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Overview of Cut Point Calculation in the Presence of Pre-existing Antibodies

The process involves statistical methods that account for variations in baseline ADA levels across the study population. Here’s a structured approach to calculate the cut point when there is a pre-existing antibody response:

1. Collect Baseline ADA Samples

  • Sample Population: Collect samples from a representative population of treatment-naïve subjects (typically 50-100 individuals). These baseline samples should reflect the typical range of pre-existing ADA levels within the target patient population.
  • Matrix Type: Use serum or plasma samples, as appropriate for the assay matrix.
  • Time Points: Ideally, collect multiple samples per subject pre-treatment to get a clear baseline.

2. Run Baseline Samples in ADA Assay

  • Perform the ADA assay on all baseline samples, running each sample in triplicate to account for intra-assay variability.
  • Record the response values (e.g., optical density (OD) in ELISA) for each sample. If using multiple replicates, calculate the mean response for each sample.

3. Select an Appropriate Statistical Approach

Several statistical approaches can be used, depending on the distribution of baseline ADA responses. Here are two common methods:

Parametric Approach (If Data is Normally Distributed)

  • Shapiro-Wilk Test: First, use the Shapiro-Wilk test to check for normality. If the data is normally distributed, proceed with a parametric approach.
  • Cut Point Calculation: Calculate the mean and standard deviation (SD) of baseline values. The cut point is generally set as the mean + (X * SD), where "X" is chosen based on the desired confidence level:
    • For a 95% confidence level, X = 1.645.
    • For a 99% confidence level, X = 2.33.
  • Formula: Cut Point=Mean Baseline Response+(X×SD)\text{Cut Point} = \text{Mean Baseline Response} + (X \times \text{SD})

Non-Parametric Approach (If Data is Not Normally Distributed)

  • Percentile-Based Cut Point: If baseline ADA levels are skewed or do not follow a normal distribution, use a percentile-based approach. The 95th percentile of baseline responses is commonly used for the cut point.
  • Procedure:
    • Rank baseline responses from lowest to highest.
    • Determine the response value corresponding to the 95th percentile of this data (for a one-sided 95% confidence interval).
  • Formula:
    • The cut point is the 95th percentile value, so values above this point are considered positive.

4. Incorporate Intra-Assay Variability Adjustment

  • Intra-Assay Precision Factor: To improve robustness, include a variability factor by calculating the intra-assay coefficient of variation (CV) from baseline replicates. Adjust the cut point by incorporating this variability factor: Adjusted Cut Point=Unadjusted Cut Point×(1+Intra-Assay CV)\text{Adjusted Cut Point} = \text{Unadjusted Cut Point} \times (1 + \text{Intra-Assay CV})
  • This adjustment accounts for minor assay fluctuations that could affect the reproducibility of cut point determination.

5. Validate the Cut Point

  • Confirm Cut Point Reproducibility: Perform reproducibility testing using additional baseline samples. This step ensures the chosen cut point accurately distinguishes between pre-existing and treatment-emergent ADA responses.
  • Negative Control Samples: Test the cut point with negative control samples to ensure it does not inadvertently classify non-specific binding as ADA-positive.

6. Apply the Cut Point to Post-Treatment Samples

  • Once established, apply the cut point to post-treatment ADA samples to classify responses as positive or negative. Samples exceeding this cut point indicate a likely treatment-induced ADA response.

Example Calculation

Assume the baseline data (OD values) follows a normal distribution with a mean of 0.250 and an SD of 0.050:

  1. Parametric Cut Point: For a 95% confidence level:

    Cut Point=0.250+(1.645×0.050)=0.250+0.08225=0.332\text{Cut Point} = 0.250 + (1.645 \times 0.050) = 0.250 + 0.08225 = 0.332
    • Here, any post-treatment ADA response above 0.332 is considered positive.

  2. Non-Parametric Cut Point: Using a 95th percentile approach, if the 95th percentile of baseline values is 0.340, then 0.340 is set as the cut point.

Summary

Calculating the ADA cut point in the presence of pre-existing antibodies involves collecting robust baseline data, selecting an appropriate statistical method, and validating the threshold. This cut point ensures accurate differentiation between natural baseline antibodies and treatment-induced ADA, critical for assessing AAV gene therapy safety and efficacy.

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