Individualized Drug Risk Assessment Method and System

Abstract

This application relates to an individualized drug risk assessment method and system. The method includes: obtaining the individual's genetic testing data, where the individual's genetic testing data 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 loci closely related to the adverse reactions of the drug to be evaluated; searching in the individual's genetic testing data for the test data associated with the genes and loci 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 a risk prompt message for the individual using the drug to be evaluated according to the determined risk level and displaying the risk prompt message. The individualized drug risk assessment method and system involved in this application 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.

Description

Technical Field

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

[0002] With the development of gene sequencing technology, gene detection has been increasingly widely applied in individualized drug treatment. Pharmacogenomics provides a theoretical basis for individualized drug use by studying the impact of gene mutations on drug effects. Currently, individualized drug use has become an important part of precision medicine. Second-generation sequencing and capture sequencing technologies can efficiently detect gene mutations, but there are still challenges in converting the detection results into specific medication recommendations and risk assessments. Existing technologies mainly rely on the experience of clinicians and existing literature guidelines, but cannot perform individualized drug risk assessment through informatics technology.

[0003] The above information disclosed in the Background Art section is only used to enhance the understanding of the background of the present application, and thus it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0004] In view of this, the present application provides an individualized 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] The present application includes the following content:

[0006] According to one aspect of the present application, an individualized drug risk assessment method is proposed, including: obtaining gene detection data of an individual, where the gene detection data of the individual includes at least one of the detection results of genotype, enzyme activity, and metabolic rate; determining a drug to be evaluated; determining genes and loci closely related to the adverse reactions of the drug to be evaluated; searching the gene detection data of the individual for detection data associated with the genes and loci 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 according to the detected data found; generating a risk prompt message for the individual using the drug to be evaluated according to the determined risk level and displaying the risk prompt message.

[0007] In an exemplary embodiment of the present application, the detection data includes detection data of enzyme activity and / or detection data of metabolic rate. Determining the risk level of the individual using the drug to be evaluated according to the detected data found includes: calculating the risk level of the drug to be evaluated according to 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.

[0008] In an exemplary embodiment of the present application, the detection data includes detection data of enzyme activity and detection data of metabolic rate. Calculating the risk level of the drug to be evaluated according to the detection data of enzyme activity, the enzyme activity risk contribution function, the detection data of metabolic rate, and the metabolic rate risk contribution function includes: calculating the integral value of the individual's enzyme activity risk contribution function according to the detection data of enzyme activity and the enzyme activity risk contribution function; calculating the integral value of the individual's metabolic rate risk contribution function according to the detection data of metabolic rate and the metabolic rate risk contribution function; calculating the risk level of the drug to be evaluated according to the integral value of the individual's enzyme activity risk contribution function and the integral value of the individual's metabolic rate risk contribution function.

[0009] 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.

[0010] In an exemplary embodiment of the present application, calculating the integral value of the individual's enzyme activity risk contribution function according to the detection data of enzyme activity and the enzyme activity risk contribution function includes: determining the minimum value and the maximum value of the individual's enzyme activity according to the detection data of enzyme activity; taking the range between the minimum value and the maximum value of the individual's enzyme activity as the integration interval, and integrating F APOE ( x )

[0011] 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.

[0012] In an exemplary embodiment of the present application, calculating an integral value of the individual's metabolic rate risk contribution function according to the detection data of the metabolic rate and the metabolic rate risk contribution function includes: determining the minimum value and the maximum value of the individual's metabolic rate according to the detection data of the metabolic rate; using the range between the minimum value and the maximum value of the individual's metabolic rate as the integration interval, and integrating F SLCO1B1 ( y )

[0013] According to another aspect of the present application, an individualized drug risk assessment system is proposed, including: an acquisition module for acquiring the individual's gene detection data, where the individual's gene detection data includes at least one of the detection results of genotype, enzyme activity, and metabolic rate; a drug to be evaluated determination module for determining the drug to be evaluated; a gene locus determination module for determining the genes and loci closely related to the adverse reactions of the drug to be evaluated; a search module for searching the detection data associated with the genes and loci closely related to the adverse reactions of the drug to be evaluated from the individual's gene detection data; a drug risk level determination module for determining the risk level of the individual using the drug to be evaluated according to the detected data found; a display module for generating a risk prompt message for the individual using the drug to be evaluated according to the determined risk level and displaying the risk prompt message.

[0014] In an exemplary embodiment of the present application, the detection data includes the detection data of enzyme activity and / or the detection data of metabolic rate, and the drug risk level determination module is used for: calculating the risk level of the drug to be evaluated according to 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.

[0015] In an exemplary embodiment of the present application, the detection data includes the detection data of enzyme activity and the detection data of metabolic rate, and the drug risk level determination module includes: a first calculation sub-module for calculating an integral value of the individual's enzyme activity risk contribution function according to the detection data of enzyme activity and the enzyme activity risk contribution function; a second calculation sub-module for calculating an integral value of the individual's metabolic rate risk contribution function according to the detection data of metabolic rate and the metabolic rate risk contribution function; a third calculation sub-module for calculating the risk level of the drug to be evaluated according to the integral value of the individual's enzyme activity risk contribution function and the integral value of the individual's metabolic rate risk contribution function.

[0016] 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 numerical value of enzyme activity, F APOE ( x ) is the enzyme activity risk contribution function.

[0017] In an exemplary embodiment of the present application, the first calculation sub-module is used to: determine the minimum value and the maximum value of the enzyme activity of an individual according to the detection data of the enzyme activity; use the range between the minimum value and the maximum value of the enzyme activity of the individual as the integration interval, and integrate F APOE ( x ).

[0018] 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 numerical value of the metabolic rate, F SLCO1B1 ( y ) is the metabolic rate risk contribution function.

[0019] In an exemplary embodiment of the present application, the second calculation sub-module is used to: determine the minimum value and the maximum value of the metabolic rate of an individual according to the detection data of the metabolic rate; use the range between the minimum value and the maximum value of the metabolic rate of the individual as the integration interval, and integrate F SLCO1B1 ( y ).

[0020] In the present application, genes and loci closely related to the adverse reactions of the drug to be evaluated are determined, detection data associated with the genes and loci closely related to the adverse reactions of the drug to be evaluated are found from the individual's gene detection data, the individual's gene detection data includes at least one of the detection results of genotype, enzyme activity, and metabolic rate, the risk level of the individual using the drug to be evaluated is determined according to the found detection data, and a risk prompt message for the individual to use the drug to be evaluated is generated according to the determined risk level and the risk prompt message is displayed, which helps to optimize the individual's drug selection, effectively improves the safety of drug use, and provides strong technical support for personalized medicine.

[0021] 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.

[0022] It should be understood that the above general description and the following detailed description are exemplary only and do not limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] 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 and do not limit the present invention.

[0024] Figure 1 is a flowchart of an individualized drug risk assessment method shown according to an exemplary embodiment.

[0025] Figure 2 is a flowchart of an individualized drug risk assessment method shown according to an exemplary embodiment.

[0026] Figure 3 is a flowchart of an individualized drug risk assessment method shown according to an exemplary embodiment.

[0027] Figure 4 is a flowchart of an individualized drug risk assessment method shown according to an exemplary embodiment.

[0028] Figure 5 is a schematic diagram of an individualized drug risk assessment system shown according to an exemplary embodiment.

[0029] Figure 6 is a block diagram of an electronic device shown according to an exemplary embodiment.

[0030] Figure 7 is a block diagram of a computer-readable medium shown according to an exemplary embodiment. DETAILED DESCRIPTION

[0031] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. Like reference numerals refer to like or similar parts throughout the figures, and thus their repetitive description will be omitted.

[0032] In addition, the described features, structures, or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present invention. However, those skilled in the art will realize that the technical solutions of the present invention may be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be employed. In other cases, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the present invention.

[0033] An embodiment of an individualized drug risk assessment method is proposed in this application, as Figure 1 shown, including steps S101 to S106.

[0034] Step S101: Obtain the individual's gene detection data, where the individual's gene detection data includes the detection results of at least one of genotype, enzyme activity, and metabolic rate.

[0035] Step S102: Determine the drug to be evaluated.

[0036] Step S103: Determine the genes and loci closely related to the adverse reactions of the drug to be evaluated.

[0037] Step S104: Search for the detection data associated with the genes and loci closely related to the adverse reactions of the drug to be evaluated from the individual's gene detection data.

[0038] Step S105: Determine the risk level of the individual using the drug to be evaluated based on the detected data found.

[0039] Step S106: Generate a risk prompt message for the individual using the drug to be evaluated based on the determined risk level and display the risk prompt message.

[0040] In this application, by determining the genes and loci closely related to the adverse reactions of the drug to be evaluated, searching for the detection data associated with the genes and loci closely related to the adverse reactions of the drug to be evaluated from the individual's gene detection data, where the individual's gene detection data includes the detection results of at least one of genotype, enzyme activity, and metabolic rate, determining the risk level of the individual using the drug to be evaluated based on the detected data found, and generating a risk prompt message for the individual using the drug to be evaluated based on the determined risk level and displaying the risk prompt message, it helps to optimize the individual's drug selection, effectively improve the safety of drug use, and provide strong technical support for personalized medicine.

[0041] The drug to be evaluated is a drug associated with the individual's symptoms.

[0042] Patient A has symptoms such as coughing, expectorating phlegm, and running nose. Patient A comes to the hospital. After the doctor examines Patient A, it is found that Patient A has a disease of 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 locus rs9274407. Then the gene and locus closely related to the adverse reactions of the drug to be evaluated are the gene HLA-DQB1 and the locus rs9274407.

[0043] Patient B is unwell and comes to the hospital. After the doctor examines Patient B, it is found that Patient B is 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 locus *57:01:01. Then the gene and locus closely related to the adverse reactions of the drug to be evaluated are the gene HLA-B and the locus *57:01:01.

[0044] The analysis of the association between drugs and genes and loci can be carried out by using the drug-gene association data in the databases of NMPA (National Medical Products Administration), FDA (U.S. Food and Drug Administration), CPIC (Clinical Pharmacogenetics Implementation Consortium), DPWG (Dutch Pharmacogenetics Working Group), Swissmedic (Swiss drug regulatory authority), and PharmGKB (Pharmacogenomics Knowledgebase).

[0045] Association analysis: Analyze the association between genotypes and drug efficacy and adverse reactions, and identify the gene loci and corresponding test results that are closely related to drug efficacy or adverse reactions.

[0046] The gene loci closely related to the drug 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.

[0047] As an optional implementation manner, the method provided by 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, cautious medication or avoiding medication is required.

[0048] 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. Among them, different drug risk levels correspond to different risk descriptions.

[0049] If the individual's drug risk level is 4, the risk assessment result is that the efficacy of the drug used by the individual is reduced.

[0050] If the individual's drug risk level is 5, the risk assessment result is that the individual needs to monitor side effects when using the drug.

[0051] If the individual's drug risk level is 6 or 7, the risk assessment result is that the individual needs to use the drug with caution.

[0052] If the individual's drug risk level is 8 or 9, the risk assessment result is that the individual should avoid using the drug.

[0053] The solution of this application understands the differences in drug responses of different individuals by focusing on gene loci closely related to drug efficacy or adverse reactions, and provides support for formulating individualized drug treatment plans. By combining drug-gene association data, analyzing the individual's gene test results, thereby evaluating the risk level of individual drug use, providing a scientific basis for clinical drug use. This system can be applied to clinicians, pharmacists, and individual users. Through individualized drug use recommendations and risk assessments, it helps to optimize drug selection and dosage, reduce side effects and drug adverse reactions, and improve the treatment effect.

[0054] This application proposes an embodiment of an individualized drug risk assessment method, including steps S1 to S5.

[0055] Step S1: Obtain gene test result data.

[0056] Step S2: Pre-group the obtained test result data, summarize 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 predetermined matching rules. Among them, SNP: Single Nucleotide Polymorphism, referring to a single base variation in the gene sequence; HLA: Human Leukocyte Antigen gene, which participates in the function of the immune system; CYP2D6: Cytochrome P450 2D6 gene, which mainly participates in drug metabolism.

[0057] Step S3: Analyze the identified and extracted gene test results according to the existing drug-gene association analysis model to obtain gene results closely related to drug efficacy or adverse reactions.

[0058] Step S4: Using the pharmacogenomic scoring model, represent each gene test result as a numerical value of the drug risk factor, and define the model parameter set and the drug risk factor array.

[0059] Step S5: Merge based on each determined numerical weight to obtain the final risk level numerical value.

[0060] This application realizes the grading of individualized medication risks through the following steps:

[0061] Steps S1 and S2 are steps for collecting and processing gene test data, and these two steps are processes of collecting, cleaning, and standardizing the data.

[0062] Data collection: Collect the genotype data of the individual to ensure the accuracy and integrity of the data.

[0063] Data cleaning: Preprocess the collected data to remove noise and outliers.

[0064] Data standardization: Convert genotype data from different sources into a unified format for subsequent analysis.

[0065] Step S3 is a step for drug-gene association analysis.

[0066] Data analysis: Analyze the gene test results with the drug-gene association data in the NMPA, FDA, CPIC, DPWG, Swissmedic, and PharmGKB databases.

[0067] Association analysis: Analyze the association between genotypes and drug efficacy and adverse reactions, and identify the gene loci and corresponding test results that are closely related to drug efficacy or adverse reactions.

[0068] For example, amoxicillin is mainly related to the gene HLA-DQB1, corresponding to the locus rs9274407. The gene HLA-DQB1 corresponding to the locus rs9274407 has three genotypes: TT, AT, and AA. For people with the genotype TT at the HLA-DQB1 gene RS9274407 locus, when using amoxicillin or drugs containing amoxicillin, the risk of drug-induced liver injury is relatively low. For people with the genotype AT at the HLA-DQB1 gene RS9274407 locus, when using amoxicillin or drugs containing amoxicillin, the risk of drug-induced liver injury may increase, but it can still be used routinely. For people with the genotype AA at the HLA-DQB1 gene RS9274407 locus, when using amoxicillin or drugs containing amoxicillin, the risk of drug-induced liver injury may increase, and use should be avoided as much as possible.

[0069] The gene loci closely related to the therapeutic efficacy or adverse reactions of different drugs are different. In other words, when the drugs are different, the gene loci that need to be concerned about are different.

[0070] The solution of the present application understands the differences in the responses of different individuals to drugs by focusing on the gene loci closely related to the therapeutic efficacy or adverse reactions of drugs, and provides support for formulating individualized drug treatment plans.

[0071] Steps S4 and S5 are steps for evaluating the risk level of individual drug use.

[0072] Evaluation indicators: Construct a drug use risk level evaluation system according to indicators such as drug efficacy, incidence of adverse reactions, and the impact of gene polymorphisms on drug metabolism and pharmacodynamics.

[0073] Risk assessment: Based on the gene detection results and the results of drug-gene association analysis, evaluate the risk level of individual drug use.

[0074] Result output: Present the evaluation results to clinicians and patients in an intuitive and easy-to-understand manner, providing a scientific basis for clinical drug use.

[0075] As an optional implementation manner, the method provided by the present application divides the drug risk level into 9 levels, namely 1-9. The lower the level, the safer the drug use for this individual; the higher the level, the less safe the drug use for this individual. When the level is higher than a certain level, drug use needs to be cautious or avoided.

[0076] 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 drug adverse reactions, the drug is a single-site drug. If there are at least two gene loci closely related to drug adverse reactions, the drug is a multi-site drug.

[0077] Single-site drug: This situation is relatively simple. The drug directly takes the risk level and description of the corresponding locus result.

[0078] Multi-site drug: One gene locus corresponds to one risk level and description. The drug takes the risk level and description with the highest risk level among the locus corresponding results. The following is a detailed example.

[0079] Example: Taking the drug [Atorvastatin] as an example, this drug is related to four loci of two genes (APOE, SLCO1B1), and the corresponding risk levels of the results are as follows:

[0080] APOE: At locus rs429358, the individual's genotype was measured as CT; at locus rs7412, the individual's genotype was measured as CC. Matching these two genotypes to enzyme activity, it can be concluded that the individual has moderate enzyme activity deficiency. Then, based on the APOE gene loci rs429358 and rs7412, the risk level of this individual using the drug [Atorvastatin] is grade 5, and the risk level description is: The risk of adverse reactions of this drug for you 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: The risk of adverse reactions when you use 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.

[0081] SLCO1B1: At locus rs4149056, the individual's genotype was measured as CT; at locus rs2306283, the individual's genotype was measured as GG. Matching these two genotypes to enzyme activity, it can be concluded that the individual has enzyme activity of ε4 (one allele type). Then, based on the SLCO1B1 gene loci rs4149056 and rs2306283, the risk level of this individual using the drug [Atorvastatin] is grade 4, and the risk level description is: The efficacy of this drug for you may be reduced. Please use the drug under the guidance of a doctor and pay attention to monitoring its effects. Simplified medication reminder: The clinical efficacy of this drug when you use it may be reduced. It is recommended that you pay attention to monitoring its efficacy when using it.

[0082] 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 reminder information need to comprehensively consider the individual drug use risk levels corresponding to the above two genes. Based on the APOE gene loci rs429358 and rs7412, the risk level of this individual using the drug [Atorvastatin] is grade 5. Based on the SLCO1B1 gene loci rs4149056 and rs2306283, the risk level of this individual using the drug [Atorvastatin] is grade 4. Take the higher risk level (grade 5) as the risk level of the individual drug use risk assessment, and use the risk level description and simplified medication reminder corresponding to this higher risk level (grade 5) as the risk level assessment and risk reminder for the individual using the drug [Atorvastatin]. That is, for the drug [Atorvastatin], risk level: grade 5, risk level description: The risk of adverse reactions of this drug for you 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 reminder / Simplified medication reminder: The risk of adverse reactions when you use 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.

[0083] When the drug risk level assessment is affected at multiple gene loci, since the degrees of influence of different gene loci on drug risk are different, a weighted method is adopted to calculate the risk level of drugs with multiple gene loci to reflect the contributions of different gene loci.

[0084] Suppose drug D is associated with the gene locus set G ={ g 1, g 2,……, g n}, and given:

[0085] R ( g i ) is the risk level of the i th gene locus.

[0086] w i : The weight of the i th gene locus, representing the degree of influence of this gene locus on drug risk, satisfies w 1 + w 2 + …… + w i + …… + w n = 1. The greater the weight, the greater the degree of influence of this gene locus on drug risk.

[0087] R ( D ) = w 1 × R ( g ) + w 2 × R ( g ) + …… + w i × R ( g i ) + …… + w n × R ( g n )

[0088] R ( D ) is the risk level of drug D .

[0089] Taking the drug

Atorvastatin

[0090] APOE: Loci rs429358, rs7412, risk level 5, weight w 1 = 0.6.

[0091] SLCO1B1: Loci rs4149056, rs2306283, risk level 4, weight w 2 = 0.4.

[0092] R ( D ) = 0.6 × 5 + 0.4 × 4 = 3 + 1.6 = 4.6 ≈ 5

[0093] Therefore, the finally calculated risk level is still level 5.

[0094] As an alternative implementation, the concept of a risk contribution function is introduced.

[0095] f ( g i ) : The contribution function of the i th gene locus to the drug use risk maps the genotype result of the gene locus to a continuous risk level value. This function can be set according to experimental data or statistical models.

[0096] To comprehensively consider the weight of each gene locus, the risk level of the drug is defined as:

[0097] R ( D ) = w 1 × f ( g 1)+ w 2 × f ( g 2)+……+ w i × f ( g i )+……+ w n × f ( g n )

[0098] where f ( g i ) usually ranges from [0, 9] and can be set by fitting historical data or expert scoring.

[0099] Set the contribution function f ( g i) can make the model more flexible, especially when the influence of different gene loci on drug risk is not simply linear. The contribution function f ( g i ) is mainly used to convert the genotype information of each gene locus into an impact on the risk level. The following introduces some specific contribution function algorithms suitable for different scenarios:

[0100] 1) Linear contribution function

[0101] If the influence of the gene locus on the risk is linear (i.e., the risk level is proportional to the genotype association strength), it can be directly represented by a linear function:

[0102] f ( g i ) = a · R ( g i ) + b

[0103] R ( g i ):The original risk level of the gene locus.

[0104] 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 according to clinical significance.

[0105] For example, if you want the risk level to become more obvious under the influence of the genotype strength, you can set a > 1 to expand the influence.

[0106] 2) Logarithmic contribution function

[0107] For some gene loci, where the influence on the risk gradually decreases or increases, a logarithmic function can be used to compress or amplify the change in the risk level:

[0108] f ( g i ) = log k ( R ( g i ) +c )

[0109] k : The base of the logarithm, usually selected as k = 2 or k = e .

[0110] c : Offset, ensuring that the input of the logarithmic function is always positive. It can take c = 1 or a positive value greater than 1.

[0111] The logarithmic function is suitable for assigning smaller impacts to some lower risk levels (such as 1 - 3) or more weights to higher risk levels.

[0112] 3) Exponential contribution function

[0113] When the impact of a certain gene locus on risk is extremely significant, that is, the risk increases sharply with the genotype result, the exponential function can be used to amplify its impact:

[0114] f ( g i ) = d · exp λ · R ( g i )]

[0115] d and λ : Constant coefficient, controlling the baseline value and growth rate of the function.

[0116] R ( g i ) : Risk level of the gene locus.

[0117] This formula can enhance the weight of high - risk loci. For example, if the risk of a certain gene locus is 5 and the impact of this risk locus on drug efficacy is more significant, the exponential function can be used to increase its impact.

[0118] 4) S - shaped (Sigmoid) contribution function

[0119] The S - shaped function (such as the logistic function) is suitable for non - linear and threshold - effect situations. For example, when the 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 of the risk level:

[0120] f ( g i ) = L / {1 + exp [ - α R ( g i ) - β}

[0121] L ​​: Represents the upper limit of the function and can be set to the maximum risk level of 9.

[0122] α : The parameter that controls the growth rate. The larger the value, the steeper the function.

[0123] β : The offset, indicating that a significant impact will occur only when a certain genotype risk level is reached.

[0124] This function is applicable to simulating the "switching effect" of gene loci, that is, the genotype significantly increases the risk only when it is higher than a certain threshold.

[0125] 5) Weighted multi-factor function

[0126] If the risk level is affected by multiple factors, multiple contribution functions can be combined. For example, combine the linear and logarithmic contribution functions to consider the base value and change rate of the risk level:

[0127] f ( g i ) = w 1 a · R ( g i ) + b + w 2 log k ( R ( g i ) +c )]

[0128] w 1 and w 2 are the weights, indicating the influence degrees of the linear and logarithmic functions.

[0129] The meanings of other parameters are the same as above.

[0130] This combined function allows for different change patterns at different risk levels.

[0131] Application example

[0132] Suppose the risk level of the APOE gene locus rs429358 of

Atorvastatin

[0133] f (rs429358) = log 2(5 + 1) = log 2(6) ≈ 2.585

[0134] The finally output risk level can be weighted and integrated with the results of other gene loci for calculating the final risk assessment value.

[0135] By reasonably selecting different contribution functions and parameters, the adaptability of the model can be improved, so as to better reflect the complex influence of multiple gene loci on the drug use risk.

[0136] The method provided by the embodiment of the present application can adjust the weights and contribution functions of each gene locus, and is applicable not only to drugs associated with single gene loci, but also to drugs with complex associations of multiple gene loci. Taking the drug [Atorvastatin] as an example, assume that the gene locus and weight are as follows:

[0137] APOE: locus rs429358, rs7412, risk level 5, weight w 1 = 0.6.

[0138] SLCO1B1: locus rs4149056, rs2306283, risk level 4, weight w 2 = 0.4.

[0139] If f ( g i ) = R ( g i ) (i.e., directly adopt discrete values):

[0140] R ( D ) = 0.6 × 5 + 0.4 × 4 = 3 + 1.6 = 4.6 ≈ 5

[0141] Therefore, the finally risk level is still level 5.

[0142] Figure 2 、 3 、4 respectively show an implementation manner of the individualized drug risk assessment method provided by the present application. The difference is that Figure 2 the shown implementation manner evaluates the drug risk level based on enzyme activity; Figure 3 the shown implementation manner evaluates the drug risk level based on metabolic rate; Figure 4 the shown implementation manner evaluates the drug risk level based on both enzyme activity and metabolic rate.

[0143] As Figure 2 shown, this implementation manner includes step S201 to step S207.

[0144] Step S201: Obtain the gene detection data of an individual, and the gene detection data of the individual at least includes the detection result of enzyme activity.

[0145] Step S202: Determine the drug to be evaluated.

[0146] Step S203: Determine the genes and loci closely related to the adverse reactions of the drug to be evaluated.

[0147] Step S204: Search the individual's gene detection data for the detection data of the enzyme activity associated with the genes and loci closely related to the adverse reactions of the drug to be evaluated.

[0148] Step S205: Determine the minimum and maximum values of the individual's enzyme activity based on the detection data of the enzyme activity.

[0149] Step S206: Use the range between the minimum and maximum values of the individual's enzyme activity as the integration interval to integrate F APOE ( x ) = 9 / {1 + exp [0.1·( x - 50)]}, and determine the risk level of the drug to be evaluated according to the integral value.

[0150] Wherein, x is the value of the enzyme activity, F APOE ( x ) is the enzyme activity risk contribution function.

[0151] Step S207: Generate a risk warning message for the individual to use the drug to be evaluated according to the determined risk level and display the risk warning message.

[0152] As Figure 3 shown, this embodiment includes Step S301 to Step S307.

[0153] Step S301: Obtain the individual's gene detection data, and the individual's gene detection data includes at least the detection result of the metabolic rate.

[0154] Step S302: Determine the drug to be evaluated.

[0155] Step S303: Determine the genes and loci closely related to the adverse reactions of the drug to be evaluated.

[0156] Step S304: Search the individual's gene detection data for the detection data of the metabolic rate associated with the genes and loci closely related to the adverse reactions of the drug to be evaluated.

[0157] Step S305: Determine the minimum and maximum values of the individual's metabolic rate based on the detection data of the metabolic rate.

[0158] Step S306: Take the range between the minimum and maximum values of the individual's metabolic rate as the integration interval, and integrate F SLCO1B1 ( y ) = 9 / {1 + exp [0.2·( y - 30)]}, and determine the risk level of the drug to be evaluated according to the integral value.

[0159] Among them, y is the value of the metabolic rate, F SLCO1B1 ( y ) is the metabolic rate risk contribution function.

[0160] Step S307: Generate a risk prompt message for the individual to use the drug to be evaluated according to the determined risk level and display the risk prompt message.

[0161] As Figure 4 shown, this embodiment includes steps S401 to S411.

[0162] Step S401: Obtain the individual's gene detection data, and the individual's gene detection data includes at least the detection results of enzyme activity and metabolic rate.

[0163] Step S402: Determine the drug to be evaluated.

[0164] Step S403: Determine the genes and loci closely related to the adverse reactions of the drug to be evaluated.

[0165] Step S404: Search the individual's gene detection data for the detection data of enzyme activity and metabolic rate associated with the genes and loci closely related to the adverse reactions of the drug to be evaluated.

[0166] Step S405: Determine the minimum and maximum values of the individual's enzyme activity according to the detection data of enzyme activity.

[0167] Step S406: Take the range between the minimum and maximum values of the individual's enzyme activity as the integration interval, and integrate F APOE ( x ) = 9 / {1 + exp [0.1·( x - 50)]} to obtain an integral value.

[0168] Among them, x is the value of the enzyme activity, F APOE ( x ) is the enzyme activity risk contribution function.

[0169] Step S407: Determine the minimum and maximum values of the individual's metabolic rate based on the detection data of the metabolic rate.

[0170] Step S408: Use the range between the minimum and maximum values of the individual's metabolic rate as the integration interval to integrate F SLCO1B1 ( y ) = 9 / {1 + exp [0.2·( y - 30)]} to obtain an integral value.

[0171] Wherein, y is the value of the metabolic rate, F SLCO1B1 ( y ) is the metabolic rate risk contribution function.

[0172] Step S409: Weight the integral value obtained by integrating F APOE ( x ) with the integral value obtained by integrating F SLCO1B1 ( y ) according to a preset weight to obtain a total integral value.

[0173] Step S410: Determine the risk level of the drug to be evaluated based on the total integral value.

[0174] Step S411: Generate a risk prompt message for the individual to use the drug to be evaluated based on the determined risk level and display the risk prompt message.

[0175] Figures 2 to 4 The integration model adopted in the illustrated embodiment is used to handle the relationship between the risk level and continuous variables such as enzyme activity and metabolic rate. The following is a specific example showing Figure 4 how the illustrated embodiment uses the integration model to calculate the risk level of the drug [Atorvastatin].

[0176] The correlation between the drug [Atorvastatin] (i.e., the drug to be evaluated) and two genes (APOE and SLCO1B1) is as follows:

[0177] APOE gene:

[0178] The combination of loci rs429358 and rs7412 affects enzyme activity.

[0179] The range of enzyme activity values is [0, 100], and the lower the enzyme activity, the higher the risk.

[0180] Risk contribution function FAPOE ( x ) = 9 / {1 + exp [0.1·( x - 50)]}。

[0181] x It represents enzyme activity, 50 is the median value of enzyme activity, and 9 is the maximum risk level.

[0182] SLCO1B1 gene:

[0183] The combination of loci rs4149056 and rs2306283 affects the drug metabolism rate.

[0184] The value range of the metabolism rate is [0, 100]. The lower the metabolism rate, the higher the risk.

[0185] Risk contribution function F SLCO1B1 ( y ) = 9 / {1 + exp [0.2·( y - 30)]}。

[0186] y It represents the metabolism rate, 30 is a medium - low metabolism rate value, and 9 is the maximum risk level.

[0187] 1) Integral model formula

[0188] The overall risk of drug [Atorvastatin] R ( D ) is calculated by integrating and weighting the risk contributions of two gene loci:

[0189] R ( D ) = w 1 × R 1 + w 2 × R 2

[0190] R 1 is the result of integrating the risk contribution function F APOE ( x ) = 9 / {1 + exp [0.1·( x - 50)]} in the interval from x min to x max The result of integration.

[0191] R 2 is the risk contribution functionF SLCO1B1 ( y ) = 9 / {1 + exp [0.2·( y - 30)]} integrated over the interval from y min to y max The result of the integration.

[0192] Among them, 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 SLCO1B1 metabolic rate range, y max is the maximum value of the SLCO1B1 metabolic rate range.

[0193] When the effects of the two genes on risk are not equal, w 1 and w 2 are not equal, and w 1 + w 2 = 1.

[0194] When the effects of the two genes on risk are equal, w 1 and w 2 are equal, and w 1 = w 2 = 0.5.

[0195] 2) Input data

[0196] Assume:

[0197] Measuring the APOE enzyme activity of an individual, 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.

[0198] Measuring the SLCO1B1 metabolic rate of an individual, the result is [20, 50]. That is, the minimum value of the metabolic rate of the individual is 20, and the maximum value is 50.

[0199] 3) Calculation steps

[0200] For the risk contribution function F APOE ( x ) = 9 / {1 + exp [0.1·( x - 50)]} integrated over the interval [30, 60], the obtained result is 163.23.

[0201] For the 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.

[0202] R ( D ) = w 1×163.23 + w 2×94.90

[0203] When the effects of two genes on risk are equal, w 1 and w 2 are equal, and w 1 = w 2 = 0.5.

[0204] R ( D ) = 0.5×(163.23 + 94.90) ≈ 129.06

[0205] 4) Risk level description

[0206] Since the integral value is too large (because the integral accumulates the total effect of risk), the result can be normalized or directly converted to a discrete risk level in [0, 9] by proportion. For example, dividing the result by the total possible integral range ([0, 100]) and taking the integer can obtain the specific drug risk level.

[0207] Figures 2 to 4 The method shown does not require measuring the genotype of an individual, and evaluates the drug risk level by detecting the enzyme activity and metabolic rate of the individual.

[0208] This application aims to analyze the gene test results of an individual by combining drug-gene association data in 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 medication. This system can be applied to clinicians, pharmacists, and individual users, and helps optimize drug selection and dosage, reduce side effects and drug adverse reactions, and improve the treatment effect through individualized medication advice and risk assessment.

[0209] This application also provides an individualized drug risk assessment system, as Figure 5 shown, this system includes: an acquisition module 10, a drug to be evaluated determination module 20, a gene locus determination module 30, a search module 40, a drug risk level determination module 50, and a display module 60.

[0210] An acquisition module 10 is configured to acquire genetic test data of an individual, where the genetic test data of the individual includes at least one of the test results of genotype, enzyme activity, and metabolic rate.

[0211] A drug to be evaluated determination module 20 is configured to determine a drug to be evaluated.

[0212] A gene locus determination module 30 is configured to determine genes and loci that are closely related to the adverse reactions of the drug to be evaluated.

[0213] A search module 40 is configured to search for test data associated with genes and loci that are closely related to the adverse reactions of the drug to be evaluated from the genetic test data of the individual.

[0214] A drug risk level determination module 50 is configured to determine the risk level of the individual using the drug to be evaluated according to the found test data.

[0215] A display module 60 is configured to generate a risk prompt message for the individual using the drug to be evaluated according to the determined risk level and display the risk prompt message.

[0216] The individualized drug risk assessment system provided by the present application can determine genes and loci that are closely related to the adverse reactions of the drug to be evaluated, search for test data associated with genes and loci that are closely related to the adverse reactions of the drug to be evaluated from the genetic test data of the individual, where the genetic test data of the individual includes at least one of the test results of genotype, enzyme activity, and metabolic rate, determine the risk level of the individual using the drug to be evaluated according to the found test data, generate a risk prompt message for the individual using the drug to be evaluated according to the determined risk level and display the risk prompt message, help optimize the individual drug selection, effectively improve the safety of drug use, and provide strong technical support for personalized medicine.

[0217] Optionally, the test data includes test data of enzyme activity and / or test data of metabolic rate, and the drug risk level determination module 50 is configured to: calculate the risk level of the drug to be evaluated according to the test data of enzyme activity, the enzyme activity risk contribution function, and / or the test data of metabolic rate, the metabolic rate risk contribution function.

[0218] Optionally, the test data includes test data of enzyme activity and test data of metabolic rate, and the drug risk level determination module 50 includes: a first calculation sub-module, a second calculation sub-module, and a third calculation sub-module.

[0219] The first calculation sub-module is configured to calculate the integral value of the enzyme activity risk contribution function of the individual according to the test data of enzyme activity and the enzyme activity risk contribution function.

[0220] The second calculation sub-module is used to calculate the integral value of the individual's metabolic rate risk contribution function according to the detection data of the metabolic rate and the metabolic rate risk contribution function.

[0221] The third calculation sub-module is used to calculate the risk level of the drug to be evaluated according to the integral value of the individual's enzyme activity risk contribution function and the integral value of the individual's metabolic rate risk contribution function.

[0222] Optionally, the enzyme activity risk contribution function is:

[0223] F APOE ( x ) = 9 / {1 + exp [0.1·( x - 50)]}, where x is the value of the enzyme activity, F APOE ( x ) is the enzyme activity risk contribution function.

[0224] Optionally, the first calculation sub-module is used to:

[0225] Determine the minimum value and the maximum value of the individual's enzyme activity according to the detection data of the enzyme activity;

[0226] Take the range between the minimum value and the maximum value of the individual's enzyme activity as the integration interval, and integrate F APOE ( x )

[0227] Optionally, the metabolic rate risk contribution function is:

[0228] F SLCO1B1 ( y ) = 9 / {1 + exp [0.2·( y - 30)]}, where y is the value of the metabolic rate, F SLCO1B1 ( y ) is the metabolic rate risk contribution function.

[0229] Optionally, the second calculation sub-module is used to:

[0230] Determine the minimum value and the maximum value of the individual's metabolic rate according to the detection data of the metabolic rate;

[0231] Take the range between the minimum value and the maximum value of the individual's metabolic rate as the integration interval, and integrate F SLCO1B1 (y ) Perform integration.

[0232] Figure 6 It is a block diagram of an electronic device shown according to an exemplary embodiment.

[0233] Refer to the following Figure 6 to describe the electronic device 600 according to this embodiment of the present application. Figure 6 The displayed electronic device 600 is merely an example and should not impose any limitation on the functions and usage scope of the embodiments of the present application.

[0234] As Figure 6 shown, the electronic device 600 is presented in the form of a general-purpose 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.

[0235] Among them, the storage unit stores program codes, and the program codes can be executed by the processing unit 610, so that the processing unit 610 executes the steps according to various exemplary embodiments of the present application described in this specification. For example, the processing unit 610 can execute the steps as shown in Figures 1 to 4 .

[0236] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 6201 and / or a cache storage unit 6202, and may further include a read-only storage unit (ROM) 6203.

[0237] The storage unit 620 may further include a program / utility 6204 having a set (at least one) of program modules 6205. Such program modules 6205 include but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment.

[0238] The bus 630 may represent one or more of several types of bus structures, including a storage unit bus or a storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any bus structure in a variety of bus structures.

[0239] The electronic device 600 can also communicate with one or more external devices 600' (such as a keyboard, a pointing device, a Bluetooth device, etc.), devices that enable a user to interact with the electronic device 600, and / or any device (such as a router, a modem, etc.) through which the electronic device 600 can communicate with one or more other computing devices. Such communication can be carried out through the input / output (I / O) interface 650. Moreover, the electronic device 600 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 660. The network adapter 660 can communicate with other modules of the electronic device 600 through the bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules can 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.

[0240] From the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software, or by a combination of software and necessary hardware. Therefore, as Figure 7 shown, the technical solutions according to the embodiments 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, including several 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 embodiments of the present application.

[0241] The software product can adopt any combination of one or more readable media. The readable media can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, but not be limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium 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 of the above.

[0242] The computer-readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, in which the readable program code is carried. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. The readable storage medium may also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0243] The program code for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's 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 can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or, it can be connected to an external computing device (for example, by using an Internet service provider to connect through the Internet).

[0244] The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the computer-readable medium realizes the following functions: obtaining genetic test data of an individual, where the genetic test data of the individual includes at least one of the test results of genotype, enzyme activity, and metabolic rate; determining a drug to be evaluated; determining genes and loci closely related to adverse reactions of the drug to be evaluated; searching the genetic test data of the individual for test data associated with genes and loci closely related to 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 a risk prompt message for the individual to use the drug to be evaluated based on the determined risk level and displaying the risk prompt message.

[0245] Those skilled in the art can understand that the above-mentioned modules can be distributed in the device according to the description of the embodiments, or can be correspondingly changed and distributed in one or more devices that are only different from this embodiment. The modules of the above embodiments can be combined into one module, or can be further split into multiple sub-modules.

[0246] Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (such as a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.

[0247] The above specifically shows and describes the exemplary embodiments of the present application. It should be understood that the present application is not limited to the detailed structures, settings, or implementation methods described herein; on the contrary, the present application is intended to cover various modifications and equivalent settings included within the spirit and scope of the appended claims.

Claims

1. An individualized drug risk assessment method, characterized in that, Comprising: Obtaining the genetic test data of an individual, where the genetic test data of the individual includes the test results of enzyme activity and metabolic rate; Determining the drug to be evaluated; Determining the genes and loci that are closely related to the adverse reactions of the drug to be evaluated; Searching the genetic test data of the individual for the test data associated with the genes and loci 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 according to the found test data; Generating a risk prompt message for the individual using the drug to be evaluated according to the determined risk level and displaying the risk prompt message, where the test data includes the test data of enzyme activity and the test data of metabolic rate, and determining the risk level of the individual using the drug to be evaluated according to the found test data includes: Calculating the integral value of the enzyme activity risk contribution function of the individual according to the test data of enzyme activity and the enzyme activity risk contribution function; Calculating the integral value of the metabolic rate risk contribution function of the individual according to the test data of metabolic rate and the metabolic rate risk contribution function; Calculating the risk level of the drug to be evaluated according to the integral value of the enzyme activity risk contribution function of the individual and the integral value of the metabolic rate risk contribution function of the individual.

2. The method according to claim 1, wherein 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.

3. The method according to claim 2, wherein Calculating the integral value of the enzyme activity risk contribution function of the individual according to the test data of enzyme activity and the enzyme activity risk contribution function includes: Determining the minimum value and the maximum value of the enzyme activity of the individual according to the test data of enzyme activity; Using the range between the minimum and maximum values of an individual's enzyme activity as the integration interval, integrate F APOE ( x ) 4. The method according to claim 1, wherein The metabolic rate risk contribution function is: F SLCO1B1 ( y ) = 9 / {1 + exp [0.2·( y - 30)]}, where y is the value of the metabolic rate, F SLCO1B1 ( y ) is the metabolic rate risk contribution function.

5. The method according to claim 4, wherein Calculating the integral value of the metabolic rate risk contribution function of the individual according to the test data of metabolic rate and the metabolic rate risk contribution function includes: Determining the minimum value and the maximum value of the metabolic rate of the individual according to the test data of metabolic rate; Taking the range between the minimum and maximum values of the metabolic rate of an individual as the integration interval, integrate F SLCO1B1 ( y ) 6. An individualized drug risk assessment system, characterized in that, Comprising: An acquisition module for obtaining the genetic test data of an individual, where the genetic test data of the individual includes the test results of enzyme activity and metabolic rate; A drug-to-be-evaluated determination module for determining the drug to be evaluated; A gene locus determination module for determining the genes and loci that are closely related to the adverse reactions of the drug to be evaluated; A search module for searching the genetic test data of the individual for the test data associated with the genes and loci that are closely related to the adverse reactions of the drug to be evaluated; A drug risk level determination module for determining the risk level of the individual using the drug to be evaluated according to the found test data; A display module for generating a risk prompt message for the individual using the drug to be evaluated according to the determined risk level and displaying the risk prompt message, where the test data includes the test data of enzyme activity and the test data of metabolic rate, and the drug risk level determination module is used to: calculate the risk level of the drug to be evaluated according to the test data of enzyme activity, the enzyme activity risk contribution function, the test data of metabolic rate, and the metabolic rate risk contribution function, and the drug risk level determination module includes: A first calculation sub-module for calculating the integral value of the enzyme activity risk contribution function of the individual according to the test data of enzyme activity and the enzyme activity risk contribution function; A second calculation sub-module, configured to calculate an integral value of the metabolic rate risk contribution function of an individual according to the detection data of the metabolic rate and the metabolic rate risk contribution function; A third calculation sub-module, configured to calculate the risk level of the drug to be evaluated according to the integral value of the enzyme activity risk contribution function of the individual and the integral value of the metabolic rate risk contribution function of the individual.

7. The system according to claim 6, 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.

8. The system according to claim 7, wherein The first calculation sub-module is configured to: Determine the minimum value and the maximum value of the enzyme activity of an individual according to the detection data of the enzyme activity; Integrate over the range between the minimum and maximum values of the enzyme activity of an individual as the integration interval for F APOE ( x ) 9. The system according to claim 6, wherein The metabolic rate risk contribution function is: F SLCO1B1 ( y ) = 9 / {1 + exp [0.2·( y -30)]}, where y is the value of the metabolic rate, F SLCO1B1 ( y ) is the metabolic rate risk contribution function.

10. The system according to claim 9, wherein The second calculation sub-module is configured to: Determine the minimum value and the maximum value of the metabolic rate of an individual according to the detection data of the metabolic rate; Integrate over the range between the minimum and maximum values of the metabolic rate of an individual as the integration interval for F SLCO1B1 ( y ).

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