Antagonism competitive

BicucuUine [485-49-4] (8) is another analeptic compound known to act by competitively antagonizing GABA at its receptor (9). There is no evidence that bicucuUine has been evaluated in humans. Several other compounds, such as the steroid R5135 (10) or the arylarninopytidazines SR 95103 (11) and SR 95531 (12), appear to antagonize GABA competitively in a manner similar to that of bicucuUine. 

In contrast to heparin, the coumarinic acid anticoagulants are inactive in vitw ]6k.e heparin they are active in vivo. The phenylindanedione-type compounds (7) (36) and warfarin (2) produce their in vivo inhibitory effect on the coagulation system by competitively antagonizing the normal activity of vitamin (8) (37—44). 

The model of simple competitive antagonism predicts that the slope of the Schild regression should be unity. However, experimental data is a sample from the complete population of infinite DR values for infinite concentrations of the antagonist. Therefore, random sample variation may produce a slope that is not unity. Under these circumstances, a statistical estimation of the 95% confidence limits of the slope (available in most... 

There are patterns of dose-response curves that preclude Schild analysis. The model of simple competitive antagonism predicts parallel shifts of agonist dose-response curves with no diminution of maxima. If this is not observed it could be because the antagonism is not of the competitive type or some other factor is obscuring the competitive nature of the antagonism. The shapes of dose-response curves can prevent measurement of response-independent... 

FIGURE 6.12 Schild analysis for constitutively active receptor systems, (a) Competitive antagonism by the inverse agonist in a constitutively active receptor system with DR values calculated at the EC80. 

Ideally, pharmacological data should directly be fit to specific models and parameters derived from the direct fit. However, there are cases where the specific models predict surrogate parameters that can be derived without fitting data to the specific model. This can be an advantage. For example, the equation for simple competitive antagonism of receptors (see Section 6.3) is... 

There are instances where it is important to know if a given regression line is linear. For example, simple competitive antagonism should yield a linear Schild regression (see Chapter 6). A statistical method used to assess whether or not a regression is linear utilizes analysis of covariance. A prerequisite to this approach is that there... 

It can be seen that the IC50 increases with increasing values of [A ]/Kd in accordance with simple competitive antagonism. This can be tested by comparison of the data to the Cheng-Prusoff equation (Equation 4.12). The data in Table 12.2b... 

The squared deviations between the calculated and actual responses are shown in Table 12.6c (see column labeled SSq). The AIC values are calculated according to Equation 11.30. The values are shown in Table 12.6c. It can be seen that the fit to the curves with a mean Emax and slope gives a lower AIC value. Therefore, this model is statistically preferable. It is also the most unambiguous model for simple competitive antagonism since it fulfills the criteria of parallel dextral displacement of dose-response curves with no diminution of maxima. The calculated curves are shown in Figure 12.7b. 

Aim This procedure measures the affinity and cooperativ-ity constant of an allosteric antagonist. It is used for known allosteric antagonists or molecules that produce a saturable antagonism that does not appear to follow the Gaddum equation for simple competitive antagonism. 

Gadduin equation (competitive antagonism), the pivotal simple equation (see Chapters 6.2 and 6.8.1) describing the competition between two ligands for a single receptor site. It forms the basis for Schild analysis. 

Insiirinoiintable antagonism, a receptor blockade that results in depression of the maximal response. Under these circumstances, unlike competitive antagonism, no increase in the concentration of agonist will regain the control maximal response in the presence of the antagonist. 

Cheng-Prasoff relationship, 65-66, 214 Cholecystokinin receptor antagonists, 80 Cimetidine, 9-10 Clark, Alfred J., 3, 3f, 12, 41 Clark plot, 114 Clearance, 165—166 Clinical pharmacokinetics, 165 Cocaine, 149, 150f Competitive antagonism description of, 114 Gaddum equation for, 101-102, 113,... 

Schild analysis is a very powerful method to quantify the potency of a competitive antagonist and to test whether the blockade of response by a molecule is consistent with simple competitive antagonism. Devised by Arunlakshana and Schild (1959), it is based on the principle that the antagonist-induced dextral displacement of a dose-response curve is due to its potency (Keq value, affinity) and its concentration in the receptor compartment. Since the antagonism can be observed and the concentration of antagonist is known, the Keq (denoted KB for antagonist) can be calculated. Die relationship between antagonism and concentration must be log-linear with a unit slope to adhere to true competitive kinetics. 

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