4. Determining NI IC50 Values
IC50: The concentration of NI which reduces NA
activity by 50% of the virus-control, or upper
asymptote
Estimated by: Measuring the NA activity (RFU or
RLU) of an isolate against a range of dilutions of the
drug, as well as without drug (virus-control)
5. Determining IC50 Values:
Calculation Options
• Curve fitting-Statistical software
• Graph Pad Prism (www.graphpad.com)
• $595
• Grafit (www.erithacus.com/grafit/)
• $400-500
• Jaspr (Developed by CDC)
• Contact CDC for suitability and further details
• Point-to-Point calculation
• Excel templates (Created by Health Protection Agency, UK)
6. IC50 Calculation: Curve-Fitting (Graph-pad)
Isolate
Mean
IC50(nM)
A/Eng/683/2007 1.2
A/Eng/684/2007 586
A/Eng/685/2007 1.1
A/Eng/686/2007 1.5
Upper Asymptote
Non linear regression analysis
Sigmoidal dose response curve
8. IC50 Calculation: Point to Point
Isolate
Mean
IC50(nM)
A/Eng/683/2007 1.1
A/Eng/684/2007 608.5
A/Eng/685/2007 1.0
A/Eng/686/2007 1.4
9. IC50 Calculation: Comparison of IC50
values from different calculation methods
Isolate GraphPad Grafit Point to Point
A/Eng/683/2007 1.2 1.1 1.1
A/Eng/684/2007 586 620 608.5
A/Eng/685/2007 1.1 1.1 1.0
A/Eng/686/2007 1.5 1.5 1.4
10. • Method of calculation
• Using point-to-point or curve fitting software
• Choice of curve fitting software used
• Intra-assay variation
• Difference between 2 or more replicates
• Inter-assay variation
• Difference in calculated value for a given isolate in multiple assays
Determining IC50 Values: Sources of
Variation
11. IC50 Calculation: Comparison of Point
to Point versus curve-fitting
Isolate Point to Point GraphPad Ratio
292R 0.69 0.69 1.00
292K >4000 8551 -
119V 40.3 40.7 1.01
A/Lisbon/22/2007 0.59 0.56 1.05
A/Latvia/685/2007 0.68 0.69 1.01
A/Denmark/1/2007 0.43 0.41 1.05
A/Denmark/2/2007 0.54 0.51 1.06
Curve Fitting WILL give an IC50 value by extrapolating the
curve when drug dilutions do not reach a true end point:
This does not necessarily give an accurate IC50 value
12. IC50 Calculation: Troubleshooting
Low VC: technical error Poor curve fit
Poor curve fit
Drug titration error
• Important to examine curves carefully to ensure IC50 is valid
13. Comments: Choice of IC50 Calculation
Method
• Choice of IC50 calculation method will make no more
than about 5% difference to IC50 value, for most
samples
• Must be clear exactly how the curve fitting and
calculation of IC50 is working
• Is IC50 based on 50% of RFU/RLU of VC or 50% of fitted upper
asymptote.
• Regardless of method used, careful examination of
the curve produced is required to identify technical
issues.
• See presentation on validation and troubleshooting of IC50 testing
methods
15. Comments: Intra-assay Variation
• Variation between replicates in the same assay can
be 15%-20%.
• This variation is greater than that seen with changes
to curve-fitting method
• Using replicates and taking the average reduces this
effect.
• A large difference between replicates (e.g. >30%) of a
given virus indicates a technical issue
• In these instances repeat testing should be performed
18. Comments: Inter-assay Variation
• Variation in IC50 values for a virus in multiple assays
can be 50%.
• Control viruses should be included in every assay to
identify technical issues.
• Control viruses should be validated, and have a
defined range between which the IC50 is valid.
• Assays in which the control virus IC50 falls outside the accepted
range should be reaped in their entirety.
19. Conclusions: Determining IC50 Values
• Several methods for IC50 calculation available at a range of price
and sophistication
• Variation due to choice of IC50 calculation method is minimal (5-
10%) in comparison with intra-assay (20%) and inter-assay (50%)
variation.
• Choice of curve-fitting method should be made based on
individual laboratory circumstances
• All variation can be minimised using appropriate assay controls
(reference/control viruses, validation of curves generated)
• Consistency in methodology used (statistical and laboratory) is
important for long term analysis (time trends)
20. Identifying IC50 outliers
• Aim: identify isolates with higher (or lower) than
expected IC50 values (outliers)
• First determine the ‘normal range’ of IC50 values
• Each
• Various statistical methods may be used
• Critical to ensure that any outliers do not unduly
affect the cut-off/threshold
• Outlier does not equal resistant
• Identifies isolates that may be worth further investigation
(retesting/sequencing)
21. Identifying IC50 outliers: Commonly
Used Statistical Methods
• SMAD
Robust estimate of the standard deviation based on the median
absolute deviation from the median
• Box and Whisker plots
Graphical representation of the 5 number summary of the data (the
sample minimum, the lower or first quartile, the median, the upper
or third quartile, the sample maximum)
• Both methods require a minimum dataset to perform
robust analyses (>20)
• Cut offs can be calculated mid-season, once a reasonable number
of samples has been tested, to monitor outliers
• At the end of the season, cut offs can be updated and a
retrospective analysis of all season data performed.
22. Using SMAD Analysis
• Create a scatter plot of all data
• Useful to see the spread and trend of the data
• Log transform the data
• Calculate a robust estimate of the standard deviation
based on the median absolute deviation from the
median using log10 data
Templates for this analyses are available from HPA, UK
• Major outliers: all those with values more than 3SD
above the median
• Minor Outliers: all those more than 1.65SD above the
median
23. Using SMAD Analysis: Example Data
Early-Mid Season Estimate
Median 0.97
Robust SD 1.27
Minor Outlier (1.65SD) 1.45
Outlier (3SD) 1.99
Post Season Estimate
Median 1.1
Robust SD 1.25
Minor Outlier (1.65SD) 1.56
Outlier (3SD) 2.11
24. Using Box and Whisker Analysis
• This analysis can be performed in Graphpad Prism,
with the box and whisker plots drawn automatically
• Calculations can be done in excel, but drawing the
box and whisker plots is more complicated
A template for plotting the graphs is available from Adam Meijer
• Log transform the data
• Calculate the median, upper quartile, lower quartile,
interquartile range, upper minor and major fences,
and lower major and minor fences
• Mild outliers lie between the minor and major fences
• Extreme outliers lie outside the major fence
26. Using Box and Whisker Analysis:
Example Data
Box and Whisker
Median 1.08
Mild outlier lower fence (1.5*IQR) 0.55
Extreme outlier lower fence (3*IQR) 0.34
Mild outlier higher fence (1.5*IQR) 1.95
Extreme outlier higher fence (3*IQR) 3.14
Graphpad Prism Output Excel Output
27. Do we need to log-transform?
• Most results from dilution assays produce ‘geometric
results’ so likely to be sensible
• Sometimes data are skewed. (e.g. lower quartile
much closer to median than upper quartile)
• Important to log transform as robust methods
assume data are normal once outliers are removed.
28. Using Log10 versus non logged Data
Non Log Data
Median 0.72
Robust SD 0.50
Minor Outlier (1.65SD) 1.55
Outlier (3SD) 2.23
Log10 Data
Median 0.72
Robust SD 0.50
Minor Outlier (1.65SD) 1.55
Outlier (3SD) 2.23
29. Impact of Excess Numbers of Outliers
• If the data have a large number of outliers, both SMAD and B+W
struggle to determine sensible cut offs.
• As resistant virus is very clearly different, these values can be
removed prior to analysis to allow sensible calculations of cut offs
for the remaining data.
• Below, data is shown for H1N1 in 2007/8, when sensitive and
resistant virus co-circulated.
• Cut offs calculated do not accurately apply to the sensitive IC50 data
Resistant Isolates removed
Sensitive and Resistant Isolates
30. Conclusions: Identifying Outliers
• Determining cut offs/thresholds identifies those isolates with IC50
values higher than the normal range
• Cut offs/thresholds need to be subtype specific
• Season specific cut offs are useful, if enough data is generated in one season,
but data from multiple seasons can be merged to perform a more reliable
analyses
• Box and whisker plots and SMAD analyses generate slightly different
cut offs
• Q3+1.5xIQR (mild outlier cut off) is equivalent to 2.7SD from SMAD
• Q3+3xICR is equivalent to 4.7SD from SMAD.
• Cut offs calculated by box and whisker analyses are higher than those from
SMAD analyses.
• Choice depends on individual laboratory preference
• Box and whisker plots present the data well
• Both methods minimise the impact of outlier values on the analyses,
but both will fail once too many outliers are present
• Data begins to have two populations
31. Monitoring Trends Over Time
• The normal range of IC50 values for a particular
subtype can change over time
• This could be seen by an increase in the number of
outliers, or by changes in the median
• Simple to monitor, using the methods already
described for identifying outliers
• scatter plots/box-whisker
• Other statistical methods can be used to further
analyse data from several seasons
34. Summary
• Good use of statistical methods can help interpret the
IC50 results and ensure assay results are reliable.
• Analyses of data not only identifies individual
outliers, but allows continuous monitoring of trends
• Retrospective analyses of multiple seasons of data
can identify changes in viral characteristics and
susceptibilities
• Do not use statistics without first looking at the data
by scatter plot to find obvious deviations which
require an adapted statistical approach
• Challenge is to find explanations for trends
