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Biomedical subjects

D Verotta

Publications and source records attributed to D Verotta.

At least 37 records · Page 2Linked to original sources
PubMed1996

Chronopharmacokinetics of nicotine.

RATIONALE: For high-clearance drugs such as nicotine, hemodynamic changes throughout the day may be expected to influence the rate of metabolism. MATERIAL AND METHODS: To assess the effects of meals and diurnal rhythms on nicotine clearance, an intravenous infusion of nicotine bitartrate was administered for 48 hours to 11 subjects. Two models to determine nicotine clearance variation throughout the day are described. Both models were used to estimate the mean effect of meal and diurnal rhythms on nicotine clearance and individual parameters that were regressed against baseline covariates. Clearance was modeled as a function of time [CL(t)] and split it in three components: a (constant) baseline value (theta 1), its circadian (diurnal) variation, and the effect of meal: CL(t) = [theta 1 + circadian(t)] [1 + meal(t)]. A two-compartmental (time-variant) model incorporating CL(t) was then fitted to the data providing estimates of CL(t) conditional on literature values of the time-invariant parameters (volume of distribution and intercompartmental clearances). RESULTS: The estimated circadian(t) showed a maximum at approximately 11 AM and a flat minimum from 6 PM to 3 AM; the estimated meal(t) showed a sharp increase up to 1 hour (after the meal), at which point clearance is increased 42%, and a slower decrease thereafter, returning to baseline (zero) after 2.8 hours. Individual estimates of baseline clearance are found to have a linear relationship with body weight. No other covariate, sex in particular, effect could be found.

Adult↗
PubMed1996

A descriptive tool to characterize nonlinear kinetics, with applications to meperidine and lidocaine.

We describe an explorative data analysis tool which can detect and describe the presence of nonlinearities in multiple dose kinetics studies. The method is nonparametric (i.e. not dependent on modeling assumptions), uses regression to estimate the functions representing the kinetics, and makes the detection of nonlinearity a part of the model selection process. Flexible functions (splines) are used to describe the kinetics corresponding to the lowest given dose, and to describe the (possible) departure of the kinetics corresponding to higher doses from the reference kinetics. The estimated kinetics and departures can be examined to offer possible insight into the nature of the nonlinearity. The methodology is applied to the analysis of meperidine and lidocaine kinetics through the lungs and the heart. We find that the lung kinetics of lidocaine and meperidine are linear. However their myocardial kinetics are complex and nonlinear.

Animals↗
PubMed1996

Modeling a bivariate control system: LH and testosterone response to the GnRH antagonist antide.

A pharmacodynamic analysis of the input-response relationship between the gonadotropin-releasing hormone antagonist antide and luteinizing hormone (LH) and testosterone concentrations is presented. A control compartmental model is developed using pharmacokinetic and pharmacodynamic data from experiments in which different short intravenous antide infusions were given to healthy male volunteers. Because of the control interdependence between serum LH and testosterone a separation principle similar to one we have used previously to analyze physiological pharmacokinetic data is used for model exploration: testosterone and LH are first modeled separately, conditioning on the other observed response. This reveals that the LH effect on testosterone depends on previous LH exposure and that LH depends not on current but on previous testosterone exposure, resulting in an LH overshoot after antide-induced suppression. Both submodels are combined into one global model, which in addition includes a model for testosterone circadian variation. This model describes the data well and can be used to predict responses for some nonstudied antide dosages. However, the sensitivity of predictions to model assumptions limits the range of valid extrapolation, and this, too, is illustrated.

Humans↗
PubMed1996

Age and autonomic effects on interrelationships between lung volume and heart rate.

To determine effects of aging and autonomic input on interrelationships between respiratory and heart rate variability, we collected 5 min of lung volume of R-R interval data from 7 young [27 +/- 3(SD) yr] and 10 older (69 +/- 6 yr) healthy supine humans before and after double pharmacological autonomic blockade with propranolol (0.2 mg/kg iv) and atropine (0.04 mg/kg iv). Estimates of respiratory and heart rate power spectra and linear transfer functions between the two groups were generated by Fourier analysis. Age, double blockade effects, the age-drug interactions were determined by analysis of variance for repeated measures. Basal R-R intervals were unaffected by age. Double blockade decreased R-R intervals and variability in both age groups (P < 0.0001), but R-R intervals decreased less in older than in young subjects (P < 0.0001). In contrast, basal respiratory intervals and standard deviation were greater in older subjects (P = 0.05) and were unaffected by double blockade in young and older subjects. Lung volume-to-heart rate spectral coherence was highest at frequencies associated with respiration and greater in young than in older subjects (P < 0.07). Double blockade decreased lung volume-to-heart rate variability transfer function magnitude (P < 0.007) and increased phase angle (P < 0.02) without age effects or age-drug interactions. In conclusion, heart rate, respiration, and respiration-heart rate interrelations are altered by aging, and double autonomic pharmacological blockade does not eliminate all age-related differences.

Adult↗
PubMed1996

Concepts, properties, and applications of linear systems to describe distribution, identify input, and control endogenous substances and drugs in biological systems.

The response at time t (R(t)) of a (causal linear time invariant) system to an input A(t) is represented by: [equation: see text] where K(t) is called the unit impulse response function of the system, and the integration on the right side of the equation (above) is called the convolution (from the latin cum volvere: to interwine) of A(t) and K(t). The system described by this equation is at zero (initial conditions) when t = 0. Although it does not even begin to describe the incredible variety of possible responses of biological systems to inputs, this representation has large applicability in biology. One of the most frequently used applications is known as deconvolution: to deinterwine R(t) given a known K(t) (or A(t)) and observations of R(t), to obtain A(t) (or K(t)). In this paper attention is focused on a greater variety of aspects associated with the use of linear systems to describe biological systems. In particular I define causal linear time-invariant systems and their properties and review the most important classes of methods to solve the deconvolution problem, address. The problem of model selection, the problem of obtaining statistics and in particular confidence bands for the estimated A(t) (and K(t)), and the problem of deconvolution in a population context is also addressed, and so is the application of linear system analysis to determine fraction of input absorbed (bioavailability). A general model to do so in a multiinput-site linear system is presented. Finally the application of linear system analysis to control a biological system, and in particular to target a desired response level, is described, and a general method to do so is presented. Applications to simulated, endocrinology, and pharmacokinetics data are reported.

Bayes Theorem↗
PubMed1995

A nonparametric subject-specific population method for deconvolution: I. Description, internal validation, and real data examples.

In a pharmacokinetics context deconvolution facilitates the following: (i) Given data obtained after intravascular (generally intravenous) input one may estimate the disposition function; (ii) given the disposition function and data obtained after extravascular administration one may estimate the extravascular to vascular input rate function. In general if the data can be represented by the convolution of two functions, of which one is unknown, deconvolution allows the estimation of the unknown one. Attention has been given in the past to deconvolution and in particular to its nonparametric variants. However, in a population context (multiple observations collected in each of a group of subjects) the use of nonparametric deconvolution is limited to either analyzing each subject separately or to analyzing the aggregate response from the population without specifying subject-specific characteristics. To our knowledge a fully nonparametric deconvolution method in which subject specificity is explicitly taken into account has not been reported. To do so we use so-called "longitudinal splines." A longitudinal spline is a nonparametric function composed of a template spline, in common to all subjects, and of a distortion spline representing the difference of the subject's function from the template. Using longitudinal splines for input rate or disposition function one obtains a solution to the problem of taking subject specificity into account in a nonparametric deconvolution context. To obtain estimates of longitudinal splines we consider three different methods: (1) parametric nonlinear mixed effect, (2) least squares, and (3) two-stage. Results obtained in one simulated and two real data analyses are shown.

Least-Squares Analysis↗
PubMed1995

A nonparametric subject-specific population method for deconvolution: II. External validation.

A lot of attention has been given in the past to deconvolution and in particular to its nonparametric variants. In a companion paper (1), we present a fully nonparametric deconvolution method in which subject specificity is explicitly taken into account. To do so we use so-called "longitudinal splines." A longitudinal spline is a nonparametric function composed of a template spline, in common to all subjects, and of a distortion spline representing the difference of the subject's function from the template. In this paper we concentrate on testing and documenting the performance of this nonparametric methodology in terms of the approximation of unknown functions. We simulate population data using parametric functions, and use longitudinal splines to recover the unknown functions. We consider different estimation methods including (1) parametric nonlinear mixed effect, (2) least squares, and (3) two-stage. Methods 2-3 are more robust than Method 1, and obtain reliable estimates of the unknown functions. The lack of robustness of Method 1 appears to be due to the misspecifications of the distribution of the subjects' parameters. Results also suggest that in a data-rich situation nonparametric nonlinear mixed-effect models should be preferred.

Models, Theoretical↗
PubMed1995

A new method to explore the distribution of interindividual random effects in non-linear mixed effects models.

This article presents a new approach for exploring the distribution of interindividual random effects in nonlinear mixed effect models. The approach introduces a spline function, which transforms an assumed normally distributed interindividual random effect to an arbitrary distribution approximating that of the data. The performance of this tool is illustrated using simulated pharmacokinetic data with non-normally distributed random effects. Results of the analyses of two real kinetic data sets are also presented.

Biometry↗
PubMed1994

Pharmacokinetic-pharmacodynamic (PK-PD) modelling in non-steady-state studies and arterio-venous drug concentration differences.

In conducting a non-steady-state pharmacokinetic (PK)-pharmacodynamic (PD) study there is potential for the observed effect (E) vs time, and venous plasma drug concentration (C) vs time, profiles to display temporal displacement with respect to each other. This is most frequently observed when there exists a distributional nonequilibrium across the effect organ giving rise to hysteresis, i.e. observed C preceding E in the time domain, with the resulting potential for a counterclockwise loop to be generated in the observed E vs C plot (when data are connected in time-order). Such temporal displacement does not afford direct prediction of the steady-state E vs C PD relationship. When an arterio-venous (A-V) difference exists across the tissues of the blood sampling compartment (i.e. the arm), and this arises solely from an elimination process then drug concentration in the respective peripheral arterial plasma and venous plasma compartments will be in equilibrium at all times during a non-steady-state PK experiment. If there are no other sources of temporal displacement in the relationship between E and C then the observed E vs C plot will be a direct predictor of the steady-state E vs C PD relationship. In contrast when the A-V difference is of a distributional nature then proteresis, i.e. observed E preceding C in the time domain, will arise with the potential for the generation of a clockwise loop in the observed E vs C relationship. Simulated error-incorporated E vs time, and C vs time, data was analysed by semi-parametric implementation of an effect-compartment link-model that affords accurate steady-state E vs C PD predictions (without the requirement of sampling arterial blood) from data that incorporates the concurrent presence of: (i) distributional nonequilibrium across the effect organ, and (ii) distributional A-V non-equilibrium. Accurate steady-state E vs C PD predictions were achieved irrespective of the comparative magnitudes of the two nonequilibria, i.e. whether the rate of equilibration across the effect organ was faster than, or slower than, the rate of equilibration across the arm (resulting in a clockwise or counterclockwise loop in the observed E vs C plot, respectively), or indeed if one or other of the nonequilibria is essentially absent. When the rate of equilibration across the effect organ is slower than the rate of A-V equilibration (i.e. counterclockwise loop generated in the observed E vs C plot) then the need to model for the underlying A-V nonequilibrium is redundant, i.e. accurate steady-state E vs C PD predictions can be achieved with implementation (strictly incorrectly) of a more simple link parameterised solely to model for distributional nonequilibrium across the effect organ.(ABSTRACT TRUNCATED AT 400 WORDS)

Computer Simulation↗
PubMed1994

Semiparametric models for antagonistic drug interactions.

A new class of models to describe antagonistic drug interactions are presented. They are semiparametric in that they use nonparametric functions (splines) but are forced to obey certain constraints corresponding to reasonable assumptions. We propose the models primarily for exploratory data analysis, but they may also be definitive models for such purposes as predicting future responses. Certain problems that arise in semiparametric modeling, such as model selection, are addressed so that we can propose a relatively automatic and objective approach to model determination. We demonstrate the applicability of the class of models we propose to two real data set examples involving pain relief response to opioid agonists/antagonists. The results suggest that the semiparametric approach is particularly useful when unusual shapes link dose to response.

Animals↗
PubMed1993

Two constrained deconvolution methods using spline functions.

This paper describes two new methods to solve the following estimation problem. Given n1 noisy measurements (yi1, i = 1,..., n1) of the response of a system to a known input [A1(t) where t indicates time], and n2 noisy measurements (yi2, i = 1,..., n2) of the response of a system to an unknown input [A2(t)], obtain an estimate of A2(t) and K(t) (the unit impulse response function of the system) under the model: [formula: see text] where Eij are independent identically distributed random variables. Both methods use spline functions to represent the unknown functions, and they automatically select the spline functions representing the unknown input and unit impulse response functions. The first method estimates separately the unit impulse response function and the input, recasting the problem in terms of inequality-constrained linear regression. The second method jointly estimates the unit impulse response function and the input function, recasting the problem in terms of inequality-constrained nonlinear regression. Simulated and real data analysis are reported.

Algorithms↗
PubMed1993

Aging effects on stereoselective pharmacokinetics and pharmacodynamics of verapamil.

Pharmacokinetics and pharmacodynamics were studied after separate single 15-min infusions of each of verapamil's enantiomers (d, 10-11 mg/kg; l, 0.10-0.11 mg/kg) in 16 healthy non-smoking subjects ranging in age from 24 to 40 (young) and from 63 to 83 years (elderly). Verapamil clearance was found to be decreased in an age-related stereoselective manner, with significant reductions in l-verapamil clearance in older subjects (P < .03), but no age-related change in d-verapamil clearance. Greater l- vs. d-verapamil clearance rates were only seen in younger male subjects. Trends for increased elimination half-lives for both enantiomers were seen with increasing age (for d-, P < .09, l- P = .10). Protein binding was stereoselective, with greater binding of d- vs. l-verapamil in both age groups (P < .0001) with no age-related differences in binding detected. Vd beta was greater for d- vs. l-verapamil (P < .05). l-Verapamil was more potent than d-verapamil (P < .001) for all pharmacodynamic variables measured. Both verapamil enantiomers decreased blood pressure (P < .0001), increased P-R intervals during sinus rhythm (P < .0001) and atrioventricular Wenckebach block cycle lengths (P < .0001) and transiently increased heart rate (P < .0001) in both young and elderly subjects. Age-related differences in responses were seen for blood pressure (greater decreases in systolic pressure in the elderly after d-verapamil, P < .002), heart rate (smaller and only transient increases followed by decreases after d-verapamil) and P-R intervals during sinus rhythm (less prolongation in the elderly after both enantiomers, P < .02).(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗
PubMed1992

Building population pharmacokinetic--pharmacodynamic models. I. Models for covariate effects.

One major task in clinical pharmacology is to determine the pharmacokinetic-pharmacodynamic (PK-PD) parameters of a drug in a patient population. NONMEM is a program commonly used to build population PK-PD models, that is, models that characterize the relationship between a patient's PK-PD parameters and other patient specific covariates such as the patient's (patho) physiological condition, concomitant drug therapy, etc. This paper extends a previously described approach to efficiently find the relationships between the PK-PD parameters and covariates. In a first step, individual estimates of the PK-PD parameters are obtained as empirical Bayes estimates, based on a prior NONMEN fit using no covariates. In a second step, the individual PK-PD parameter estimates are regressed on the covariates using a generalized additive model. In a third and final step, NONMEM is used to optimize and finalize the population model. Four real-data examples are used to demonstrate the effectiveness of the approach. The examples show that the generalized additive model for the individual parameter estimates is a good initial guess for the NONMEM population model. In all four examples, the approach successfully selects the most important covariates and their functional representation. The great advantage of this approach is speed. The time required to derive a population model is markedly reduced because the number of necessary NONMEM runs is reduced. Furthermore, the approach provides a nice graphical representation of the relationships between the PK-PD parameters and covariates.

Adult↗
PubMed1992

Elderly, conscious patients have an accentuated hypotensive response to nitroglycerin.

There is no adequate explanation for the highly variable response of systemic blood pressure to nitroglycerin (glyceryl trinitrate [GTN]). Aging produces cardiovascular changes that should alter the effects of GTN, but elderly patients usually have been excluded from studies of GTN. Accordingly, the authors compared the effects of GTN on systemic blood pressure in elderly and younger patients. Fifty-three patients, aged 49-87 (with 30 patients older than 70), were studied. Before elective vascular surgery, 14 patients received an infusion of placebo; 26, a constant infusion of GTN; and 13, a stepwise increasing infusion of GTN. After a standardized anesthetic induction and the start of surgery, the identical infusion protocols were repeated in each group. Data on GTN infusion rate, arterial blood pressure, and GTN concentrations versus time, age, and other potentially influencing variables were pooled for analysis. Before anesthesia and surgery, GTN more commonly caused excessive hypotension in patients older than 70 yr than in younger patients, but none of the patients had complications. A repeated-measures model analysis indicated that age significantly influenced the effects of GTN on blood pressure. That is, patients who are in their 70s who receive 0.5 micrograms.kg-1.min-1 of GTN are predicted to experience a twofold greater decrease in systolic arterial pressure (approximately 33 mmHg) than patients in their 50s. However, no apparent effect of age on intraoperative GTN responsiveness was discernible nor was a predictable relationship found between the preoperative and intraoperative responsiveness or between arterial concentrations of GTN and blood pressure or age. Therefore, the authors conclude that, in the absence of the effects of anesthesia and surgery, elderly patients have a more pronounced blood pressure response to GTN than younger patients. Furthermore, the authors conclude that preoperative blood pressure responsiveness to GTN is not a reliable predictor of intraoperative responsiveness.

Aged↗
PubMed1992

Comparing responses when each response is a curve.

We describe, generalize, and demonstrate the application of a method (W. H. Lawton, A. Sylvestre, and M. S. Maggio, Technometrics 14: 513-532, 1972) that can be used to partially analyze population data. The data from each subject consist of a series of responses observed at distinct values of a predictor variable. The method assumes that all subjects' data originate from a common process but differ because the "units" of predictor and response variables differ among subjects. For example, if the predictor variable is time, time can be "faster" or "slower" from subject to subject. We deal with two different problems. In the first one the response at x for the ith subject is of the form beta 1i + beta 2iG[(x - beta 3i)/beta 4i] + epsilon, where G(x) is a mathematical "shape" function (of the predictor variable x) representing the process and epsilon is observation error. The units of observed and predictor variable are then defined by the values of beta 1i, beta 2i and beta 3i, beta 4i, respectively. In particular beta 1i and beta 3i express shifts and beta 2i, beta 4i express scales of the observed and predictor variables, respectively. In the second problem the response at x for the ith subject is of the form beta 1i + beta 2i integral of x0 G[(s - beta 3i)/beta 4i].Hi(x - s) ds + epsilon, where Hi(x) is a known function. In both problems, the method estimates the common "shape" function G(x) nonparametrically and the parameters beta 1i, beta 2i, beta 3i, and beta 4i for each subject.(ABSTRACT TRUNCATED AT 250 WORDS)

Animals↗