SPIKESmate
  • Development Tool
  • Self-Assessment Tool
  • Quiz
  • -log Ki Reference Table
  • Assumptions of the Model
-log Ki Reference Table

Hypothetical ligands have been included in the model to increase its applicability.

Ligand A and Ligand B are examples of ligands that have marked differences in affinities (-log Ki values) for receptor subtypes (not evident with established ligands).

You may nominate affinities (-log Ki values) for Ligands C and D.

Competing
Ligand
-log Ki Value
M1 M2 M3 M4 M5
Established
Atropine 9.0 8.8 9.3 8.9 9.2
Pirenzepine 8.2 6.5 6.9 7.4 7.2
Methoctramine 6.7 7.7 6.0 7.0 6.3
Darifenacin 7.8 7.0 8.8 7.7 8.0
MT-3 6.7 5.9 6.0 8.1 6.0
S-Secoverine 8.0 7.9 7.7 7.7 6.5
Solifenacin 7.6 6.8 7.9 7.0 7.5
DAU-5884 8.9 7.1 8.9 8.5 8.1
Hypothetical
Ligand A 9.0 9.0 6.0 6.0 2.0
Ligand B 8.0 5.0 8.0 5.0 8.0
Ligand C * * * * *
Ligand D * * * * *
Assumptions of the Model
  1. All of the usual assumptions associated with Law of Mass Action that typically describe drug binding to receptors
    • All receptors are equally accessible to ligands.
    • All receptors are either free or bound to ligand - i.e. the model ignores any states of partial binding.
    • Neither ligand nor receptor are altered by binding.
    • Binding is reversible.

  2. Assumptions associated with competition binding studies
    • Only a small fraction of both the labelled and unlabelled ligands has bound, so the free concentration is virtually the same as the added concentration.
    • There is no co-operativity – binding to one binding site does not alter affinity at another site.
    • The experiment has reached equilibrium.
    • Binding is reversible and follows the law of mass action.

  3. Other model-specific assumptions
    • Analysis is conducted using % Specific Binding (no non-specific binding)
    • [3H-QNB] used in studies was << KA of 3H-QNB for receptors present, i.e. Cheng-Prusoff equation reduces to IC50 ≅ Ki for all competing ligands.
Quiz

Ready to take the quiz?

In this quiz you will be given a graph containing competition binding data for 5 randomly selected muscarinic cholinoceptor ligands.

Your task is to analyse the data and determine which muscarinic receptor subtypes are present, and their relative proportions.

to generate answers, use SPIKES approach together with the -log ki values presented in the reference table.

Enter your answer into the boxes provided - note that the answers will be either one receptor subtype with a relative density of 100%, or more likely, a mixture of two receptor subtypes with relative proportions between 20% and 80%.

The will be questions asked, and with practise, you should be able to obtain the answers within 25 minutes.

Don't forget to use the SPIKES approach to formally justify your answer in writing too!

Good luck!

25:00

Which receptor subtypes are present and what are their relative densities?

Receptor
M1 M2 M3 M4 M5
Subtypes
Relative Density (%)
Competition Binding Curves

You may use the space below to justify your answers:

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CLICK QUESTION TO REVIEW

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Creative Commons 2017

Instructions

Firstly, you select which receptor subtypes are present in the cell/tissue of interest.

You may select up to two options by clicking on the appropriate buttons below the names of the subtypes.

Once a particular receptor subtype has been selected, the relative density box appears immediately below.

If only one receptor subtype is selected, then the relative density value must be 100.

If two receptor subtypes have been selected, then the combined value must be 100, i.e. increasing the value of one will decrease the value of the other to maintain a total value of 100.

If three receptor subtypes have been selected, then the combined value must again be 100. In this case, receptor densities should always be entered left receptor first, i.e. This time the left-most active receptor density must be specified first.

Once this has been done either of the remaining two densities can be entered and the final density will automatically be calculated.

For example, if Receptor 1 has density 60% and then Receptor 2 is specified as 30% density, Receptor 3 will automatically be given 10% density.

Similarly, if Receptor 1 has density 50% and Receptor 3 has density 40% then Receptor 2 will automatically be given 10% density.

This is how the problem of 2 unknown variables is managed.

Changing the relative densities is achieved by using the arrows for increments of 1%, or by typing in the desired value.

Secondly, you select which ligands you wish to use in the competition binding studies.

From the drop down menu, you select from a list of 8 established ligands and 4 hypothetical ligands.

Established ligands are durgs that have been used extensively in the past, and for which there are known -log Ki values.

Hypothetical ligands have been included in the model to increase applicability.

There are 2 types of hypothetical ligands - one type includes Ligand A and Ligand B, which have marked (but defined) differences in affinities for receptor subtypes.

The second type of hypothetical ligand includes Ligand C and Ligand D, and these are drugs where you may nominate (and change) affinities between 3 and 10.

Again, these values may be changed by clicking the arrows in the input box for a ±0.1 increment, or by typing in a value.

The ligands included in the drop down menu and their corresponding -log Ki values can be viewed in the '-log Ki Reference Table' tab.

Lastly, once receptor subtypes and ligands have been selected, a plot of the specific binding % is displayed in real time within the competition binding curve window.

By inserting the appropriate -log Ki values into the one- or two-site binding models, accompanied by the selected subtype receptors, the program will calculate the % specific binding values over the ligand range 10-12 - 10-2 M and will generate the competition binding curve for viewing.

Any modifications to the receptor subtype or the ligands input will allow for the curves to dynamically update in real time to reflect the new data selected.

Firstly, you select which ligands you wish to use in the competition binding studies.

From the drop down menu, you select from a list of 8 established ligands and 4 hypothetical ligands.

Established ligands are durgs that have been used extensively in the past, and for which there are known -log Ki values.

Hypothetical ligands have been included in the model to increase applicability.

There are 2 types of hypothetical ligands - one type includes Ligand A and Ligand B, which have marked (but defined) differences in affinities for receptor subtypes.

The second type of hypothetical ligand includes Ligand C and Ligand D, and these are drugs where you may nominate (and change) affinities between 3 and 10.

Again, these values may be changed by clicking the arrows in the input box for a ±0.1 increment, or by typing in a value.

The ligands included in the drop down menu and their corresponding -log Ki values can be viewed in the '-log Ki Reference Table' tab.

Secondly, the competition binding curves will appear for the selected ligands, showing the specific binding % over the ligand range 10-12 - 10-2 M.

The competition binding curves generated will depend on which receptor subtypes are present and their relative densities, which will be randomly selected from a defined bank of options.

This is based on the premise that there are 10 possible mixed receptor combinations - each receptor subtype present solo, and each unique combination of two receptor subtypes.

For each of these mixed receptor subtype combinations, the density combinations will be limited between 80% and 20%, remembering that they must always sum to 100%.

90%/10% and small incremental combinations have been omitted for your benefit as they cannot be reliably interpreted by the eye.

Thus, there is a total of 70 options for mixed receptor subtype combinations, plus another 5 options for pure receptor subtypes, giving a grand total of 75 options.

This is where you view the graph and calculate which receptor subtype combination is present.

Lastly, after you have worked out your answer, you may reveal the solution to validate your work.

This is done by clicking the Reveal Answer button, and the answer will appear in place of where the button was.

After viewing the solution, the ligands may be modified in the ligand table to view other competition binding curves, including the custom -log Ki values for ligands C and D, and the graph will dynamically update.

To then generate a new question, click the Reset button and a new, different receptor subtype combination will be plotted.

Firstly, read the instructions given on the quiz page before you get started.

As a brief overview, the quiz contains 5 questions and is timed for 25 minutes, giving you 5 minutes per question to work out the solution.

The quiz works off the same premise as the self-assessment tool, where a mixed subtype receptor combination is select from a list of 75 options.

Aside from being timed and scored, the quiz is different from the self-assessment tool as you do not get the opportunity of selecting which ligands to plot, instead there are 5 pre-selected randomised ligands already plotted on the competition binding curve graph.

These 5 pre-selected ligands consists of 4 established ligands, out of Pirenzepine, Methoctramine, Darifenacin, MT-3, S-Sercoverine, Solifenacin and DAU-5884, and one hypothetical ligand, either Ligand A or Ligand B.

You then use this information to work out which receptor subtypes are present, and what their densities are. Again, for a combination of two receptor subtypes will only allow for relative densities up to 80% and 20%.

Secondly, after the instructions have been read, and you have prepared the necessary tools you need for working out, it's time to begin the quiz.

Click the Start Quiz button, and the first question will appear.

At the top of the page, you will notice the timer appear, counting down from 25 minutes. When the timer reaches 0, the quiz is terminated and you are taken to the answer review page. Any answer not submitted before time runs out will not be accepted.

The graph will appear on the right hand side, showing the 5 pre-selected ligands plotted with the randomised mixed subtype receptor combination.

Use this information to work out your solution.

Thirdly, once you have worked out your solution, submit your answer.

This is done by selecting from the checkboxes which receptor subtype(s) you believe to be present, and then filling in the boxes for the relative denisities.

There is space provided for you to explain your answers using the SPIKES approach, however this is for practise only and your responses will not be assessed.

After you have completed that and you are happy with your answer, click the Next button, and the next question will be generated.

If you would like to go back to the previous question, click the Back button.

Repeat these steps until you reach the final question.

Lastly, after you have completed all the questions, submit your quiz.

This is done by clicking the Submit button that is in place of where the Next Question button used to be.

After submitting, a review page will pop up, showing your score and listing all the answers.

You may click on each question to view the competition binding curves again, and if you selected the wrong answer you will also be able to view what competition binding curves your selection would have created.

Click Restart to restart the quiz, with different questions of course!

About

SPIKESmate is an educational application to boost students' proficiency in interpreting competition binding data by providing self-development, self-assessment and quiz-me options.

Most drugs work by binding to specific receptors, which are large proteins typically expressed on the surface of cells.

The process of drug binding requires the existence of affinity (chemical forces of attraction) between the drug and receptor, and can be readily described by relatively simple mathematical relationships.

As drug binding is essential for drug action, pharmacology students need to have an advanced level of understanding of how changes in the affinity of a drug for a receptor can influence binding, and be able to readily interpret drug binding data.

SPIKESmate was created by Madeline King, Jason Gan, Robert Fernandez, Timothy Mennell, Roarke Holland and Sooho Moon; students of CITS3200: Professional Computing at the University of Western Australia 2017, under the guidance of Associate Professor Peter Henry.

-log Ki Values:

D'Agostino, G., Condino, A., Gioglio, L., Zonta, F., Tonini, M. and Barbieri, A. (2008). Isolated porcine bronchi provide a reliable model for development of bronchodilator anti-muscarinic agents for human use. British Journal of Pharmacology, 154(8), pp.1611-8.

Eglen, RM. & Nahorski, SR. (2000). The muscarinic M(5) receptor: a silent or emerging subtype?. British Journal of Pharmacology, 130(1), pp.13-21.

Ohtake, A., Saitoh, C., Yuyama, H., Ukai, M., Okutsu, H., Noguchi, Y., Hatanaka, T., Suzuki, M., Sato, S., Sasamata, M. and Miyata, K. (2007). Pharmacological characterization of a new antimuscarinic agent, solifenacin succinate, in comparison with other antimuscarinic agents. Biological and Pharmaceutical Bulletin, 30(1), pp.54-8.

Assumptions of the Model:

University of Alcalá. (1995). The GraphPad Guide to Analyzing Radioligand Binding Data.
Available at: http://www3.uah.es/farmamol/Public/GraphPad/radiolig.htm.

Contact

Found a bug in our application?
Or simply wanted to give some feedback?
Please don't hesitate to let us know!

Peter Henry
Associate Professor
The University of Western Australia

Room 1.34, M Block,
School of Biomedical Sciences,
QEII Medical Centre

peter.henry@uwa.edu.au