Document ID: DQ159 Relevant to following products: WorkOut 2.0 Document description: Application Example: Inhibition Concentration of Malaria Parasites in Red Blood Cells
In this fluorescence assay the IC50 (inhibition concentration) of malaria parasites is used to determine the amount of drug that inhibits the growth of the parasite/cell line to 50% of maximal growth.
The 96 well microplate layout is setup using the top and bottom rows and the end column. This leaves 6 wells in each column. The top 3 wells in each column for assay 1 and the bottom 3 wells in each column for assay 2. These are completely independent of each other.
Column 1 contains a blank this consists of red blood cells in media with the SYBR green. (No DNA as red blood cells have no DNA). Columns 6 and 7 contain our control these have parasitised red blood cells with media with no drug. These will grow the most and will be 100%
Column 2 contains parasitised red blood cells with 0.45 nM drug
Column 3 as 2
37.037 nM drug
111.111 nM drug
An average of the blank is made (B1, C1, D1). An average of the control is made (B6,7 C 6,7 D 6,7). An average of B2, C2, D2 etc.
The blank average is taken away from all the average values. Each blanked value is then divided by the control blanked value and multiplied by 100 to give the percentage of control growth. A graph is drawn x= concentration nM (log scale) y= percentage of control growth (linear scale). A four parameter curve fit is performed to give the concentration of drug at which the control growth is at 50% (IC50).
An example assay protocol and results files are available for download from here. Some points about the data/protocol:
The microplate layout is divided in between two "sub-assays" (with groups labelled 1 and 2). The blanks and controls are used on their associated unknowns. The manually flagged were described as "operator error" - these are excluded from the calculations (such as the averaging). The analysis method used is slightly different in that it calculates the % for each sample and uses each % in the curve fit (rather than the average of the replicates) this may result in slightly different results but may be considered better practice.