Opioid individualized analgesia evaluation method and system based on gene polymorphism

Abstract

The present application discloses a method and system for individualized analgesia evaluation of opioid drugs based on gene polymorphism, which relates to the field of digital medical technology. An evidence-based evaluation is carried out on SNP loci related to opioid analgesia to establish a recommendation strength standard; SNP loci related to the analgesic effect of opioid drugs detected in all databases and samples are screened, and pain influence parameters of SNPs in different levels are respectively set according to the classification levels of the recommendation strength standard; a drug dosage prediction scheme is established according to the pain influence parameters and the number of SNP variations, and the drug dosage prediction scheme is adjusted based on the analgesic attribute characteristics corresponding to the patient to be evaluated determined by the overall score of the patient to be evaluated under different clinical variables. By incorporating the individualized information of the patient and the pain assessment data and adjusting the drug dosage according to the SNP locus variation situation, the clinical medication decision-making of opioid analgesic drugs based on gene polymorphism is realized, and intelligent medication decision support for drug dosage can be provided according to the detection results.

Description

Technical Field

[0001] The present application relates to the field of digital medical technology, and in particular to a method and system for evaluating individualized analgesia of opioid drugs based on gene polymorphism. Background Art

[0002] Opioids are the main analgesic drugs. However, clinical practice has found that due to differences in genetic polymorphism, the analgesic effect of opioids varies greatly from person to person. Some patients with the same basic conditions and pain assessments use the same dose of drugs, but some patients experience insufficient analgesia or adverse drug reactions such as nausea, vomiting, constipation, and respiratory depression. Clinical cases of opioid tolerance are also common due to excessively rapid increases in doses. The above problems complicate the clinical application of opioids.

[0003] Chinese patent CN113707263B discloses a method, device and computer equipment for evaluating drug effectiveness based on population division. By dividing the sample population into multiple subgroups and determining the target subgroup to which the patient to be evaluated belongs from the multiple subgroups, it is possible to evaluate whether a specific drug is effective for the patient to be evaluated based on the effectiveness parameters corresponding to the target subgroup. The above scheme can accurately evaluate the effectiveness of a specific drug for individual patients, but does not consider the influencing factors of individual gene polymorphism. Chinese patent CN115547513B discloses a kit for realizing a method for predicting the dosage of intrathecal opioid analgesics, by detecting the genotype of each SNP (single nucleotide polymorphism) site of the patient's ABCB1, and performing a combined analysis of the genotype of each site to determine the dosage of intrathecal opioid analgesics required by the patient. The above patent takes into account the influence of individual gene polymorphism on the dosage of opioid drugs, mainly based on the limited primer pairs in the kit to achieve the determination of the limited gene sites of the ABCB1 gene.

[0004] Intelligent medication decision support has been a research hotspot in the field of medical informationization in recent years. How to achieve intelligent medication decision support based on individual genetic polymorphism is a technical problem that needs to be solved urgently in the field of medical informationization. Summary of the invention

[0005] In order to solve the above technical problems, this application proposes the following technical solutions:

[0006] In a first aspect, the present application provides a method for evaluating individualized analgesia of opioid drugs based on gene polymorphism, comprising:

[0007] Evidence-based evaluation of opioid analgesia-related SNP sites to establish recommendation strength standards;

[0008] Screen all the SNP sites related to the analgesic effect of opioids detected in the databases and samples, and set the pain impact parameters of SNPs in different levels according to the classification levels of the recommended strength criteria;

[0009] Establish a drug dosage prediction plan based on the pain impact parameters and the number of SNP variations, and adjust the drug dosage prediction plan based on the analgesic attribute characteristics corresponding to the patient to be evaluated determined by the overall score of the patient to be evaluated under different clinical variables.

[0010] In a possible implementation manner, the establishment of the recommended strength criteria for the SNP sites related to the analgesia of opioids based on evidence-based evaluation includes:

[0011] Based on the sample data of each patient in the evidence-based materials under different clinical variables, evaluate the analgesic effectiveness parameters of opioids for the sample according to their opioid use information and pain relief degree, and the clinical variables include: tumor type, pain score, and basic information;

[0012] According to the analgesic effectiveness parameters corresponding to the sample, divide them into a 6-level scoring system of level 1A, level 1B, level 2A, level 2B, level 3, and level 4 according to the parameter values to evaluate the importance of different opioid analgesic SNP sites.

[0013] In a possible implementation manner, the screening of all the SNP sites related to the analgesic effect of opioids detected in the databases and samples, and setting the pain impact parameters of SNPs in different levels according to the classification levels of the recommended strength criteria includes:

[0014] Determine the increase or decrease multiple of the drug demand, the number of allele types, and the allele frequency;

[0015] Establish the pain impact parameter formula of the SNP , is the increase or decrease multiple of the drug demand, where an increase is a positive value and a decrease is a negative value, is the number of allele types, is the frequency of the

[0016] In a possible implementation manner, the establishment of a drug dosage prediction plan based on the pain impact parameters and the number of SNP variations includes:

[0017] First, select the target population for detection at the SNP sites with high correlation and strong recommended strength;

[0018] The target population is respectively selected from cancer pain patients using morphine and oxycodone. All patients need to be included in the same cancer type and the same stage of cancer pain patients who have been taking drugs for a long time and have good pain control;

[0019] Statistically analyze the pain relief situation before and after the use of opioid drugs in patients, the dosage of opioid drugs, and the occurrence of nausea, vomiting, and respiratory depression in patients. Use correlation statistical analysis to evaluate the correlation between the polymorphism of the above gene loci and drug dosage and drug adverse reactions;

[0020] Use the stepwise two-way selection method for multiple linear regression analysis of the dosage situation to establish a dose prediction formula based on the sensitivity of gene loci.

[0021] In a possible implementation, the dose prediction formula is: Opioid analgesic demand = (1.356 + 0.530) * (SNP variant number of gene locus classification 1 * PI + 0.283) * (SNP variant number of gene locus classification 2 * PI + 0.164) *... * (SNP variant number of gene locus classification m * PI + 0.155) * (pain sensation delay time * pain score of different tumor stages - 0.006) * body weight, where the unit of opioid analgesic demand is μg / kg.

[0022] In a possible implementation, adjusting the drug dose prediction plan according to the analgesic attribute characteristics corresponding to the patient to be evaluated determined by the overall score of the patient to be evaluated under different clinical variables includes:

[0023] First, select the patient to be evaluated for grouping. The experimental group is first given genomic testing and then re-grouped according to the detected genotype;

[0024] For each group, use the dose prediction formula and the basic situation of the patient to formulate the initial administration dose and administration frequency of the analgesic drug according to different genotypes. At the same time, pre-treat patients with genes sensitive to adverse reactions or replace the drug;

[0025] Finally, during the specific evaluation period, determine the titration duration, pain relief degree, and the incidence and degree of adverse reactions of the analgesic drug in each group, judge the actual application effect of gene locus polymorphism in the clinical application of opioid drugs, and correct the dose prediction formula.

[0026] In a possible implementation, the correction value for correcting the dose prediction formula Is expressed as:

[0027]

[0028] Among them, N is the total number of alleles, Is the Allele frequency adjacent to the

[0029] In a possible implementation, the gene loci with relevance after screening are comprehensively evaluated again, and the Delphi expert consultation method is used to assign values according to the metabolic-related genomic loci, pain relief effect-related gene loci, and drug adverse reaction-related loci. Finally, the gene locus with the greatest relevance and the highest score is selected as the basic detection combination; the detection criteria, clinical application consensus, and knowledge base rules are determined; and finally, an opioid analgesic clinical medication decision-making system that incorporates the patient's individual information, genomic information, and pain assessment data is formed.

[0030] Second, the embodiments of the present application provide an individualized analgesic evaluation system for opioid drugs based on gene polymorphisms, including:

[0031] A standard determination module for establishing a recommended strength standard by evidence-based evaluation of SNP loci related to opioid analgesia;

[0032] An influence parameter setting module for screening SNP loci related to the analgesic effect of opioid drugs detected in all databases and samples, and setting pain influence parameters for SNPs in different levels according to the classification levels of the recommended strength standard;

[0033] An analgesic evaluation module for establishing a drug dosage prediction scheme based on the pain influence parameters and the number of SNP variations, and adjusting the drug dosage prediction scheme based on the analgesic attribute characteristics corresponding to the patient to be evaluated determined by the overall score of the patient to be evaluated under different clinical variables.

[0034] Third, the embodiments of the present application provide an individualized analgesic evaluation device for opioid drugs based on gene polymorphisms, including:

[0035] A processor;

[0036] A memory;

[0037] And a computer program, where the computer program is stored in the memory, and the computer program includes instructions that, when executed by the processor, cause the individualized analgesic evaluation device for opioid drugs based on gene polymorphisms to execute the individualized analgesic evaluation method for opioid drugs based on gene polymorphisms according to any possible implementation manner of the first aspect.

[0038] In the embodiments of the present application, the association between different genotypes of cancer pain patients and the demand for opioid drugs was evaluated based on evidence, individual patient information and pain assessment data were incorporated, and the dosage was adjusted according to the SNP locus variation, realizing clinical medication decision-making of opioid analgesic drugs based on gene polymorphism, and intelligent medication decision support for providing dosage according to the test results. It can significantly improve the standardization degree of the use of opioid drugs in cancer pain patients, especially in patients with advanced cancer pain, reduce the occurrence of non-standard use and abuse of opioid analgesic drugs, and benefit cancer patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 FIG. is a schematic flow chart of an individualized analgesic evaluation method for opioid drugs based on gene polymorphism provided by an embodiment of the present application;

[0040] Figure 2 FIG. is a schematic molecular diagram related to the action of opioid analgesics provided by an embodiment of the present application;

[0041] Figure 3 FIG. is a schematic diagram of an individualized analgesic evaluation system for opioid drugs based on gene polymorphism provided by an embodiment of the present application;

[0042] Figure 4 FIG. is a schematic diagram of an individualized analgesic evaluation device for opioid drugs based on gene polymorphism provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] The following describes the present solution in conjunction with the accompanying drawings and specific embodiments.

[0044] See Figure 1 , the individualized analgesic evaluation method for opioid drugs based on gene polymorphism provided by the embodiments of the present application includes:

[0045] S101, establishing a recommendation strength standard by evidence-based evaluation of SNP loci related to opioid analgesia.

[0046] Based on the sample data of each patient in different clinical variables in the evidence-based materials, the analgesic effectiveness parameters of opioid drugs for the samples are evaluated according to their use information and pain relief degree of opioid analgesics. The clinical variables include: tumor type, pain score and basic information.

[0047] According to the relevant pathways of the molecular mechanism of opioid analgesia, preliminary gene locus screening and analysis have been carried out on the associated molecules, and the relevant loci with more research evidence included are preliminarily screened and evaluated, which are specifically divided into three types of gene loci related to drug metabolism, transporters and drug targets, such as Figure 2 and Table 1, Figure 2Among them, CYP is cytochrome P450 enzyme, UGT is UDP-glucuronosyltransferase, GIRK is G protein-activated inwardly rectifying potassium ion, cAMP is cyclic adenosine monophosphate, and PKA is protein kinase A. Next, a systematic evidence-based evaluation will be carried out.

[0048] Table 1 Screening of Studies on Opioid Pharmacogenomic Related Loci

[0049]

[0050] At the same time, an evidence-based evaluation of the correlation of loci related to opioid adverse reactions was carried out, as shown in Table 2. For the above loci, the drug adverse reaction sentinel surveillance system was used to retrieve the population with various drug adverse reactions after using opioids for analysis and evaluation. At the same time, gene detection was carried out on the relevant associated loci. Two opioid adverse reaction-associated genes with statistical significance were screened and detected. One of them was CYP2D6, which was related to constipation caused by opioids. Constipation is an intolerable adverse reaction of opioid analgesics and generally persists during opioid treatment. One of the possible reasons is that after the concentration of opioids increases, it causes changes in the patient's bowel habits or bowel patterns. By retrieving 30 patients with severe constipation after using oxycodone from 2018 to 2021, the CYP2D6 gene typing was detected by the XL-PCR method. The IM type and NM type were significantly more than the PM type, and the IM type showed a tendency to be more than the NM type, as shown in Table 3. Subsequently, it is necessary to increase the sample size for stratified evaluation of the correlation.

[0051] Table 2 Summary of Studies on the Association between Gene Loci and Opioid Adverse Reactions

[0052]

[0053] Table 3 CYP2D6 Gene Typing in 30 Patients with Constipation Caused by Opioids

[0054]

[0055] At the same time, the intestinal function index questionnaire was used to evaluate the severity of constipation among different groups for the above-mentioned groups. Eight patients in the IM group and six patients in the NM group with no differences in gender, age, KPS score, BMI, liver and kidney functions, metastasis status, tumor type, and oxycodone dosage were selected to evaluate the correlation. The average BFI score of the IM group was significantly higher than that of the NM group, as shown in Table 4.

[0056] Table 4 Evaluation of the Severity of Constipation Caused by Opioids and CYP2D6 Genotype

[0057]

[0058] Adopt the evidence-based medicine evaluation method, fully search clinical guidelines related to cancer pain treatment, and drug genomics research databases (such as PharmGKB, 1000Genomes, CYP database, SNPedia, WarfarinDosing, SNPs3D, ugt-pharmacogenomics, etc.), fully determine candidate genes related to pain, candidate genes related to the efficacy of opioid drugs, and candidate genes related to toxic reactions, especially respiratory depression, and determine the analgesic effectiveness of different drugs at different gene loci. Establish a standard for recommendation strength, list the recommendation strength: a 6-level scoring system including 1A level, 1B level, 2A level, 2B level, 3 level, and 4 level, to evaluate the importance of analgesic SNP loci of different opioid drugs.

[0059] The above are the key points of the embodiments of this application. The focus is on comprehensively screening candidate genes, strictly following the evidence-based medicine evaluation method, collecting candidate genes related to pain and gene loci related to the efficacy and adverse reactions of opioid drugs, establishing a target gene group database, and evaluating the association strength.

[0060] S102, screen SNP loci related to the analgesic effect of opioid drugs detected in all databases and samples, and set pain impact parameters for SNPs in different levels according to the classification levels of the recommended strength standard.

[0061] Determine the increase or decrease multiple of drug demand, the number of allele types, and the frequency of alleles. Establish the pain impact parameter formula for the SNP , is the increase or decrease multiple of drug demand, where an increase is a positive value and a decrease is a negative value, is the number of allele types, is the frequency of the

[0062] S103, establish a drug dosage prediction plan based on the pain impact parameter and the SNP variance number, and adjust the drug dosage prediction plan based on the analgesic attribute characteristics corresponding to the patient to be evaluated determined by the overall score of the patient to be evaluated under different clinical variables.

[0063] In this embodiment, sites with high correlation and strong recommendation intensity (1A level, 1B level) are first selected to detect the target population. For example, gene sites related to drug metabolism: CYP3A4*18B; gene sites related to drug efficacy: u-opioid receptor gene site (OPRMl A118G), dopamine transporter gene site (DAT1 VNTR), ABCB1 gene, etc. 100 cancer pain patients using morphine and oxycodone respectively are selected as the target population. All patients need to be included in the same cancer type and the same stage of cancer pain patients who have been taking drugs for a long time and have good pain control (lung cancer stage III is planned to be selected) for the study. The pain relief situation before and after opioid drug use, the dosage of opioid drugs, and the occurrence of nausea, vomiting, and respiratory depression in patients are counted. The correlation between the polymorphisms of the above gene sites and drug dosage and drug adverse reactions is evaluated by correlation statistical analysis, and a stepwise two-way selection method is used for multiple linear regression analysis of the dosage situation to establish a dose prediction formula based on gene site sensitivity.

[0064] Specifically, the sample can be selected as the patient's blood sample or oral exfoliated cells for genotype detection. The detection method adopts XL-PCR, immunofluorescence method or sequencing according to the genotype; SPSS 20.0 statistical software is used for data analysis. Count data uses chi-square test, and Logistic regression analysis is used for comparison of descriptive classification data. One-way ANOVA is used for comparison between different genotype groups.

[0065] The finally established dose prediction formula is: opioid analgesic demand = (1.356 + 0.530) * (SNP variation number of gene site classification 1 * PI + 0.283) * (SNP variation number of gene site classification 2 * PI + 0.164) *... * (SNP variation number of gene site classification m * PI + 0.155) * (pain sensation delay time * pain score of different tumor stages - 0.006) * body weight, where the unit of opioid analgesic demand is μg / kg.

[0066] In this embodiment, in order to further correct the dose prediction formula, lung cancer patients are first selected for grouping (100 people in each of the experimental group and the control group). The experimental group is first given genomic testing, and secondary grouping is carried out according to the detected genotype. Each subgroup formulates the initial dosing dose and dosing frequency of analgesic drugs according to the prediction formula and the basic situation of the patients. At the same time, patients with genes sensitive to adverse reactions are pretreated or the drugs are replaced. Finally, the titration duration of analgesic drugs, the degree of pain relief (VAS score), and the incidence and degree of adverse reactions in each group during hospitalization are specifically evaluated to determine the practical application effect of gene site polymorphism in the clinical application of opioid drugs and correct the dose prediction formula.

[0067] In this embodiment, the correction value for correcting the dose prediction formula Expressed as:

[0068]

[0069] Where N is the total number of alleles, is the allele frequency adjacent to the th allele.

[0070] Comprehensively evaluate the gene loci with strong correlation after screening. Using methods such as the Delphi expert consultation method, assign values according to three categories: metabolic-related gene loci, pain relief effect-related gene loci, and drug adverse reaction-related loci. Finally, select the gene locus with the greatest correlation and the highest score as the basic detection combination, and determine the detection standard, clinical application consensus, and knowledge base rules. Finally, using information technology and artificial intelligence algorithms, finally form an opioid analgesic clinical medication decision-making system that incorporates patient individual information, genomics information, and pain assessment data. This system can provide relevant warning information and initial recommended dosing.

[0071] Corresponding to the method for individualized analgesia evaluation of opioid drugs based on gene polymorphism provided in the above embodiment, the present application also provides an embodiment of a system for individualized analgesia evaluation of opioid drugs based on gene polymorphism.

[0072] See Figure 3 , the individualized analgesia evaluation system 20 of opioid drugs based on gene polymorphism in this embodiment includes:

[0073] A standard determination module 201 for establishing a recommended strength standard by evidence-based evaluation of SNP loci related to opioid analgesia.

[0074] An influence parameter setting module 202 for screening SNP loci related to the analgesic effect of opioid drugs detected in all databases and samples, and setting pain influence parameters for SNPs in different levels according to the classification levels of the recommended strength standard.

[0075] An analgesia evaluation module 203 for establishing a dosing prediction plan based on the pain influence parameter and the number of SNP variations, and adjusting the dosing prediction plan based on the analgesic attribute characteristics corresponding to the patient to be evaluated determined by the overall score of the patient to be evaluated under different clinical variables.

[0076] The present application also provides an embodiment of an individualized analgesia evaluation device for opioid drugs based on gene polymorphism. See Figure 4 , the individualized analgesia evaluation device 30 of opioid drugs based on gene polymorphism in this embodiment includes:

[0077] A processor 301, a memory 302, and a communication unit 303. These components communicate via one or more buses. Those skilled in the art can understand that the structure of the opioid individualized analgesia evaluation device based on gene polymorphism shown in the figure does not limit the embodiments of the present application. It can be a bus structure, a star structure, or can include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0078] Among them, the communication unit 303 is used to establish a communication channel so that the opioid individualized analgesia evaluation device based on gene polymorphism can communicate with other electronic devices.

[0079] The processor 301 is the control center of the opioid individualized analgesia evaluation device based on gene polymorphism. It uses various interfaces and lines to connect all parts of the opioid individualized analgesia evaluation device based on gene polymorphism, and by running or executing software programs and / or modules stored in the memory 302, as well as calling data stored in the memory, to execute various functions of the opioid individualized analgesia evaluation device based on gene polymorphism and / or process data.

[0080] The processor can be composed of an integrated circuit (IC). For example, it can be composed of a single-packaged IC, or can be composed of multiple connected packaged ICs with the same or different functions. For example, the processor 301 can only include a central processing unit (CPU). In the embodiments of the present application, the CPU can be a single-operation core or can include multiple operation cores.

[0081] The memory 302 is used to store the execution instructions of the processor 301. The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disc.

[0082] When the execution instructions in the memory 302 are executed by the processor 301, the opioid individualized analgesia evaluation device 30 based on gene polymorphism can execute some or all of the steps in the above method embodiments.

[0083] In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "And / or" describes the association relationship of associated objects and indicates that there can be three relationships. For example, A and / or B can represent the cases of A existing alone, A and B existing simultaneously, and B existing alone. Here, A and B can be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "At least one of the following" and its similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, and c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, c can be single or multiple.

[0084] As described above, the foregoing are only specific embodiments of the present application. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. The protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. An individualized analgesia evaluation method for opioid drugs based on gene polymorphism, characterized in that, Comprising: Establishing a recommended strength standard by evidence-based evaluation of SNP loci related to opioid analgesia; Screening SNP loci related to the analgesic effect of opioids detected in all databases and samples, and setting pain impact parameters for SNPs in different levels according to the classification levels of the recommended strength standard, including: Determining the increase or decrease multiple of drug demand, the number of allele types, and the allele frequency; Establish the pain impact parameter formula for the SNP , is the multiple of increase or decrease in drug demand, where increase is a positive value and decrease is a negative value, is the number of allele types, is the frequency of the Establishing a drug dosage prediction scheme based on the pain impact parameters and the number of SNP variations, and adjusting the drug dosage prediction scheme based on the analgesic attribute characteristics corresponding to the patient to be evaluated determined by the overall score of the patient to be evaluated under different clinical variables.

2. The method for individualized analgesia evaluation of opioid drugs based on gene polymorphism according to claim 1, wherein The establishing of the recommended strength standard by evidence-based evaluation of SNP loci related to opioid analgesia includes: Based on the sample data of each patient in evidence-based materials under different clinical variables, evaluating the analgesic effectiveness parameters of opioids for the sample according to the usage information and pain relief degree of opioid analgesics, and the clinical variables include: tumor type, pain score, and basic information; According to the analgesic effectiveness parameters corresponding to the sample, dividing them into a 6-level scoring system of level 1A, level 1B, level 2A, level 2B, level 3, and level 4 according to the parameter values to evaluate the importance of different opioid analgesic SNP loci.

3. The method for individualized analgesia evaluation of opioid drugs based on gene polymorphism according to claim 1, characterized in that The establishing of the drug dosage prediction scheme based on the pain impact parameters and the number of SNP variations includes: Selecting target populations for detection at SNP loci with high correlation and strong recommended strength first; The target populations respectively select cancer pain patients using morphine and oxycodone, and all need to include cancer pain patients of the same cancer type and the same stage who have been using drugs for a long time and have good pain control; Statistically analyzing the pain relief situation before and after the use of opioid drugs by patients, the dosage of opioid drugs, and the occurrence of nausea, vomiting, and respiratory depression in patients, and using correlation statistical analysis to evaluate the correlation between gene locus polymorphism and drug dosage and drug adverse reactions; Performing multiple linear regression analysis on the dosage situation by the stepwise two-way selection method to establish a dose prediction formula based on gene locus sensitivity.

4. The method for individualized analgesia evaluation of opioid drugs based on gene polymorphism according to claim 3, wherein The dose prediction formula is: demand for opioid analgesics = (1.356 + 0.530) * (number of SNP variations of gene locus classification 1 * PI + 0.283) * (number of SNP variations of gene locus classification 2 * PI + 0.164) *... * (number of SNP variations of gene locus classification m * PI + 0.155) * (pain sensation delay time * pain score of different tumor stages - 0.006) * body weight, where the unit of demand for opioid analgesics is μg / kg.

5. The method for individualized analgesia evaluation of opioid drugs based on gene polymorphism according to claim 4, wherein The adjusting of the drug dosage prediction scheme based on the analgesic attribute characteristics corresponding to the patient to be evaluated determined by the overall score of the patient to be evaluated under different clinical variables includes: First, select the patient to be evaluated for grouping, and the experimental group is first given genomic testing and then re-grouped according to the detected genotypes; Each group formulates the initial drug dosage and dosing frequency of analgesic drugs according to different genotypes using the dose prediction formula and the basic situation of the patient, and at the same time, pre-treats patients with genes sensitive to adverse reactions or replaces the drugs. During the final specific evaluation period, determine the titration duration, pain relief degree, incidence and degree of adverse reactions of analgesic drugs in each group, and judge the practical application effect of gene locus polymorphism in the clinical application of opioid drugs and correct the dose prediction formula.

6. The method for individualized analgesia evaluation of opioid drugs based on gene polymorphism according to claim 5, wherein Correction value for the correction dose prediction formula Expressed as: where N is the total number of alleles, is the allele frequency adjacent to the th allele.

7. The method for individualized analgesia evaluation of opioid drugs based on gene polymorphism according to claim 6, wherein, Comprehensively evaluate the relevant gene loci again after screening, and use the Delphi expert consultation method to assign values according to the metabolism-related genomic loci, pain relief-related gene loci and drug adverse reaction-related loci. Finally, select the gene locus with the greatest relevance and the highest score as the basic detection combination. Determine the detection standards, clinical application consensus and knowledge base rules; finally, form an opioid analgesic drug clinical medication decision-making system that incorporates the individual information, genomic information and pain assessment data of the included patients.

8. An individualized analgesia evaluation system for opioid drugs based on gene polymorphism, characterized in that, It includes: A standard determination module for establishing a recommended strength standard by evidence-based evaluation of opioid analgesia-related SNP loci. An influence parameter setting module for screening SNP loci related to the analgesic effect of opioid drugs detected in all databases and samples, and setting pain influence parameters of SNPs in different levels according to the classification levels of the recommended strength standard, including: Determine the increase or decrease multiple of drug demand, the number of allele types and the allele frequency. Establish the pain impact parameter formula for the SNP , is the multiple of increase or decrease in drug demand, where increase is a positive value and decrease is a negative value, is the number of allele types, is the frequency of the An analgesia evaluation module for establishing a drug dosage prediction plan based on the pain influence parameter and the number of SNP variations, and adjusting the drug dosage prediction plan based on the analgesic attribute characteristics corresponding to the patient to be evaluated determined by the overall score of the patient to be evaluated under different clinical variables.

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