A SNP site detection kit for predicting the use of drugs for treating depression

Abstract

This invention relates to the field of gene sequencing technology, specifically to a SNP locus detection kit for predicting medication use in the treatment of depression. The kit includes a data acquisition module, a gene restriction analysis module, a drug recommendation analysis module, and a drug recommendation generation module. It collects gene interpretation data of SNP loci from the current patient, including at least a gene interpretation table and a gene-drug correspondence table. Based on the gene interpretation table, it analyzes the common metabolic effects of different detection loci on the metabolism of the same antidepressant, determining the overall gene restriction degree of different antidepressant drugs. Based on the gene-drug correspondence table, it analyzes the burden on the patient's metabolic capacity from combinations of antidepressants, and, combined with the overall gene restriction degree, determines the predictive power of different antidepressants, thereby generating a drug recommendation table for the current patient. This invention improves the accuracy of the generated medication recommendation report by accurately determining the predictive power of different antidepressants.

Description

Technical Field

[0001] This invention relates to the field of gene sequencing technology, specifically to a SNP site detection kit for predicting medication use in the treatment of depression. Background Technology

[0002] Current antidepressants (such as SSRIs: sertraline, escitalopram, etc.; SNRIs: venlafaxine, etc.) face a common dilemma: 1. Significant individual variability: The same drug and dosage may be effective for some patients but ineffective or cause serious side effects for others; 2. Prolonged trial-and-error process: Patients often need to spend weeks or even months, undergoing multiple medication changes and dosage adjustments, to find a relatively suitable regimen. This not only delays treatment but also increases the physical and financial burden on patients. The root cause of this dilemma lies in individual genetic differences, particularly SNP sites related to the metabolism and action of antidepressants, which determine how the body processes and responds to these drugs. To address this, existing technologies are developing SNP testing kits. By sampling patients with depression and analyzing their samples using laboratory equipment, a predictive report is generated for patients and doctors to refer to, reducing the cost of trial and error.

[0003] In real-world scenarios, the metabolism and response of the same antidepressant may be influenced by multiple genes simultaneously. For example, venlafaxine is metabolized by both CYP2D6 and CYP2C19. When the phenotypes of these two genes are inconsistent—for instance, CYP2D6 is a slow metabolizer while CYP2C19 is an ultra-fast metabolizer—the built-in intelligent algorithm of the data system may struggle to make a judgment. Consequently, it may be unable to assign appropriate reference weights, making it difficult to accurately determine the recommended order of different antidepressants. This results in an inability to generate more accurate medication recommendation reports, increasing the difficulty for patients and doctors to refer to reports and raising the cost of trial and error. Summary of the Invention

[0004] To address the problem that the metabolism and response of the same antidepressant may be influenced by multiple genes simultaneously, making it difficult to accurately determine the recommended order of different antidepressants and thus unable to generate more accurate medication recommendation reports, the present invention aims to provide a SNP locus detection kit for predicting medication use in the treatment of depression. The specific technical solution adopted is as follows:

[0005] In a first aspect, the present invention provides a SNP locus detection kit for predicting medication use in the treatment of depression, comprising a medication prediction module, wherein the medication prediction module includes:

[0006] The data acquisition module is used to collect gene interpretation data of the current patient's SNP sites. The gene interpretation data includes at least a gene interpretation table and a gene-drug correspondence table.

[0007] The gene restriction analysis module is used to analyze the common metabolic effects of different detected genes on the metabolism of the same antidepressant based on the gene interpretation table, and to determine the comprehensive gene restriction degree of different antidepressants.

[0008] The drug recommendation analysis module is used to analyze the burden of different combinations of anti-disease drugs on the patient's metabolic capacity based on the gene-disease correspondence table, and to determine the recommendation prediction degree of different anti-disease drugs in combination with the gene comprehensive restriction degree.

[0009] The drug recommendation generation module is used to generate a drug recommendation table for the current patient based on the recommendation prediction level.

[0010] In conjunction with the first aspect above, in some possible implementations, the data acquisition module is further used for:

[0011] The gene interpretation table of the current patient is compared with the gene interpretation table of the SNP locus of the reference patient stored in the database to determine the accuracy of the gene interpretation results of the current patient.

[0012] When the accuracy of the gene interpretation result is less than a set accuracy threshold, the gene interpretation table of the current patient is corrected. In conjunction with the first aspect above, in some possible implementations, determining the accuracy of the gene interpretation result for the current patient includes:

[0013] The gene interpretation table of the current patient is compared with the gene interpretation table of the reference patient. If the same detection gene is found, the same detection gene is regarded as the same type of detection gene.

[0014] If the SNP site detection data of the same type of detection gene are completely consistent in the gene interpretation table of the current patient and the reference patient, then the same type of detection gene is regarded as the same type of detection gene; otherwise, the same type of detection gene is classified as the specific detection gene.

[0015] For target reference patients in the database who have the same type of detection genes as the current patient, the accuracy of the current patient's gene interpretation results is determined based on the number of similar detection genes, the number of specific detection genes, and the number of dimensions where the SNP site detection data of specific detection genes are inconsistent with those of previous patients when comparing genes.

[0016] In conjunction with the first aspect mentioned above, some possible implementation methods involve correcting the current patient's gene interpretation data, including:

[0017] For all target reference patients in the database who have the same type of detection genes as the current patient, the detection data of detection genes other than the same type of detection genes and their SNP sites are added to the current patient's gene interpretation table, thereby obtaining the corrected gene interpretation data of the current patient.

[0018] In conjunction with the first aspect above, in some possible implementations, the gene restriction analysis module is used for:

[0019] Obtain the enzyme activity values ​​of all detected genes in the current patient's gene interpretation table at different stages of medication, as well as the metabolism-related antiviral drugs;

[0020] Identify several metabolically active enzymes for each antidote drug, wherein the several metabolically active enzymes refer to several detection genes related to the metabolism of each antidote drug;

[0021] Based on the enzyme activity values ​​of the aforementioned metabolically active enzymes at different drug administration stages, the high- and low-metabolic-activity enzymes of each anti-disease drug at different drug administration stages are determined.

[0022] Based on the enzyme activity values ​​of high- and low-metabolic enzymes of each anti-disease drug at different stages of administration, and the distribution of high- and low-metabolic enzymes at different stages of administration, the degree of gene restriction of each anti-disease drug is determined.

[0023] In conjunction with the first aspect above, among some possible implementation methods, the degree of gene-based restriction for each antidote is determined, including:

[0024] Based on the differences in the number of high- and low-metabolic enzymes between each antidote at each stage of administration, as well as the differences in enzyme activity values ​​between high- and low-metabolic enzymes, the high-speed metabolic support capacity of each antidote at each stage of administration was determined.

[0025] Based on the distribution of the aforementioned metabolically active enzymes of each antidote as high- and low-metabolic-activity enzymes at different stages of administration, the sustained high-metabolic-activity effect of each antidote throughout the entire administration period is determined.

[0026] Based on the distribution level of high metabolic support for each antidote at different stages of administration and the sustained high metabolic effect throughout the entire administration period, the efficient and sustained metabolic enzyme activity of each antidote throughout the entire administration period was determined.

[0027] The high efficiency and sustainability of the metabolic enzyme activity were negatively correlated and normalized to obtain the gene-integrated restriction degree of each anti-disease drug.

[0028] In conjunction with the first aspect above, among some possible implementations, the high-velocity metabolic support for each antidote at each stage of administration is determined, including:

[0029] Based on the number of highly metabolically active enzymes of each antidote at each stage of administration and the average enzyme activity of highly metabolically active enzymes of each antidote at each stage of administration, the positive metabolic support of each antidote at each stage of administration is determined.

[0030] Based on the number of low-metabolic active enzymes of each antidote in each administration phase and the average enzyme activity value of low-metabolic active enzymes of each antidote in each administration phase, the reverse metabolic inhibition of each antidote in each administration phase is determined.

[0031] The ratio of positive metabolic support to negative metabolic inhibition for each antidote in each treatment phase was determined to obtain the high-speed metabolic support for each antidote in each treatment phase.

[0032] In conjunction with the first aspect above, among some possible implementations, the high metabolic sustained activity of each antidote throughout the entire treatment phase is determined, including:

[0033] The ratio of the number of times each metabolically active enzyme of each antidote appears as a highly metabolically active enzyme in all stages of administration to the number of times it appears as a highly metabolically active enzyme in all stages of administration is determined to obtain the therapeutic efficacy of each metabolically active enzyme of each antidote in all stages of administration.

[0034] The ratio of the number of times each metabolically active enzyme of each antidote appears as a low-metabolic-activity enzyme in all treatment phases to the number of times it appears as a low-metabolic-activity enzyme in all treatment phases is determined to obtain the therapeutic inefficiency of each metabolically active enzyme of each antidote in all treatment phases.

[0035] Based on the differential distribution levels of the therapeutically potent and therapeutically ineffective effects of all metabolically active enzymes of each antidote, the sustained high metabolic potency of each antidote throughout the entire treatment period was determined.

[0036] In conjunction with the first aspect above, in some possible implementations, the drug recommendation analysis module is used for:

[0037] In the current patient's gene drug correspondence table, the anti-disease drugs associated with each detected gene are counted, and the recurring anti-disease drugs are treated as gene repeat drugs, while the other types of anti-disease drugs are treated as gene single drugs.

[0038] Based on the comprehensive gene restriction level of each gene-specific drug and the number of anti-disease drug types contained in the detected genes of each gene-specific drug in the gene-drug correspondence table of the current patient, the recommended predictive level of each gene-specific drug is determined.

[0039] The recommended predictive power of each gene duplication drug is determined based on the degree of gene-based restriction for each drug and the number of anti-disease drug types contained in each detected gene in the current patient's gene drug correspondence table.

[0040] In conjunction with the first aspect above, in some possible implementations, the drug recommendation generation module is used for:

[0041] In the current patient's gene-drug correspondence table, all types of anti-disease drugs related to the detected genes are arranged in descending order of their predictive recommendation level, and a drug recommendation table for the current patient is generated.

[0042] Secondly, the present invention also provides a method for predicting medication use in the treatment of depression using an SNP site detection kit, the method comprising:

[0043] Collect gene interpretation data of the SNP sites of the current patient, and the gene interpretation data includes at least a gene interpretation table and a gene-drug correspondence table;

[0044] Based on the gene interpretation table, the common metabolic effects of different detected genes on the metabolism of the same antidepressant were analyzed to determine the degree of comprehensive gene restriction of different antidepressants.

[0045] Based on the gene-drug correspondence table, the burden of different combinations of anti-disease drugs on the metabolic capacity of patients is analyzed, and the predictive recommendation level of different anti-disease drugs is determined in combination with the gene comprehensive restriction level.

[0046] Based on the predicted recommendation level, a drug recommendation table is generated for the current patient.

[0047] Thirdly, the present invention also provides a medication prediction system for treating depression using an SNP site detection kit, comprising a memory and a processor. The memory stores executable computer program code, and the processor retrieves and runs the executable computer program code from the memory, causing the system to perform the steps implemented by the various modules of the SNP site detection kit for predicting medication for treating depression, as described in the first aspect or any possible implementation thereof.

[0048] Fourthly, the present invention also provides a computer program product comprising: computer program code, which, when run on a computer, causes the computer to perform the steps implemented by the various modules of the SNP site detection kit for predicting medication for treating depression, as described in the first aspect or any possible implementation of the first aspect.

[0049] Fifthly, the present invention also provides a computer-readable storage medium storing computer program code that, when executed on a computer, causes the computer to perform the steps implemented by the various modules of the SNP site detection kit for predicting medication for treating depression, as described in the first aspect or any possible implementation of the first aspect.

[0050] The present invention has the following beneficial effects: by analyzing the gene interpretation table in the gene interpretation data of the current patient's SNP loci, the degree of interference caused by different gene interferences to the effect of antidepressants is analyzed, the degree of gene comprehensive restriction of different antidepressants is determined, and further combined with the gene-drug correspondence table in the gene interpretation data, the degree of physical burden on the patient's metabolic capacity of different antidepressant combinations is analyzed, the recommendation prediction degree of different antidepressants is accurately calculated, thereby improving the accuracy of the generated medication recommendation report. Attached Figure Description

[0051] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a schematic diagram of the medication prediction module of the SNP site detection kit for predicting medication use in the treatment of depression, according to an embodiment of the present invention.

[0053] Figure 2 A flowchart illustrating the steps for determining the gene synthesis restriction level of each antidote according to an embodiment of the present invention;

[0054] Figure 3 This is a flowchart illustrating the steps for determining the predictive recommendation level of different anti-disease drugs according to an embodiment of the present invention.

[0055] Figure 4 This is a flowchart illustrating the steps of a method for predicting medication use in treating depression using an SNP site detection kit, as described in an embodiment of the present invention. Detailed Implementation

[0056] To clearly illustrate the technical features of this solution, the invention will be described in detail below through specific embodiments and in conjunction with the accompanying drawings.

[0057] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the invention. It should be understood that the accompanying drawings and embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the invention.

[0058] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0059] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0060] It should be noted that the concepts of "first" and "second" mentioned in this invention are only used to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0061] Although operations or steps are described in a specific order in the accompanying drawings in the embodiments of the present invention, this should not be construed as requiring these operations or steps to be performed in the specific order or serial order shown, or requiring all of the shown operations or steps to be performed to obtain the desired result. In the embodiments of the present invention, these operations or steps may be performed serially; they may be performed in parallel; or a portion of these operations or steps may be performed.

[0062] Furthermore, it is understood that the data involved in the technical solutions of this invention (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions. Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains, and all parameters or indicators in the formulas involved in this invention are normalized values ​​that have eliminated the influence of dimensions.

[0063] The following will provide a detailed description of an SNP site detection kit for predicting medication use in the treatment of depression, provided by an embodiment of the present invention, with reference to the accompanying drawings.

[0064] Please see Figure 1 The diagram illustrates the architecture of a SNP site detection kit for predicting medication use in the treatment of depression, according to the present invention. The SNP site detection kit includes the kit body (the kit testing device, mainly used for gene detection and generating corresponding gene detection data) and a medication prediction module. This medication prediction module includes: a data acquisition module 1, a gene restriction analysis module 2, a drug recommendation analysis module 3, and a drug recommendation generation module 4, specifically:

[0065] Data acquisition module 1 is used to collect gene interpretation data of the current patient's SNP loci.

[0066] The gene interpretation data includes at least a gene interpretation table and a gene-drug correspondence table.

[0067] Taking a patient with depression as an example, the SNP site detection kit is used to collect samples from the current patient and place the samples in the test equipment to generate a report. An initial data report (including a gene interpretation table and a gene-drug correspondence table) is obtained. The gene interpretation table and gene-drug correspondence table in this data report are then used as the gene interpretation data of the current patient's SNP sites.

[0068] It should be understood that the gene interpretation table in the data report contains data in four dimensions: detected gene, detected locus, genotype, and result interpretation. The data under the three dimensions of detected locus, genotype, and result interpretation are used as the SNP locus type data for the current patient. Table 1 shows a portion of the gene interpretation table data. The gene-drug correspondence table in the data report contains data in four dimensions: detected gene, number of rs loci (SNP loci), rs locus, number of corresponding gene-drugs, and corresponding antidepressant. Table 2 shows a portion of the gene-drug correspondence table data.

[0069] Figure 1 Gene Interpretation Table

[0070]

[0071] Figure 2 Gene therapy drug correspondence table

[0072]

[0073] For SNP testing kits already in use, considering limitations such as cost and efficiency, the SNP sites detectable in predicting medication treatment for depression are typically only known SNP sites associated with depression (as recognized by current medical technology). The number of these known SNP sites is usually limited (due to the small size of the testing kits). However, for patients with depression, the same antidepressant can produce different therapeutic effects in different patients. This is mainly due to the different gene expression patterns in different patients, and the diversity of gene expression. Therefore, in addition to the SNP sites covered by the testing kits, many uncommon known SNP sites may be abnormal. Thus, directly predicting medication treatment for depression based solely on the results of existing SNP testing kits may introduce potential errors, leading to incorrect patient classifications.

[0074] Therefore, it is necessary to retrieve related gene data from the database based on the gene interpretation data of the current patient's SNP loci for comparison, determine the accuracy of the current patient's gene interpretation results, and then determine whether the current patient's gene interpretation results are accurate, and correct the gene interpretation results if they are inaccurate.

[0075] In one possible implementation of the present invention, the data acquisition module 1 is further configured to:

[0076] First, the gene interpretation table of the current patient is compared with the gene interpretation table of the reference patient's SNP locus stored in the database to determine the accuracy of the current patient's gene interpretation results.

[0077] In a specific example, the gene interpretation table of the current patient is compared with that of a reference patient. If the same detection gene is found, it is considered as a gene of the same category. Specifically, the gene interpretation tables of patients diagnosed with depression are retrieved from the database and compared with the current patient's gene interpretation table. If both tables contain the same detection gene, it is considered as a gene of the same category.

[0078] If the SNP site detection data of the same type of gene are completely consistent in the gene interpretation tables of the current patient and the reference patient, then the same type of gene is classified as a gene of the same nature; otherwise, the same type of gene is classified as a gene of specific nature. In other words, if the SNP site detection data of the same type of gene in the current patient and the reference patient are consistent across the three dimensions of detection site, genotype, and result interpretation, then the corresponding gene of the same type is classified as a gene of the same nature; otherwise, the corresponding gene of the same type is classified as a gene of specific nature.

[0079] For target reference patients in the database who share similar test genes with the current patient, the accuracy of the current patient's gene interpretation results is determined based on the number of similar test genes, the number of specific test genes, and the number of dimensions of inconsistency in SNP site detection data when comparing specific test genes with previous patients' genes in the target reference patient's gene interpretation table. Specifically, the accuracy of the current patient's gene interpretation results is calculated using the following formula, based on the relationship between the number of similar and specific test genes in the current patient and other patients with depression in the database, as well as the inconsistency dimensions of SNP site detection data corresponding to specific test genes. :

[0080]

[0081] In the formula: This indicates the number of target reference patients in the database who share the same type of detection genes as the current patient;

[0082] This indicates that compared to current patients, the first... The number of similar test genes included in the gene interpretation table of each target reference patient; This indicates that compared to current patients, the first The number of specifically tested genes included in the gene interpretation table of each target reference patient; Indicates the first The genetic interpretation table of a patient with depression contains the first... The number of dimensions where SNP site detection data is inconsistent with the current patient's specific gene detection data; This represents a normalization function used to normalize numerical values ​​to the range [0,1].

[0083] The accuracy of the current patient's gene interpretation results can be calculated using the formula described above. Accuracy of gene interpretation results The larger the value, the less the data obtained from the SNP site detection kit is affected by individual patient specificity, reflecting that the gene interpretation table obtained from the SNP site detection kit can be directly used to predict subsequent antidepressant treatment.

[0084] Secondly, when the accuracy of the gene interpretation results is less than the set accuracy threshold, the gene interpretation table of the current patient is corrected.

[0085] A pre-set accuracy threshold (e.g., 0.5) is set based on the accuracy of the current patient's gene interpretation results. A value greater than 0.5 indicates that the gene interpretation table obtained from the SNP site detection kit for the current patient can be directly used to predict subsequent antidepressant medications. Conversely, if the accuracy of the current patient's gene interpretation results is low... A value less than or equal to 0.5 indicates that the gene interpretation table obtained by the current patient through the SNP site detection kit cannot be directly used to predict subsequent antidepressant treatment. In this case, the gene interpretation table of the current patient needs to be corrected.

[0086] In a specific example, the process of correcting the current patient's gene interpretation data includes: for all target reference patients in the database who share the same type of detection genes as the current patient, adding the detection genes other than those of the same type and their SNP site detection data to the current patient's gene interpretation table, thus obtaining the corrected gene interpretation data for the current patient. In other words, among the target reference patients in the database who share the same type of detection genes as the current patient, adding the detection genes other than those of the same type to the current patient's gene interpretation table completes the correction of the current patient's gene interpretation table.

[0087] The gene restriction analysis module 2 is used to analyze the common metabolic effects of different detected genes on the metabolism of the same antidepressant based on gene interpretation data, and to determine the comprehensive gene restriction degree of different antidepressants.

[0088] In the gene interpretation data acquired by the aforementioned data acquisition module 1, each detected gene in the gene interpretation table represents an important drug-metabolizing enzyme. Its genotype information symbolizes the activity and expression of this enzyme. Result interpretation involves translating the biological function of the genotype, that is, translating the abstract genotype expression into biological language (converting abstract language into easily understandable language). Each drug-metabolizing enzyme represents the metabolism of a specific drug. Because different patients with depression exhibit different gene expression patterns, the activity and expression of the same detected gene are complex and varied among different patients, resulting in different metabolisms of the same antidepressant among patients with depression.

[0089] In general, patients with depression often have multiple detection sites that contribute to the metabolism of the same antidepressant. This means that when recommending medication based on the patient's genetic data, the metabolism and response of the same drug may be influenced by multiple detection genes. For example, venlafaxine is metabolized by both CYP2D6 and CYP2C19. When the phenotypes of these two genes are inconsistent—for instance, CYP2D6 is a slow metabolizer while CYP2C19 is an ultra-fast metabolizer—it can cause interference in the system's judgment. Therefore, it is necessary to analyze the combined metabolic effects of multiple detection sites on the metabolism of the same antidepressant based on the patient's genetic data, and to calculate the overall genetic restriction level of each antidepressant.

[0090] In one possible implementation of the present invention, such as Figure 2 As shown, the gene restriction analysis module 2 described above is used for:

[0091] First, obtain the enzyme activity values ​​of all tested genes in the current patient's gene interpretation table at different stages of medication, as well as the corresponding antidepressants. Specifically, for all tested genes in the gene interpretation table of the current patient's gene interpretation data, determine the enzyme activity value of each tested gene at different stages of medication (e.g., early, middle, and late stages), and simultaneously identify the antidepressants associated with the metabolism of each tested gene at different stages of medication.

[0092] In a specific example, the neural network was trained using gene interpretation tables from historical patient gene interpretation data. Based on the genotypes and results in the gene interpretation tables, all detected genes were manually divided into three medication phases: initial, intermediate, and late. Within each medication phase, each detected gene was assigned an enzyme activity value r (range 0-1) and an antidepressant related to the metabolism of the detected gene. Since the training process of the neural network is a well-known technique, it will not be elaborated upon here.

[0093] The gene interpretation table from the current patient's gene interpretation data is input into the trained neural network. The neural network then outputs the enzyme activity values ​​of all detected genes in the gene interpretation table from the current patient's gene interpretation data at different stages of medication, as well as the metabolism-related anti-disease drugs.

[0094] Secondly, identify several metabolically active enzymes for each antidepressant. These metabolically active enzymes refer to several detection genes related to the metabolism of each antidepressant. Specifically, for any given antidepressant, count all detection genes related to its metabolism and use them as several metabolically active enzymes for that antidepressant.

[0095] Next, based on the enzyme activity values ​​of several metabolically active enzymes at different stages of drug administration, the high- and low-activity enzymes of each antidote at different stages of drug administration were identified. Specifically, based on the enzyme activity values ​​of the metabolically active enzymes of each antidote at different stages of drug administration, enzymes with higher activity values ​​were classified as high-activity enzymes, and enzymes with lower activity values ​​were classified as low-activity enzymes.

[0096] In a specific example, for each antidepressant, during each medication phase, the metabolically active enzyme with an enzyme activity value greater than a preset enzyme activity value (e.g., 0.4) is considered the high metabolically active enzyme of the antidepressant during that medication phase, and the remaining metabolically active enzymes are considered the low metabolically active enzymes of the antidepressant during that medication phase.

[0097] Finally, based on the enzyme activity values ​​of high- and low-metabolic enzymes of each antidote at different stages of administration, and the distribution of high- and low-metabolic enzymes at different stages of administration, the degree of gene restriction of each antidote was determined.

[0098] This study analyzes the activity levels of metabolically active enzymes of each antidepressant at different stages of treatment, identifying both high and low metabolic activity levels, and their distribution across these stages. This analysis aims to determine the degree of genetic restriction associated with the antidepressant. For example, if a metabolically active enzyme of an antidepressant is predominantly a high-activity enzyme across multiple treatment stages, and its activity level is also relatively high, it indicates that the antidepressant is rapidly metabolized during treatment, corresponding to a lower degree of genetic restriction.

[0099] In one possible implementation of the present invention, determining the degree of gene synthesis restriction for each antidote includes:

[0100] First, based on the differences in the number of high- and low-metabolic enzymes between each antidote at each stage of administration, as well as the differences in enzyme activity values ​​between high- and low-metabolic enzymes, the high-speed metabolic support capacity of each antidote at each stage of administration was determined.

[0101] In a specific example, the steps for determining the high-velocity metabolic support of each antidote in each treatment phase include: determining the positive metabolic support of each antidote in each treatment phase based on the number of highly metabolically active enzymes and the average enzyme activity of highly metabolically active enzymes in each treatment phase; determining the negative metabolic inhibition of each antidote in each treatment phase based on the number of low-velocity active enzymes and the average enzyme activity of low-velocity active enzymes in each treatment phase; and determining the ratio of positive metabolic support to negative metabolic inhibition for each antidote in each treatment phase to obtain the high-velocity metabolic support of each antidote in each treatment phase.

[0102] Specifically, taking any antidepressant at any stage of administration as an example, the number of highly metabolically active enzymes of the antidepressant during that stage of administration is denoted as... The average enzyme activity values ​​of all highly metabolically active enzymes during this medication period of the antidepressant are denoted as: At this point, the positive metabolic support of the antidepressant during this treatment phase is expressed as follows: Simultaneously, the number of low-metabolic-activity enzymes during this treatment phase of the antidepressant was recorded as... The average enzyme activity of all low-metabolic enzymes during this treatment period of the antidepressant is recorded as _____. At this point, the reverse metabolic inhibition of the antidepressant during this treatment phase is expressed as: Furthermore, the positive metabolic support was calculated. With reverse metabolic inhibition ratio This ratio is taken as the support for the rapid metabolism of the antidepressant during this treatment phase and denoted as... A higher metabolic support indicates that the antidepressant is metabolized more rapidly during that treatment phase. Based on this, the metabolic support for each antidepressant during each treatment phase can be calculated.

[0103] Secondly, based on the distribution of several metabolically active enzymes of each antidote as high- and low-metabolic-activity enzymes at different stages of administration, the sustained high-metabolic-activity effect of each antidote throughout the entire administration period was determined.

[0104] In real-world scenarios, the same antidepressant may produce different metabolic efficiencies at different stages of a patient's continuous use. That is, several metabolically active enzymes of the same antidepressant may exhibit different levels of enzyme activity throughout the treatment process. Therefore, it is necessary to further analyze the same antidepressant in relation to the entire medication phase (between different medication phases) based on high-speed metabolic support, so as to comprehensively calculate the degree of genetic restriction of the antidepressant.

[0105] In a specific example, the steps for determining the high metabolic sustained potency of each antidote throughout the entire treatment phase include: determining the ratio of the number of times each metabolically active enzyme of each antidote appears as a high metabolically active enzyme in all treatment phases to the number of times it appears as a high metabolically active enzyme in all treatment phases, to obtain the therapeutically efficient potency of each metabolically active enzyme of each antidote throughout the entire treatment phase; determining the ratio of the number of times each metabolically active enzyme of each antidote appears as a low metabolically active enzyme in all treatment phases to the number of times it appears as a low metabolically active enzyme in all treatment phases, to obtain the therapeutically ineffective potency of each metabolically active enzyme of each antidote throughout the entire treatment phase; and determining the high metabolic sustained potency of each antidote throughout the entire treatment phase based on the level of difference in the distribution of the therapeutically efficient and therapeutically ineffective potencies of all metabolically active enzymes of each antidote.

[0106] Specifically, taking any antidepressant as an example... Taking the first metabolically active enzyme as an example, the statistics of this first enzyme are as follows: The number of times a metabolically active enzyme appears as a highly metabolically active enzyme throughout the entire (three) treatment phases is denoted as . ; and statistics on the first The number of times each metabolically active enzyme appeared as a highly metabolically active enzyme throughout the entire (three) treatment phases is denoted as . At the same time, statistics on this first... The number of times a metabolically active enzyme appears as a low-metabolic-activity enzyme throughout the entire (three) treatment phases is recorded as follows: ; and statistics on the first The number of times each metabolically active enzyme appeared as a low-metabolism-activity enzyme throughout the entire (three) treatment phases is denoted as . .

[0107] Number of calculations and ratio and count the number of times. and ratio . , They represent the first The therapeutically potent and therapeutically ineffective effects of each metabolically active enzyme throughout the three (drug) treatment phases are denoted as follows: , Calculate the therapeutic potency and ineffective potency of all metabolically active enzymes corresponding to the antidepressant drugs. and The sum of these ratios is used as the cumulative value of the corresponding antidepressant's high metabolic sustained effect throughout the entire treatment period. The larger the value of the high metabolic sustained effect B, the better and longer the effect of the biological enzymes produced in the body on the breakdown and absorption of the antidepressant's components when the patient is using the drug.

[0108] Next, based on the distribution level of high metabolic support for each antidote at different stages of administration and the sustained high metabolic effect throughout the entire administration period, the high efficiency and sustainability of metabolic enzyme activity of each antidote throughout the entire administration period were determined.

[0109] When a certain antidepressant exhibits higher metabolic support at different stages of treatment and a higher sustained metabolic effect throughout the entire treatment period, it indicates that the bioenzymes in the body that produce the drug's components are more likely to maintain high activity when the patient is using the drug. This also indicates a stronger ability to resist reduced activity (because multiple genes work together, and if one fails, others are still functioning, so the overall speed is not significantly affected). This reflects a better and more sustained effect of the bioenzymes produced in the body on the breakdown and absorption of the antidepressant's components. In this case, the more efficient and sustained the metabolic enzyme activity of the antidepressant is throughout the entire treatment period.

[0110] In a specific example, the high metabolic support of each antidepressant was calculated across all stages of treatment. The mean value of the activity of the metabolic enzymes of this antidepressant, combined with its high metabolic sustainability throughout the entire treatment period, is used to calculate the high efficiency and sustainability of its metabolic enzyme activity Y throughout the entire treatment period using the following formula:

[0111]

[0112] In the formula: This represents the average high metabolic support capacity of each antidepressant throughout the entire (three) treatment phases; This indicates the number of metabolically active enzymes for each type of antidepressant; Indicates the first The therapeutic efficacy of each metabolically active enzyme throughout the (three) stages of medication; Indicates the first The therapeutic efficacy of the metabolically active enzymes is low throughout the (three) stages of medication.

[0113] Finally, the high efficiency and sustainability of metabolic enzyme activity were negatively correlated and normalized to obtain the gene-based restriction level of each anti-disease drug.

[0114] The sustained high metabolic enzyme activity (Y) of each antidepressant throughout the entire treatment period was inversely normalized, and the value after inverse normalization was used as the degree of genetic restriction for that antidepressant, denoted as [missing value]. .

[0115] The drug recommendation analysis module 3 is used to analyze the burden on the metabolic capacity of patients by multiple combinations of anti-disease drugs based on the degree of gene comprehensive restriction, and to determine the predictive power of different anti-disease drugs.

[0116] The comprehensive gene restriction degree calculated by the gene restriction analysis module 2 above has clarified the extent to which each antidepressant is interfered with by the activity of the detected gene. A higher value indicates that the corresponding antidepressant is more likely to experience significant obstacles to drug absorption efficiency due to local abnormalities at the detected gene sites (usually low activity). In real-world scenarios, there are various types of antidepressants, and multiple antidepressants may be recommended for the same detected gene. However, for the human body, especially for patients with depression, their metabolic capacity is usually limited. If multiple antidepressants are recommended, all requiring the same detected gene for subsequent breakdown and absorption, the burden on the patient may be excessive, hindering the therapeutic effect. Therefore, it is necessary to analyze the burden on the patient's metabolic capacity from combinations of multiple antidepressants based on the comprehensive gene restriction degree, and calculate the predictive value of different antidepressant recommendations.

[0117] In one possible implementation of the present invention, such as Figure 3 As shown, the drug recommendation analysis module 3 is used for:

[0118] First, in the current patient's gene-drug correspondence table, count the anti-disease drugs associated with each detected gene, and treat the recurring anti-disease drugs as gene repeat drugs, and the remaining types of anti-disease drugs as gene single drugs.

[0119] Secondly, based on the gene-wide restriction level of each single-gene drug and the number of anti-disease drug types contained in the detected genes corresponding to each single-gene drug in the current patient's gene drug correspondence table, the recommended predictive level of each single-gene drug is determined.

[0120] The smaller the value of the gene-based restriction of a certain antidepressant, and the smaller the number of antidepressant types contained in the gene-drug correspondence table of the current patient, the weaker the metabolic pressure of the antidepressant, and the higher its predictive power should be.

[0121] In a specific example, the gene-based restriction level of a single drug for each gene. Based on the distribution of this single-dose gene therapy in the current patient's gene therapy drug correspondence table, the recommended predictive power of each single-dose gene therapy is calculated using the following formula. :

[0122]

[0123] In the formula: This indicates the number of antidepressant drug types contained in the gene to which each drug is detected in a single dose. This represents a normalization function (such as the max-min normalization algorithm) used to normalize numerical values ​​to the range [0,1]. It also indicates the recommended prediction level. The larger the value, the more needed the corresponding gene-specific drug is in the gene being tested, the weaker the metabolic pressure, and the more recommended the corresponding gene-specific drug should be.

[0124] Finally, based on the gene-wide restriction level of each gene duplication drug and the number of anti-disease drug types contained in each detected gene in the gene drug correspondence table of the current patient, the recommended predictive level of each gene duplication drug is determined.

[0125] In a specific example, based on the gene-comprehensive restriction level of each gene-replicating drug. Based on the distribution of this type of gene duplication drug in the current patient's gene drug correspondence table, the predictive power of recommendation for each gene duplication drug is calculated using the following formula. :

[0126]

[0127] In the formula: This indicates the number of times each gene repeats a drug; This indicates the position of each gene-drug duplication in the gene-drug correspondence table for the current patient. The number of antidepressant drugs contained in the corresponding gene when it appears for the first time; This represents a normalization function (such as the max-min normalization algorithm) used to normalize numerical values ​​to the range [0,1]. It also indicates the recommended prediction level. The larger the value, the more needed the corresponding gene duplication drug is in the detected gene, the weaker the metabolic pressure, and the more recommended the corresponding gene duplication drug should be.

[0128] Based on the above technical solution, and based on the distribution of each antidepressant in the current patient's gene drug correspondence table, the burden of multiple antidepressant combinations on the patient's metabolic capacity can be analyzed. Combined with the gene comprehensive restriction degree of each antidepressant, the recommendation prediction degree of different antidepressants can be obtained.

[0129] The drug recommendation generation module 4 is used to generate a drug recommendation table for the current patient based on the degree of recommendation prediction.

[0130] In the current patient gene-drug correspondence table, the types of antidepressants associated with each detected gene are sorted in descending order of their predictive recommendation level, generating a drug recommendation table for doctors to annotate accordingly.

[0131] It should be understood that if different antidepressants have the same predictive power, these antidepressants can be added to a neural network for training and analysis, prioritizing those included in the medical insurance list and those that are cheaper, ultimately achieving accurate recommendations for different antidepressants.

[0132] Based on the same inventive concept, embodiments of the present invention also provide a method for predicting medication use in the treatment of depression using an SNP site detection kit, such as... Figure 4 As shown, the method includes:

[0133] Collect gene interpretation data of the SNP loci of the current patient. The gene interpretation data shall include at least a gene interpretation table and a gene-drug correspondence table.

[0134] Based on the gene interpretation table, the common metabolic effects of different detected genes on the metabolism of the same antidepressant were analyzed to determine the degree of comprehensive gene restriction of different antidepressants.

[0135] Based on the gene-drug correspondence table, we analyzed the burden of different combinations of anti-disease drugs on the metabolic capacity of patients, and combined with the degree of gene comprehensive restriction, we determined the predictive power of different anti-disease drugs.

[0136] Based on the degree of recommendation prediction, a drug recommendation table is generated for the current patient.

[0137] Based on the same inventive concept, embodiments of the present invention also provide a system for predicting medication use for treating depression using an SNP site detection kit. The system includes: a memory, a processor, and computer program code stored in the memory and running on the processor. When the processor executes the computer program code, the system can perform the steps implemented by each module in any of the aforementioned SNP site detection kits for predicting medication use for treating depression.

[0138] In this embodiment of the invention, the system can be divided into functional modules according to the above method example. For example, each module can correspond to a separate functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0139] Based on the same inventive concept, embodiments of the present invention also provide a computer program product, which includes: computer program code, which, when run on a computer, causes the computer to execute the steps implemented by each module in any of the SNP site detection kits for predicting medication for treating depression described above.

[0140] Based on the same inventive concept, embodiments of the present invention also provide a computer-readable storage medium storing computer program code that, when executed on a computer, causes the computer to perform the steps implemented by the various modules in any of the aforementioned SNP site detection kits for predicting medication use in treating depression.

[0141] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A SNP site detection kit for predicting medication use in the treatment of depression, characterized in that, The medication prediction module includes: The data acquisition module is used to collect gene interpretation data of the current patient's SNP sites. The gene interpretation data includes at least a gene interpretation table and a gene-drug correspondence table. The gene restriction analysis module is used to analyze the common metabolic effects of different detected genes on the metabolism of the same antidepressant based on the gene interpretation table, and to determine the comprehensive gene restriction degree of different antidepressants. The drug recommendation analysis module is used to analyze the burden of different combinations of anti-disease drugs on the patient's metabolic capacity based on the gene-disease correspondence table, and to determine the recommendation prediction degree of different anti-disease drugs in combination with the gene comprehensive restriction degree. The drug recommendation generation module is used to generate a drug recommendation table for the current patient based on the recommendation prediction level. The gene restriction analysis module is used for: Obtain the enzyme activity values ​​of all detected genes in the current patient's gene interpretation table at different stages of medication, as well as the metabolism-related antiviral drugs; Identify several metabolically active enzymes for each antidote drug, wherein the several metabolically active enzymes refer to several detection genes related to the metabolism of each antidote drug; Based on the enzyme activity values ​​of the aforementioned metabolically active enzymes at different drug administration stages, the high- and low-metabolic-activity enzymes of each anti-disease drug at different drug administration stages are determined. Based on the enzyme activity values ​​of high- and low-metabolic enzymes of each anti-disease drug at different stages of administration, and the distribution of high- and low-metabolic enzymes at different stages of administration, the degree of gene restriction of each anti-disease drug is determined.

2. The SNP site detection kit for predicting the drug use for treating depression according to claim 1, characterized in that, The data acquisition module is also used for: The gene interpretation table of the current patient is compared with the gene interpretation table of the SNP locus of the reference patient stored in the database to determine the accuracy of the gene interpretation results of the current patient. When the accuracy of the gene interpretation result is less than the set accuracy threshold, the gene interpretation table of the current patient is corrected.

3. The SNP site detection kit for predicting drug use for treating depression according to claim 2, characterized in that, Determining the accuracy of the current patient's gene interpretation results includes: The gene interpretation table of the current patient is compared with the gene interpretation table of the reference patient. If the same detection gene is found, the same detection gene is regarded as the same type of detection gene. If the SNP site detection data of the same type of detection gene are completely consistent in the gene interpretation table of the current patient and the reference patient, then the same type of detection gene is regarded as the same type of detection gene; otherwise, the same type of detection gene is classified as the specific detection gene. For target reference patients in the database who have the same type of detection genes as the current patient, the accuracy of the current patient's gene interpretation results is determined based on the number of similar detection genes, the number of specific detection genes, and the number of dimensions where the SNP site detection data of specific detection genes are inconsistent with those of previous patients when comparing genes.

4. The SNP site detection kit for predicting drug use for treating depression according to claim 3, characterized in that, The current patient's genetic interpretation data will be revised, including: For all target reference patients in the database who have the same type of detection genes as the current patient, the detection data of detection genes other than the same type of detection genes and their SNP sites are added to the current patient's gene interpretation table, thereby obtaining the corrected gene interpretation data of the current patient.

5. The SNP site detection kit for predicting drug use for treating depression according to claim 1, characterized in that, Determine the degree of gene synthesis restriction for each antidote, including: Based on the differences in the number of high- and low-metabolic enzymes between each antidote at each stage of administration, as well as the differences in enzyme activity values ​​between high- and low-metabolic enzymes, the high-speed metabolic support capacity of each antidote at each stage of administration was determined. Based on the distribution of the aforementioned metabolically active enzymes of each antidote as high- and low-metabolic-activity enzymes at different stages of administration, the sustained high-metabolic-activity effect of each antidote throughout the entire administration period is determined. Based on the distribution level of high metabolic support for each antidote at different stages of administration and the sustained high metabolic effect throughout the entire administration period, the efficient and sustained metabolic enzyme activity of each antidote throughout the entire administration period was determined. The high efficiency and sustainability of the metabolic enzyme activity were negatively correlated and normalized to obtain the gene-integrated restriction degree of each anti-disease drug.

6. The SNP site detection kit for predicting drug treatment of depression according to claim 2, wherein Determine the high metabolic support capacity of each antiviral drug at each stage of treatment, including: Based on the number of highly metabolically active enzymes of each antidote at each stage of administration and the average enzyme activity of highly metabolically active enzymes of each antidote at each stage of administration, the positive metabolic support of each antidote at each stage of administration is determined. Based on the number of low-metabolic active enzymes of each antidote in each administration phase and the average enzyme activity value of low-metabolic active enzymes of each antidote in each administration phase, the reverse metabolic inhibition of each antidote in each administration phase is determined. The ratio of positive metabolic support to negative metabolic inhibition for each antidote in each treatment phase was determined to obtain the high-speed metabolic support for each antidote in each treatment phase.

7. The SNP site detection kit for predicting drug use for treating depression according to claim 2, characterized by, Determine the sustained metabolic activity of each antiviral drug throughout the entire treatment period, including: The ratio of the number of times each metabolically active enzyme of each antidote appears as a highly metabolically active enzyme in all stages of administration to the number of times it appears as a highly metabolically active enzyme in all stages of administration is determined to obtain the therapeutic efficacy of each metabolically active enzyme of each antidote in all stages of administration. The ratio of the number of times each metabolically active enzyme of each antidote appears as a low-metabolic-activity enzyme in all treatment phases to the number of times it appears as a low-metabolic-activity enzyme in all treatment phases is determined to obtain the therapeutic inefficiency of each metabolically active enzyme of each antidote in all treatment phases. Based on the differential distribution levels of the therapeutically potent and therapeutically ineffective effects of all metabolically active enzymes of each antidote, the sustained high metabolic potency of each antidote throughout the entire treatment period was determined.

8. The SNP site detection kit for predicting drug use for treating depression according to claim 1, characterized in that, The drug recommendation analysis module is used for: In the current patient's gene drug correspondence table, the anti-disease drugs associated with each detected gene are counted, and the recurring anti-disease drugs are treated as gene repeat drugs, while the other types of anti-disease drugs are treated as gene single drugs. Based on the comprehensive gene restriction level of each gene-specific drug and the number of anti-disease drug types contained in the detected genes of each gene-specific drug in the gene-drug correspondence table of the current patient, the recommended predictive level of each gene-specific drug is determined. The recommended predictive power of each gene duplication drug is determined based on the degree of gene-based restriction for each drug and the number of anti-disease drug types contained in each detected gene in the current patient's gene drug correspondence table.

9. A SNP site detection kit for predicting medication use in the treatment of depression according to claim 1, characterized in that, The drug recommendation generation module is used for: In the current patient's gene-drug correspondence table, all types of anti-disease drugs related to the detected genes are arranged in descending order of their predictive recommendation level, and a drug recommendation table for the current patient is generated.

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