Step-wise Procedure to Calculate Confirmation Cut Point for Anti-Drug Antibody Assay

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The cut-point of the assay is the level of response of the assay that defines the sample response as positive or negative. Establishing the appropriate cut-point is critical to minimizing the risk of false-negative results. The confirmation cut points are determined by assaying a set of approximately 50 drug-naive individual serum samples over three days by two analysts for a total of six runs. Here, we describe the stepwise procedure to calculate the statistical cut point using the parametric and robust parametric method. 

Steps for estimating confirmation cut points

1) Compile the data from 50 drug-naive individuals assayed by two analysts over 3 days for a total of six days. (See template below) 

2) Compute the % Signal Inhibition or % Immunodepletion from the assay signal generated in the presence and absence of the drug.  

3) Use the box plot analysis in JMP to identify and exclude outliers. 

4) Use the Shapiro-Wilk Test for the test of homogeneity or normality. The data is normally distributed when the p-value for the Shapiro-Wilk Test is less than 0.05. Use the nonparametric method for the data that are not normally distributed.

5) Perform ANOVA analysis (Oneway ANOVA in JMP software)  to compare the mean and variances among six runs. 
   

6) Use the Parametric method for the data sets 
         -when the pooled the %SI data is normally distributed   

The formula for parametric cut point with a 1% false-positive rate
        
          Confirmation Cut Point = Mean (Normalized) + 2.33 X Standard Deviation

7) Evaluate the cut point using Robust Parametric Method for the data sets with higher outliers

         Confirmation Cut Point = Median (Normalized) + 2.33 X (1.45 X Median Absolute Deviation)


Template for Data Complilation
Cutpoint 
Run 
 Day   Analyst A/B
 Plate 
NC-signal 
 %CV
 Drug 
Naive 
Individual ID 
Screen Sample Signal
(S)
 Confrm 
Sample Signal
(C)
 100% (1-C/S)
         


Reference 
Shankar et al 2008
FDA Guidance on ADA assay 2019

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