Concentration-effect analysis of antihypertensive drug responses.

Abstract

It is now widely recognized that a rigid stepped-care approach to antihypertensive therapy is not universally appropriate. Individualized treatment may result in good or better blood pressure control and a simpler regimen without troublesome side effects. Successful development of such a strategy depends on accurate characterization of a dose-response relation and quantitative assessment of the response in each individual. In the past such relations have proved to be hard to identify for antihypertensive drugs, often because of inappropriate study design during drug development Despite failure of earlier studies to identify drug concentration-antihypertensive response relations, use of concentration-effect modeling, which recognizes and characterizes the temporal discrepancy between drug concentration and effect, has proved more successful. By using such an approach, it has been possible to characterize the concentration-effect relations after acute and steady-state antihypertensive therapy with prazosin, doxazosin, nifedipine, verapamil, and enalapril. With enalapril and nifedipine, it has been demonstrated that the mathematical parameters of effect derived from the first dose can predict the response to these drugs after 4-6 weeks of treatment These findings suggest that the definition of individual concentration-effect relations may be of value in the rational choice of antihypertensive drug therapy and optimization of dose and dose frequency. In particular, the approach can provide valuable information on dose-effect relations and should optimize choice of dose intervals in drug development and practice. (Hypertension 1990;16:12-18)

Electronic monitoring of variation in drug intakes can reduce bias and improve precision in pharmacokinetic/pharmacodynamic population studies

“…In our simulations, we let  i = (ln(E max; i ); ln(C 50; i )) be normally distributed with mean vector and variance-covariance matrix, respectively, given by The block of the Fisher information matrix I corresponding to the ÿve e ect parameters (M and ) is denoted Ie. Mentrà e et al [5] proposed a simple algorithm based on a ÿrstorder approximation to estimate the information matrix for all unknown parameters, including variance components in (8). To examine the PD information gain due to irregular intake, we focus on a 'typical patient' whose parameters equal the population medians (4).…”

“…In Section 3 we present time-dependent compliance patterns as they were observed in a clinical trial [3]. Based on documented PK=PD models [6][7][8][9], we simulate the resulting plasma concentration and e ect proÿles for a range of plausible parameters. Several analysis approaches are then implemented and compared on these data sets in Section 4.…”

Characterization of the Pharmacokinetic and Pharmacodynamic Properties of the Angiotensin‐Converting Enzyme Inhibitor, Enalapril, in Horses

“…[29][30][31][32] Because of ACE binding, enalaprilat disposition is best described in humans by a physiologically based model. 31,[33][34][35] This model has also been used to describe enalaprilat disposition in dogs. 36,37 It is possible that the protracted terminal phase representing binding of enalaprilat to tissue and plasma ACE and prolonged t 1/2 may also exist in the horse, but we were unable to evaluate it as the LOQ of our LC/MS assay (5 ng/mL) was greater than that of the radioimmunoassay (0.4 ng/mL) used in the human studies described earlier.…”