Biomedical subjects

N B Modi

Publications and source records attributed to N B Modi.

6 recordsLinked to original sources

PubMed1992

An algorithm for constrained deconvolution based on reparameterization.

The method of deconvolution is the most general method of evaluating drug absorption. Deconvolution does not normally subscribe to a particular structured model for the input function that would ensure non-negativity of the calculated input rate. Unfortunately, the increased flexibility gained by this "model independence" sometimes results in a calculated input rate that becomes negative in the late absorption phase. A method for constrained deconvolution is proposed to overcome this deficiency. The method is based on a reparameterization of the input function in a "model-free" context. The reparameterization schemes proposed are optimal in the sense that they give the input function its maximum flexibility possible while at the same time ensuring non-negativity. The method makes use of "deconvolution through convolution." The basic procedure of this technique is, during the curve fitting, to iteratively adjust the input function so that when it is convolved with the unit impulse response function it results in a curve that best fits the data from the extravascular administration. Cimetidine data from oral and iv administrations are deconvolved by the proposed method to demonstrate the basic procedures involved. The proposed method is easy to implement since it relies on simple curve-fitting procedures as routinely performed in pharmacokinetics. The procedure can be carried out using any of the many curve-fitting programs available that allow the user to supply the function to be fitted and allow simple bounds to be specified for the fitted parameters. The convolution required by the method can be done analytically using a previously published computer program.

Algorithms↗

PubMed1992

Pharmacodynamic system analysis of the biophase level predictor and the transduction function.

This work deals with the pharmacodynamic problem of relating a drug effect E(t) to an observable pharmacokinetic (PK) predictor variable r(t), which may be a venous and/or arterial drug level, some other PK variable, or a drug infusion scheme. It is proposed that the relationship between E(t) and r(t) may, in many cases, be appropriately modeled as E(t) = N(F(r(t))) [for practical analysis reasons, F(r(t)) is denoted the biophase level, cb(t), the operator F() is accordingly denoted the biophase level predictor (BLP), and N() is denoted the transduction function (TF)]. This work proposes a method for determining the two fundamental components, BLP and TF, that define the E(t)-r(t) relationship. The BLP is determined by a hysteresis minimization (HM) technique with the following features: (1) the method considers errors in both HM variables; (2) the method is suitable for dealing with the important case in which the predictor variable is a drug infusion scheme; (3) the approach is noncompartmental in contrast to the effect-compartment approaches and it does not require a specific structured modeling of the cb(t)-r(t) relationship when dealing with drugs with linear PKs in a general operational sense; and (4) the method makes use of a dimensionless transformation of the hysteresis variable that eliminates numerical scaling problems so that a complex penalty function optimization approach can be avoided. The TF is determined by a cross validation procedure in conjunction with the BLP determined by HM. The method is demonstrated using pharmacodynamic data for several drugs, considering both concentration-based and drug input-based r(t) values. The significance of the information obtained from the determined BLP and TF is discussed, including the concepts of equilibration dynamics and overloading. The limitations and potential problems of the methodology are discussed.

Alfentanil↗

PubMed1992

Neural networks in pharmacodynamic modeling. Is current modeling practice of complex kinetic systems at a dead end?

Neural networks (NN) are computational systems implemented in software or hardware that attempt to simulate the neurological processing abilities of biological systems, in particular the brain. Computational NN are classified as parallel distributed processing systems that for many tasks are recognized to have superior processing capability to the classical sequential Von Neuman computer model. NN are recognized mainly in terms of their adaptive learning and self-organization features and their nonlinear processing capability and are considered most suitable to deal with complex multivariate systems that are poorly understood and difficult to model by classical inductive, logically structured modeling techniques. A NN is applied to demonstrate one of the potentially many applications of NN for modeling complex kinetic systems. The NN was used to predict the effect of alfentanil on the heart rate resulting from a complex infusion scheme applied to six rabbits. Drug input-drug effect data resulting from a repeated, triple infusion rate scheme lasting from 30 to 180 min was used to train the NN to recognize and emulate the input-effect behavior of the system. With the NN memory fixed from the 30- to 180-min learning phase the NN was then tested for its ability to predict the effect resulting from a multiple infusion rate scheme applied in the subsequent 180 to 300 min of the experiment. The NN's ability to emulate the system (30-180 min) was excellent and its predictive extrapolation capability (180-300 min) was very good (mean relative prediction accuracy of 78%). The NN was best in predicting the higher intensity effect and was able to identify and predict an overshoot phenomenon likely caused by a withdrawal effect from acute tolerance. Current modeling philosophy and practice is discussed on the basis of the alternative offered by NN in the modeling of complex kinetic systems. In modeling such systems it is questioned whether traditional modeling practice that insists on structure relevance and conceptually pleasing structures has any practical advantages over the empirical NN approach that largely ignores structure relevance but concentrates on the emulation of the behavior of the kinetic system. The traditional searching for appropriate models of complex kinetic systems is a painstakingly slow process. In contrast, the search for empirical models using NN will continue to improve, limited only by technological advances supporting the very promising NN developments.

Alfentanil↗

PubMed1992

A sensitive and specific erythropoietin immunoprecipitation assay: application to pharmacokinetic studies.

Previous pharmacokinetic studies with radiolabeled erythropoietin have relied on results of nonspecific methods to derive pharmacokinetic parameters. Dependence on nonspecific protein precipitation or total radioactivity may result in falsely high determinations of plasma radiolabeled erythropoietin and erroneous determinations of pharmacokinetic elimination and distribution parameters. In the present study pharmacokinetic parameters were derived by using a specific, sensitive, and reproducible immunoprecipitation assay for biologically active iodine 125-labeled recombinant human erythropoietin (125I-rhEp) and compared with those obtained by using nonspecific protein precipitation with trichloroacetic acid (TCA). Tracer amounts of 125I-rhEp were administered by bolus injection to six newborn lambs. Plasma-precipitable radioactivity assayed by the immunoprecipitation method became progressively lower with time relative to those observed with the TCA method. Pharmacokinetic parameters derived from the immunoprecipitation assay demonstrated significantly more rapid plasma and elimination clearances, shorter terminal half-life, shorter mean body residence time, and shorter distribution time when compared with the TCA assay (p less than 0.01). Volume of distribution was not different. Comparison of immunoprecipitation and TCA assay results from gel permeation fractions of iodinated erythropoietin demonstrated that immunoprecipitation assay results provide a better evaluation of biologically active hormone as determined by comparisons with SDS-PAGE and erythropoietin radioreceptor data. We conclude that 125I-rhEp pharmacokinetic parameters derived by using the immunoprecipitation assay more accurately reflect physiologic conditions than do those derived by using TCA precipitation.

Animals↗

PubMed1992

Developmental differences in erythropoietin pharmacokinetics: increased clearance and distribution in fetal and neonatal sheep.

Although erythropoietin (Ep) is considered the primary hormone responsible for erythrocyte production throughout development, the administration of recombinant human Ep (rhEp) to premature human neonates has, thus far, been ineffective in the prevention and treatment of their anemia. To determine if developmental pharmacokinetic differences might in part be responsible for this lack of efficacy, Ep pharmacokinetic studies were carried out in four groups of sheep: late gestation fetal, neonatal, adult and pregnant. After i.v. bolus injection of tracer amounts of the biologically active [125I]rhEp, plasma [125I]rhEp was measured using a sensitive and specific Ep immunoprecipitation assay. Pharmacokinetic parameters were derived using noncompartmental system analysis. Significantly greater clearances, shorter half-lives, shorter residence times, greater distribution volumes and greater Ep production rates were found in the fetal and neonatal groups compared to pregnant and nonpregnant adults (P less than .01). These developmental differences likely reflect more rapid metabolism and greater distribution of Ep in less mature individuals. As such, they could result in a reduction in Ep's erythropoietic effect similar to that reported in premature infants. We speculate that treatment of anemic premature neonates using rhEp doses shown to be effective in adults may be inadequate.

Animals↗

PubMed1991

Optimal extravascular dosing intervals.

An explicit formula is presented for simple calculations of the dosing time, tau, that results in a steady-state peak-to-trough ratio of 2 in extravascular dosings. Contrary to other formulae presented, the calculations are guaranteed to be well bound in the percentage error (less than 1%) for any parameter value combination. It is shown that the biexponential dosing interval problem can be transformed into a general, dimensionless problem enabling a global error analysis in the approximation. The proposed formula is demonstrated in the calculation of an "optimal" dosing interval for quinidine. An algorithm and FORTRAN computer program OPTAU for exact calculation of tau and dosing simulations is also demonstrated in the quinidine example.

Algorithms↗