Use pIC50 Instead of IC50 to Think About Your Assays Logarithmically
By Marc Navare, Ph.D.
You’ve been there — watching a colleague’s presentation where they review their assay results and try to create a meaningful SAR story — but, it’s not working.
Their slides consist of table after table of IC50 values and you are struggling to compare the result of one R group substitution with another.
It’s frustrating to watch a presentation like this and you end up missing the conclusion of the presentation.
Why is it so difficult to present clear SAR data slides?
Because you are presenting the values the wrong way.
IC50 values are holding you back.
Why is presenting potency data as IC50 values so problematic?
Well, there are two issues to consider:
One is that when you’re presenting IC50 data, there are usually too many digits.
It’s difficult to use consistent significant figures when you’re varying in potency ranges from micromolar to nanomolar.
There’s just no way around it.
Secondly, reporting IC50 values encourages linear, or arithmetic, thinking about what is actually an exponential value.
Using IC50 implies that zero or negative values are possible.
It also encourages arithmetic versus geometric averaging, which is incorrect.
It implies that cutting the IC50 in half means you’re doubling potency and it encourages nonoptimal experimental design.
It also makes data visualization very difficult, causing not only confusing presentations but also a misunderstanding of your SAR study.
So a key question, then, is how can you discourage this arithmetic or linear thinking?
By thinking logarithmically.
By using pIC50 values instead of IC50 values.
5 Reasons pIC50 will Improve Your SAR Story
Switching from IC50 to pIC50 causes you to change how you think about your data and your experimental design.
It will encourage you to think logarithmically about your potency data and stop thinking about arithmetic scales.
Fundamentally dosedependent inhibition is a logarithmic phenomenon, so it makes more sense to think of it that way.
Here are 5 reasons you should replace IC50 with pIC50 to improve your SAR story…

1. pIC50 will encourage you to look at in vitro assay data logarithmically.
Think about when you plot your IC50 values — your potency data.
You use some system for curve fitting, and all these systems use an IC50 equation, also known as the four parameter logistic, the Hill, or the sigmoid equation.
But, if you get under the covers and you look at the actual equation that’s being fit, you’ll see that it is actually the log of your drug concentration being used to determine the log of the IC50 value.
The software may report an IC50 for you, but it’s back converting it from the log IC50.
It’s important to remember that.
pIC50 encourages logarithmic thinking.
The idea is, when you’re looking at the data in your electronic lab notebook using pIC50 and not using IC50, the transition from the micromolar to the nanomolar inhibitors is smoother and the spacing between potency values is more relevant.
You want to think logarithmically.

2. pIC50 will allow you to present in vitro assay data in an easytoread form.
When you use pIC50, you can pretty much use two significant figures to cover both micromolar and nanomolar potencies.
With this method, you’re using a consistent number of digits and significant figures.
You’ve got one digit before the dot and one digit after the dot.
Done.
No further thinking.
What’s so great about this?
Now your audience can focus on the SAR, and stop doing mental gymnastics trying to understand your IC50 values.
pIC50 values are easy for an audience to understand.
You’ll be able to communicate clearly with your audience, and your audience will hear what you are saying, not what they think they wanna hear.
It’s a much clearer way of presenting your data.

3. pIC50 will make it easy and intuitive to average your in vitro assay data.
Let’s say you’ve had a bad assay day.
You’ve gotten the following three replicates of the same IC50 determination: 1 mM, 10 mM, and 5 mM.
If you calculate the arithmetic mean, you’re going to sum your IC50 values and divide it by n.
For the case above, you get an average IC50 of 3.5 mM.
But, that is incorrect.
The correct way to average IC50 data is to use what’s called the geometric mean because you’re working with an exponential value.
That requires some more difficult math.
You have to take the product of the three IC50s and take the nth root, with n being how many samples there were.
If you use this method in the above example, you end up with an average IC50 of 3.7 mM.
But, if you use pIC50s you can avoid doing the geometric mean math and you can just take the arithmetic mean of the pIC50 values because you’re already in a logarithmic space.
What’s the lesson learned here?
Don’t average IC50 values using arithmetic means.
Either do it the difficult way by doing a geometric mean or do it the smart and easy way using arithmetic means of your pIC50 values.

4. pIC50 and logarithmic thinking will improve how you plan your experiments.
Using pIC50 will help before you even start taking out the test tubes for your experiment.
For example, do you typically set up half decade dilution curves?
In other words, do you set up your dilutions like this: 1,000, 500, 100, etc.?
And then, do you notice that your data points are clumped up when you plot the data on your logarithmic compound concentration scale, and not evenly spaced?
That’s because five is not halfway between one and ten on the logarithm scale.
This causes your data to be bunched up and it doesn’t give you as high a quality of a result because you’re missing large amounts of concentration space.
However, if you’re thinking logarithmically, like using pIC50 encourages you to, you would realize that the number halfway between one and ten is actually around three.
You can then set up your dilution so it’s 1,000, 300, 100, 30, etc.
This will allow your data points to fall nicely and smooth out the spacing of the points as they fall along the curve.
This is a much nicer way to sample concentration space and this is a way to get more reliable data from the same amount of work.
This applies not only for in vitro experiments but for in vivo experiments as well.

5. pIC50 and logarithmic thinking will improve how you look at the reliability of your data.
Reporting the reliability of IC50 determinations is complex.
You’re probably familiar with standard error, but the other less common, but more useful, way to look at the reliability of your data is the 95% confidence interval.
The 95% confidence interval is an estimate of the precision of your measurement.
Another way of putting it is if the experiment were repeated 100 times there’s a 95% chance that your true value will be in this range.
If you use modern software that does curve fitting, you’ll notice that it will report the 95% confidence interval of an IC50, but not the standard error.
That’s because standard error of an arithmetic value (IC50) doesn’t make sense with logarithmic data.
Your IC50s are actually being calculated as a log IC50 so the standard error, and thus the confidence intervals, exist in log space.
It doesn’t make sense to use standard error when dealing with IC50 values.
Using this erroneous method will give you incorrect results, such as negative IC50 values.
What is a negative IC50 value?
The answer is you should scream because there’s no such thing.
This is the type of error that occurs when you start thinking in an arithmetic space about a logarithmic value.
In summary, using pIC50 instead of IC50 will force, or more hopefully, encourage you to think about your assays logarithmically. Reviews of assay data will be easier to present, you’ll average your potency properly by using arithmetic means of the pIC50 values, your reliability ranges will be correct, and you’ll never encounter negative IC50 values again.