Application of DBP gene mutation and / or CYP2A6 gene mutation in individualized therapy of tacrolimus

Abstract

The present application relates to the technical field of biomedical diagnosis, and particularly to application of DBP gene variation and / or CYP2A6 gene variation in individualized medication guidance of tacrolimus, and more particularly to an individualized medication guidance system of tacrolimus, which comprises a sample information processing module, a diagnosis module and an information output module; the sample information processing module is used for receiving patient subject information, wherein the subject information at least comprises information of DBP gene variation and / or CYP2A6 gene variation; the diagnosis module receives information input of the sample information processing module, obtains blood drug concentration probability according to the information, and outputs medication guidance suggestion by the information output module.

Description

Technical Field

[0001] This invention relates to the field of biomedical diagnostic technology, and in particular to the application of DBP gene mutations and / or CYP2A6 gene mutations in personalized tacrolimus dosing guidance. Background Technology

[0002] Tacrolimus (chemical name FK506), a potent immunosuppressant, has been proven to have broad and significant applications in clinical medicine. Tacrolimus is a calcineurin inhibitor that binds to the intracellular FK506-binding protein (FKBP), forming a complex that inhibits calcineurin activity. This, in turn, inhibits the transcription of various cytokines, including interleukin-2 (IL-2), IL-3, and TNF-α, further blocking the activation and proliferation of T lymphocytes, thereby achieving therapeutic effects against target diseases.

[0003] In the clinical treatment of transplantation medicine (such as the prevention and treatment of immune rejection after organ transplantation) and autoimmune diseases (such as rheumatoid arthritis and atopic dermatitis), the application and related research of the immunosuppressant tacrolimus have received widespread attention in order to meet the clinical needs of optimizing treatment outcomes and reducing treatment risks. Based on its potent immunosuppressive activity, tacrolimus has been widely used in the aforementioned fields and has achieved significant clinical therapeutic effects.

[0004] However, the clinical application of tacrolimus faces two key challenges: a narrow therapeutic window and significant individual variability. This variability leads to blood drug concentrations deviating from the target range in approximately 38.4% of patients receiving standard treatment, resulting in two major clinical problems: on the one hand, insufficient blood drug concentrations can increase the incidence of acute rejection, seriously threatening graft survival; on the other hand, excessively high blood drug concentrations can induce serious adverse reactions such as nephrotoxicity, neurotoxicity, and diabetes.

[0005] Currently, clinical dosage adjustments mainly rely on therapeutic drug monitoring (TDM), but this method has a significant lag. Traditional TDM requires a 3-5 day feedback cycle, which cannot guide dosage adjustments in a timely manner during the critical period after transplantation (the first two weeks), precisely the period with the highest risk of graft loss. Currently, only the CYP3A5 gene testing kit is approved for personalized tacrolimus treatment. Besides CYP3A5, other genes such as CYP3A4, POR, and IL-10 have also been found to potentially affect the efficacy and safety of tacrolimus, but the level of evidence is low, and they have not yet been applied clinically. Five-year follow-up data from the 2016 French Tactique study showed that although gene-guided dosage optimization improved early blood drug concentration achievement rates, it did not significantly improve long-term clinical outcomes (graft survival rate and rejection rate). This result exposes the limitations of current personalized tacrolimus treatment strategies—single genetic factors (such as CYP3A5)... The three genotypes can only explain 18%-30% of individual differences, while age, liver and kidney function, drug interactions and other genes together constitute a complex regulatory network. Summary of the Invention

[0006] This invention covers the following technical solutions: One aspect of the present invention relates to the use of reagents for detecting DBP gene variants and / or CYP2A6 gene variants in the preparation of a test kit for personalized tacrolimus dosing guidance.

[0007] Another aspect of the present invention relates to a personalized tacrolimus dosing guidance system, the system comprising: Sample information processing module, diagnostic module, and information output module; The sample information processing module is used to receive patient subject information, which includes at least the information on DBP gene mutations and / or CYP2A6 gene mutations as described in the above application. The diagnostic module receives information input from the sample information processing module, obtains the probability of blood drug concentration based on this information, and outputs medication guidance suggestions by the information output module.

[0008] Another aspect of the present invention relates to a computer-readable storage medium for storing computer instructions, programs, code sets, or instruction sets, which, when run on a computer, cause the computer to execute the sample information processing module, diagnostic module, and information output module in the medication guidance system described above.

[0009] Another aspect of the present invention relates to an electronic device comprising: One or more processors; and A computer-readable storage medium for storing computer instructions, programs, code sets, or instruction sets, which, when run on a computer, cause the one or more processors to implement the sample information processing module, diagnostic module, and information output module in the medication guidance system described above.

[0010] This invention combines DBP / CYP2A6 gene mutation detection with routine clinical indicators to establish an efficient and prospective predictive model, which can significantly improve the accuracy and clinical safety of personalized tacrolimus medication guidance and solve the problems of insufficient predictive ability and delayed feedback in existing technologies. Attached Figure Description

[0011] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0012] Figure 1 Pharmacokinetic parameters of tacrolimus in 46 BXD mouse strains; A. Area under the curve for tacrolimus in 46 BXD mouse strains; B. Peak plasma concentrations of tacrolimus in 46 BXD mouse strains; C. Time to peak plasma concentration of tacrolimus in 46 BXD mouse strains; D. Half-life of tacrolimus in 46 BXD mouse strains; E. Mean retention time of tacrolimus in 46 BXD mouse strains.

[0013] Figure 2 Machine learning-driven identification of key genetic regulators of tacrolimus metabolism; A. Multimodal feature selection process; B. Venn diagram of quantitative trait loci (QTLs) and differentially expressed genes in the liver, kidney, and small intestine transcriptomes; C. Liver tissue-specific consensus feature genes obtained through machine learning screening; D. Kidney tissue-specific consensus feature genes obtained through machine learning screening; E. Genes of intestinal tissue-specific consensus characteristics obtained by integrating machine learning screening.

[0014] Figure 3 : Verify that Cyp2a22 is a key gene affecting the blood concentration of tacrolimus metabolism; A. Blood concentration-time curves of tacrolimus in the CYP2a22 overexpression group and the control group (n=3); B. Area under the curve of tacrolimus in the CYP2a22 overexpression group and the control group (n=3); C. Relative gene expression levels of CYP2a22 in the CYP2a22 overexpression group and the control group (n=3).

[0015] Figure 4 : To verify that Dbp is a key gene affecting the blood concentration of tacrolimus metabolism; A. Blood concentration-time curves of tacrolimus in the Dbp overexpression group and the control group (n=3); B. Area under the curve of tacrolimus in the Dbp overexpression group and the control group (n=3). C. Relative gene expression levels of Dbp in the Dbp overexpression group and the control group (n=3). D. Relative fluorescence intensity of dual-luciferase reporter genes of Dbp and CYP3A4 (n=4). The relative fluorescence intensity of the dual-luciferase reporter gene of E. Dbp and CYP3A5 (n=4).

[0016] Figure 5 Development and validation of a tacrolimus pharmacokinetic prediction model; A. Patient stratification: 168 kidney transplant recipients were stratified according to their trough concentration C. min Layered, divided into high C min Group (n=84) and low C min Groups (n=84); B. Circular plot analysis of clinical data distribution, including creatinine, ALT, AST, and tacrolimus dosage; C. ROC curve of Lasso training set model: Area under the ROC curve (AUC=0.861 (95% confidence interval: 0.794-0.918) (n=119); D. Lasso test set model ROC curve: Area under the ROC curve (AUC) = 0.791 (95% confidence interval: 0.654-0.905) (n=49). Detailed Implementation

[0017] Reference will now be made to detailed embodiments of the present invention, one or more of which are described below. Each example is provided for explanation and not for limitation of the invention. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made to the invention without departing from its scope or spirit. For example, features described or illustrated as part of one embodiment may be used in another embodiment to produce further embodiments.

[0018] Unless otherwise stated, all terms used to disclose this invention (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Further guidance is provided below for a better understanding of the teachings of this invention. The terminology used herein in the specification of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0019] In this invention, unless otherwise stated, scientific and technical terms used herein have the meanings commonly understood by those skilled in the art.

[0020] The terms "and / or," "or / and," and "and / or" as used herein include any one of two or more of the related listed items, as well as any and all combinations of the related listed items. These arbitrary and all combinations include any two related listed items, any more related listed items, or a combination of all related listed items. It should be noted that when at least three items are connected using at least two conjunctions selected from "and / or," "or / and," and "and / or," it should be understood that in this invention, the technical solution undoubtedly includes solutions connected by "logical AND," and also undoubtedly includes solutions connected by "logical OR." For example, "A and / or B" includes three parallel solutions: A, B, and A+B. For example, the technical solution of "A, and / or, B, and / or, C, and / or, D" includes any one of A, B, C, and D (that is, a technical solution that is connected by "logical OR"), as well as any and all combinations of A, B, C, and D, that is, combinations of any two or three of A, B, C, and D, and also combinations of all four of A, B, C, and D (that is, a technical solution that is connected by "logical AND").

[0021] The terms “containing,” “comprising,” and “including” as used in this invention are synonyms and are inclusive or open-ended, not excluding additional, uncited members, elements, or method steps.

[0022] In this invention, the numerical range represented by endpoints includes all numerical values ​​and fractions contained within that range, as well as the endpoints mentioned.

[0023] As used in this invention, the term "about" or "approximately" means within 20%, preferably within 10%, and more preferably within 5%, of a given value or range. It also includes specific numbers, such as about 20 including 20.

[0024] Furthermore, in describing representative embodiments of the invention, this specification may present the methods and / or processes of the invention as a specific sequence of steps. However, the method or process should not be limited to the specific order of the steps described herein, to the extent that the method or process does not depend on the specific order of the steps presented herein. As will be understood by those skilled in the art, other sequences of steps are also possible. Therefore, the specific order of steps presented in the specification should not be construed as a limitation of the claims. Additionally, the claims relating to the methods and / or processes of the invention should not be limited to the execution of their steps in the order they are written, and those skilled in the art will readily recognize that the sequence can be changed while still remaining within the spirit and scope of the invention.

[0025] This invention relates to concentration values, which include fluctuations within a certain range. For example, fluctuations are allowed within a corresponding precision range. For instance, 2% can fluctuate within ±0.1%. For larger values ​​or values ​​that do not require overly precise control, even greater fluctuations are permitted. For example, 100mM can fluctuate within ranges of ±1%, ±2%, ±5%, etc. Regarding molecular weight, fluctuations of ±10% are allowed.

[0026] As used in this invention, unless otherwise stated, the singular forms of the articles “a,” “an,” and “the” include plural referents.

[0027] In this invention, the terms "multiple" or "various" are used unless otherwise specified, referring to a quantity of 2 or more.

[0028] In this invention, the technical features described in an open-ended manner include both closed-ended technical solutions composed of the listed features and open-ended technical solutions that include the listed features.

[0029] In this invention, terms such as "preferred," "better," "more suitable," and "ideal" merely describe implementation methods or embodiments with better effects and should be understood not to limit the scope of protection of this invention. In this invention, terms such as "optionally," "optionally," and "optional" mean that something is optional, that is, selected from either "with" or "without" a parallel solution. If multiple "optional" statements appear in a technical solution, unless otherwise specified and without contradiction or mutual constraint, each "optional" statement is independent.

[0030] All references to this invention are incorporated herein by reference as if each document were individually incorporated herein by reference. Unless they conflict with the inventive purpose and / or technical solution of this invention, the referenced documents are incorporated herein by reference in their entirety and for all purposes. When references are made in this invention, the definitions of relevant technical features, terms, nouns, phrases, etc., are also incorporated herein by reference. Examples and preferred embodiments of the referenced technical features may also be incorporated herein by reference, but only to the extent that they enable the implementation of this invention. It should be understood that when the cited content conflicts with the description in this invention, this invention shall prevail or modifications shall be made adaptively according to the description in this invention.

[0031] This invention relates to the use of reagents for detecting DBP gene variants and / or CYP2A6 gene variants in the preparation of test kits for personalized tacrolimus dosing guidance.

[0032] This invention can achieve many technical effects: 1. Improve the predictive accuracy of personalized tacrolimus dosing By detecting specific variant sites in the DBP and / or CYP2A6 genes, combined with clinical indicators (such as creatinine, ALT, and AST), the predictive ability of tacrolimus blood concentration can be significantly improved.

[0033] According to the embodiments of the present invention, the ROC curve AUC of the LASSO model constructed can reach 0.827, which is better than the traditional detection based solely on CYP3A5.

[0034] 2. Overcoming the limitations of existing personalized drug delivery strategies Traditional CYP3A5 genotyping can only explain about 18% to 30% of individual differences. However, this invention introduces the dual gene factors of DBP and CYP2A6 and combines them with multi-parameter analysis to explain more variability and solve the problem of insufficient single genetic factors.

[0035] 3. To provide rapid and forward-looking medication guidance By combining genetic testing and routine clinical laboratory indicators, blood drug concentration levels can be predicted before or early in the course of drug administration, overcoming the lag of long feedback cycles (3-5 days) in traditional therapeutic drug monitoring (TDM), thus enabling rapid dose optimization during the critical period after transplantation.

[0036] 4. Reduce clinical risks and improve transplant outcomes Predictive models can help doctors strike a balance between avoiding insufficient blood drug concentrations (risk of acute rejection) and avoiding excessive blood drug concentrations (side effects such as nephrotoxicity, neurotoxicity, and diabetes), thereby improving patient safety and graft survival rates.

[0037] In some embodiments, the variant corresponding to the DBP gene variant includes at least one of the following: Nucleotide variation: NM_001352.5:c.160_161del; Nucleotide variation: NM_001352.5:c.551-102del; Nucleotide variation: NM_001352.5:c.562del; Protein variant: NP_001343.1:p.Arg188Glyfs 15; The CYP2A6 gene mutation includes at least one of the following: Nucleotide variation: NM_000762.6:c. 95del; Nucleotide variation: NM_000762.6:c.813del; Protein variant: NP_000753.3:p.Leu272Serfs 17; Single nucleotide polymorphism (SNP) site: rs148948768; Single nucleotide polymorphism (SNP) site: rs1235580651; Nucleotide variation: NM_000762.6:c.1162-71del; Nucleotide variation: NM_000762.6:c.832-18del.

[0038] In some embodiments, the reagent is selected from at least one of the following: (a) Sequencing reagents; (b) Polymerase chain reaction (PCR) reagents; (c) Fluorescent probes or molecular beacons; (d) Primer composition; (e) Chip hybridization reagents; (f) At least one of the following: a specific antibody, an immunoassay reagent, and a mass spectrometry reagent capable of detecting amino acid alterations or truncated proteins caused by gene mutations.

[0039] In some embodiments, the kit further comprises at least one of the following reagents: Serum creatinine test reagent, alanine aminotransferase (ALT) activity test reagent, aspartate aminotransferase (AST) activity test reagent, and tacrolimus blood concentration test reagent.

[0040] The above indicators are all routine clinical tests, with good availability and clinical application value. Commonly used exemplary testing methods include: Commonly used exemplary methods for detecting serum creatinine concentration include alkaline picric acid (Jaffe method), enzymatic methods, or dry chemical methods; results are expressed in μmol / L. It reflects the patient's renal function status and is an important factor affecting tacrolimus clearance rate. Measuring Scr can correct for differences in renal function among patients, avoiding drug accumulation due to renal impairment and improving the safety of dose prediction.

[0041] Commonly used exemplary methods for detecting alanine aminotransferase (ALT) enzyme activity levels typically employ the rate method recommended by the International Federation for Clinical Chemistry (IFCC), based on NADH / NAD ratios. + Absorbance changes were determined colorimetrically, and results are expressed in U / L. A commonly used exemplary method for detecting aspartate aminotransferase (AST) activity levels also employs the IFCC-recommended rate method, measuring enzyme activity colorimetrically, with results expressed in U / L. Both methods reflect the patient's liver function level. Since tacrolimus is primarily metabolized in the liver, abnormal ALT / AST levels significantly affect its metabolic rate. Incorporating ALT / AST information improves the explanatory power of fluctuations in blood drug concentrations, thereby increasing the model's sensitivity to individual variability.

[0042] Commonly used exemplary methods for detecting tacrolimus blood concentrations are immunoassay and LC-MS / MS. Both immunoassay and LC-MS / MS methods for tacrolimus blood concentration detection express concentrations in ng / mL. Immunoassays are based on the principle of specific antigen-antibody binding and include types such as CLIA, ELISA, and FPIA. The procedure involves serum processing, reaction incubation, signal detection, and concentration calculation using a standard curve. It is simple to operate and provides results in 15-60 minutes, making it suitable for routine monitoring and initial dose adjustment after transplantation. However, it requires assessment of renal function using serum creatinine to avoid drug accumulation. LC-MS / MS is an international reference method that combines chromatographic separation and mass spectrometry detection. Samples are pretreated and then analyzed using CLC-MS / MS. 18 Column separation and mass spectrometry for characteristic ion pairs (with internal standard correction) provide high specificity and sensitivity (lower limit 0.1 ng / mL, range 0.1-50 ng / mL) and can also measure metabolites. Combined with tacrolimus blood concentration analysis, this allows for more precise dosage adjustment of tacrolimus and facilitates rapid stabilization of blood drug concentrations.

[0043] According to another aspect of the invention, a personalized tacrolimus dosing guidance system is also provided, the system comprising: Sample information processing module, diagnostic module, and information output module; The sample information processing module is used to receive patient subject information, which includes at least the information on DBP gene mutations and / or CYP2A6 gene mutations as described above. The diagnostic module receives information input from the sample information processing module, obtains the probability of blood drug concentration based on this information, and outputs medication guidance suggestions by the information output module.

[0044] The patient referred to in this invention is a primate, preferably a human.

[0045] In some implementations, the inspected object information further includes: The first dose of tacrolimus, tacrolimus blood concentration, serum creatinine concentration, alanine aminotransferase activity, and aspartate aminotransferase activity are at least one of the following:

[0046] The information on the tested individuals can further increase the accuracy of medication guidance.

[0047] Initial dose: Calculated as mg / kg / day based on patient weight (kg) and actual dose (mg). Initial doses vary significantly among patients, and relying solely on genetic factors is insufficient to fully explain this. Using the initial dose as an input variable can further correct for drug exposure levels and improve the correlation between predicted results and actual blood drug concentrations.

[0048] Tacrolimus blood concentration: It can determine the level of drug metabolism and is a commonly used predictive indicator in the field of medicine.

[0049] In some implementations, the diagnostic module calculates the probability of blood drug concentration using the following formula: P(probability of blood drug concentration) = 1 / (1 + exp(-Log-Odds)) Log-Odds = 0.023 + 0.3686×creatinine - 0.2553×ALT - 0.0534×AST - 0.8343×dose + 4.8873×[CYP2A6:NM_000762.6:c]. 95del] + 4.3669×[CYP2A6:rs148948768]- 1.5311×[CYP2A6:NM_000762.6:c.813del or NP_000753.3:p.Leu272Serfs 17] - 0.4321×[CYP2A6:NM_000762.6:c.1162-71del]+ 4.3342×[CYP2A6:NM_000762.6:c.832-18del] + 6.3478×[CYP2A6:rs1235580651]+ 0.4085×[DBP:NM_001352.5:c.160_161del] - 1.1919×[DBP:NM_001352.5:c.551-102del]-6.3946×[DBP:NM_001352.5:c.562del or NP_001343.1:p.Arg188Glyfs 15]; For the DBP gene mutation and CYP2A6 gene mutation results, wild type is recorded as 0, heterozygote as 1, and homozygous mutation as 2.

[0050] This formula utilizes a weighted integration of multidimensional variables to quantify the probability distribution of tacrolimus blood concentrations in patients with specific genotypes and clinical conditions. Its technical advantages are: 1. By incorporating multiple locus information from DBP and CYP2A6, it significantly improves the model's explanatory power for differences in blood drug concentrations compared to using only CYP3A5 genotyping; 2. By combining renal function (creatinine), liver function (ALT / AST), and initial dose, it further reduces bias in blood drug concentration prediction, making the prediction results closer to clinical reality; 3. Using a logistic regression probability model, it transforms complex multifactorial information into intuitive probability values, facilitating risk stratification and individualized dosage adjustments by physicians.

[0051] Therefore, the diagnostic module proposed in this invention predicts the blood drug concentration of patients through the above-mentioned logistic regression calculation formula, which can significantly improve the prediction accuracy and clinical application value compared with the traditional single-factor model.

[0052] In some embodiments, the medication guidance recommendations include: If P < 0.2, it is recommended to significantly increase the dose and monitor blood drug concentration; If 0.2 ≤ P < 0.4, it is recommended to appropriately increase the dose and monitor blood drug concentration; If 0.4 ≤ P < 0.6, it is recommended to adjust the dosage based on blood drug concentration. If 0.6 ≤ P < 0.8, it is recommended to appropriately reduce the dosage and monitor blood drug concentration; If P ≥ 0.8, it is recommended to significantly reduce the dose and monitor blood drug concentration.

[0053] The present invention also relates to a computer-readable storage medium for storing computer instructions, programs, code sets or instruction sets, which, when run on a computer, cause the computer to execute the sample information processing module, diagnostic module and information output module in the medication guidance system described above.

[0054] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0055] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0056] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0057] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, C++, Swift, and Python—as well as conventional procedural programming languages—such as the "C" language or similar programming languages. Additionally, scripting languages ​​such as Python and R can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0058] The present invention also relates to an electronic device, comprising: One or more processors; and A computer-readable storage medium for storing computer instructions, programs, code sets, or instruction sets, which, when run on a computer, cause the one or more processors to implement the sample information processing module, diagnostic module, and information output module in the medication guidance system described above.

[0059] In some embodiments, the electronic device may also include a transceiver. The processor and the transceiver are connected, such as via a bus. It should be noted that in practical applications, the transceiver is not limited to one unit, and the structure of the electronic device does not constitute a limitation on the embodiments of this application.

[0060] The processor can be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0061] A bus can include a pathway for transmitting information between the aforementioned components. The bus can be a PCI bus or an EISA bus, etc. Buses can be categorized as address buses, data buses, control buses, etc.

[0062] The embodiments of the present invention will be described in detail below with reference to examples. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. For experimental methods in the following embodiments where specific conditions are not specified, please refer to the guidelines given in this invention, or follow experimental manuals or conventional conditions in the art, or other experimental methods known in the art, or follow the conditions recommended by the manufacturer.

[0063] In the specific embodiments described below, the measurement parameters involving raw material components may have slight deviations within the weighing accuracy range unless otherwise specified. Temperature and time parameters are subject to acceptable deviations due to instrument testing accuracy or operational precision.

[0064] Example 1: Screening of key genes affecting tacrolimus blood concentration based on pharmacokinetics, QTL mapping, transcriptomics, and machine learning. This study used 46 different strains of BXD mice, each strain was randomly divided into 3 groups (n=3 per group, totaling 414 mice). Mice were fasted for 12 hours before the experiment (with free access to water). A 1 mg / mL tacrolimus suspension was prepared using pharmaceutical-grade olive oil and administered via gavage at a dose of 10 mg / kg. At 5, 15, 30 min, 1, 2, 4, 6, 8, 12, 24, 48, 72, and 96 h, 80 μL of whole blood was collected from each mouse via orbital venous plexus puncture. The blood was mixed in pre-chilled blood collection tubes containing EDTA and stored at -80℃. Subsequently, 50 μL of whole blood was collected, and the blood drug concentration was determined using a validated HPLC-MS / MS method. The obtained concentration-time data were analyzed using a non-compartmental model with WinNonLin® 8.3 software to calculate T0. max C max AUC 0-t T 1 / 2 Pharmacokinetic parameters such as MRT are shown in the following figures. Figure 1 (AE).

[0065] The R / qtl package was used to perform QTL mapping for the above PK parameters. The Haley-Knott regression method was used to scan the entire genome for QTLs, and significance was determined by a permutation test of 10,000 iterations. A total of 14 genomic loci significantly associated with tacrolimus metabolism were identified. Furthermore, the QTL analysis sites for blood drug concentration largely overlapped with the regulatory sites of the core PK parameters. The results are shown in [Figure number missing]. Figure 2 A and B in the middle.

[0066] Based on AUC levels, BXD mice were divided into High-AUC and Low-AUC groups. Total RNA was extracted from their liver, kidney, and jejunum tissues for transcriptome sequencing. Differentially expressed genes were identified using STAR alignment, DESeq2 differential analysis, and functional enrichment analysis with clusterProfiler and fgsea. Gene expression was correlated with AUC, and candidate QTGs were obtained by intersecting QTLs with LOD ≥ 3.0 with the differentially expressed genes using bedtools.

[0067] While maintaining class balance, the samples were divided into 70% training set and 30% test set, and four machine learning algorithms were used for screening using a unified process: (1) Elastic Network Logistic Regression (Enetα=0.9, 10-fold CV, λmin), (2) LASSO Logistic Regression (α=1, 100-fold CV, λmin), (3) Stepwise Logistic Regression (glm, family=binomial, bidirectional AIC selection), and (4) Radial Basis Kernel Support Vector Machine (caret::rfe, svmRadial, 10-fold CV). Genes appearing in at least two algorithm retention lists were defined as common feature genes. Their importance was then assessed based on the average reduction of Gini impurities using a random forest model (ntree=5000, mtry=√p). Finally, the intersection genes of QTL-analyzed genes and RNA-seq differentially expressed genes were subjected to the above machine learning secondary screening. Combining the results of random forest analysis, literature research, and correlation with tacrolimus metabolism, Cyp2a22 and Dbp were identified as key genes affecting tacrolimus blood concentration. The results are shown in […]. Figure 2 CE.

[0068] Example 2: Cyp2a22 has a regulatory effect on tacrolimus blood concentration. BXD139 mice with low basal Cyp2a22 gene expression levels in their livers were selected and randomly divided into an experimental group (BXD139-OE-Cyp2a22) and a control group (BXD139-Control). The experimental group was injected via tail vein with an adeno-associated virus (AAV) suspension (titer 1×10⁻⁶) carrying the mouse Cyp2a22 gene overexpression sequence. 11The control group was injected with an equal volume and titer of empty vector AAV suspension (μg / mouse). Both groups of mice were then housed in a standard SPF-grade environment for 4 weeks to achieve stable overexpression of Cyp2a22 in the liver. Subsequently, tacrolimus was administered to both groups of mice via a single gavage at a dose of 10 mg / kg. Whole blood samples were collected via orbital venous plexus puncture at 5, 15, 30 min, 1, 2, 4, 6, 8, 12, 24, 48, 72, and 96 h post-administration. The concentration of tacrolimus in whole blood was quantitatively determined using methodologically validated LC-MS / MS, and the AUC was calculated using Phoenix WinNonlin software based on a non-compartmental model. 0-t C max T max T 1 / 2 Key pharmacokinetic parameters such as MRT were measured. Differences in parameters between the two groups were compared using Student's T-test (with P < 0.05 defined as statistically significant). Results showed that the AUC of tacrolimus in the experimental group mice was significantly higher than that in the control group. 0-t The levels were significantly lower than those in the control group, confirming that hepatic Cyp2a22 overexpression can effectively promote tacrolimus metabolism and reduce its systemic exposure, thus clarifying its regulatory role in tacrolimus blood concentration. Results are shown in [Figure Number]. Figure 3 .

[0069] Example 3: Dbp has a regulatory effect on tacrolimus blood concentration. BXD170 mice with low basal expression levels of the Dbp gene in their livers were selected and randomly divided into an experimental group (BXD170-OE-Dbp) and a control group (BXD170-Control). The experimental group was injected via tail vein with an adeno-associated virus (AAV) suspension (titer 1×10⁻⁶) carrying the mouse Dbp gene overexpression sequence. 11 The control group received an equal volume and titer of empty vector AAV suspension (µg / mouse). Both groups of mice were housed in a standard SPF-grade environment for 4 weeks to achieve stable overexpression of Dbp in the small intestine. Subsequently, both groups of mice were administered tacrolimus via a single gavage at a dose of 10 mg / kg. Whole blood samples were collected via orbital venous plexus puncture at 5, 15, 30 min, 1, 2, 4, 6, 8, 12, 24, 48, 72, and 96 h post-administration. The concentration of tacrolimus in whole blood was quantitatively determined using methodologically validated LC-MS / MS. The AUC was calculated using Phoenix WinNonlin 8.0 software based on a non-compartmental model. 0-t C max T max T 1 / 2And key pharmacokinetic parameters such as MRT. The differences in parameters between the two groups were compared using Student's T test (with P < 0.05 defined as statistically significant). The results showed that the AUC of tacrolimus in the experimental group mice was significantly higher. 0-t The levels were significantly lower than those in the control group, suggesting that Dbp overexpression can regulate tacrolimus disposal in vivo. (See results below.) Figure 4 (AC).

[0070] To investigate the mechanism, CYP3A4 and CYP3A5 promoter luciferase reporter vectors were constructed. Using 293T cells, the two reporter vectors were co-transfected with the internal control vector pRL-TK and the Dbp overexpression vector pcDNA3.1-Dbp (experimental group) or the empty vector pcDNA3.1-empty (control group), respectively. Cells were lysed 48 h after transfection, and the luciferase activities of firefly and Renilla were detected, and the relative activities (Firefly / Renilla) were calculated. The results were then analyzed using GraphPad Prism. Independent samples t-test analysis using software 9.0 (n=4, P<0.05) showed that the relative activity of the CYP3A4 promoter in the experimental group (0.4194±0.0045) was significantly higher than that in the control group (0.2886±0.0024, P<0.0001), and the relative activity of the CYP3A5 promoter (0.2954±0.0184) was significantly higher than that in the control group (0.2147±0.0122, P<0.001). This indicates that Dbp can specifically promote the transcriptional activity of the CYP3A4 and CYP3A5 promoters, thereby regulating the blood concentration of tacrolimus. (See results below.) Figure 4 D and E in the middle.

[0071] Example 4: Construction and application of a tacrolimus blood concentration prediction model based on DBP / CYP2A6 gene mutations and clinical indicators Given that the human homolog of mouse Cyp2a22 is CYP2A6 and the human homolog of mouse Dbp is DBP, the target genes for genomic detection based on patient samples in this embodiment are CYP2A6 and DBP. The clinical samples in this embodiment are from a retrospective single-center study, which has been approved by the Ethics Committee of Beijing Chaoyang Hospital affiliated to Capital Medical University (Approval No.: 2018-Ke-288).

[0072] EDTA-anticoagulated whole blood samples were collected from 168 kidney transplant patients receiving tacrolimus-based immunosuppressive therapy. Demographic data, tacrolimus trough concentration, daily dose, and routine biochemical indicators such as serum creatinine (µmol / L), ALT (U / L), and AST (U / L) were extracted from their electronic medical records. Genomic DNA was extracted from the whole blood samples, and full-length sequencing of the CYP2A6 and DBP genes was performed. The sequences were compared with the GRCh38 human reference genome, variant recall was performed, and functional annotation of variant sites was conducted using ANNOVAR software.

[0073] Analysis of the expression distribution characteristics of CYP2A6 and DBP in this kidney transplant patient population, according to the target trough concentration of tacrolimus (C min Patients were divided into high-concentration and low-concentration groups, and a composite dataset was constructed including CYP2A6, DBP gene mutation sites, initial tacrolimus dose (mg / kg / d), serum creatinine, ALT, and AST (results are shown in [link to dataset]). Figure 5 (A and B). Eight machine learning algorithms, namely Enet, Gradient Boosting Machine (GBM), GLM, j48 classifier (JCS), Random Forest, Ridge Regression, Support Vector Machine (SVM), and Lasso, were used to independently rank the input features of the dataset. Variables selected by at least six of the algorithms were retained as consensus predictors. Using only these consensus predictors as input, seven modeling strategies (Enet, LASSO, rpart, glm, Partial Least Squares Generalized Linear Model (plsRglm), RF, and SVM) were trained on 70% of the samples (training set) and validated on 30% of the samples (test set), maintaining class balance between the two sets of samples throughout the process.

[0074] We used machine learning algorithms to build a model and studied the correlation between DBP and different loci and clinical information of CYP2A6 on the training and test sets. We used the pROC package to generate receiver operating characteristic (ROC) curves to evaluate model performance; and calculated the area under the curve (AUC) for the training set, test set, and the entire cohort. The LASSO model was ultimately determined as the optimal model, achieving an AUC of 0.827, demonstrating good predictive performance (see results below). Figure 5 (C and D). Model predictive parameters include the initial tacrolimus dose (mg / kg / d), serum creatinine (µmol / L), ALT (U / L), and AST (U / L); and the CYP2A6 variant site: NM_000762.6:c. 95del, NM_000762.6:c.813del:p.Leu272Serfs 17. rs148948768, rs1235580651, NM_000762.6:c.1162-71del, NM_000762.6:c.832-18del; DBP variant sites: NM_001352.5:c.160_161del, NM_001352.5:c.551-102del, NM_001352.5:c.562del:p.Arg188GlyfsTer15. The variant sites are calculated as follows: wild type = 0, heterozygote = 1, homozygous mutation = 2.

[0075] The calculation formula based on the LASSO model is as follows: P(probability of blood drug concentration) = 1 / (1 + exp(-Log-Odds)) Log-Odds = 0.023 + 0.3686×creatinine - 0.2553×ALT - 0.0534×AST - 0.8343×dose + 4.8873×[CYP2A6:NM_000762.6:c]. 95del] + 4.3669×[CYP2A6:rs148948768]- 1.5311×[CYP2A6:NM_000762.6:c.813del:p.Leu272Serfs 17]- 0.4321×[CYP2A6:NM_000762.6:c.1162-71del]+ 4.3342×[CYP2A6:NM_000762.6:c.832-18del] + 6.3478×[CYP2A6:rs1235580651]+ 0.4085×[DBP:NM_001352.5:c.160_161del] - 1.1919×[DBP:NM_001352.5:c.551-102del] - 6.3946×[DBP:NM_001352.5:c.562del:p.Arg188GlyfsTer15].

[0076]

[0077] Maintaining stable tacrolimus blood concentrations is crucial for solid organ transplant recipients. Fluctuations in tacrolimus blood concentrations increase the risk of rejection and can even lead to graft loss. However, the narrow therapeutic window of tacrolimus and significant individual variability make dosage requirements difficult to predict. Therefore, we developed a tacrolimus blood concentration prediction model to assist clinicians in developing precise, individualized dosing regimens, thereby helping patients maintain stable blood drug concentrations.

[0078] This invention combines DBP and CYP2A6 gene mutation detection with clinical biochemical indicators and drug dosage to establish a tacrolimus blood concentration prediction system, solving the problems of poor predictive ability and slow feedback from traditional single-gene testing and routine monitoring. In clinical application, it can provide graded dosage adjustment recommendations at critical stages after transplantation, effectively balancing the risk of rejection caused by insufficient drug concentration and the toxic side effects caused by excessive concentration, thus improving the safety and scientific basis of medication. The accompanying medication guidance system, computer storage media, and electronic equipment integrate data processing, concentration probability calculation, and medication recommendation output, reducing the difficulty of clinical use. Combined with routine testing items, this technology is easier to promote clinically, providing technical support for the standardized use of tacrolimus and having practical clinical value in improving patient treatment outcomes and increasing graft survival rates.

[0079] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims, and the specification and drawings can be used to interpret the content of the claims.

Claims

1. The use of reagents for detecting DBP gene variants and / or CYP2A6 gene variants in the preparation of test kits for personalized tacrolimus dosing guidance.

2. The application according to claim 1, wherein the variant corresponding to the DBP gene variant includes at least one of the following: Nucleotide variation: NM_001352.5:c.160_161del; Nucleotide variation: NM_001352.5:c.551-102del; Nucleotide variation: NM_001352.5:c.562del; Protein variant: NP_001343.1:p.Arg188Glyfs 15; The CYP2A6 gene mutation includes at least one of the following: Nucleotide variation: NM_000762.6:c. 95del; Nucleotide variation: NM_000762.6:c.813del; Protein variant: NP_000753.3:p.Leu272Serfs 17; Single nucleotide polymorphism (SNP) site: rs148948768; Single nucleotide polymorphism (SNP) site: rs1235580651; Nucleotide variation: NM_000762.6:c.1162-71del; Nucleotide variation: NM_000762.6:c.832-18del.

3. The application according to claim 1, wherein the reagent is selected from at least one of the following: (a) Sequencing reagents; (b) Polymerase chain reaction (PCR) reagents; (c) Fluorescent probes or molecular beacons; (d) Primer composition; (e) Chip hybridization reagents; (f) At least one of the following: a specific antibody, an immunoassay reagent, and a mass spectrometry reagent capable of detecting amino acid alterations or truncated proteins caused by gene mutations.

4. The application according to any one of claims 1 to 3, wherein the kit further comprises at least one of the following reagents: Serum creatinine test reagent, alanine aminotransferase (ALT) activity test reagent, aspartate aminotransferase (AST) activity test reagent, and tacrolimus blood concentration test reagent.

5. A personalized tacrolimus medication guidance system, the system comprising: Sample information processing module, diagnostic module, and information output module; The sample information processing module is used to receive patient subject information, which includes at least the information on DBP gene variation and / or CYP2A6 gene variation as described in claim 1 or 2. The diagnostic module receives information input from the sample information processing module, obtains the probability of blood drug concentration based on this information, and outputs medication guidance suggestions by the information output module.

6. The tacrolimus personalized medication guidance system according to claim 5, wherein the subject information further includes: The first dose of tacrolimus, tacrolimus blood concentration, serum creatinine concentration, alanine aminotransferase activity, and aspartate aminotransferase activity are at least one of the following:

7. The tacrolimus personalized medication guidance system according to claim 6, wherein the diagnostic module calculates the probability of blood drug concentration using the following formula: P(probability of blood drug concentration) = 1 / (1 + exp(-Log-Odds)) Log-Odds = 0.023 + 0.3686×creatinine - 0.2553×ALT - 0.0534×AST - 0.8343×dose + 4.8873×[CYP2A6:NM_000762.6:c]. 95del] + 4.3669×[CYP2A6:rs148948768] - 1.5311×[CYP2A6:NM_000762.6:NM_000762.6:c.813del and / or NP_000753.3:p.Leu272Serfs 17] - 0.4321×[CYP2A6:NM_000762.6:c.1162-71del] +4.3342×[CYP2A6:NM_000762.6:c.832-18del] + 6.3478×[CYP2A6:rs1235580651] +0.4085×[DBP:NM_001352.5:c.160_161del] - 1.1919×[DBP:NM_001352.5:c.551-102del] - 6.3946×[DBP:NM_001352.5:c.562del or NP_001343.1:p.Arg188Glyfs 15]; For the DBP gene mutation and CYP2A6 gene mutation results, wild type is recorded as 0, heterozygote as 1, and homozygous mutation as 2.

8. The tacrolimus personalized medication guidance system according to claim 7, wherein the medication guidance recommendations include: If P < 0.2, it is recommended to significantly increase the dose and monitor blood drug concentration; If 0.2 ≤ P < 0.4, it is recommended to appropriately increase the dose and monitor blood drug concentration; If 0.4 ≤ P < 0.6, it is recommended to adjust the dosage based on blood drug concentration. If 0.6 ≤ P < 0.8, it is recommended to appropriately reduce the dosage and monitor blood drug concentration; If P ≥ 0.8, it is recommended to significantly reduce the dose and monitor blood drug concentration.

9. A computer-readable storage medium for storing computer instructions, programs, code sets, or instruction sets, which, when executed on a computer, cause the computer to perform the sample information processing module, diagnostic module, and information output module of the medication guidance system as described in any one of claims 5 to 8.

10. An electronic device, comprising: One or more processors; as well as A computer-readable storage medium for storing computer instructions, programs, code sets, or instruction sets, which, when executed on a computer, cause the one or more processors to implement the sample information processing module, diagnostic module, and information output module of the medication guidance system as described in any one of claims 5 to 8.

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