Thomas W. Schnider

Influence of age and gender on the pharmacokinetics and pharmacodynamics of remifentanil I. Model development

BackgroundPrevious studies have reported conflicting results concerning the influence of age and gender on the pharmacokinetics and pharmacodynamics of fentanyl, alfentanil, and sufentanil. The aim of this study was to determine the influence of age and gender on the pharmacokinetics and pharmacodyn

The influence of method of administration and covariates on the pharmacokinetics of propofol in adult volunteers.

Background Unresolved issues with propofol include whether the pharmacokinetics are linear with dose, are influenced by method of administration (bolus vs. infusion), or are influenced by age. Recently, a new formulation of propofol emulsion, containing disodium edetate (EDTA), was introduced in the United States. Addition of EDTA was found by the manufacturer to significantly reduce bacterial growth. This study investigated the influences of method of administration, infusion rate, patient covariates, and EDTA on the pharmacokinetics of propofol. Methods Twenty‐four healthy volunteers aged 26–81 yr were given a bolus dose of propofol, followed 1 h later by a 60‐min infusion. Each volunteer was randomly assigned to an infusion rate of 25, 50, 100, or 200 micro gram [center dot] kg‐1 [center dot] min‐1. Each volunteer was studied twice under otherwise identical circumstances: once receiving propofol without EDTA and once receiving propofol with EDTA. The influence of the method of administration and of the volunteer covariates was explored by fitting a three‐compartment mamillary model to the data. The influence of EDTA was investigated by direct comparison of the measured concentrations in both sessions. Results The concentrations of propofol with and without EDTA were not significantly different. The concentration measurements after the bolus dose were significantly underpredicted by the parameters obtained just from the infusion data. The kinetics of propofol were linear within the infusion range of 25–200 micro gram [center dot] kg‐1 [center dot] min‐1. Age was a significant covariate for Volume2 and Clearance2, as were weight, height, and lean body mass for the metabolic clearance. Conclusions These results demonstrate that method of administration (bolus vs. infusion), but not EDTA, influences the pharmacokinetics of propofol. Within the clinically relevant range, the kinetics of propofol during infusions are linear regarding infusion rate.

The Influence of Age on Propofol Pharmacodynamics

BACKGROUND The authors studied the influence of age on the pharmacodynamics of propofol, including characterization of the relation between plasma concentration and the time course of drug effect. METHODS The authors evaluated healthy volunteers aged 25-81 yr. A bolus dose (2 mg/kg or 1 mg/kg in persons older than 65 yr) and an infusion (25, 50, 100, or 200 microg x kg(-1) x min(-1)) of the older or the new (containing EDTA) formulation of propofol were given on each of two different study days. The propofol concentration was determined in frequent arterial samples. The electroencephalogram (EEG) was used to measure drug effect. A statistical technique called semilinear canonical correlation was used to select components of the EEG power spectrum that correlated optimally with the effect-site concentration. The effect-site concentration was related to drug effect with a biphasic pharmacodynamic model. The plasma effect-site equilibration rate constant was estimated parametrically. Estimates of this rate constant were validated by comparing the predicted time of peak effect with the time of peak EEG effect. The probability of being asleep, as a function of age, was determined from steady state concentrations after 60 min of propofol infusion. RESULTS Twenty-four volunteers completed the study. Three parameters of the biphasic pharmacodynamic model were correlated linearly with age. The plasma effect-site equilibration rate constant was 0.456 min(-1). The predicted time to peak effect after bolus injection ranging was 1.7 min. The time to peak effect assessed visually was 1.6 min (range, 1-2.4 min). The steady state observations showed increasing sensitivity to propofol in elderly patients, with C50 values for loss of consciousness of 2.35, 1.8, and 1.25 microg/ml in volunteers who were 25, 50, and 75 yr old, respectively. CONCLUSIONS Semilinear canonical correlation defined a new measure of propofol effect on the EEG, the canonical univariate parameter for propofol. Using this parameter, propofol plasma effect-site equilibration is faster than previously reported. This fast onset was confirmed by inspection of the EEG data. Elderly patients are more sensitive to the hypnotic and EEG effects of propofol than are younger persons.

Pharmacokinetics and Pharmacodynamics of Remifentanil: Ii. Model Application

Background The pharmacokinetics and pharmacodynamics of remifentanil were studied in 65 healthy volunteers using the electroencephalogram (EEG) to measure the opioid effect. [1] In a companion article, the authors developed complex population pharmacokinetic and pharmacodynamic models that incorporated age and lean body mass (LBM) as significant covariates and characterized intersubject pharmacokinetic and pharmacodynamic variability. In the present article, the authors determined whether remifentanil dosing should be adjusted according to age and LBM, or whether these covariate effects were overshadowed by the interindividual variability present in the pharmacokinetics and pharmacodynamics. Methods Based on the typical pharmacokinetic and pharmacodynamic parameters, nomograms for bolus dose and infusion rates at each age and LBM were derived. Three populations of 500 individuals each, ages 20, 50, and 80 yr, were simulated base on the interindividual variances in model parameters as estimated by the NONMEM software package. The peak EEG effect in response to a bolus, the steady‐state EEG effect in response to an infusion, and the time course of drug effect were examined in each of the three populations. Simulations were performed to examine the time necessary to achieve a 20%, 50%, and 80% decrease in remifentanil effect site concentration after a variable‐length infusion. The variability in the time for a 50% decrease in effect site concentrations was examined in each of the three simulated populations. Titratability using a constant‐rate infusion was also examined. Results After a bolus dose, the age‐related changes in V1 and ke0 nearly offset each other. The peak effect site concentration reached after a bolus dose does not depend on age. However, the peak effect site concentration occurs later in elderly individuals. Because the EEG shows increased brain sensitivity to opioids with increasing age, an 80‐yr‐old person required approximately one half the bolus dose of a 20‐yr old of similar LBM to reach the same peak EEG effect. Failure to adjust the bolus dose for age resulted in a more rapid onset of EEG effect and prolonged duration of EEG effect in the simulated elderly population. The infusion rate required to maintain 50% EEG effect in a typical 80‐yr‐old is approximately one third that required in a typical 20‐yr‐old. Failure to adjust the infusion rate for age resulted in a more rapid onset of EEG effect and more profound steady‐state EEG effect in the simulated elderly population. The typical times required for remifentanil effect site concentrations to decrease by 20%, 50%, and 80% after prolonged administration are rapid and little affected by age or duration of infusion. These simulations suggest that the time required for a decrease in effect site concentrations will be more variable in the elderly. As a result, elderly patients may occasionally have a slower emergence from anesthesia than expected. A step change in the remifentanil infusion rate resulted in a rapid and predictable change of EEG effect in both the young and the elderly. Conclusions Based on the EEG model, age and LBM are significant demographic factors that must be considered when determining a dosage regimen for remifentanil. This remains true even when interindividual pharmacokinetic and pharmacodynamic variability are incorporated in the analysis.

Response surface model for anesthetic drug interactions

Background Anesthetic drug interactions traditionally have been characterized using isobolographic analysis or multiple logistic regression. Both approaches have significant limitations. The authors propose a model based on response-surface methodology. This model can characterize the entire dose–response relation between combinations of anesthetic drugs and is mathematically consistent with models of the concentration–response relation of single drugs. Methods The authors defined a parameter, &thgr;, that describes the concentration ratio of two potentially interacting drugs. The classic sigmoid Emax model was extended by making the model parameters dependent on &thgr;. A computer program was used to estimate response surfaces for the hypnotic interaction between midazolam, propofol, and alfentanil, based on previously published data. The predicted time course of effect was simulated after maximally synergistic bolus dose combinations. Results The parameters of the response surface were identifiable. With the test data, each of the paired combinations showed significant synergy. Computer simulations based on interactions at the effect site predicted that the maximally synergistic three-drug combination tripled the duration of effect compared with propofol alone. Conclusions Response surfaces can describe anesthetic interactions, even those between agonists, partial agonists, competitive antagonists, and inverse agonists. Application of response-surface methodology permits characterization of the full concentration–response relation and therefore can be used to develop practical guidelines for optimal drug dosing.

Using the time of maximum effect site concentration to combine pharmacokinetics and pharmacodynamics.

Background To simulate the time course of drug effect, it is sometimes necessary to combine the pharmacodynamic parameters from an integrated pharmacodynamic–pharmacodynamic study (e.g., volumes, clearances, ke0 [the effect site equilibration rate constant], C50 [the steady state plasma concentration associated with 50% maximum effect], and the Hill coefficient) with pharmacokinetic parameters from a different study (e.g., a study examining a different age group or sampling over longer periods of time). Pharmacokinetic–pharmacodynamic parameters form an interlocked vector that describes the relationship between input (dose) and output (effect). Unintended consequences may result if individual elements of this vector (e.g., ke0) are combined with pharmacokinetic parameters from a different study. The authors propose an alternative methodology to rationally combine the results of separate pharmacokinetic and pharmacodynamic studies, based on tpeak, the time of peak effect after bolus injection. Methods The naive approach to combining separate pharmacokinetic and pharmacodynamic studies is to simply take the ke0 from the pharmacodynamic study and apply it naively to the pharmacokinetic study of interest. In the tpeak approach, ke0 is recalculated using the pharmacokinetics of interest to yield the correct time of peak effect. The authors proposed that the tpeak method would yield better predictions of the time course of drug effect than the naive approach. They tested this hypothesis in three simulations: thiopental, remifentanil, and propofol. Results In each set of simulations, the tpeak method better approximated the postulated “true” time course of drug effect than the naive method. Conclusions Tpeak is a useful pharmacodynamic parameter and can be used to link separate pharmacokinetic and pharmacodynamic studies. This addresses a common difficulty in clinical pharmacology simulation and control problems, where there is usually a wide choice of pharmacokinetic models but only one or two published pharmacokinetic–pharmacodynamic models. The results will be immediately applicable to target-controlled anesthetic infusion systems, where linkage of separate pharmacokinetic and pharmacodynamic parameters into a single model is inherent in several target-controlled infusion designs.

Modeling and closed-loop control of hypnosis by means of bispectral index (BIS) with isoflurane

A model-based closed-loop control system is presented to regulate hypnosis with the volatile anesthetic isoflurane. Hypnosis is assessed by means of the bispectral index (BIS), a processed parameter derived from the electroencephalogram. Isoflurane is administered through a closed-circuit respiratory system. The model for control was identified on a population of 20 healthy volunteers. It consists of three parts: a model for the respiratory system, a pharmacokinetic model and a pharmacodynamic model to predict BIS at the effect compartment. A cascaded internal model controller is employed. The master controller compares the actual BIS and the reference value set by the anesthesiologist and provides expired isoflurane concentration references to the slave controller. The slave controller maneuvers the fresh gas anesthetic concentration entering the respiratory system. The controller is designed to adapt to different respiratory conditions. Anti-windup measures protect against performance degradation in the event of saturation of the input signal. Fault detection schemes in the controller cope with BIS and expired concentration measurement artifacts. The results of clinical studies on humans are presented.

Decreased Factor XIII Availability for Thrombin and Early Loss of Clot Firmness in Patients with Unexplained Intraoperative Bleeding

To explore relevant changes in unexplained intraoperative bleeding, we evaluated elements of the final steps of the coagulation cascade in 226 consecutive patients undergoing elective surgery. Patients were stratified for the occurrence of unexplained intraoperative bleeding according to predefined criteria. Twenty patients (8.8%) developed unexplained bleeding. The median intraoperative blood loss was 1350 mL (bleeders) and 400 mL (nonbleeders) (P < 0.001). Fibrinogen and Factor XIII (F. XIII) were more rapidly consumed in bleeders (P < 0.001). Soluble fibrin formation (fibrin monomer) was increased in bleeders throughout surgery (P ≤ 0.014). However, F. XIII availability per unit thrombin generated was significantly decreased in bleeders before, during, and after surgery (P ≤ 0.051). Computerized thrombelastography showed a parallel, significant reduction in clot firmness. We suggest that mild preexisting coagulopathy is not rare in surgical patients and probably can result in clinically relevant intraoperative bleeding. This hemostatic disorder shows impaired clot firmness, probably secondary to decreased cross-linking (due to a loss of F. XIII, both in absolute measures and per unit thrombin generated). We suggest that the application of F. XIII might be worthwhile to test in a prospective clinical trial to increase clot firmness in patients at risk for this intraoperative coagulopathy.

A new paradigm for the closed-loop intraoperative administration of analgesics in humans

We present a new paradigm for the closed-loop administration of analgesics during general anesthesia. The manipulated variable in the control system is the infusion rate of the opiate alfentanil, administered intravenously through a computer-controlled infusion pump (CCIP). The outputs to be controlled are the patients mean arterial pressure (MAP) and the drug concentration in the plasma. Maintaining MAP within appropriate ranges provides optimal treatment of the patients reactions to surgical stimuli. Maintaining plasma drug concentrations close to a reference value specified by the anesthesiologist allows to titrate analgesic administration to qualitative clinical end-points of insufficient analgesia. MAP is acquired invasively through a catheter cannula. Since plasma drug concentrations cannot be measured on-line, they are estimated via a pharmacokinetic model. We describe an explicit model-predictive controller which achieves the above-mentioned objectives. An upper constraint on drug concentrations is maintained to avoid overdosing. Constraints on the MAP are introduced to trigger a prompt controller reaction during hypertensive and hypotensive periods. Measurement artifacts in the MAP signal are rejected to prevent harmful misbehavior of the controller. We discuss the results of the clinical validation of the controller on humans.

Factor XIII Substitution in Surgical Cancer Patients at High Risk for Intraoperative Bleeding

Background:Excessive intraoperative bleeding is associated with significant morbidity and mortality. The authors and others have shown that fibrin monomer allows preoperative risk stratification for intraoperative blood loss, likely due to an imbalance between available factor XIII and prothrombin conversion. The authors hypothesized that the use of factor XIII would delay the decrease of clot firmness in high-risk patients. Methods:The concept was tested in a prospective, randomized, double-blind, placebo-controlled trial in elective gastrointestinal cancer surgery. Patients were randomized to receive factor XIII (30 U/kg) or placebo in addition to controlled standard therapy. Results:Twenty-two patients were evaluable for a planned interim analysis. For the primary outcome parameter maximum clot firmness, patients receiving factor XIII showed a nonsignificant 8% decrease, and patients receiving placebo lost 38%, a highly significantly difference between the two groups (P = 0.004). A reduction in the nonprimary outcome parameters fibrinogen consumption (–28%, P = 0.01) and blood loss (–29%, P = 0.041) was also observed in the factor XIII group. Three patients experienced adverse events that seemed unrelated to factor XIII substitution. The trial was stopped early after a planned interim analysis with the primary endpoint reached. Conclusions:This proof of concept study confirms the hypothesis that patients at high risk for intraoperative blood loss show reduced loss of clot firmness when factor XIII is administered early during surgery. Further clinical trials are needed to assess relevant clinical endpoints such as blood loss, loss of other coagulation factors, and use of blood products.