Pharmacokinetic Model For Propofol Research Articles

A comparison of the predictive performance of three pharmacokinetic models for propofol using measured values obtained during target‐controlled infusion

We compared the predictive performance of the existing Diprifusor and Schnider models, used for target-controlled infusion of propofol, with a new modification of the Diprifusor model (White) incorporating age and sex covariates. The bias and inaccuracy (precision) of each model were determined using computer simulation to replicate the infusion profiles in an earlier study of 41 patients undergoing surgery with propofol administered by target-controlled infusion and in which timed, measured blood propofol concentrations were available. Bias with the White model (5%) was significantly less (p < 0.0001) than with the Diprifusor (16%) or Schnider (15%) models. The White model showed a significant improvement in accuracy (inaccuracy 19%, p < 0.0001) relative to the Diprifusor (26%). Temporal changes were such that with all three models, bias differed at induction and recovery. None of the models accounted fully for the extent of inter-individual variation in propofol clearance, but the improved performance with the White model suggests it has merit.

Validation of pharmacokinetic model of propofol in Indian population

The pharmacokinetics of propofol has been evaluated extensively in a variety of patients groups after either bolus doses or continuous infusions. Presently there are multiple models available based on western data including China. So far, the pharmacokinetics of propofol has not been studied in the Indian population. With this background we planned to evaluate pharmacokinetics of propofol in Indian patients which will help in better management of these patients undergoing surgery using propofol infusion in total intravenous anesthesia. Venous blood samples (3 ml) for estimation of propofol concentrations were taken at different time intervals. Plasma propofol concentration was estimated by using High Performance Liquid Chromatography (HPLC) method. Maximum performance error occurred at 2 min with a median of -3.85 and it varied from -1.7 to -9.5 showing a consistent over prediction of the concentration at two minutes after the loading dose and start of infusion. Subsequently the error decreased to median of -0.9 (-0.9 to 4.6) at 10 minutes and median of -0.3 (range-0.3 to 2.8) at 30 and in 60 min -1.55(-0.28 to 1). When we compare the performance of our pharmacokinetic model of propofol in this study with other western studies, we observed less error with our pharmacokinetic model. Key words: Propofol, pharmacokinetics, median performance error (MDPE), median absolute performance error (MDAPE).

Prospective randomised comparison of Marsh and Schnider pharmacokinetic models for propofol during induction of anaesthesia in elective cardiac surgery

Haemodynamic stability during induction is a cornerstone of cardiac anaesthesia. The evaluation of pharmacokinetic models for propofol during induction is lacking. To compare haemodynamics during cardiac anaesthesia induction with two pharmacokinetic models. Randomised controlled trial. Department of Cardiothoracic Surgery, São João Hospital; July to December 2010. Ninety consecutive elective adult cardiac surgical patients. Random assignment to effect-site target-controlled infusion by Marsh (n = 45) or Schnider (n = 45) pharmacokinetic models with an equilibration constant of 1.2 min(-1) adapted to Marsh's model. Invasive blood pressure measurements, propofol dose, and bispectral index (BIS) were recorded. After an initial target concentration of 1.5 μg ml(-1), concentrations were upward-titrated in 0.5 μg ml(-1) increments until the BIS was <50. No differences were observed between Marsh and Schnider models in required propofol dose (0.99 ± 0.26 vs. 0.93 ± 0.31 mg kg(-1), P = 0.322), decrease in mean blood pressure (25 ± 13 vs. 22 ± 14%, P = 0.192) or the need for vasopressors (20 vs. 24%, P = 0.800), but the use of the Marsh model resulted in a lower predicted effect-site concentration (2.3 ± 0.4 vs. 2.7 ± 0.6 μg ml(-1), P = 0.006) and shorter time to induction (296 ± 59 vs. 338 ± 87 s, P = 0.024). There was a greater decrease in mean blood pressure in older patients (>60 years; 29 ± 10 vs. 22 ± 11%, P = 0.004) irrespective of model, but obese (BMI ≥30 kg m(-2)) and nonobese patients did not differ. Decrease in mean blood pressure. In effect-site targeting with a 1.2 min(-1) equilibration constant, Marsh's model is comparable to Schnider's during induction of anaesthesia.

Propofol Clearance in Morbidly Obese Children and Adolescents

Given the alarming increase in obesity among children undergoing surgery, the main aim of this study was to characterize propofol clearance in a cohort of morbidly obese children and adolescents in relation to their age and body weight characteristics. A prospective pharmacokinetic study in morbidly obese children and adolescents undergoing elective surgery was conducted. Serial blood samples were collected and nonlinear mixed-effects modelling using NONMEM(®) was performed to characterize propofol pharmacokinetics with subsequent evaluation of age and body size descriptors. Twenty obese and morbidly obese children and adolescents with a mean age of 16 years (range 9-18 years), a mean total body weight (TBW) of 125 kg (range 70-184 kg) and a mean body mass index of 46 kg/m(2) (range 31-63 kg/m(2)) were available for pharmacokinetic modelling using a two-compartment pharmacokinetic model (n = 294 propofol concentration measurements). Compared with lean body weight and ideal body weight, TBW proved to be the most predictive covariate for clearance [CL (L/min) = 1.70 × (TBW/70)(0.8)]. Central volume of distribution, peripheral volume and intercompartmental clearance were 45.2 L, 128 L and 1.75 L/min, respectively, with no predictive covariates identifiable. In the population pharmacokinetic model for propofol in morbidly obese children and adolescents, TBW proved to be the most significant determinant for clearance. As a result, it is anticipated that dosage of propofol for maintenance of anaesthesia in morbidly obese children and adolescents should be based on TBW using an allometric function.

The peak bispectral index time cannot predict early phase propofol pharmacodynamics with effect site-controlled infusion algorithm

Objectives:The plasma-effect site equilibration rate constant (ke0) of propofol was determined with peak bispectral index (BIS) time (TPEAK) in our previous study. The present study has been conducted to evaluate the ke0's performance with effect site-controlled infusion algorithm.Materials and Methods:Forty unpremedicated patients were randomized to group TE1 (Schnider's pharmacokinetic model with ke0 adapted to TPEAK = 74s) and TE2 (TPEAK = 96s). In stage 1, all patients received propofol with effect-site concentration (Ce) controlled infusion. Once the pump had injected the mass of propofol necessary to achieve pre-set Ce and while the infusion was stopped, target was reset at 0 μg/ml. When BIS returned to 80 or above, then, in stage 2, the patients received plasma concentration controlled infusion for 10 min. The time of loss of responsiveness (LOR) and BIS were recorded. The differences of Ce at the time of LOR, lowest BIS between stages 1 and 2, hysteresis loop were used to evaluate the performance of ke0.Results:In both groups, the calculated propofol Ce at the time of LOR in stages 1 and 2 differed significantly (P<0.01); the mean lowest BIS in stage 1 were significantly higher than those in stage 2 (P < 0.05).The relations of propofol Ce versus BIS revealed the apparent hysteresis loop.Conclusions:The study cannot clinically validate the accuracy of application of ke0 derived from the TPEAK = 74 s of BIS with Schnider propofol pharmacokinetic model.

Performance evaluation of paediatric propofol pharmacokinetic models in healthy young children

The performance of eight currently available paediatric propofol pharmacokinetic models in target-controlled infusions (TCIs) was assessed, in healthy children from 3 to 26 months of age. Forty-one, ASA I-II children, aged 3-26 months were studied. After the induction of general anaesthesia with sevoflurane and remifentanil, a propofol bolus dose of 2.5 mg kg(-1) followed by an infusion of 8 mg kg(-1) h(-1) was given. Arterial blood samples were collected at 1, 2, 3, 5, 10, 20, 40, and 60 min post-bolus, at the end of surgery, and at 1, 3, 5, 30, 60, and 120 min after stopping the infusion. Model performance was visually inspected with measured/predicted plots. Median performance error (MDPE) and the median absolute performance error (MDAPE) were calculated to measure bias and accuracy of each model. Performance of the eight models tested differed markedly during the different stages of propofol administration. Most models underestimated propofol concentration 1 min after the bolus dose, suggesting an overestimation of the initial volume of distribution. Six of the eight models tested were within the accepted limits of performance (MDPE<20% and MDAPE<30%). The model derived by Short and colleagues performed best. Our results suggest that six of the eight models tested perform well in young children. Since most models overestimate the initial volume of distribution, the use for TCI might result in the administration of larger bolus doses than necessary.

Total Intravenous Anesthesia with Target-Controlled Infusion of Remifetanil and Propofol for Ablation of Atrial Fibrillation

Although ablation of atrial fibrillation (AF) is common in other centers, among us it is a new procedure. The choice of anesthesia, monitors, and anesthesiologic care for this procedure performed outside the surgical center has not been described. The objective of this report was to describe an anesthesia technique for ablation of AF. This is a 49-year old female weighing 73 kg, 155 cm, and ASA II due to hypertension. The patient was monitored with a 12-lead ECG, pulse oximetry, heart rate, bispectral electroencephalography for BIS measurement, suppression rate (SR), and SEF95, and mean arterial pressure (MAP). Intravenous target-controlled infusion (TCI) of propofol with a target of 4 microg x mL(-1), intravenous TCI of remifentanil with a target of 3 ng x mL(-1), and intravenous bolus of rocuronium 0.2 mg x kg(-1) were used for induction of anesthesia. The pharmacokinetic model of propofol described by Marsh was used and incorporated into the propofol PFS pump. The pharmacokinetic model of remifentanil described by Minto was incorporated into the Alaris PK infusion pump. Local effector, or biophase, concentrations corresponded to the information obtained from the infusion pumps and represented predictive measurements of the concentrations of both drugs on their sites of action. The concentrations of propofol and remifentanil were regulated according to BIS and MAP, respectively. Total intravenous anesthesia for ablation of AF can be a safe option considering the lack of electrophysiological changes in accessory pathways. The literature on this subject is scarce and new publications could justify, or not, this type of anesthesia during ablation of AF.

Evaluation of the predictive performance of four pharmacokinetic models for propofol

This study has compared the predictive performance of four pharmacokinetic models, two of which are currently incorporated in commercial target-controlled infusion pumps for the administration of propofol. Arterial propofol concentrations and patient characteristic data were available from nine patients who, in a published study, had received a standardized infusion of propofol. Predicted concentrations with 'Diprifusor' (Marsh), 'Schnider', 'Schuttler', and 'White' models were obtained by computer simulation. The predictive performance of each model was assessed overall and over the following phases: rapid infusion (1-5 min), early (1-21 min), maintenance (21-min end-infusion), and recovery (2-20 min post-infusion). The overall assessment, based on 29-36 samples from each patient, indicated that all four models were clinically acceptable. However, the negligible bias (-0.1%) with the 'Schnider' model was accompanied by overprediction in the rapid infusion phase and underprediction during recovery. This changing bias over time was not detected as 'divergence' when assessed on absolute performance error (APE), (1.4% h(-1)) but became significant (13.2% h(-1)) when based on changes in signed PE over time. The 'Schuttler' model performed well at most phases but overpredicted concentrations during recovery. The White model led to a marginal improvement over 'Diprifusor' and would be expected to reduce the positive bias usually seen with 'Diprifusor' systems. In assessing the predictive performance of pharmacokinetic models, additional information can be obtained by analysis of bias at different phases of an infusion. The evaluation of divergence should involve linear regression analysis of both absolute and signed PEs.

Mixed-Effects Modeling of the Influence of Midazolam on Propofol Pharmacokinetics

The combined administration of anesthetics has been associated with pharmacokinetic interactions that induce concentration changes of up to 30%. Midazolam is often used as a preoperative sedative in advance of a propofol-based anesthetic. In this study, we identified the influence of midazolam on the pharmacokinetics of propofol. Eight healthy male volunteers were studied on two occasions in a random crossover manner. During Session A, volunteers received propofol 1 mg/kg in 1 min followed by an infusion of 2.5 mg x kg(-1) x h(-1) for 59 min. During Session B, in addition to this propofol infusion scheme, a target-controlled infusion of midazolam (constant C(t): 125 ng/mL) was given from 15 min before the start until 6 h after termination of the propofol infusion. Arterial blood samples for blood propofol and plasma midazolam concentration analysis were taken until 6 h after termination of the propofol infusion. Nonlinear mixed-effects models examining the influence of midazolam and hemodynamic variables on propofol pharmacokinetics were constructed using Akaike criterion for model selection. In the presence of midazolam (C(blood): 224.8 +/- 41.6 ng/mL), the blood propofol concentration increased by 25.1% +/- 13.3% compared with when propofol was given as single drug. Midazolam (C(blood): 225 ng/mL) reduced propofol Cl(1) from 1.94 to 1.61 L/min, Cl(2) from 2.86 to 1.52 L/min, and Cl(3) from 0.95 to 0.73 L/min. Inclusion of mean arterial blood pressure further improved the propofol pharmacokinetic model. Midazolam reduces the metabolic and rapid and slow distribution clearances of propofol. In addition, a reduction in mean arterial blood pressure is associated with propofol pharmacokinetic alterations that increase the blood propofol concentration.

Encouraging the Move Towards Predictive Population Models for the Obese Using Propofol as a Motivating Example

To develop a predictive pharmacokinetic model for propofol that could inform development of a dosing strategy for the obese population. A prior model that included a nonlinear relationship between clearance (CL) and Total Body Weight (TBW) was re-parameterized with a linear relationship between CL and Lean Body Weight (LBW). The predictive performance of both models was compared and the LBW model used to explore propofol exposure from normal to obese patients. A dosing strategy was evaluated that normalized awakening time across a range of patient weights. The predictive performance of the LBW model was similar to the nonlinear TBW model for normal weighted subjects. Simulations in 70-160 kg subjects indicated that dosing linearly on TBW (label recommendation), in contrast to LBW, resulted in increased plasma concentrations in the larger weight groups. This result might explain why obese subjects take longer to awaken from anesthesia compared to normal weighted subjects. Dosing by LBW normalized patient awakening times across this weight range. LBW as a covariate provides a plausible mechanistic explanation for an observed nonlinear increase in drug CL with TBW and may be suitable for developing dosing strategies that are appropriate for use in the obese population.

TCI : Target Controlled Infusion, or Totally Confused Infusion? Call for an Optimised Population Based Pharmacokinetic Model for Propofol.

Different pharmacokinetic models for target controlled infusion (TCI) of propofol are available in the recently launched open TCI systems. There is also a compelling choice to work with either plasma- or effect-site targets. Knowledge about the clinical consequences of different alternatives is of importance. We aimed to illustrate the potential differences in the actual drug delivery/output between three present commercially available and clinically used pharmacokinetic models: the original Marsh model, which is also implemented in the Diprifusor®, the “modified Marsh-” and the Schnider models.Simulations were made in the TivaTrainer program (eurosiva.com). Firstly, our standard plasma target regimen was simulated, and secondly an effect-site target of 3.5 μg/mL was chosen. Thirdly, real infusors were used for measuring the time to reach defined predicted effect-site concentrations when aiming at a plasma target of 6 μg/mL. Identical patient characteristics were used in all simulations: male, 170 cm, 70 kg, 40 years of age. Resulting predicted effect-site peak concentrations, and used bolus doses were recorded, as were the resulting plasma over-shoot, and time frames.The plasma target regimen gave predicted effect-site peaks in the different models ranging from 3.6 to 7.2 μg/mL, reached after 2¾ to 4 minutes. To reach the same effect-site target, the three models used bolus doses ranging from 68 to 150 mg given during 22 to 46 seconds. The predicted plasma concentration over-shoots varied from 5.0 to 13.4 μg/mL. There were obvious differences between the models in the time taken to reach defined effect-site concentrations.We observed clinically significant different results between the models. The choice of model will make a difference for the patient. To eliminate confusion – not necessarily to improve precision – we call for an optimised population based pharmacokinetic model for propofol – a consensus model!

Brain monitoring in dogs using the cerebral state index during the induction of anaesthesia via target-controlled infusion of propofol

The aim of this study was to evaluate the correlation between the cerebral state index (CSI) and the estimated propofol plasma concentrations in dogs during induction of anaesthesia. Fifteen healthy dogs undergoing scheduled routine surgical procedures were enrolled in this study. Target controlled infusion (TCI) software, based on the pharmacokinetic model for propofol, was used to control the syringe pump and to estimate plasma propofol concentrations (PropCp) and the CSI values every five-seconds. Three electrodes placed in the centre of the forehead, on the left side of the forehead and on the left mastoid were used to collect the electroencephalographic (EEG) signal converted by the cerebral state monitor into the CSI. The cerebral electrical changes induced by increasing propofol concentrations appear to be detected by CSI monitoring in dogs. The negative correlation between CSI and PropCp demonstrates that the CSI could be used to assess electrical brain activity in dogs during the induction of anaesthesia with propofol.

34th Annual Scientific Meeting of the Association of Paediatric Anaesthetists of Great Britain and Ireland, 8?10 March 2007, Manchester. Paediatric propofol pharmacokinetics: a multicentre study

Introduction: Schuttler and Imhsens propofol pharmacokinetic (PK) model (1) based on pooled adult and paediatric data lacked information for the smallest patients. We conducted a pooled population analysis of available neonatal and paediatric propofol PK data. The variable clinical circumstances of the individual studies allowed us to explore health status as a PK model covariate. Methods: We combined raw propofol blood/plasma concentration vs time data, dosing information and demographic data from eight paediatric studies conducted by six research groups, with our data (2,3). The pooled data set comprised 197 individuals (2315 observations), aged 0.02–12.25 years (2.75–60.5 kg, median 15 kg). In this preliminary population PK analysis using NONMEM, the basic model structure was established before all model parameters were allometrically scaled to body weight. The influence of health status on paediatric propofol PK was explored. Results: In this 3-compartment preliminary model, postcardiac surgery patients have significantly reduced metabolic clearance rates (31–45% less when compared with healthy children or noncardiac PICU patients). The volume of the deep peripheral compartment in critically ill and postcardiac surgery children is 319% and 205% larger, respectively, than in healthy children, see Table 1. Table 1. PK values for a child weighing 15 kg Parameter Typical Value 95% Confidence Interval CL (l·min-1) Healthy 0.614 0.563–0.665 PICU 0.767 0.628–0.906 PICU cardiac 0.421 0.366–0.476 Q2 (l·min-1) 0.839 0.703–0.975 Q3 (l·min-1) 0.252 0.221–0.283 V1 (l) 7.76 6.33–9.19 V2 (l) 14.4 12.8–16.0 V3 (l) Healthy 83.9 61.4–106 PICU 268 183–353 PICU cardiac 172 117–227

Pharmacokinetics and effects of propofol 6% for short-term sedation in paediatric patients following cardiac surgery.

This paper describes the pharmacokinetics and effects of propofol in short-term sedated paediatric patients. Six mechanically ventilated children aged 1-5 years received a 6 h continuous infusion of propofol 6% at the rate of 2 or 3 mg kg-1 h-1 for sedation following cardiac surgery. A total of seven arterial blood samples was collected at various time points during and after the infusion in each patient. Pharmacokinetic modelling was performed using NONMEM. Effects were assessed on the basis of the Ramsay sedation score as well as a subjective sedation scale. The data were best described by a two-compartment pharmacokinetic model. In the model, body weight was a significant covariate for clearance. Pharmacokinetic parameters in the weight-proportional model were clearance (CL) = 35 ml kg-1 min-1, volume of central compartment (V1) = 12 l, intercompartmental clearance (Q) = 0.35 l min-1 and volume of peripheral compartment (V2) = 24 l. The interindividual variabilities for these parameters were 8%, < 1%, 11% and 35%, respectively. Compared with the population pharmacokinetics in adults following cardiac surgery and when normalized for body weight, statistically significant differences were observed the parameters CL and V1 (35 vs 29 ml kg-1 min-1 and 0.78 vs 0.26 l kg-1P < 0.05), whereas the values for Q and V2 were similar (23 vs 18 ml kg-1 min-1 and 1.6 vs 1.8 l kg-1, P > 0.05). In children, the percentage of adequately sedated patients was similar compared with adults (50% vs 67%) despite considerably higher propofol concentrations (1.3 +/- 0.10 vs 0.51 +/- 0.035 mg l-1, mean +/- s.e. mean), suggesting a lower pharmacodynamic sensitivity to propofol in children. In children aged 1-5 years, a pharmacokinetic model for propofol was described using sparse data. In contrast to adults, body weight was a significant covariate for clearance in children. The model may serve as a useful basis to study the role of covariates in the pharmacokinetics and pharmacodynamics of propofol in paediatric patients of different ages.

Target-controlled anaesthesia: concepts and first clinical experiences.

Propofol has a favourable pharmacokinetic profile for total intravenous anaesthesia and several manual infusion schemes have been proposed to maintain a constant blood concentration during anaesthesia. However, such schemes cannot respond predictably to changing surgical and anaesthetic requirements. A pharmacokinetic model for propofol has been incorporated into a target-controlled infusion system. This allows the desired target blood concentration appropriate for any individual patient and level of surgical stimulation to be achieved and maintained at any time. There is no single blood concentration of an anaesthetic agent which will result in satisfactory anaesthesia for all patients and all surgical conditions. It is necessary to titrate the target concentration against each patient's clinical response. Target-controlled systems provide the best estimate of the blood concentration at any time and permit the required target concentration to be achieved as accurately and as rapidly as possible.