model. The stepwise modeling strategy using PsN was applied for covariate modeling. The criteria of forward addition and backward elimination were (α = 0.01 and α = 0.005, χ2, df = 1). The final model was evaluated by bootstrap using PDx and visual predictive check using PsN. 500 bootstraps and 1000 simulation were run.
Result : The propofol population model was described by 3-compartment model with inter-individual variability of CL, V 1 , Q 2, and Q 3 describing by exponential model. The inter-individual variability of V 2 , V 3 were not included because it is reported that the parameter was near its boundary. The typical value of CL, V1, Q2, V2, Q3 and V3 were 1.28 L · min-1 , 10.1 × (age/44)-0.465 × (1 + 0.352 × sex) L, 0.819 L · min-1 , 36.0 L, 0.405 × (bodyweight/60)1.58 L · min-1 and 272 L, respectively. Coefficients of inter-individual variability of CL, V1, Q2 and Q3 were 30.5%, 35.6%, 43.7% and 66.9%, respectively, and the coefficients of variation of HPLC-UV, GC-MS and HPLC-FLU were 13.3%, 16.9% and 24.2%, respectively. The bootstrap evaluation showed that the final model parameter estimates were within ± 3.39% compared with bootstrap median. The curves of observations percentiles were distributed within the corresponding 95 prediction percentiles by the visual predictive check.
Conclusion: The three-compartment model with first-order elimination could describe the pharmacokinetics of propofol fairly well. The involved fixed effects are age, body weight and sex. The population model was evaluated to be stable by bootstrap and visual predictive check.
Keywords: Propofol, multicenter, nonlinear mixed effect model
|How to cite this article:|
Ye Hb, Li Jh, Rui Jz, Zheng H, Zhang Xa, Chi Xj, Chen Wy, Xu Jg. Propofol pharmacokinetics in China: A multicentric study. Indian J Pharmacol 2012;44:393-7
|How to cite this URL:|
Ye Hb, Li Jh, Rui Jz, Zheng H, Zhang Xa, Chi Xj, Chen Wy, Xu Jg. Propofol pharmacokinetics in China: A multicentric study. Indian J Pharmacol [serial online] 2012 [cited 2022 Jan 16];44:393-7. Available from: https://www.ijp-online.com/text.asp?2012/44/3/393/96346
| » Introduction|| |
Propofol is widely used in the surgical anesthesia with the characteristics of: Fast induction, rapid elimination, short duration of action, smooth recovery from anesthesia. However, the apparent volume and clearance varied over a huge range because of tremendous body uptakes and rapid elimination. The concentrations were difficult to predict. As a solution the target-controlled infusion system (TCI) was introduced into propofol anesthesia. TCI with infusion pump driven by computer meant that the fairly steady concentration in the central or the effect compartment was maintained by presetting the pharmacokinetic parameter. TCI systems require appropriate pharmacokinetics parameters to make sure that a defined concentration could be achieved rapidly and maintained at constant level.
In the past 20 years, more than 20 studies about the propofol population pharmacokinetics with different characteristics were retrieved in Medline. But only one by Schuttler  was a multicenter study. The benefits of multicenter population research included a large number of participants, a possibility of including a wide range of population group, and a more representative population model. There were several single center studies about propofol population pharmacokinetics in China but no multicenter study. In this study, we performed a multicenter population pharmacokinetic analysis with 220 individual data from anesthesia division of five large hospitals widely located in China to build a more representative population model for Chinese. Our data were analyzed with NONMEM, and a first-order conditional estimation with interaction , was used.
| » Materials and Methods|| |
We collected data of propofol for anesthesia in department of anesthesiology from four large hospitals [Table 1]. The research was approved by the Hospital Ethics Committee and all patients provided the signed written consent.
A mean of 2.58 μg · ml -1 with the standard deviation of 2.33 μg · ml -1 and the range of 0.2 ~ 24.42 μg · ml -1 propofol concentrations from 220 patients were measured from artery. Seventy-six participants accepted single bolus dose before TCI infusion for a loading dose which was 34.55% of 220 participants. The rest accepted continuous infusion before TCI. The sampling periods (min) were various among four hospitals, with the 2-1500 min for Jinling Hospital, 1-495 min for Huaxi Hospital, 1-60 min for First Affiliated Hospital of Xinjiang Medical University, and 2-180 min for Third affiliated Hospital of SUN-Yat-Sen University. The covariates involved sex, age and body weight, analysing instruments. The blood samples were measured by any of High Performance Liquid Chromatography - Ultraviolet (HPLC-UV), Gas Chromatography-Mass Spectrum (GC-MS) and High Performance Liquid Chromatography - Fluorescence (HPLC-FLU) instruments. The lower quantitative limits of HPLC-UV, GC-MS and HPLC-FLU were 20 ng · ml -1 , 10 ng · ml -1 and 5 ng · ml -1 .
Propofol concentration-time data were analyzed using NONMEM  (double precision, Version VI, Level 2.2) integrated with PD × 3.1 (version for trial, Icon developmental solutions, Hanover, MD, USA). The complier was G77 Fortran under environment of Window XP® .
Two and three-compartment pharmacokinetic models were compared. The inter-individual variability was described by an exponential , (EXP) model and for the residual variability, we used a constant coefficient of variation , (CCV) model. By the instructions of NONMEM manuals, , the covariate analyzing instrument (INST) was analyzed as a potential influence on random error (Eq.1), and therefore the codes ε 1, ε2, ε3 were residual error of HPLC-UV, GC-MS and HPLC-FLU instrument The first order conditional estimation method with interaction , method was applied as an estimation algorithm for model fitting. The subroutine ADVAN11 TRANS4 in PREDPP was chosen.
In which, Y was the measured concentration and F was the corresponding predicted concentration. ε1, ε2, ε3 were the residual errors which obey the normal distribution with mean of zero and variances of . The code 1, 2 and 3 represented HPLC, GC-MS and HPLC-FLU.
Stepwise Covariate Modeling
The stepwise covariate modeling tool of PsN  implements Forward Selection and Backward Elimination of covariates to a model. In short, one model for each relevant parameter-relationship was tested in a univariate manner. The relationship of continuous covariates such as age, bodyweight was was:
where P j was the model predicted value of a pharmacokinetic parameter P in the jth patient for a given covariate value-Covj. represented a scaling factor for the influence of that covariate. Cov j /Cov m was the normalized transformation for Covj where Covm was the median of covariate. TVP represented the typical parameter value.
The relationship of categorical variates such as sex was
where Covj was the numeric index value of the covariate, the θcov represented a multiplicative factor for the influence of that covariate. For example, if the θcov is negative, the effect was a decrease in the typical value, and an increase if the was θcov positive on the other hand. The rest codes were the same as the corresponding one in the continuous expression.
The covariates selected by PsN  were based on the maximum of drop of objective function value. In first step, the model that gives the most drop of objective value is retained and taken forward to the next step. However, we added the criteria that the inter-individual variabilities and residual variabilities should decrease. After a forward modeling run finished, we selected the qualified models whose drop of objective function valuewas larger than 6.64 (a = 0.01, c2, df = 1) and made a comparison. Only the model which gives the most decrease in inter-individual variabilities could be taken forward to the next step. The forward selection step stopped when there were not any increased inter-individual variabilities even a forward model give the qualified drop of objective function value which was larger than 6.64.
An increase in the objective function valueof at least 7.87 (α = 0.005, χ2 , df = 1) was used as a criterion for retaining relationship during the backward elimination. The final population model was the one which contained all significant covariate after backward elimination process.
The shrinkage value was calculated in the final model using PsN.  The final model was evaluated by bootstrap and visual predictive check (VPC) to check its accuracy and p robustness. VPC were executed using Perl-speak-NONMEM (PsN 3.1.0, Uppsala University, Uppsala, Sweden) under the environment of Perl language (ActiveState Software Inc.). X-pose  4.1.0 (Uppsala University, Sweden) was used for data visualization under the environment of the R  2.9.0 (The R Development Core Team). A total of 1000 replicates of the original dataset were simulated using the final model to generate the expected concentrations and 90% prediction interval. The percentile of the observations was plotted with the 90% prediction interval. Those plots were separated by four hospitals.
Bootstrap which was internal evaluation assessed the model reliability by the 95% confidence interval. Five hundred bootstraps were executed using PD × 3.1 (version for trial, Icon developmental solutions, Hanover, MD, USA) and the result was reported in the form of 95% CI and the bias (%) between the median and the final value.
| » Results|| |
The minimal objective function value (OFV) was - 1485.73 when the 2-compartment was adopted, and - 2585.06 of 3-compartment, respectively. The 3-compartment model which was parameterized in terms of the elimination clearance (CL), inter-compartmental clearances (Q2, Q3), the volume of central compartment (V1), the shallow peripheral compartment (V2) and the deep peripheral compartment (V3) was selected as the base model according to the comparison as well as the suggestion from the correlated references. 
The results of base model were displayed in [Table 2]. The inter-individual variabilities of V2 and V3 were not considered into the base model because the parameter was near its boundary according the NONMEM report. The largest inter-individual variability among parameters was 72.2% (Q3), and the least one was 30.9% (CL). As shown in the [Table 2], the residual variability of three measuring instruments was different in which HPLC-UV of 13.2% was the smallest and the most precise.
Final Population Model
The expression of final model was expressed as follow:
After stepwise covariate modeling process, covariates which remained significant in the final population model were the body weight on Q3, and the age, sex on V1. The final model had a decrease in OFV of 58.95 points compared with base model. The inter-individual variability decreased from 30.9% to 30.5% for CL, from 41.0% to 35.6% for V1, from 44.2% to 43.7% for Q2, from 72.2% to 66.9% for Q3. The final parameter estimates were presented in [Table 3].
|Table 3: Parameter estimates from the final population model and bootstrap validation (400 bootstraps were successful out of 500 runs)|
Click here to view
The whole process of establishing the final model including stepwise forward addition and backward elimination was displayed in [Table 4]. None was excluded from the final model after the backward elimination because the minimum increase of OFV was 21.19 which was larger than 7.87.
The final model indicated that covariate of body weight had a positive affect on Q3, instead of CL which was reported by Schuttler.  The covariate bodyweight was excluded during the forward selection because it made the inter-individual variabilities an increase from 41.0 to 41.6% for V1, from 44.2 to 44.4% for Q2 and from 72.2 to 72.5% for Q3 although a decrease from 30.9 to 26.7% for CL. V1 in males were higher than those in females at 35.2%.
The coefficients of variation of HPLC-UV, GC-MS and HPLC-FLU were 13.3%, 16.9% and 24.2% which were square roots of the variances 0.0176, 0.0285 and 0.0590, respectively, in the final model.
The weighted residuals were homogeneously distributed over individuals [Figure 1]. The estimates of shrinkage for CL, V1, Q2, Q3, were 8.83%, 8.92%, 15.15%, and 21.34%, respectively.
Four hundred bootstraps were minimized successfully from 500 runs. The final model parameter estimates were within ± 3.39% compared with bootstrap median. Parameter estimates were within their 95% confidence interval which was displayed in [Table 3].
The percentiles of the observation were plotted with the corresponding percentiles of the prediction s over the whole time courses separated by hospitals are shown in [Figure 2]. Because the sampling time was not regular, a small part of the percentile line of observations were located outside of the 95 percentile predictive interval of simulations. Most part of percentile lines of observations were within the predictive interval of simulations.
|Figure 2: Visual predictive check for four hospitals. The upper and lower reds dashed depict the 95 percentile of observations. The median red line is the 50 percentile of the observation. The rest 6 black lines are the corresponding prediction intervals of simulated data|
Click here to view
| » Discussion|| |
A multiplicative equation instead of a linear equation was applied to describe the relationships between the continuous covariates and parameters because of the large size of data, covariate age, and bodyweight. The clearance (Q3) was estimated to be most influenced by body weight obviously, which was the different with other published literature. ,,, Some of sampling data that we reanalyzed have been analyzed in the published literatures. ,, But those literatures reported that the residual variability was described by exponential model and the first order estimation was used. It was proven to be not a proper choice for population analysis because of the Taylor expansion. 
Though the model including the affect of bodyweight on clearance (CL) had a significant drop on OFV, the inter-individual variability of V1, Q2, and Q3 increased. After comparison with the model including the bodyweight affect on Q3, we abandoned the covariate bodyweight on CL. The normalized transformation BW/60 could be put into practise simply. The body weight had an increasing affect on Q3, which was confirmed through the published literature. , Although both of the two studies discovered the affect of age on V1, this is a limitation that the patients' age ranged from 19 to 77 in our study, when compared to those ranged from 2 to 88 in Schuttler  study.
The phenomenon mentioned by Schuttler  that the model resulted in a underestimation (observed > predicted) when concentration >8 μg × ml -1 still appeared in our analysis, for a high concentration caused a reduced blood flow in liver, and a decreased clearance as a chain reaction. And also, to be mixed instantaneously was a little difficult for high concentrations.  Therefore, high concentrations were still unable to predict ideal values. Maybe propofol at high concentration may follow nonlinear pharmacokinetics. ,
The difference of the precision among the three measuring instruments was discovered during the NONMEM analysis when including the instrument as an indicator variable for residual variability.
The limitation of this study, when compared to the one by Schuttler et al. is that no children and elderly patients were included in present study. However, our study represents the Chinese population. The data collected from four hospitals which were widely located in main land of China are more representative than any paper reported before. ,,,,
| » References|| |
|1.||Schuttler J, Ihmsen H. Population pharmacokinetics of propofol: A multicenter study. Anesthesiology 2000;92:727-38. |
|2.||Wang Y. Derivation of various NONMEM estimation methods. J Pharmacokinet Pharmacodyn 2007;34:575-93. |
|3.||Wahlby U, Jonsson EN, Karlsson MO. Assessment of actual significance levels for covariate effects in NONMEM. J Pharmacokinet Pharmacodyn 2001;28:231-52. |
|4.||Boeckmann AJ, Sheiner LB, Beal SL. NONMEM Users Guide-Part V Introduction Guide. San Francisco: University of California; 1994. p. 81. |
|5.||Guo T. Modern Pharmacokinetics. 1st. Beijing: Chinese Science and Technology Press; 2005. p. 167-9. |
|6.||Boeckmann AJ, Sheiner LB, Beal SL. NONMEM Users Guide-Part V Introdution Guide and PART VIII Help Guide. San Francisco and ICON development Ltd: University of California; 2010. p. 81-2. |
|7.||Lindbom L, Ribbing J, Jonsson EN. Perl-speaks-NONMEM (PsN)-A Perl module for NONMEM related programming. Comput Methods Programs Biomed 2004;75:85-94. |
|8.||Jonsson EN, Karlsson MO. Xpose-an S-PLUS based population pharmacokinetic/pharmacodynamic model building aid for NONMEM. Comput Methods Programs Biomed 1999;58:51-64. |
|9.||Team R, Development, Core. R: A Language and Environment for Statistical Computing [Internet]. (R Foundation for Statistical Computing), . Available from: http://www.R-project.org. |
|10.||Li YH, Rui JZ, Zhou YG, Wang LQ. Evaluation of population pharmacokinetic parameter for Chinese using NONMEM program. Chin J Anesthesiol 2003;23:173-7. |
|11.||Zhang XA, Rui JZ, Wu QL, Shi C, Wang RS. Evaluation of population pharmacokinetics of propofol with NONMEM. Chin J Clin Pharmacol 2004;6:444-8. |
|12.||Zheng H, Cao X, Xuan Y. Effect of acute normovolemic hemodilution on the pharmacokinetics of propofol by TCI during hip joint replacement. Chin J Anesthesiol 2006;5:428-31. |
|13.||Li YH, Rui JZ, Zhou YG, Wang LQ, Fu SE, Yang JJ, et al. Population pharmacokinetics of propofol in Chinese patients. Acta Pharmacol Sin 2003;24:581-8. |
|14.||Major E, Aun C, Yate PM, Savege M, Verniquet AJ, Adam H, et al. Influence of sample site on blood concentration of ICI 35868. Br J Anaesth 1983;55:371-5. |
|15.||Coetzee JF, Glen JB, Wium CA, Boshoff L. Pharmacokinetic model selection for TCIs of propofol. Assessment of three parameter sets. Anesthesiology 1995;82:1328-45. |
|16.||Vuyk J, Engbers FH, Brurm AG, Vletter AA, Bovill JG. Performance of computer-controlled infusion of propofl: An evaluation of five pharmacokinetic parameter sets. Anesth Analg 1995;81:1275-82. |
|17.||Zheng H, Wang J, Cao XH, Rui JZ, Liu J. Population pharmacokinetics of propofol administrated by TCI in chinese. Chin J Anesthesiol 2004;5:12-4. |
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3], [Table 4]
|This article has been cited by|
||Three-bottle exponential decay infusion: A novel intravenous infusion strategy for drugs which conform to three-compartment model with observations on propofol kinetics
| ||Huang, X.-h., Xie, H.-t., Wang, K., Huang, J.-h., Sun, R.-y. |
| ||Bangladesh Journal of Pharmacology. 2012; 7(4): 223-235 |