Ki values, agonism, partial agonism and antagonism

[Psychferlyfe3000]

I am working on a project involving ki values. However, I am struggling to represent agonism, partial agonism and antagonism within a single variable. Eg, an agonist might have a ki value of .20 and an antagonist might have a ki value of .20 and my regression would have no way of distinguishing this. This might be a longshot, but does anyone have any thoughts on how this might be done?

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[WisNeuro]

I'm not sure I understand what you're trying to do. Are you trying to represent continuous variables in a categorical way? Also, are you trying to represent two different continuous variables as one categorical variable? If so, a regression is not the way to go even if you do find a good way to do so.

 

[Psychferlyfe3000]

I'm not sure I understand what you're trying to do. Are you trying to represent continuous variables in a categorical way? Also, are you trying to represent two different continuous variables as one categorical variable? If so, a regression is not the way to go even if you do find a good way to do so.

Sorry for the lack of clarity on my part! I am trying to avoid creating three different continuous variables for agonist ki values, partial agonist ki values and antagonist ki values due to the lack of power this would result in in my study. I would like to combine eg D2 partial agonist ki values with D2 antagonist ki values within a single variable to maintain sufficient power for my regression.

 

[WisNeuro]

Sorry for the lack of clarity on my part! I am trying to avoid creating three different continuous variables for agonist ki values, partial agonist ki values and antagonist ki values due to the lack of power this would result in in my study. I would like to combine eg D2 partial agonist ki values with D2 antagonist ki values within a single variable to maintain sufficient power for my regression.

You want to combine 2 independent continuous variables into one continuous variable?

 

[Psychferlyfe3000]

You want to combine 2 independent continuous variables into one continuous variable?

Yes, I do. I would like to encode both affinity and agonism/partial agonism/antagonism into a single variable.

 

[WisNeuro]

Yes, I do. I would like to encode both affinity and agonism/partial agonism/antagonism into a single variable.

Are these values related to each other with a 1.0 correlation?

 

[hamstergang]

You could set agonists to positive numbers and antagonists to negative, but that wouldn't take partial agonists into account.

You could instead create a new variable = 1/(Ki * A) where:

• For agonists, A = 1
• For antagonists, A = -1
• For partial agonists, 0 < A < 1
• For inverse agonists, A < -1

However, while this would achieve what you want in that the number indicates some measure of how strongly turned on or off the receptor is, I don't know that it would be fully valid. Can you compare different drugs with this variable in the ways you would expect? Are the regressions you want to do actually going to indicate what you think they do?

 

[WisNeuro]

It would help to know more about what specifically you are looking at in these analyses to see if something like Dummy coding or similar transformations make sense or not.

 

[FlowRate]

Wis is also alluding to the idea that what you're trying to do is probably not a great idea. Partial agonists by their unique nature don't have correlations with effect the same way a pure agonist or antagonist with the same Ki would.

 

[clausewitz2]

Even a "pure" agonist is only pure in the sense that it promotes one particular conformation of a receptor as strongly as whatever the reference agonist being used to measure the Ki values favors that receptor conformation. Furthermore, there is pretty good evidence these days for agonism that is selective to particular second messenger pathways and not others independent of how tightly the molecule binds to the receptor in question. You are trying to use binding affinity to measure efficacy and they are just not the same.