System and application for accurate selection of tacrolimus therapeutic dose for myasthenia gravis patients

Abstract

The present invention relates to a prediction system for accurately selecting the therapeutic dose of tacrolimus for myasthenia gravis patients and its application, wherein the prediction system comprises: a data acquisition unit and a prediction unit, wherein the data acquisition unit is used to acquire the characteristic data of the patient, and the prediction unit is used to input the characteristic data into a prediction model to obtain a predicted probability value of a low concentration of tacrolimus. The accurate prediction model for the initial dose and dose adjustment of tacrolimus for myasthenia gravis patients of the present invention is helpful to guide the individualized medication of myasthenia gravis patients receiving tacrolimus treatment, reduce the insufficient blood drug concentration in the early stage of treatment caused by insufficient tacrolimus dosage, significantly improve the early efficacy, and is of great significance to the accurate medication of immunosuppressive treatment for myasthenia gravis patients.

Description

Technical Field

[0001] The present invention relates to the field of drug information technology, and in particular to a system and application for accurately selecting a tacrolimus treatment dose for myasthenia gravis patients. Background Art

[0002] A study in 2017 showed that maintaining tacrolimus concentrations within the range of 4.8-10ng / ml can enable 92% of myasthenia gravis patients to achieve treatment goals after treatment. Too low blood drug concentrations can lead to slow onset of action or even ineffective treatment. Therefore, tacrolimus treatment requires regular drug concentration monitoring and adjustment of tacrolimus dosage based on blood drug concentration results to improve the effectiveness of treatment.

[0003] However, drug concentration monitoring cannot guide the selection of the initial dose, and it is difficult to achieve the target blood drug concentration in the early stage of treatment. Due to the different medical conditions in different places, patients in some remote areas cannot monitor drug concentrations in time, delaying dose adjustments. In order to avoid adverse reactions in the early stages of treatment, the starting dose of tacrolimus for patients with myasthenia gravis is 2 mg / day, which is then gradually increased to 3-4 mg / day. The dose needs to be stabilized for 5-7 days before drug concentration monitoring. The first drug concentration monitoring is often performed in the 3rd to 4th week after taking the medicine, which makes it difficult for some patients to reach the effective concentration range within the first month of tacrolimus treatment. Therefore, analyzing the influencing factors of tacrolimus blood concentration and applying them to the selection of initial doses will help increase the proportion of early blood drug concentrations that meet the target in myasthenia gravis patients and improve early efficacy.

[0004] A variety of clinical and genetic factors affect the blood concentration of tacrolimus, which can be used to guide the individualized use of tacrolimus. Among them, the single nucleotide mutation at the CYP3A5*3 (rs776746) site is the most clear influencing factor, which has been widely used in the optimal dose selection of tacrolimus for the treatment of organ transplantation, inflammatory bowel disease, rheumatic immune diseases, myasthenia gravis and other diseases. However, the polymorphism of the CYP3A5*3 site can only explain 29%-35% of the inter-individual differences in tacrolimus blood concentration. In a randomized controlled study of tacrolimus use in renal transplant patients, drug dose selection based on CYP3A5*3 genotype could not help more patients achieve effective blood concentrations or improve prognosis.

[0005] For example, Chen Di et al. (Chen Di, Zhang Hua, Yin Jian, Tian Xiaoxin, et al. Monitoring of tacrolimus blood concentration and CYP3A5 gene detection analysis in patients with myasthenia gravis [J]. Chinese Journal of Neuroimmunology and Neurology, 2020, 27(03): 206-209+218.) used chemiluminescent microparticle immunoassay to determine tacrolimus blood concentration, and fluorescent staining in situ hybridization to detect patients' CYP3A5*3 gene polymorphism. The multiple linear regression method was used to analyze the correlation between blood concentration and factors such as gender, age, weight, genotype, dosage, and concomitant medication. The results showed that there was a correlation between tacrolimus dosage, CYP3A5 genotype, and concomitant use of omeprazole or esomeprazole and tacrolimus blood concentration (all P < 0.05). However, this study only analyzed many factors related to tacrolimus blood concentration, such as tacrolimus dose, CYP3A5 genotype, etc. through a large amount of sample data, but did not provide a method for accurately selecting or even predicting tacrolimus blood concentration. In addition, this study believed that patient age had no obvious correlation with the C / D value of tacrolimus blood concentration, and therefore did not use it as a reference factor for studying tacrolimus blood concentration. More importantly, this study believed that the impact of tacrolimus dose on tacrolimus blood concentration monitoring was far greater than the impact of genotype, which is in stark contrast to the international mainstream view that when the CYP3A5 genotype is a fast metabolizer, the tacrolimus dose needs to be significantly increased to achieve an effective concentration. In other words, the impact of genotype is usually far greater than the tacrolimus dose.

[0006] For another example, Chinese patent application CN111662975A discloses the use of products for detecting mutations in CYP3A4rs2242480 and CYP3A4rs4646437 gene sites in the preparation of products for predicting or evaluating the metabolic status of patients after taking tacrolimus. However, its application only includes single nucleotide site mutations in the CYP3A4 gene, and does not include the CYP3A5 site, which is the most clear influencing factor of tacrolimus, and its clinical practicality is low.

[0007] For another example, Chinese patent application CN112786145A discloses a method for accurately predicting the dosage of tacrolimus for organ transplant patients. The method includes collecting personal data of organ transplant patients, clinical tacrolimus medication data, and genetic testing data, and constructing a model-verified tacrolimus dosage prediction model, which helps to individualize the medication for organ transplant patients. However, in this method, the model is established based on the data of organ transplant patients. The medication regimen, effective concentration range, combined medication, drug concentration monitoring frequency and time point of other tacrolimus treatments are significantly different from those of myasthenia gravis patients, and cannot be applied to the initial tacrolimus dosage selection for myasthenia gravis patients.

[0008] Therefore, there is still an urgent need for a prediction system / model or prediction method for the accurate selection of tacrolimus treatment dose for patients with myasthenia gravis.

[0009] In addition, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making the present invention, but due to space limitations, not all details and contents are listed in detail. However, this does not mean that the present invention does not have the characteristics of these prior arts. On the contrary, the present invention already has all the characteristics of the prior art, and the applicant reserves the right to add relevant prior art to the background technology. Summary of the invention

[0010] In view of the deficiencies of the prior art, the present invention provides a system and application for accurately selecting the tacrolimus treatment dose for myasthenia gravis patients, aiming to solve at least one or more technical problems existing in the prior art.

[0011] To achieve the above objectives, the inventors conducted in-depth research on the factors affecting the blood concentration of tacrolimus in the myasthenia gravis population, constructed an early prediction system for the blood concentration of tacrolimus, and verified the prediction model used therein internally and externally, providing a basis for accurately guiding the initial dose selection and early dose adjustment of tacrolimus in myasthenia gravis patients.

[0012] Specifically, the present invention provides a system for accurately selecting the tacrolimus treatment dose for myasthenia gravis patients, comprising:

[0013] A data acquisition unit, configured to acquire at least one feature data of a myasthenia gravis patient;

[0014] A prediction unit configured to determine a predicted probability value of a low tacrolimus concentration in a myasthenia gravis patient based on at least one feature data input;

[0015] Among them, the characteristic data include age, single nucleotide mutation sites of candidate genes related to tacrolimus metabolism, selected tacrolimus dose, whether to use Wuzhi capsule and one or more of hematocrit. In particular, the prior art is usually based on the data of organ transplant patients, in which the medication regimen, effective concentration range, combined medication, drug concentration monitoring frequency and time point of tacrolimus treatment are significantly different from those of myasthenia gravis patients, and cannot be applied to the initial dose selection of tacrolimus for myasthenia gravis patients; in the study of individual differences in metabolism of myasthenia gravis patients, the existing tacrolimus treatment only analyzes the factors related to tacrolimus concentration. On the one hand, the alternative influencing factors are limited, and on the other hand, a practical concentration prediction model has not yet been proposed to guide the selection and adjustment of clinical medication doses. In the present invention, by establishing an accurate prediction model for the initial dose and dose adjustment of tacrolimus for patients, it is helpful to guide the individualized medication of myasthenia gravis patients receiving tacrolimus treatment, reduce the insufficient blood drug concentration in the early stage of treatment caused by insufficient tacrolimus dosage, improve the early efficacy, and have important significance for the accurate medication of immunosuppressive treatment for myasthenia gravis patients. The system for accurately selecting the therapeutic dose of tacrolimus for myasthenia gravis patients provided by the present invention, namely, the collection of clinical data based on clinical cohorts, the screening of gene loci based on previous studies, and the model verification based on internal cross-validation and external validation, provides a template for the research on the precise use of immunosuppressants for the treatment of other autoimmune diseases and has important reference value.

[0016] Preferably, the data acquisition unit can be an electronic input device, which can be a keyboard, a mouse, a scanner, a microphone, etc. The data acquisition unit sends the acquired feature data to the prediction unit. The prediction unit is a data processor, which can calculate and output the predicted probability value of low concentration of tacrolimus based on the received feature data according to preset computer program instructions. Preferably, the prediction unit can also output an indication of adjusting the dosage of tacrolimus and / or an indication of whether to use Wuzhi capsules based on the calculated predicted probability value of low concentration of tacrolimus. Preferably, the prediction unit sends the calculated predicted probability value of low concentration of tacrolimus and / or an indication of adjusting the dosage of tacrolimus and / or an indication of whether to use Wuzhi capsules to the output unit, and the output unit outputs the above information. Preferably, the output unit can be a display, a sound output device, etc.

[0017] Preferably, the prediction model in the present invention is Logit (P low concentration), and Logit (P low concentration) is determined in association with at least age, tacrolimus dose, and at least two candidate genes related to tacrolimus metabolism. Specifically, as the optimal method, Logit (P low concentration) = 10.023-0.047×age-1.263×tacrolimus dose-4.325×whether Wuzhi capsule is used+3.039×rs776746 genotype-2.111×rs1045642 genotype-0.117×hematocrit. In particular, in the present invention, the corresponding coefficients of tacrolimus dose and rs776746 gene are 1.263 and 3.039, respectively, and the influence of surface genotype is much greater than that of tacrolimus dose, which is consistent with the existing mainstream view. This conclusion can better guide physicians to choose the dosage of tacrolimus. In addition, the present invention also determines the correlation between hematocrit, the synthetic drug Wuzhi capsule, MDR1 (rs1045642) and the tacrolimus dosage, and further determines the respective weight coefficients.

[0018] Preferably, in the present invention, the predicted probability value of tacrolimus low concentration is calculated by the logistic regression principle in a manner related to the prediction model Logit (P low concentration). Specifically, the predicted probability value of tacrolimus low concentration can be calculated by the following formula:

[0019]

[0020] Preferably, when the low concentration probability value is greater than a preset threshold, it is determined that the patient has a high risk of low tacrolimus concentration, and an instruction to increase the tacrolimus dosage or to add Wuzhi capsules is output;

[0021] When the low concentration probability value is less than the preset threshold, the patient is judged to have a low risk of tacrolimus low concentration, and an instruction to administer the selected dose is output. Preferably, in the present invention, the preset threshold can be 50%, or 40%, or 30%, or 20%, or 10%, or any value between 0 and 50%. In particular, the need to increase the tacrolimus dose or add the five-ester capsule and the selected dose administration are not particularly limited, and a specific dosage control plan can be given according to the patient's specific health status and the experience of the medical physician to achieve the control of the tacrolimus concentration.

[0022] Preferably, low tacrolimus concentration means that the patient's tacrolimus blood concentration is less than a concentration threshold. In particular, in the present invention, the concentration threshold is 4.8 ng / ml.

[0023] Preferably, the candidate genes include CYP3A5 and ABCB1 genes.

[0024] Preferably, the single nucleotide mutation sites of the candidate gene include a dominant inheritance model at the rs776746 site and a recessive inheritance model at the rs1045642 site.

[0025] Preferably, in the present invention, the prediction model is constructed based on the variables through multivariate analysis, wherein the multivariate analysis includes but is not limited to stepwise regression analysis and binary logistic regression analysis.

[0026] Preferably, the prediction model is obtained as follows:

[0027] Obtain clinical data information of myasthenia gravis patients;

[0028] Based on clinical data, factors associated with tacrolimus were identified;

[0029] A set of variables correlated with tacrolimus concentrations was obtained by univariate analysis based on the factors;

[0030] Perform multi-factor analysis of variable sets based on stepwise regression analysis model to obtain characteristic data;

[0031] A tacrolimus low concentration prediction model is constructed based on the characteristic data. In particular, clinical data information can be derived from clinical data collection, genetic testing, and laboratory test data.

[0032] In particular, univariate analysis may include: expressing normally distributed continuous variables as mean ± standard deviation, expressing non-normally distributed continuous variables as median, and expressing categorical variables as frequency; using Hardy-Weinberg equilibrium test to analyze the genetic balance of single nucleotide site polymorphisms and to test whether the genotype frequency is representative of the population; using Haploview software to analyze linkage disequilibrium between different single nucleotide sites; using independent sample t-test, rank sum test or chi-square test to compare between groups of demographic characteristics, clinical data and single nucleotide sites, and screen out a set of variables that can be included in multivariate analysis.

[0033] Further, the present invention also relates to the process of internal cohort validation and external cohort validation of the prediction model, wherein the internal cohort validation includes the use of random method and Bootstrap resampling method to evaluate the internal validity of the prediction model; the external validation cohort validation includes the use of validation cohort data to validate the constructed prediction model, and calculate the area under the receiver operating characteristic curve of the prediction result and its 95% confidence interval, specificity, sensitivity, positive prediction rate and negative prediction rate to evaluate the external validity of the prediction model. In particular, the model of the present application has confirmed the repeatability and universality of the model through internal and external validation methods, especially in external validation, the area under the receiver operating characteristic curve and its 95% confidence interval are above 0.85, indicating that the prediction system and model of the present invention can be widely used in this field without worrying about the low prediction efficiency caused by population differences.

[0034] Preferably, the factors correlated with tacrolimus in the present invention include medication regimen, tacrolimus dose, gender, age, height, weight, body mass index, alanine aminotransferase, glutamate aminotransferase, urea nitrogen, creatinine, hematocrit, concomitant medication, concomitant diseases, and single nucleotide mutation sites of candidate genes related to tacrolimus metabolism.

[0035] Preferably, the set of variables correlated with tacrolimus concentration in the present invention includes age, whether glucocorticoids are used, whether Wuzhi capsule is used, hematocrit, tacrolimus dose and three single nucleotide mutation sites: dominant inheritance model of rs776746 (T / T, T / C vs C / C), recessive inheritance model of rs2242480 (C / C vs C / T, T / T), recessive inheritance model of rs1045642 (A / A vs A / G, G / G).

[0036] Preferably, the prediction system for accurate selection of tacrolimus therapeutic dose for myasthenia gravis patients involved in the present invention also includes a visualization unit, which is configured to input characteristic data of myasthenia gravis patients into the prediction model so that the prediction model can achieve visualized output.

[0037] Preferably, the present invention also relates to an electronic device, comprising:

[0038] one or more processors;

[0039] a memory for storing one or more computer programs;

[0040] When one or more computer programs are executed by one or more processors, the one or more processors implement the step of obtaining the predicted probability value of low tacrolimus concentration by using the prediction model: including inputting at least one characteristic data of a myasthenia gravis patient into the prediction model to obtain the predicted probability value of low tacrolimus concentration.

[0041] Preferably, the present invention also relates to a storage medium comprising computer executable instructions, which, when executed by a computer processor, are used to implement the steps of obtaining a predicted probability value of a low concentration of tacrolimus using a prediction model: comprising inputting at least one characteristic data of a myasthenia gravis patient into the prediction model to obtain a predicted probability value of a low concentration of tacrolimus.

[0042] Preferably, the present invention also relates to the use of a prediction system for accurately selecting a tacrolimus therapeutic dose for myasthenia gravis patients in predicting a tacrolimus therapeutic dose for myasthenia gravis patients.

[0043] The beneficial technical effects of the present invention include: the present invention provides an accurate prediction system for the initial dose and dose adjustment of tacrolimus for myasthenia gravis patients, which helps to guide the individualized medication of myasthenia gravis patients receiving tacrolimus treatment, reduces the insufficient blood drug concentration in the early stage of treatment caused by insufficient tacrolimus dosage, improves the early efficacy, and is of great significance for the accurate medication of immunosuppressive treatment for myasthenia gravis patients. In addition, the model construction process and method of the present invention, namely, clinical data collection based on clinical cohorts, gene site screening based on previous studies, and model verification based on internal cross-validation and external validation, provides a model for the accurate medication research of immunosuppressant treatment for other autoimmune diseases, and has important reference value.

[0044] In the prior art, for the treatment of myasthenia gravis patients, tacrolimus can only be administered according to the empirical dose. In order to avoid adverse reactions in the early stage of treatment, the starting dose of tacrolimus for myasthenia gravis patients is 2 mg / day, and then gradually increased to 3-4 mg / day. The dose needs to be stabilized for 5-7 days before drug concentration monitoring. The first drug concentration monitoring is often performed in the 3rd to 4th week after taking the medicine, which makes it difficult for some patients to reach the effective concentration range within the first month of tacrolimus treatment. The present invention creatively proposes that the patient's tacrolimus low concentration probability value can be calculated by correlation through the patient's characteristic data, and the indication of adjusting the initial dose of tacrolimus and whether the five-ester capsule should be used can be given according to the predicted probability value of the low concentration of tacrolimus, or the recommended value of the initial dose of tacrolimus and the indication of whether the five-ester capsule should be used can be calculated according to the characteristic data combined with the target tacrolimus low concentration probability value, so that the dosing regimen can be formulated more scientifically and personalized, effectively overcoming the problem of poor early efficacy caused by individual differences in patients in the prior art. Moreover, through this system, patients can also avoid multiple blood drug concentration tests. Among them, the characteristic data include age, single nucleotide mutation sites of candidate genes related to tacrolimus metabolism, selected tacrolimus dosage, whether to use Wuzhi capsule and one or more of hematocrit. The characteristic data selected by the present invention is the data with the highest correlation with the possibility of low tacrolimus concentration of the patient, with a small amount of data and accurate calculation results. After verification, the accuracy of the tacrolimus low concentration probability value of the system disclosed by the present invention is as high as 90%, or even more than 95%, and the accuracy of the tacrolimus dosage indication is also as high as 90%, or even more than 95%, which significantly improves the drug treatment effect of patients with myasthenia gravis. Statistically speaking, it significantly improves the early treatment effect, shortens the treatment cycle of a large number of patients, improves the treatment experience of patients, and saves a lot of medical resources at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of prediction results of a visual nomogram prediction system according to a preferred embodiment of the present invention;

[0046] Figure 2 It is an AUC curve diagram of a prediction system according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following is a detailed description with reference to the accompanying drawings.

[0048] The prediction method or prediction model and system for accurately selecting the tacrolimus treatment dose for myasthenia gravis patients of the present invention are used for non-diagnostic and non-therapeutic purposes.

[0049] In the present invention, the scope of clinical data information collection is not particularly limited, as long as the collection of patient information can be achieved, the scope of collection includes but is not limited to the myasthenia gravis clinical cohort database, the first visit and follow-up information in the hospital inpatient and outpatient electronic medical record system, wherein the general information of the patient includes at least: the patient's name and number; demographic characteristics: gender, race, permanent residence, age, height, weight, occupation, education level. The patient's medical history data at least includes: myasthenia gravis related information: onset time, disease classification, previous treatment medication regimen, previous treatment outcome; basic disease: basic disease and current treatment medication, including but not limited to the name of the treatment drug for diabetes, hypertension, and coronary heart disease, dosage, and frequency of administration. The detailed medication record of tacrolimus includes at least: the starting medication time, medication time, frequency of administration, dosage, dosage adjustment time, withdrawal time, and adverse drug reactions. Tacrolimus drug concentration monitoring includes at least: collecting venous blood samples, collection date and time, concentration detection method, and tacrolimus drug concentration detection value. In addition, laboratory tests include at least: blood routine, liver function, and kidney function.

[0050] In the present invention, the gene detection data is the gene detection result obtained by sequencing the genes related to tacrolimus metabolism, including but not limited to CYP3A5, CYP3A4, ABCB1, POR, CYP2C19, NR1L2 genes. Sequencing can be performed using sequencing techniques known in the art, including but not limited to sequencing by synthesis, single molecule sequencing, and nanopore sequencing.

[0051] In the present invention, the term "factors correlated with tacrolimus" refers to a set of related factors that are correlated with the tacrolimus blood concentration level in myasthenia gravis patients but have not been screened for variables. The term "variable set correlated with tacrolimus concentration" refers to variables with strong correlation obtained by screening variables through univariate analysis, which are used for multivariate analysis. The term "characteristic data" refers to independent risk factors associated with low tacrolimus concentrations obtained through multivariate analysis, and is used to construct a factor set for predictive model.

[0052] It will be appreciated by those skilled in the art that the various exemplary embodiments described in the present invention may be implemented by software or by software combined with necessary hardware. Therefore, the specific embodiments according to the present invention may be embodied in the form of a software product, which may be stored in a non-volatile storage medium or a non-transient computer-readable storage medium (which may 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 may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the present invention.

[0053] In an exemplary embodiment, the program product of the present invention can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared or semiconductor, or any combination of the above. More specific examples of readable storage media include, but are not limited to: 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. Preferably, a computer executable instruction for obtaining a low concentration prediction probability value of tacrolimus can be deployed in the readable medium: including inputting at least one characteristic data of a myasthenia gravis patient into the system predicted by the present invention to obtain a low concentration prediction probability value of tacrolimus.

[0054] Correspondingly, based on the same inventive concept, the present invention also provides an electronic device.

[0055] In an exemplary embodiment, the electronic device is embodied in the form of a general-purpose computing device. Components of the electronic device may include, but are not limited to: at least one processor, at least one memory, and a bus connecting different system components (including the memory and the processor).

[0056] The memory stores a program code, and the program code can be executed by the processing unit, so that the processing unit executes the method of the present invention, that is, in response to at least one characteristic data of the patient with myasthenia gravis inputted, outputs a predicted probability value of a low concentration of tacrolimus. The processor includes at least the data processing unit of the present invention (the present invention is sometimes also referred to as a "module"). The memory may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) and / or a cache storage unit, and may further include a read-only storage unit (ROM).

[0057] The memory of the present invention may also include a program / utility having a set (at least one) of program modules, such program modules 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.

[0058] The bus may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0059] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), with one or more devices that enable a user to interact with the electronic device, and / or with any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.).

[0060] Such communication can be carried out through an input / output (I / O) interface. In addition, the electronic device 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 a network adapter. The network adapter communicates with other modules of the electronic device through a bus. It should be understood that, although not shown herein, other hardware and / or software modules can be used in conjunction with the electronic device, 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.

[0061] The detection / identification value of the system or method of the present invention can be judged by, for example, calculating the area under the receiver operating characteristic curve (AUC), sensitivity, specificity and other evaluation indicators. AUC is also called the area under the receiver operating characteristic curve, which is defined as the area under the ROC curve and the coordinate axis, and the numerical range of the area is between 0.5 and 1. The closer the AUC is to 1.0, the higher the authenticity of the detection method.

[0062] Those skilled in the art should understand that, as long as the purpose of the present invention can be achieved, other steps or operations may be included before, after, or between the above steps a, b and (1)-(3), for example, to further optimize and / or improve the method described in the present invention.

[0063] Example 1

[0064] 1. Experimental design

[0065] The general information, medical history, detailed tacrolimus medication records, tacrolimus drug concentration monitoring, laboratory tests, and genetic testing data of the enrolled myasthenia gravis patients were collected, and a model was constructed using statistical analysis methods to identify the influencing factors of inter-individual differences in tacrolimus blood concentrations in myasthenia gravis patients and to determine a prediction model for the risk of low tacrolimus blood concentrations in the early stage.

[0066] 2. Research subjects

[0067] (1) Meet the following inclusion criteria: patients with myasthenia gravis registered in the Myasthenia Gravis Clinical Cohort of Xuanwu Hospital (National Key R&D Program of the Ministry of Science and Technology-Precision Medicine Research-Neurological Disease Cohort-Myasthenia Gravis Disease Cohort); meet the diagnostic criteria for myasthenia gravis (refer to the "Guidelines for the Diagnosis and Treatment of Myasthenia Gravis in China 2020"); receive oral tacrolimus treatment; complete tacrolimus drug concentration monitoring and genetic testing; patients and their families sign informed consent.

[0068] (2) According to the time of joining the study, the patients were divided into a modeling cohort and an external validation cohort: patients who joined this study between July 1, 2017, and December 31, 2019, were the modeling cohort, with a total of 93 patients included (the sample size required for cross-sectional study sample size calculation was 83 cases); patients who joined this study between January 1, 2020, and October 1, 2020, were the external validation cohort, with a total of 36 patients included (the sample size required for diagnostic study sample size calculation was 36 cases).

[0069] 3. Data collection and grouping basis

[0070] (1) Tacrolimus dosing regimen: The initial dose is 2 mg / day, and the daily dose is taken twice a day (once in the morning and once in the evening, on an empty stomach or 1 hour before or 2-3 hours after a meal); if there are no obvious discomfort symptoms, the dose is increased to 3-4 mg / day in the 2nd-3rd week; peripheral venous blood is drawn in the 3rd-4th week for blood drug concentration, blood routine, and blood biochemistry tests. In the 4th week after taking the medicine, the clinical physician will adjust the dosage according to the degree of improvement of myasthenia gravis, tacrolimus drug tolerance, blood drug concentration, and related test results. The target range of tacrolimus blood concentration is 4.8-10 ng / ml. Increase the tacrolimus dose or give Wuzhi capsules to increase the TAC blood drug concentration level. The maximum daily dose does not exceed 5 mg.

[0071] (2) Tacrolimus drug concentration monitoring: The first blood drug concentration test was performed 3-4 weeks after taking the drug, using microparticle enzyme-linked immunosorbent assay to detect the blood drug concentration; single blood drug concentration measurements between 1-3 months after the patient took the drug were recorded. If there were multiple measurements, the first measurement value was used (the first measurement value more accurately reflected the concentration level in the early stage of treatment, which was conducive to analyzing the influencing factors of the early concentration); the target blood drug concentration range was 4.8-10 ng / ml. If the concentration was lower than 4.8 ng / ml, the treatment might be ineffective. With 4.8 ng / ml as the cutoff point, the patients were divided into two concentration groups: a low concentration group (<4.8 ng / ml) and an effective concentration group (≥4.8 ng / ml), which were used as binary dependent variables for statistical analysis.

[0072] (3) Selection and detection of gene loci: Based on the evidence level of the Pharmacogenomic Knowledge Base (PharmGKB) and the guidelines of the Clinical Pharmacogenetics Implementation Consortium (CPIC), genes and single nucleotide mutation sites that affect the blood concentration of tacrolimus were selected. A total of 6 candidate genes related to tacrolimus metabolism were included, including CYP3A5, CYP3A4, ABCB1, POR, CYP2C19 and NR1L2 genes. DNA was extracted from the whole blood of patients using the salting-out method, and the NovaSeq6000 sequencer (Illumina Inc.) was used for second-generation sequencing. Bioinformatics analysis obtained information on single nucleotide mutation sites of 120 genes available for analysis. The Hardy-Weinberg equilibrium test was used to analyze the genetic balance of single nucleotide site polymorphisms and to test whether the genotype frequency was representative of the population; Haploview software was used to analyze the linkage disequilibrium between different sites, and finally 14 single nucleotide site mutations available for statistical analysis were obtained.

[0073] (4) Collection of clinical data: Clinical data such as demographic characteristics, clinical data, and auxiliary examination results were collected from the myasthenia gravis clinical cohort database, including medication regimen, tacrolimus dose, gender, age, height, weight, body mass index, alanine aminotransferase, glutamate aminotransferase, urea nitrogen, creatinine, hematocrit, concomitant medications, and comorbidities (diabetes, hypertension). A total of 17 clinically relevant factors were included.

[0074] 4. Statistical analysis to build a prediction model

[0075] (1) Univariate analysis of variables: independent sample t test (continuous variables with normal distribution), rank sum test (continuous variables with non-normal distribution) or chi-square test (categorical variables) were used to compare demographic characteristics, clinical data and single nucleotide mutations between groups (low concentration group vs effective concentration group), and variables with P value ≤ 0.10 or clinically considered to be strongly correlated were included in the multivariate analysis. Finally, 5 clinical factors (age, whether to use glucocorticoids, whether to use Wuzhi capsules, hematocrit, and tacrolimus dose) and 3 single nucleotide mutation sites [dominant genetic model of rs776746 site (T / T, T / C vs C / C), recessive genetic model of rs2242480 site (C / C vs C / T, T / T), recessive genetic model of rs1045642 site (A / A vs A / G, G / G)] were screened and included in the multivariate analysis;

[0076] (2) Multi-factor analysis model construction

[0077] a. Stepwise regression analysis was used to express the correlation between clinical or genetic factors and tacrolimus blood concentration <4.8 ng / ml (i.e., low concentration group) with odds ratio and its 95% confidence interval. Five independent risk factors for the low concentration group were obtained, including age, tacrolimus dose, use of Wuzhi capsule, rs776746 genotype and rs1045642 genotype; hematocrit was not statistically different (P value = 0.068), but was included in the final model of stepwise regression. The final tacrolimus low concentration risk prediction model was Logit (P low concentration) = 10.023-0.047 × (age) -1.263 × (TAC dose) -4.325 × (whether Wuzhi capsule was used) + 3.039 × (rs776746 genotype) -2.111 × (rs1045642 genotype) -0.117 × (hematocrit). In the formula, age is a continuous variable, taking the integer age, the unit is years. TAC dose is a continuous variable, taking the actual dose, the unit is mg. Whether Wuzhi capsules are used is a categorical variable, not using Wuzhi capsules is 0, and using Wuzhi capsules is 1. The rs776746 genotype is a categorical variable, the C / C genotype is 0, and the T / T or T / C genotype is 1. The rs1045642 genotype is a categorical variable, the A / G or G / G genotype is 0, and the A / A genotype is 1. Hematocrit is a continuous variable, without unit, and the value is hematocrit × 100.

[0078] Prediction function of the model: According to the following formula, the predicted probability value of low tacrolimus concentration can be obtained:

[0079] b. Using binary logistic regression, with age, tacrolimus dose, use of Wuzhi capsules, genotype of rs776746 and rs1045642, and hematocrit as independent variables, odds ratio and its 95% confidence interval were used to indicate the correlation between clinical or genetic factors and tacrolimus blood concentration <4.8 ng / ml (i.e., low concentration group), and the same tacrolimus concentration prediction model as the stepwise regression result was obtained; the nomogram prediction model was established using the R (R3.6.0) software rms package, as shown in the following figure: Figure 1 shown.

[0080] c. The predictive performance of the prediction model was evaluated by using discrimination and calibration. The discrimination evaluation index was the area under the receiver operating characteristic curve and its 95% confidence interval, and the calibration evaluation index was the goodness of fit test.

[0081] like Figure 2As shown, the prediction model has good discrimination [area under the receiver operating characteristic curve and its 95% confidence interval = 0.877 (0.809-0.945)] ( Figure 2 The dark curve shows that the calibration degree [test χ2 = 2.252, P value = 0.972];

[0082] The nomogram prediction model had good discrimination [area under the receiver operating characteristic curve and its 95% confidence interval = 0.877 (0.810-0.945)] and calibration (the predicted probability in the calibration curve was close to the actual probability);

[0083] d. Before myasthenia gravis patients receive tacrolimus treatment, their age, rs776746 genotype, and rs1045642 genotype are obtained through clinical data collection, genetic testing, and laboratory tests. The clinician selects the tacrolimus dose and whether to use Wuzhi capsules, and obtains the predicted probability value of low concentration through the prediction model or nomogram prediction model. When the low concentration probability value is greater than 50%, it indicates that the patient has a high risk of low tacrolimus concentration and needs to increase the tacrolimus dose or add Wuzhi capsules appropriately. When the low concentration probability value is less than 50%, it indicates that the patient has a low risk of low tacrolimus concentration and can be given the selected dose.

[0084] To facilitate understanding, some examples are given below:

[0085] Patient A is 40 years old, with a TAC dose of 2 mg, and has not used Wuzhi capsules (value 0). The genotype of rs776746 is C / C (value 0), the genotype of rs1045642 is A / A (value 1), and the hematocrit is 0.45 (value 45). Logit(Plow concentration) = 10.023-0.047×(40)-1.263×(2)-4.325×(0)+3.039×(0)-2.111×(1)-0.117×(45), and the result is -1.705. After conversion by the formula, the predicted probability value of tacrolimus low concentration is 0.15381, indicating that the risk of tacrolimus low concentration is low and tacrolimus can be administered at the selected dose.

[0086] Patient B, age 60 years, TAC dose 2 mg, no use of Wuzhi capsule (value 0), rs776746 genotype is T / T (value 1), rs1045642 genotype is G / G (value 0), hematocrit is 0.45 (value 45), then Logit (P low concentration) = 10.023-0.047 × (60)-1.263×(2)-4.325×(0)+3.039×(1)-2.111×(0)-0.117×(45), the result is 2.505; after conversion by the formula, the predicted probability value of tacrolimus low concentration is 0.92449, indicating that the risk of tacrolimus low concentration is relatively high; based on this prediction result, increasing the tacrolimus dose to 3mg (the final predicted probability is 0.78) still has a high risk of low concentration; or maintaining the tacrolimus dose of 2mg and adding Wuzhi Capsule (the final predicted probability is 0.139), the risk of low concentration is relatively low.

[0087] (3) Internal validation of the model

[0088] a. Internal cross-validation of the prediction model: The modeling population was divided into a training set and a validation set at a ratio of 2:1. The training set was used to establish the prediction model, and the internal validity of the prediction model was evaluated in the validation set. The simple cross-validation method was used, and the random grouping was repeated 200 times. The area under the receiver operating characteristic curve of the 200 validation sets and its 95% confidence interval were calculated = 0.799 (0.670-0.908), indicating that the low-concentration prediction model has good internal validity and repeatability in the modeling cohort;

[0089] b. Internal validation of the nomogram prediction model: The Bootstrap resampling method was used to calculate the area under the receiver operating characteristic curve and the square root of 0.844, indicating that the low-concentration prediction model has good internal validity and repeatability in the modeling cohort.

[0090] (4) External cohort validation of the model:

[0091] a. Substituting the relevant data of 36 patients in the validation cohort into the calculation formula of the low-concentration risk prediction model, the results showed that the low-concentration predicted risk of 14 patients was greater than 50%, and the predicted risk of 22 patients was less than or equal to 50%; compared with the actual results, the prediction model successfully judged the area under the receiver operating characteristic curve of the low-concentration group and its 95% confidence interval = 0.855 (0.708-1) (such as Figure 2 The sensitivity and specificity were 82.4% and 100%, respectively, the positive predictive rate was 100%, and the negative predictive rate was 86.4%, indicating that the low-concentration prediction model has good external validity and is universal for external data;

[0092] b. The relevant data of 36 patients in the validation cohort were substituted into the low-concentration risk nomogram prediction model. The results showed that the low-concentration predicted risk of 15 patients was greater than 50%, and the predicted risk of 21 patients was less than or equal to 50%. Compared with the actual results, the prediction model successfully judged the area under the receiver operating characteristic curve of the low-concentration group and its 95% confidence interval = 0.854 (0.712-1), the sensitivity and specificity were 82.4% and 94.7% respectively, the positive predictive rate was 93.3%, and the negative predictive rate was 85.7%, indicating that the low-concentration prediction model has good external validity and is universal for external data.

[0093] In some optional embodiments, the recommended initial TAC dose can also be calculated based on a preset or required target tacrolimus low concentration probability value. In particular, calculating the initial recommended TAC dose based on the expected target tacrolimus low concentration probability value can greatly save the long early monitoring process of tacrolimus, help to increase the proportion of early blood drug concentrations in patients with myasthenia gravis and improve early efficacy.

[0094] In particular, the prediction method provided by the present invention, i.e., associating various characteristic data of myasthenia gravis patients with each other to construct a prediction system for predicting or guiding the initial dose selection and early dose adjustment of tacrolimus for myasthenia gravis patients, can also be used to guide the selection and adjustment of the initial dose of corresponding drugs for treating other diseases, or can also be used to guide the selection and adjustment of the initial dose of tacrolimus in the treatment of other autoimmune diseases.

[0095] It should be noted that the above is only the optimal embodiment provided by the present invention. Based on the concept of the present invention, that is, calculating the probability of low tacrolimus concentration in myasthenia gravis patients by correlation with characteristic data, thereby adjusting their drug dosage and whether to use Wuzhi capsules, wherein the characteristic data includes age, single nucleotide mutation sites of candidate genes related to tacrolimus metabolism, selected tacrolimus dosage, whether to use Wuzhi capsules and / or hematocrit, any other calculation model obtained should fall within the protection scope of the present invention. On the basis of the calculation model disclosed in the present invention, other optional implementations obtained by adjusting parameters, coefficients, etc. do not deviate from the inventive concept of the present invention and should also fall within the protection scope of the present invention.

[0096] It should be noted that the above-mentioned specific embodiments are exemplary, and those skilled in the art can come up with various solutions inspired by the disclosure of the present invention, and these solutions also belong to the disclosure scope of the present invention and fall within the protection scope of the present invention. Those skilled in the art should understand that the present invention specification and its drawings are illustrative and do not constitute a limitation on the claims. The scope of protection of the present invention is defined by the claims and their equivalents. The present invention specification contains multiple inventive concepts, such as "preferably", "according to a preferred embodiment" or "optionally", all of which indicate that the corresponding paragraph discloses an independent concept, and the applicant reserves the right to file a divisional application based on each inventive concept.

Claims

1. A system for accurately selecting the therapeutic dose of tacrolimus for myasthenia gravis patients, characterized in that: include: A data acquisition unit, configured to acquire at least one feature data of the myasthenia gravis patient; A prediction unit, configured to determine a predicted probability value of a low tacrolimus concentration for the myasthenia gravis patient based on at least one feature data input, and to provide an indication of adjusting an initial tacrolimus dose and whether Wuzhi capsules should be used according to the predicted probability value of the low tacrolimus concentration, or to calculate a recommended initial tacrolimus dose and an indication of whether Wuzhi capsules should be used according to the feature data combined with a target predicted probability value of a low tacrolimus concentration; The characteristic data include age, single nucleotide mutation sites of candidate genes related to tacrolimus metabolism, selected tacrolimus dose, whether Wuzhi capsule is used, and hematocrit; The predicted probability value of low tacrolimus concentration is calculated by the logistic regression principle in a manner related to the prediction model Logit (P): ; The prediction model Logit (P) is determined in association with at least age, tacrolimus dose, and at least two candidate genes associated with tacrolimus metabolism, wherein: The candidate genes include CYP3A5 and ABCB1 genes, and the single nucleotide mutation sites of the candidate genes include the dominant inheritance model of the rs776746 site and the recessive inheritance model of the rs1045642 site; The prediction model Logit (P) = 10.023-0.047 × age-1.263 × tacrolimus dose-4.325 × whether Wuzhi capsule was used + 3.039 × rs776746 genotype-2.111 × rs1045642 genotype-0.117 × hematocrit; When the low concentration prediction probability value is greater than a preset threshold, it is determined that the risk of low tacrolimus concentration in the myasthenia gravis patient is high, and an instruction to increase the tacrolimus dosage or to add Wuzhi capsules is output; When the low concentration prediction probability value is less than a preset threshold, it is determined that the risk of low tacrolimus concentration in the myasthenia gravis patient is low, and an instruction to administer the drug according to the selected dose is output; The preset threshold is 50%, or 40%, or 30%, or 20%, or 10%, or any value between 0 and 50%.

2. The system for accurately selecting the tacrolimus therapeutic dose for myasthenia gravis patients according to claim 1, characterized in that: The low tacrolimus concentration means that the tacrolimus blood concentration of the myasthenia gravis patient is less than the concentration threshold, and the concentration threshold is 4.8 ng / ml.

3. The system for accurately selecting the tacrolimus therapeutic dose for myasthenia gravis patients according to claim 1, characterized in that: The prediction model is constructed based on variables through multivariate analysis, wherein the multivariate analysis includes stepwise regression analysis or binary logistic regression analysis.

4. A system for accurately selecting a therapeutic dose of tacrolimus, characterized in that: include: A data acquisition unit, configured to acquire at least one feature data of a myasthenia gravis patient; A prediction unit, configured to determine a tacrolimus low concentration prediction probability value associated with the at least one feature data inputted; The characteristic data include age, single nucleotide mutation sites of candidate genes related to tacrolimus metabolism, selected tacrolimus dose, whether Wuzhi capsules are used and / or hematocrit; The predicted probability value of low tacrolimus concentration is calculated by the logistic regression principle in a manner related to the prediction model Logit (P): ; The prediction model Logit (P) is determined in association with at least age, tacrolimus dose, and at least two candidate genes associated with tacrolimus metabolism, wherein: The candidate genes include CYP3A5 and ABCB1 genes, and the single nucleotide mutation sites of the candidate genes include the dominant inheritance model of the rs776746 site and the recessive inheritance model of the rs1045642 site; The prediction model Logit (P) = 10.023-0.047 × age-1.263 × tacrolimus dose-4.325 × whether Wuzhi capsule was used + 3.039 × rs776746 genotype-2.111 × rs1045642 genotype-0.117 × hematocrit; When the low concentration prediction probability value is greater than a preset threshold, it is determined that the risk of low tacrolimus concentration in the myasthenia gravis patient is high, and an instruction to increase the tacrolimus dosage or to add Wuzhi capsules is output; When the low concentration prediction probability value is less than a preset threshold, it is determined that the risk of low tacrolimus concentration in the myasthenia gravis patient is low, and an instruction to administer the drug according to the selected dose is output; The preset threshold is 50%, or 40%, or 30%, or 20%, or 10%, or any value between 0 and 50%.

5. An electronic device, characterized in that: include, one or more processors; a memory for storing one or more computer programs; When the one or more computer programs are executed by the one or more processors, the one or more processors implement the step of obtaining the predicted probability value of low concentration of tacrolimus using the prediction model: including inputting at least one characteristic data of a myasthenia gravis patient into the system described in any one of claims 1 to 4 to obtain the predicted probability value of low concentration of tacrolimus.

6. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions, when executed by a computer processor, are used to implement the step of obtaining a predicted probability value of a low tacrolimus concentration using a prediction model: comprising inputting at least one characteristic data of a myasthenia gravis patient into the system described in any one of claims 1 to 4 to obtain a predicted probability value of a low tacrolimus concentration.

7. Use of the system for accurately selecting the therapeutic dose of tacrolimus for myasthenia gravis patients according to any one of claims 1 to 3, or the system for accurately selecting the therapeutic dose of tacrolimus for myasthenia gravis according to claim 4, in predicting the therapeutic dose of tacrolimus for myasthenia gravis patients.

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