The role of enzyme and substrate concentration in the evaluation of serum angiotensin converting enzyme (ACE) inhibition by enalaprilat in vitro

Abstract

The relationship between serum angiotensin converting enzyme (ACE) activity and concentration of the ACE inhibitor enalaprilat was determined in vitro in the presence of different concentrations ( S = 4–200 mM) of the substrate Hip-Gly-Gly. From Henderson plots, a competitive tight-binding relationship between enalaprilat and serum ACE was found yielding a value of ≈5 nM for serum ACE concentration ( E t ) and an inhibition constant ( K t ) for enalaprilat of ≈0.1 nM. A plot of reaction velocity ( V i ) versus total inhibitor concentration ( I t ) exhibited a non-parallel shift of the inhibition curve to the right with increasing S. This was reflected by apparent Hill coefficients > 1 when the commonly used inhibitory sigmoid concentration-effect model ( E max model) was applied to the data. Slopes > 1 were obviously due to discrepancies between the free inhibitor concentration ( I f ) present in the assay and I t plotted on the abscissa and could, therefore, be indicators of tight-binding conditions. Thus, the sigmoid E max model leads to an overestimation of K i . Therefore, a modification of the inhibitory sigmoid E max model (called “ E max tight model”) was applied, which accounts for the depletion of I f by binding, refers to I t and allows estimation of the parameters E t and IC 50f (free concentration of inhibitor when 50% inhibition occurs) using non-linear regression analysis. This model could describe the non symmetrical shape of the inhibition curves and the results for K i and E i , correlated very well with those derived from the Henderson plots. The latter findings confirm that the degree of ACE inhibition measured in vitro is, in fact, dependent on the concentration of substrate and enzyme present in the assay. This is of importance not only for the correct evaluation of K i but also for the interpretation of the time course of serum ACE inhibition measured ex vivo. The non-linear model has some advantages over the linear Henderson equation: it is directly applicable without conversion of the data and avoids the stochastic dependency of the variables, allowing non-linear regression of all data points contributing with the same weight.