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.

[Modern concepts in pharmacokinetics of intravenous anesthetics]. / Moderne Konzepte der Pharmakokinetik intravenöser Anästhetika.

From a pharmacological perspective, anesthesia is concerned with controlling the time course of drug effect. Mathematical models are commonly used to relate the administered drug dose to the measured drug concentration (a pharmacokinetic model) and to relate the measured drug concentrations to the measured drug effects (a pharmacodynamic model). With such models, the time course of the drug effect for different drug regimens can be predicted. Although the conventional pharmacokinetic parameters such as the volume of distribution, clearance, distribution and elimination half-lives can be used to accurately describe the time course of the plasma concentration, the plasma is usually not the site of drug effect. An understanding of the "effect compartment concept" and the "time of the peak effect site concentration," together with the concepts of" context sensitive"half-time and "relevant decrement time,' contribute substantially to the anesthetist's understanding of the principles governing the onset and offset of drug effect. As part of a computer-controlled infusion system, the pharmacokinetic model facilitates optimized and rational dosing. These systems, also called target-controlled infusion systems (TCI), calculate the infusion rates for rapidly achieving and then maintaining a target concentration.

Concurrent ketamine and alfentanil administration: pharmacokinetic considerations.

BACKGROUND: A ketamine-alfentanil combination has been suggested for total i.v. anaesthesia. We determined the pharmacokinetics of ketamine and alfentanil, alone and together, in three groups of adult male rats, to assess any pharmacokinetic interaction. METHODS: Group 1 animals were infused with i.v. ketamine for 5 min; in group 2, constant low plasma concentrations of alfentanil were maintained by computer-controlled infusion; in group 3, the treatments were combined. Serial plasma and terminal tissue concentrations were measured by high performance liquid chromatography or gas chromatography-mass spectrometry. RESULTS: In the presence of alfentanil, the mean plasma ketamine concentration-time area under the curve (AUC) value was significantly lower (by 13%, P<0.05), while clearance (CIT) and volume of distribution (Vss) were significantly higher (by 16 and 28%, respectively, both P<0.05). Tissue:plasma distribution coefficients for ketamine in the presence of alfentanil were significantly higher in forebrain (by 128%, P<0.005), hindbrain (by 207%, P<0.01), gut (by 254%, P<0.005), and fat (by 344%, P<0.0001). Mean AUC values for alfentanil did not differ significantly in the presence of ketamine, but alfentanil tissue concentrations were significantly lower in forebrain (by 77%, P<0.0001), hindbrain (by 28%, P<0.01), heart (by 33%, P<0.01), lung (30%, P<0.05), and gut (by 21%, P<0.05). Corresponding tissue:plasma distribution coefficients were significantly lower for forebrain (by 69%, P<0.0001) alone. CONCLUSIONS: The finding that the distribution of ketamine into the brain was increased by low plasma concentrations of alfentanil could have important clinical applications for pain management.

Predictors of onset and offset of drug effect.

Anaesthesiologists administer a wide variety of drugs, including benzodiazepines, opioids, intravenous anaesthetic agents, volatile anaesthetic agents, muscle relaxants, local anaesthetics, and other drugs, especially those influencing the cardiovascular system. Sometimes a drug is chosen because of its better effect and/or side-effect profile. However, many of the drugs within each group have similar effect and/or side-effect profiles and differ mainly in their pharmacokinetics. The choice of one drug over another may then need to be based on differences in their pharmacokinetic profiles. Traditional predictors of onset of drug effect, such as time to a specified effect, are dose-dependent. Traditional predictors of offset of drug effect, such as 'terminal half-life', often have little clinical relevance. Newer descriptors offer significant advantages. The time to peak effect-site concentration is an informative dose-independent descriptor of the onset of drug effect following an intravenous bolus dose. The relevant decrement time (for continuous measures of drug effect) and mean effect time (for binary measures of drug effect) build upon the context-sensitive half-time concept, by considering the time required for the concentrations to decrease from one clinically relevant level of drug effect to another.

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, theta, that describes the concentration ratio of two potentially interacting drugs. The classic sigmoid Emax model was extended by making the model parameters dependent on theta. 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.

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.

[Characterization of dose profile of remifentanil with computer simulation: comparative study with fentanyl and alfentanyl]. / Caracterización del perfil de dosificación del remifentanilo mediante simulación con ordenador: estudio comparativo con fentanilo y alfentanilo.

OBJECTIVES: To estimate the optimum dosing regimen and delivery system for remifentanil, a new opioid, using computer simulations based on information from pharmacokinetic and pharmacodynamic models available for fentanyl, alfentanil and remifentanil, as well as from clinical trials of fentanyl and alfentanil. PATIENTS AND METHODS: We estimated the site concentration ranges likely to be needed to blunt response to anesthetic or surgical stimuli and to recover from spontaneous ventilation. Dosing guidelines for remifentanil, fentanyl and alfentanil were estimated for three methods of administration (bolus, bolus + variable continuous infusion or constant continuous infusion). To that end, the time course of opioid concentration was simulated for hypothetical balanced anesthesia lasting 60 min. We then studied the number of boluses, the number of infusion rate steps, time taken to reach the terapeutic threshold, and time from turning off the infusion until reaching a concentration compatible with spontaneous ventilation. RESULTS: The estimated "effect site" concentration ranges for remifentanil were 6 to 10 ng.ml-1 during intubation; 4 to 6 ng.ml-1 during cutaneous incision; 4 to 7 ng.ml-1 for maintenance; and less than 2.5 ng.ml-1 for recovery of spontaneous ventilation. Simulated bolus administration indicated that 21 boluses of remifentanil, 4 boluses of fentanyl and 7 boluses of alfentanil were needed during one hour. The therapeutic threshold was reached within the first minute with remifentanil, within 2 minutes with fentanyl and within 1 min with alfentanil. Time until recovery of spontaneous ventilation was 7 min with remifentanil, 22 min with fentanyl and 14 min with alfentanil. In the simulation of bolus plus variable infusion, the initial bolus of remifentanil was 100 micrograms, the infusion rate for induction and maintenance was 25 micrograms.min-1 and the maintenance rate was 15 micrograms.min-1. The initial bolus of fentanyl was 300 micrograms, the infusion rate for induction and maintenance was 5 micrograms.min-1. The initial bolus of alfentanil was 2,000 micrograms, the infusion rate for induction was 200 micrograms.min-1 and the maintenance rates were 75 and 25 micrograms.min-1. The therapeutic threshold was reached in 1 min with remifentanil, in 2 min with fentanyl and within 1 min with alfentanil. Spontaneous ventilation was recovered 4 min after turning off the infusion of remifentanil, 4 min afterwards with fentanyl and 6 min afterwards with alfentanil. The simulated constant infusion rate for remifentanil of 15 micrograms.min1 (8 micrograms.min-1 for fentanyl and 75 micrograms.min-1 for alfentanil) allowed the therapeutic threshold to be reached in 10 min with remifentanil, in 22 min with fentanyl and in 17 min with alfentanil. Recovery of spontaneous ventilation occurred 5 min after closure of the infusion pump with remifentanil (24 min with fentanyl and 17 min with alfentanil). CONCLUSIONS: Information from pharmacokinetic and pharmacodynamic models allows us to establish the effect site concentration ranges for remifentanil and determine the ideal administration technique for this drug. The simulation also allows us to compare the properties of remifentanil to those of other common opioids such as fentanyl and alfentanil. The results are fairly consistent with clinical evidence, demonstrating the power of pharmacokinetic and pharmacodynamic models for rationally establishing opioid dosing guidelines.

Pharmacokinetics of intravenous dynorphin A(1-13) in opioid-naive and opioid-treated human volunteers.

BACKGROUND: Dynorphin A(1-13) is a fragment of the endogenous opioid neuropeptide dynorphin A. Previous research suggested that intravenously administered dynorphin A(1-13) has the ability to modulate morphine-induced analgesia. We designed this study to characterize the disposition of intravenous dynorphin immunoreactivity in humans and to determine whether concomitant long-term opioid therapy influenced the pharmacokinetics or side-effects profile of dynorphin A(1-13). METHODS: The study subjects comprised 20 volunteers divided into two groups of 10 each, stratified by dose (low dose, 250 micrograms/kg; high dose, 1000 micrograms/kg). There were four volunteers receiving long-term opioid therapy and six opioid-naive volunteers (nonopioid group) within each dosing group. Dynorphin A(1-13) was infused over 10 minutes, and arterial blood samples were drawn and assayed for dynorphin immunoreactivity. A population modeling approach was used to characterize the pharmacokinetics. Dynorphin effects on heart rate and arterial blood pressure were also studied. RESULTS: The pharmacokinetics of dynorphin immunoreactivity were linear over the dose range studied and were best described by a three-compartment mammillary model whose parameters were volume 1, 5.0 L; volume 2, 0.80 L; volume 3, 12 L; clearance 1, 6.0 L/min; clearance 2, 0.054 L/min; and clearance 3, 0.044 L/min. Concomitant opioid medication did not affect the disposition of dynorphin immunoreactivity. Tachycardia and flushing were commonly observed side effects. The incidence of side effects was dose dependent and was not influenced by long-term opioid use. CONCLUSIONS: Intravenously administered dynorphin A(1-13) is very rapidly metabolized, on the basis of the time course of immunoreactivity in the blood. Long-term opioid therapy did not influence either the pharmacokinetics or incidence of side effects.

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 microg x kg(-1) x 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 microg x kg(-1) x 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.

Pharmacokinetics and pharmacodynamics of nandrolone esters in oil vehicle: effects of ester, injection site and injection volume.

We studied healthy men who underwent blood sampling for plasma nandrolone, testosterone and inhibin measurements before and for 32 days after a single i.m. injection of 100 mg of nandrolone ester in arachis oil. Twenty-three men were randomized into groups receiving nandrolone phenylpropionate (group 1, n = 7) or nandrolone decanoate (group 2, n = 6) injected into the gluteal muscle in 4 ml of arachis oil vehicle or nandrolone decanoate in 1 ml of arachis oil vehicle injected into either the gluteal (group 3, n = 5) or deltoid (group 4, n = 5) muscles. Plasma nandrolone, testosterone and inhibin concentrations were analyzed by a mixed-effects indirect response model. Plasma nandrolone concentrations were influenced (P < .001) by different esters and injection sites, with higher and earlier peaks with the phenylpropionate ester, compared with the decanoate ester. After nandrolone decanoate injection, the highest bioavailability and peak nandrolone levels were observed with the 1-ml gluteal injection. Plasma testosterone concentrations were also influenced (P < .001) by the ester and injection site, with the most rapid, but briefest, suppression being due to the phenylpropionate ester, whereas the most sustained suppression was achieved with the 1-ml gluteal injection. Plasma inhibin concentrations were also significantly influenced by injection volume and site, with the lowest nadir occurring after the nandrolone decanoate 1-ml gluteal injection. Thus, the bioavailability and physiological effects of a nandrolone ester in an oil vehicle are greatest when the ester is injected in a small (1 ml vs. 4 ml) volume and into the gluteal vs. deltoid muscle. We conclude that the side-chain ester and the injection site and volume influence the pharmacokinetics and pharmacodynamics of nandrolone esters in an oil vehicle in men.

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

BACKGROUND: Previous 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 pharmacodynamics of the new short-acting opioid remifentanil. METHODS: Sixty-five healthy adults (38 men and 27 women) ages 20 to 85 y received remifentanil by constant-rate infusion of 1 to 8 micrograms.kg-1.min-1 for 4 to 20 min. Frequent arterial blood samples were drawn and assayed for remifentanil concentration. The electroencephalogram was used as a measure of drug effect. Population pharmacokinetic and pharmacodynamic modeling was performed using the software package NONMEM. The influence of volunteer covariates were analyzed using a generalized additive model. The performances of the simple (without covariates) and complex (with covariates) models were evaluated prospectively in an additional 15 healthy participants ages 41 to 84 y. RESULTS: The parameters for the simple three-compartment pharmacokinetic model were V1 = 4.98 l, V2 = 9.01 l, V3 = 6.54 l, Cl1 = 2.46 l/min, Cl2 = 1.69 l/min, and Cl3 = 0.065 l/min. Age and lean body mass were significant covariates. From the ages of 20 to 85 y, V1 and Cl1 decreased by approximately 25% and 33%, respectively. The parameters for the simple sigmoid Emax pharmacodynamic model were Ke0 = 0.516 min-1, E0 = 20 Hz, Emax = 5.62 Hz, EC50 = 11.2 ng/ml, and gamma = 2.51. Age was a significant covariate of EC50 and Ke0, with both decreasing by approximately 50% for the age range studied. The complex pharmacokinetic-pharmacodynamic model performed better than did the simple model when applied prospectively. CONCLUSIONS: This study identified (1) an effect of age on the pharmacokinetics and pharmacodynamics of remifentanil; (2) an effect of lean body mass on the pharmacokinetic parameters; and (3) no influence of gender on any pharmacokinetic or pharmacodynamic parameter.

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. 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.

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 patient's 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 subject's 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 effects: T4, which was coded for the purpose of the calculation as 18 (14%); (2) rate of offset of effect: 0.0118 (26%) min-1; (3) rate of onset of effect: 0.061 (45%) min-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...

Derivation and cross-validation of pharmacokinetic parameters for computer-controlled infusion of lidocaine in pain therapy.

BACKGROUND: Lidocaine administered intravenously is efficacious in treating neuropathic pain at doses that do not cause sedation or other side effects. Using a computer-controlled infusion pump (CCIP), it is possible to maintain the plasma lidocaine concentration to allow drug equilibration between the plasma and the site of the drug effect. Pharmacokinetic parameters were derived for CCIP administration of lidocaine in patients with chronic pain. METHODS: Thirteen patients (mean age 45 yr, mean weight 66 kg) were studied. Eight subjects received a computer-controlled infusion, targeting four increasing lidocaine concentrations (1-7 micrograms.ml-1) for 30 min each, based on published kinetic parameters in which venous samples were obtained infrequently after bolus administration. From the observations in these eight patients, new lidocaine pharmacokinetic parameters were estimated. These were prospectively tested in five additional patients. From the complete data set (13 patients), final structural parameters were estimated using a pooled analysis approach. The interindividual variability was determined with a mixed-effects model, with the structural model parameters fixed at the values obtained from the pooled analysis. Internal cross-validation was used to estimate the residual error in the final pharmacokinetic model. RESULTS: The lidocaine administration based on the published parameters consistently produced higher concentrations than desired, resulting in acute lidocaine toxicity in most of the first eight patients. The highest measured plasma concentration was 15.3 micrograms.ml-1. The pharmacokinetic parameters estimated from these eight patients differed from the initial estimates and included a central volume one-sixth of the initial estimate. In the subsequent prospective test in five subjects, the new parameters resulted in concentrations evenly distributed around the target concentration. None of the second group of subjects had evidence of acute lidocaine toxicity. The final parameters ( +/- population variability expressed as %CV) were estimated as follows: V1 0.101 +/- 53% 1.kg-1, V2 0.452 +/- 33% 1.kg-1, Cl1 0.0215 +/- 25% 1.kg-1.min-1, and Cl2 0.0589 +/- 35% 1.kg-1.min-1. The median error measured by internal cross-validation was +1.9%, and the median absolute error was 14%. CONCLUSIONS: Pharmacokinetic parameters for lidocaine were derived and administration was prospectively tested via computer-controlled infusion pumps for patients with chronic neuropathic pain. The estimated parameters performed well when tested prospectively. A second estimation step further refined the parameters and improved performance, as measured using internal cross-validation.

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 (VD(SS)), but remifentanil's central clearance (CLc)) is substantially greater. The NONMEM analysis population pharmacokinetic parameters for remifentanil include a CLc of 2.9 l x min(-1), a VDss of 21.81, and a terminal half-life of 35.1 min. Corresponding NONMEM parameters for alfentanil are 0.36 l x min(-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 T(12)k(e0) for remifentanil of 0.75 min [corrected] 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 x ml(-1) versus 375.9 ng x ml(-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 remifentanil's nearly 20-times greater potency (30-times if alfentanil partitioning between whole blood and plasma is considered), the drugs are pharmacodynamically similar.

Semilinear canonical correlation applied to the measurement of the electroencephalographic effects of midazolam and flumazenil reversal.

BACKGROUND: The electroencephalographic (EEG) effect of benzodiazepines, and midazolam in particular, has been described using simple measures such as total power in the beta band, waves.s(-1) in the beta band and total power from aperiodic analysis. All these parameters failed to consistently describe the EEG effect of midazolam in a study in which large doses of midazolam were infused, and the effect subsequently reversed with flumazenil. Using a technique called semilinear correlation it is possible to extract a parameter from the EEG that is statistically optimally correlated with the apparent concentration of the benzodiazepine in the effect site. This method has been used to develop new univariate measures of the effects of opioids on the EEG but has not previously been applied to the EEG effects of benzodiazepines. METHODS: Data from ten subjects who received an infusion of midazolam were analyzed. The data were divided into "learning" and "test" sets. The learning set consisted of ten studies in which the volunteers received an infusion of 2.5 mg.min(-1) midazolam. Semilinear canonical correlation was used to extract an univariate descriptor of the EEG effect by weighting the different frequency bands of the EEG power spectrum. The test set comprised the same subjects on subsequent visits, in which the subjects received a continuous infusion of midazolam to maintain 20% or 80% of the peak drug effect for 3h. Twenty minutes after start of the midazolam infusion, the patient received an infusion of flumazenil to acutely reverse the benzodiazepine drug effect. The weights obtained from the learning set were tested prospectively in the test set, based on the coefficient of multiple determination, R(2), obtained by fitting the EEG effect to a sigmoid Emax model. RESULTS: The canonical univariate parameter of benzodiazepine drug effect on the EEG, when applied to the test set receiving the midazolam infusion with flumazenil reversal, yielded a median R(2) of 0.78. The median R(2) of six commonly used empirical EEG measures of drug effect ranged from 0.18 to 0.55. CONCLUSIONS: The canonical univariate parameter for benzodiazepine drug effect on the EEG correlates more accurately and consistently with the predicted EEG effects of midazolam and its reversal than previously reported EEG measures of benzodiazepine effect.

The pharmacokinetics of 8-methoxypsoralen following i.v. administration in humans.

1. 8-methoxypsoralen (8-MOP) is a naturally occurring photoreactive substance which, in the presence of u.v. light, forms covalent adducts with pyrimidine bases in nucleic acids. For many years, 8-MOP has been used in PUVA therapy for treatment of psoriasis. Recently, the drug has been found to inactivate effectively bacteria spiked into platelet concentrates. The purpose of this study was to determine the pharmacokinetics and safety of 8-MOP administered intravenously in the bactericidal dosage range. 2. Eighteen volunteers were divided into three treatment groups to receive, respectively, 5, 10, and 15 mg 8-MOP infused over 60 min. Frequent arterial samples were gathered, and the blood and plasma were assayed for 8-MOP concentration. The pharmacokinetic parameters were determined by moment and compartmental population analysis, the latter performed with the program NONMEM. Haemodynamics, ventilatory pattern, and subjective effects were recorded throughout the study. 3. The intravenously administered 8-MOP was well tolerated in all individuals, and no acute toxicity was observed. 4. The pharmacokinetics of 8-MOP were best described by a three-compartment mammillary model in which the volumes and clearances were proportional to weight. The mean pharmacokinetic parameters for the plasma concentrations were: V1 = 0.045 1 kg-1, V2 = 0.57 1 kg-1, V3 = 0.15 1 kg-1, CL1 (systemic) = 0.010 1 kg-1 min-1, CL2 = 0.0067 1 kg-1 min-1, CL3 = 0.012 1 kg-1 min-1. The mean pharmacokinetic parameters for the blood concentrations were: V1 = 0.061 1 kg-1, V2 = 1.15 1 kg-1, V3 = 0.21 1 kg-1, CL1 (systemic) = 0.015 1 kg-1 min-1, CL2 = 0.011 1 kg-1 min-1 and CL3 = 0.015 1 kg-1 min-1. 5. The plasma pharmacokinetic model described the observations with a median absolute error of 17%, and the blood pharmacokinetic model described the observations with a median absolute error of 18%. Analysis of the relative concentration of 8-MOP between plasma and red blood cells suggested concentration-dependent partitioning. 6. The addition of 7.5 mg 8-MOP to 300 ml platelet concentrate would produce bactericidal concentrations of 25 micrograms ml-1. Simulations based upon our data show that intravenous administration of 7.5 mg over 60 min would result in systemic concentrations of 8-MOP similar to those observed with conventional PUVA therapy. We conclude that the extensive safety history established in PUVA therapy will be applicable to this new application of 8-MOP.