![]() ![]() This quantitative measure indicates how much of a particular drug or other substance is needed to inhibit a given biological process by half. The half maximal inhibitory concentration is a measure of the effectiveness of a compound in inhibiting biological or biochemical function. Top and Bottom are plateaus in the units of the Y axis. Depending on which units Y is expressed in, and the values of Bottom and Top, the IC50 may give a response nowhere near "50". This is not the same as the response at Y=50. ![]() IC50 is the concentration of agonist that gives a response half way between Bottom and Top. ![]() Y=Bottom + (Top-Bottom)/(1+10^((X-LogIC50))) Interpret the parameters If you have subtracted off any basal response, consider constraining Bottom to a constant value of 0. ![]() If you prefer to enter concentrations, rather than the logarithm of concentrations, use Prism to transform the X values to logs.įrom the data table, click Analyze, choose nonlinear regression, choose the panel of equations "Dose-response curves - Inhibition" and then choose the equation "log(inhibitor) vs. Enter one data set into column A, and use columns B, C. Enter response into Y in any convenient units. Enter the logarithm of the concentration of the inhibitor into X. If you have lots of data points, pick the variable slope model to determine the Hill slope from the data. If you don't have many data points, consider using the standard slope model. This is the slope expected when a ligand binds to a receptor following the law of mass action, and is the slope expected of a dose-response curve when the second messenger created by receptor stimulation binds to its receptor by the law of mass action. This model assumes that the dose response curves has a standard slope, equal to a Hill slope (or slope factor) of -1.0. The goal is to determine the IC50 of the inhibitor - the concentration that provokes a response half way between the maximal (Top) response and the maximally inhibited (Bottom) response. response curves follow the familiar symmetrical sigmoidal shape. ![]()
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |