Transcript Influenza Neuraminidase Inhibitor IC50 Calculations: Methods

Influenza Neuraminidase Inhibitor IC50 Data:
Calculation, Interpretation and Statistical Analyses
Presentation Outline
• Determining IC50 values
• Curve-fitting methods
• Sources of variation
• Identifying IC50 outliers
• Determining cut-offs/thresholds
• Outlier values versus resistant viruses
• Monitoring trends over time in IC50 data
Abbreviations
NA: Neuraminidase
NI: Neuraminidase Inhibitor
RFU: Relative Fluorescence Units
RLU: Relative Luminescence Units
VC: Virus Control
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)
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)
IC50 Calculation: Curve-Fitting (Graph-pad)
Upper Asymptote
Isolate
A/Eng/683/2007
A/Eng/684/2007
A/Eng/685/2007
A/Eng/686/2007
Non linear regression analysis
Sigmoidal dose response curve
Mean
IC50(nM)
1.2
586
1.1
1.5
IC50 Calculation: Curve-Fitting (Grafit)
UK Surveillance
40000
A/Eng/683/2007 #1
A/Eng/683/2007 #2
Response
A/Eng/684/2007 #1
A/Eng/684/2007 #2
A/Eng/685/2007 #1
20000
A/Eng/685/2007 #2
A/Eng/686/2007 #1
A/Eng/686/2007 #2
0
0.01
0.1
1
10
Oseltamivir
100
1000
Isolate
A/Eng/683/2007
A/Eng/684/2007
A/Eng/685/2007
A/Eng/686/2007
Mean
IC50(nM)
1.1
620
1.1
1.5
IC50 Calculation: Point to Point
Isolate
A/Eng/683/2007
A/Eng/684/2007
A/Eng/685/2007
A/Eng/686/2007
Mean
IC50(nM)
1.1
608.5
1.0
1.4
IC50 Calculation: Comparison of IC50
values from different calculation methods
Isolate
A/Eng/683/2007
A/Eng/684/2007
A/Eng/685/2007
A/Eng/686/2007
GraphPad
1.2
586
1.1
1.5
Grafit
1.1
620
1.1
1.5
Point to Point
1.1
608.5
1.0
1.4
Determining IC50 Values: Sources of
Variation
• 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
IC50 Calculation: Comparison of Point
to Point versus curve-fitting
Isolate
Point to Point
0.69
292R
>4000
292K
40.3
119V
0.59
A/Lisbon/22/2007
0.68
A/Latvia/685/2007
0.43
A/Denmark/1/2007
0.54
A/Denmark/2/2007
GraphPad
0.69
8551
40.7
0.56
0.69
0.41
0.51
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
Ratio
1.00
1.01
1.05
1.01
1.05
1.06
IC50 Calculation: Troubleshooting
• Important to examine curves carefully to ensure IC50 is valid
Low VC: technical error
Poor curve fit
Drug titration error
Poor curve fit
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
Analysis of intra-assay variation
Isolate
Replicate 1
0.75
292R
3769
292K
42.0
119V
0.57
A/Lisbon/22/2007
0.70
A/Latvia/685/2007
0.40
A/Denmark/1/2007
0.58
A/Denmark/2/2007
Replicate 2
0.63
>4000
38.6
0.61
0.66
0.46
0.49
Ratio
1.19
1.09
1.07
1.06
1.15
1.18
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
Analysis of Inter-assay Variation
Introduction of new drug batch
Analysis of Inter-assay Variation
Isolate
Assay 1 Assay 2 Ratio
0.42
0.36
1.17
274H Oseltamivir
0.17
0.22
1.29
274H Zanamivir
300
303
1.01
274Y Oseltamivir
0.31
0.37
1.19
274Y Zanamivir
43.7
34.0
1.29
119V Oseltamivir
1.41
1.32
1.07
119V Zanamivir
920
1316
1.43
152K Oseltamivir
239
3.51
1.47
152K Zanamivir
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.
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 (510%) 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)
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)
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.
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
Using SMAD Analysis: Example Data
Early-Mid Season Estimate
Post Season Estimate
Median
1.1
Robust SD
1.25
Minor Outlier (1.65SD)
1.56
Outlier (3SD)
2.11
Median
0.97
Robust SD
1.27
Minor Outlier (1.65SD)
1.45
Outlier (3SD)
1.99
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
Box and Whisker Plots Principle
Excel Formulae
Upper quartile (Q3): (QUARTILE(B2:B150,3)
Lower quartile )Q1): (QUARTILE(B2:B150,1)
IQR: Q3-Q1
Mild outlier Extreme outlier
Equivalent values
in SMAD analysis
Using Box and Whisker Analysis:
Example Data
Excel Output
Graphpad Prism Output
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
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.
Using Log10 versus non logged Data
Non Log Data
Log10 Data
Median
0.72
Robust SD
0.50
Minor Outlier (1.65SD)
1.55
Outlier (3SD)
2.23
Median
0.72
Robust SD
0.50
Minor Outlier (1.65SD)
1.55
Outlier (3SD)
2.23
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
Sensitive and Resistant Isolates
Resistant Isolates removed
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
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
Trends in IC50 Data: Scatter Plot
H1N1 Zanamivir 2005-2007
10.00
3SD
1.00
Median
0.10
2005/06
2006/07
H3N2 Zanamivir 2005-2007
100.00
10.00
3SD
IC50
IC50
1.65 SD
1.65 SD
1.00
Median
0.10
2005/06
2006/07
Trends in IC50 Data: Box and
Whisker
H1N1 isolates 2004-2007 EU+ UK: Oseltamivir
5.0
4.5
4.0
IC50 (nM)
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
H1N1 EU+UK 04-05
H1N1 EU+UK 05-06
H1N1 EU+UK 06-07
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