SPIKESbind
  • Development Tool
  • Self-Assessment Tool
  • Quiz
  • -log Ki Table
  • Assumptions of the Model
-log Ki Table
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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 2.0 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


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 use the SPIKES approach to analyse the data and determine which muscarinic receptor subtypes are present, and their relative proportions.

To generate answers, use the SPIKES approach together with the -logKi values presented in the reference table. Provided will be a table to practise your SPIKES approach (this table will not be assessed).

Enter your answer into the box provided - note that the answer(s) 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% (in 10% increments).

With practise, you should be able to identify which receptor subtypes are present and their relative proportions within ~5-10 minutes.

The quiz is taken 1 question at a time and feedback is provided.

Click the Start Quiz button to commence the Quiz!

Don't forget to formally justify your answer in writing using the SPIKES approach, as may be required in an examination situation - this should take another 5-10 minutes.

Prior to taking the Quiz, you may wish to look through seven examples (of increasing complexity) that demonstrate how the SPIKES approach can be used to definitively determine which receptor subtypes are present within a particular cell/tissue and if more than one receptor is present, their relative densities (SPIKES examples)


25:00

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

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

You may use the table below to determine your answer

SPIKES Approach ​
Ligands
-log Ki for M1 - M5
Shape of Curve Position
-logIC50
Eliminated Receptors
M1 M2 M3 M4 M5
Competition Binding Curves

Time spent:

CLICK QUESTION TO REVIEW

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  • About
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Creative Commons 2018

Instructions

The Development Tool can be used to visualise how the shape and position of competition binding curves can be influenced by:

1.  the relative densities of receptor subtypes expressed by cells or tissues, and

2.  the affinities of ligands for the receptor subtypes.

The following instructions demonstrate how these changes in the shape and position of competition binding curves can be visualised using the Development Tool.

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

Within the Receptor window, select up to three receptor subtype combinations by clicking on the appropriate buttons below the name of the subtype. Once a particular subtype has been selected, the Relative Density (%) box appears immediately below.

If only one receptor subtype is selected, then the Relative Density (%) value will be 100.

If two receptor subtypes are selected, then the sum of their Relative Density (%) values must be 100, i.e. increasing the value of one will decrease the value of the other to maintain a total value of 100. The Relative Density (%) values can be changed by either by using the arrows for increments of 1%, or by typing in the desired value (0-100).

If three receptor subtypes are selected, then the combined value must again be 100. In this case, enter the receptor density values in order from left to right, i.e. start with the receptor subtype with the lowest number selected (e.g. M1) to the receptor subtype with the highest number (e.g. M5).

Once the first two receptor densities have been selected, the final density will automatically be calculated.

For example, if you wished to use a cell that expresses a combination of M2, M4 and M5 receptors in the relative proportions 20:30:50, respectively, then select the M2, M4 and M5 subtypes. In order, enter the values of 20% for M2 and 30% for M4. M5 will automatically be assigned a 50% density.

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

Within the Ligands window, use the drop down menu to select up to 6 ligands from a list of 8 Established ligands and 4 Hypothetical ligands.

Established ligands are ligands that have been used extensively in the past, and for which there are known -log Ki values (e.g. Atropine).

Hypothetical ligands, as their name suggests do not exist in the real world, but have been included in SPIKESbind 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 ligands where you can assign (and change) a –log Ki value (affinity) for each receptor subtype. The assigned –log Ki values must be between 3 and 10 and may be changed by either using the arrows (increments of 0.1), or by typing in the desired value (3-10).

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

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

A feature of SPIKESbind is that any changes to the receptor subtype or the ligands selected will cause the Competition Binding Curves window to be dynamically updated in real time, to reflect the new values. For example, you can directly observe (in real time) changes in the shape and position of Competition Binding Curves caused by (1) altering the Relative Densities of receptor subtypes or (2) altering ligand affinity (use Ligands C (or D) to observe this effect).

In the Self-Assessment Tool, you choose up to 6 ligands (from the list of 12 Established and Hypothetical ligands) that you wish to use in a competition binding study. The Competition Binding Curves for these selected ligands are then displayed in the Competition Binding Curves window based on a receptor profile (the receptor subtypes and their relative densities) that has been randomly generated by SPIKESbind.

The task for you is to analyse the competition binding curves using the SPIKES approach, and deduce which receptor subtypes are present and their relative densities. The actual receptor profile can be revealed at any time by clicking the Reveal Subtype button.

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

From within the Ligands window, select up to 6 Ligands from a list of 8 Established ligands and 4 Hypothetical ligands. As indicated above, Established ligands are ligands that have been used extensively in the past, whereas Hypothetical ligands, as their name suggests, do not exist in the real world, and have been included in SPIKESbind 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 ligands where you can assign (and change) a –log Ki value (affinity) for each receptor subtype. The assigned –log Ki values must be between 3 and 10 and may be changed by either using the arrows (increments of 0.1), or by typing in the desired value (3-10).

The ligands included in the drop down menu and their corresponding -log Ki values can be viewed in the '-log Ki 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 shape and position of the competition binding curves generated depends on which specific receptor subtypes are present and their relative densities (e.g. 30% M1 and 70% M2). The receptor profile is randomly selected by SPIKESbind from a defined bank of approximately 75 options.

The bank of 75 options is based on the premise (1) that there are 10 possible combinations involving two receptor subtypes (M1/M2, M1/M3, M1/M4, M1/M5, M2/M3, M2/M4, M2/M5, M3/M4, M3/M5 and M4/M5), and (2) that for each receptor combination there are 7 possible receptor density combinations (20/80, 30/70, 40/60/ 50/50, 60/40, 70/30 and 80/20) – giving a total of 70 options for two-receptor subtype combinations. This 70 options, together with another 5 options for pure receptor subtypes (100%), gives a grand total of 75 options from with SPIKESbind can choose to present. All 90%/10% and small incremental combinations have been omitted for your benefit as they cannot be reliably interpreted by eye.

Using the SPIKES approach to deduce which receptor subtypes are present and their relative densities. Your answer should be one of the 75 options.

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

You can reveal which receptor subtype population exists and their relative densities by clicking the Reveal Subtype button, and the answer will appear.

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 generate a new question in the Self-Assessment Tool, click the Reset button and a new receptor subtype combination option will be randomly generated by SPIKESbind.

The Quiz Tool works off the same premise as the Self-Assessment tool, where any one question is randomly selected from a list of 75 options (i.e. up to two receptor subtypes with relative densities between 20-80%, using 10% increments).

However, the Quiz Tool is different from the Self-Assessment tool as the ligands used in the Competition Binding studies are pre-selected by SPIKESbind. In the Quiz Tool, SPIKESbind selects 5 ligands – 4 of which will be randomly selected from the list of 7 selective Established ligands (Pirenzepine, Methoctramine, Darifenacin, MT-3, S-Secoverine, Solifenacin and DAU-5884), and one Hypothetical ligand, either Ligand A or Ligand B. This provides the opportunity for over 500 different questions to be posed by the Quiz Tool – sufficient to gain expert proficiency in interpreting competition binding data.

Analyse the Competition Binding Curves using the SPIKES approach to solve which receptor subtypes are present, and what their densities are. Note that not all questions within the Quiz Tool can be answered by eye – for these questions you should be able to indicate which additional ligand(s) would allow the question to be answered unequivocally (and why).

Prior to commencing the quizzes, you may wish to read through the SPIKES examples to gain an insight into how to systematically approach solving competition binding data using the SPIKES approach.

Follow the instructions below to undertake the Quiz.

From the Quiz homepage, click the Start Quiz button, and the quiz will commence with the question appearing.

At the top of the page, you will notice the timer appear, counting up from 0 seconds until your answer is submitted. This allows for you to record the time spent evaluating each question, and over time you should be able to see your times improve. Once the quiz is complete you are taken to the answer review page.

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

Use this information to work out your solution (i.e. which receptor subtypes are present and their relative densities). Provided is a ‘SPIKES Approach’ table for you to help determine the answer. In this table you are able to view the list of ligands displayed in the graph, along with their corresponding -log Ki values, a drop down menu to determine the shape of the curve, a position textbox to enter information for the -logIC50 value, and checkboxes to eliminate receptor subtypes (i.e. apply the SPIKES approach).

Once you have completed your analysis, enter which receptor subtype(s) you believe to be present and their relative densities, using the checkboxes and drop down menus provided.

After you are happy with your answer, click the Submit button to head to the answer 'Review' page.

At the ‘Review’ page, "Correct!" or "Incorrect" will appear, as will the competition binding curve again. If you answered incorrectly, another graph will be displayed to view what competition binding curves your selection would have created. You have the option to Amend your Answers or to start a New Quiz.

About

SPIKESbind 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.

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

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Liu H, Hofmann J, Fish I, et al. (2018) Structure-guided development of selective M3 muscarinic acetylcholine receptor antagonists. Proc Natl Acad Sci U S A. 115:12046-12050.

Mansfield KJ, Liu L, Mitchelson FJ, Moore KH, Millard RJ, Burcher E (2005). Muscarinic receptor subtypes in human bladder detrusor and mucosa, studied by radioligand binding and quantitative competitive RT-PCR: changes in ageing. Br J Pharmacol. 144:1089-99.

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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