Method for detecting double-target agonist drug neutralizing antibody in cynomolgus monkey serum

Abstract

The invention provides a method for detecting a double-target agonist drug neutralizing antibody in cynomolgus monkey serum, and belongs to the technical field of biological detection. The invention establishes and verifies a stable, reliable and efficient detection method which can sensitively and specifically detect the neutralizing antibody activity of the cynomolgus monkey serum to the double-target agonist drug. The method has good precision, accuracy and reproducibility, effectively overcomes the interference of complex components in a serum sample, and ensures the accuracy and reliability of a detection result. The successful application of the method can provide key technical support for immunogenicity evaluation of double-target agonist drugs before clinical application and in clinical research, and the method has important practical significance for guaranteeing smooth drug research and development and medication safety.

Description

Technical Field

[0001] This invention relates to the field of biodetection technology, and in particular to a method for detecting dual-target agonist drug neutralizing antibodies in the serum of cynomolgus monkeys. Background Technology

[0002] Immunogenicity assessment is a crucial step in the development of biopharmaceuticals. After a drug enters the body, it may be recognized by the immune system, leading to the production of corresponding anti-drug antibodies. These antibodies, especially those with neutralizing activity, can specifically bind to the drug, potentially inhibiting its biological function, affecting its pharmacokinetic characteristics, reducing efficacy, and even, in rare cases, triggering serious adverse reactions. Therefore, establishing sensitive, specific, and robust methods to detect and characterize anti-drug antibodies, especially neutralizing antibodies, produced by the body during both pre-clinical and clinical research is a core component of evaluating drug safety and efficacy.

[0003] For agonist drugs, especially those acting on the G protein-coupled receptor family, their pharmacological effects are achieved by activating intracellular signaling pathways. The detection of neutralizing antibodies against these drugs typically requires cell-function-based biological methods, as only these methods can accurately reflect whether the antibody blocks the downstream biological activities triggered by drug binding to the target. Compared to traditional ligand binding assays, cell-based detection methods can more directly assess antibody neutralizing capacity, but they are also more challenging to develop, requiring the construction of suitable reporter gene cell lines and the optimization of complex detection conditions to ensure the sensitivity and specificity of the method.

[0004] With advancements in drug development technology, bispecific or multispecific agonists have become a new trend in the treatment of complex diseases such as type 2 diabetes and obesity. These drugs act on multiple targets simultaneously, leading to better efficacy, but also presenting new challenges in assessing their immunogenicity. How to effectively and independently evaluate the activity of neutralizing antibodies against different targets within a single assay system, avoiding mutual interference, and establishing unified judgment criteria are currently technical challenges faced by those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method for detecting the activity of a dual-target agonist drug neutralizing antibody in cynomolgus monkey serum, which has good precision, accuracy and reproducibility.

[0006] To achieve the above-mentioned objectives, the present invention provides the following technical solution: This invention provides a method for detecting neutralizing antibodies against a dual-target agonist drug in cynomolgus monkey serum, comprising the following steps: The recombinant cells expressing target receptors, including GLP-1R and GIPR, are provided, and the recombinant cells contain fluorescent reporter genes; The cynomolgus monkey serum sample was mixed and incubated with the dual-target agonist drug to obtain a mixture; The mixture is brought into contact with the cells; after incubation, the fluorescence signal generated by the cells based on a fluorescent reporter gene is detected. The neutralizing antibody activity against the target receptor in the serum sample is calculated based on the fluorescence signal.

[0007] Preferably, when the target receptor is GLP-1R, the recombinant cells expressing the target receptor are HEK293 / CRE-Luc / GLP-1R cells.

[0008] Preferably, when the target receptor is GIPR, the recombinant cell expressing the target receptor is HEK293-GIPR(Luc) cell.

[0009] Preferably, the fluorescence signal is characterized by relative luminescence intensity.

[0010] Preferably, calculating the neutralizing antibody activity includes calculating the signal-to-noise ratio S / N, where the signal-to-noise ratio S / N is the ratio of the mean RLU of the serum sample to the median RLU of the negative control sample; Calculating the neutralizing antibody activity also includes calculating the signal-to-noise ratio inhibition rate (SIR), where the SIR is (1-S / N)×100%. It also includes setting a threshold value CP, which is used to distinguish between neutralizing antibody positive or negative, wherein the threshold value CP is determined by statistical methods based on the signal-to-noise ratio inhibition rate (SIR) of multiple blank individual serum samples; The statistical methods include: obtaining SIR values ​​from multiple blank individual serum samples; removing and analyzing outliers and biological outliers; examining the distribution of the remaining SIR values; and selecting a critical value (CP) calculation method based on the distribution.

[0011] Preferably, the distribution of the remaining SIR values ​​is tested using the Shapiro-Wilk test and the skewness coefficient.

[0012] Preferably, when the distribution satisfies the Shapiro-Wilk test p-value greater than 0.05 and the absolute value of the skewness coefficient less than or equal to 1, the critical value CP is the mean of the SIR values ​​plus 2.2 to 2.5 times the standard deviation.

[0013] Preferably, in the process of testing the distribution of the remaining SIR values, when the distribution satisfies the Shapiro-Wilk test P-value less than or equal to 0.05 and the absolute value of the skewness coefficient less than or equal to 1, the critical value CP is the median of the SIR values ​​plus 1.45 to 1.55 times the absolute deviation of the median of 2.2 to 2.5 times.

[0014] Preferably, in the process of testing the distribution of the remaining SIR values, when the distribution satisfies the Shapiro-Wilk test P-value less than or equal to 0.05 and the absolute value of the skewness coefficient greater than 1, the critical value CP is the value corresponding to the 99th percentile.

[0015] Preferably, the process of examining the distribution of the remaining SIR values ​​further includes detection using a gradient sample of neutralizing antibody concentrations, wherein the concentration range of the gradient sample is 0.5 ng / mL to 100 ng / mL.

[0016] The beneficial effects of this invention are: This invention establishes and validates a stable, reliable, and efficient detection method capable of sensitively and specifically detecting neutralizing antibody activity against dual-target agonist drugs in cynomolgus monkey serum. This method exhibits good precision, accuracy, and reproducibility, effectively overcoming interference from complex components in serum samples and ensuring the accuracy and reliability of the detection results. Its successful application can provide crucial technical support for the immunogenicity evaluation of dual-target agonist drugs in preclinical and clinical studies, and has significant practical implications for ensuring the smooth progress of drug development and medication safety. Attached Figure Description

[0017] Picture 1 Flowchart for calculating the critical value of neutralizing antibodies. Detailed Implementation

[0018] The technical solutions provided by the present invention will be described in detail below with reference to the embodiments, but they should not be construed as limiting the scope of protection of the present invention.

[0019] Key terms used in the examples: HPC represents high concentration positive control; MPC represents medium concentration positive control; LPC represents low concentration positive control; NC represents negative control; CC represents cell control; CP represents critical value; RLU represents relative luminescence intensity; SD represents standard deviation; %CV represents percentage of coefficient of variation; h represents hour; S / N represents signal-to-noise ratio; SIR represents signal-to-noise ratio inhibition rate.

[0020] Example Calculation of Neutralizing Antibody Cut Point (CP) The cut point (CP) is a numerical value used to distinguish whether a test sample is negative or positive for neutralizing antibodies; a value greater than or equal to the cut point is positive, and a value below the cut point is negative.

[0021] The CP value was statistically analyzed and calculated using Stata software, as follows: Picture 1 As shown.

[0022] (1) If the instrument response value (RLU)%CV of the blank individual S is greater than 30%, the individual is directly removed and not included in the statistics.

[0023] (2) Data processing: calculate the signal-to-noise ratio suppression rate for each sample.

[0024] (3) The Box-plot method is used to analyze all valid detection data of each blank individual and exclude outliers. After removing outliers, the median of all valid detection data of each blank individual is calculated. Then the Box-plot method is used to analyze the median of the multiple individuals and exclude abnormal individuals (biological outlier judgment).

[0025] (4) After removing outliers (at least 3 / 4 of the individuals are valid), the distribution of the data is determined by the Shapiro-Wilk test.

[0026] If the distribution of the test data satisfies the Swartz-Stokes test P If the skewness coefficient is greater than 0.05 and the absolute value of the skewness coefficient is less than or equal to 1 (i.e., |skewness|≤1), the CP value can be calculated. CP = Mean + 2.326 × SD (where Mean and SD are the mean and standard deviation of the SIR of all blank individuals after removing outliers in the validation phase, respectively, and 2.326 is the coefficient corresponding to the one-sided 99th percentile of the normal distribution). If P≤0.05 and the absolute value of the skewness coefficient is less than or equal to 1 (i.e., |skewness|≤1), CP= Median+2.326×(1.4826×MAD) (where Median is the median of the SIR values ​​of all blank individuals after removing outliers in the validation phase, 2.326 is the coefficient corresponding to the one-sided 99th percentile of the normal distribution, and MAD is the absolute deviation of the median).

[0027] like P≤ If the skewness coefficient is 0.05 and the absolute value of the skewness coefficient is greater than 1 (i.e., |skewness|>1), then CP = the value corresponding to the 99th percentile.

[0028] Serum from 20 Tirzepatide-neutralizing antibody-negative cynomolgus monkeys was used as a negative control (NC) and diluted 10-fold with complete culture medium. Wells containing only complete culture medium without Tirzepatide were used as cell controls (CC). Two replicates were set up for each sample. The mean RLU (relative light unit) values ​​were calculated, and the signal-to-noise ratio (S / N) for the blank samples was calculated as: S / N = mean RLU of the sample / median RLU values ​​of all negative control samples in the analytical batch. Statistical analysis was performed based on the signal-to-noise ratio inhibition rate (SIR) of all samples = (1 - S / N) × 100% to obtain the critical value.

[0029] Verification results: Critical value (CP) In this embodiment, a total of 3 batches of valid tests were conducted. Each analytical batch contained blank serum from 20 selected Tirzepatide neutralizing antibody-negative cynomolgus monkeys. According to the statistical calculation using Stata software, the critical value CP (SIR) obtained for GLP1R validation was 19.054% (Table 1-1), and the critical value CP (SIR) obtained for GIPR validation was 24.282% (Table 1-2).

[0030] Table 1-1 CP Value Verification Hashimoto analysis criticism SIR outSIR Median outSIRout 1 Run1 -1.000 -1.000 -1.000 1 Run2 -3.000 -3.000 -3.000 1 RunSite -10.000 -10.000 -3.000 -10.000 2 Run1 -1.000 -1.000 -1.000 2 Run2 -6.000 -6.000 -6.000 2 RunSite -5.000 -5.000 -5.000 -5.000 3 Run1 -6.000 -6.000 -6.000 3 Run2 -10.000 -10.000 -10.000 3 RunSite -12.000 -12.000 -10.000 -12.000 4 Run1 5.000 5.000 5.000 4 Run2 -10.000 -10.000 -10.000 4 RunSite -9.000 -9.000 -9.000 -9.000 5 Run1 7.000 7.000 7.000 5 Run2 2.000 2.000 2.000 5 RunSite -1.000 -1.000 2.000 -1.000 6 Run1 14.000 14.000 14.000 6 Run2 -6.000 -6.000 -6.000 6 RunSite 2.000 2.000 2.000 2.000 7 Run1 6.000 6.000 6.000 7 Run2 -6.000 -6.000 -6.000 7 RunSite -3.000 -3.000 -3.000 -3.000 8 Run1 1.000 1.000 1.000 8 Run2 -4.000 -4.000 -4.000 8 RunSite -5.000 -5.000 -4.000 -5.000 9 Run1 10.000 10.000 10.000 9 Run2 3.000 3.000 3.000 9 RunSite -1.000 -1.000 3.000 -1.000 10 Run1 14.000 14.000 14.000 10 Run2 3.000 3.000 3.000 10 RunSite 3.000 3.000 3.000 3.000 11 Run1 1.000 1.000 1.000 11 Run2 -5.000 -5.000 -5.000 11 RunSite -5.000 -5.000 -5.000 -5.000 12 Run1 5.000 5.000 5.000 12 Run2 -4.000 -4.000 -4.000 12 RunSite -8.000 -8.000 -4.000 -8.000 13 Run1 6.000 6.000 6.000 13 Run2 -1.000 -1.000 -1.000 13 RunSite -2.000 -2.000 -1.000 -2.000 14 Run1 13.000 13.000 13.000 14 Run2 7.000 7.000 7.000 14 RunSite 3.000 3.000 7.000 3.000 15 Run1 19.000 19.000 19.000 15 Run2 5.000 5.000 5.000 15 RunSite 6.000 6.000 6.000 6.000 16 Run1 13.000 13.000 13.000 16 Run2 4.000 4.000 4.000 16 RunSite 6.000 6.000 6.000 6.000 17 Run1 14.000 14.000 14.000 17 Run2 7.000 7.000 7.000 17 RunSite 6.000 6.000 7.000 6.000 18 Run1 16.000 16.000 16.000 18 Run2 6.000 6.000 6.000 18 RunSite 8.000 8.000 8.000 8.000 19 Run1 13.000 13.000 13.000 19 Run2 7.000 7.000 7.000 19 RunSite 3.000 3.000 7.000 3.000 20 Run1 4.000 4.000 4.000 20 Run2 -2.000 -2.000 -2.000 20 RunSite -10.000 -10.000 -2.000 -10.000 Table 1-2 CP Value Verification Hashimoto analysis criticism SIR outSIR Median outSIRout 1 Run1 -9.000 -9.000 -9.000 1 Run2 -11.000 -11.000 -11.000 1 RunSite -10.000 -10.000 -10.000 -10.000 2 Run1 11.000 11.000 11.000 2 Run2 6.000 6.000 6.000 2 RunSite 9.000 9.000 9.000 9.000 3 Run1 11.000 11.000 11.000 3 Run2 16.000 16.000 16.000 3 RunSite 19.000 19.000 16.000 19.000 4 Run1 10.000 10.000 10.000 4 Run2 14.000 14.000 14.000 4 RunSite 13.000 13.000 13.000 13.000 5 Run1 14.000 14.000 14.000 5 Run2 11.000 11.000 11.000 5 RunSite 10.000 10.000 11.000 10.000 6 Run1 8.000 8.000 8.000 6 Run2 9.000 9.000 9.000 6 RunSite 10.000 10.000 9.000 10.000 7 Run1 0.000 0.000 0.000 7 Run2 4.000 4.000 4.000 7 RunSite 12.000 12.000 4.000 12.000 8 Run1 -2.000 -2.000 -2.000 8 Run2 0.000 0.000 0.000 8 RunSite 2.000 2.000 0.000 2.000 9 Run1 -6.000 -6.000 -6.000 9 Run2 -11.000 -11.000 -11.000 9 RunSite -3.000 -3.000 -6.000 -3.000 10 Run1 -11.000 -11.000 -11.000 10 Run2 -6.000 -6.000 -6.000 10 RunSite 4.000 4.000 -6.000 4.000 11 Run1 5.000 5.000 5.000 11 Run2 9.000 9.000 9.000 11 RunSite 9.000 9.000 9.000 9.000 12 Run1 1.000 1.000 1.000 12 Run2 1.000 1.000 1.000 12 RunSite 2.000 2.000 1.000 2.000 13 Run1 2.000 2.000 2.000 13 Run2 -4.000 -4.000 -4.000 13 RunSite 8.000 8.000 2.000 8.000 14 Run1 11.000 11.000 11.000 14 Run2 22.000 22.000 22.000 14 RunSite 21.000 21.000 21.000 21.000 15 Run1 -7.000 -7.000 -7.000 15 Run2 4.000 4.000 4.000 15 RunSite -1.000 -1.000 -1.000 -1.000 16 Run1 -8.000 -8.000 -8.000 16 Run2 4.000 4.000 4.000 16 RunSite 12.000 12.000 4.000 12.000 17 Run1 -9.000 -9.000 -9.000 17 Run2 -2.000 -2.000 -2.000 17 RunSite 8.000 8.000 -2.000 8.000 18 Run1 4.000 4.000 4.000 18 Run2 8.000 8.000 8.000 18 RunSite 17.000 17.000 8.000 17.000 19 Run1 2.000 2.000 2.000 19 Run2 4.000 4.000 4.000 19 RunSite 16.000 16.000 4.000 16.000 20 Run1 1.000 1.000 1.000 20 Run2 4.000 4.000 4.000 20 RunSite 13.000 13.000 4.000 13.000 Sensitivity and low-concentration positive control samples (LPC) To validate the GLP1R receptor, four analytical batches were performed by two analysts at different times. Each batch included a set of sensitivity samples with antibody concentrations of 80.00 ng / mL, 40.00 ng / mL, 20.00 ng / mL, 10.00 ng / mL, 5.00 ng / mL, 2.50 ng / mL, and 1.25 ng / mL, for a total of seven gradient samples used to calculate sensitivity. The calculated sensitivity of this method was 17.787 ng / mL, and the low-concentration positive control (LPC) was 23.198 ng / mL (Table 2-1).

[0031] Table 2-1 Sensitivity of cell-based detection of Tirzepatide neutralizing antibodies in cynomolgus monkey serum

[0032] Note: The sensitivity calculation formula is = Mean + t0.05 , df × SD, LPC = Mean + t 0.01 , df × SD。

[0033] The GIPR receptor was verified. A total of 3 batches of valid detections were carried out by 2 analysts at different times. Each analytical batch included a set of sensitivity samples. The antibody concentrations included 5 gradient samples of 86.000 ng / mL, 43.000 ng / mL, 21.500 ng / mL, 10.750 ng / mL, and 5.380 ng / mL, which were used for the calculation of sensitivity. After calculation, the sensitivity of this method was 19.999 ng / mL, and the low-concentration positive control sample (LPC) was 34.946 ng / mL (Table 2-2).

[0034] Table 2-2 Sensitivity of detecting neutralizing antibodies of Tirzepatide in cynomolgus monkey serum by cell method

[0035] Note: The sensitivity calculation formula is = Mean + t 0.05 , df × SD, LPC = Mean + t 0.01 , df × SD。

[0036] Precision The GLP1R receptor was verified, and a total of 3 valid batches of precision verification were carried out. The precision results showed that the within-batch precision %CV of the signal-to-noise ratio inhibition rate of each positive control sample was between 2.000% and 7.210%, and the between-batch precision %CV was between 3.546% and 5.074%; the within-batch precision %CV of the relative fluorescence intensity signal value of the negative control sample was between 0.510% and 4.510%, and the between-batch precision %CV was 4.200%.

[0037] The above results all met the requirements that the signal-to-noise ratio inhibition rate of each positive control sample within and between batches satisfied %CV ≤ 30%, and all positive control signal-to-noise ratio inhibition rates ≥ CP; the relative fluorescence intensity signal value of the negative control %CV ≤ 30% and the signal-to-noise ratio inhibition rate of the negative control < CP, indicating that the precision of this method was good (Table 3-1).

[0038] Table 3-1 Precision of detecting neutralizing antibodies of Tirzepatide in cynomolgus monkey serum by cell method

[0039] The GIPR receptor was verified. A total of 3 valid batches of precision verification were carried out by 2 analysts over 4 days. The precision results showed that for each positive control sample, the within-batch precision %CV of the signal-to-noise ratio inhibition rate was between 1.590% and 8.300%, and the between-batch precision %CV was between 1.948% and 6.486%; for the negative control sample, the within-batch precision %CV of the relative fluorescence intensity signal value was between 2.700% and 4.590%, and the between-batch precision %CV was 3.470%.

[0040] The above results all met the requirements that for each positive control sample within and between batches, the signal-to-noise ratio inhibition rate satisfied %CV ≤ 30%, and all positive control signal-to-noise ratio inhibition rates ≥ CP; for the negative control, the relative fluorescence intensity signal value %CV ≤ 30% and the negative control signal-to-noise ratio inhibition rate < CP, indicating that the precision of this method was good (Table 3-2).

[0041] Table 3-2 Precision of detecting Tirzepatide neutralizing antibodies in cynomolgus monkey serum by cell method

[0042] Investigation of cell passages The GLP1R receptor was verified. Two batches of cells HEK 293 / CRE-Luc / GLP-1R at passages 15 and 25 were selected respectively to compare the signal-to-noise ratio inhibition rates of positive controls LPC (23 ng / mL), MPC (30 ng / mL), HPC (40 ng / mL) and the %CV of the relative fluorescence intensity signal value of negative control NC. The between-batch %CV of the signal-to-noise ratio inhibition rate of positive control samples was between 4.820% and 4.960%, and the between-batch %CV of the relative fluorescence intensity signal value of negative control samples was 20.500%, both within 30%. Each positive control sample was positive (signal-to-noise ratio inhibition rate ≥ CP), and the negative control sample was negative (signal-to-noise ratio inhibition rate < CP), indicating good stability between cell passages, meaning that different cell passages did not affect the detection of Tirzepatide neutralizing antibodies in cynomolgus monkey serum samples, and cell passages between 15 and 25 could be used for subsequent sample detection (Table 4-1).

[0043] Table 4-1 Investigation of cell passages for detecting Tirzepatide neutralizing antibodies in cynomolgus monkey serum by cell method

[0044] Note: The CP value (SIR%) is 19.054; " / " has no relevant information.

[0045] Verify the GIPR receptor. Select two batches of cells, HEK293-GIPR (Luc) at passage 15 and passage 25 respectively, and compare the signal-to-noise ratio inhibition rates of positive controls LPC (35 ng / mL), MPC (50 ng / mL), HPC (65 ng / mL) and the %CV of the relative fluorescence intensity signal value of the negative control NC. The between-batch %CV of the signal-to-noise ratio inhibition rate of the positive control samples is 3.45% - 6.00%, and the between-batch %CV of the relative fluorescence intensity signal value of the negative control samples is 15.25%, both within 30%. Each positive control sample is positive (signal-to-noise ratio inhibition rate ≥ CP), and the negative control sample is negative (signal-to-noise ratio inhibition rate < CP), indicating good stability between cell passages, meaning that different cell passages do not affect the detection of Tirzepatide neutralizing antibodies in cynomolgus monkey serum samples. Cell passages between 15 and 25 can be used for subsequent sample detection (Table 4-2).

[0046] Table 4-2 Investigation of cell passages for detecting anti-Tirzepatide neutralizing antibodies in cynomolgus monkey serum by cell method

[0047] Note: The CP value (SIR%) is 24.282; " / " indicates no relevant information.

[0048] As can be seen from the above embodiments, the present invention provides a comprehensively verified cytological method for detecting neutralizing antibodies of dual-target agonist drugs in cynomolgus monkey serum. For the two key receptors (GLP-1R and GIPR) targeted by the drug, independent detection systems are established respectively, and its critical value, sensitivity, precision and the influence of different cell passages on the detection results are systematically evaluated. The verification results show that all performance indicators of the method established by the present invention meet the expected requirements, with excellent reliability, repeatability and durability, can effectively distinguish positive and negative samples, and are suitable for accurately and stably evaluating the immunogenicity of the dual-target agonist drug in the cynomolgus monkey model.

[0049] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for detecting neutralizing antibodies against a dual-target agonist drug in cynomolgus monkey serum, characterized in that, Includes the following steps: The recombinant cells expressing target receptors, including GLP-1R and GIPR, are provided, and the recombinant cells contain fluorescent reporter genes; The cynomolgus monkey serum sample was mixed and incubated with the dual-target agonist drug to obtain a mixture; The mixture is brought into contact with the cells; after incubation, the fluorescence signal generated by the cells based on a fluorescent reporter gene is detected. The neutralizing antibody activity against the target receptor in the serum sample is calculated based on the fluorescence signal.

2. The method as described in claim 1, characterized in that, When the target receptor is GLP-1R, the recombinant cells expressing the target receptor are HEK293 / CRE-Luc / GLP-1R cells.

3. The method as described in claim 1, characterized in that, When the target receptor is GIPR, the recombinant cells expressing the target receptor are HEK293-GIPR(Luc) cells.

4. The method as described in claim 1, characterized in that, The fluorescence signal is characterized by relative luminescence intensity.

5. The method as described in claim 1, characterized in that, Calculating the neutralizing antibody activity includes calculating the signal-to-noise ratio (S / N), where S / N is the ratio of the mean RLU of the serum sample to the median RLU of the negative control sample. Calculating the neutralizing antibody activity also includes calculating the signal-to-noise ratio inhibition rate (SIR), where the SIR is (1-S / N)×100%. It also includes setting a threshold value CP, which is used to distinguish between neutralizing antibody positive or negative, wherein the threshold value CP is determined by statistical methods based on the signal-to-noise ratio inhibition rate (SIR) of multiple blank individual serum samples; The statistical methods include: obtaining SIR values ​​from multiple blank individual serum samples; removing and analyzing outliers and biological outliers; examining the distribution of the remaining SIR values; and selecting a critical value (CP) calculation method based on the distribution.

6. The method as described in claim 5, characterized in that, The distribution of the remaining SIR values ​​was tested using the Shapiro-Wilk test and the skewness coefficient.

7. The method as described in claim 6, characterized in that, In the process of testing the distribution of the remaining SIR values, when the distribution satisfies the Shapiro-Wilk test P-value greater than 0.05 and the absolute value of the skewness coefficient less than or equal to 1, the critical value CP is the mean of the SIR values ​​plus 2.2 to 2.5 times the standard deviation.

8. The method as described in claim 6, characterized in that, In the process of testing the distribution of the remaining SIR values, when the distribution satisfies the Shapiro-Wilk test P-value less than or equal to 0.05 and the absolute value of the skewness coefficient less than or equal to 1, the critical value CP is the median of the SIR values ​​plus 1.45 to 1.55 times the absolute deviation of the median of 2.2 to 2.5 times.

9. The method as described in claim 6, characterized in that, In the process of testing the distribution of the remaining SIR values, when the distribution satisfies the Shapiro-Wilk test P-value less than or equal to 0.05 and the absolute value of the skewness coefficient greater than 1, the critical value CP is the value corresponding to the 99th percentile.

10. The method as described in claim 6, characterized in that, It also includes detection using a gradient of neutralizing antibody concentrations, wherein the concentration range of the gradient samples is 0.5 ng / mL to 100 ng / mL.

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