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L B Sheiner

Publications and source records attributed to L B Sheiner.

At least 91 records · Page 5Linked to original sources
PubMed1986

Simultaneous modeling of pharmacokinetics and pharmacodynamics with nonparametric kinetic and dynamic models.

Three models, linked in series, can be used to analyze combined pharmacokinetic (PK) and pharmacodynamic (PD) data arising from non--steady-state experiments. A PK model relates dose to plasma drug concentration (Cp); a link model relates Cp to drug concentration at the effect site (Ce); and a PD model relates Ce to drug effect (E). All three submodels can be stated parametrically. Recently the use of a nonparametric PD submodel has been proposed (CLIN PHARMACOL THER 1984;35:733-41). In this article we use an extended nonparametric approach that represents both the PK and PD models nonparametrically, but retains a parametric link model. Cp data from several PK models and E data from several PD models were simulated. After the addition of noise to both the Cp and E data, they were analyzed by both the parametric and extended nonparametric methods. The methods were compared by how well they estimated the PD model. To assess robustness, the effect of misspecification of the PK submodel on the goodness of estimation of both methods was also compared. In the absence of model misspecification, the parametric method usually estimates the PD model better than the nonparametric method. However, this difference in the performances diminishes and even reverses when the PK model is misspecified. Because one can rarely be certain that model misspecification is absent, the nonparametric approach may offer a distinct advantage for routine analysis of PK/PD data.

Kinetics↗
PubMed1986

The extent of metabolism of inhaled anesthetics in humans.

To determine the percentage of anesthetic metabolized and to assess the role of metabolism in the total elimination of inhaled anesthetics, the authors administered isoflurane, enflurane, halothane, and methoxyflurane simultaneously, for 2 h, to nine healthy patients. Total anesthetic uptake during the 2 h of washin and total recovery of unchanged anesthetic in exhaled gases during 5 to 9 days of washout were measured, and from these the per cent of anesthetic uptake that was recovered was calculated. Of the isoflurane taken up, 93 +/- 4% (mean +/- SE) was recovered. To compensate for factors other than metabolism that limit complete recovery of unchanged anesthetic, the percentage recovery of each anesthetic was normalized to the percentage recovery of isoflurane (which it was assumed undergoes no metabolism). Deficits in normalized recovery were assumed to be due to metabolism of the anesthetics. The resulting estimates of metabolism of anesthetic taken up were: enflurane 8.5 +/- 1.0%, halothane 46.1 +/- 0.9%, and methoxyflurane 75.3 +/- 1.6%. These results indicate that elimination is primarily via the lungs for isoflurane and enflurane, equally via the lungs and via metabolism for halothane, and primarily via metabolism for methoxyflurane.

Adult↗
PubMed1986

An integrated model for the interaction of muscle relaxants with their antagonists.

An integrated model describing the interaction of nondepolarizing neuromuscular blocking agents with reversible anticholinesterase agents is derived and compared with a naive model using experimental data obtained from four anesthetized dogs. Three consecutive but separate steady-state d-tubocurarine blocks (approximately 50, 70, and 90%) were induced in each of the four dogs and reversed by short edrophonium infusions. Edrophonium arterial concentrations and twitch tension of the anterior tibialis muscle were measured. Both the integrated and the naive model were fit to the twitch tension data using a model with a hypothetical "effect" compartment. The integrated model consistently fit the twitch tension data better than the naive model; the sum of squared deviations was lower by 46, 45, 87, and 69%, respectively, with the integrated model than with the naive model. Also, in contrast to the naive model, the integrated model is capable of describing the interaction of the anticholinesterase agent and the neuromuscular blocking agent when the concentration of either varies with time.

Animals↗
PubMed1986

Pharmacokinetics of inhaled anesthetics in humans: measurements during and after the simultaneous administration of enflurane, halothane, isoflurane, methoxyflurane, and nitrous oxide.

To determine the relative washin and washout characteristics of isoflurane, enflurane, halothane, and methoxyflurane, we administered all four anesthetics simultaneously (total = 1.1 MAC) to nine healthy patients for 2 hr. Concentrations of anesthetics in end-tidal gases were measured during washin and for 5-9 days during washout. Multiexponential (multicompartment) models were fit to the washin and washout curves using least-squares analysis. Slowly equilibrating compartments could only be identified during washout. For 27 of the 36 data sets, five-compartment models fit the washout curves significantly better than four-compartment models. The time constant for our first compartment is consistent with that predicted for washout of the lungs. Time constants for the second, third, and fifth compartments were consistent with current data for blood flows and solubilities of vessel-rich, muscle, and fat tissue groups, respectively. The fourth compartment has a time constant that lies between the time constants predicted for muscle and fat.

Adult↗
PubMed1986

Elimination of atracurium in humans: contribution of Hofmann elimination and ester hydrolysis versus organ-based elimination.

Atracurium, a nondepolarizing muscle relaxant, is eliminated through several pathways, including Hofmann elimination (spontaneous degradation in plasma and tissue at normal body pH and temperature) and ester hydrolysis (catalysis by nonspecific esterases). Because elimination of atracurium occurs in both tissue and plasma, traditional pharmacokinetic models assuming elimination from a single central compartment are inaccurate for atracurium. The authors developed a two-compartment pharmacokinetic model in which hepatic and/or renal elimination occurs from the central compartment (Cl organ), and Hofmann elimination and ester hydrolysis occur from both central and peripheral compartments (Cl nonorgan). To determine the in vitro rate constant for Hofmann elimination and ester hydrolysis, atracurium was added to whole blood kept at each patient's pH and temperature. The values for this rate constant ranged from 0.0193 to 0.0238 per min. When these values were applied to the pharmacokinetic model, Cl total, Cl organ, and Cl nonorgan were 4.8 +/- 1.1, 3.0 +/- 0.9, and 1.9 +/- 0.6 ml . kg-1 . min-1, respectively. The authors conclude that more than one-half of the clearance of atracurium occurs via pathways other than Hofmann elimination and ester hydrolysis.

Adult↗
PubMed1985

The usefulness of preoperative laboratory screening.

We assessed the usefulness of routine laboratory screening of preoperative patients. Computer-readable laboratory, demographic, and discharge diagnostic data were assembled for 2,000 patients undergoing elective surgery over a four-month period, and randomly selected samples of patients were studied. Several tests ordered by protocol and performed by the laboratory at the time of admission were examined in these samples, including complete blood cell count, differential cell count, prothrombin time, partial thromboplastin time, platelet count, six-factor automated multiple analysis, and glucose level. Sixty percent of these routinely ordered tests would not have been performed if testing had only been done for recognizable indications, and only 0.22% of these revealed abnormalities that might influence perioperative management. Chart review indicated that these few abnormalities were not acted on nor did they have adverse surgical or anesthetic consequences. In the absence of specific indications, routine preoperative laboratory tests contribute little to patient care and could reasonably be eliminated.

Adolescent↗
PubMed1985

Analysis of pharmacokinetic data using parametric models. II. Point estimates of an individual's parameters.

This is the second in a series of tutorial articles discussing the analysis of pharmacokinetic data using parametric models. In this article the basic issue is how to estimate the parameters of such models. Primary emphasis is placed on point estimates of the parameters of the structural (pharmacokinetic) model. All the estimation methods discussed are least squares (LS) methods: ordinary least squares, weighted least squares, iteratively reweighted least squares, and extended least squares. The choice of LS method depends on the variance model. Some discussion is also provided of computer methods used to find the LS estimates, identifiability, and robust LS-based estimation methods.

Data Collection↗
PubMed1985

Pharmacokinetic parameter estimates from several least squares procedures: superiority of extended least squares.

The precision of pharmacokinetic parameter estimates from several least squares parameter estimation methods are compared. The methods can be thought of as differing with respect to the way they weight data. Three standard methods, Ordinary Least Squares (OLS-equal weighting), Weighted Least Squares with reciprocal squared observation weighting [WLS(y-2)], and log transform OLS (OLS(ln))--the log of the pharmacokinetic model is fit to the log of the observations--are compared along with two newer methods, Iteratively Reweighted Least Squares with reciprocal squared prediction weighting (IRLS,(f-2)), and Extended Least Squares with power function "weighting" (ELS(f-xi)--here xi is regarded as an unknown parameter). The values of the weights are more influenced by the data with the ELS(f-xi) method than they are with the other methods. The methods are compared using simulated data from several pharmacokinetic models (monoexponential, Bateman, Michaelis-Menten) and several models for the observation error magnitude. For all methods, the true structural model form is assumed known. Each of the standard methods performs best when the actual observation error magnitude conforms to the assumption of the method, but OLS is generally least perturbed by wrong error models. In contrast, WLS(y-2) is the worst of all methods for all error models violating its assumption (and even for the one that does not, it is out performed by OLS(ln)). Regarding the newer methods, IRLS(f-2) improves on OLS(ln), but is still often inferior to OLS. ELS(f-xi), however, is nearly as good as OLS (OLS is only 1-2% better) when the OLS assumption obtains, and in all other cases ELS(f-xi) does better than OLS. Thus, ELS(f-xi) provides a flexible and robust method for estimating pharmacokinetic parameters.

Analysis of Variance↗
PubMed1984

Extended least squares nonlinear regression: a possible solution to the "choice of weights" problem in analysis of individual pharmacokinetic data.

It is often difficult to specify weights for weighted least squares nonlinear regression analysis of pharmacokinetic data. Improper choice of weights may lead to inaccurate and/or imprecise estimates of pharmacokinetic parameters. Extended least squares nonlinear regression provides a possible solution to this problem by allowing the incorporation of a general parametric variance model. Weighted least squares and extended least squares analyses of data from a simulated pharmacokinetic experiment were compared. Weighted least squares analysis of the simulated data, using commonly used weighting schemes, yielded estimates of pharmacokinetic parameters that were significantly biased, whereas extended least squares estimates were unbiased. Extended least squares estimates were often significantly more precise than were weighted least squares estimates. It is suggested that extended least squares regression should be further investigated for individual pharmacokinetic data analysis.

Computers↗
PubMed1984

Analysis of pharmacokinetic data using parametric models--1: Regression models.

This is the first in a series of tutorial articles discussing the analysis of pharmacokinetic data using parametric models. In this article, the purposes of modelling are discussed; regression models for individuals and populations are defined; and structural and variance models are discussed as the two required submodels of the overall regression model. Topics of future articles are: point estimates of parameters; interval estimates of parameters; model criticism and choosing among contending models; population kinetic models and estimation; and elements of optimal design.

Humans↗
PubMed1984

Simultaneous modeling of pharmacokinetics and pharmacodynamics with a nonparametric pharmacodynamic model.

We describe a variation on an approach to simultaneous modeling of pharmacokinetics (PK) and pharmacodynamics (PD). Both approaches model the often-observed time lag between plasma drug concentration (Cp) and drug effect (E) in non-steady-state experiments by postulating an E site whose concentration (Ce) is kinetically linked to Cp by a first-order process. With the linking model, the time lag can be removed from the data and the underlying concentration-response (Ce-E) relationship can be estimated. The original method requires the analyst to postulate a particular parametric form for the Ce-E model, whereas ours does not. It estimates the rate constant of the linking model as the value that causes the hysteresis curve (Ce vs E points connected in time order) to collapse to a single curve that represents the (empirical) Ce-E relationship. The method is presented as an algorithm and is tested by means of simulation and a real-world example. The results suggest that the method can faithfully estimate the Ce-E curve for a variety of PD models and degrees of experimental error when its basic assumption of time-invariant PD holds.

Dose-Response Relationship, Drug↗
PubMed1984

The population approach to pharmacokinetic data analysis: rationale and standard data analysis methods.

Population pharmacokinetics describe the typical relationships between physiology (both normal and disease altered) and pharmacokinetics, the interindividual variability in these relationships, and their residual intraindividual variability. Knowledge of population kinetics can help one to choose initial drug dosage, to modify dosage appropriately in response to observed drug levels, to make rational decisions regarding certain aspects of drug regulation, and to investigate and elucidate certain research questions in pharmacokinetics. Experimental data from which population kinetics might be estimated often come from only those few individuals both willing and available to be studied. Clinical data from patients undergoing care might be more representative. These data, however, are marked by varying quality, accuracy, and precision, as well as there being few data per patient. Population pharmacokinetic parameters have traditionally been estimated either by fitting all individuals' data together as though there were no individual kinetic differences [the naive pooled data (NPD) approach], or by fitting each individual's data separately and then combining the individual parameter estimates [the two-stage (TS) approach]. These methods have certain theoretical problems which can only be aggravated when the deficiencies of data typical of clinical data are present. In this paper, the standard approaches are discussed and illustrated (using nondeficient data) in order to introduce subsequent papers in which alternative data analysis methods for population parameter estimation are defined, discussed, and compared.

Humans↗
PubMed1984

Experience with NONMEM: analysis of routine phenytoin clinical pharmacokinetic data.

NONMEM, a program package the produces the extended least squares estimates of population parameters for a nonlinear mixed-effect model, has been applied to two data sets from patients routinely receiving phenytoin. A general model for the data is proposed. The models used in previous, standard-method analyses of each data set are compared to the general model using NONMEM. The comparison involves two questions: The first asks whether the parameters estimated previously agree with NONMEM estimates when the original model is used. We find that for fixed-effect parameters they generally do, while for interindividual random-effect parameters the previous methods' estimates appear upward biased relative to NONMEM. Second, the original model per se is compared to the general model by comparing the best fit to each. The general model is clearly superior. NONMEM's ability to distinguish among models, and to precisely estimate their parameters from sparse individual data, is illustrated and verified.

Computers↗
PubMed1983

Evaluation of methods for estimating population pharmacokinetic parameters. III. Monoexponential model: routine clinical pharmacokinetic data.

Individual pharmacokinetic parameters quantify the pharmacokinetics of an individual, while population pharmacokinetic parameters quantify population mean kinetics, interindividual kinetic variability, and residual variability, including intraindividual variability and measurement error. Individual pharmacokinetics are estimated by fitting a pharmacokinetic model to individual data. Population pharmacokinetic parameters have traditionally been estimated by doing this separately for each individual, and then combining the individual parameter estimates, the Standard Two Stage (STS) approach. Another approach, NONMEM, appropriately pools data across individuals and is therefore less dependent on individual parameter estimates. This study provides further evidence of NONMEM's validity and usefulness by comparing both approaches on simulated routine-type pharmacokinetic data arising from a monoexponential model. The estimates of population parameters (notably those describing interindividual variability) provided by the STS method are poorer than those provided by NONMEM, especially when there is considerable residual error. Further, NONMEM's estimates of population parameters do not require that the data be restricted to special types of routine data such as those obtained only at steady state, or only at peak or trough, nor do the estimates improve with such data. NONMEM's estimates do improve, however, when a data set is enhanced by the addition of single-observation-per-individual type data. Thus, population parameters can be estimated efficiently from data that simulate real clinical pharmacokinetic conditions.

Humans↗
PubMed1982

Bayesian individualization of pharmacokinetics: simple implementation and comparison with non-Bayesian methods.

One may attempt to individualize drug dosage by estimating an individual's pharmacokinetic parameters. Information useful for this purpose consists of certain population pharmacokinetic parameters (notably those describing the typical relationship between dosage and drug concentrations) and also measured drug concentrations from the individual of concern. Both types of information should be used. A (Bayesian) method that does so has been described in the pharmacokinetic literature. In this report an implementation of the Bayesian method that is readily adapted to a microcomputer is presented. Using simulated data it is compared with two other methods proposed by others, for estimating individual theophylline clearances. Both previously suggested methods are shown to be less precise than the Bayesian method: their typical error magnitudes are 20-70% larger.

Bayes Theorem↗