Dose response assays are generally characterized by a nonlinear relationship between the response and the amount of drug/agonist/antagonist given. When plotted, this response will usually exhibit a sigmoidal curve that is best described using the 4 Parameter Logistic (4-PL) or 5 Parameter Logistic (5-PL) nonlinear regression model equation.
The C parameter in the 4-PL model represents the concentration value for the inflection point of the curve. Depending on whether you have an inhibition or activation assay, the C parameter also represents the half maximal inhibitory concentration (IC50) or the half maximal effective concentration (EC50)respectively. Due to the symmetry around the inflection point, the 4PL makes it very convenient to determine the EC50/IC50 value.
The 5PL is a different story because of the asymmetry around the inflection point of the curve. The formula for calculating the EC50/IC50 from the 5PL still involves the C parameter but also a couple of others:
- E parameter – This is fifth or the “asymmetry factor” for the 5PL model equation. A value of 1 for this parameter represents perfect symmetry around the inflection point making it equal to the 4PL model equation.
- B parameter – This is the Hill Slope and it a measure of the rapidity of the curve’s transition between the asymptotes.
The equation for calculating the EC50/IC50 from the 5PL is as follows:
x = (C(2^(1/E) – 1))^(1/B)
MasterPlex ReaderFit offers full support for the 4 Parameter Logistic (4-PL) and 5 Parameter Logistic (5-PL) model equation making it an ideal tool for automated fitting of dose response curves and calculating the EC50 or IC50 values.
Here is a Quick Guide for this procedure:
- To acquire MasterPlex ReaderFit, download and install the free 14-day trial.
- Importing the raw response values is a simple copy & paste OR you can use the file import wizard to import Excel, .csv, or text files.
- This is a sample dose response data set which contains 3 different sets of data from which we will generate 3 dose response curves (each outlined with a different color). Each curve set contains 8 data points with duplicates that are side-by-side.
- Select 1 entire set of data points(including replicates) and click the blue Sample Curve button. Please note that you can hold down the CTRL button and left click non-contiguous wells.
- The Auto Fill dialog will pop up. This feature is only helpful if you have a serial dilution for your drug concentrations. If not, you will have to enter them in manually.
In this example, Drug ABC was serially diluted from top (20,000 pg/mL) to bottom (0.26 pg/mL) with a dilution factor of 5.
- Click Apply & View to preview the values in plate tab to confirm.
- Repeat steps 2-4 for any other data sets you may have. In this example, there are 2 other data sets.
For each cell, you will notice 2 numbers: the response value on the top and the drug dose on the bottom.
- Click on the Fit Curves tab.
- Make sure the Use Best Fit feature checkbox is unchecked and select either the 4-PL or 5-PL model equation for the curve fit and then click Calculate.
- MasterPlex ReaderFit will then draw the dose response curve(s) according to the parameters you have chosen.
- To view the EC50 or IC50 calculations, click on the View Results tab and press the Show Columns Selector button.
- Drag and drop the EC50 / IC50 column to the table header row.
- (Optional) You can click on the Merge Cells By Group button for a cleaner output. This function basically merges cells of replicate groups and just outputs a single result instead of repeating the same value for all replicates.
- (Optional) If you have more than 1 dose response curve, you may want to group them together by dragging and dropping the Replicate Group Name column header onto Analyte Name.
The end results will look something like this:
If you are looking for curve-fitting software with EC50 and IC50 determination we have a couple options:
ReaderFit.com – Free online curve-fitting application
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ReaderFit Desktop – Robust curve-fitting, quality control and reporting desktop software
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