Biomedical subjects

D Verotta

Publications and source records attributed to D Verotta.

60 records · Page 4Linked to original sources

PubMed1986

MODDIS: a microcomputer program for model discrimination.

A computer program for a microcomputer (HP 86) is presented to discriminate between different models and to design new experiments for model discrimination. By a non-linear fitting algorithm a set of experimental data is fitted with different models suggested by the user. The parameters characterizing each model are estimated by minimizing the sum of squared residuals; different criteria are used to test the choice between two or more models at different levels of probability and the smallest number of additional experiments required for discrimination is computed. If discrimination is not achieved a direct search method is used to find the local maxima of the divergence (or information for discrimination) over a user-chosen domain of independent variables (x R'''). The x value corresponding to the absolute maximum of the divergence is the best choice to run a new experiment for discrimination.

Biometry↗

PubMed1986

Allosteric inhibition as a model to explain flatter displacement curves in binding experiments: application to "heterogeneous" serotonin receptors.

Flattened or biphasic inhibition curves are usually interpreted by postulating the presence of two sites which are labelled with the same affinity by the ligand and can be recognized using the appropriate inhibitor with different selective affinities. We found that a priori this type of curve, can be equally fitted by another model, the allosteric model, on account of the mathematical equivalence of the two model functions when the 3H-ligand concentration is kept constant (i.e. inhibition experiments). A new approach consisting of three-dimensional analysis of the experimental data (3H-ligand binding as a function of ligand and inhibitor concentrations, simultaneously) permitted a statistical discrimination between the two models. The examples, used as tool for the present study, are the flattened or biphasic inhibition curves obtained by displacing 3H-serotonin with the neuroleptic spiperone. The results are discussed in relation to the general interpretation of this type of "anomalous" binding data and to the specific field of serotonergic receptor subtypes.

Allosteric Regulation↗

PubMedDate not supplied

Estimation and model selection in constrained deconvolution.

We analyze in detail the estimation problem associated with the following problem. Given n noisy measurements (yi, i = 1, ..., n) of the response of a system to an input (A(t) where t indicates time), obtain an estimate of A(t) given a known K(t) (the unit impulse response function of the system) under the model: yi = integral of 0(ti) A(s)K(ti - s)ds + epsilon i where epsilon 1, ... ,epsilon n are independent identically distributed random variables with mean zero and common finite variance. In the solution to the problem, the unknown function is represented by a spline function, and the problem is recast in terms of (inequality constrained) linear regression. The main issues addressed are: (a) the comparison of different nonparametric regression methods in this context, and (b) how to do model selection, i.e., given a (finite) set of candidate spline functions, select the (possibly unique) best one using some (statistically based) selection criteria. Different spline candidate sets, and different asymptotic and resampling-based statistical selection criteria are compared by means of simulations. Due to the particular nature of the estimation problem, modifications to the criteria are suggested. Applications to simulated and real pharmacokinetics data are reported.

Animals↗

PubMedDate not supplied

Pharmacokinetic and pharmacodynamic modelling of metoprolol in rabbits with liver failure.

The pharmacokinetic and pharmacodynamic profiles of metoprolol were studied in adult male rabbits given 3.2 mg/kg i.v. before and during liver failure. The partition of metoprolol between blood cells and plasma averaged 1.14 in both conditions. Plasma protein binding, concentration-independent, was 32% and 17% in normal and pathological status, respectively. With normal liver function the terminal elimination half-life for the drug was 0.54-0.96 h, rising to 1.0-2.1 h in liver failure. Differences of the same order were observed for total plasma drug clearance (average 3.7 vs 1.5 1/h/kg), MRT (0.77 vs 1.92 h), AUC (0.9 vs 2.2 mg h/l) and k10 (3.17 vs 1.80 h-1). Liver impairment did not affect the volume of distribution of the central compartment, the steady-state volume of distribution and the other intercompartmental rate constants. Although metoprolol was eliminated in the urine, the amount excreted was low (1.5% of the administered dose) in both conditions. The pharmacokinetic model was extended by an 'effect compartment', which has no influence on the predetermined mass of drug in the body, to analyse the relationship between heart rate fall and changes in metoprolol plasma concentrations. After drug administration, heart rate fell rapidly about 90 beats in both states. The mean unbound plasma concentration producing 50% of this reduction was double during liver failure compared to normal condition (0.03 vs 0.07 mg/l), but the temporal aspects of drug equilibration with site of action were similar.

Animals↗

PubMedDate not supplied

Software for experimental design: the computer program EXCAD.

A computer program (EXCAD) dedicated to the optimization of experimental designs to estimate parameters of a mathematical model, is presented. EXCAD computes D-optimal designs and sequentially augmented designs. D-optimal designs minimize the determinant of the variance-covariance matrix and parameters, thus obtaining the average most accurate estimate of parameters. D-optimal designs have generally as many support points as the number of parameters in the mathematical model, so sample scheduling is minimal, not extensive. Augmented designs add to an original design the point that maximizes the decrement of the determinant of the variance-covariance matrix. The general model, linearly or not linearly parametrized Y = F(X,P), that relates two independent variables and P parameters to different responses may be written in the program, while a set of prewritten models is provided.

Algorithms↗

PubMedDate not supplied

Characterizing the variability of system kernel and input estimates.

The identification of the input to, or kernels of, a system using nonparametric representations and least-squares estimation is becoming increasingly popular. Nonparametric representations avoid making a priori assumptions about the input or having detailed knowledge about the system, and only need to guarantee known general characteristics (for example, positivity), which are obtained through the imposition of constraints on the estimates. An often overlooked problem is how to characterize the variability of the estimates so obtained. This problem is caused by the presence of constraints--and/or the nonlinearities of the estimates, or the complexity of the (regression based) estimation algorithms used--which make standard methods of estimating variability incorrect. In this article we investigate the use of a resampling technique called the "bootstrap" to obtain the desired estimates of variability. We present real data analysis demonstrating the approach, and through simulations we test the performance of a novel bootstrap technique obtaining confidence bands for the estimated functions.

Algorithms↗