The effects on Schild regressions of antagonist removal from the receptor compartment by a saturable process

Publisher Website
Google Scholar
Abstract
A theoretical model of the effects of a saturable removal mechanism for an antagonist diffusing into the receptor compartment of a tissue is used to calculate expected deviations in Schild regressions. At concentrations of antagonist which do not saturate the removal mechanism, there can be a deficit of antagonist in the receptor compartment as compared to the concentration of antagonist bathing the tissue. This results in a shift to the right of the Schild regression and a corresponding underestimation of antagonist potency. The model predicts that as the concentration of antagonist exceeds the K m for removal (saturation of the removal process), this concentration deficit is eliminated, resulting in a proportionate increase in antagonist concentration at the receptor and a concomitant increase in receptor antagonism. This results in a steepening of the Schild regression; the slope in the region of saturation is greater than one. Experimental evidence in support of this model was found in studies of the antagonism of responses to bethanechol by atropine in rabbit ileum; this species is known to have an atropinesterase capable of hydrolyzing atropine. The Schild regression for atropine was curvilinear with an overall slope of 1.42 (1.34–1.5) and pK B = 8.5 (8.36–8.8); in the ileum from guinea pigs, a species which does not possess this enzyme, the Schild regression for atropine was linear, had a slope not significantly different from unity (1.1; 0.95–1.2) and a pK b of 9.0 (8.9–9.2). The slope of the regression in rabbit ileum was corrected to unity by the addition of an excess concentration of methylbutyrate, an alternate substrate for atropinesterase. Experimental factors such as rate of permeation of drug into the receptor compartment, avidity of removal, the effects of unstirred layers and geometrical configuration of tissues are discussed with reference to the expected verisimilitude of the predictions of the model to experimental results.