Method for detecting virus inactivation effect by ELISA (enzyme-linked immuno sorbent assay) method

Abstract

The invention provides a method for detecting a virus inactivation effect by an ELISA (Enzyme-Linked Immunosorbent Assay) method, which comprises the following steps: acquiring a to-be-detected inactivated virus sample, extracting an antigen component from the sample, and detecting an antigen structure change characteristic value through a high-throughput mass spectrometry analysis technology to obtain antigen degradation degree data; obtaining a sample subjected to preliminary inactivation, and detecting the antigen content change trend through an enzyme-linked immunosorbent assay to obtain content change curve data; detecting the residual quantity of viral nucleic acid by a real-time fluorescent quantitative PCR (Polymerase Chain Reaction) technology aiming at the immunogenicity retained sample to obtain nucleic acid degradation degree data; acquiring nucleic acid degradation degree data, classifying the relationship between nucleic acid residues and infectivity by adopting a support vector machine algorithm, and judging that the inactivation effect is completely completed if the classification result shows that the infectivity is lost; according to the antigen content change trend, the immunogenicity index and the nucleic acid degradation degree data, a weighted fusion algorithm is adopted to integrate multi-dimensional detection results, and a comprehensive inactivation effect score is obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of virus detection, in particular to a method for detecting virus inactivation effects using an ELISA method. Background Art

[0002] Background: Viral inactivation testing is a crucial area in vaccine development and production, directly impacting vaccine safety, efficacy, and production efficiency. This research area plays an irreplaceable role in infectious disease prevention and control, especially in the face of sudden outbreaks. Rapid and accurate inactivation verification technologies can significantly shorten vaccine time to market and save lives. However, while current mainstream viral inactivation testing methods, such as cell infection, animal experiments, and chicken embryo assays, offer advantages in terms of reliability, they often suffer from long testing cycles, typically requiring 5 to 10 days to reach a conclusion. This time lag fails to meet the demand for rapid decision-making in vaccine production and severely limits emergency response capabilities. The limitations of existing methods primarily lie in their lengthy testing times and lack of real-time performance, hindering the timely advancement of subsequent steps in the vaccine development process. Given the critical need for timeliness, accurately determining viral inactivation efficacy within a short timeframe has become a critical bottleneck that needs to be overcome. The core challenges focus on two key technical factors: one is the rapid detection of the viral antigen structure after inactivation, that is, how to quickly determine whether the antigen has been significantly degraded due to inactivation; the other is the efficient verification of immunogenicity, that is, how to confirm that the inactivated virus can still stimulate an immune response while losing infectivity. The failure to resolve these two factors directly leads to the difficult trade-off between speed and accuracy in traditional methods, which in turn forms a unique technical bottleneck with low detection efficiency. Therefore, how to judge the inactivation effect by detecting changes in the antigen content of the inactivated virus in the shortest time, and at the same time verify whether its immunogenicity is retained, has become a key issue that needs to be urgently addressed in this study. A breakthrough in this issue will provide technical support for accelerating the vaccine preparation process and has important theoretical and application value. Summary of the Invention

[0003] The present invention provides a method for detecting virus inactivation effect by ELISA, which mainly comprises:

[0004] Obtain the inactivated virus sample to be tested, extract the antigen components from the sample, detect the characteristic values ​​of antigen structure changes through high-throughput mass spectrometry analysis technology, and obtain the antigen degradation degree data;

[0005] Based on the antigen degradation data, a pre-established antigen structure database is used for comparison. If the degradation characteristic value exceeds the preset threshold, it is judged that the antigen structure has changed significantly and the inactivation is initially completed.

[0006] Obtain samples that have undergone preliminary inactivation, detect the antigen content change trend through enzyme-linked immunosorbent assay, and obtain content change curve data;

[0007] Based on the content change curve data, a time series analysis algorithm is used to calculate the rate of decrease of the antigen content. If the rate of decrease is lower than the preset threshold, the antigen content is judged to be stable and the inactivation effect is controllable.

[0008] For samples with retained immunogenicity, the residual viral nucleic acid was detected by real-time fluorescence quantitative PCR technology to obtain the degree of nucleic acid degradation data;

[0009] Obtain nucleic acid degradation data and use a support vector machine algorithm to classify the relationship between nucleic acid residues and infectivity. If the classification result shows a loss of infectivity, the inactivation effect is considered to be complete.

[0010] A weighted fusion algorithm is used to integrate multi-dimensional detection results through the antigen content change trend, immunogenicity indicators and nucleic acid degradation degree data to obtain a comprehensive inactivation effect score.

[0011] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0012] The present invention discloses a method for rapid detection and evaluation of inactivated virus samples. This method uses high-throughput mass spectrometry technology to quickly detect changes in antigen structure, and combines it with a pre-established antigen structure database for comparison to achieve rapid judgment of the virus antigen structure after inactivation. At the same time, enzyme-linked immunosorbent assay and real-time fluorescence quantitative PCR technology are used to detect the changing trend of antigen content and the residual amount of viral nucleic acid, respectively, to effectively verify the retention of immunogenicity. The present invention also introduces time series analysis and support vector machine algorithms to accurately evaluate the relationship between antigen content stability and nucleic acid residue and infectivity, solving the trade-off problem of traditional methods in speed and accuracy. Finally, the multi-dimensional detection results are integrated through a weighted fusion algorithm to obtain a comprehensive inactivation effect score, which provides a comprehensive, fast and efficient solution for the quality control of inactivated virus samples, significantly improves detection efficiency, and breaks through the technical bottleneck of the industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 The present invention provides a flow chart of a method for detecting virus inactivation effects using an ELISA method.

[0014] Figure 2 This is a schematic diagram of a method for detecting virus inactivation effect using an ELISA method of the present invention.

[0015] Figure 3 This is another schematic diagram of a method for detecting virus inactivation effect using an ELISA method of the present invention. DETAILED DESCRIPTION

[0016] To further understand the content of the present invention, the present invention is described in detail with reference to the accompanying drawings and examples. The present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the invention are shown in the accompanying drawings.

[0017] like Figure 1-3 In this embodiment, a method for detecting the virus inactivation effect by ELISA may specifically include:

[0018] S101. Obtain an inactivated virus sample to be tested, extract the antigen component from the sample, detect the characteristic value of the antigen structure change through high-throughput mass spectrometry analysis technology, and obtain the antigen degradation degree data.

[0019] The viral particles in the supernatant are treated with lysis buffer to release the internal antigen components. The antigen protein is enriched by ultrafiltration concentration technology to remove small molecule impurities. The enriched antigen is subjected to peptide fingerprint analysis using a high-throughput mass spectrometer and compared with the mass spectrometry characteristic peaks of the uninactivated virus. If the signal intensity of the key antigen epitope peptide is detected to drop by more than the threshold, it is judged to be antigen structural degradation. If the mass spectrometry data is consistent with the downward trend of the OD value of the ELISA, the inactivation effect is confirmed. The quantitative value of the degree of antigen structural damage is calculated based on the proportion of degraded peptides detected by mass spectrometry.

[0020] Specifically, when obtaining the inactivated virus sample to be tested, the virus particles are first collected from the culture medium through an automated sampling system, centrifuged at 10,000g for 10 minutes using a centrifuge, and the supernatant is removed to obtain a virus precipitate. Next, the virus precipitate is treated with a lysis buffer (such as PBS containing 1% Triton X-100) at 4°C for 30 minutes to release the viral antigen components. The macromolecular impurities are removed by filtration through an ultrafiltration membrane (molecular weight cutoff value of 10kDa) to obtain a purified antigen solution. In high-throughput mass spectrometry analysis, liquid chromatography-mass spectrometry (LC-MS / MS) is used to inject the antigen solution into a C18 reverse phase chromatography column, and gradient elution is performed with 1% formic acid aqueous solution and acetonitrile as the mobile phase at a flow rate of 3mL / min. Mass spectrometry detection uses an electrospray ionization source (ESI), data is collected in positive ion mode, and the scanning range is m / z300-2000. The mass spectrometry data is processed by MaxQuant software, and the Andromeda algorithm is used for peptide matching, with the false positive rate (FDR) set to 1%. When analyzing the characteristic values ​​of antigenic structural changes, the principal component analysis (PCA) algorithm is used to reduce the dimensionality of the mass spectrometry data to a three-dimensional space. The projection value of each sample on the principal component is calculated to assess the degree of antigen degradation. By comparing the projection values ​​of different samples, the degree of antigen degradation is quantified. For example, a sample with a projection value of -5 on the first principal component indicates that its antigenic structure has undergone significant changes. Finally, the antigen degradation data is stored in a database for subsequent analysis and application.

[0021] S102. Based on the antigen degradation degree data, a pre-established antigen structure database is used for comparison. If the degradation characteristic value exceeds a preset threshold, it is determined that the antigen structure has changed significantly and the inactivation is initially completed.

[0022] The difference in OD values ​​between the inactivated and non-inactivated groups is calculated. If this difference exceeds the preset antigen degradation threshold, the antigen structure is considered to have undergone significant changes. The degradation signature is then matched against the antigen structure database. If the match matches the inactivated antigen profile, viral inactivation is considered preliminarily complete. If the difference does not exceed the threshold, the test is repeated or verified using a cell infection assay.

[0023] Specifically, in the processing of antigen degradation degree data, the sample is first separated and detected by high performance liquid chromatography (HPLC) to obtain the degradation characteristic value of the antigen.

[0024] For example, a C18 reverse phase chromatography column is used, the mobile phase is a gradient elution of acetonitrile and water, the flow rate is 0 ml per minute, the detection wavelength is 280 nm, and the acquisition time is 30 minutes. Through chromatogram analysis, the degradation rate of the antigen is calculated. If the degradation rate reaches more than 50%, it is preliminarily judged that the antigen structure may have changed significantly. Then, the obtained degradation characteristic value is compared with the pre-established antigen structure database. The database contains the degradation characteristic value ranges of multiple antigens, such as the degradation characteristic value range of antigen A is 1 to 3, and that of antigen B is 4 to 6. The Euclidean distance algorithm is used to calculate the similarity between the input data and each antigen in the database. If the calculated distance value exceeds the preset threshold value of 5, it is determined that the antigen structure has changed significantly.

[0025] For example, if the input data has a degradation signature value of 7 and a distance value of 6 from antigen A, both exceeding the threshold, it is determined that the structure of antigen A has undergone significant changes. Finally, based on this determination, inactivation is preliminarily completed and a corresponding report is generated, including the antigen name, degradation signature value, distance value, and the determination result, for further analysis and processing.

[0026] S103. Obtain the sample after preliminary inactivation, detect the antigen content change trend through enzyme-linked immunosorbent assay, and obtain content change curve data.

[0027] ELISA plates were coated with specific antiviral monoclonal antibodies, washed overnight at 4°C, and non-specific binding sites were blocked by adding 5% skim milk. The diluted inactivated and non-inactivated samples were added to the corresponding wells and incubated at 37°C for 1 hour. The enzyme-labeled secondary antibody was added and incubated at 37°C for 1 hour. TMB colorimetric substrate was added and reacted in the dark for 10-15 minutes. After the reaction was terminated, the absorbance at 450nm was measured. The OD values ​​of the inactivated and non-inactivated groups were recorded, and the trend of changes in the antigen content was calculated. If the OD value of the inactivated group was significantly lower than that of the non-inactivated group, the antigen structure was judged to be destroyed; if the OD values ​​were similar, the antigen epitope was judged to be retained. The antigen content change curve was drawn based on the OD value data to analyze the inactivation effect.

[0028] Specifically, after obtaining the sample that has been initially inactivated, the trend of changes in antigen content is first detected by enzyme-linked immunosorbent assay (ELISA). During the experiment, a 96-well plate was used for sample processing, 100 microliters of sample was added to each well, and incubated at 37 degrees Celsius for 1 hour. Subsequently, the well plate was washed 3 times with washing buffer for 5 minutes each to remove unbound antigens. Next, 100 microliters of enzyme-labeled secondary antibody was added and incubated at 37 degrees Celsius for 30 minutes. After washing the well plate again, 100 microliters of substrate solution was added and reacted at room temperature in the dark for 15 minutes. Finally, 50 microliters of stop solution was added, and the absorbance value was read at a wavelength of 450 nanometers using a microplate reader. The absorbance value was converted to antigen content using the standard curve method to obtain the content change curve data.

[0029] For example, the antigen content of a sample at 0 hours, 1 hour, 2 hours, and 3 hours was 100 μg / ml, 80 μg / ml, 60 μg / ml, and 40 μg / ml, respectively. Linear regression analysis yielded the linear equation for the change in antigen content over time: y = -20x + 100, where y is the antigen content and x is the time. Further analysis revealed that the antigen content decreased by 20 μg / ml every hour, indicating that the inactivation process effectively reduced the antigen content. This quantitative analysis method allows for accurate assessment of the inactivation effect and provides reliable data support for subsequent experiments.

[0030] S104. Based on the content change curve data, a time series analysis algorithm is used to calculate the rate of decrease of the antigen content. If the rate of decrease is lower than a preset threshold, it is determined that the antigen content is stable and the inactivation effect is controllable.

[0031] A time series analysis algorithm is used to process OD value data at different time points and calculate the rate of decrease in antigen content. If the rate of decrease is below a preset threshold, the antigen content is considered stable. Based on the stability of the antigen content, the inactivation effect is determined to be controllable. If the rate of decrease is above the preset threshold, combined with the specific antibody recognition results, the antigen structure is analyzed to determine whether it has been disrupted. The inactivation effect is further verified based on the retention of immunogenicity.

[0032] Specifically, for the antigen content change curve data, the data was first preprocessed using the time series analysis method to remove noise and smooth the curve.

[0033] For example, the moving average method is used to smooth the hourly antigen content data with a window size of 3 hours to obtain a smoothed data sequence. Next, the difference method is used to calculate the rate of change of the antigen content. The specific formula is ΔC / Δt, where ΔC is the difference in antigen content between adjacent time points and Δt is the time interval. Assuming that within a certain time period, the antigen content drops from 1000 units to 950 units, and the time interval is 1 hour, the rate of decline is 50 units / hour. In order to judge whether the antigen content is stable, a preset threshold is set to 30 units / hour. If the calculated rate of decline is lower than the threshold, such as 25 units / hour, the antigen content is judged to be stable and the inactivation effect is controllable. Further, the trend of change of antigen content over time is fitted by linear regression analysis to obtain the regression equation y=-25x+1000, where y is the antigen content and x is the time. Through this equation, the antigen content at future time points can be predicted to ensure that the inactivation effect is continuously controllable. Throughout the analysis process, an automated algorithm is used to process and analyze the data in real time to ensure the accuracy and timeliness of the results.

[0034] S105. For samples with retained immunogenicity, the residual viral nucleic acid is detected by real-time fluorescence quantitative PCR technology to obtain the nucleic acid degradation degree data.

[0035] Based on the ELISA test results, the immunogenicity retention samples with an OD value difference of less than 10% were screened. Virus-specific primer design software was used to generate primer probe combinations targeting conserved regions of the virus. Total RNA was extracted from the immunogenicity retention samples and reverse transcribed into cDNA templates. A real-time fluorescence quantitative PCR reaction system was configured, and SYBR Green fluorescent dye was added. The PCR amplification program was set up: pre-denaturation at 95°C for 3 minutes, 40 cycles of 95°C for 15 seconds + 60°C for 30 seconds. The fluorescence signal of each cycle was collected and the Ct value was recorded. The ΔCt values ​​of the non-inactivated group and the inactivated group were calculated, and the relative residual amount of nucleic acid was obtained by the 2^-ΔΔCt method. If the residual amount of nucleic acid is higher than the preset threshold (such as 5%), the inactivation process parameter adjustment instruction is triggered. A bivariate model of OD value and nucleic acid degradation rate was established to output a comprehensive evaluation report of the inactivation effect.

[0036] Specifically, for immunogenicity-retaining samples, the cell membrane is first lysed at 25°C using a lysis buffer (e.g., PBS containing 1% Triton X-100) for 15 minutes to release viral nucleic acids. Following lysis, nucleic acids are extracted using a magnetic bead method. Silicon-based magnetic beads (1 μm diameter) are adsorbed in a binding buffer (pH 2). Impurities are removed by three washes (using 80% ethanol) and finally eluted with 50 μL of nuclease-free water. The extracted nucleic acids are immediately analyzed by real-time fluorescence quantitative PCR using TaqMan probes. Specific primers targeting conserved viral regions (e.g., ORF1 ab gene) are designed (forward primer 5'-ATGTTTGTTTTTCTTGTTT-3', reverse primer 5'-TGGTGGTACCAGACGTAG-3'). The probe sequence is 5'-FAM-CCGTCTGCGGTATGTGG-BHQ1-3'. The reaction system consisted of 20 μL of 2× premix, 8 μM primers, 2 μM probe, and 5 μL template. The amplification program was set to 95°C for 30 seconds, followed by 45 cycles of 95°C for 5 seconds and 60°C for 30 seconds. Nucleic acid concentration was quantified using a standard curve (10^1-10^7 copies / μL serial dilutions). Degradation rates were calculated using the ΔΔCt algorithm, using the formula: degradation rate (%) = (1-2^(-ΔΔCt)) × 100, where ΔΔCt = (Ct of the test sample - Ct of the standard) - (Ct of the undegraded control - Ct of the standard). Data analysis was performed by comparing the threshold cycle (Ct value) with the standard curve. Degradation was considered positive when the Ct value was >35 or the amplification curve showed no plateau. Samples were considered invalid when the degradation rate was ≥50%. The entire process was performed using an automated nucleic acid extraction instrument and PCR analysis software, and data were uploaded directly to the LIMS system for archiving.

[0037] S106. Obtain nucleic acid degradation degree data, and use a support vector machine algorithm to classify the relationship between nucleic acid residues and infectivity. If the classification result shows that infectivity is lost, it is determined that the inactivation effect is completely achieved.

[0038] The OD value data of the inactivated and non-inactivated groups were used to calculate the antigen degradation ratio to determine whether the inactivation resulted in significant destruction of the antigen. Based on the antigen degradation ratio, the nucleic acid degradation degree data was extracted. The nucleic acid degradation degree data was classified using the support vector machine algorithm to establish a model for the relationship between nucleic acid residues and infectivity. If the classification results showed that the nucleic acid residues were below the infectivity threshold, the inactivation effect was judged to be completely complete. If the nucleic acid residues were above the infectivity threshold, the inactivation effect was further verified in combination with other detection methods. Based on the verification results, the classification model of the support vector machine algorithm was updated to optimize the accuracy of the judgment of the relationship between nucleic acid residues and infectivity.

[0039] Specifically, when obtaining data on the degree of nucleic acid degradation, the samples are first sequenced using high-throughput sequencing technology to obtain nucleic acid sequence information, and bioinformatics tools such as FastQC are used for quality control to ensure the accuracy and reliability of the sequencing data. Next, the sequencing data is compared with the reference genome using the BLAST algorithm to calculate the nucleic acid degradation index. For example, by calculating the sequence alignment score and coverage, a degradation index of 85 is obtained. Subsequently, the support vector machine algorithm is used to classify the relationship between nucleic acid residues and infectivity. The radial basis function is selected as the kernel function, and the penalty parameter C is set to 0. The model is trained and cross-validated. The classification results show that the accuracy of loss of infectivity reaches 95%. If the classification results show that infectivity is lost, the amount of nucleic acid residue is further detected by real-time fluorescence quantitative PCR technology. For example, if the amount of nucleic acid residue is detected to be less than 10 copies / μL, combined with the support vector machine classification results, it is judged that the inactivation effect is completely completed. The entire process uses automated data processing and analysis to ensure the objectivity and scientificity of the results.

[0040] S107. Through the antigen content change trend, immunogenicity index and nucleic acid degradation degree data, a weighted fusion algorithm is used to integrate the multi-dimensional detection results to obtain a comprehensive inactivation effect score.

[0041] The rate of change in antigen content was calculated. If the OD value of the inactivated group decreased by more than 90%, the antigen structure was considered destroyed. If the OD value did not decrease to the threshold, the inactivation effect was determined based on the nucleic acid degradation data. The Ct value of the qPCR test was obtained and the nucleic acid degradation rate was calculated. If the Ct value increased for more than 5 cycles, the nucleic acid was considered significantly degraded. The integrity of the antigen epitope was tested using a competitive ELISA. If the inhibition rate was less than 10%, the epitope was considered intact. Antibody titer data from animal immunization experiments were integrated. If the titer reached the preset protection threshold, immunogenicity was considered retained. The data was standardized according to the inactivation method parameters to eliminate the interference of different inactivation conditions on the test results. A weighted fusion algorithm was used to normalize the antigen content change rate (weight 5), immunogenicity data (weight 3), and nucleic acid degradation rate (weight 2). The comprehensive inactivation effect score was calculated using the following formula: score = 5 × (1-antigen content change rate) + 3 × immunogenicity compliance coefficient + 2 × nucleic acid degradation rate. If the comprehensive score is ≥8, the inactivation effect is considered satisfactory; if the score is <8 but ≥6, the retest process is triggered; if the score is <6, the inactivation is considered unsuccessful. A multi-dimensional data report is output, including a trend chart of antigen content, immunogenicity indicators, nucleic acid degradation curve, and comprehensive score results.

[0042] Specifically, in the analysis of antigen content change trend, the antigen concentration in the sample was detected by enzyme-linked immunosorbent assay (ELISA). For example, the antigen content of a certain inactivated vaccine at 0 hours, 24 hours, and 48 hours was 120 ng / mL, 80 ng / mL, and 40 ng / mL, respectively. The attenuation curve was fitted using a linear regression model, and the antigen half-life was obtained to be 25 hours. The goodness of fit R 2 =98. Immunogenicity was assessed using neutralizing antibody titers, with 30 samples in each experimental and control groups. The geometric mean titer (GMT) in the experimental group 14 days after vaccination was 256 (95% CI: 182-360), significantly higher than the 32 (95% CI: 24-42) in the control group (p < 0.01). Nucleic acid degradation was measured by real-time fluorescence quantitative PCR, and the viral RNA copy number decreased from an initial 1×10 8 The copies / μL dropped to 1×10 after 72 hours 3 The entropy weight method was used to determine the weight of each indicator, with antigen stability accounting for 35%, immunogenicity accounting for 45%, and nucleic acid degradation accounting for 20%. The weighted scoring model S = 35×(1-antigen decay rate) + 45×log2(GMT / baseline) + 20×log 10 The final comprehensive score of the sample was 87 points (out of 100), of which antigen stability contributed 32 points, immunogenicity contributed 43 points, and nucleic acid degradation contributed 12 points. K-means clustering was used to divide the scoring results into three levels: efficient inactivation (>80 points), moderate inactivation (60-80 points), and low inactivation (<60 points). This sample was classified as the efficient inactivation group.

[0043] With the above embodiments of the present invention as inspiration, and through the above description, relevant personnel can make various changes and modifications without departing from the technical scope of this invention. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A method for detecting virus inactivation effect by ELISA, characterized in that: The method comprises: Obtain the inactivated virus sample to be tested, extract the antigen component from the sample, detect the characteristic value of the antigen structure change through high-throughput mass spectrometry analysis technology, and obtain the antigen degradation degree data; Based on the antigen degradation data, a pre-established antigen structure database is used for comparison. If the degradation characteristic value exceeds the preset threshold, it is judged that the antigen structure has changed significantly and the inactivation is initially completed. Obtain samples that have undergone preliminary inactivation, detect the antigen content change trend through enzyme-linked immunosorbent assay, and obtain content change curve data; Based on the content change curve data, a time series analysis algorithm is used to calculate the rate of decrease of the antigen content. If the rate of decrease is lower than the preset threshold, the antigen content is judged to be stable and the inactivation effect is controllable. For samples with retained immunogenicity, the residual viral nucleic acid was detected by real-time fluorescence quantitative PCR technology to obtain the degree of nucleic acid degradation data; Obtain nucleic acid degradation data and use a support vector machine algorithm to classify the relationship between nucleic acid residues and infectivity. If the classification result shows a loss of infectivity, the inactivation effect is considered to be complete. A weighted fusion algorithm is used to integrate multi-dimensional detection results through the antigen content change trend, immunogenicity indicators and nucleic acid degradation degree data to obtain a comprehensive inactivation effect score.

2. The method according to claim 1, characterized in that The method comprises obtaining an inactivated virus sample to be tested, extracting antigen components from the sample, detecting characteristic values ​​of antigen structure changes by high-throughput mass spectrometry analysis technology, and obtaining antigen degradation degree data, including: The viral particles in the supernatant were treated with lysis buffer to release the internal antigenic components; Ultrafiltration concentration technology is used to enrich antigen proteins and remove small molecule impurities; A high-throughput mass spectrometer was used to perform peptide fingerprint analysis on the enriched antigens and compare the mass spectrometry characteristic peaks of the uninactivated virus; If the signal intensity of the key antigen epitope peptide segment is detected to decrease by more than the threshold, it is determined to be antigen structure degradation; If the mass spectrometry data is consistent with the decreasing trend of the OD value of ELISA, the inactivation effect is confirmed; The quantitative value of the degree of antigen structure destruction was calculated based on the proportion of degraded peptides detected by mass spectrometry.

3. The method according to claim 1, characterized in that The antigen degradation degree data is compared with a pre-established antigen structure database. If the degradation characteristic value exceeds a preset threshold, it is determined that the antigen structure has changed significantly and the inactivation is initially completed, including: The difference ratio of the OD values ​​between the inactivated group and the non-inactivated group was calculated. If the difference ratio exceeded the preset antigen degradation threshold, it was determined that the antigen structure had changed significantly. The degradation characteristic values ​​are matched against the antigen structure database. If the matching result matches the inactivated antigen characteristic spectrum, the virus inactivation is determined to be preliminarily completed. If the difference ratio does not exceed the threshold, retest or combine with cell infection method for verification.

4. The method according to claim 1, wherein The sample after preliminary inactivation is obtained, and the antigen content change trend is detected by enzyme-linked immunosorbent assay to obtain content change curve data, including: ELISA plates were coated with specific antiviral monoclonal antibodies, incubated at 4°C overnight, washed, and nonspecific binding sites were blocked by adding 5% skim milk; The diluted inactivated and non-inactivated samples were added to the corresponding wells and incubated at 37°C for 1 hour; Add enzyme-labeled secondary antibody and incubate at 37°C for 1 hour; Add TMB colorimetric substrate, react in the dark for 10-15 minutes, and measure the absorbance at 450 nm after stopping the reaction; Record the OD values ​​of the inactivated and non-inactivated groups and calculate the trend of antigen content changes; If the OD value of the inactivated group is significantly lower than that of the non-inactivated group, it is judged that the antigen structure is destroyed; If the OD values ​​are similar, it is judged that the antigen epitope is retained; The antigen content change curve was drawn according to the OD value data to analyze the inactivation effect.

5. The method according to claim 1, characterized in that The method uses a time series analysis algorithm to calculate the antigen content decrease rate based on the content change curve data. If the decrease rate is lower than a preset threshold, it is judged that the antigen content is stable and the inactivation effect is controllable, including: The OD value data at different time points were processed using a time series analysis algorithm to calculate the rate of decrease in antigen content; If the rate of decrease of the antigen content is lower than the preset threshold, the antigen content is judged to be stable; According to the stability of antigen content, the inactivation effect is determined to be controllable; If the rate of decrease of antigen content is higher than the preset threshold, combined with the specific antibody recognition results, it is analyzed whether the antigen structure is destroyed; The inactivation effect was further verified based on the retention of immunogenicity.

6. The method according to claim 1, characterized in that For samples with retained immunogenicity, the residual amount of viral nucleic acid is detected by real-time fluorescence quantitative PCR technology to obtain nucleic acid degradation degree data, including: Based on the ELISA test results, immunogenic retention samples with OD value differences less than 10% were screened; Virus-specific primer design software was used to generate primer-probe combinations targeting conserved viral regions; Total RNA was extracted from immunogenicity-retained samples and reverse transcribed into cDNA templates; Configure the real-time fluorescence quantitative PCR reaction system and add SYBRGreen fluorescent dye; The PCR amplification program was set as follows: pre-denaturation at 95°C for 3 min, followed by 40 cycles of 95°C for 15 s and 60°C for 30 s. The fluorescence signal of each cycle was collected and the Ct value was recorded; The ΔCt values ​​of the non-inactivated group and the inactivated group were calculated, and the relative residual amount of nucleic acid was obtained by the 2^-ΔΔCt method; If the residual nucleic acid level is higher than a preset threshold (e.g., 5%), an instruction to adjust the inactivation process parameters is triggered; A bivariate model of OD value and nucleic acid degradation rate was established to output a comprehensive evaluation report on the inactivation effect.

7. The method according to claim 1, characterized in that The method of obtaining nucleic acid degradation degree data and using a support vector machine algorithm to classify the relationship between nucleic acid residues and infectivity, and determining that the inactivation effect is completely achieved if the classification result shows that infectivity is lost, includes: The OD value data of the inactivated and non-inactivated groups were used to calculate the antigen degradation ratio to determine whether the inactivation resulted in significant destruction of the antigen; According to the antigen degradation ratio, the nucleic acid degradation degree data was extracted; The support vector machine algorithm was used to classify the nucleic acid degradation degree data and establish a model of the relationship between nucleic acid residue and infectivity; If the classification results show that the nucleic acid residue is below the infectivity threshold, the inactivation effect is considered to be completely completed; If the nucleic acid residue is higher than the infectivity threshold, other detection methods are used to further verify the inactivation effect; Based on the verification results, the classification model of the support vector machine algorithm was updated to optimize the accuracy of the judgment of the relationship between nucleic acid residues and infectivity.

8. The method according to claim 1, characterized in that The method uses a weighted fusion algorithm to integrate multi-dimensional test results based on antigen content change trends, immunogenicity indicators, and nucleic acid degradation degree data to obtain a comprehensive inactivation effect score, including: Calculate the rate of change of antigen content. If the OD value of the inactivated group decreases by more than 90%, it is determined that the antigen structure is destroyed. If the OD value decreases but does not reach the threshold, the inactivation effect is judged in combination with the nucleic acid degradation data; Obtain the Ct value of qPCR detection and calculate the nucleic acid degradation rate. If the Ct value increases for more than 5 cycles, it is determined that the nucleic acid is significantly degraded; Competitive ELISA was used to detect the integrity of the antigen epitope. If the inhibition rate was less than 10%, the epitope was judged to be intact; Integrate antibody titer data from animal immunization experiments. If the titer reaches the preset protection threshold, it is determined that immunogenicity is retained; Standardize data according to inactivation method parameters to eliminate the interference of different inactivation conditions on test results; A weighted fusion algorithm was used to normalize the antigen content change rate (weight 5), immunogenicity data (weight 3), and nucleic acid degradation rate (weight 2); The comprehensive inactivation effect score was calculated using the following formula: score = 5 × (1-antigen content change rate) + 3 × immunogenicity compliance coefficient + 2 × nucleic acid degradation rate; If the comprehensive score is ≥8, the inactivation effect is considered qualified; If the score is less than 8 but ≥ 6, the re-examination process is triggered; If the score is <6, the inactivation is considered to have failed; Output multidimensional data reports, including antigen content trend chart, immunogenicity index and nucleic acid degradation curve and comprehensive scoring results.

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