The intellectual health of clinical drug evaluation.
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Clinical Trials as Topic↗
Biomedical subjects
L B Sheiner
Publications and source records attributed to L B Sheiner.
Combining physiologic models and symbolic methods to interpret time-varying patient data.
This paper describes a methodology for representing and using medical knowledge about temporal relationships to infer the presence of clinical events that evolve over time. The methodology consists of three steps: (1) the incorporation of patient observations into a generic physiologic model, (2) the conversion of model states and predictions into domain-specific temporal abstractions, and (3) the transformation of temporal abstractions into clinically meaningful descriptive text. The first step converts raw observations to underlying model concepts, the second step identifies temporal features of the fitted model that have clinical interest, and the third step replaces features represented by model parameters and predictions into concepts expressed in clinical language. We describe a program, called TOPAZ, that uses this three-step methodology. TOPAZ generates a narrative summary of the temporal events found in the electronic medical record of patients receiving cancer chemotherapy. A unique feature of TOPAZ is its use of numeric and symbolic techniques to perform different temporal reasoning tasks. Time is represented both as a continuous process and as a set of temporal intervals. These two temporal models differ in the temporal ontology they assume and in the temporal concepts they encode. Without multiple temporal models, this diversity of temporal knowledge could not be represented.
Adult↗
Implications of an alternative approach to dose-response trials.
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Analysis of Variance↗
A semiparametric approach to physiological flow models.
By regarding sampled tissues in a physiological model as linear subsystems, the usual advantages of flow models are preserved while mitigating two of their disadvantages, (i) the need for assumptions regarding intratissue kinetics, and (ii) the need to simultaneously fit data from several tissues. To apply the linear systems approach, both arterial blood and (interesting) tissue drug concentrations must be measured. The body is modeled as having an arterial compartment (A) distributing drug to different linear subsystems (tissues), connected in a specific way by blood flow. The response (CA, with dimensions of concentration) of A is measured. Tissues receive input from A (and optionally from other tissues), and send output to the outside or to other parts of the body. The response (CT, total amount of drug in the tissue (T) divided by the volume of T) from the T-th one, for example, of such tissues is also observed. From linear systems theory, CT can be expressed as the convolution of CA with a disposition function, F(t) (with dimensions 1/time). The function F(t) depends on the (unknown) structure of T, but has certain other constant properties: The integral integral infinity0 F(t) dt is the steady state ratio of CT to CA, and the point F(0) is the clearance rate of drug from A to T divided by the volume of T. A formula for the clearance rate of drug from T to outside T can be derived. To estimate F(t) empirically, and thus mitigate disadvantage (i), we suggest that, first, a nonparametric (or parametric) function be fitted to CA data yielding predicted values, CA, and, second, the convolution integral of CA with F(t) be fitted to CT data using a deconvolution method. By so doing, each tissue's data are analyzed separately, thus mitigating disadvantage (ii). A method for system simulation is also proposed. The results of applying the approach to simulated data and to real thiopental data are reported.
Models, Biological↗
Study designs for dose-ranging.
Premarketing dose-ranging studies of a drug are done to establish a reasonable initial dose. According to the current procedure sanctioned by the Food and Drug Administration, each patient is given one of several possible doses, including placebo, after an initial placebo run-in period. Data analysis is based on a model in which the mean response at each dose is independent of the magnitude of the dose. The initial dose is the lowest dose tested that has a response that is statistically significantly greater than the response after placebo administration. We suggest that the present conceptual approach to, and standard study design and analysis for, dose-ranging studies be changed. We believe one must begin with a parametric model for patient-specific dose-response curves. Knowledge of the distribution of these curves in a population provides a basis for choice of an initial dose (e.g., the dose that achieves a given response in a given fraction of patients) and, after observation of response to an initial dose, for choice of an incremental dose for a specific patient (by use of Bayes rule). The current parallel-dose design can provide only poor information about the distribution of dose-response curves, biased estimates of the typical curve, and little information on interpatient variability. Crossover studies provide better information. In studies in which a parametric patient-specific dose-response model is used, a dose-escalation design provides no less information than a crossover design, and it has ethical advantages that allow a more representative patient group and clinical setting to be studied.
Analysis of Variance↗
The pharmacokinetics and pharmacodynamics of diltiazem and its metabolites in healthy adults after a single oral dose.
A potential complicating factor in the characterization of the pharmacokinetics and pharmacodynamics of diltiazem after an oral dose in the presence of two metabolites, N-demethyldiltiazem and desacetyldiltiazem, in plasma. Both N-demethyldiltiazem and desacetylditiazem have been shown to have pharmacologic activity in animal tissues. It is therefore possible that these metabolites contribute to the pharmacologic effect of diltiazem, but this possibility has not been explored. The purpose of this study was to investigate the pharmacokinetics and pharmacodynamics of diltiazem, N-demethyldiltiazem, and desacetyldiltiazem. Particular attention was paid to the effect of diltiazem on atrioventricular conduction. Six healthy men received a 120 mg oral dose of diltiazem. Concentrations of diltiazem, N-demethyldiltiazem, and desacetyldiltiazem in plasma and urine were measured by a sensitive HPLC method. Measures of pharmacologic response (heart rate, blood pressure, and PR interval) were obtained at each blood sampling time. Mean (+/- SD) peak plasma concentrations of diltiazem, N-demethyldiltiazem, and desacetyldiltiazem were 174.3 +/- 72.7, 42.6 +/- 10.0, and 14.9 +/- 3.3 ng/ml, respectively. The apparent half-lives of diltiazem, N-demethyldiltiazem, and desacetyldiltiazem were 6.5 +/- 1.4 hours, 9.4 +/- 2.2 hours, and 18 +/- 6.2 hours, respectively. Both N-demethyldiltiazem and diltiazem were eliminated by net secretion, whereas the renal clearance of desacetyldiltiazem did not exceed clearance by filtration. Both N-demethyldiltiazem and desacetyldiltiazem are bound to plasma proteins, with unbound fractions of 0.323 +/- 0.035 and 0.230 +/- 0.021. These values are similar to the unbound fraction of diltiazem (0.254 +/- 0.027). No significant effect of diltiazem on blood pressure or heart rate was noted. However, a prolongation of the PR interval was observed in all six subjects. Furthermore, an apparent clockwise hysteresis in the concentration-effect relationship was found in four of the six subjects. These findings suggest that some form of acute tolerance to the electrophysiologic effect of diltiazem develops, but the results of pharmacodynamic modeling suggest that this is not caused by the antagonistic effects the metabolites.
Administration, Oral↗
Clinical pharmacology and the choice between theory and empiricism.
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Animals↗
Semiparametric approach to pharmacokinetic-pharmacodynamic data.
A semiparametric model for analysis of pharmacokinetic (PK) and pharmacodynamic (PD) data arising from non-steady-state experiments is presented. The model describes time lag between drug concentration in a sampling compartment, e.g., venous blood (Cv), and drug effect (E). If drug concentration at the effect site (Ce) equilibrates with arterial blood concentration (Ca) slower than with Cv, a non-steady-state experiment yields E vs. Cv data describing a counterclockwise hysteresis loop. If Ce equilibrates with Ca faster than with Cv, clockwise hysteresis is observed. To model hysteresis, a parametric model is proposed linking (unobserved) Ca to Cv with elimination rate constant kappa ov and also linking Ca to Ce with elimination rate constant kappa oe. When kappa oe is greater than (or less than) kappa ov clockwise (or counterclockwise) hysteresis occurs. Given kappa oe and kappa ov, numerical (constrained) deconvolution is used to obtain the disposition function of the arterial compartment (Ha), and convolution is used to calculate Ce given Ha. The values of kappa oe and kappa ov are chosen to collapse the hysteresis loops to single curves representing the Ce-E (steady-state) concentration-response curve. Simulations, and an application to real data, are reported.
Humans↗
Improving the homogeneity of diagnosis-related groups (DRGs) by using clinical laboratory, demographic, and discharge data.
For 48 of the most common diagnosis-related groups (DRGs) at our hospital, we examined the ability of clinical laboratory tests, demographic data, and ICD-9-CM codes, which provide a measure of severity of illness, to predict patients' length of stay (LOS) more accurately than DRGs alone. For 10 of 20 medical DRGs and 13 of 23 surgical DRGs examined, we were able to increase the ability to predict LOS by at least 10 per cent. The laboratory tests that proved most predictive of LOS over all DRGs were the mean serum sodium, potassium, bicarbonate, and albumin. The system is data driven, objective, and flexible, thus ensuring its utility for the purpose of equitable reimbursement.
California↗
Pharmacodynamic modeling of verapamil effects under steady-state and nonsteady-state conditions.
Pharmacodynamic models relating the plasma concentration (Cp) of verapamil to the drug's effect (E) on the P-R interval were investigated after single dose infusions of (0.15-0.22 mg/kg) verapamil in 22 normal subjects. Model predictions of the steady-state Cp-E relationship were then compared to results from actual steady-state drug infusions in the same subjects. Two methods of estimating the steady-state concentration response relationship from the single dose data were examined: 1) the relationship of descending limb Cp vs. E and 2) the relationship of estimated effect site concentrations (Ce) vs. E. When compared to experimental steady-state measurements, the absolute errors of predictions from the Ce vs. E method were less than those from the Cp vs. E predictions (6.8 +/- 4.4 vs. 9.6 +/- 6.6, mean +/- S.D.). Similarly, the slope of the linear regression of E on Cp differed more from the observed steady-state slope than the slope of E on Ce. Sigmoid Emax models fit to Cp vs. E. data gave false Emax values even when data immediately following drug infusion were disregarded whereas Ce vs. E plots demonstrated that Emax was not reached (and Ce much less than Cp). Neither the postinfusion (descending limb) Cp vs. E nor Ce vs. E plots allowed analysis of higher concentration vs. effect relationships after usual (0.15-0.22 mg/kg) doses of verapamil. In summary, we have demonstrated that nonsteady-state postdrug infusion effect vs. plasma concentration data for verapamil does not reflect the true steady-state relationship and that use of a model to estimate effect site concentration provides a closer estimate of the true steady-state relationship.
Adult↗
Commentary on "extended least squares (ELS) for pharmacokinetic models".
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Models, Statistical↗
Pharmacodynamic model of tolerance: application to nicotine.
The authors propose a model of pharmacodynamic response that, when integrated with a pharmacokinetic model, allows characterization of the development of functional tolerance. The model may be conceived of in several equivalent ways; one of these sees tolerance as a result of (noncompetitive) antagonism of agonist effect by a hypothetical substance (e.g., metabolite) produced by a first-order process, driven by agonist concentration. Tolerance thus lags behind, and is approximately proportional to, agonist concentrations. Two new parameters quantifying tolerance are introduced: kantO, which describes the elimination kinetics of the antagonist and determines the rate of development and disappearance of tolerance, and Cant50, which determines the magnitude of tolerance that can be achieved. The model was tested in eight volunteers on data produced after three sequences of paired i.v. administrations of nicotine separated by different intervals of time. Blood concentrations of nicotine and heart rate were measured. The proposed tolerance model was fitted to the nicotine data. The estimate of kantO suggests a half-life of development and regression of tolerance of 35 min, and the estimate of Cant50 suggests that tolerance, at its full development, causes an approximately 80% reduction of initial (nontolerant) effect. This model provides a quantitative pharmacokinetic-pharmacodynamic description of the development of acute tolerance that also carries physiologic meaning. The quantitative information provided by this model may improve understanding of the temporal patterns of drug abuse and complications thereof.
Adult↗
A note on confidence intervals with extended least squares parameter estimates.
It has previously been shown that the extended least squares (ELS) method for fitting pharmacokinetic models behaves better than other methods when there is possible heteroscedasticity (unequal error variance) in the data. Confidence intervals for pharmacokinetic parameters, at the target confidence level of 95%, computed in simulations with several pharmacokinetic and error variance models, using a theoretically reasonable approximation to the asymptotic covariance matrix of the ELS parameter estimator, are found to include the true parameter values considerably less than 95% of the time. Intervals with the ordinary least squares method perform better. Two adjustments to the ELS confidence intervals, taken together, result in better performance. These are: (i) apply a bias correction to the ELS estimate of variance, which results in wider confidence intervals, and (ii) use confidence intervals with a target level of 99% to obtain confidence intervals with actual level closer to 95%. Kineticists wishing to use the ELS method may wish to use these adjustments.
Kinetics↗
Pharmacodynamic modeling of the EEG effects of ketamine and its enantiomers in man.
The pharmacodynamics of a racemic mixture of ketamine R,S(+/-)-ketamine and of each enantiomer, S(+)-ketamine and R(-)-ketamine, were studied in five volunteers. The median frequency of the electroencephalogram (EEG) power spectrum, a continuous noninvasive measure of the degree of central nervous system (CNS) depression (pharmacodynamics), was related to measured serum concentrations of drug (pharmacokinetics). The concentration-effect relationship was described by an inhibitory sigmoid Emax pharmacodynamic model, yielding estimates of both maximal effect (Emax) and sensitivity (IC50) to the racemic and enantiomeric forms of ketamine. R(-)-ketamine was not as effective as R,S(+/-)-ketamine or S(+)-ketamine in causing EEG slowing. The maximal decrease (mean +/- SD) of the median frequency (Emax) for R(-)-ketamine was 4.4 +/- 0.5 Hz and was significantly different from R,S(+/-)-ketamine (7.6 +/- 1.7 Hz) and S(+)-ketamine (8.3 +/- 1.9 Hz). The ketamine serum concentration that caused one-half of the maximal median frequency decrease (IC50) was 1.8 +/- 0.5 micrograms/mL for R(-)-ketamine; 2.0 +/- 0.5 micrograms/mL for R,S(+/-)-ketamine; and 0.8 +/- 0.4 microgram/mL for S(+)-ketamine. Because the maximal effect (Emax) of the R(-)-ketamine was different from that of S(+)-ketamine and R,S(+/-)-ketamine, it was not possible to directly compare the potency (i.e., IC50) of these compounds. Accordingly, a classical agonist/partial-agonist interaction model was examined, using the separate enantiomer results to predict racemate results. Although the model did not predict racemate results well, its failure was not so great as to provide clear evidence of synergism (or excess antagonism) of the enantiomers.
Adult↗
Simultaneous modeling of pharmacokinetics and pharmacodynamics: an improved algorithm.
An algorithm and computer program is presented that fits a largely non-parametric model to pharmacokinetic (PK) and pharmacodynamic (PD) data; it is an extension of a recently proposed approach. A PK model relates dose to plasma concentrations (Cp), a link model relates plasma concentrations to the concentration in the effect site (Ce), a PD model relates Ce to the effect. Both the PK and the PD model are non-parametric, but the link model is parametric. The extension presented here allows modeling of PK/PD data arising from non-steady-state experiments after arbitrary dosage. In addition, several data sets from the same individual (or from different individuals) can now be analyzed simultaneously, assuming the same link model for all, but allowing either all the PD models to be the same, or all to be different.
Adrenergic beta-Antagonists↗
Apparent tolerance to the acute effect of nicotine results in part from distribution kinetics.
Persons exposed to nicotine develop tolerance to many of its effects. When heart rate and forearm venous blood concentration are plotted against time after intravenous administration of nicotine, a greater increase in heart rate is seen for a given nicotine concentration during the rising phase of nicotine concentrations than during the decreasing phase. This could be due to acute tolerance or to more rapid distribution of drug to effect site (brain) than to venous blood. To distinguish between these possibilities, six rabbits were given nicotine intravenously. Blood samples were taken from the internal jugular vein (reflecting brain concentration), and the femoral vein and artery. Brain concentrations peaked before femoral venous concentrations. Seven men received intravenous infusions of nicotine. Peripheral venous blood concentrations and cardiovascular responses were measured. Heart rate peaked before venous concentrations. A physiological kinetic model, fit to the rabbit data, was scaled to humans and used to predict "brain" concentrations in them. Heart rate and predicted brain concentrations peaked simultaneously. We conclude that the rapid development of tolerance to the cardioaccelerating effect of nicotine can be attributed, at least in part, to its distribution kinetics.
Adult↗
Does the duration of anesthetic administration affect the pharmacokinetics or metabolism of inhaled anesthetics in humans?
To define the effect of anesthetic duration on the pharmacokinetics of inhaled anesthetics, we determined the pharmacokinetics of isoflurane, enflurane, halothane, and methoxyflurane given simultaneously to seven healthy subjects for exactly 30 min and compared the results with data from a previous study in which these four anesthetics were administered for 120 min. End-tidal and mixed-expired anesthetic concentrations were measured during washin of anesthetic and for 3-9 days of washout. Multiexponential (multicompartment) models were fit by least squares to the alveolar washin and washout curves. We estimated the percentage of anesthetic that was metabolized from total uptake and recovery of anesthetic. Alveolar washout was more rapid after the shorter period of anesthetic administration. However, duration of administration did not affect the time constants determined, the number of compartments identified (i.e., five compartments were identified in both studies), or the percentages of anesthetic metabolized.
Adult↗
Analysis of pharmacokinetic data using parametric models. III. Hypothesis tests and confidence intervals.
This is the third in a series of tutorial articles discussing the analysis of pharmacokinetic data using parametric models. In this article the concern is how to test hypotheses about, and assign confidence intervals to, the values of the parameters of such models. The basic approach to both tasks involves determining the goodness of fit of the model to the data for alternative values of the parameters and using the change in goodness of fit to assess the plausibility of the alternative values. The goodness of fit is measured by the value of a (least-squares-type) objective function. An approximation to the dependence of the latter on the parameter values yields an estimate of the familiar asymptotic covariance matrix of the estimates. The latter can also be used to test hypotheses about, and assign confidence intervals to, functions of parameters.
Data Collection↗