Practical 8 Quantitating Agonist Efficacy & Antagonist Affinity PDF

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BLOCK 3: Quantitation of Drug Effects to Characterise Receptors Learning to Love the Concentration-Response Curve

Practical 8: Quantitating Agonist Efficacy & Antagonist Affinity Practical 7: Determination of Drug Selectivity

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PHRM30009 Drugs in Biomedical Experiments

Objectives:

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Gain an appreciation of some pitfalls that can affect single dose drug studies; gain skills in the evaluation of concentration-response curve data and determination of competitive antagonist affinity. understand the classical approach to characterisation of drug targets; become familiar with the similarities and differences in behaviour of full and partial agonists

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and consolidate skills of data generation, curation, analysis and interpretation.

Resources: Required software •

Download and install the Concentration-Response Curve Simulator (CRC CAL) from Canvas (Practical 8 → Software (CRC CAL)).

Pre-reading is recommended •

Description of Schild analysis of antagonism (and other aspects of quantitative pharmacological analysis). Chapter 8 of Molecular Pharmacology: a Short Course, by TP Kenakin.

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Introduction Pharmacologists have long appreciated the fact that there is a lot of useful information in full concentration-response curves. However, if you look through the basic biomedical research literature you will see that it is common to examine the effects of drugs by simply testing each drug at a single concentration. There can be no doubt that such a strategy is efficient from the point of view of effort, but it is sometimes very inefficient in terms of the quality of scientific inference that can be drawn from the results. Concentration-response curves are one of the major tools for quantitative research employed by pharmacologists, and in this practical you will see how important they can be. There is a relationship between concentration and effect for all drugs, and for agonist drugs that relationship is relatively easily obtained and interpreted—it’s the standard concentration-response curve. However, for antagonists the effect is inhibition and there is often no effect observed when an antagonist is tested by itself. A concentration-response curve is not quite appropriate. There are several ways to see a relationship between antagonist concentration and antagonist effect, and the classical Schild plot is often the best.

Methods To facilitate the rapid, accurate and repeatable acquisition of the data needed for this practical, the experiments will be conducted using a computer-based simulator. The results from the simulations are displayed in a manner that is analogous to the PowerLab data acquisition system employed for ‘wet’ experiments in the practical lab. You should be able to generate full concentration-response curves in just a few minutes and, because there is no need to wait for tissues to equilibrate or to ‘rest’, you can generate a lot of data in a short period of time. Five different drugs have been programmed into the simulator: acetylcholine; atropine; NMS (N-methylscopolamine); muscatelamine and UM123. The first three are real drugs and their properties in the simulation match closely their real-world properties. Muscatelamine and UM123 are fictitious drugs with properties carefully adjusted to the learning objectives of this practical. How to work the CRC simulator The simulator provides an idealised (no noise, no pipetting errors, no inter-tissue variability, no studentinduced tissue damage et cetera) environment for exploration of patterns of agonism and antagonism. The underlying model is based solely on the law of mass-action and should provide results entirely in accordance with classical receptor theory (Furchgott’s version of Stephenson’s theory). The efficacy control adjusts the agonist intrinsic efficacy and the receptor density control adjusts the total receptor concentration. There is no facility to vary the agonist or antagonist affinities and so they are constant in all runs.

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PHRM30009 Drugs in Biomedical Experiments

As you will see in the following picture, the controls of the simulator are labeled clearly.

The Run, Pause and Stop buttons do what you would expect. Pause and un-pause do not delete the current chart but restarting after the Stop button is clicked will delete the previous chart. You have a choice of agonists and antagonists available from pop-up lists, and you can control the concentrations of both using scrollbar controls. The drug concentrations are indicated as log molar, so –8 is 10 nM. You can adjust the drug concentrations using the scrollbars at any time; there is no need to pause or stop the simulation, but you may find it convenient to pause when you change concentrations. To remove a drug entirely simply set the scrollbar to –inf (negative infinity) which is equivalent to zero molar. Note that while the agonist equilibrates with the receptors during the simulation, the antagonist is instantaneously equilibrated to make the calculations easier for the programming. Thus, if you make changes to the antagonist concentration while the simulation is running, the apparent time-course of any change in response is unrealistic. You can alter the receptor density using the pop-up list if you want to investigate its influence on the agonist responses. In the above picture, the simulation has been run for a short time with the agonist (acetylcholine) at 10 8.5 M, and then for a while with the agonist at 10-8M. When you move the cursor over the curve, a box pops up with useful information, as you can see in the picture. The first line contains the x,y coordinates of the marked point (94 seconds and 15.5 response units in the picture), then the agonist and antagonist concentrations (log M). Clicking on the curve puts that information into the Data Pad area, and you can copy the data pad (using the Copy button) so that you can move the data to a graphing program for plotting the concentration-response curves. As usual, a chocolate prize is offered for the first student to point out any real bug in the software.

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Scenario Imagine that a medicinal chemist synthesised a novel drug designed to be an agonist at muscarinic receptors. They called the drug muscatelamine because they are quite keen on a certain type of grape. In order to test whether muscatelamine had the intended properties they tested it for bradycardic activity1 in isolated rat right atria, activity that can be mediated by muscarinic M 2 receptors. The results show that muscatelamine did cause a strong bradycardic response, similar to that mediated by acetylcholine. However, because some other types of receptors can also mediate bradycardia the medicinal chemist needed to confirm that the receptor mediating the responses was muscarinic and so they tested the effect of a modest concentration (3 nM) of the archetypical muscarinic antagonist, atropine, on the responses. Atropine almost abolished the response to acetylcholine but only cased a small diminution of the response to muscatelamine (Figure 1). Because muscatelamine appeared to be relatively resistant to antagonism by atropine, our medicinal chemist concluded that muscatelamine was not acting predominantly through the intended target of muscarinic receptors. On the strength of that conclusion, no further characterization or development of the muscatelamine molecule was undertaken.

Figure 1. Effect of atropine on the bradycardic responses of rat isolated right atria to muscatelamine and acetylcholine. The data are from a single experiment and the concentrations of muscatelamine and acetylcholine were not recorded.

Was the medicinal chemist’s conclusion reliable? Was it correct?

1. Bradycardia is a slowing of the heartbeat.

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PHRM30009 Drugs in Biomedical Experiments

TASK 1 Perform a full concentration-response curve experiment to test the hypothesis that atropine (3 nM) is more effective at antagonising acetylcholine than muscatelamine. • Plot your results using Prism. • Record any observations that you’ve made about the effectiveness of atropine against the two agonist drugs. Do you agree with the conclusion of the medicinal chemist? In pharmacology it is common to use the pA2 of a competitive antagonist as the index of antagonist potency. The pA2 is the negative logarithm of the concentration of antagonist needed to cause a two times rightward shift of the agonist concentration-response curve. It is strictly an empirical index but in ideal circumstances it is the same value as the negative logarithm of the antagonist’s equilibrium dissociation constant, pKB. You can estimate the pA2 by fiddling around with the concentration of antagonist until you find one that yields the required two-fold shift, but because the relationship between competitive antagonist concentration and shift of the agonist curve can be determined from the law of mass action we can determine the pA2 much more conveniently with the relevant formula (called the Schild formula because Heinz O. Schild was the first to show its utility): log(r – 1) = log[B] + pA2 (rearrange to get pA2 = log(r – 1) – log[B] )* The ratio, r, is the shifted EC50 divided by the control EC50. • Calculate the pA2 of atropine as an antagonist of acetylcholine and of muscatelamine by plugging the appropriate EC50s into the formula above. • Calculate the pA2 of the antagonists that shifted your noradrenaline curve in practical 7.

*Which is equivalent to r = 1 + [B]/K B where pA2 = pKB . You can go to the GraphPad website for more information and formulae: http://www.graphpad.com/curvefit/schild.htm

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TASK 2 Presumably your pA2 values for atropine were quite similar against both agonists. How then did the medicinal chemist obtain data that made it seem that muscatelamine was relatively resistant to the antagonism by atropine? The pA2s that you calculated in task 1 are determined by the degree to which the antagonist shifted the agonist concentration-response curves to the right, but in the scenario the medicinal chemist formed their conclusions on the basis of how much the antagonist reduced the responses to single concentrations of the agonists. In effect, the medicinal chemist examined the vertical shift of individual points on the concentration-response curves. Let’s see how well that works out in general. Use your results from task 1 to fill in the following table. Percentage reduction of responses to agonists by atropine at 3 nM Agonist concentration 10-9M 10-8M 10-7M 10-6M 10-5M

10-4M

Acetylcholine Muscatelamine [Inaccurate] It is often assumed that a ‘fair’ comparison of agonists can only be made when they are tested at the same concentration, and according to that standard we should compare the effects of the antagonist on the two agonists with the identical agonist concentrations. •

Is there any particular concentration of agonist in the table that gives a reasonable picture of the effectiveness of the antagonist?

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Compare the table of inhibition values with the graphs of concentration-response curves from Task 1. Which representation gives an easier to understand and describe picture of the effect of the antagonist?

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PHRM30009 Drugs in Biomedical Experiments

TASK 3 In task 1 you should have found that atropine had a similar effect on both agonists. A competitive antagonist like atropine will have a particular affinity for its target receptors and so it will generally affect all agonists acting through that target similarly. We can estimate the affinity of a competitive antagonist in functional experiments using a Schild analysis. Where possible, a Schild plot should be used to determine the pA2 instead of just using the Schild formula as in Task 1 because if the interaction appears to meet the criteria for a simple competitive interaction, we get a useful estimate of the antagonist pKB. The relevant criteria are: 1. Surmountable antagonism (you see this in the concentration-response curves); 2. The expected relationship between antagonist concentration and agonist curve shift. The second criterion cannot be assessed in experiments where a single concentration of antagonist is used, but instead requires the use of several (ideally, many) concentrations of antagonist. Agonist-antagonist interactions that meet criterion 2 will yield data whereby the Schild plot is linear and has a slope of unity*. Do a full Schild analysis for atropine as an antagonist of acetylcholine and of muscatelamine. To generate a Schild plot you need to: •

Generate agonist concentration-response curves in the absence and presence of a range of concentrations of antagonist and determine the agonist EC 50 from each one. To speed up the process, you may want to assign each member to an agonist (acetylcholine or muscatelamine) and

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2-3 different concentrations of atropine. Determine the concentration ratio (r). Note that the concentration ratio is always the shifted EC50 divided by the control EC50, so if you have a ratio of less than 1 you have made an error.

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You can use the table below as a template for collecting and calculating the results (you may need to add more rows - remember to include this table in your ELN).

The Schild plot is a graph of log(r-1) versus log(antagonist concentration). Plot the Schild plots using Prism and determine the pA2 (and pKB) estimates from them. A brief example of how to construct a Schild plot from a set of CRCs can be found in the ELN in General Resources → Practical 8. antagonist conc. (log M)

log EC50

EC50

concentration ratio (r)

log(r-1)

* Interpretation of non-linear Schild plots is interesting but esoteric. See the Kenakin’s book Pharmacological Analysis of Drug-Receptor Interactions for more information (copies are available on Canvas)....