It is the concentration needed to bring the curve halfway between the top and bottom plateaus. With this concept of IC50, the NS values are completely ignored. This is the term that is used in traditional pharmacological study of agonist and antagonist interactions.
The absolute IC50 is the concentration that produces a result halfway between the Blank and NS values. The horizontal dotted lines indicate how percent and 0 percent are described, which then defines 50 percent. Neubig, personal communication. I agree. For what we refer to as the total IC50, the abbreviation GI50 is used. Attempting to calculate IC50 by fitting a curve to the data in the graph above would be futile. A curve fitting software may or may not be able to match the data to a dose-response curve.
However, if the curve matches, the IC50 value is likely to be irrelevant, with a very large confidence interval. The data does not shape a top plateau which would define or a bottom plateau which would define 0. The curve can be easily fit if you already have control values that describe and 0. Fitting a dose response curve when constraining the Top plateau to be a constant value equal to the mean of the Blanks values and the Bottom plateau to be equal to the mean of the NS values yielded the curve below.
The IC50 value fits this way only if you believe that higher concentrations of the inhibitor would ultimately inhibit down to the NS values. That is a hypothesis that cannot be verified with the available data. In this case, the distinction between relative and absolute IC50 is irrelevant. The IC50 must be specified in relation to the NS control values because the data do not describe a bottom plateau.
As you can see from the examples above, normalising the data to run from percent to 0 percent is not needed. Curves may be fitted using data in their natural units. You should employ one of three strategies:. There are two ways to constrain the curve:. Unless you define the values clearly for percent and 0 percent, the definition of IC50 or EC50 is a little unclear.
Neubig et al. Update on terms and symbols in quantitative pharmacology. Pharmacol Rev vol. Search for:. Select your country. Saudi Arabia. Prism free trial.
Geneious free trial. SnapGene free trial. May 11, IC50 and EC IC50 — The perfect situation This figure shows a perfect situation: Measurements taken with controls are indicated by the green symbols. There are three options available to you: Ignore the Blank control values and just fit the data.
Set the parameter Top to be a constant value equal to the mean of the blanks, and average the Blank control values. The rest of this article is about IC50 I for inhibition, for downward sloping dose-response curves. All the ideas can be applied to stimulatory curves and EC50 E for effective as well.
Just stand on your head when you view the figures. The green symbols show measurements made with controls. The data of the experimental dose-response curve red dots extend all the way between the two control values.
When fitting this curve, you need to decide how to fit the top plateau of the curve. You have three choices:. The results will be very similar with any of these methods, because the data form a complete dose-response curve with a clear top plateau that is indistinguishable from the blank. I prefer the third method, as it analyzes all the data, but that is not a strong preference.
Similarly, there are three ways to deal with the bottom plateau: Fit the data only, set Bottom to be a constant equal to the average of the NS controls, and put the NS controls into the fit as if they were a very high concentration of inhibitor. This figure shows an unusual situation where the inhibition curve plateaus well above the control values NS defined by a high concentration of a standard drug. This leads to alternative definitions of IC Clearly, a single value cannot summarize such a curve.
You'd need at least two values, one to quantify the middle of the curve the drug's potency and one to quantify how low it gets the drug's maximum effect. The relative IC50 is by far the most common definition, and the adjective relative is usually omitted. It is the concentration required to bring the curve down to point half way between the top and bottom plateaus of the curve.
The NS values are totally ignored with this definition of IC This definition is the one upon which classical pharmacological analysis of agonist and antagonist interactions is based. With appropriate consideration of the biological system and concentrations of interacting ligands, estimated Kd values can often be derived from the IC50 value defined this way not so for the "so-called absolute IC50" mentioned below. This term is not entirely standard. Since this value does not quantify the potency of a drug, the authors of the International Union of Pharmacology Committee on Receptor Nomenclature 1 think that the concept of absolute IC50 and that term is not useful R.
Neubig, personal communication. I agree. The concept but not the term "absolute IC50" is used to quantify drugs that slow cell growth. The abbreviation GI50 is used for what we call here the absolute IC They don't use the terms relative and absolute. If you really want to use the absolute IC50, here are instructions for fitting a curve to find it. Any attempt to determine an IC50 by fitting a curve to the data in the graph above will be useless.
A curve fitting program might, or might not, be able to fit a dose-response curve to the data. But if the curve fits, the value of the IC50 is likely to be meaningless and have a very wide confidence interval. The data simply don't form a top plateau which would define or a bottom plateau which would define 0. If data haven't defined or 0, then 50 is undefined too, as is the IC If you also have control values that define and 0, then the curve can be easily fit. The curve below was created by fitting a dose response curve, but constraining the Top plateau to be a constant value equal to the mean of the Blanks values, and the Bottom plateau equal to the mean of the NS values.
The value of the IC50 fit this way only makes sense if you assume that higher concentrations of the inhibitor would eventually inhibit down to the NS values. That is an assumption that can't be tested with the data at hand. The distinction between relative and absolute IC50 doesn't really apply to these data.
Because the data don't define a bottom plateau, the IC50 must be defined relative to the NS control values. You can fit curves using data in their natural units.
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