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Background: Intraoperation hypotension (IOH) is commonly observed in patients undergoing surgery under general anesthesia, and even a brief episode of IOH can lead to unfavorable outcomes. To reduce the risk, blood pressure is closely measured during general anesthesia, and norepinephrine (NE) is frequently administered if hypotension is detected. Despite its routine application, information on the dose-exposure-response relationship of NE remains limited. Additionally, quantification of the influence of general anesthesia on the pharmacokinetics (PK) of NE is lacking., Objective: In this study, we aimed to describe NE PK in healthy volunteers and the influence of general anesthesia on its PK., Methods: A single-center, cross-over study was conducted in healthy volunteers. The volunteers received a step-up NE dosing scheme (0.04, 0.08, 0.12, 0.16 and 0.20 mcg -1 /kg -1 /min -1 ) first in the awake state and then under general anesthesia. General anesthesia was administered using a propofol/remifentanil Eleveld target-controlled infusion. During general anesthesia, a 30-second electrical stimulus was given as surrogate for surgical incision to the volunteers at each dosage step. Blood samples were drawn before the initial dosing and after each dosing step, and plasma NE, propofol and remifentanil concentrations were subsequently determined. A population PK model was developed using non-linear mixed effects modelling. Simulations were conducted to predict the plasma NE concentration in patients at different measured propofol concentrations., Results: A total of 1219 samples were analyzed from 36 volunteers. A two-compartment model with a first-order elimination best described the data. Weight, age, and session effect (awake vs general anesthesia) were identified as relevant covariates on the clearance (CL) of NE. A 10% decrease in NE CL was observed after general anesthesia induction. This difference between sessions is better explained by the measured concentration of propofol, rather than the anticipated impact of cardiac output. The estimated post-stimulation NE concentration is 0.66 nmol/L -1 (95% CI 0.06-1.20 nmol/L -1 ) lower than the pre-stimulation NE concentration. Model simulation indicates that patients at a higher measured propofol concentration (e.g., 6 mcg/mL -1 ) exhibited higher NE concentrations (95% PI 18.10-43.89 nmol/L -1 ) than patients at a lower measured propofol concentration (e.g., 3 mcg/mL -1 ) (95% PI 16.81-38.91 nmol L -1 )., Conclusion: The NE PK is well described with a two-compartment model with a first-order elimination. NE CL exhibiting a 10% decrease under general anesthesia, with this difference being attributed to the measured concentration of propofol. The impact of stimulation on NE PK under general anesthesia is very limited., Clinical Trials Registration Number: NL9312., Competing Interests: Declarations Funding The study is supported by the Department of Anesthesiology at University Medical Center Groningen, The Netherlands. Conflicts of Interest M.M.R.F Struys: His research group/department received (over the last 3 years) research grants and consultancy fees from The Medicines Company (Parsippany, NJ, USA), Masimo (Irvine, CA, USA), Becton Dickinson (Eysins, Switzerland), Fresenius (Bad Homburg, Germany), Dräger (Lübeck, Germany), Paion (Aachen, Germany), Medtronic (Dublin, Ireland). He receives royalties on intellectual property from Demed Medical (Sinaai, Belgium) and the Ghent University (Gent, Belgium). He is an editorial board member and Director for the British Journal of Anaesthesia. D.J. Eleveld: Has received travel from Becton Dickenson and is an editorial board member of Anesthesiology journal. P.J. Colin: Over the last 3 years his research group has been involved in contract research for PAION UK Ltd. (London, England) and Acacia Pharma Ltd. (Cambridge, England). Yingxue Li, Jeroen V. Koomen, Johannes P. van den Berg, Jaap Jan Vos, Ilonka N. de Keijzer declare that they have no conflicts of interest that are relevant to the content of this article. Ethics Approval The protocol of the study has been approved by the local medical ethics committee. Consent to Participate Informed consent was acquired from each individual participant incorporated in the study. Consent for Publication Not applicable. Availability of Data and Material The data and material are accessible upon reasonable request by contacting the corresponding author. Code Availability The model code is accessible upon reasonable request by contacting the corresponding author. Authors’ Contributions M.M.R.F. Struys, P.J. Colin, J.P. Van den Berg, J.J. Vos: conception and design of the study and manuscript revision; J.P. Van den Berg, J.J. Vos, I.N. de Keijzer: data collection, bioanalysis and manuscript revision; Y Li, J.V. Koomen, D.J. Eleveld, P.J. Colin: data analysis and interpretation, modelling, manuscription preparation and revision., (© 2024. The Author(s).)

Background: Target-controlled infusion (TCI) systems incorporating pharmacokinetic (PK) or PK-pharmacodynamic (PK-PD) models can be used to facilitate drug administration. Existing models were developed using data from select populations, the use of which is, strictly speaking, limited to these populations. Recently a propofol PK-PD model was developed for a broad population range. The aim of the study was to prospectively validate this model in children, adults, older subjects, and obese adults undergoing general anaesthesia., Methods: The 25 subjects included in each of four groups were stratified by age and weight. Subjects received propofol through TCI with the Eleveld model, titrated to a bispectral index (BIS) of 40-60. Arterial blood samples were collected at 5, 10, 20, 30, 40, and 60 min after the start of propofol infusion, and every 30 min thereafter, to a maximum of 10 samples. BIS was recorded continuously. Predictive performance was assessed using the Varvel criteria., Results: For PK, the Eleveld model showed a bias < ±20% in children, adults, and obese adults, but a greater bias (-27%) in older subjects. Precision was <30% in all groups. For PD, the bias and wobble were <5 BIS units and the precision was close to 10 BIS units in all groups. Anaesthetists were able to achieve intraoperative BIS values of 40-60 using effect-site target concentrations about 85-140% of the age-adjusted Ce 50 ., Conclusions: The Eleveld propofol PK-PD model showed predictive precision <30% for arterial plasma concentrations and BIS predictions with a low (population) bias when used in TCI in clinical anaesthesia practice., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

We present a race-specific model of propofol-induced loss of consciousness that is based on pharmacodynamic data collected and adapted from the peer-reviewed literature. In the proposed race-specific model that includes EC05 and EC95 concentrations, the median (EC50) (and where available 95%CI) propofol concentrations at the effect site compartment for propofol-induced loss of consciousness for whites, Chinese, blacks, and Indians are 2.8 (2.7-2.9), 2.2 (2.2-2.3), 2.0, and 1.9 μg/mL, respectively., (© 2016, The American College of Clinical Pharmacology.)

Background: Hemodynamic stability is one of the most critical concerns during induction of anesthesia. Whether the pharmacokinetic model by Marsh or the one by Schnider will produce better hemodynamic stability remains unclear. This study compared hemodynamic changes during induction between the two models., Methods: 60 patients who underwent elective surgery were randomly assigned to plasma target-controlled infusion by Marsh's (n = 30) or Schnider's (n = 30) model with an initial target concentration of 4 μg×mL-1. The target was then reset and gradually titrated to a sedation level with a narcotrend index (NI) below 64. Stroke volume, cardiac output, systemic vascular resistance, arterial pressure, target, and effect site concentration, and dose of propofol infused were recorded every minute during the first 25 minutes of infusion., Results: Throughout the first 25 minutes, stroke volume index and cardiac index were decreased significantly in both Marsh and Schnider groups, but no statistical difference was detected between the groups (p > 0.05). Central venous pressure (CVP), systemic vascular resistance index (SVRI), and heart rate (HR) did not significantly change during induction (p > 0.05). Time to loss of responsiveness (LOR), and time for NI to decrease to 64 was faster in Marsh than in Schnider (1.51 ± 0.8 minutes vs. 2.8 ± 1.2 min, p < 0.001; 3.3 ± 2.0 minutes vs. 5.2 ± 2.3 minutes, p < 0.01, respectively)., Conclusions: When target concentrations are titrated according to NI during induction of anesthesia, Marsh's model could induce sedation faster than Schnider's. Meanwhile, hemodynamic changes were not observed to be statistically different between the two models. Hypotension induced by plasma target-controlled infusion of propofol could mainly be attributed to decreased stroke volume instead of vascular dilation.

Online measurement of drug concentrations in patient's breath is a promising approach for individualized dosage. A direct transfer from breath- to blood-concentrations is not possible. Measured exhaled concentrations are following the blood-concentration with a delay in non-steady-state situations. Therefore, it is necessary to integrate the breath-concentration into a pharmacological model. Two different approaches for pharmacokinetic modelling are presented. Usually a 3-compartment model is used for pharmacokinetic calculations of blood concentrations. This 3-compartment model is extended with a 2-compartment model based on the first compartment of the 3-compartment model and a new lung compartment. The second approach is to calculate a time delay of changes in the concentration of the first compartment to describe the lung-concentration. Exemplarily both approaches are used for modelling of exhaled propofol. Based on time series of exhaled propofol measurements using an ion-mobility-spectrometer every minute for 346 min a correlation of calculated plasma and the breath concentration was used for modelling to deliver R(2) = 0.99 interdependencies. Including the time delay modelling approach the new compartment coefficient k(e0lung) was calculated to k(e0lung) = 0.27 min(-1) with R(2) = 0.96. The described models are not limited to propofol. They could be used for any kind of drugs, which are measurable in patient's breath.

We studied the use of a new ke0 value (0.6 min(-1)) for the Marsh pharmacokinetic model for propofol. Speed of induction and side-effects produced were compared with three other target-controlled infusion systems. Eighty patients of ASA physical status 1-2 were studied in four groups in a prospective, randomised study. Median (IQR [range]) induction times were shorter with the Marsh model in effect-site control mode with a ke0 of either 0.6 min(-1) (81 (61-101 [49-302])s, p < 0.01), or 1.2 min(-1) (78 (68-208 [51-325])s, p < 0.05), than with the Marsh model in blood concentration control (132 (90-246 [57-435])). The Schnider model in effect-site control produced induction times that were longer (298 (282-398 [58-513])s) than those observed with the Marsh model in blood control (p < 0.05), or either effect-site control mode (p < 0.001). There were no differences in the magnitude of blood pressure changes or frequency of apnoea between groups., (© 2014 The Association of Anaesthetists of Great Britain and Ireland.)

Background: Propofol target-controlled infusion (TCI) in effect site mode has become popular since it became commercially available., Objective: We have performed a study to assess the pharmacokinetic performance of the Marsh model in effect site mode in an unselected group of patients during neurosurgery during the maintenance phase of anaesthesia., Design: Fifty American Society of Anesthesiologists (ASA) physical status classes 1 to 3 adults underwent elective neurosurgery receiving propofol TCI using the Marsh model in effect site mode. Propofol dose titration and level of patient monitoring was determined by the attending anaesthesiologist. Arterial blood was sampled at regular intervals during the maintenance phase of anaesthesia and measured plasma propofol concentrations were compared with those estimated using TCI., Setting: Large tertiary referral centre in Birmingham, UK, with a specialist neuroanaesthesia service., Patients: Fifty ASA status I to III adult patients undergoing elective neurosurgery., Main Outcome Measures: Performance of Marsh model as assessed by median performance error (bias) and median absolute performance error (imprecision)., Results: Performance of the Marsh model showed a positive bias (median performance error) of 27.6%, and imprecision (median absolute performance error) of 29.4%. Analysis of pooled data demonstrated greatest bias in the early phase (15 to 30 min) of anaesthesia (mean prediction error of 51.6%). Analysis of covariates demonstrated that obesity (BMI >30 kg m(-2)) contributed around half of the bias detected (mean prediction error 47 vs. 23%, P < 0.001). Patients with advanced age and significant comorbidity (ASA physical status class >2) actually demonstrated significantly lower prediction errors., Conclusion: Pharmacokinetic analysis suggests that the performance of the Marsh model in effect site mode is poor in this broad patient population. The greatest bias demonstrated occurred in the early maintenance phase of anaesthesia. Of the covariates analysed, obesity contributed most significantly to an increased bias. Despite overall poor performance of the Marsh model, attending anaesthesiologists modified targeted propofol concentrations only 0.3 times per hour on average, using remifentanil dose modification nine times more frequently, with good surgical conditions in all patients.

Objective: Many processed electroencephalographic signals are used now to help the anaesthesiologist titrate the depth of sedation. We investigated the relationship between target plasma propofol concentration and objective end-points of sedation- Bispectral Index (BIS), State Entropy (SE) and Response Entropy (RE)-at clinical end-points as assessed by Modified Observer Assessment of Alertness/sedation Scale (MOAAS) in Indian patients., Methods: Eighteen ASA 1 and 2 Indian adult patients scheduled to undergo elective surgery were included. The target control infusion (TCI) of propofol was administered using 'Diprifusor'. The level of sedation was assessed using MOAAS by the anaesthesiologist. BIS, SE, RE were recorded throughout. TCI was started at 0.5 μg/ml and increased by 0.5 μg/ml every 6 min till MOAAS scores reached 0 or there was sustained BIS value less than 30., Results: The EC(50) and EC(95) of predicted plasma propofol concentration for loss of consciousness (assessed by loss of response to verbal command), were 2.3 and 2.8 μg/ml respectively and for loss of response to painful stimuli (trapezius squeeze) were 4.0 and 5.0 μg/ml respectively. The BIS and entropy values (EC(50) and EC(95)) for loss of consciousness and response to painful stimuli in Indian patients were estimated. The preliminary relation of target plasma propofol concentration with BIS was found to be BIS = 100.5-16.4 × (Target concentration)., Conclusions: The target plasma propofol concentrations required to produce unconsciousness and loss of response to painful stimuli in Indian patients have been estimated. Also, the relations between target plasma concentration and objective measures of different levels of anaesthesia have been established.

Background: The objective of this study was to develop a pharmacokinetic (PK) model to characterize the influence of obesity on propofol PK parameters., Methods: Nineteen obese ASA II patients undergoing bariatric surgery were studied. Patients received propofol 2 mg kg(-1) bolus dose followed by a 5-20-40-120 min, 10-8-6-5 mg kg(-1) h(-1) infusion. Arterial blood samples were withdrawn at 1, 3, 5 min after induction, every 10-20 min during propofol infusion, and every 10-30 min for 2 h after stopping the propofol infusion. Arterial samples were processed by high-performance liquid chromatography. Time-concentration data profiles from this study were pooled with data from two other propofol PK studies available at http://www.opentci.org. Population PK modelling was performed using non-linear mixed effects model., Results: The study involved 19 obese adults who contributed 163 observations. The pooled analysis involved 51 patients (weight 93 sd 24 kg, range 44-160 kg; age 46 sd 16 yr, range 25-81 yr; BMI 33 sd 9 kg m(-2), range 16-52 kg m(-2)). A three-compartment model was used to investigate propofol PK. An allometric size model using total body weight (TBW) was superior to all other models investigated (linear TBW, free fat mass, lean body weight, normal fat mass) for all clearance parameters. Variability in V2 and Q2 was reduced by a function showing a decrease in both parameters with age., Conclusions: We have derived a population PK model using obese and non-obese data to characterize propofol PK over a wide range of body weights. An allometric model using TBW as the size descriptor of volumes and clearances was superior to other size descriptors to characterize propofol PK in obese patients.

Background: With the growing use of pharmacokinetic (PK)-driven drug delivery and/or drug advisory displays, identifying the PK model that best characterizes propofol plasma concentration (Cp) across a variety of dosing conditions would be useful. We tested the accuracy of 3 compartmental models and 1 physiologically based recirculatory PK model for propofol to predict the time course of propofol Cp using concentration-time data originated from studies that used different infusion schemes., Methods: Three compartmental PK models for propofol, called the "Marsh," the "Schnider," and the "Schüttler" models, and 1 physiologically based recirculatory model called the "Upton" model, were used to simulate the time course of propofol Cp. To test the accuracy of the models, we used published measured plasma concentration data that originated from studies of manual (bolus and short infusion) and computer-controlled (target-controlled infusion [TCI] and long infusion) propofol dosing schemes. Measured/predicted (M/P) propofol Cp plots were constructed for each dataset. Bias and inaccuracy of each model were assessed by median prediction error (MDPE) and median absolute prediction error (MDAPE), respectively., Results: The M/P propofol Cp in the 4 PK models revealed bias in all 3 compartmental models during the bolus and short infusion regimens. In the long infusion, a worse M/P propofol Cp at higher concentration was seen for the Marsh and the Schüttler models than for the 2 other models. Less biased M/P propofol Cp was found for all models during TCI. In the bolus group, after 1 min, a clear overprediction was seen for all 3 compartmental models for the entire 5 min; however, this initial error resolved after 4 min in the Schnider model. The Upton model did not predict propofol Cp accurately (major overprediction) during the first minute. During the bolus and short infusion, the Marsh model demonstrated worse MDPE and MDAPE compared with the 3 other models. During short infusion, MDAPE for the Schnider and Schüttler models was better than the Upton and the Marsh models. All models showed similar MDPE and MDAPE during TCI simulations. During long infusion, the Marsh and the Schüttler models underestimated the higher plasma concentrations., Conclusion: When combining the performance during various infusion regimens, it seems that the Schnider model, although still not perfect, is the recommended model to be used for TCI and advisory displays.

Background: Few studies address the dynamic effect of opioids on respiration. Models with intact feedback control of carbon dioxide on ventilation (non-steady-state models) that correctly incorporate the complex interaction among drug concentration, end-tidal partial pressure of carbon dioxide concentration, and ventilation yield reliable descriptions and predictions of the behavior of opioids. The authors measured the effect of remifentanil on respiration and developed a model of remifentanil-induced respiratory depression., Methods: Ten male healthy volunteers received remifentanil infusions with different infusion speeds (target concentrations: 4-9 ng/ml; at infusion rates: 0.17-9 ng x ml x min) while awake and at the background of low-dose propofol. The data were analyzed with a nonlinear model consisting of two additive linear parts, one describing the depressant effect of remifentanil and the other describing the stimulatory effect of carbon dioxide on ventilation., Results: The model adequately described the data including the occurrence of apnea. Most important model parameters were as follows: C50 for respiratory depression 1.6 +/- 0.03 ng/ml, gain of the respiratory controller (G) 0.42 - 0.1 l x min x Torr, and remifentanil blood effect site equilibration half-life (t(1/2)ke0) 0.53 +/- 0.2 min. Propofol caused a 20-50% reduction of C50 and G but had no effect on t(1/2)ke0. Apnea occurred during propofol infusion only. A simulation study revealed an increase in apnea duration at infusion speeds of 2.5-0.5 ng x ml x min followed by a reduction. At an infusion speed of < or = 0.31 ng x ml x min, no apnea was seen., Conclusions: The effect of varying remifentanil infusions with and without a background of low-dose propofol on ventilation and end-tidal partial pressure of carbon dioxide concentration was described successfully using a non-steady-state model of the ventilatory control system. The model allows meaningful simulations and predictions.

The recently introduced open-target-controlled infusion (TCI) systems can be programmed with any pharmacokinetic model, and allow either plasma- or effect-site targeting. With effect-site targeting the goal is to achieve a user-defined target effect-site concentration as rapidly as possible, by manipulating the plasma concentration around the target. Currently systems are pre-programmed with the Marsh and Schnider pharmacokinetic models for propofol. The former is an adapted version of the Gepts model, in which the rate constants are fixed, whereas compartment volumes and clearances are weight proportional. The Schnider model was developed during combined pharmacokinetic-pharmacodynamic modelling studies. It has fixed values for V1, V3, k(13), and k(31), adjusts V2, k(12), and k(21) for age, and adjusts k(10) according to total weight, lean body mass (LBM), and height. In plasma targeting mode, the small, fixed V1 results in very small initial doses on starting the system or on increasing the target concentration in comparison with the Marsh model. The Schnider model should thus always be used in effect-site targeting mode, in which larger initial doses are administered, albeit still smaller than for the Marsh model. Users of the Schnider model should be aware that in the morbidly obese the LBM equation can generate paradoxical values resulting in excessive increases in maintenance infusion rates. Finally, the two currently available open TCI systems implement different methods of effect-site targeting for the Schnider model, and in a small subset of patients the induction doses generated by the two methods can differ significantly.

Background: 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., Methods: 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)., Results: 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., Conclusions: 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.

Background: A previously published pharmacokinetic simulation suggested a simple manual infusion regimen to achieve propofol plasma concentrations of 3 microg.ml(-1). This study investigated if a simple variation in propofol infusion rates is able to achieve distinct propofol plasma concentrations and whether these are close to the propofol plasma concentrations predicted by the Kataria model., Methods: With Research Ethics Board approval and written parental consent, a total of 17 healthy children requiring general anaesthesia were enrolled. Following inhalational induction of anaesthesia, a propofol bolus of 5 mg.kg(-1) was given and anaesthesia maintained using an adaptation of the McFarlan continuous propofol infusion regimen to achieve three distinct depths of propofol anaesthesia. Weight and propofol infusion data were used to calculate simulated propofol concentrations using the Kataria dataset and the TIVA simulation program. The performance of the infusion regimen was assessed by calculating the median performance error, median absolute performance error, wobble, and divergence., Results: Measured propofol concentrations were (mean +/- sd) 7.15 +/- 1.4, 4.3 +/- 0.85, and 2.85 +/- 0.53 microg.ml(-1) against simulation values of 6.6, 4.1, and 2.8 microg.ml(-1), respectively, at 30, 50, and 70 min using the Kataria dataset. These differences were not significant. Formal assessment of the infusion regimen's performance was acceptable., Conclusion: The manual propofol infusion regimen achieved three distinct depths of propofol anaesthesia. The manual infusion regimen produced higher plasma propofol concentrations than predicted during the early part of the infusion period but was more accurate for later time points.

Target controlled infusion (TCI) for Kids is a computer controlled system designed to administer propofol for general anesthesia. A controller establishes infusion rates required to achieve a specified concentration at the drug's effect site (C(e)) by implementing a continuously updated pharmacokinetic-pharmacodymanic model. This manuscript provides an overview of the system's design, preclinical tests, and a clinical pilot study. In pre-clinical tests, predicted infusion rates for 20 simulated procedures displayed complete convergent validity between two software implementations, Labview and Matlab, at computational intervals of 5, 10, and 15s, but diverged with 20s intervals due to system rounding errors. The volume of drug delivered by the TCI system also displayed convergent validity with Tivatrainer, a widely used TCI simulation software. Further tests, were conducted for 50 random procedures to evaluate discrepancies between volumes reported and those actually delivered by the system. Accuracies were within clinically acceptable ranges and normally distributed with a mean of 0.08 +/- 0.01 ml. In the clinical study, propofol pharmacokinetics were simulated for 30 surgical procedures involving children aged 3 months to 9 years. Predicted C(e) values during standard clinical practice, the accuracy of wake-up times predicted by the system, and potential correlations between patient wake-up times, C(e), and state entropy (SE) were assessed. Neither Ce nor SE was a reliable predictor of wake-up time in children, but the small sample size of this study does not fully accommodate the noted variation in children's response to propofol. A C(e) value of 1.9 mug/ml was found to best predict emergence from anesthesia in children.

This work presents the improvement of a software for a clinical setup, the Anesthesia Synchronization Software (ASYS). The first version presented the data acquisition from cerebral monitors and a partial control with Target Controlled Infusion (TCI) system. Based on pharmacokinetic models, the effect-site and plasma concentrations can be related with the drug dose infused and vice versa. This later version comes with the implementation of the hemodynamic monitor always present in operating rooms and with the full TCI system developed to determine the infusion rates of the drug which are given as commands to the infusion pumps.

Background: The authors hypothesized a difference in plasma-effect site equilibration, depicted by a first-order constant k(e0), depending on the injection rate of propofol., Methods: Sixty-one patients received 2.5 mg/kg propofol given as a bolus or as a 1-, 2-, or 3-min infusion. The Bispectral Index was used to monitor drug effect. Propofol predicted plasma concentration was calculated using a three-compartment model and the effect site concentration over time as the convolution between the predicted plasma concentration and the disposition function of the effect site concentration. The authors evaluated the influence of the infusion rate on the k(e0) by comparing the model with one k(e0) for all groups with models estimating different k(e0) values for each group. The authors also assessed the accuracy of two pharmacokinetic models after bolus injection., Results: The best model based was a fixed (Bispectral Index > or = 90) plus sigmoidal model (Bispectral Index < 90) with two values of k(e0), one for the bolus (t(1/2) k(e0) = 1.2 min) and one for the infusions (t(1/2) k(e0) = 2.2 min). However, the tested pharmacokinetic models poorly predicted the arterial concentrations in the first minutes after bolus injection. Simulations showed the requirement for two k(e0) values for bolus and infusion was mostly a compensation for the inaccurate prediction of arterial concentrations after a bolus., Conclusion: Propofol plasma-effect site equilibration occurs more rapidly after a bolus than after rapid infusion, based on the electroencephalogram as a drug effect measure, mostly because of misspecification of the pharmacokinetic model in the first minutes after bolus.

Target controlled infusion (TCI) pumps function using a programme based on a pharmacokinetic/pharmacodynamic model. We compared the Marsh and Schnider models to find out which better correlates with the clinically observed effect of propofol as assessed by the Observer Assessment of Alertness/Sedation (OAAS) score and the Bispectral index. We assessed the sedation score and Bispectral index score in 40 un-premedicated patients undergoing surgical procedures under spinal anaesthesia with propofol sedation to a target concentration of 2 microg.ml(-1). Half of the patients received TCI propofol driven by the Schnider model in effect site control, the other half were sedated with TCI propofol driven by the Marsh model in plasma control. We calculated the effect site concentration predicted by both models for all the patients. Changes in the sedation score and Bispectral index correlated better with the Marsh than with the Schnider effect site prediction in both study groups.

The effect of drugs' interaction on the brain signal Bispectral Index (BIS) is of great importance for an anesthesia control drug infusion system. In this study, the objective was to inspect the influence of patient's heart rate on the effect of the drugs on BIS. With this goal, the patient's heart rate was incorporated in an drug interaction model. The model was fitted per patient during anesthesia induction, and tested for prediction under surgery. The results showed that the model with time changing parameters incorporating patient's heart rate has a better performance than a non adjusted model. Three clusters of models were also identified using the fuzzy c-means algorithm. These clusters will help to distinguish between different patients' dynamics.

Propofol is commonly administered, as a single bolus dose, for the induction of general anesthesia. The purpose of this study was to mathematically assess the ability to model propofol induction-dose serum levels with a recursive finite difference equation (RFDE). Using data obtained from a prior published study, propofol induction pharmacokinetics were accurately modeled, on a subject-specific basis, with a third-order homogeneous finite difference equation with constant coefficients: P((k + 3)) = AP((k + 2)) + BP((k + 1)) + CP((k)). Furthermore, each RFDE model is derived directly from the coefficients of a traditional three-compartment pharmacokinectic exponential equation. Based on this study, third-order RFDE models can have identical accuracy as three-compartment exponential models. In this particular application, it should be noted that each RFDE model required only three coefficients whereas each exponential model required six. Also, there was overall less patient-to-patient variability of the coefficients of the RFDE models. In general, it appears that RFDE models uniquely allow for predicting subsequent drug levels from preexisting ones. However, RFDE models require initial conditions whereas exponential models do not. Additional studies and applications of exponentially-derived RFDE pharmacokinetic models may be warranted.

We describe a six-compartment physiological model of the kinetics and dynamics of induction of anaesthesia with propofol in sheep. It includes a faithful description of initial bolus kinetics caused by accurate representations of the inter-relationships between initial vascular mixing, lung kinetics and cardiac output, the use of the brain as the target organ for propofol anaesthesia (two-compartment sub-model with slight membrane limitation), a description of the effects of propofol-induced changes in cerebral blood flow and a combined description of systemic kinetics as two tissue pools. Variables for the model were estimated from an extensive in vivo data set using hybrid modelling. Propofol was characterized by rapid transit through the lungs, but a slower transit time though the brain, leading to significant delay between arterial blood concentrations and cerebral effects.

The performance of 10 pharmacokinetic models in predicting blood propofol concentrations was evaluated in patients during neurosurgical anesthesia. Eight patients-ASA category I or II, aged 49 +/- 18-years, weighing 71 +/- 20 kg, and scheduled for routine neurosurgery-were anesthetized with propofol and sufentanil using Ohmeda pumps controlled with a personal computer. Sufentanil was administered as a bolus of 0.3 microgram.kg-1, 5 min before induction of anesthesia, and infused at a constant rate of 0.5 microgram.kg-1.h-1 throughout the study. At induction, propofol was administered as a bolus of 1.5 mg.kg-1 followed by a continuous infusion of 6 mg.kg-1.h-1. During surgery, the propofol infusion rate was deliberately increased by 2 mg.kg-1.h-1 every 15 min up to 12 mg.kg-1.h-1. Arterial blood samples were drawn at the end of each infusion step for measurement of propofol concentrations by high-performance liquid chromatography. Measured propofol concentrations were compared with theoretical concentrations derived from 10 published pharmacokinetic models designed in different clinical settings. Each model has been assessed by calculating the median of the performance error, the median of the absolute performance error, and their 10th and 90th percentiles. Models designed for certain categories, such as children, young, or elderly patients who received propofol as a bolus injection, showed a bad predictive accuracy. The models of Gepts et al. (Anesth Analg 1987; 66:1256-1263, Anaesthesia 1988; 43(suppl):8-13), Tackley et al. (Br J Anaesth 1989;62:46-53), and Cockshott (Postgrad Med J 1985;61:55), derived from healthy patients receiving continuous propofol infusions, provided the best agreement between expected and measured propofol concentrations; they showed bias and inaccuracy lower than 17%. In conclusion, the accurate prediction of blood propofol concentrations from different continuous infusion rates in ASA I or II patients requires the selection of appropriate pharmacokinetic models derived from similar categories of patients and using a similar technique of propofol administration. However, in clinical practice, the selection of a specific set among the appropriate models is balanced by the interindividual variability in blood propofol concentrations adjusted to clinical effects.

For model identification and parameter estimation in the framework of linear pharmacokinetics it is most often assumed that the disposition function is a finite sum of exponential functions with time constants lambda i and associated coefficients Ci. Least-square fitting procedures are used to estimate the coefficients Ci and the corresponding discrete locations lambda i on the lambda-axes. This work presents an alternative approach. It does not assume that the non-zero coefficients are located at sharply defined values of lambda, but that they are represented by a continuous function h(lambda), the spectrum of the disposition function. This turns the non-linear least-square problem into a linear problem, which is known to be as so-called "ill-posed". Regularization methods have been developed in recent years as suitable tools for the treatment of such ill-posed problems. Application of Tikhonov regularization to the case of the bolus kinetics of propofol in 8 volunteers is demonstrated. In 7 of the 8 cases a spectrum with 4 to 5 peaks was found, and in one volunteer there were only 2 peaks. All spectra with more than 2 peaks showed negative values of h(lambda). The method used is described and the results are compared with those of conventional compartment analysis.

A computer controlled infusion device for propofol was used to induce and maintain general anaesthesia in 20 children undergoing minor surgical procedures. The device was programmed with an adult pharmacokinetic model for propofol. During and after anaesthesia, blood samples were taken for measurement of propofol concentrations and it was found that the values obtained were systematically overpredicted by the delivery system algorithm. New pharmacokinetic microconstants were derived from our data which reflected more accurately the elimination and distribution of propofol in a prospective study involving another 10 children.