Aslamo Alikom (Greetings) Everyone,
I've MTT results for the cell viability after an exposure to an inhibitor alone and combining it with another drug.
Now I wanna see how the other drug affects the IC50 of the main inhibitor.
Cell viability% for the drug alone and with combination ranges from 100 to nearly 40s and 30, respectively.
I was wondering whether I've to normalize the data before doing non-linear regression to calculate the IC50 on Graphpad Prism.
I would highly appreciate your advice and guidance in this matter. Thanks in advance
Hello Mariam Ahmed Galal
Clearly, the answer to this question is no, and there is no need to normalize the data.
As you can see (on the same data), whether the data is normalized or not, the answer is obtained for IC50.
Of course, provided that you use the correct equation.
See the link below
https://www.graphpad.com/guides/prism/latest/curve-fitting/reg_dr_inhibit_normalized_variable.htm
If you have good control data, it can make sense to normalize the response to run between 0% and 100%. This model assumes that the data have been normalized, so forces the curve to run from 100% down to 0%. The goal is to determine the IC50 of the inhibitor - the concentration that provokes a response equal to 50%.
It only makes sense to fit a normalized model when you are sure you have defined 0% and 100% quite accurately. If your data define a complete sigmoidal curve, it is best to fit the entire curve and let Prism fit the Top and Bottom plateaus. If your data don't form a full sigmoidal curve, but you can define the bottom and top by solid control data, then fitting to a normalized model is preferable.
When looking at the effect of inhibitor A on inhibitor B, the maximum signal at zero concentration of inhibitor B will decrease as the concentration of inhibitor A increases. This will reduce the range of values between the baseline (no cell growth) and zero inhibitor B, but it does not change the way you calculate the % inhibition at each concentration of B. Therefore, it is not necessary to normalize the data.
If the signal at no cell growth is called MIN, the signal at zero concentration of inhibitor B at any particular concentration of A is called MAXA, and the signal at any given concentration of inhibitor B at this concentration of A is called XA, then
% inhibition = 100[1-(XA-MIN)/(MAXA-MIN)]
and the equation to use for nonlinear regression is (for example)
% inhibition = 100[I]n/(IC50n+[I]n)
where [I] is inhibitor concentration and n is the Hill slope.
Abolfazl Ghoodjani Thank you very much for ur answer. I was wondering why in the example=u kindly referenced the IC50s are different? is it based on the analysis method used?
If yes, how would I know the best method to use?
Another Q if u don't mind, if I wanna get a graph representing the change in IC50s, can I normalize the data in this case? or should I just fit a sigmoidal curve or will both work?
@Adam B Shapiro thank you so much for the equations. They r so helpful!
Hello Mariam Ahmed Galal
1- To answer this question, look at the degrees of freedom in each model. In a model, two more parameters are estimated.
In order to know which one is better, it can be said that R Square is an almost useful measure. (although in non-linear models, you can't rely on them too much)
2- I did not understand this question exactly. However, you can still work with Prism. Both with normal and non-normal data
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