Charles F. Minto

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.

Remifentanil versus alfentanil : Comparative pharmacokinetics and pharmacodynamics in healthy adult male volunteers

Background Remifentanil is an esterase-metabolized opioid with a rapid clearance. The aim of this study was to contrast the pharmacokinetics and pharmacodynamics of remifentanil and alfentanil in healthy, adult male volunteers. Methods Ten volunteers received infusions of remifentanil and alfentanil on separate study sessions using a randomized, open-label crossover design. Arterial blood samples were analyzed to determine drug blood concentrations. The electroencephalogram was employed as the measure of drug effect. The pharmacokinetics were characterized using a moment analysis, a nonlinear mixed effects model (NONMEM) population analysis, and context-sensitive half-time computer simulations. After processing the raw electroencephalogram to obtain the spectral edge parameter, the pharmacodynamics were characterized using an effect compartment, inhibitory maximum effect model. Results Pharmacokinetically, the two drugs are similar in terms of steady-state distribution volume (VDss), but remifentanils central clearance (CLc) is substantially greater. The NONMEM analysis population pharmacokinetic parameters for remifentanil include a CLc of 2.9 l *symbol* min sup -1, a VDss of 21.81, and a terminal half-life of 35.1 min. Corresponding NONMEM parameters for alfentanil are 0.36 l *symbol* min sup -1, 34.11, and 94.5 min. Pharmacodynamically, the drugs are similar in terms of the time required for equilibration between blood and the effect-site concentrations, as evidenced by a T12 Ke0 for remifentanil of 1.6 min and 0.96 min for alfentanil. However, remifentanil is 19 times more potent than alfentanil, with an effective concentration for 50% maximal effect of 19.9 ng *symbol* ml sup -1 versus 375.9 ng *symbol* ml sup -1 for alfentanil. Conclusions Compared to alfentanil, the high clearance of remifentanil, combined with its small steady-state distribution volume, results in a rapid decline in blood concentration after termination of an infusion. With the exception of remifentanils nearly 20-times greater potency (30-times if alfentanil partitioning between whole blood and plasma is considered), the drugs are pharmacodynamically similar.

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.

Efficient trial design for eliciting a pharmacokinetic-pharmacodynamic model-based response surface describing the interaction between two intravenous anesthetic drugs.

Background The authors published a pharmacokinetic– pharmacodynamic model for two drugs based on response surface methodology. Because of the complexity of the model, they performed a simulation study to answer two questions about use of the model: (1) which study design would be most satisfactory; and (2) how many patients would need to be studied to adequately describe an entire response surface. Methods Data were simulated using realistic variability for two hypothetical intravenous anesthetic drugs that interact synergistically and that could be given by computer-controlled infusion. Three trial designs were simulated, one that made a series of parallel slices of the response surface, one that crisscrossed the response surface, and one that made a series of radial slices across the surface. Series of 5, 10, 20, and 40 “subjects” were simulated. A pooled data approach was used to assess the ability of the various trial designs and numbers of subjects to adequately identify the interaction response surface and estimate the original response surface. Results The crisscross design was shown to be the most robust in terms of its ability to both discriminate the correct order of the interaction term and to discriminate the original response surface using the least number of patients. Twenty subjects would be required to adequately define a surface using the crisscross study design, and 40 subjects would be required using the other trial designs. Conclusions The results showed that a number of trial designs would be viable, but a design that crossed the surface in a crisscross fashion would give the most robust result with the least patients.

Contributions of PK/PD Modeling to Intravenous Anesthesia

Pharmacokinetic (PK)/pharmacodynamic (PD) modeling has made an enormous contribution to intravenous anesthesia. PK/PD models have provided us with insight into the factors affecting the onset and offset of drug effect. For example, we are now able to describe the influence of cardiac output on the disposition of intravenous drugs within the first few minutes after administration of the drug. We are able to calculate intravenous loading doses that allow for the delay between the concentration of the drug in the plasma and the rising concentration at the site of drug effect. We are able to achieve and maintain a stable level of anesthetic effect using computerized infusion pumps that target the site of drug effect rather than the plasma. Importantly, on the basis of models of drug interaction and an understanding of how drug offset varies with duration of administration, we are now able to rationally combine hypnotics and opioids.

Population pharmacodynamic modeling and covariate detection for central neural blockade

Background In spinal anesthesia, often a large interindividual variability in analgesic response is observed after administration of a certain fixed dose of anesthetic to a patient population. To improve therapeutic outcome it is important to characterize the variability in response by means of a population model (e.g., mixed-effects models or two-stage approaches). The purpose of this investigation is to derive a population model for spinal anesthesia with plain bupivacaine. Based on the population models, a description of a patients time course of drug action is obtained, the influence of patient covariates on clinically important endpoints is examined, and the success of Bayesian forecasting of the offset of effect in a specific patient from the data obtained during onset is evaluated. Methods The level of central neural blockade after intrathecal injection of plain bupivacaine was assessed by testing analgesia to pinprick. A total of 714 measurements in 96 patients (4-10 per subject) were available for analysis. Two pharmacodynamic models, based on the understanding of the physiology of the spread of local anesthetic in the spinal fluid, were evaluated to characterize the time course of analgesia in a specific patient. The first model is a combination of a biexponential pharmacokinetic model, describing the onset and offset of effect and a linear pharmacodynamic model. The second model combines the biexponential pharmacokinetic model with an Emax type pharmacodynamic model. The interindividual variability in model parameters was modeled by an exponential variance model. An additional term characterized the residual error. The population mean parameters, interindividual variance, and residual variance were estimated using the first-order conditional estimate method in the NONMEM software package. Clinically important endpoints such as onset time, time to reach the maximal level, the maximal level, and the duration of analgesia were estimated from the Bayesian fit of each subjects data and correlated with patient-specific covariates. Using Bayesian forecasting, the offset of spinal analgesia was predicted for each patient based on the population model and measurements from the first 30 min and from the first 60 min, respectively. Results The Emax type pharmacodynamic model was superior based on the improvement in likelihood (P < 0.001) and on visual inspection of the fits. The estimates of the population mean parameters (coefficient of variation) were: (1) maximal effect: T4, which was coded for the purpose of the calculation as 18 (14%); (2) rate of offset of effect: 0.0118 (26%) min sup -1; (3) rate of onset of effect: 0.061 (45%) min. sup -1 The standard deviation of the residual error was 1.4. Large interindividual differences were observed in the time course of analgesic response and clinically important endpoints. The mean onset time; that is, time to reach T10 (interindividual variability) was 4.2 min (90%), the mean time to maximal level was 35.5 min (29%), the mean duration of effect was 172 min (28%), and the mean maximal achieved level was T6 (12%). Significant correlations between onset time and height and weight, between time to maximal level and age, between maximal level and weight and height, and between duration and height were found. Bayesian regression using the population model and data from the first 30 min and from the first 60 min predicted the offset of effect in each patient reasonably well, with coefficients of determination (R2) of 0.71 and 0.72. This is a significant improvement over the population mean prediction. Conclusion A population model was derived for the description of the time course of central neural blockade. Based on the population model, a continuous effect profile over time was obtained for each person. Clinically important endpoints such as onset time, maximal level of analgesia, time to reach maximal level, and duration were correlated to patient covariates such as age, height, weight, puncture site, and kind of preparation of bupivacaine used to explain the large interindividual variability. The mixed-effects modeling approach is of particular importance for the analysis of incomplete and sparse data from large patient populations.