Individualized drug risk assessment method and system

Abstract

The invention relates to an individualized drug risk assessment method and system. The method comprises the following steps: obtaining gene detection data of an individual, wherein the gene detection data of the individual comprises a detection result of at least one of genotype, enzyme activity and metabolic rate; determining a to-be-evaluated drug; determining genes and loci closely related to the adverse reaction of the to-be-evaluated drug; searching detection data related to genes and loci closely related to the adverse reaction of the to-be-evaluated drug from the gene detection data of the individual; according to the searched detection data, determining the risk level of the individual using the to-be-evaluated drug; and according to the determined risk level, generating risk prompt information of using the to-be-evaluated drug by the individual, and displaying the risk prompt information. According to the individualized medicine risk assessment method and system, the risk level of individual medicine use can be assessed, optimization of individual medicine selection is facilitated, the safety of medicine use is effectively improved, and powerful technical support is provided for individualized medical treatment.

Description

Technical Field

[0001] The present invention relates to the field of computer information processing, and in particular to an individualized drug risk assessment method and system. Background Art

[0002] With the development of gene sequencing technology, the application of genetic testing in personalized drug treatment is becoming more and more widespread. Pharmacogenomics provides a theoretical basis for personalized medication by studying the impact of gene variation on drug effects. At present, personalized medication has become an important part of precision medicine. Second-generation sequencing and capture sequencing technologies can efficiently detect gene mutations, but how to convert the test results into specific medication recommendations and risk assessments remains a challenge. Existing technologies mainly rely on the experience of clinicians and existing literature guidelines, but cannot perform personalized medication risk assessments through informatics technology.

[0003] The above information disclosed in the Background section is only for enhancement of understanding of the background of the present application and therefore it may contain information that does not constitute the prior art that is already known to a person of ordinary skill in the art. Summary of the invention

[0004] In view of this, the present application provides a personalized drug risk assessment method and system, which can evaluate the risk level of individual drug use, help optimize individual drug selection, effectively improve the safety of drug use, and provide strong technical support for personalized medicine.

[0005] This application contains the following: According to one aspect of the present application, a personalized drug risk assessment method is proposed, comprising: obtaining genetic test data of an individual, the genetic test data of the individual including the test results of at least one of genotype, enzyme activity, and metabolic rate; determining the drug to be evaluated; determining genes and sites that are closely related to the adverse reactions of the drug to be evaluated; searching for test data associated with genes and sites that are closely related to the adverse reactions of the drug to be evaluated from the genetic test data of the individual; determining the risk level of the individual using the drug to be evaluated based on the found test data; generating risk warning information for the individual using the drug to be evaluated based on the determined risk level and displaying the risk warning information.

[0006] In an exemplary embodiment of the present application, the detection data includes detection data of enzyme activity and / or detection data of metabolic rate, and the risk level of an individual using the drug to be evaluated is determined based on the found detection data, including: calculating the risk level of the drug to be evaluated based on the detection data of enzyme activity, the enzyme activity risk contribution function and / or the detection data of metabolic rate, the metabolic rate risk contribution function.

[0007] In an exemplary embodiment of the present application, the detection data includes detection data of enzyme activity and detection data of metabolic rate, and the risk level of the drug to be evaluated is calculated based on the detection data of enzyme activity, the enzyme activity risk contribution function and the detection data of metabolic rate, and the metabolic rate risk contribution function, including: calculating the integral value of the individual enzyme activity risk contribution function based on the detection data of enzyme activity and the enzyme activity risk contribution function; calculating the integral value of the individual metabolic rate risk contribution function based on the detection data of metabolic rate and the metabolic rate risk contribution function; calculating the risk level of the drug to be evaluated based on the integral value of the individual enzyme activity risk contribution function and the integral value of the individual metabolic rate risk contribution function.

[0008] In an exemplary embodiment of the present application, the enzyme activity risk contribution function is: F APOE ( x ) = 9 / {1+ exp [0.1·( x -50)]}, where x is the value of enzyme activity, F APOE ( x ) is the enzyme activity risk contribution function.

[0009] In an exemplary embodiment of the present application, the integral value of the enzyme activity risk contribution function of an individual is calculated according to the detection data of the enzyme activity and the enzyme activity risk contribution function, including: determining the minimum and maximum values ​​of the enzyme activity of the individual according to the detection data of the enzyme activity; taking the range between the minimum and maximum values ​​of the enzyme activity of the individual as the integral interval, F APOE ( x ) for integration.

[0010] In an exemplary embodiment of the present application, the metabolic rate risk contribution function is: F SLCO1B1 ( y ) = 9 / {1+ exp [0.2·( y -30)]}, where y is the value of metabolic rate, F SLCO1B1 ( y ) is the metabolic rate risk contribution function.

[0011] In an exemplary embodiment of the present application, the integral value of the metabolic rate risk contribution function of an individual is calculated according to the metabolic rate detection data and the metabolic rate risk contribution function, including: determining the minimum and maximum values ​​of the metabolic rate of the individual according to the metabolic rate detection data; taking the range between the minimum and maximum values ​​of the metabolic rate of the individual as the integral interval, F SLCO1B1 ( y ) for integration.

[0012] According to another aspect of the present application, a personalized drug risk assessment system is proposed, including: an acquisition module, used to acquire individual genetic test data, the individual genetic test data including at least one test result of genotype, enzyme activity, and metabolic rate; a drug determination module to be evaluated, used to determine the drug to be evaluated; a gene site determination module, used to determine genes and sites that are closely related to the adverse reactions of the drug to be evaluated; a search module, used to search for test data associated with genes and sites that are closely related to the adverse reactions of the drug to be evaluated from the individual's genetic test data; a drug risk level determination module, used to determine the risk level of the individual using the drug to be evaluated based on the found test data; and a display module, used to generate risk warning information for the individual using the drug to be evaluated based on the determined risk level and display the risk warning information.

[0013] In an exemplary embodiment of the present application, the detection data includes detection data of enzyme activity and / or detection data of metabolic rate, and the drug risk level determination module is used to calculate the risk level of the drug to be evaluated based on the detection data of enzyme activity, the enzyme activity risk contribution function and / or the detection data of metabolic rate, the metabolic rate risk contribution function.

[0014] In an exemplary embodiment of the present application, the detection data includes detection data of enzyme activity and detection data of metabolic rate, and the drug risk level determination module includes: a first calculation submodule, used to calculate the integral value of the individual enzyme activity risk contribution function based on the detection data of enzyme activity and the enzyme activity risk contribution function; a second calculation submodule, used to calculate the integral value of the individual metabolic rate risk contribution function based on the detection data of metabolic rate and the metabolic rate risk contribution function; a third calculation submodule, used to calculate the risk level of the drug to be evaluated based on the integral value of the individual enzyme activity risk contribution function and the integral value of the individual metabolic rate risk contribution function.

[0015] In an exemplary embodiment of the present application, the enzyme activity risk contribution function is: F APOE ( x ) = 9 / {1+ exp [0.1·(x -50)]}, where x is the value of enzyme activity, F APOE ( x ) is the enzyme activity risk contribution function.

[0016] In an exemplary embodiment of the present application, the first calculation submodule is used to: determine the minimum and maximum values ​​of the enzyme activity of the individual according to the detection data of the enzyme activity; take the range between the minimum and maximum values ​​of the enzyme activity of the individual as the integral interval, F APOE ( x ) for integration.

[0017] In an exemplary embodiment of the present application, the metabolic rate risk contribution function is: F SLCO1B1 ( y ) = 9 / {1+ exp [0.2·( y -30)]}, where y is the value of metabolic rate, F SLCO1B1 ( y ) is the metabolic rate risk contribution function.

[0018] In an exemplary embodiment of the present application, the second calculation submodule is used to: determine the minimum and maximum metabolic rates of the individual according to the metabolic rate detection data; use the range between the minimum and maximum metabolic rates of the individual as the integration interval, F SLCO1B1 ( y ) for integration.

[0019] In the present application, genes and sites closely related to the adverse reactions of the drug to be evaluated are determined, and test data associated with genes and sites closely related to the adverse reactions of the drug to be evaluated are searched from the individual's genetic test data. The individual's genetic test data includes at least one test result of genotype, enzyme activity, and metabolic rate. The risk level of the individual's use of the drug to be evaluated is determined based on the found test data. Risk warning information for the individual's use of the drug to be evaluated is generated based on the determined risk level and the risk warning information is displayed, which helps optimize individual drug selection, effectively improves the safety of drug use, and provides strong technical support for personalized medicine.

[0020] In addition, the technical solution of the present invention also brings many other advantages, which will be described in detail in the specific implementation manner.

[0021] It is to be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings of the embodiments will be briefly introduced below. Obviously, the drawings in the following description only relate to some embodiments of the present invention, but are not intended to limit the present invention.

[0023] Figure 1 The figure is a flow chart of a method for individualized drug risk assessment according to an exemplary embodiment.

[0024] Figure 2 The figure is a flow chart of a method for individualized drug risk assessment according to an exemplary embodiment.

[0025] Figure 3 The figure is a flow chart of a method for individualized drug risk assessment according to an exemplary embodiment.

[0026] Figure 4 The figure is a flow chart of a method for individualized drug risk assessment according to an exemplary embodiment.

[0027] Figure 5 is a schematic diagram of a personalized drug risk assessment system according to an exemplary embodiment.

[0028] Figure 6 It is a block diagram of an electronic device according to an exemplary embodiment.

[0029] Figure 7 It is a block diagram of a computer-readable medium according to an exemplary embodiment. DETAILED DESCRIPTION

[0030] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that the present invention will be comprehensive and complete and fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted.

[0031] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present invention. However, those skilled in the art will appreciate that the technical solution of the present invention can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring various aspects of the present invention.

[0032] This application proposes an embodiment of a method for individualized drug risk assessment, such as Figure 1 As shown, it includes steps S101 to S106.

[0033] Step S101: Obtaining individual genetic testing data, where the individual genetic testing data includes at least one test result of genotype, enzyme activity, and metabolic rate.

[0034] Step S102: Determine the drug to be evaluated.

[0035] Step S103: Determine genes and loci that are closely related to adverse reactions of the drug to be evaluated.

[0036] Step S104: searching the gene and site-related test data closely related to the adverse reactions of the drug to be evaluated from the individual's gene test data.

[0037] Step S105: Determine the risk level of the individual using the drug to be assessed based on the found test data.

[0038] Step S106: Generate risk warning information for individual use of the drug to be evaluated according to the determined risk level and display the risk warning information.

[0039] In the present application, genes and sites that are closely related to the adverse reactions of the drug to be evaluated are determined, and test data associated with genes and sites that are closely related to the adverse reactions of the drug to be evaluated are searched from the individual's genetic test data. The individual's genetic test data includes at least one test result of genotype, enzyme activity, and metabolic rate. The risk level of the individual's use of the drug to be evaluated is determined based on the found test data. Risk warning information for the individual's use of the drug to be evaluated is generated and displayed based on the determined risk level, which helps optimize individual drug selection, effectively improves the safety of drug use, and provides strong technical support for personalized medicine.

[0040] The medications to be evaluated are those that are associated with the individual's symptoms.

[0041] Patient A suffers from symptoms such as cough, sputum, and runny nose. Patient A comes to the hospital. After the doctor examines Patient A, he finds that Patient A suffers from an upper respiratory tract infection. The commonly used drug for this disease is amoxicillin, so amoxicillin is the drug to be evaluated. Amoxicillin is mainly related to the gene HLA-DQB1, corresponding to the site rs9274407. The genes and sites closely related to the adverse reactions of the drug to be evaluated are the gene HLA-DQB1, site rs9274407.

[0042] Patient B was feeling unwell and came to the hospital. After the doctor examined Patient B, he found that Patient B was infected with HIV. The commonly used drug for this disease is abacavir, so abacavir is the drug to be evaluated. Abacavir is mainly related to the gene HLA-B, corresponding to the site *57:01:01. The gene and site closely related to the adverse reactions of the drug to be evaluated are the gene HLA-B, site *57:01:01.

[0043] The association analysis between drugs and genes and sites can be analyzed with the help of drug-gene association data in the NMPA (National Medical Products Administration), FDA (US Food and Drug Administration), CPIC (Clinical Pharmacogenetics Implementation Consortium), DPWG (Dutch Pharmacogenetics Working Group), Swissmedic (Swiss Medicines Agency) and PharmGKB (Pharmacogenomics Knowledgebase) databases.

[0044] Association analysis: Analyze the association between genotype and drug efficacy and adverse reactions, identify gene loci closely related to drug efficacy or adverse reactions and the corresponding test results.

[0045] The gene loci closely related to the efficacy or adverse reactions of different drugs are different. In other words, when the drugs are different, the gene loci to be concerned about are different.

[0046] As an optional implementation, the method provided in this application divides the drug risk level into 9 levels, namely 1-9. The lower the level, the safer the individual's medication; the higher the level, the less safe the individual's medication. When the level is higher than a certain level, it is necessary to use the medication with caution or avoid using the medication.

[0047] If the individual's drug risk level is 1, 2, or 3, the risk assessment result is that the individual can use the drug normally. Different drug risk levels correspond to different risk descriptions.

[0048] If the individual's drug risk level is level 4, the risk assessment result is that the efficacy of the drug used by the individual is reduced.

[0049] If an individual's drug risk level is level 5, the risk assessment result is that the individual needs to monitor side effects when using the drug.

[0050] If an individual's drug risk level is 6 or 7, the risk assessment result is that the individual needs to use the drug with caution.

[0051] If an individual's drug risk level is 8 or 9, the risk assessment result is that the individual should avoid using the drug.

[0052] The scheme of this application focuses on gene loci that are closely related to drug efficacy or adverse reactions to understand the differences in drug responses among different individuals and provide support for the development of personalized drug treatment plans. By combining drug-gene association data, the individual's genetic test results are analyzed to assess the risk level of individual drug use and provide a scientific basis for clinical drug use. The system can be applied to clinicians, pharmacists and individual users to help optimize drug selection and dosage, reduce side effects and adverse drug reactions, and improve treatment effects through personalized medication recommendations and risk assessments.

[0053] The present application proposes an embodiment of a method for individualized drug risk assessment, including steps S1 to S5.

[0054] Step S1: Obtain gene test result data.

[0055] Step S2: Pre-group the acquired test result data, aggregate the gene regions with similar biological functions or drug response mechanisms into more concentrated categories (such as SNP, HLA, CYP2D6, etc.), and identify and extract the grouped gene test results according to the predetermined matching rules. Among them, SNP: Single Nucleotide Polymorphism, refers to a single base variation in a gene sequence; HLA: Human Leukocyte Antigen gene, involved in the function of the immune system; CYP2D6: Cytochrome P450 2D6 gene, mainly involved in drug metabolism.

[0056] Step S3: According to the existing drug-gene association analysis model, the identified and extracted gene test results are analyzed to obtain gene results that are closely related to drug efficacy or adverse reactions.

[0057] Step S4: Using the drug genome scoring model, each gene test result is expressed as a numerical value of a drug risk factor, and a model parameter set and a drug risk factor array are defined.

[0058] Step S5: Merge based on each determined numerical weight to obtain a final risk level value.

[0059] This application implements the grading of individualized medication risks through the following steps: Step S1 and step S2 are steps for collecting and processing genetic testing data. These two steps are the process of collecting, cleaning and standardizing data.

[0060] Data collection: Collect individual genotype data and ensure the accuracy and completeness of the data.

[0061] Data cleaning: Preprocess the collected data to remove noise and outliers.

[0062] Data standardization: Convert genotype data from different sources into a unified format for subsequent analysis.

[0063] Step S3 is the step of drug-gene association analysis.

[0064] Data analysis: Genetic test results were analyzed with drug-gene association data from the NMPA, FDA, CPIC, DPWG, Swissmedic, and PharmGKB databases.

[0065] Association analysis: Analyze the association between genotype and drug efficacy and adverse reactions, identify gene loci closely related to drug efficacy or adverse reactions and the corresponding test results.

[0066] For example, amoxicillin is mainly related to the gene HLA-DQB1, and the corresponding site is rs9274407. The corresponding site rs9274407 of the gene HLA-DQB1 has three genotypes: TT, AT, and AA. For people whose genotype is TT at the RS9274407 site of the HLA-DQB1 gene, the risk of drug-induced liver injury when using amoxicillin or drugs containing amoxicillin is relatively low. For people whose genotype is AT at the RS9274407 site of the HLA-DQB1 gene, the risk of drug-induced liver injury when using amoxicillin or drugs containing amoxicillin may increase, but it can still be used routinely. For people whose genotype is AA at the RS9274407 site of the HLA-DQB1 gene, the risk of drug-induced liver injury when using amoxicillin or drugs containing amoxicillin may increase, and its use should be avoided as much as possible.

[0067] The gene loci closely related to the efficacy or adverse reactions of different drugs are different. In other words, when the drugs are different, the gene loci to be concerned about are different.

[0068] The scheme of this application focuses on gene loci that are closely related to drug efficacy or adverse reactions to understand the differences in drug responses among different individuals and provide support for the development of individualized drug treatment plans.

[0069] Steps S4 and S5 are steps for evaluating the risk level of individual medication.

[0070] Evaluation indicators: A drug use risk level evaluation system is constructed based on indicators such as drug efficacy, incidence of adverse reactions, and the impact of genetic polymorphisms on drug metabolism and efficacy.

[0071] Risk assessment: Based on the results of genetic testing and drug-gene association analysis, the risk level of individual drug use is assessed.

[0072] Result output: The evaluation results are presented to clinicians and patients in an intuitive and easy-to-understand manner, providing a scientific basis for clinical medication.

[0073] As an optional implementation, the method provided in this application divides the drug risk level into 9 levels, namely 1-9. The lower the level, the safer the individual's medication; the higher the level, the less safe the individual's medication. When the level is higher than a certain level, it is necessary to use the medication with caution or avoid using the medication.

[0074] Drugs are divided into single-site drugs and multi-site drugs. The difference between single-site drugs and multi-site drugs is that if there is only one gene locus closely related to adverse drug reactions, the drug is a single-site drug. If there are at least two gene loci closely related to adverse drug reactions, the drug is a multi-site drug.

[0075] Single-site drug: This situation is relatively simple. The drug can directly take the risk level and description of the corresponding site result.

[0076] Multi-site drugs: One gene site corresponds to one risk level and description. The drug value site corresponds to the highest risk level and description in the result, as explained in detail below.

[0077] For example, taking the drug [atorvastatin] as an example, this drug is associated with two genes (APOE, SLCO1B1) and four loci. The corresponding risk levels are as follows: APOE: At site rs429358, the individual's genotype was CT; at site rs7412, the individual's genotype was CC. Matching these two genotypes to enzyme activity, it can be concluded that the individual's enzyme activity is moderately deficient. Based on the APOE gene sites rs429358 and rs7412, the risk level of this individual's use of the drug [atorvastatin] is level 5. The risk level is described as: The risk of adverse reactions to this drug may be higher than that of the general population. Please use the drug under the guidance of a doctor and pay attention to monitoring its adverse reactions. Simplified medication reminder: Your risk of adverse reactions to this drug is higher than that of the general population. It is recommended that you pay attention to monitoring its adverse reactions when using it.

[0078] SLCO1B1: At site rs4149056, the individual's genotype was CT; at site rs2306283, the individual's genotype was GG. Matching these two genotypes to enzyme activity, it can be concluded that the individual's enzyme activity is ε4 (one allele type). Based on the SLCO1B1 loci rs4149056 and rs2306283, the risk level of this individual's use of the drug [atorvastatin] is level 4. The risk level is described as: The efficacy of this drug may be reduced for you. Please use the drug under the guidance of a doctor and pay attention to monitoring its effects. Simplified medication reminder: The clinical efficacy of your use of this drug may be reduced. It is recommended that you pay attention to monitoring its efficacy when using it.

[0079] Since the risk level of the drug [atorvastatin] is related to the above two genes and four loci, the final drug risk level assessment and risk warning information need to integrate the individual drug risk levels corresponding to the above two genes. The risk level of the individual using the drug [atorvastatin] obtained according to the gene APOE loci rs429358 and rs7412 is 5, and the risk level of the individual using the drug [atorvastatin] obtained according to the gene SLCO1B1 loci rs4149056 and rs2306283 is 4. The higher risk level (level 5) of the two is taken as the risk level for individual drug risk assessment, and the risk level description and simplified medication reminder corresponding to the higher risk level (level 5) are used as the risk level assessment and risk warning for the individual using the drug [atorvastatin]. That is, the drug [atorvastatin], risk level: level 5, risk level description: The risk of adverse reactions to this drug may be higher than that of the general population. Please use the drug under the guidance of a doctor and pay attention to monitoring its adverse reactions. Risk Warning / Simplified Medication Reminder: Your risk of adverse reactions from using this drug is higher than that of the general population. It is recommended that you pay attention to monitor its adverse reactions when using it.

[0080] When multiple gene loci affect the assessment of drug risk level, since different gene loci have different degrees of influence on drug risk, a weighted method is used to calculate the risk level of multi-gene loci drugs to reflect the contribution of different gene loci.

[0081] Hypothetical drug D Gene locus set G ={ g 1, g 2,……, g n} associated, given: R ( g i ): No. i The risk level of each gene locus.

[0082] w i : No. i The weight of a gene locus represents the degree of influence of the gene locus on drug risk, satisfying w 1+ w 2+……+ w i +……+ w n = 1. The larger the weight, the greater the impact of the gene locus on drug risk.

[0083] R ( D ) = w 1× R ( g 1)+ w 2× R ( g 2)+……+ w i × R ( g i )+……+ w n × R ( g n ) R ( D ) is a drug D level of risk.

[0084] Taking the drug [atorvastatin] as an example, assume that the gene loci and weights are as follows: APOE: sites rs429358, rs7412, risk level 5, weight w 1=0.6.

[0085] SLCO1B1: sites rs4149056, rs2306283, risk level 4, weight w 2=0.4.

[0086] R ( D )=0.6×5+0.4×4=3+1.6=4.6≈5 Therefore, the final calculated risk level is still level 5.

[0087] As an optional implementation method, the concept of risk contribution function is introduced.

[0088] f ( g i ): About i The contribution function of each gene locus to the risk of drug use maps the genotype results of the gene locus to a continuous risk level value. This function can be set based on experimental data or statistical models.

[0089] In order to comprehensively consider the weight of each gene locus, the risk level of the drug is defined as: R ( D ) = w 1× f ( g 1)+ w 2× f ( g 2)+……+ w i × f ( g i )+……+ w n × f ( g n ) in f ( g i ) usually ranges from [0,9] and can be set by fitting historical data or expert scoring.

[0090] Setting the contribution function f ( g i ) can make the model more flexible, especially when the impact of different gene loci on drug risk is not simply linear. Contribution function f ( g i) is mainly used to convert the genotype information of each gene locus into the impact on the risk level. The following are some specific contribution function algorithms suitable for different situations: 1) Linear contribution function

[0091] If the effect of a gene locus on risk is linear (i.e., the risk level is proportional to the strength of the genotype association), it can be directly represented by a linear function: f ( g i ) = a · R ( g i )+ b R ( g i ): Raw risk level of the gene locus.

[0092] a and b are linear coefficients that control the slope and offset of the function. They can be set by fitting historical data or adjusted based on clinical significance.

[0093] For example, if you want to make the risk level more sensitive to the strength of the genotype, you can a >1 to expand the impact. 2) Logarithmic contribution function

[0094] For some gene loci, the impact on risk gradually decreases or increases, and a logarithmic function can be used to compress or amplify the change in risk level: f ( g i ) = log k ( R ( g i ) +c ) k : The base of the logarithm, usually chosen k = 2 or k = e .

[0095] c : Offset, ensuring that the input to the logarithmic function is always positive. c =1 or a greater positive value.

[0096] The logarithmic function is suitable for giving less impact to some lower risk levels (such as 1-3), or giving more weight to higher risk levels. 3) Exponential contribution function

[0097] When a gene locus has a very significant effect on risk, that is, the risk increases sharply with the genotype result, an exponential function can be used to amplify its effect: f ( g i ) = d · exp [ λ · R ( g i )] d and λ : Constant coefficient, controlling the baseline value and growth rate of the function.

[0098] R ( g i ): Risk level of the gene locus.

[0099] This formula can increase the weight of high-risk sites. For example, if the risk of a certain gene site is 5, and the risk site has a more significant impact on the efficacy of the drug, its impact can be increased through an exponential function. 4) Sigmoid contribution function

[0100] S-shaped functions (such as logistic functions) are suitable for nonlinear situations with threshold effects. For example, when a gene locus reaches a certain intensity, the impact on the risk level no longer increases, and the S-shaped function can be used to smooth the change in risk level: f ( g i ) = L / {1+ exp [- α [ R ( g i )- β ]} L : Indicates the upper limit of the function, which can be set to the maximum risk level 9.

[0101] α : Parameter that controls the growth rate. The larger the value, the steeper the function.

[0102] β : Offset, indicating that a significant effect will only occur when a certain genotype risk level is reached.

[0103] This function is suitable for simulating the "switch effect" of a gene locus, that is, the genotype significantly increases the risk only when it is above a certain threshold. 5) Weighted multi-factor function

[0104] If the risk level is affected by multiple factors, multiple contribution functions can be combined. For example, linear and logarithmic contribution functions can be combined to take into account the base value and rate of change of the risk level: f ( g i ) = w 1[ a · R ( g i )+ b ]+ w 2[ log k ( R ( g i ) +c )] w 1 and w 2Weight, indicating the influence of linear and logarithmic functions.

[0105] The meanings of other parameters are the same as above.

[0106] This combination function allows for different change patterns at different risk levels.

[0107] Application Examples Assuming that the risk level of APOE gene locus rs429358 of [atorvastatin] is 5, a logarithmic contribution function is used, k =2 and c =1: f (rs429358) = log 2(5+1)= log 2(6) ≈ 2.585 The final output risk level can be weighted and integrated with the results of other gene loci to calculate the final risk assessment value.

[0108] By rationally selecting different contribution functions and parameters, the adaptability of the model can be improved to better reflect the complex effects of multiple gene loci on the risk of drug use.

[0109] The method provided in the embodiment of the present application can adjust the weight and contribution function of each gene locus, which is not only applicable to drugs associated with a single gene locus, but also to drugs with complex associations of multiple gene loci. Taking the drug [atorvastatin] as an example, assuming that the gene loci and weights are as follows: APOE: sites rs429358, rs7412, risk level 5, weight w 1=0.6.

[0110] SLCO1B1: sites rs4149056, rs2306283, risk level 4, weight w 2=0.4.

[0111] like f ( g i )= R ( g i ) (that is, directly use discrete values): R ( D )=0.6×5+0.4×4=3+1.6=4.6≈5 Therefore, the final risk level remains at level 5.

[0112] Figure 2 , 3 4 respectively show an implementation of the individualized drug risk assessment method provided by the present application, the difference is Figure 2 The illustrated embodiment assesses the risk level of a drug based on enzyme activity; Figure 3 The illustrated embodiment assesses the risk level of a drug based on metabolic rate; Figure 4 The illustrated embodiment assesses the risk level of a drug based on both enzyme activity and metabolic rate.

[0113] like Figure 2 As shown, this implementation includes steps S201 to S207.

[0114] Step S201: Obtaining individual genetic testing data, where the individual genetic testing data at least includes the test result of enzyme activity.

[0115] Step S202: Determine the drug to be evaluated.

[0116] Step S203: Determine genes and loci that are closely related to adverse reactions of the drug to be evaluated.

[0117] Step S204: searching the gene detection data of the individual for the detection data of the enzyme activity associated with the genes and sites that are closely related to the adverse reactions of the drug to be evaluated.

[0118] Step S205: Determine the minimum and maximum values ​​of the enzyme activity of the individual according to the detection data of the enzyme activity.

[0119] Step S206: The range between the minimum and maximum values ​​of the enzyme activity of the individual is taken as the integral interval. F APOE ( x ) = 9 / {1+ exp[0.1·( x -50)]} to perform integration and determine the risk level of the drug to be evaluated based on the integrated value.

[0120] in, x is the value of enzyme activity, F APOE ( x ) is the enzyme activity risk contribution function.

[0121] Step S207: Generate risk warning information for individual use of the drug to be evaluated according to the determined risk level and display the risk warning information.

[0122] like Figure 3 As shown, this implementation includes steps S301 to S307.

[0123] Step S301: Obtaining individual genetic testing data, where the individual genetic testing data at least includes a metabolic rate test result.

[0124] Step S302: Determine the drug to be evaluated.

[0125] Step S303: Determine genes and loci that are closely related to adverse reactions of the drug to be evaluated.

[0126] Step S304: searching the individual's genetic test data for metabolic rate test data associated with genes and sites that are closely related to the adverse reactions of the drug to be evaluated.

[0127] Step S305: Determine the minimum and maximum metabolic rate of the individual according to the metabolic rate detection data.

[0128] Step S306: Take the range between the minimum and maximum metabolic rates of the individual as the integration interval, F SLCO1B1 ( y ) = 9 / {1+ exp [0.2·( y -30)]} to perform integration and determine the risk level of the drug to be evaluated based on the integrated value.

[0129] in, y is the value of metabolic rate, F SLCO1B1 ( y ) is the metabolic rate risk contribution function.

[0130] Step S307: Generate risk warning information for individual use of the drug to be evaluated according to the determined risk level and display the risk warning information.

[0131] like Figure 4As shown, this implementation includes steps S401 to S411.

[0132] Step S401: Obtaining individual genetic testing data, where the individual genetic testing data at least includes test results of enzyme activity and metabolic rate.

[0133] Step S402: Determine the drug to be evaluated.

[0134] Step S403: Determine genes and loci that are closely related to adverse reactions of the drug to be evaluated.

[0135] Step S404: searching the gene detection data of the individual for the detection data of the enzyme activity and metabolic rate associated with the genes and sites closely related to the adverse reactions of the drug to be evaluated.

[0136] Step S405: Determine the minimum and maximum values ​​of the enzyme activity of the individual according to the detection data of the enzyme activity.

[0137] Step S406: The range between the minimum and maximum values ​​of the enzyme activity of the individual is taken as the integral interval. F APOE ( x ) = 9 / {1+ exp [0.1·( x -50)]} to integrate and obtain the integral value.

[0138] in, x is the value of enzyme activity, F APOE ( x ) is the enzyme activity risk contribution function.

[0139] Step S407: Determine the minimum and maximum metabolic rates of the individual according to the metabolic rate detection data.

[0140] Step S408: The range between the minimum and maximum metabolic rates of the individual is taken as the integration interval. F SLCO1B1 ( y ) = 9 / {1+ exp [0.2·( y -30)]} to integrate and obtain the integral value.

[0141] in, y is the value of metabolic rate, F SLCO1B1 ( y ) is the metabolic rate risk contribution function.

[0142] Step S409: FAPOE ( x ) and the integral value obtained by integrating F SLCO1B1 ( y ) is weighted according to the preset weight to obtain the total integral value.

[0143] Step S410: Determine the risk level of the drug to be evaluated according to the total integral value.

[0144] Step S411: Generate risk warning information for individual use of the drug to be evaluated according to the determined risk level and display the risk warning information.

[0145] Figures 2 to 4 The integral model used in the illustrated embodiment is used to handle the relationship between risk level and continuous variables such as enzyme activity and metabolic rate. The following is a specific example showing Figure 4 The illustrated embodiment uses an integral model to calculate the risk level of the drug [atorvastatin].

[0146] The correlation between the drug [atorvastatin] (i.e. the drug to be evaluated) and two genes (APOE and SLCO1B1) is as follows: APOE gene:

[0147] The combination of sites rs429358 and rs7412 affected the enzyme activity.

[0148] The enzyme activity value range is [0,100]. The lower the enzyme activity, the higher the risk.

[0149] Risk Contribution Function F APOE ( x ) = 9 / {1+ exp [0.1·( x -50)]}.

[0150] x Indicates enzyme activity, 50 is the median value of enzyme activity, and 9 is the maximum risk level. SLCO1B1 gene:

[0151] The combination of loci rs4149056 and rs2306283 affects the rate of drug metabolism.

[0152] The metabolic rate range is [0,100]. The lower the metabolic rate, the higher the risk.

[0153] Risk Contribution Function F SLCO1B1 ( y ) = 9 / {1+ exp [0.2·( y -30)]}.

[0154] y Indicates metabolic rate, 30 is a medium-low metabolic rate value, and 9 is the maximum risk level. 1) Integral model formula

[0155] Overall risk of atorvastatin R ( D ) is calculated by integrating and weighting the risk contributions of the two loci: R ( D )= w 1× R 1+ w 2× R 2 R 1 is the risk contribution function F APOE ( x ) = 9 / {1+ exp [0.1·( x -50)]} x min to x max The result of integration in the interval .

[0156] R 2 is the risk contribution function F SLCO1B1 ( y ) = 9 / {1+ exp [0.2·( y -30)]} y min to y max The result of integration in the interval .

[0157] in, x min is the minimum value of the APOE enzyme activity range, x max is the maximum value of the APOE enzyme activity range; y min is the minimum value of the range of SLCO1B1 metabolic rate, y max The maximum value of the range of SLCO1B1 metabolic rate.

[0158] When the two genes have unequal effects on risk, w 1 and w2 are not equal, and w 1+ w 2=1.

[0159] When two genes have equal effects on risk, w 1 and w 2 are equal, and w 1= w 2=0.5. 2) Input data

[0160] Assumptions: The APOE enzyme activity of an individual is measured and the result is [30,60]. That is, the minimum value of the enzyme activity of the individual is 30 and the maximum value is 60.

[0161] The individual's SLCO1B1 metabolic rate is measured and the result is [20,50]. That is, the minimum metabolic rate of the individual is 20 and the maximum metabolic rate is 50. 3) Calculation steps

[0162] Risk Contribution Function F APOE ( x ) = 9 / {1+ exp [0.1·( x -50)]} is integrated in the interval [30,60] and the result is 163.23.

[0163] Risk Contribution Function F SLCO1B1 ( y ) = 9 / {1+ exp [0.2·( y -30)]} is integrated over the interval [20,50] and the result is 94.90.

[0164] R ( D )= w 1×163.23+ w 2×94.90 When two genes have equal effects on risk, w 1 and w 2 are equal, and w 1= w 2=0.5.

[0165] R ( D )=0.5×(163.23+94.90)≈129.06 4) Risk level description

[0166] Since the integral value is too large (because the integral accumulates the total effect of the risk), the result can be normalized or directly converted to a discrete risk level of [0, 9]. For example, the specific drug risk level can be obtained by dividing the result by the total possible integral range ([0,100]) and then rounding.

[0167] Figures 2 to 4 The method shown does not require measuring the individual's genotype, and assesses the drug risk level by detecting the individual's enzyme activity and metabolic rate.

[0168] This application aims to analyze the individual's genetic test results by combining the drug-gene association data in the databases (such as NMPA, FDA, CPIC, DPWG, Swissmedic and PharmGKB), so as to evaluate the risk level of individual drug use and provide a scientific basis for clinical drug use. The system can be applied to clinicians, pharmacists and individual users to help optimize drug selection and dosage, reduce side effects and adverse drug reactions, and improve treatment effects through personalized medication recommendations and risk assessment.

[0169] The present application also provides a personalized drug risk assessment system, such as Figure 5 As shown, the system includes: an acquisition module 10 , a drug to be evaluated determination module 20 , a gene site determination module 30 , a search module 40 , a drug risk level determination module 50 , and a display module 60 .

[0170] The acquisition module 10 is used to acquire individual gene detection data, where the individual gene detection data includes at least one detection result of genotype, enzyme activity, and metabolic rate.

[0171] The drug to be evaluated determining module 20 is used to determine the drug to be evaluated.

[0172] The gene site determination module 30 is used to determine genes and sites that are closely related to the adverse reactions of the drug to be evaluated.

[0173] The search module 40 is used to search for detection data associated with genes and sites that are closely related to adverse reactions of the drug to be evaluated from the individual's genetic detection data.

[0174] The drug risk level determination module 50 is used to determine the risk level of the individual's use of the drug to be evaluated based on the found test data.

[0175] The display module 60 is used to generate risk warning information for an individual using the drug to be evaluated according to the determined risk level and to display the risk warning information.

[0176] The personalized drug risk assessment system provided by the present application can determine the genes and sites that are closely related to the adverse reactions of the drug to be evaluated, and search for test data associated with the genes and sites that are closely related to the adverse reactions of the drug to be evaluated from the individual's genetic test data. The individual's genetic test data includes at least one test result of genotype, enzyme activity, and metabolic rate. The risk level of the individual's use of the drug to be evaluated is determined based on the found test data. Risk warning information for the individual's use of the drug to be evaluated is generated and displayed based on the determined risk level, so as to help optimize individual drug selection, effectively improve the safety of drug use, and provide strong technical support for personalized medicine.

[0177] Optionally, the detection data includes detection data of enzyme activity and / or detection data of metabolic rate, and the drug risk level determination module 50 is used to calculate the risk level of the drug to be evaluated based on the detection data of enzyme activity, the enzyme activity risk contribution function and / or the detection data of metabolic rate, the metabolic rate risk contribution function.

[0178] Optionally, the detection data includes detection data of enzyme activity and detection data of metabolic rate, and the drug risk level determination module 50 includes: a first calculation submodule, a second calculation submodule, and a third calculation submodule.

[0179] The first calculation submodule is used to calculate the integral value of the enzyme activity risk contribution function of the individual according to the detection data of the enzyme activity and the enzyme activity risk contribution function.

[0180] The second calculation submodule is used to calculate the integral value of the metabolic rate risk contribution function of the individual according to the metabolic rate detection data and the metabolic rate risk contribution function.

[0181] The third calculation submodule is used to calculate the risk level of the drug to be evaluated according to the integral value of the individual enzyme activity risk contribution function and the integral value of the individual metabolic rate risk contribution function.

[0182] Optionally, the enzyme activity risk contribution function is: F APOE ( x ) = 9 / {1+ exp [0.1·( x -50)]}, where x is the value of enzyme activity, F APOE ( x ) is the enzyme activity risk contribution function.

[0183] Optionally, the first computing submodule is used for: Determine the minimum and maximum values ​​of the enzyme activity of the individual according to the detection data of the enzyme activity; The range between the minimum and maximum values ​​of the enzyme activity of the individual is taken as the integral interval. F APOE ( x ) for integration.

[0184] Optionally, the metabolic rate risk contribution function is: F SLCO1B1 ( y ) = 9 / {1+ exp [0.2·( y -30)]}, where y is the value of metabolic rate, F SLCO1B1 ( y ) is the metabolic rate risk contribution function.

[0185] Optionally, the second computing submodule is used for: Determine the minimum and maximum metabolic rates of an individual according to the metabolic rate detection data; The range between the minimum and maximum metabolic rates of individuals is taken as the integration interval. F SLCO1B1 ( y ) for integration.

[0186] Figure 6 It is a block diagram of an electronic device according to an exemplary embodiment.

[0187] Refer to the following Figure 6 The electronic device 600 according to this embodiment of the present application is described. Figure 6 The electronic device 600 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0188] like Figure 6 As shown, the electronic device 600 is in the form of a general computing device. The components of the electronic device 600 may include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different system components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0189] The storage unit stores a program code, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps described in this specification according to various exemplary embodiments of the present application. For example, the processing unit 610 can perform the following steps: Figures 1 to 4 Follow the steps shown in .

[0190] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0191] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include the implementation of a network environment.

[0192] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0193] The electronic device 600 may also communicate with one or more external devices 600' (e.g., keyboards, pointing devices, Bluetooth devices, etc.) so that a user can communicate with the device that interacts with the electronic device 600, and / or the electronic device 600 can communicate with any device that communicates with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface 650. In addition, the electronic device 600 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 through a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0194] Through the above description of the implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by combining software with necessary hardware. Figure 7 As shown, the technical solution according to the implementation mode of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, or a network device, etc.) to execute the above method according to the implementation mode of the present application.

[0195] The software product may be any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0196] The computer readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by an instruction execution system, an apparatus, or a device or used in combination with it. The program code contained on the readable storage medium may be transmitted with any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0197] Program code for performing the operations of the present application may be written in any combination of one or more programming languages, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0198] The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by a device, the computer-readable medium realizes the following functions: obtaining the genetic test data of an individual, wherein the genetic test data of the individual includes the test results of at least one of genotype, enzyme activity, and metabolic rate; determining the drug to be evaluated; determining the genes and sites that are closely related to the adverse reactions of the drug to be evaluated; searching the genetic test data of the individual for test data associated with the genes and sites that are closely related to the adverse reactions of the drug to be evaluated; determining the risk level of the individual using the drug to be evaluated based on the found test data; generating risk warning information for the individual using the drug to be evaluated based on the determined risk level and displaying the risk warning information.

[0199] Those skilled in the art will appreciate that the above modules can be distributed in the device according to the description of the embodiment, or can be changed accordingly and only used in one or more devices different from the embodiment. The modules of the above embodiments can be combined into one module, or further divided into multiple sub-modules.

[0200] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiment of the present application.

[0201] The exemplary embodiments of the present application are specifically shown and described above. It should be understood that the present application is not limited to the detailed structures, configurations or implementations described herein; on the contrary, the present application is intended to cover various modifications and equivalent configurations included in the spirit and scope of the appended claims.

Claims

1. A method for individualized drug risk assessment, characterized in that: include: Acquiring genetic testing data of an individual, wherein the genetic testing data of the individual includes a test result of at least one of a genotype, an enzyme activity, and a metabolic rate; Identify drugs to be evaluated; Determine the genes and loci that are closely related to the adverse reactions of the drug to be evaluated; Searching for gene and site-related test data closely related to adverse reactions of the drug to be evaluated from the individual's gene test data; Determining the risk level of the individual using the drug to be evaluated based on the found test data; Generate risk warning information for individual use of the drug to be evaluated based on the determined risk level and display the risk warning information.

2. The method according to claim 1, characterized in that The detection data includes detection data of enzyme activity and / or detection data of metabolic rate. The risk level of an individual using the drug to be evaluated is determined based on the found detection data, including: calculating the risk level of the drug to be evaluated based on the detection data of enzyme activity, the enzyme activity risk contribution function and / or the detection data of metabolic rate, the metabolic rate risk contribution function.

3. The method according to claim 2, characterized in that The detection data includes detection data of enzyme activity and detection data of metabolic rate. The risk level of the drug to be evaluated is calculated according to the detection data of enzyme activity, the enzyme activity risk contribution function and the detection data of metabolic rate, and the metabolic rate risk contribution function, including: Calculate the integral value of the individual enzyme activity risk contribution function according to the enzyme activity detection data and the enzyme activity risk contribution function; Calculating the integral value of the individual metabolic rate risk contribution function according to the metabolic rate detection data and the metabolic rate risk contribution function; The risk level of the drug to be evaluated is calculated according to the integral value of the individual enzyme activity risk contribution function and the integral value of the individual metabolic rate risk contribution function.

4. The method according to claim 3, characterized in that The enzyme activity risk contribution function is: F APOE ( x ) = 9 / {1+ exp [0.1·( x -50)]}, where x is the value of enzyme activity, F APOE ( x ) is the enzyme activity risk contribution function.

5. The method according to claim 4, characterized in that The integral value of the individual enzyme activity risk contribution function is calculated based on the enzyme activity detection data and the enzyme activity risk contribution function, including: Determine the minimum and maximum values ​​of the enzyme activity of the individual according to the detection data of the enzyme activity; The range between the minimum and maximum values ​​of the enzyme activity of the individual is taken as the integral interval. F APOE ( x ) for integration.

6. The method according to claim 3, characterized in that The metabolic rate risk contribution function is: F SLCO1B1 ( y ) = 9 / {1+ exp [0.2·( y -30)]}, where y is the value of metabolic rate, F SLCO1B1 ( y ) is the metabolic rate risk contribution function.

7. The method according to claim 6, characterized in that The integral value of the individual metabolic rate risk contribution function is calculated based on the metabolic rate detection data and the metabolic rate risk contribution function, including: Determine the minimum and maximum metabolic rates of an individual according to the metabolic rate detection data; The range between the minimum and maximum metabolic rates of individuals is taken as the integration interval. F SLCO1B1 ( y ) for integration.

8. A personalized drug risk assessment system, characterized in that: include: An acquisition module, used to acquire individual genetic test data, wherein the individual genetic test data includes a test result of at least one of genotype, enzyme activity, and metabolic rate; A module for determining drugs to be evaluated, used for determining drugs to be evaluated; A gene locus determination module, used to determine genes and loci closely related to the adverse reactions of the drug to be evaluated; A search module, used to search the gene and site-related detection data closely related to the adverse reaction of the drug to be evaluated from the gene detection data of the individual; A drug risk level determination module, used to determine the risk level of an individual using the drug to be evaluated based on the found test data; The display module is used to generate risk warning information for an individual using the drug to be evaluated according to the determined risk level and to display the risk warning information.

9. The system according to claim 8, characterized in that The detection data includes detection data of enzyme activity and / or detection data of metabolic rate, and the drug risk level determination module is used to calculate the risk level of the drug to be evaluated based on the detection data of enzyme activity, the enzyme activity risk contribution function and / or the detection data of metabolic rate, the metabolic rate risk contribution function.

10. The system according to claim 9, characterized in that The detection data includes detection data of enzyme activity and detection data of metabolic rate, and the drug risk level determination module includes: A first calculation submodule is used to calculate the integral value of the enzyme activity risk contribution function of the individual according to the detection data of the enzyme activity and the enzyme activity risk contribution function; A second calculation submodule is used to calculate the integral value of the metabolic rate risk contribution function of the individual according to the metabolic rate detection data and the metabolic rate risk contribution function; The third calculation submodule is used to calculate the risk level of the drug to be evaluated according to the integral value of the individual enzyme activity risk contribution function and the integral value of the individual metabolic rate risk contribution function.

11. The system according to claim 10, characterized in that The enzyme activity risk contribution function is: F APOE ( x ) = 9 / {1+ exp [0.1·( x -50)]}, where x is the value of enzyme activity, F APOE ( x ) is the enzyme activity risk contribution function.

12. The system according to claim 11, characterized in that The first calculation submodule is used for: Determine the minimum and maximum values ​​of the enzyme activity of the individual according to the detection data of the enzyme activity; The range between the minimum and maximum values ​​of the enzyme activity of the individual is taken as the integral interval. F APOE ( x ) for integration.

13. The system of claim 10, wherein: The metabolic rate risk contribution function is: F SLCO1B1 ( y ) = 9 / {1+ exp [0.2·( y -30)]}, where y is the value of metabolic rate, F SLCO1B1 ( y ) is the metabolic rate risk contribution function.

14. The system of claim 13, wherein: The second calculation submodule is used for: Determine the minimum and maximum metabolic rates of an individual according to the metabolic rate detection data; The range between the minimum and maximum metabolic rates of individuals is taken as the integration interval. F SLCO1B1 ( y ) for integration.

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