Hi
I used the MTT assay for drug-inhibiting cancer cells
Then, I used GraphPad prism to calculate the IC50 of this drug following these equations
- log (inhibitor)vs response (three parameters)
- log (inhibitor)vs response--variable slope (four parameters)
- log (inhibitor)vs normalized response
- log (inhibitor)vs normalized response --variable slope
However, the result of each equation is not equal, especially both log(inhibitor) vs response (3 and 4 parameters) IC50 of both equations is significantly less than the concentration at 50% cell viability. When compared with log (inhibitor)vs normalized response IC50 of this equation is related to concentration at 50% cell viability.
As mentioned above, I wonder what is the suitable equation to calculate IC50 for further experiment such as apoptosis analysis?
Prism fits varieties of mathematical statistical models to data.. There are statistical methods to measure goodness of fitof models but it is up to the investigator, with knowledge of biological mechanisms, to select the model which best fits his needs, bearing in mind what can be measured or controlled.
Dear James Leigh ,
Thank you for your kind answer.
I’ll consult my PI about biological mechanism to select model as your suggestion.
Best regard,
Chidchanok
Have you done some pre-processing of your mortality data by converting it into probits or logits?
Hello Chidchanok Chawiwithaya Of course, there are ideas about the inappropriateness of R square in non-linear regression models.
However, compare the R square of each model with each other. Whichever was better and higher has a higher chance to be selected as the optimal model.
Of course, I think you can test other non-linear models, especially S (5PL) and special models.
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