Individualized dosing method of sirolimus combined with voriconazole in treatment of children with pid

Abstract

This invention belongs to the field of intelligent healthcare, specifically relating to a personalized dosing method for sirolimus combined with voriconazole in the treatment of PID in children. The method includes: obtaining the age of the PID child; determining the sirolimus dosage when combined with voriconazole based on the PID child's age; if the PID child's age is in the range of [1, 6) years, the dosage is [0.100, 0.330] mg / QD; if the age is in the range of [6, 13) years, the dosage is [0.280, 0.540] mg / QD. This application is the first to develop a PPK model for PID children using sirolimus combined with voriconazole (VRC), and based on the PPK, explores the optimal dosage for PID children using sirolimus combined with VRC, providing concise dosage guidance for children of different age groups; and through posterior Bayesian methods, it can provide individualized sirolimus medication guidance.

Description

Technical Field

[0001] This invention relates to the field of intelligent healthcare, and more specifically, to a personalized dosing method, device, medium, and procedure for treating children with PID using sirolimus in combination with voriconazole. Background Technology

[0002] Primary immunodeficiency diseases (PID) are a group of rare, inherited immune disorders caused by gene mutations that affect the normal function of an individual's immune system. While the incidence is low, the mortality rate is extremely high, ranging from 40% to 100% depending on the specific type. Clinical manifestations of PID primarily include recurrent infections, autoimmune diseases, autoinflammatory diseases, and increased susceptibility to tumors. Approximately 75% of PID patients have antibody deficiencies, with infection being the most common clinical manifestation. Current treatment status and challenges for PID: Hematopoietic stem cell transplantation is currently the preferred clinical treatment, but patients with autoimmune diseases have a significantly increased risk of mortality and complications after transplantation. Immunomodulatory agents have a good prognosis in treating autoimmune diseases, but their efficacy and safety are influenced by various factors. Children with PID often require long-term, early use of anti-infective drugs, but drug interactions and individual differences pose challenges to treatment.

[0003] The study, "Advances in Population Pharmacokinetic Studies of Sirolimus in Pediatric Patients," revealed that clinical applications often rely on empirical dosing, followed by monitoring steady-state trough concentrations to gradually adjust the dosage to an appropriate level. This method requires multiple doses to reach steady-state before therapeutic drug monitoring (TDM) and treatment regimen adjustments can be performed, a process that is time-consuming. Furthermore, blood drug concentrations can be affected by factors such as blood collection time and covariates, leading to imprecise dosing regimens. Population pharmacokinetics (PPK) studies can address this by analyzing the pharmacokinetics of a specific population. By combining cokinetics (PK) parameters with analysis of relevant patient data and taking into account factors such as disease progression, the best individualized dosing regimen can be developed for the patient.

[0004] Individual differences in pharmacokinetic parameters can be explained by demographic, physiological, genetic, and biochemical variables: Djebli N et al. confirmed that the in vivo disposal of SRL is highly correlated with age; Reichen J et al. found that the clearance rate (CL) of SRL is negatively correlated with age (22-73 years); Tejani A et al. showed that the clearance rate of SRL in children aged 5-11 years [(544±463) mL / h / kg, n=7] was 90% higher than that in healthy adults aged 19-36 years [(287±111) mL / h / kg, n=25]; a study by CEmoto administered SRL 0.8 mg / m2 twice daily to transplant patients; after adjusting the pharmacokinetic parameters based on the first dose, 90% of the children achieved the ideal blood drug concentration.

[0005] Population pharmacokinetic (PPK) models integrate patient, drug, and disease-related information through mathematical modeling and simulation techniques, revealing the variability of drugs among different individuals and providing a basis for precise drug administration. Existing technologies have established a PopPK model of SRL in children with immune cytopenic purpura (ICP): it can predict exposure and guide dose adjustment (including 27 patients with a total of 107 blood sampling points; internal validation demonstrated the model's stability and reliability, and clinical application evaluation showed good results). Existing technologies have also established a PopPK model of SRL in children with iLM: exploring the bidirectional prediction of blood drug concentration and dosage, accurately predicting patient pharmacokinetic parameters, and developing individualized dosing regimens, thereby improving efficacy and safety.

[0006] Key Applications and Combination Therapy Strategies of Voriconazole (VRC) in the Treatment of Children with Particulate Developing Disease (PID): Children with PID are at high risk of invasive fungal infections due to compromised immune function. Guidelines recommend early use of antifungal drugs. Voriconazole (VRC) is a second-generation antifungal drug with broad-spectrum antifungal activity and is the preferred clinical treatment for invasive Aspergillus and Candida infections. Voriconazole is primarily metabolized by CYP2C19, and secondarily by CYP2C9 and CYP3A4, and is a potent inhibitor of CYP3A4 (DDI). Early empirical use of VRC can effectively prevent and control fungal infections and reduce the risk of complications.

[0007] The combined use of VRC and SRL can synergistically enhance efficacy, and the combination of SRL and VRC is an important treatment option for PID. However, this combination faces multiple challenges and risks. The drug-in-dose (DDI) mechanism of this combination is complex: SRL is a substrate of CYP3A and P-gp, while VRC is a substrate and / or inhibitor of CYP3A and / or P-gp. The combined use of these two drugs inevitably leads to uncertainty in the efficacy and safety of SRL. In combination with VRC, the peak plasma concentration of SRL can increase by approximately 556%, requiring a reduction in the SRL dose. Existing studies have revealed a significant increase in SRL plasma concentration when SRL and VRC are used in combination and have explored dose adjustment strategies; however, these studies are mostly based on small sample sizes and have not focused on pediatric PID patients. Summary of the Invention

[0008] In view of the above problems, the present invention provides an individualized dosing method for treating children with PID using sirolimus in combination with voriconazole. A PopPK model is constructed to realize a model-guided individualized dosing regimen for the combined use of SRL and VRZ in children with PID, so as to improve the effectiveness and safety of combination therapy.

[0009] This application (first aspect) discloses a personalized dosing method for treating children with PID using sirolimus in combination with voriconazole, comprising:

[0010] Obtain the age of the child with PID;

[0011] The dosage of sirolimus in combination with voriconazole is determined based on the child's age. If the child's age is in the range of [1, 6) years, the dosage is [0.100, 0.330] mg / QD; if the age is within... The recommended dosage is [0.280, 0.540] mg / QD for the age range.

[0012] Furthermore, the dosage of sirolimus when used in combination with voriconazole is determined based on the age of the child with PID, including:

[0013] If the age is in the range of [1, 1.5) years, the dosage range is [0.100, 0.150] mg / QD;

[0014] If the age is in the range of [1.5, 2.5) years, the dosage range is [0.160, 0.240] mg / QD;

[0015] If the age is in the range of [2.5, 3.5) years, the dosage range is [0.180, 0.270] mg / QD;

[0016] If the age is in the range of [3.5, 4.5) years, the dosage range is [0.220, 0.330] mg / QD;

[0017] If the age is in the range of [4.5, 6) years, the dosage range is [0.220, 0.330] mg / QD;

[0018] If the age is in the range of [6, 8) years, the dosage range is [0.280, 0.420] mg / QD;

[0019] If the age is in the range of [8, 11.5) years, the dosage range is [0.360, 0.540] mg / QD;

[0020] If the age is in the range of [11.5, 13) years, the dosage range is [0.360, 0.540] mg / QD;

[0021] Optionally, if the child is 1–1.5 years old, the dosage is 0.125 mg / QD.

[0022] If the child is between 1.5 and 2.5 years old, the dosage is 0.2 mg / QD.

[0023] If the child is between 2.5 and 3.5 years old, the dosage is 0.225 mg / QD.

[0024] If the child is between 3.5 and 4.5 years old, the dosage is 0.275 mg / day.

[0025] If the child is between 4.5 and 6 years old, the dosage is 0.275 mg / QD.

[0026] If the child is 6-8 years old, the dosage is 0.35 mg / day.

[0027] If the child is between 8 and 11.5 years old, the dosage is 0.45 mg / day.

[0028] If the child is between 11.5 and 13 years old, the dosage is 0.45 mg / day.

[0029] Optionally, the method further includes: determining the dosage of sirolimus without voriconazole based on the age of the child with PID; if the age of the child with PID is in the range of [1, 6) years, the dosage is [0.500, 1.500] mg / QD; if the age is in the range of [6, 13) years, the dosage is [1.200, 2.400] mg / QD.

[0030] Optionally, the dosage of sirolimus without voriconazole can be determined based on the child's age, including:

[0031] If the age is in the range of [1, 1.5), the dosage range is [0.500, 0.750] mg / QD;

[0032] If the age is in the range of [1.5, 2.5), the dosage range is [0.600, 0.900] mg / QD;

[0033] If the age is in the range of [2.5, 3.5), the dosage range is [0.800, 1.200] mg / QD;

[0034] If the age is in the range of [3.5, 4.5), the dosage range is [0.900, 1.350] mg / QD;

[0035] If the age is in the range of [4.5, 6), the dosage range is [1.000, 1.500] mg / QD;

[0036] If the age is in the range of [6, 8), the dosage range is [1.200, 1.800] mg / QD;

[0037] If the age is in the range of [8, 11.5), the dosage range is [1.500, 2.250] mg / QD;

[0038] If the age is in the range of [11.5, 13), the dosage range is [1.600, 2.400] mg / QD.

[0039] The second aspect of this application discloses a method for constructing a PPK model of sirolimus in children with PID when voriconazole is used in combination, including:

[0040] Obtain a dataset of children with PID, including the child's age, ALB level, and whether voriconazole was used in combination.

[0041] The first PPK model was obtained by deriving the model parameters of the PPK model based on the dataset of PID patients.

[0042] Furthermore, the PPK model is expressed as:

[0043]

[0044]

[0045]

[0046] in, Indicates the clearance rate. Indicates age, Indicates albumin, Indicate whether voriconazole should be used concurrently; This represents the typical value of the clearance rate for the population. The median age. This is the age exponential term in the clearance rate model; The median of ALB. This is the exponential term of ALB in the clearance rate model; This refers to the exponential term of VRC in the clearance rate model when VRC is used in conjunction with other methods. Indicates the inter-individual variability in clearance rate; Represents the absorption rate constant;

[0047] Optional, , , , , , , , To determine the PPK model to be built based on the training set data;

[0048] Optional, Fixed at 0.7521 / h;

[0049] Optionally, when voriconazole is used in combination, the VRC is 1; when voriconazole is not used in combination, the VRC is 0.

[0050] The third aspect of this application discloses a method for constructing a dosage calculation model for sirolimus in children with PID when using voriconazole in combination with voriconazole, including:

[0051] Obtain the dataset of children with PID;

[0052] Based on the dataset of children with PID and the first PPK model, the Monte Carlo simulation method was used to obtain the dosage of sirolimus in children with PID of different ages when voriconazole was used in combination; the first PPK model was constructed based on the construction method of the sirolimus dosage calculation model when PID children were used in combination with voriconazole.

[0053] A dosage calculation model was constructed based on different age groups, whether voriconazole was used in combination, and the corresponding sirolimus dosage.

[0054] Optionally, the dosage calculation model is a decision tree model, and the decision tree nodes include: different age groups and whether voriconazole is used in combination;

[0055] Optionally, when using the Monte Carlo simulation method to simulate different age groups, each age group can be divided into 0.5-year segments.

[0056] The fourth aspect of this application discloses a personalized dosing method for treating children with PID using sirolimus in combination with voriconazole, including:

[0057] Obtain the age of the child with PID and whether voriconazole is being used in combination;

[0058] Based on the age group to which the PID patient belongs and whether voriconazole is being used in combination, the first drug dose is obtained by constructing a drug dose calculation model based on the aforementioned drug dose calculation model construction method. The first drug dose is used as the individualized dosing dose.

[0059] Furthermore, the method further includes: adjusting the first drug dose based on posterior Bayesian method to obtain a second drug dose, and using the second drug dose as the individualized dosing dose. The posterior Bayesian method includes:

[0060] Step 1: Simultaneously obtain the ALB of the PID patient, and formulate a first medication regimen for the PID patient based on the first drug dosage. The first medication regimen includes: planned medication date, medication time, first drug dosage, dosing interval, blood drug concentration, and number of dosings between two blood drug concentration measurements.

[0061] Step 2: Based on the first medication regimen, the ALB of the PID patient, and the first PPK model, obtain the individual distribution volume and clearance rate of the PID patient;

[0062] Step 3: Based on the distribution volume and clearance rate, simulate the time to reach steady state of the first dosing regimen to obtain the predicted blood drug concentration. Compare the predicted blood drug concentration with the reference value. If it is higher than the reference value, reduce the dosage of the first dosing regimen. If it is lower than the reference value, increase the dosage of the first dosing regimen to obtain an updated dosing regimen.

[0063] Step 4: Replace the first medication regimen with the updated medication regimen and repeat steps 2-3 until the predicted blood drug concentration is within the reference range to obtain the second medication dose;

[0064] Optionally, the method for obtaining the volume of distribution and clearance rate of the PID patient based on the first medication regimen, the ALB of the PID patient, and the first PPK model includes:

[0065] Based on the age of the PID patient, the ALB level of the PID patient, and the first medication regimen, pharmacokinetic analysis was performed to obtain the individual pharmacokinetic parameters of the PID patient. The individual pharmacokinetic parameters are the individual typical values ​​of the volume of distribution and the individual typical values ​​of the clearance rate.

[0066] After updating the population typical values ​​of the volume of distribution and the clearance rate of the first PPK model using individual pharmacokinetic parameters, the second PPK model is obtained. Based on the second PPK model, the volume of distribution and clearance rate of the individual child are obtained.

[0067] The fifth aspect of this application discloses a personalized drug delivery system for treating children with PID using sirolimus in combination with voriconazole, comprising:

[0068] First acquisition module: used to acquire the age of children with PID;

[0069] First output module: Used to determine the dosage of sirolimus when using voriconazole in combination with PID based on the child's age. If the child's age is in the range of [1, 6) years, the dosage is [0.100, 0.330] mg / QD; if the age is in the range of [1, 6) years, the dosage is [0.100, 0.330] mg / QD. The recommended dosage is [0.280, 0.540] mg / QD for the age range.

[0070] The sixth aspect of this application discloses a system for constructing a PPK model of sirolimus in children with PID treated with voriconazole, including:

[0071] The second acquisition module is used to acquire the dataset of children with PID. The dataset includes the child's age, ALB, and whether voriconazole is being used in combination.

[0072] The second output module is used to obtain the first PPK model after deriving the model parameters of the PPK model based on the dataset of the PID patients.

[0073] The seventh aspect of this application discloses a system for constructing a dosage planning model for sirolimus in children with PID when using voriconazole in combination with voriconazole, including:

[0074] The third acquisition module is used to acquire the dataset of children with PID.

[0075] PPK model calculation module: Used to obtain the dosage of sirolimus for children with PID based on the dataset of children with PID and the first PPK model using Monte Carlo simulation method, whether or not voriconazole is used in combination.

[0076] The dosage model construction module is used to construct a dosage calculation model based on different age groups, whether or not voriconazole and the corresponding sirolimus are used in combination;

[0077] The eighth aspect of this application discloses a personalized drug delivery system for treating children with PID using sirolimus in combination with voriconazole, comprising:

[0078] The fourth acquisition module is used to obtain the age of children with PID and whether voriconazole is being used in combination.

[0079] The fourth output module is used to calculate the first dosage based on the age group of the PID patient and whether voriconazole is being used in combination, and then use the first dosage as the individualized dosing dose.

[0080] A third aspect of this application discloses a computer device, the device comprising: a memory and a processor; the memory being used to store program instructions; the processor being used to invoke the program instructions, which, when executed, are used to perform the steps of the method described above.

[0081] The fourth aspect of this application discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0082] The fifth aspect of this application discloses a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0083] This application has the following beneficial effects:

[0084] (1) This application is the first to explore the optimal dosage of SRL combined with VRC in children with PID based on PPK, providing simple dosage guidance for children of different age groups;

[0085] (2) This application is the first to develop a PPK model for children with PID who use SRL in combination with VRC. Through the posterior Bayesian method, it can provide individualized SRL medication guidance for individuals.

[0086] (3) The model obtained in this application has been verified by real clinical data and used in clinical drug dosage guidance, which improves the problem of determining the SRL dosage when SRL and VRC are administered in combination in children with PID. Attached Figure Description

[0087] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0088] Figure 1 This is a schematic diagram of the method flow provided in the first aspect of the present invention;

[0089] Figure 2 This is a schematic diagram of a program product provided in the second aspect of the present invention;

[0090] Figure 3 This is a schematic diagram of a computer device provided in an embodiment of the present invention;

[0091] Figure 4 This is a schematic diagram of the architecture of an exemplary computing device provided in an embodiment of the present invention;

[0092] Figure 5This is a schematic diagram of the storage medium provided in an embodiment of the present invention;

[0093] Figure 6 This is a research roadmap provided in an embodiment of the present invention;

[0094] Figure 7 This is a schematic diagram of the enrollment of research subjects provided in an embodiment of the present invention;

[0095] Figure 8A , 8B Figures 8C, 8D, 8E, and 8F are graphical methods for screening covariates provided in embodiments of the present invention. The upper figure for the same variable represents the correlation of V, and the lower figure represents the correlation with CL.

[0096] Figure 9 This is a comparative diagram of the basic model (AF) and the final model (A'-F') provided in this embodiment of the invention: where A, A': observed values ​​vs. individual predicted values ​​(IPRED); B, B': individual weighted residuals (IWRES) vs. IPRED; C, C': observed values ​​vs. population predicted values; D, D': conditionally weighted residual QQ plot; E, E': CWRES vs. time to start dosing; F, F': CWRES vs. population predicted values;

[0097] Figure 10 This is a schematic diagram of SRL in evaluating PPK model in children with PID, provided by an embodiment of the present invention;

[0098] Figure 11 This is a data illustration of a Monte Carlo-based population prediction method provided in an embodiment of the present invention;

[0099] Figure 12 This is a schematic diagram of basic data for personalized medication recommendation based on Bayesian methods, provided by an embodiment of the present invention.

[0100] Figure 13 This is a schematic diagram illustrating the individual pharmacokinetic parameters obtained during a Bayesian posterior method according to an embodiment of the present invention;

[0101] Figure 14 This is a schematic diagram of an individual optimal drug dosage simulation scheme provided by an embodiment of the present invention for Bayesian posterior method optimization. Detailed Implementation

[0102] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0103] In some of the processes described in the specification, claims, and accompanying drawings of this invention, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as S101, S102, etc., are merely used to distinguish different operations and do not represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0104] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0105] Figure 1 This is a schematic flowchart of an individualized dosing method based on the evaluation of sirolimus combined with voriconazole for the treatment of children with PID, provided by an embodiment of the present invention. Specifically, the method includes the following steps:

[0106] S101: Obtain the age of the child with PID;

[0107] S102: The dosage of sirolimus in combination with voriconazole is determined based on the age of the child with PID. If the child's age is in the range of [1, 6) years, the dosage is [0.100, 0.330] mg / QD; if the age is in the range of [1, 6) years, the dosage is [0.100, 0.330] mg / QD. The recommended dosage is [0.280, 0.540] mg / QD for the age range.

[0108] Research Objective: To provide a reference for individualized precision medicine treatment in children with PID who require combined use of SRL and VRC. Overall technical approach, such as... Figure 6 As shown, the specific research is as follows.

[0109] I. Research Methods

[0110] 1. Exploring the influencing factors of SRL blood drug concentration in children based on prior knowledge and real data.

[0111] 1.1 Literature Review:

[0112] We reviewed existing domestic and international literature and our hospital's database to analyze the current status of screening "influencing factors" of SRL blood drug concentration;

[0113] 1.2 Real-world research:

[0114] 1.2.1 Study subjects: Patients who visited the outpatient or inpatient departments of Beijing Children's Hospital between August 2018 and June 2023;

[0115] Inclusion criteria:

[0116] A. The patient's age is <18 years old;

[0117] B. Record detailed information such as general details, medication usage, and laboratory test results;

[0118] C. The patient has been taking SRL for more than 5 days and has reached a steady-state blood drug concentration;

[0119] D. Monitor concomitant medications used during patient treatment;

[0120] Exclusion criteria:

[0121] E. Developing a concurrent diarrheal infection;

[0122] F. Those who have used SRL treatment for less than 5 days or whose relevant information records are incomplete;

[0123] Sample size: 249 children who underwent SRL, with a total of 1535 sampling points;

[0124] 1.2.2 Data Collection

[0125] Data collection for PID patients: A. Demographic data (name, age, gender, weight, body surface area); B. Laboratory tests (complete blood count, urinalysis, blood biochemistry); C. Medication information (dosage time, dosage, blood drug concentration); Among them, the blood drug concentration was obtained by fluorescence polarization immunoassay to obtain the steady-state trough concentration (C0) of the drug after continuous administration: blood was collected 15-30 minutes before taking the medication in the morning;

[0126] 1.2.3 Observation Indicators

[0127] Follow-up began on the day of administration of the first dose of SRL, and was conducted at weeks 1, 2, 4 and 12 after medication to adjust the dosage.

[0128] Key observation indicators: Steady-state trough concentration of SRL in blood at each detection site;

[0129] Secondary observation indicators: complete blood count, blood biochemistry, immunological examination, urinalysis, liver function indicators, and kidney function indicators of patients at each testing time point.

[0130] 1.2.4 Statistical description of clinical data

[0131] For continuous data: those conforming to a normal distribution are expressed as mean ± standard deviation; those not conforming to a normal distribution are expressed as median (range). For categorical data: those expressed as frequency and rate.

[0132] 1.2.5 Univariate Analysis

[0133] For factors that conform to a normal distribution, Pearson correlation analysis was used to analyze the correlation between each factor and the SRL trough concentration; otherwise, Spearman correlation analysis was used to screen for factors with P < 0.05.

[0134] Statistically significant factors were included as independent variables in the multivariate analysis.

[0135] Multivariate analysis: With C / D as the dependent variable, factors of SRL that have an impact in the univariate analysis are included in the multiple linear regression analysis to screen for statistically significant factors (i.e., independent influencing factors).

[0136] 2. Construct and optimize the PPK model of SRL combined with VRC for children with PID based on influencing factors.

[0137] 2.1 Modeling Process

[0138] Data preprocessing → Building the basic model → Optimizing the basic model → Comparing the goodness of fit using goodness-of-fit plots → Validating the model using Bootstrap and VPC methods → Monte Carlo simulation of the optimal dosing regimen

[0139] 2.1.1 Building the basic model

[0140] We analyzed the blood concentration data of SRL in enrolled children, including administration time, dosage, and blood collection time. We then used a nonlinear mixed-effects model to establish a basic structural model, a statistical model, and a residual model.

[0141] 2.1.2 Constructing a covariate model

[0142] The stepwise regression method was used to screen variables and establish a covariate model, which consisted of two steps:

[0143] ① Positive inclusion method: When including a covariate reduces OFV by more than 6.63 (P < 0.01), the variable is retained.

[0144] ② Reverse elimination method: If excluding the covariate causes OFV to increase by more than 10.83 (P < 0.001), then the variable is retained.

[0145] 2.1.3 Internal Model Validation:

[0146] The goodness-of-fit plot was used to compare the model's fit, and the Bootstrap method was used to verify the model's stability. The VPC method was used to verify the model's predictive ability and predict the optimal dosing regimen.

[0147] 2.1.4 The Monte Carlo method was used to simulate the optimal dosage for children aged 1-13 years.

[0148] 3. Evaluate the predictive efficacy of the PPK model combining SRL and VRC in children with PID based on real data.

[0149] The collected subjects were children who visited the outpatient or inpatient department of the Department of Immunology at Beijing Children's Hospital from April to November 2024.

[0150] Inclusion criteria: A. Patient age <18 years; B. Patient clinical diagnosis: PID; C. Detailed records of general information, medication use, laboratory test results, etc.; D. Patient has been treated with SRL for more than 5 days and has reached steady-state blood drug concentration; E. Monitoring of concomitant medications during treatment.

[0151] Exclusion criteria: F. Comorbid diarrhea and infection; G. Patients treated with SRL for less than 5 days and those with incomplete related records; Sample size: 38 sampling points from 16 PID patients; Observation index: Steady-state trough concentration of SRL in blood at each detection site; Evaluation of model accuracy: Compare the relative deviation between the model predictions and the actual observations to evaluate the accuracy and precision of the model.

[0152] II. Research Results

[0153] 1. Exploring the influencing factors of SRL blood drug concentration in children based on prior knowledge and real data.

[0154] By reviewing previous literature, clinical indicators including drug absorption, distribution, metabolism, and excretion were included. A retrospective study of 249 patients from August 2018 to June 2023, totaling 1535 plasma drug concentration points, was conducted. Patient demographic and pathophysiological results were also included. Figure 7 As shown in Table 1.

[0155] Univariate analysis results (Table 2) showed that age, weight, body surface area, WBC, HGB, AST, ALT, ALB, and CREA and SRL blood drug concentrations were significantly correlated.

[0156] Table 1. Statistics of Basic Patient Information

[0157]

[0158] Table Note: Group A was 1 week after SRL administration, Group B was 2 weeks after SRL administration, Group C was 4 weeks after SRL administration, and Group D was 12 weeks after SRL administration.

[0159] Table 2. One-way correlation analysis of each factor with C / D

[0160]

[0161] Table Note: Group A was 1 week after SRL administration, Group B was 2 weeks after SRL administration, Group C was 4 weeks after SRL administration, and Group D was 12 weeks after SRL administration.

[0162] Multivariate analysis of factors influencing SRL (Table 3) showed that the child's weight, body surface area, PLT, AST, ALT, and ALB were statistically correlated with the C / D ratio of SRL and were independent factors affecting SRL blood drug concentration (P < 0.05). Therefore, it is recommended that clinicians optimize the medication regimen by combining the child's weight, body surface area, ALT, AST, PLT, and ALB, and adjust the dosage in a timely manner according to the individualized dosing regimen to reduce the occurrence of adverse reactions.

[0163] Table 3 Multifactor analysis of factors influencing SRL

[0164]

[0165] Table Note: Group A was 1 week after SRL administration, Group B was 2 weeks after SRL administration, Group C was 4 weeks after SRL administration, and Group D was 12 weeks after SRL administration.

[0166] 2. Construct and optimize the PPK model of SRL combined with VRC for children with PID based on influencing factors.

[0167] A retrospective study included 52 children diagnosed with PID between January 2017 and March 2024, with a total of 399 SRL trough concentration points. Missing data were processed; based on Andrea Mashall's research, multiple imputation was used for missing values ​​<50%. Figure 7 (As shown).

[0168] Taking OFV as the primary factor for comprehensive consideration, a first-order absorption elimination proportional error model was ultimately selected to establish the basic model. Covariates were included to explain intra- and inter-individual differences in SRL, optimizing the model: age, albumin, and VRC combination therapy were ultimately included as covariates. Figures 8A-8F The diagram illustrates the process of selecting covariates using a graphical method: age ( Figure 8A Middle Age), albumin ( Figure 8A (ALB) and combined with VRC for treatment.

[0169] Covariate screening criteria: Positive inclusion: OFV decreases by more than 6.63 (P < 0.01), then include; Reverse exclusion: OFV increases by more than 10.83 (P < 0.001), then retain.

[0170] The final results of covariate screening: CL-Age, ALB, VRC, V-VRC ( Figures 8A-8F (As shown in Table 2).

[0171]

[0172] Therefore, the PPK model after covariate selection is expressed as:

[0173]

[0174]

[0175]

[0176] in, Indicates the clearance rate. This represents the typical value of the clearance rate for the population. The median age. This is the age exponential term in the clearance rate model; The median of ALB. This is the exponential term of ALB in the clearance rate model; This refers to the exponential term of VRC in the clearance rate model when VRC is used in conjunction with other methods. Indicates the inter-individual variability in clearance rate;

[0177] Represents the distributed volume. The typical value of the distribution volume for the population. This refers to the exponential term of VRC in the distributed volume model when VRC is used in conjunction with it. Indicates the inter-individual variability of the volume of distribution;

[0178] , , , , , , , To determine the PPK model to be built based on the training set data; Represents the absorption rate constant;

[0179] To determine the parameters of the PPK model, the final model and typical parameters were calculated by substituting the collected data into the covariates, as shown in Table 5.

[0180] Table 5. Final model parameter estimation and bootstrap validation results

[0181]

[0182] Table Note: θ represents the typical value of each parameter in the population; f represents the exponent of the covariate model corresponding to each parameter, ω 2 Vd Representing the covariate model ω 2 CLRepresenting the covariate model .

[0183] Therefore, based on the median of the dataset, we obtain... ; After substituting the parameter values ​​calculated based on the dataset into Table 4, the final model is expressed as follows:

[0184]

[0185]

[0186]

[0187] Note: When using VRC in conjunction, VRC=1 in the model; otherwise, VRC=0.

[0188] CL: Clearance; V: Distribution volume.

[0189] The absorption rate constant (Ka) is fixed at 0.7521 / h.

[0190] 3. Evaluation of the PPK model of SRL combined with VRC in children with PID

[0191] Internal validation of the model was performed to evaluate its predictive power (VPC) and stability (Bootstrap). Bootstrap results: The bootstrap method converged successfully 1000 times out of 1000 attempts. Validation results are as follows... Figure 9 As shown in Table 4.

[0192] The model was internally validated, and the goodness-of-fit plot was obtained. Figure 9 The results show that the final model significantly outperforms the baseline model. VPC analysis reveals that the model performs well in predicting low concentrations of SRL, but its performance in predicting high concentrations needs improvement. This is likely because the modeling sampling points are primarily distributed in the range of 5-15 ng / ml. The results are as follows... Figure 10 As shown, the model stability was verified using the bootstrap method. The results showed that the success rate was 100% and the deviations were all less than 30%, proving that the model has excellent stability.

[0193] Monte Carlo simulation of initial dosing doses for children of different ages with and without VRC: Based on Monte Carlo simulation, the optimal dosing regimen for children aged 1-13 years with SRL alone or in combination was simulated. The evaluation index was the probability that the patient's steady-state trough concentration reached the therapeutic window (5-15 ng / mL). The results showed (Table 6): for patients of the same age, the dosage needed to be reduced by approximately 73.3%-80.0% when VRC was used in combination compared to when VRC was not used.

[0194] Table 6. Monte Carlo method simulation of medication dosage for children of different ages.

[0195]

[0196] Note: The results of Monte Carlo simulations are affected by the collected dataset. Based on the dataset of this study, the probability of reaching the corresponding target therapeutic range when the dose is as shown in Table 5 is given. The above results have been applied to clinical studies, and a fluctuation of up to or below 20% is considered within the clinically safe and effective dose range.

[0197] 4. Evaluate the predictive efficacy of the PPK model combining SRL and VRC in children with PID based on real data.

[0198] According to the inclusion and exclusion criteria, 38 sampling points from 16 patients who visited our hospital between March and November 2024 were included for external evaluation of the model. MPE, MAE, MIPE, and MAIE were calculated using the following formulas to evaluate the model's accuracy and precision.

[0199]

[0200]

[0201]

[0202]

[0203]

[0204]

[0205] External evaluation results: MPE%=-13.34%, MAE%=32.34%, MIPE%=-7.79%, MAIE%=20.41%.

[0206] The results showed that MPE and MIPE were less than ±20% and MAIE was less than 30%, indicating that the model had good predictive performance.

[0207] Where MPE = (Median Prediction Error), MAE = (Median Absolute Prediction Error), MIPE = (Median Individual Prediction Error), and MAIE = (Median Absolute Individual Prediction Error).

[0208] The model ultimately incorporates age, albumin (ALB), and VRC combination therapy into the model, as shown in the formula below:

[0209]

[0210]

[0211]

[0212] It should be noted that the model parameters in the above model were calculated based on the dataset collected in this study. When the collected dataset changes, the model parameters may change slightly.

[0213] The final model shows that:

[0214] ① Age is positively correlated with CL: This further confirms that in previous studies of pediatric PPK, age is an important factor affecting the pharmacokinetics of SRL and is positively correlated with pharmacokinetic parameters.

[0215] Unlike previous SRL PPK studies, previous PPK studies often included body weight or body surface area as covariates.

[0216] ② ALB and CL are positively correlated:

[0217] Currently, no relevant PPK studies have included this covariate. Although the mechanism is not yet clear, the principle may be: ① SRL has a 92% binding rate with plasma albumin in vivo. ② Albumin can reflect liver function indicators to a certain extent, while SRL is mainly metabolized in the liver.

[0218] The model's final results showed that age and albumin levels are important factors affecting SRL blood concentration. Specifically, age was positively correlated with clearance, further confirming that age is a significant factor influencing SRL blood concentration in previous studies. Unlike other previous PPK studies, which often included body weight as a covariate, this study found that body weight was not a significant covariate. In this study, age had a more significant impact on SRL pharmacokinetic parameters than body weight in the study population. Furthermore, unlike previous studies, this study included albumin as a covariate, a result consistent with our previous correlation analysis of SRL blood concentration with physiological and biochemical indicators. That is, as ALB levels increase, SRL clearance increases, while the trough concentration of SRL decreases. This may be because SRL binds to plasma albumin at a rate of 92%, with 97% binding to serum albumin. Therefore, an increase in ALB may lead to an increase in SRL clearance, resulting in a decrease in the trough concentration of SRL.

[0219] This study further demonstrates that VRC significantly affects the pharmacokinetics of SRL: In previous PPK studies of SRL in adult liver transplantation, VRC combination therapy was included as a categorical variable in the model, and the results showed a negative correlation between VRC combination therapy and SRL pharmacokinetic parameters. Previous studies (3 / 28 cases) and case reports have only shown that in the real world, the combination of SRL and VRC leads to an approximately 4.5-fold increase in the AUC of SRL. This is mainly because VRC, as a triazole antifungal drug, is an inhibitor of cytochrome P450 CYP3A4, and SRL is a substrate of this enzyme.

[0220] We further used Monte Carlo simulations to predict the optimal dosage of voriconazole in combination with or without other medications in children of different ages. We found that, for the same age, children receiving voriconazole in combination required a 73.3%-80% reduction in dosage compared to those not receiving it. This is similar to the 75%-87.5% reduction reported in previous real-world studies, but lower than the 90% reduction suggested in another study. This may be because previous studies were conducted in adult populations, while voriconazole could have been used in children, and the sample size in this study was significantly larger than that of the three previous studies.

[0221] The Monte Carlo simulation method was used to determine the optimal dosage for children of different ages using VRC in combination or without: Children of the same age using VRC required an empirical dose reduction of 73.3%-80% compared to those not using VRC. This is higher than the study by Peksa et al., which required a reduction of 50%-75%, but lower than another previous study that suggested an empirical dose reduction of 90%. The main reasons are: different populations (previous studies were all adults) and different sample sizes (previous studies all had small sample sizes).

[0222] This study identified factors influencing SRL blood concentration through correlation analysis. It is recommended that clinicians optimize medication regimens by considering factors such as the child's weight, body surface area, ALT, AST, PLT, and ALB, and adjust the dosage promptly according to individualized dosing regimens to reduce the occurrence of adverse reactions.

[0223] This study optimized the PPK model for applying SRL to children with PID and preliminarily explored the effect of VRC on the pharmacokinetics of SRL. Internal and external evaluations were conducted on the established PPK model, and the results showed that the model has good predictive performance.

[0224] This study predicts the optimal dosing regimens for PID patients aged 1-13 years, with and without VRC:

[0225] When children are given VRC in combination, the SRL dose needs to be reduced by 73.3%-80.0% to maintain the SRL trough concentration in the blood within the therapeutic window.

[0226] III. Personalized Medication Guidance Based on the Constructed PPK Model

[0227] Monte Carlo simulation and Bayesian feedback are two different methods. Monte Carlo simulation is for groups, while Bayesian simulation is for specific individuals.

[0228] 3.1 Optimal Dosing Regimen for Monte Carlo Simulation of PID Patients

[0229] 1) Setting up the dosing regimen includes dosing information (date, time, amt, dv, II, mdv) and covariate information (Age, ALB, VRC), such as Figure 11 As shown;

[0230] 2) In the "run option" section, set "simulation" to output the steady-state trough concentration (half an hour before administration), and simulate 1000 times, using the target therapeutic range (5-15 ng / mL) as the criterion. Simulate the optimal dosing regimen for children of different ages using sirolimus alone or in combination with voriconazole (as shown in Table 5). Results indicate that for children with PID, when combined with voriconazole, the sirolimus dose needs to be empirically reduced by 73.3%-80.0% compared to sirolimus alone.

[0231] 3.2. Posterior Bayesian method for individualized drug dosage optimization

[0232] Step 1: First, obtain the characteristics of the PID patient to be tested.

[0233] In some embodiments, basic information about the child is obtained, such as Figure 12 As shown, it includes:

[0234] DATE: Date; TIME: Time; AMT: Dosage; DV: Blood drug concentration; II: Dosage interval (h); ADDL: Number of doses given between two blood drug concentration measurements (32 doses given between the DV measurement on October 7 and the DV measurement on November 10); Age: Age; ALB: Albumin; VOL: Whether VRC was used in combination;

[0235] Step 2: Determine the final model parameters

[0236] 2-1 Importing the dosing information (date, time, amt, dv, II) and covariate information (Age, ALB, VRC) into the final model yields the individual-typical values ​​of clearance rate and volume of distribution for each subject; this is to calculate the individual's pharmacokinetic parameters using individual observations. Figure 13 (As shown).

[0237] 2-2 Replace the population typicality parameters in the final model formula with individual pharmacokinetic parameters (CL, Vd).

[0238] In Monte Carlo simulations, the population canonical parameter θ of the PPK model vd / F (L) is 136.46, θ CL / F (L / h) is 4.96 (Table 4);

[0239] In some embodiments, the calculated individual typical value of the clearance rate is 4.93, and the individual typical value of the distribution volume is 135. Therefore, the replaced PPK model is:

[0240]

[0241]

[0242]

[0243] Step 3: Configure an individual optimal dosing simulation protocol, including TIME (time) and AMT (dosage). Figure 14 In the run option interface, select simulation to perform posterior Bayesian simulation, set the output time point, and output the predicted concentration of the simulation scheme.

[0244] The volume of distribution and clearance rate were imported into the parameters of the drug administration simulation. The simulation was set to run 1000 times, with sirolimus reaching steady state in 5-7 days. The simulation duration was set to 10 days. After the software ran, the output blood drug concentration was 0.0126, which is higher than the reference value. Therefore, [further details are needed]. Figure 14 After adjusting the setting from 2mg to 1.5mg, run the program again until the blood drug concentration output by the software is within the reference range.

[0245] In some embodiments, the simulated dosage is set based on clinical experience, and then a posterior Bayesian posterior method is applied to obtain an individualized dosage.

[0246] In some embodiments, the simulated dosing dose is set based on the results of the Monte Carlo population. For example, for a 9-year-old child with PID, when using VRC in combination, the initial planned dosing dose is set to 0.45 mg / day; then the final SRL dosing dose is further adjusted using a Bayesian method to obtain the optimal dosing dose.

[0247] Clinically, an initial treatment plan is first developed based on the child's basic information. Then, personalized data analysis using a model is used to predict the child's blood drug concentration. If the target concentration is not met, a new dosing regimen is adjusted. Simultaneously, if the child's measured blood drug concentration is low, the model can also be used to estimate the appropriate dosage for subsequent treatments. Treatment is a process that requires continuous dynamic adjustments, and the model serves as the basis for these adjustments.

[0248] In this model, Ka is a fixed parameter of 0.7521.

[0249] In some embodiments, the above simulation process and PPK model are implemented using a programming language, and the output blood drug concentration after the run is automatically compared to see if it is within the reference range. If it is higher than the reference range, the dosing schedule is automatically lowered and the simulation is repeated until a satisfactory solution is given.

[0250] 3.3. External Validation

[0251] We conducted external validation of the model using 46 blood drug concentration points from 17 patients at our center, evaluating the model's accuracy and precision using MPE, MAE, MIPE, and MAIE. The results showed MPE%=17.39%, MAE%=31.72%, MIPE%=5.58%, and MAIE%=21.91%. MPE, MIPE, and MAIE were within ±20% and 30%, respectively. Although MAE slightly exceeded 30%, it remained within acceptable limits. Therefore, the final model is considered to have good predictive performance for both population and individual predictions. Notably, when using this study to predict the effects of SRL combined with VRC at four observation points, the MAPE ranged from 6.76% to 22.4%, indicating that the model has good predictive ability for PID patients using sirolimus combined with voriconazole.

[0252] Figure 3 This is a schematic diagram of a computer device provided in an embodiment of the present invention, such as... Figure 3 As shown, the device 2000 may include: one or more processors 2010 and one or more memories 2020; wherein the memories store computer-readable code that, when run by the one or more processors, can perform the methods described above.

[0253] The processor in this embodiment can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, operations, and logic block diagrams disclosed in this embodiment. The general-purpose processor can be a microprocessor or any conventional processor, and can be based on an x86 or ARM architecture.

[0254] In general, the various exemplary embodiments of this disclosure can be implemented in hardware or dedicated circuitry, software, firmware, logic, or any combination thereof. Some aspects can be implemented in hardware, while others can be implemented in firmware or software that can be executed by a controller, microprocessor, or other computing device. When aspects of embodiments of this disclosure are illustrated or described as block diagrams, flowcharts, or using some other graphical representation, it will be understood that the blocks, apparatuses, systems, techniques, or methods described herein can be implemented as non-limiting examples in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0255] For example, the method or apparatus according to embodiments of this disclosure can also be used by means of Figure 4 The architecture of the computing device 3000 shown is used for implementation. For example... Figure 4 As shown, the computing device 3000 may include a bus 3010, one or more CPUs 3020, a read-only memory (ROM) 3030, a random access memory (RAM) 3040, a communication port 3050 connected to a network, an input / output component 3060, a hard disk 3070, etc. The storage devices in the computing device 3000, such as the ROM 3030 or the hard disk 3070, may store various data or files used for processing and / or communication of the methods provided in this disclosure, as well as program instructions executed by the CPU. The computing device 3000 may also include a user interface 3080. Of course, Figure 4 The architecture shown is merely exemplary and can be omitted as needed when implementing different devices. Figure 4 One or more components in the computing device shown.

[0256] This invention also includes a computer-readable storage medium, such as... Figure 5The diagram illustrates a storage medium 4000 provided in an embodiment of the present invention. The computer storage medium 4020 stores computer-readable instructions 4010. When the computer-readable instructions 4010 are executed by a processor, the method described above according to embodiments of the present disclosure can be performed. The computer-readable storage medium in the embodiments of the present disclosure may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Synchronous Link Dynamic Random Access Memory (SLDRAM), and Direct Memory Bus Random Access Memory (DR RAM). It should be noted that the memory used in the methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0257] This disclosure also provides a computer program product or computer program that, when executed by a processor, implements the steps of the above-described method, such as... Figure 2 As shown, the computer program product or computer program includes:

[0258] First acquisition module 201: Used to acquire the age of the child with PID;

[0259] First output module 202: Used to determine the dosage of sirolimus when using voriconazole in combination with PID based on the child's age. If the child's age is in the range of [1, 6) years, the dosage is [0.100, 0.330] mg / QD; if the age is in the range of [1, 6) years, the dosage is [0.100, 0.330] mg / QD. The recommended dosage is [0.280, 0.540] mg / QD for the age range.

[0260] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0261] In general, the various exemplary embodiments of this disclosure can be implemented in hardware or dedicated circuitry, software, firmware, logic, or any combination thereof. Some aspects can be implemented in hardware, while others can be implemented in firmware or software that can be executed by a controller, microprocessor, or other computing device. When aspects of embodiments of this disclosure are illustrated or described as block diagrams, flowcharts, or using some other graphical representation, it will be understood that the blocks, apparatuses, systems, techniques, or methods described herein can be implemented as non-limiting examples in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0262] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0263] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0264] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0265] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0266] The exemplary embodiments of this disclosure described in detail above are merely illustrative and not restrictive. Those skilled in the art will understand that various modifications and combinations of these embodiments or their features can be made without departing from the principles and spirit of this disclosure, and such modifications should fall within the scope of this disclosure.

Claims

1. A method for constructing a PPK model of sirolimus in combination with voriconazole in children with PID, characterized in that, The method includes: Obtain a dataset of children with PID, including the child's age, ALB level, and whether voriconazole was used in combination. The first PPK model is obtained by deriving the model parameters of the PPK model based on the dataset of PID patients. The PPK model is represented as follows: in, Indicates the clearance rate. Indicates age, Indicates albumin, Indicate whether voriconazole should be used concurrently; This represents the typical value of the clearance rate for the population. The median age. This is the age exponential term in the clearance rate model; The median of ALB. This is the exponential term of ALB in the clearance rate model; This refers to the exponential term of VRC in the clearance rate model when VRC is used in conjunction with other methods. Indicates the inter-individual variability in clearance rate; Represents the absorption rate constant; , , , , , , , The VRC is determined for constructing the PPK model based on the training set data; when voriconazole is used in combination, the VRC is 1, and when voriconazole is not used in combination, the VRC is 0.

2. The method for establishing the PPK model of sirolimus in children with PID in combination with voriconazole according to claim 1, characterized in that, Fixed at 0.7521 / h.

3. A method for constructing a dosage calculation model for sirolimus in children with PID when using voriconazole in combination with voriconazole, characterized in that, The method includes: Obtain the dataset of children with PID; Based on the dataset of children with PID and the first PPK model, the Monte Carlo simulation method was used to obtain the dosage of sirolimus in children with PID of different ages when voriconazole was used in combination; the first PPK model was constructed based on the method described in any one of claims 1-2; A dosage calculation model was constructed based on different age groups, whether voriconazole was used in combination, and the corresponding sirolimus dosage. The dosage calculation model is a decision tree model, and the decision tree nodes include: different age groups and whether or not voriconazole is used in combination.

4. The method for constructing a dosage regimen model of sirolimus in combination with voriconazole for a patient with PID according to claim 3, characterized in that, When using the Monte Carlo simulation method to simulate different age groups, each age group is defined as 0.5 years.

5. The method for constructing a dosage regimen model of sirolimus in combination with voriconazole in children with PID according to claim 3, characterized in that, The method further includes: adjusting the first drug dose based on posterior Bayesian method to obtain a second drug dose, using the second drug dose as the individualized dosing dose, wherein the first drug dose is obtained by: inputting the drug dose calculation model obtained using the construction method described in claim 3 according to the age of the PID child and whether voriconazole is used in combination; the posterior Bayesian method includes: Step 1: Simultaneously obtain the ALB of the PID patient, and formulate a first medication regimen for the PID patient based on the first drug dosage. The first medication regimen includes: planned medication date, medication time, first drug dosage, dosing interval, blood drug concentration, and number of dosings between two blood drug concentration measurements. Step 2: Based on the first medication regimen, the ALB of the PID patient, and the first PPK model, obtain the individual distribution volume and clearance rate of the PID patient; Step 3: Based on the distribution volume and clearance rate, simulate the time to reach steady state of the first dosing regimen to obtain the predicted blood drug concentration. Compare the predicted blood drug concentration with the reference value. If it is higher than the reference value, reduce the dosage of the first dosing regimen. If it is lower than the reference value, increase the dosage of the first dosing regimen to obtain an updated dosing regimen. Step 4: Replace the first medication regimen with the updated medication regimen and repeat steps 2-3 until the predicted blood drug concentration is within the reference range to obtain the second medication dose.

6. The method for constructing a dosage regimen model of sirolimus in combination with voriconazole for a patient with PID according to claim 5, wherein, The method for obtaining the volume of distribution and clearance rate of the PID patient based on the first medication regimen, the ALB of the PID patient, and the first PPK model includes: Based on the age of the PID patient, the ALB level of the PID patient, and the first medication regimen, pharmacokinetic analysis was performed to obtain the individual pharmacokinetic parameters of the PID patient. The individual pharmacokinetic parameters are the individual typical values ​​of the volume of distribution and the individual typical values ​​of the clearance rate. After updating the population typical values ​​of the volume of distribution and the clearance rate of the first PPK model using individual pharmacokinetic parameters, the second PPK model is obtained. Based on the second PPK model, the volume of distribution and clearance rate of the individual child are obtained.

7. The method for constructing a dosage calculation model for sirolimus in children with PID when using voriconazole in combination with voriconazole, as described in claim 5, is characterized in that... The method further includes: Obtain the age of the child with PID; The dosage of sirolimus in combination with voriconazole is determined based on the child's age. If the child's age is in the range of [1, 6) years, the dosage is [0.100, 0.330] mg / QD; if the age is within... The dosage is [0.280, 0.540] mg / QD for children aged 1 to 6 years; the dosage of sirolimus without voriconazole is determined based on the age of the PID child. If the age of the PID child is in the range of [1, 6) years, the dosage is [0.500, 1.500] mg / QD; if the age is in the range of [6, 13) years, the dosage is [1.200, 2.400] mg / QD. The dosage is the second dosage as described in claim 5.

8. The method for constructing a dosage calculation model for sirolimus in children with PID when using voriconazole in combination with voriconazole, as described in claim 7, is characterized in that... The dosage of sirolimus when used in combination with voriconazole is determined based on the child's age and includes: If the age is in the range of [1, 1.5) years, the dosage range is [0.100, 0.150] mg / QD; If the age is in the range of [1.5, 2.5) years, the dosage range is [0.160, 0.240] mg / QD; If the age is in the range of [2.5, 3.5) years, the dosage range is [0.180, 0.270] mg / QD; If the age is in the range of [3.5, 4.5) years, the dosage range is [0.220, 0.330] mg / QD; If the age is in the range of [4.5, 6) years, the dosage range is [0.220, 0.330] mg / QD; If the age is in the range of [6, 8) years, the dosage range is [0.280, 0.420] mg / QD; If the age is in the range of [8, 11.5) years, the dosage range is [0.360, 0.540] mg / QD; If the age is in the range of [11.5, 13) years, the dosage range is [0.360, 0.540] mg / QD.

9. The method for constructing a dosage regimen model of sirolimus in combination with voriconazole in children with PID according to claim 7, characterized in that, If the child is 1-1.5 years old, the dosage is 0.125 mg / QD. If the child is between 1.5 and 2.5 years old, the dosage is 0.2 mg / QD. If the child is between 2.5 and 3.5 years old, the dosage is 0.225 mg / QD. If the child is between 3.5 and 4.5 years old, the dosage is 0.275 mg / day. If the child is between 4.5 and 6 years old, the dosage is 0.275 mg / day. If the child is 6-8 years old, the dosage is 0.35 mg / day. If the child is between 8 and 11.5 years old, the dosage is 0.45 mg / day. If the child is between 11.5 and 13 years old, the dosage is 0.45 mg / day.

10. The method for constructing a dosage regimen model of sirolimus in combination with voriconazole in children with PID according to claim 7, characterized in that, The dosage of sirolimus without voriconazole is determined based on the age of the child with PID, including: If the age is in the range of [1, 1.5), the dosage range is [0.500, 0.750] mg / QD; If the age is in the range of [1.5, 2.5), the dosage range is [0.600, 0.900] mg / QD; If the age is in the range of [2.5, 3.5), the dosage range is [0.800, 1.200] mg / QD; If the age is in the range of [3.5, 4.5), the dosage range is [0.900, 1.350] mg / QD; If the age is in the range of [4.5, 6), the dosage range is [1.000, 1.500] mg / QD; If the age is in the range of [6, 8), the dosage range is [1.200, 1.800] mg / QD; If the age is in the range of [8, 11.5), the dosage range is [1.500, 2.250] mg / QD; If the age is in the range of [11.5, 13), the dosage range is [1.600, 2.400] mg / QD.

11. A computer device, characterized by The device includes: a memory and a processor; the memory is used to store a computer program; the processor executes the computer program to implement the steps of the method according to any one of claims 1-10.

12. A computer-readable storage medium, characterized in that, It stores a computer program thereon, which, when executed by a processor, implements the steps of the method as described in any one of claims 1-10.

13. A computer program product comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method described in any one of claims 1-10.

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