CN110923311B - Polymorphic site for guiding nephrotic syndrome CYP3A5 non-expression children to use tacrolimus - Google Patents

Polymorphic site for guiding nephrotic syndrome CYP3A5 non-expression children to use tacrolimus Download PDF

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CN110923311B
CN110923311B CN201911347476.1A CN201911347476A CN110923311B CN 110923311 B CN110923311 B CN 110923311B CN 201911347476 A CN201911347476 A CN 201911347476A CN 110923311 B CN110923311 B CN 110923311B
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tacrolimus
cyp3a5
nephrotic syndrome
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莫小兰
李嘉丽
陈秀娟
黄民
梁会营
孙新
谭梅
何艳玲
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Guangzhou Women and Childrens Medical Center
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Abstract

The invention relates to the field of biomedicine, in particular to a method for guiding nephrotic syndrome CYP3A5 non-expression children to use tacrolimus polymorphic sites. The polymorphic site is at least one of IL2RA rs12722489, MYH9rs2239781, MYH9rs4821478, ACTN4rs56113315, ACTN4rs62121818, ACTN4rs3745859 and INF2rs1128880 sites. The loci are closely related to tacrolimus pharmacokinetics of a nephrotic syndrome CYP3A5 non-expression infant patient, and detection of the loci belongs to peripheral blood indexes, so that the loci can be conveniently obtained and are convenient to popularize and use in the future.

Description

Polymorphic site for guiding nephrotic syndrome CYP3A5 non-expression children to use tacrolimus
Technical Field
The invention relates to the field of biomedicine, in particular to a polymorphic site for guiding nephrotic syndrome CYP3A5 non-expression children to use tacrolimus.
Background
Tacrolimus (FK 506) is a first line therapeutic for the treatment of refractory nephrotic syndrome in children. However, the drug has narrow treatment window (the blood and grain concentration needs to be controlled at 5-10 ng/mL), large pharmacokinetic individual difference (the oral bioavailability is about 3-77%, and the half-life period of children is about 8-16.8 h), and the administration dosage is difficult to control, which is always a key problem which troubles the clinical administration of FK 506. The clinician usually needs to perform a trial and error type search for 1-3 months to ensure that the concentration of FK506 grains of the infant patient reaches a proper treatment range, and the curative effect of the infant patient can be guaranteed. During such a long period, the infant patient may delay treatment and cause further damage to the kidney, and may even suffer serious complications such as severe infection and renal failure, and suffer economic, physical and mental losses. Therefore, the real reason influencing the individual difference of FK506 pharmacokinetics is found out, the biomarker capable of accurately predicting the FK506 steady-state blood concentration is excavated, a prospective concentration prediction model of FK506 is established, the initial dosage which is exactly suitable before the administration of a patient is realized, and the method is a key problem which needs to be solved urgently for realizing the individual administration of FK 506.
The important reason for individual difference in pharmacokinetics is due to polymorphism of genes encoding related drug metabolizing enzymes, transporters, drug targets and related expression regulatory factors. At present, research on FK506 personalized medicine at home and abroad mainly focuses on gene polymorphism of drug metabolizing enzyme (CYP 3A) and transporter (P-gp coded by MDR 1) of organ and blood transplant population. However, the research on the population with nephrotic syndrome is very rare, and only several SNPs such as FK506 metabolic enzyme coding gene CYP3A5 x 3, transporter coding gene MDR 1C 3435T, MDR1 1236C > -T, MDR 12677G > T/A and the like are involved. While it is currently agreed in the literature that what is recommended and also recommended by organ transplant guidelines is that CYP3A5 x 3 significantly affects FK506 blood levels, the impact of other SNPs on FK506 pharmacokinetics remains controversial. The pharmacokinetic individual differences of FK506 are not fully explained by the limited number of SNPs described above. In addition, nephrotic syndrome is a syndrome in which the permeability of the filter membrane of the glomerulus is increased and proteins in the blood are lost from the urine, resulting in a decrease in plasma albumin and an increase in urine protein. While FK506 is a drug with a protein binding rate of about 99%. Therefore, the pharmacokinetic behavior of FK506 in renal heald population tends to be different from that of healthy population and organ transplant population, and FK506 may be combined with plasma protein and leaked from the damaged glomerular filtration membrane into urine, thereby significantly changing the pharmacokinetic process. Then is the gene polymorphism encoding the relevant protein on the glomerular filtration membrane affecting the pharmacokinetics of FK 506? The literature reports that the NPHS 2R 229Q polymorphism of renal podocytes may influence the blood concentration of FK 506. However, the research report that the polymorphism of genes encoding other podocyte related proteins of the glomerulus influences the pharmacokinetics of FK506 is not found, and the influence of the polymorphism of genes encoding FK506 metabolic enzymes, transporters, related transcription regulation factors of target proteins and inflammatory cytokines on the FK506 concentration of the infant with the renal heddle is not reported. Therefore, it is necessary to find the influence of the SNPs on the plasma concentration of FK506, and to sufficiently clarify the cause of the large individual difference in FK506 pharmacokinetics.
After a biomarker which may influence the FK506 blood concentration is found, a concentration prediction model is established, and a reliable and convenient medication decision support can be provided for doctors and pharmacists. At present, most of domestic and foreign related researches still adopt the traditional analysis method such as multiple linear regression to establish a concentration model, but the traditional method has poor fitting performance on a multi-dimensional complex variable model, poor model generalization performance and the like. The only population pharmacokinetic model that included too few influencing factors, including only age, weight and one pharmacokinetic site (CYP 3A5 x 3), was not sufficient to account for individual differences in actual clinical concentrations. The existing research only uses the traditional statistical method logistic regression method to analyze and model, but the method is not suitable for modeling of complex, multidimensional and interactive variables, so that the obtained model has poor fitting performance and the phenomenon of under-fitting or over-fitting. In addition, the group pharmacokinetics must collect blood samples at multiple time points to establish a model with better performance. These clinical results or models have poor generalization performance and are difficult to popularize.
Therefore, it is difficult to analyze and obtain which gene polymorphism sites are sites mainly influencing the administration dosage of the FK506 medicament by the prior art, and an accurate medication guidance suggestion cannot be given.
Disclosure of Invention
Traditional statistical methods logistic regression methods analyze modeling, and the methods are not suitable for modeling complex, multidimensional and interactive variables. According to the method, a machine learning algorithm is adopted for the first time to establish and verify a prediction model of the steady-state blood drug valley concentration of the tacrolimus in the stabilization period, the machine learning algorithm carries out more reasonable preprocessing and importance sequencing on data, and the data noise is effectively reduced; and various modeling methods are compared and analyzed according to the characteristics of different data, so that a machine learning model which is excellent in performance on training data (such as small enough, low VC dimension of the model and moderate degree of freedom) is found out. By the method, large-scale high-throughput analysis of complex data can be performed on the dysolimus pharmacokinetic data for the first time, and the learning model can be adjusted more flexibly and more pertinently according to different expression types of CYP3A5, so that the correlation between relevant coding gene polymorphisms such as tacrolimus pharmacokinetic pathway protein, pharmacodynamics pathway protein, nephropathy related protein and transcription regulation factor and the blood and valley concentration of tacrolimus in the stable phase of Chinese nephrotic syndrome patients can be analyzed comprehensively and accurately for the first time, and further an objective medication guiding method for using tacrolimus by nephrotic syndrome CYP3A5 non-expression patients is provided.
Specifically, the invention relates to application of a detection agent of at least one of IL2RA rs12722489, MYH9rs2239781, MYH9rs4821478, ACTN4rs56113315, ACTN4rs62121818, ACTN4rs3745859 and INF2rs1128880 loci in preparation of a medication instruction kit for a nephrotic syndrome CYP3A5 non-expression infant to use tacrolimus.
IL2RA rs12722489, MYH9rs2239781, MYH9rs4821478, ACTN4rs56113315, ACTN4rs62121818, ACTN4rs3745859 and INF2rs1128880 sites are closely related to tacrolimus pharmacokinetics of nephrotic syndrome CYP3A5 non-expression type patients, and detection of the tacrolimus pharmacokinetics belongs to peripheral blood indexes, so that the tacrolimus pharmacokinetic markers can be obtained conveniently, and the tacrolimus pharmacokinetic markers are convenient to popularize and use in the future.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic flow chart of the construction and validation of a model used in the present invention;
FIG. 2 is a graph showing the relationship between the different genotypes of IL2RA rs12722489 and the CDRs (dose-corrected trough concentrations of tacrolimus) in a CYP3A5 non-expressing population according to an embodiment of the invention;
FIG. 3 is a graph showing the relationship between the different genotypes and CDRs of MYH9rs2239781 in a CYP3A5 non-expressing population according to an embodiment of the invention;
FIG. 4 is a graph showing the relationship between the different genotypes and CDRs of MYH9rs4821478 in a CYP3A5 non-expressing population according to an embodiment of the invention;
FIG. 5 is a graph showing the relationship between the different genotypes and CDRs of ACTN4rs56113315 in a CYP3A5 non-expressing population according to one embodiment of the present invention;
FIG. 6 is a graph showing the relationship between the different genotypes and CDRs of ACTN4rs62121818 in a non-expressed population of CYP3A5 according to an embodiment of the present invention;
FIG. 7 is a graph showing the relationship between the different genotypes and CDRs of ACTN4rs3745859 in a CYP3A5 non-expressing population according to an embodiment of the present invention;
FIG. 8 is a graph showing the relationship between the various genotypes and CDRs of INF2rs1128880 in a CYP3A5 non-expressing population according to an embodiment of the invention;
FIG. 9 is a block diagram of a training set and test set R according to an embodiment of the present invention 2 And drawing an optimal characteristic variable graph.
Detailed Description
Reference will now be made in detail to embodiments of the invention, one or more examples of which are described below. Each example is provided by way of explanation, not limitation, of the invention. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the scope or spirit of the invention. For instance, features illustrated or described as part of one embodiment, can be used on another embodiment to yield a still further embodiment.
It is therefore intended that the present invention cover the modifications and variations of this invention provided they come within the scope of the appended claims and their equivalents. Other objects, features and aspects of the present invention are disclosed in or are apparent from the following detailed description. It is to be understood by one of ordinary skill in the art that the present discussion is a description of exemplary embodiments only, and is not intended as limiting the broader aspects of the present invention.
The invention relates to application of a detection agent of at least one of IL2RA rs12722489, MYH9rs2239781, MYH9rs4821478, ACTN4rs56113315, ACTN4rs62121818, ACTN4rs3745859 and INF2rs1128880 loci in preparation of a medication instruction kit for a Nephrotic Syndrome (NS) CYP3A5 non-expression infant to use tacrolimus (FK 506).
"medication guide" as used herein means that one skilled in the art can adjust the dosage (dosage, dosage interval, etc.) of tacrolimus in nephrotic syndrome CYP3A5 non-expressing children according to IL2RA rs12722489, MYH9rs2239781, MYH9rs4821478, ACTN4rs56113315, ACTN4rs62121818, ACTN4rs3745859 and INF2rs1128880 sites. Based on the present disclosure, those skilled in the art can know without inventive step that these several gene polymorphism sites are closely related to the pharmacokinetics of tacrolimus.
The interleukin 2 receptor alpha chain is a protein encoded by the IL2RA gene. Interleukin 2 (IL 2) receptors the alpha (IL 2 RA) and beta (IL 2 RB) chains together with the common gamma chain (IL 2 RG) constitute a high protein affinity IL2 receptor. The IL2RA gene is thus associated with a number of immune diseases. Myosin heavy chain 9 (MYH 9) encodes myosin IIA heavy chain, which is involved in a number of cellular functional activities, including cell division, migration and adhesion. Aberrant MYH9 expression or function changes caused by MYH9 mutants result in cytoskeletal damage and, in some cases, proteinuria or renal failure. Alpha-actinin-4 (ACTN 4) is a member of the spectrin superfamily, and is a membrane-associated actin cross-linked protein which forms a cytoskeletal protein, and researches show that the ACTN is highly expressed in various tumor tissues, participates in the occurrence of EMT in the process of tumor progression through mediating various protein molecules, and is closely related to the invasion and metastasis of tumors. Inverted protamine formate 2 (INF 2) is a protein encoded by the INF2 gene in humans that is associated with focal segmental glomerulosclerosis.
There are no reports in the prior art relating to the relationship between IL2RA rs12722489, MYH9rs2239781, MYH9rs4821478, ACTN4rs56113315, ACTN4rs62121818, ACTN4rs3745859 and INF2rs1128880 with tacrolimus concentrations in renal integrated CYP3A5 non-expressing populations. The invention discovers that the polymorphism of the gene loci is closely related to the pharmacokinetics of tacrolimus of children with nephrotic syndrome for the first time. Genotyping of IL2RA rs12722489, MYH9rs2239781, MYH9rs4821478, ACTN4rs56113315, ACTN4rs62121818, ACTN4rs3745859, and INF2rs1128880 polymorphisms helps to better guide tacrolimus dosing in pediatric renal heddle CYP3A5 non-expressing patients.
In some embodiments, the kit further comprises a detection agent for other gene polymorphism sites which can be used for guiding the administration of tacrolimus.
In some embodiments, the additional gene polymorphic sites are CYP3A5 x 3/'3 and CYP3A5 x 1/' 1+ '1/' 3 gene polymorphic sites.
The detection reagents for the polymorphic sites of the genes of CYP3A5 x 3/x 3 and CYP3A5 x 1/x 1+ 1/x 3 can be used to double-check the non-expressed form of CYP3A 5.
In some embodiments, the pediatric patient with nephrotic syndrome is under 14 years of age.
In some embodiments, the infant with nephrotic syndrome has an age below 10 years.
In some embodiments, the nephrotic syndrome infant is from asia.
In some embodiments, the pediatric nephrotic syndrome is from china.
In some embodiments, the pediatric patient has substantially normal liver or kidney function.
In some embodiments, the pediatric patient with nephrotic syndrome is a pediatric patient with primary nephrotic syndrome.
In some embodiments, the pediatric patient with nephrotic syndrome is a refractory nephrotic syndrome pediatric patient.
In some embodiments, the detection agent is used to perform any one of the following methods:
restriction fragment length polymorphism PCR-RFLP, single-strand conformation polymorphism PCR-SSCP, competitive Allele-Specific PCR (Kompetitive Allele Specific PCR), denaturing gradient gel electrophoresis, allele-Specific PCR (ASPCR), DNA sequencing, DNA typing chip detection, flight mass spectrometry (MALDI-TOFMS) detection, denaturing High Performance Liquid Chromatography (DHPLC), snapshot method, taqman probe method, biological mass spectrometry, and HRM method.
The above methods are all known in the art for detecting a gene polymorphism site, and reagents used therefor are also known to those skilled in the art.
In some embodiments, DNA extraction reagents are also included in the kit.
In some embodiments, the DNA extraction reagent is used to perform any one of the following methods:
phenol chloroform method, naOH method, resin extraction method, salting out method, hexadecyl trimethyl ammonium bromide method, silica gel membrane adsorption method, FTA card method, silica bead method or magnetic bead extraction method.
Wherein:
the phenol chloroform method generally refers to a DNA extraction method in which a protein-like organic substance in a DNA solution is extracted by a phenol chloroform mixture, and the DNA is retained in an aqueous solution.
The NaOH method generally dissolves and denatures proteins by strong alkali, destroys cell membranes and nuclear membranes, denatures nuclease and releases DNA, and NaOH does not destroy the primary structure of DNA.
The resin extraction method is usually a Chelex100 method, and is a DNA extraction method for inactivating nuclease for degrading DNA by chelating magnesium, sodium and potassium ions through Chelex.
The salting-out method is generally carried out by disrupting cells and centrifuging, then precipitating the protein with about 6M saturated NaCl, precipitating the DNA in the supernatant from the centrifugation with anhydrous ethanol, and dissolving the DNA in TE.
The cetyltrimethylammonium bromide method is generally a DNA extraction method in which a nonionic detergent CTAB destroys cell walls and cell membranes and hard tissues, forms a complex with DNA, and separates DNA from proteins and polysaccharides.
The silica gel membrane adsorption method generally refers to a method for extracting and purifying DNA by adsorbing cell lysate to release DNA after cracking through a silica gel membrane, and removing impurities such as protein, lipid, polysaccharide and the like through protease digestion and rinsing liquid cleaning.
The FTA card method generally refers to a method for obtaining DNA from blood and oral epithelial cells by the lysis of cells by the FTA card to release the DNA.
The silica bead method generally refers to a DNA extraction method in which DNA molecules in an organic solution are specifically captured by silica microparticles in the presence of high concentration of guanidine thiocyanate.
The magnetic bead method generally refers to a method for extracting and purifying DNA, in which a layer of magnetic beads of magnetic resin is coated on the surface of silica gel in the presence of guanidine salt, and DNA is released after cell lysis is specifically adsorbed and lysed.
Based on the present invention, one skilled in the art will readily appreciate that the markers provided herein, IL2RA rs12722489, MYH9rs2239781, MYH9rs4821478, ACTN4rs56113315, ACTN4rs62121818, ACTN4rs3745859, and INF2rs1128880 sites, may also be used as pharmacokinetic markers. As used herein, a "pharmacokinetic marker" is an objective biochemical marker that is specifically associated with a drug effect. The presence or amount of the pharmacokinetic marker is not associated with the disease condition or disorder to which the drug is administered; thus, the presence or amount of a pharmacokinetic marker may serve as an indicator of the presence or activity of the drug in the subject. For example, a pharmacokinetic marker may indicate the concentration of a drug in a biological tissue, since the marker is expressed or transcribed or not expressed or transcribed in the tissue in relation to the level of the drug. In this way, the distribution or uptake of the drug is monitored with a pharmacokinetic marker. Similarly, the presence or amount of a pharmacokinetic marker may be correlated with the presence or amount of a drug metabolite, such that the presence or amount of the marker is indicative of the relative rate of drug breakdown in the body. Pharmacokinetic markers are particularly useful for increasing the sensitivity of detecting the effect of a drug, especially when the drug is administered at low doses. Moreover, the label is easier to detect due to the nature of the label itself; for example, the labels can be readily detected by methods such as probe hybridization or sequencing using the methods described herein. In addition, the use of pharmacokinetic markers may provide a mechanism-based prediction of risk due to drug treatment outside the range of possible direct observation.
Further, the invention also relates to a medication guidance method for using tacrolimus in children patients with nephrotic syndrome CYP3A5 non-expression, which comprises the following steps:
a) Obtaining a sample containing the DNA of the child patient; b) Measuring at least one of IL2RA rs12722489, MYH9rs2239781, MYH9rs4821478, ACTN4rs56113315, ACTN4rs62121818, ACTN4rs3745859, and INF2rs1128880 sites in the sample; c) Using the measurement of step b) to guide the drug dosage of tacrolimus.
In some embodiments, the sample is a bodily fluid of the subject, such as blood, serum or plasma and cerebrospinal fluid, tissue or tissue lysate, cell culture supernatant, semen, saliva sample, or other suitable genomic DNA-containing sample, preferably peripheral blood.
In some embodiments, step d may further comprise:
d 1 ) If the genotype of the IL2RA rs12722489 site of the infant is CT, the dosage of tacrolimus needs to be increased relative to other genotypes (CC + TT);
d 2 ) If the genotype of the MYH9rs2239781 locus of the child patient is CC, the dosage of tacrolimus needs to be reduced relative to other genotypes (TC + TT);
d 3 ) If the genotype of the MYH9rs4821478 locus of the child patient is AA, the dosage of tacrolimus needs to be reduced relative to other genotypes (AG + GG);
d 4 ) If the genotype of the ACTN4rs56113315 site of the child patient is CT, the dosage of tacrolimus needs to be reduced relative to other genotypes (CC + TT);
d 5 ) If the genotype of the ACTN4rs62121818 locus of the child patient is CC, the dosage of tacrolimus needs to be increased relative to other genotypes (CT + TT);
d 6 ) If the genotype of the ACTN4rs3745859 site of the child patient is TC, the dosage of tacrolimus needs to be reduced relative to other genotypes (TT + CC);
d 7 ) If the genotype of INF2rs1128880 site of the infant is GT, the dosage of tacrolimus needs to be increased relative to other genotypes (GG + TT);
d 8 ) If the genotype of the INF2rs1128880 site of the child patient is TT, the amount of tacrolimus needs to be reduced relative to other genotypes (GT + GG).
Embodiments of the present invention will be described in detail with reference to examples.
Examples
In the embodiment, a model for predicting the blood concentration of FK506 in the renal integrated CYP3A5 non-expression infant before administration is constructed by adopting an advanced machine learning algorithm.
1. Materials and methods
1. Patient and treatment
The patients have a visit at the maternal-child healthcare center in Guangzhou city from 7 months in 2013 to 12 months in 2017. Inclusion criteria were as follows: symptoms appeared before age 14, clinically diagnosed as refractory NS (including hormone-dependent NS, hormone-resistant NS, recurrent NS), and tacrolimus was taken orally for at least three months. Excluding the acceptance of drugs other than prednisone that affect tacrolimus blood levels, such as verapamil, ketoconazole, itraconazole, erythromycin, clarithromycin, diltiazem or certain traditional Chinese herbal medicines. Patients with secondary NS or liver or kidney dysfunction were also excluded.
All patients received a dual immunosuppressive regimen, including tacrolimus (prograf) TM Astellas, killorglin, ireland) and low dose prednisone (south china pharmaceutical limited, guangdong, china). The initial dose of tacrolimus is given to the patient at 0.10-0.15 mg/kg twice a day, and then the dose is adjusted to maintain the steady-state concentration of tacrolimus at 5-10 ng/ml.
The patient information is shown in Table 1
Table 1 demographic and clinical characteristics of all patients with pediatric primary nephrotic syndrome (n = 87)
Figure BDA0002333786960000111
The present invention was analyzed only for the CYP3A5 non-expressed group.
2. Data acquisition
Three months after tacrolimus administration, weight, age, tacrolimus dose and whole blood trough concentration (C) were obtained when steady state concentrations were consistently achieved 0 ). Venous blood samples were collected prior to the morning dose of tacrolimus. The whole blood concentration of tacrolimus was determined by enzyme linked immunoassay (Viva-E, siemens, germany). CDR (i.e. C) 0 the/D) is the dose-corrected trough concentration expressed as tacrolimus trough concentration divided by mg/kg body weight. In addition, corresponding laboratory parameters were obtained, including serum creatinine, alanine aminotransferase and aspartate aminotransferase.
DNA extraction and genotyping
Venous blood samples (2 ml) were collected from the patients for genotyping after informed consent was obtained. Total genomic DNA was extracted from peripheral blood leukocytes using Genome TIANGEN blood DNA extraction kit (DP 348, beijing, china). CYP3A4 x 1G (20230C)>T)，CYP3A5*3(6985A>G) And MDR1 (1236C)>T，2677G>T/A and 3435C>T) polymorphism was determined by PCR-RFLP method. 75 Single Nucleotide Polymorphisms (SNPs) including ACTN4 (rs 62121818), ACTN4 (rs 56113315), ACTN4 (rs 3745859), NPHS1 (rs 437168), NPHS2 (rs 2274622), MYH9 (rs 2239781), ITGB4 (rs 871443), LAMB2 (YO 11987E) (rs 58948844), CD2AP (rs 4711880), INF2 (rs 12147772), TRPC6 (rs 10501986), PLCE1 (rs 17109671), WT1 (rs 1799925), ANGPTL4 (rs 2042899) and the like by Agena Bioscience
Figure BDA0002333786960000121
system (Agena Bioscience, san Diego, calif., USA).
4. Data pre-processing
Firstly, removing variables with deletion values of more than 10 percent, analyzing the correlation between each variable and an outcome variable by adopting a single-factor analysis method, and rejecting the variables with negative analysis results (P values of more than 0.05); selecting CYP3A5 x 3 non-carriers (namely CYP3A5 non-expression type) according to whether the CYP3A5 x 3 is carried for typing, and then filling missing continuous type variables by adopting a mean value; and finally, standardizing the continuous variable and carrying out dummification on the classified variable.
5. Feature extraction
Firstly, performing single factor analysis on variables after data preprocessing, screening initial characteristic variables (characteristic subsets a) related to outcome variables, and then further selecting proper characteristic variable subsets by using an integrated model, wherein the proper characteristic variable subsets comprise four algorithms of eXtreme Random Tree (ET), gradient Boost Decision Tree (GBDT), random Forest (RF) and eXtreme Gradient boost (XGBoost): different data transformations are firstly carried out on continuous data variables, namely min-max normalization (min-max normalization), Z-score normalization (Z-score normalization), L2 regularization and prototype keeping unchanged, and four types of variable data sets are formed by combining classified variables respectively. And then modeling the four form variables by adopting the four algorithms to obtain 16 models. Each model carries out importance sequencing on data variables, a total of 16 variable importance ranking sequences are obtained, and the median importance ranking is taken as the final importance ranking of the variables. Finally, selecting a five-fold cross validation and XGboost method, carrying out forward stepwise modeling from the most important variable according to the ranking sequence of the important contribution degrees of the variables, only adding one variable each time, and re-modeling and evaluating the R of the model 2 Until the end of the last variable addition and according to R 2 And determining the optimal variable number to form the final characteristic variable (the characteristic subset B).
6. Model construction
Analyzing the feature subset A by using a Lasso algorithm (least absolute shrinkage and selection operator, lasso), realizing feature selection and regularization and constructing a multiple linear regression model; in addition, feature subset B was analyzed and regression prediction models were constructed using Lasso, ET, GBDT, RF, and XGBoost algorithms. In all model building processes, a training set and a testing set are randomly divided into data sets according to the ratio of 8. The flow chart of modeling and verifying is shown in fig. 1.
2. Results of the experiment
1. Determination of initial feature variables (feature subset A)
All the characteristic variables tested, including the clinical characteristics and genotype before administration, were combined in 118 variables and subjected to one-way analysis with CDRs to yield variables with p <0.05 including: GENDER (p = 0.033), IL 2rs 12722489 (p = 0.046), MYH9rs4821478 (p = 0.027), ACTN4rs56113315 (p = 0.002), ACTN4rs62121818 (p = 0.003), MYH9rs2239781 (p = 0.039), INF2rs1128880 (p = 0.008), IL2RArs2104286 (p = 0.043), ACTN4rs3745859 (p = 0.002), LMX1Brs71497630 (p = 0.048). Further, the positive variables were further muted, and the muted variables were subjected to one-factor analysis again, resulting in positive variables with p <0.05, including GENDER (p = 0.033), IL 2rs 12722489_ CC (p = 0.022), IL 2rs 12722489_ CT (p = 0.013), MYH9rs4821478_ AA (p = 0.008), ACTN4rs56113315_ CT (p = 0.004), ACTN4rs62121818_ CC (p = 0.013), MYH9rs2239781_ CC (p = 0.013), INF2rs1128880_ GG (p = 0.030), INF2rs 8880_ GT (p = 0.040), INF2rs 8880_ TT (p = 0.005), IL 2rs 4286_ TT (p = 0.2100.21086), IL 2rs = 1124286 (p = 2240.014), IL 2rs 4205 _ CC (p = 429) and initial characteristics of p = 4289 (p = 2 _ ctl = 2). All variables and CDRs of the characteristic subset A are mapped, wherein the relations between different genotypes and CDRs of IL2RA rs12722489, MYH9rs2239781, MYH9rs4821478, ACTN4rs56113315, ACTN4rs62121818, ACTN4rs3745859 and INF2rs1128880 in the CYP3A5 non-expression population are shown in sequence in FIGS. 2-8.
2. Determination of the optimal characteristic variable (characteristic subset B)
Modeling 4 variable subsets formed by performing data conversion on the characteristic subset A by adopting 4 machine learning algorithms to obtain 16 models, obtaining 16 important contribution degrees of each variable, ranking the final important contribution degrees by taking the median of the important contribution degrees of each variable,the smaller the median value, the more important the variable. The importance of variables such as MYH9rs2239781_ CC, MYH9rs4821478_ AA, ACTN4rs56113315_ CT, INF2rs1128880_ TT, GENDER, etc. are ranked in the top row. Further adopting an XGboost method (5-fold cross validation) to rank according to the important contribution degrees of the variables, carrying out forward gradual modeling from the most important variables, adding one variable each time, and reconstructing and evaluating R of the model 2 Ending modeling until the last variable is added to obtain a training set and a test set R 2 Best feature variables plotted figure 9. As can be seen from the figure, the training set R of the model is set when one of the added variables reaches the 4 th variable 2 And test set R 2 To achieve the best R of the two 2 The value is obtained. Therefore, the 9 variables (MYH 9rs2239781_ CC, MYH9rs4821478_ AA, ACTN4rs56113315_ CT, INF2rs1128880_ TT, GENDER, ACTN4rs3745859_ TC, ACTN4rs62121818_ CC, INF2rs1128880_ GT, IL2RArs12722489_ CT) are obtained as the best characteristic variables (characteristic subset B), and the establishment and verification of the final concentration prediction model are performed.
3. Establishment and verification of concentration prediction model
The above-described optimal feature variables (feature subset B) were modeled and verified using 5 machine learning algorithms. XGboost, ET, RF, GBDT and Lasso regression are respectively modeled and verified to obtain 5 concentration prediction models. Training set and test set prediction performance (including R) for 5 models 2 Mean square error, mean absolute error, median absolute error) results are shown in tables 2-3. As can be seen from the table, the training set of each model predicts R 2 Values of approximately 19.7% to 26.7%, test set prediction R 2 The value is about 19.7% -26.4%, R obtained by GBDT algorithm modeling 2 Highest value, training set and test set R 2 26.7% and 26.4%, respectively, and the degree of important contribution of the variables in the model is shown in table 4. The error values are within acceptable ranges.
TABLE 2 comparison of predicted Performance of five models in the CYP3A5 non-expressing population training set
Figure BDA0002333786960000151
TABLE 3 comparison of predicted Performance of five models in the CYP3A5 non-expressing population test set
Figure BDA0002333786960000152
TABLE 4 significance values for feature screening of five models in the CYP3A5 non-expressing population
Figure BDA0002333786960000153
Figure BDA0002333786960000161
Note: each row in Table 4 is the degree of contribution of each variable incorporated by a model to the entire model. The contribution of each variable to the entire model can be seen from the magnitude of the value of each variable.
From the above tables, the GBDT model is optimal, and therefore, it can be determined that the IL2RA rs12722489, MYH9rs2239781, MYH9rs4821478, ACTN4rs56113315, ACTN4rs62121818, ACTN4rs3745859, and INF2rs1128880 sites have high contribution, which indicates that these sites have important influence on the pharmacokinetics of tacrolimus in CYP3A5 non-expressing population.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is specific and detailed, but not to be understood as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (7)

  1. Application of detection reagents of IL2RA rs12722489, MYH9rs2239781, MYH9rs4821478, ACTN4rs56113315, ACTN4rs62121818, ACTN4rs3745859 and INF2rs1128880 loci in preparation of a medication instruction kit for using tacrolimus in nephrotic syndrome CYP3A5 non-expression children patients,
    the nephrotic syndrome CYP3A5 non-expression type is CYP3A5 x 3/' 3 genotype, and the nephrotic syndrome is primary nephrotic syndrome;
    if the genotype of the IL2RA rs12722489 site of the infant is CT, the dosage of tacrolimus needs to be increased relative to other genotypes CC and TT;
    if the genotype of the MYH9rs2239781 locus of the child patient is CC, the dosage of tacrolimus needs to be reduced relative to other genotypes TC and TT;
    if the genotype of MYH9rs4821478 locus of the child patient is AA, the dosage of tacrolimus needs to be reduced compared with that of AG and GG of other genotypes;
    if the genotype of the ACTN4rs56113315 site of the child patient is CT, the dosage of tacrolimus needs to be reduced relative to other genotypes CC and TT;
    if the genotype of the ACTN4rs62121818 locus of the child patient is CC, the dosage of tacrolimus needs to be increased relative to other genotypes CT and TT;
    if the genotype of the ACTN4rs3745859 locus of the child patient is TC, the dosage of tacrolimus needs to be reduced relative to other genotypes TT and CC;
    if the genotype of INF2rs1128880 locus of the child patient is GT, the dosage of tacrolimus needs to be increased relative to other genotypes GG and TT;
    if the genotype of the INF2rs1128880 site in the child is TT, the tacrolimus dosage needs to be reduced relative to other genotypes GT and GG.
  2. 2. The use according to claim 1, wherein the nephrotic syndrome CYP3A5 non-expressing infant patient is under 14 years of age.
  3. 3. The use according to claim 2, wherein the nephrotic syndrome CYP3A5 non-expressing infant patient is under 10 years of age.
  4. 4. The use according to claim 1, wherein the nephrotic syndrome CYP3A5 non-expressing infant patient is a refractory nephrotic syndrome infant patient.
  5. 5. The use according to any one of claims 1 to 4, wherein the detection reagent is for performing any one of the following methods:
    restriction fragment length polymorphism, single-strand conformation polymorphism, competitive allele-specific PCR, denaturing gradient gel electrophoresis, allele-specific PCR, DNA sequencing, DNA typing chip detection, flight mass spectrometer detection, denaturing high performance liquid chromatography, snapshot method, taqman probe method, biological mass spectrometry and HRM method.
  6. 6. The use according to any one of claims 1 to 4, wherein the kit further comprises a DNA extraction reagent.
  7. 7. Use according to claim 6, wherein the DNA extraction reagent is used to perform any one of the following methods:
    phenol chloroform method, naOH method, resin extraction method, salting out method, hexadecyl trimethyl ammonium bromide method, silica gel membrane adsorption method, FTA card method, silica bead method or magnetic bead extraction method.
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