Individualized administration method for treating PID child patient by combining sirolimus with voriconazole

Abstract

The invention belongs to the field of intelligent medical treatment, and particularly relates to an individualized administration method for treating a PID child patient by combining sirolimus with voriconazole. The method comprises the following steps: acquiring the age of a PID child patient; based on the age of the PID child patient, determining the dosage of sirolimus when the voriconam is combined, and if the age of the PID child patient is in a [1, 6] year interval, the dosage is [0.100, 0.330] mg / QD; if the age is in the interval of [6, 13], the dosage is [0.280, 0.540] mg / QD. According to the method, the PPK model is formulated for the first time when the PID child patient is combined with the VRC in the SRL mode, the optimal administration dosage is explored based on PPK when the PID child patient is combined with the VRC in the SRL mode, and simple medication dosage guidance is provided for child patients of different age groups; individualized and customized SRL medication guidance can be provided for individuals through a posterior Bayesian method.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent medical treatment, and more particularly, to a method, device, medium and program product for individualized dosing of sirolimus in combination with voriconazole for treating PID in children. BACKGROUND

[0002] Primary immunodeficiency diseases (PID) are a group of rare immunological genetic diseases caused by gene mutations, which affect the normal function of the immune system of individuals. The disease has a low incidence, but the mortality rate is very high, and the mortality rate of different diseases can reach 40%-100%. The clinical manifestations of PID mainly include recurrent infections, autoimmune diseases, autoinflammatory diseases and increased susceptibility to tumors. About 75% of PID patients have antibody deficiency, and infection is the most common clinical manifestation. Current treatment status and challenges of PID: the current clinical preferred treatment method is hematopoietic stem cell transplantation, but the mortality rate and complication risk of patients with autoimmune diseases after transplantation are significantly increased. Immune modulators have good prognosis in the treatment of autoimmune diseases, but their efficacy and safety are affected by many factors. PID children often need long-term and early use of anti-infective drugs, but drug interactions and individual differences bring challenges to treatment.

[0003] The research of "Population Pharmacokinetics of Sirolimus in Pediatric Patients" reveals that the clinical application is often based on empirical dosing, and then the steady-state trough concentration after drug administration is monitored to gradually adjust to the appropriate dosing. This method requires multiple dosing to reach steady state before therapeutic drug monitoring (TDM) and treatment regimen adjustment can be performed, which is a time-consuming process. In addition, blood drug concentration is also affected by blood sampling time, covariates and other factors, resulting in inaccurate dosing regimen. The development of population pharmacokinetics (PPK) research can be based on the pharmacokinetics (PK) parameters of a specific population, combined with the analysis of patient-related data, and taking into account factors such as disease progression, to develop the best individualized dosing regimen.

[0004] The individual differences in pharmacokinetic parameters can be explained by demographic, physiological, genetic and biochemical variables: Djebli N et al. demonstrated that the in vivo disposition of SRL was highly correlated with age; Reichen J et al. suggested that the clearance of SRL (CL) was negatively correlated with age (22-73 years old); Tejani A et al. showed that the clearance of SRL in children aged 5-11 years [(544 ± 463) mL / h / kg, n = 7] was 90% higher than that in young adults aged 19-36 years [(287 ± 111) mL / h / kg, n = 25]; CEmoto's study showed that 90% of children reached the ideal blood concentration after SRL 0.8 mg / m2 was given twice a day to transplant patients, and the dose was adjusted based on the pharmacokinetic parameters of the first time.

[0005] The population pharmacokinetic model (PPK) integrates relevant information such as patients, drugs and diseases through mathematical modeling and simulation techniques, reveals the variation rules of drugs among different individuals, and provides the basis for precise drug use. The existing technology has established a PopPK model of SRL in children with immune cytopenia: it can predict exposure and guide dose adjustment (27 patients were included, a total of 107 blood sample points, internal validation proved the stability and reliability of the model, and the effect of clinical application evaluation was good); the existing technology has established a PopPK model of SRL in children with iLM: it explores the bidirectional prediction of blood concentration and dose, can accurately predict the pharmacokinetic parameters of patients, and develop individualized dosing regimens, so as to improve efficacy and safety.

[0006] The key application of Voriconazole (VRC) in the treatment of PID children and the application of combined drug strategy in PID children: due to the abnormal immune function, it is a high-risk group to develop invasive fungal infection; guidelines recommend that antifungal drugs should be used as soon as possible, Voriconazole (VRC) is a second-generation antifungal drug with broad-spectrum antifungal activity, and is the clinical preferred treatment drug for invasive aspergillosis and Candida infection. Voriconazole is mainly metabolized by CYP2C19, secondarily by CYP2C9 and CYP3A4, and is a strong inhibitor of CYP3A4 (DDI). Early empirical use of VRC can effectively prevent and control fungal infection and reduce the risk of complications.

[0007] VRC and SRL are used in combination to achieve synergistic effect, and SRL combined with VRC is an important regimen for treating PID. However, the combination of the two drugs faces multiple challenges and risks, and the DDI mechanism of the combination of the two drugs is complex: SRL is a substrate of CYP3A and P-gp, and VRC is a substrate and / or inhibitor of CYP3A and / or P-gp. The combination of the two drugs will inevitably lead to uncertainty in the efficacy and safety of SRL. The combination with VRC can increase the peak concentration of SRL by about 556%, and the dose of SRL needs to be reduced. Existing studies have revealed significant increase in the blood concentration of SRL when SRL is used in combination with VRC, and have explored dose adjustment strategies. However, these studies are mostly based on small sample sizes, and do not focus on pediatric PID patients. SUMMARY

[0008] In view of the above problems, the present application provides a method for individualized dosing of sunitinib combined with voriconazole for treating pediatric patients with PID, and a PopPK model is constructed to realize model-based individualized dosing regimen for pediatric patients with PID when SRL and VRZ are used in combination, so as to improve the effectiveness and safety of combined use of drugs.

[0009] The present application (first aspect) discloses a method for individualized dosing of sunitinib combined with voriconazole for treating pediatric patients with PID, comprising:

[0010] obtaining the age of the pediatric patient with PID;

[0011] determining the dosage of sunitinib when combined with voriconazole based on the age of the pediatric patient with PID, if the age of the pediatric patient with PID is in the interval [1, 6), the dosage is [0.100, 0.330] mg / QD; if the age is in the interval [6, 13), the dosage is [0.280, 0.540] mg / QD.

[0012] Further, determining the dosage of sunitinib when combined with voriconazole based on the age of the pediatric patient with PID comprises:

[0013] if the age is in the interval [1, 1.5), the interval of the dosage is [0.100, 0.150] mg / QD;

[0014] if the age is in the interval [1.5, 2.5), the interval of the dosage is [0.160, 0.240] mg / QD;

[0015] if the age is in the interval [2.5, 3.5), the interval of the dosage is [0.180, 0.270] mg / QD;

[0016] if the age is in the interval [3.5, 4.5), the interval of the dosage is [0.220, 0.330] mg / QD;

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

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

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

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

[0021] Optionally, if the age is in 1-1.5 years old, the dosage is 0.125 mg / QD,

[0022] If the age is in 1.5-2.5 years old, the dosage of the drug is 0.2 mg / QD,

[0023] If the age is in 2.5-3.5 years old, the dosage of the drug is 0.225 mg / QD,

[0024] If the age is in 3.5-4.5 years old, the dosage of the drug is 0.275 mg / QD,

[0025] If the age is in 4.5-6 years old, the dosage of the drug is 0.275 mg / QD,

[0026] If the age is in 6-8 years old, the dosage of the drug is 0.35 mg / DQ,

[0027] If the age is in 8-11.5 years old, the dosage of the drug is 0.45 mg / DQ;

[0028] If the age is in 11.5-13 years old, the dosage of the drug is 0.45 mg / DQ;

[0029] Optionally, the method further comprises: determining the dosage of sirolimus without combination of vorinostat based on the age of the PID child, if the age of the PID child is in the interval [1, 6), the dosage is [0.500, 1.500] mg / QD; if the age is in the interval [6, 13), the dosage is [1.200, 2.400] mg / QD;

[0030] Optionally, determining the dosage of sirolimus without combination of vorinostat based on the age of the PID child comprises:

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

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

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

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

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

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

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

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

[0039] The second aspect of the application discloses a method for constructing a PPK model of sirolimus combined with VRC for a PID child, comprising:

[0040] obtaining a data set of the PID child, the data set comprising the age, ALB and whether the VRC is combined of the child;

[0041] obtaining the model parameters of the PPK model based on the data set of the PID child, and obtaining a first PPK model.

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

[0043]

[0044] wherein CL represents the clearance rate, Age represents the age, ALB represents the albumin, and VRC represents whether the VRC is combined; θ CL is a population typical value of the clearance rate, median age is a median of the age, f CL-Age is an exponential term of the age in the clearance rate model; median ALB is a median of the ALB, f CL-ALB is an exponential term of the ALB in the clearance rate model; f CL-VRC is an exponential term of the VRC in the clearance rate model when the VRC is combined, η CL represents the inter-individual variability of the clearance rate; represents the absorption rate constant;

[0045] Optionally, θ CL , median age , f CL-Age , median ALB , f CL-ALB , f CL-VRC , θ V , f Vd-VRC is determined based on the training set data;

[0046] Optionally, is fixed at 0.7521 / h;

[0047] Optionally, when used with voriconazole, VRC is 1, and when not used with voriconazole, VRC is 0.

[0048] The third aspect of the present application discloses a method for constructing a dosage calculation model of sirolimus used in combination with voriconazole for PID children, comprising:

[0049] Obtaining a data set of PID children;

[0050] Based on the data set of PID children and the first PPK model, the Monte Carlo simulation method is used to obtain the dosage of sirolimus used in combination with voriconazole for PID children in different age groups; the first PPK model is constructed based on the method for constructing the dosage calculation model of sirolimus used in combination with voriconazole for PID children;

[0051] Based on different age groups, whether or not used in combination with voriconazole, and the corresponding dosage of sirolimus, a dosage calculation model is constructed.

[0052] Optionally, the dosage calculation model is a decision tree model, and the decision tree nodes include different age groups and whether or not used in combination with voriconazole.

[0053] Optionally, the Monte Carlo simulation method is used to simulate different age groups, and each 0.5 year is an age group.

[0054] The fourth aspect of the present application discloses an individualized dosing method for treating PID children with sirolimus in combination with voriconazole, comprising:

[0055] Obtaining the age of PID children and whether or not used in combination with voriconazole;

[0056] Based on the age group to which the age of PID children belongs and whether or not used in combination with voriconazole, a first dosage is obtained by inputting the dosage calculation model constructed based on the method for constructing the dosage calculation model, and the first dosage is used as the individualized dosage.

[0057] Further, the method further comprises: adjusting the first drug dosage to obtain a second drug dosage based on a posteriori Bayesian method, wherein the posteriori Bayesian method comprises:

[0058] Step 1: obtaining the ALB of the PID child, and formulating a first drug scheme for the PID child based on the first drug dosage, wherein the first drug scheme comprises: a planned drug date, a drug time, a first drug dosage, a drug interval length, a blood drug concentration, and a number of drugs between two measurements of the blood drug concentration;

[0059] Step 2: obtaining the individual distribution volume and clearance of the PID child based on the first drug scheme, the ALB of the PID child, and the first PPK model;

[0060] Step 3: obtaining a predicted blood drug concentration based on the distribution volume and clearance and the time to reach a steady state of the first drug scheme, comparing the predicted blood drug concentration with a reference value, if the predicted blood drug concentration is higher than the reference value, reducing the drug dosage of the first drug scheme, if the predicted blood drug concentration is lower than the reference value, increasing the drug dosage of the first drug scheme, and obtaining an updated drug scheme;

[0061] Step 4: repeating Steps 2-3 until the predicted blood drug concentration is in a reference value interval after the first drug scheme is replaced by the updated drug scheme to obtain a second drug dosage;

[0062] Optionally, the method for obtaining the distribution volume and clearance of the PID child based on the first drug scheme, the ALB of the PID child, and the first PPK model comprises:

[0063] performing pharmacokinetic analysis based on the age of the PID child, the ALB of the PID child, and the first drug scheme to obtain individual pharmacokinetic parameters of the PID child, wherein the individual pharmacokinetic parameters are an individual typical value of the distribution volume and an individual typical value of the clearance;

[0064] updating the population typical value of the distribution volume and the population typical value of the clearance of the first PPK model using the individual pharmacokinetic parameters to obtain a second PPK model, and obtaining the individual distribution volume and clearance of the child based on the second PPK model.

[0065] The fifth aspect of the present application discloses an individualized drug administration system for treating PID children with sirolimus in combination with voriconazole, comprising:

[0066] The first obtaining module is configured to obtain the age of the PID child.

[0067] The first output module is configured to determine the dosage of sirolimus combined with voriconazole based on the age of the PID patient. If the age of the PID patient is in the interval of [1, 6), the dosage is [0.100, 0.330] mg / QD. If the age of the PID patient is in the interval of [6, 13), the dosage is [0.280, 0.540] mg / QD.

[0068] The sixth aspect of the present application discloses a system for constructing a PPK model of sirolimus combined with voriconazole for PID patients, comprising:

[0069] The second acquisition module is configured to acquire a data set of the PID patient, wherein the data set comprises the age, ALB, and whether the PID patient is combined with voriconazole.

[0070] The second output module is configured to obtain the first PPK model based on the model parameters of the PPK model obtained based on the data set of the PID patient.

[0071] The seventh aspect of the present application discloses a system for constructing a dosage planning model of sirolimus combined with voriconazole for PID patients, comprising:

[0072] The third acquisition module is configured to acquire a data set of the PID patient.

[0073] The PPK model calculation module is configured to obtain the dosage of sirolimus combined with voriconazole for PID patients in different age groups by using a Monte Carlo simulation method based on the data set of the PID patient and the first PPK model.

[0074] The dosage model construction module is configured to construct a dosage calculation model based on the dosages of sirolimus in different age groups, whether the PID patient is combined with voriconazole, and the corresponding dosages.

[0075] The eighth aspect of the present application discloses an individualized dosing system for treating PID patients with sirolimus combined with voriconazole, comprising:

[0076] The fourth acquisition module is configured to acquire the age of the PID patient and whether the PID patient is combined with voriconazole.

[0077] The fourth output module is configured to input the dosage calculation model based on the age group to which the age of the PID patient belongs and whether the PID patient is combined with voriconazole to obtain the first dosage, and use the first dosage as the dosage of individualized dosing.

[0078] The third aspect of the present application discloses a computer device, comprising a memory and a processor; the memory is configured to store program instructions; the processor is configured to invoke the program instructions, and when the program instructions are executed, the steps of the above-mentioned method are executed.

[0079] The fourth aspect of the present application discloses a computer readable storage medium, which stores a computer program, and the computer program realizes the steps of the above method when executed by a processor.

[0080] The fifth aspect of the present application discloses a computer program product, which comprises a computer program, and the computer program realizes the steps of the above method when executed by a processor.

[0081] The present application has the following beneficial effects:

[0082] (1) The present application first explores the optimal dosing of SRL combined with VRC for PID children based on PPK, and provides simple dosing guidance for children of different age groups;

[0083] (2) The present application first formulates the PPK model for SRL combined with VRC for PID children, and can provide individualized SRL dosing guidance for individuals through the posterior Bayesian method;

[0084] (3) The model obtained by the present application has been verified by clinical real data and put into clinical dosing guidance, which improves the problem of SRL dose determination when SRL is combined with VRC for PID children. BRIEF DESCRIPTION OF DRAWINGS

[0085] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0086] Figure 1 is a method flowchart provided by the first aspect of the present application;

[0087] Figure 2 is a program product schematic diagram provided by the second aspect of the present application;

[0088] Figure 3 is a schematic diagram of a computer device provided by the present application;

[0089] Figure 4 is a schematic diagram of the architecture of an exemplary computing device provided by the present application;

[0090] Figure 5 is a schematic diagram of a storage medium provided by the present application;

[0091] Figure 6 is a research roadmap provided by the present application;

[0092] Figure 7is a group entry schematic diagram of a research subject provided by an embodiment of the present application;

[0093] Figure 8A 、 8B , 8C, 8D, 8E, 8F are methods for illustrating the screening of covariates provided by an embodiment of the present application, the upper graph of the same variable represents the correlation of V, and the lower graph represents the correlation with CL;

[0094] Figure 9 is a comparison schematic diagram of a basic model (A-F) and a final model (A'-F') provided by an embodiment of the present application: A, A': observed value VS individual predicted value (IPRED); B, B': individual weighted residual (Iwres) VS IPRED; C, C': observed value VS population predicted value; D, D': conditional weighted residual QQ plot; E, E': CWRES VS start time of administration; F, F': CWRES VS population predicted value;

[0095] Figure 10 is a PPK model evaluation schematic diagram of SRL in children with PID provided by an embodiment of the present application;

[0096] Figure 11 is a data schematic diagram for population prediction based on Monte Carlo provided by an embodiment of the present application;

[0097] Figure 12 is a basic data schematic diagram for individualized drug recommendation based on Bayesian provided by an embodiment of the present application;

[0098] Figure 13 is a schematic diagram of individual pharmacokinetic parameters obtained in the process of Bayesian posterior method provided by an embodiment of the present application;

[0099] Figure 14 is a schematic diagram of individual optimal drug dosage simulation scheme in the process of Bayesian posterior method optimization provided by an embodiment of the present application. DETAILED DESCRIPTION

[0100] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application.

[0101] In some of the flowcharts described in the description and claims of the present application and in the above-described figures, a plurality of operations are included which occur in a particular order, but it should be clearly understood that the operations can be performed in an order other than that in which they are presented herein or in parallel, and the serial numbers of the operations, such as S101, S102, etc., are merely used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, the flowcharts can include more or fewer operations, and the operations can be executed in sequence or in parallel. It should be noted that the descriptions herein, such as "first", "second", etc., are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence, nor do "first" and "second" represent different types.

[0102] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0103] Figure 1 is a flowchart of a method for individualized dosing of a pediatric patient with PID based on evaluation of the combination of sirolimus and voriconazole provided by the embodiments of the present application. Specifically, the method comprises the following steps:

[0104] S101: obtaining the age of the pediatric patient with PID;

[0105] S102: determining the dosage of sirolimus combined with voriconazole based on the age of the pediatric patient with PID. If the age of the pediatric patient with PID is in the interval [1, 6), the dosage is [0.100, 0.330] mg / QD; if the age is in the interval [6, 13), the dosage is [0.280, 0.540] mg / QD.

[0106] Research purpose: to provide a reference for individualized precision dosing of pediatric patients with PID who need to use SRL and VRC in combination. The total technical route is as shown in Figure 6 , and the specific research is as follows.

[0107] I. Research method

[0108] 1. Based on prior knowledge and real data, explore the influencing factors of SRL blood drug concentration in children

[0109] 1.1 Literature review:

[0110] Review existing literature at home and abroad and the hospital database, analyze the current situation, and screen the "influencing factors" of SRL blood drug concentration;

[0111] 1.2 Real-world research:

[0112] 1.2.1 Research subjects: from August 2018 to June 2023, patients who visited the outpatient department and were hospitalized in Beijing Children's Hospital;

[0113] Inclusion criteria:

[0114] A. Patients aged <18 years;

[0115] B. Detailed records of general information, medication, laboratory test results, and other data;

[0116] C. Patients have been treated with SRL for more than 5 days, reaching steady-state blood drug concentration;

[0117] D. Monitoring of concurrent medications during patient treatment;

[0118] Exclusion criteria:

[0119] E. Presence of concurrent diarrhea infection;

[0120] F. Use of SRL for less than 5 days and incomplete related information;

[0121] Sample size: 249 children using SRL, with a total of 1535 sampling points;

[0122] 1.2.2 Data collection

[0123] PID child data collection: A. Demographic data (name, age, gender, weight, body surface area); B. Laboratory tests (blood routine, urine routine, blood biochemistry); C. Drug information (time of administration, dosage, blood drug concentration); among them, the steady-state blood drug trough concentration (C0) is obtained by fluorescence polarization immunoassay after continuous administration; blood sampling time is within 15-30 minutes before morning administration;

[0124] 1.2.3 Observation indicators

[0125] The follow-up starting point is the day when the first dose of SRL is given, and follow-up is conducted at 1 week, 2 weeks, 4 weeks, and 12 weeks after administration to adjust the dosage;

[0126] Main observation indicators: steady-state blood drug trough concentration of SRL at each detection site;

[0127] Secondary observation indicators: blood routine, blood biochemistry, immunology, urine routine, liver function indicators, and renal function indicators of patients at each detection time point.

[0128] 1.2.4 Statistical description of clinical data

[0129] Measurement data: for normally distributed data, mean ± standard deviation is used; for non-normally distributed data, median (range) is used; count data: frequency and rate.

[0130] 1.2.5 Single factor analysis

[0131] Pearson correlation analysis was used to analyze the correlation between each factor and SRL valley concentration, otherwise Spearman correlation analysis was used, and factors with P<0.05 were screened out;

[0132] Statistically significant factors were used as independent variables and were included in the multiple factor analysis;

[0133] Multiple factor analysis: C / D as the dependent variable, the factors affecting SRL in single factor analysis were included in multiple linear regression analysis, and statistically significant factors (i.e. independent factors) were screened out.

[0134] 2. Based on the influencing factors, the PPK model of SRL combined with VRC for PID children was constructed and optimized

[0135] 2.1 Modeling process

[0136] Data preprocessing→construction of basic model→optimization of basic model→comparison of fitting degree→Bootstrap method and VPC method to verify the model→Monte Carlo simulation of the best dosing regimen

[0137] 2.1.1 Construction of basic model

[0138] The SRL blood concentration data, dosing time, dosing dose, blood sampling time, etc. of the enrolled children were analyzed, and the non-linear mixed effect model method was used to establish the basic structure model, statistical model and residual model.

[0139] 2.1.2 Construction of covariate model

[0140] Stepwise regression method was used to screen variables to establish a covariate model, which was divided into two steps:

[0141] ① Forward inclusion method: when the inclusion of covariates reduces the OFV by more than 6.63 (P<0.01), the variable is retained.

[0142] ② Reverse exclusion method: when the exclusion of the covariate increases the OFV by more than 10.83 (P<0.001), the variable is retained.

[0143] 2.1.3 Model internal verification:

[0144] The fitting degree of the model was compared by using the Bootstrap method to verify the stability of the model, and the VPC method was used to verify the predictive ability of the model.

[0145] 2.1.4 Monte Carlo method was used to simulate the best dosing dose for children aged 1-13 years

[0146] 3. Evaluate the predictive performance of the PPK model of SRL combined with VRC in children with PID based on real data

[0147] The collected subject set: from 2024.4 to 2024.11, the patients treated in the outpatient department and hospitalized in the immunology department of Beijing Children's Hospital;

[0148] Inclusion criteria: A. Patients aged <18 years; B. Patients clinically diagnosed as PID; C. Detailed records of general information, medication, laboratory test results, etc. D. Patients have taken SRL for more than 5 days to achieve steady-state blood drug concentration; E. Monitoring of concurrent medication during patient treatment;

[0149] Exclusion criteria: F. Combined with diarrhea infection; G. Using SRL for less than 5 days and incomplete information; Sample size: 16 PID patients with 38 sampling points; Observation index: SRL steady-state blood drug trough concentration at each detection site; Evaluation model accuracy: comparison of relative deviation between model predicted value and actual observed value; evaluation of model accuracy and precision.

[0150] II. Research results

[0151] 1. Based on prior knowledge and real data, explore the influencing factors of SRL blood drug concentration in children

[0152] By learning from previous literature, clinical indicators including drug absorption, distribution, metabolism, and excretion were included. Retrospective inclusion of 249 patients from 2018.8 to 2023.6 with a total of 1535 blood drug concentration points. The statistical results of patient demographic and pathophysiological indicators, such as Figure 7 , are shown in Table 1.

[0153] The results of single factor analysis (Table 2) showed that age, weight, body surface area, WBC, HGB, AST, ALT, ALB, and CREA were significantly correlated with SRL blood drug concentration.

[0154] Table 1 Statistical results of patient basic information

[0155]

[0156]

[0157] Note: Group A is 1 week after SRL administration, Group B is 2 weeks after SRL administration, Group C is 4 weeks after SRL administration, and Group D is 12 weeks after SRL administration.

[0158] Table 2 Single factor correlation analysis of each factor and C / D

[0159]

[0160] Note: Group A is 1 week after SRL administration, Group B is 2 weeks after SRL administration, Group C is 4 weeks after SRL administration, and Group D is 12 weeks after SRL administration.

[0161] Multivariate analysis of SRL influencing factors (Table 3) showed that the weight, body surface area, PLT, AST, ALT and ALB of the children were statistically correlated with the C / D of SRL, and were independent factors affecting the blood concentration of SRL (P<0.05); therefore, it is recommended to optimize the medication regimen in combination with the weight, body surface area, ALT, AST, PLT and ALB of the children, follow individualized dosing regimen and adjust the dose in time to reduce adverse reactions.

[0162] Table 3 Multivariate analysis of SRL influencing factors

[0163]

[0164] Note: Group A is 1 week after SRL administration, Group B is 2 weeks after SRL administration, Group C is 4 weeks after SRL administration, and Group D is 12 weeks after SRL administration.

[0165] 2. Based on the influencing factors, the PPK model of SRL combined with VRC in children with PID was constructed and optimized

[0166] Retrospective inclusion of 52 children diagnosed with PID from January 2017 to March 2024, a total of 399 SRL blood drug concentration points. For missing data, according to Andrea Mashall's research, missing values accounted for <50% can be processed by multiple imputation ( Figure 7 ).

[0167] Based on OFV, the first-order absorption and elimination proportional error model was finally selected to establish the basic model. The covariates were included to explain the individual differences within and between individuals to optimize the model: age, albumin, and VRC combination therapy were finally included as covariates. Figure 8A- Figure 8F The process of screening covariates by graphical method is shown in the figure: age ( Figure 8A Age), albumin ( Figure 8A ALB), and VRC combination therapy.

[0168] Covariate screening conditions: positive inclusion: OFV decrease greater than 6.63 (P<0.01), then included; reverse elimination: OFV increase greater than 10.83 (P<0.001), then retained.

[0169] The final result of covariate screening: CL-Age, ALB, VRC, V-VRC ( Figure 8A- Figure 8F , shown in Table 2).

[0170] Table 4 Covariate screening process table

[0171]

[0172] Thus, the PPK model after covariate screening is represented as:

[0173]

[0174] where CL represents clearance, θ CL is the population typical value of clearance, median age is the median of age, f CL-Age is the exponential term of age in the clearance model; median ALB is the median of ALB, f CL-ALB is the exponential term of ALB in the clearance model; f CL-VRC is the exponential term of VRC in the clearance model when VRC is co-administered, η CL represents the inter-individual variability of clearance; V represents the volume of distribution, θ V is the population typical value of volume of distribution, f Vd-VRC is the exponential term of VRC in the volume of distribution model when VRC is co-administered, η v represents the inter-individual variability of volume of distribution.

[0175] θ CL , median age , f CL-Age , median ALB , f CL-ALB , f CL-VRC , θ V , f Vd-VRC are determined based on the PPK model constructed based on the training set data. represents the absorption rate constant;

[0176] In order to determine the parameters of the above PPK model, the final model and the model typical value parameters are calculated based on the collected data and the covariates, as shown in Table 5.

[0177] Table 5. Final model parameter estimation and bootstrap verification results

[0178]

[0179] Notes: θ represents the population typical value corresponding to each parameter; f represents the exponential of the covariate model corresponding to each parameter, ω 2 Vd represents η v in the covariate model, ω 2 CL represents η CL in the covariate model.

[0180] Thus, based on the median of the dataset, the median age = 7.29; median ALB = 41.22, the final model is expressed as:

[0181]

[0182] K a = 0.7521 / h

[0183] Note: when combined with VRC, VRC = 1 in the model; otherwise, VRC = 0;

[0184] CL: clearance; V: volume of distribution.

[0185] The absorption rate constant (Ka) was fixed at 0.7521 / h.

[0186] 3. Evaluation of PPK model of SRL combined with VRC in children with PID

[0187] Internal validation was performed on the model to evaluate the predictive ability (VPC) and stability (Bootstrap) of the model. Bootstrapping results: 1000 times of bootstrapping were successfully converged 1000 times. The validation results are shown in Figure 9 , Table 4.

[0188] Internal validation was performed on the model, and the goodness-of-fit plot ( Figure 9 ) showed that the final model was significantly better than the base model. Through VPC, it can be seen that the model has good prediction effect at low and medium concentrations of SRL, but the prediction effect at high concentrations needs to be improved, and the main reason may be that the sampling points for modeling are mainly distributed in the range of 5-15 ng / ml. The results are shown in Figure 10 . The bootstrapping method was used to verify the stability of the model, and the results showed that the success probability of running was 100%, and the bias was less than 30%, proving that the stability of the model was excellent.

[0189] Monte Carlo simulation of initial dosing in children of different ages with or without VRC: based on Monte Carlo, the optimal dosing regimen of children aged 1-13 years old was simulated under the conditions of SRL alone or combined administration, and the evaluation index was the probability of patients reaching the therapeutic window (5-15 ng / mL) at steady state. The results showed (Table 6) that for patients of the same age, the dosing dose needs to be reduced by about 73.3%-80.0% when combined with VRC than not combined with VRC.

[0190] Table 6 Dosing dose of children of different ages simulated by Monte Carlo method

[0191]

[0192] Note: The results of the Monte Carlo simulation method are affected by the data set collected. Based on the data set of this study, the probability of reaching the corresponding target therapeutic range when the dose is the dose in Table 5 is achieved. The above research results have been applied to clinical research, and on the basis of the results, both within the floating range of 20% are within the clinically safe and effective dose.

[0193] 4. Evaluation of the predictive performance of the PPK model of SRL combined with VRC in children with PID based on real data

[0194] According to the criteria of the National Emission Standard, 16 patients from our hospital during 2024.3-2024.11 were included, a total of 38 sampling points, and the model was externally evaluated. According to the following formula, MPE, MAE, MIPE, and MAIE were calculated respectively to evaluate the accuracy and precision of the model.

[0195]

[0196] MDPE% = median(PE i , i = 1, 2, 3, …, N i )

[0197] MAPE% = median(|PE i |, i = 1, 2, 3, …, N i )

[0198]

[0199] MDIPE% = median(IPE i , i = 1, 2, 3, …, N i )

[0200] MAIPE% = median(|IPE i |, i = 1, 2, 3, …, N i )

[0201] External evaluation results: MPE% = -13.34%, MAE% = 32.34%, MIPE% = -7.79%, MAIE% = 20.41%.

[0202] The results show that: MPE, MIPE < ± 20%; MAIE < 30%; the model has good predictive performance.

[0203] Wherein, MPE = (Median Prediction Error), MAE = (Median Absolute Prediction Error), MIPE = (Median Individual Prediction Error), MAIE = (Median Absolute Individual Prediction Error)

[0204] The final model incorporated age, albumin (ALB), and VRC co-medication into the model, and the formula is as follows:

[0205]

[0206] K a = 0.7521 / h

[0207] It should be noted that the model parameters in the above model are calculated based on the data set collected in this study. When the data set changes, the model parameters may change slightly.

[0208] The final model shows that:

[0209] ① Age is positively correlated with CL: This again confirms the results of previous pediatric PPK studies that age is an important factor affecting the pharmacokinetics of SRL and is positively correlated with pharmacokinetic parameters.

[0210] Unlike previous SRL PPK studies: Most previous PPK studies have included body weight or body surface area as a covariate.

[0211] ② ALB is positively correlated with CL:

[0212] There is currently no related PPK study that includes this covariate. Although the mechanism is not clear, the principle may be: ① Because SRL has a 92% binding rate with plasma albumin in the body. ② Albumin can reflect liver function to some extent, and SRL is mainly metabolized in the liver.

[0213] The final results of the model showed that age and albumin were important factors affecting the blood concentration of SRL. Among them, age was positively correlated with clearance rate, which confirmed that age was an important factor affecting the blood concentration of SRL in previous studies. Unlike other PPK studies, body weight was not a significant covariate in this study. Age was more significant than body weight in affecting the pharmacokinetic parameters of SRL in this study. In addition, unlike previous studies, this study included albumin as a covariate, which was consistent with our previous analysis of the correlation between SRL blood concentration and physiological and biochemical indicators. That is, as ALB rises, the clearance rate of SRL will rise, and the blood trough concentration of SRL will fall. This may be due to the fact that the binding rate of SRL to plasma albumin is 92%, of which the binding to serum albumin accounts for 97%. Therefore, the increase in ALB may lead to an increase in SRL clearance rate, resulting in a decrease in SRL blood trough concentration.

[0214] This study again proves that VRC significantly affects the pharmacokinetic process of SRL: in previous PPK studies of SRL used in adult liver transplantation, the combination of VRC and SRL was included as a categorical variable in the model, and the results showed that the combination of VRC and SRL was negatively correlated with the pharmacokinetic parameters. Previous studies with small sample sizes (3 / 28 cases) and case reports have shown that the combination of SRL and VRC in the real world can increase the AUC of SRL by about 4.5 times. The main reason is that VRC, as a triazole antifungal drug, is an inhibitor of cytochrome P450 CYP3A4, while SRL is a substrate of this enzyme.

[0215] We further use Monte Carlo simulation method to predict the best dose of different age children with or without voriconazole, and found that the same age with voriconazole children than not with, the dose should be reduced by 73.3%-80%, which is similar to the results of previous real-world studies that the dose needs to be reduced by 75%-87.5%, but lower than another study suggests to reduce 90%. This may be due to the fact that previous studies were conducted in adult populations, while this study was conducted in children, and the sample size of this study was significantly higher than that of the previous three studies.

[0216] The best dose of different age children with or without VRC was predicted by Monte Carlo simulation method: the same age with VRC children than not with VRC, the dose should be reduced by 73.3%-80%. This is higher than Peksa's study: needs to be reduced by 50%-75%. Lower than another previous study suggests to reduce 90% of the dose. The main reason: different populations: previous studies were all adult populations; different sample sizes: previous studies all have small sample sizes.

[0217] The factors affecting the blood concentration of SRL were obtained through correlation analysis: it is suggested to optimize the drug regimen in combination with the body weight, body surface area, ALT, AST, PLT, ALB, etc. of the child, follow the individualized dosing regimen, and timely adjust the dose to reduce adverse reactions.

[0218] The PPK model of SRL applied to PID children was optimized, and the influence of VRC on the pharmacokinetics of SRL was preliminarily explored. The established PPK model was internally and externally evaluated, and the results showed that the model had good prediction performance.

[0219] The optimal dosing regimen of 1-13 year-old PID children with or without VRC was predicted:

[0220] After the child is combined with VRC, the SRL dose needs to be reduced by 73.3%-80.0% to maintain the SRL blood drug concentration in the therapeutic window.

[0221] III. Individualized medication guidance using the constructed PPK model

[0222] Monte Carlo simulation and Bayesian feedback are two different methods. Monte Carlo is for population simulation, while Bayesian is for simulation of specific individuals.

[0223] 3.1 Monte Carlo simulation of the optimal dosing regimen of PID children

[0224] 1) Set the dosing regimen including dosing information (date, time, amt, dv, II, mdv) and covariate information (Age, ALB, VRC), as shown in Figure 11 ;

[0225] 2) Set "simulation" in the run option block to output the steady-state trough concentration (half an hour before dosing), simulate 1000 times, and take the target therapeutic range (5-15 ng / mL) as the basis for judgment. Simulate the optimal dosing regimen of children of different ages using sirolimus alone or combined with voriconazole (shown in Table 5). The results suggest that after PID children are combined with voriconazole, the sirolimus dose needs to be empirically reduced by 73.3%-80.0% compared to sirolimus alone.

[0226] 3.2. Posterior Bayesian individualized medication dose optimization method

[0227] Step 1: First, obtain the characteristics of the PID child to be tested,

[0228] In some embodiments, the basic information of the child is obtained as shown in Figure 12 , including:

[0229] DATE: date; TIME: time; AMT: dose; DV: drug concentration; II: interval length (h); ADDL: how many doses were given between two measured drug concentrations (32 doses were given between the measured DV on October 7 and the measured DV on November 10); Age: age; ALB: albumin; VOL: whether VRC was used;

[0230] Step 2: Determine the individual typical values of the final model parameters

[0231] 2-1 Import the dosing information (date, time, amt, dv, II) and covariate information (Age, ALB, VRC) into the final model to obtain the individual typical values of the clearance and volume of distribution; here, the individual pharmacokinetic parameters (CL, Vd) are calculated from the individual observations (shown in the table). Figure 13

[0232] 2-2 Replace the population typical value parameters in the final model formula with the individual pharmacokinetic parameters (CL, Vd)

[0233] In the Monte Carlo simulation method, the population typical value parameters θ vd / F (L) is 136.46, and θ CL / F (L / h) is 4.96 (Table 4);

[0234] In some embodiments, the calculated individual typical value of the clearance is 4.93, and the individual typical value of the volume of distribution is 135, so the replaced PPK model is:

[0235]

[0236] K a = 0.7521 / h

[0237] Step 3: Configure the individual optimal dosing simulation scheme, which includes TIME (time) and AMT (dose) (shown in the table). Figure 14 In the interface of run option, select simulation to perform the posterior Bayesian simulation, set the time points for output, and output the predicted concentrations of the simulation scheme.

[0238] Import the volume of distribution and clearance into the parameter items for drug simulation, set the simulation to 1000 times, and set the simulation run time to 10 days, as sirolimus reaches steady state in 5-7 days. After the software runs, the output drug concentration is 0.0126, which is higher than the reference value, so the 2 mg set in the simulation is lowered to 1.5 mg, and the simulation is re-run until the output drug concentration after the software runs is within the reference value range. Figure 14

[0239] ​​In some embodiments, the simulated dosing is set according to clinical experience, and then an individualized dosing is obtained by using a posterior Bayesian method.

[0240] In some embodiments, the simulated dosing is set based on the results of a Monte Carlo population, for example, for a PID child of 9 years old, when combined with VRC, the initial planned dosing is set to 0.45 mg / DQ; then the final SRL dosing is further adjusted to obtain the optimal dosing by using a Bayesian method.

[0241] In clinical practice, an initial plan is first made according to the basic information of the child, and then the blood concentration value of the child is predicted by using the individualized data of the model, and if it does not meet the standard, a new dosing plan is adjusted. At the same time, if the measured blood concentration value of the child is determined, the subsequent dosing plan can also be estimated by using the model. Treatment is a process that needs to be dynamically adjusted, and the model is the basis for adjustment.

[0242] In the present model, Ka is a fixed parameter of 0.7521.

[0243] In some embodiments, the above simulation process and PPK model are programmed by using a program language, and whether the output blood concentration after running is in the reference range is automatically compared, if it is higher than the reference range, the planned dosing table is automatically adjusted and then re-simulated until a satisfactory plan is given.

[0244] 3.3. External verification

[0245] We use 46 blood concentration points of 17 patients in our center to externally verify the model to evaluate the accuracy and precision of the model by using MPE, MAE, MIPE, and MAIE. The results show that MPE% = 17.39%, MAE% = 31.72%, MIPE% = 5.58%, MAIPE% = 21.91%, MPE, MIPE and MAIE are within ± 20% and 30% respectively, and MAE is slightly more than 30% but within an acceptable range, so it is considered that the final model has good prediction performance for both population prediction and individual prediction. It is worth mentioning that the MAPE of the 4 observation points of SRL combined with VRC in this subject is between 6.76% and 22.4%, which shows that the model has good prediction ability for the part of PID patients using sirolimus combined with voriconazole.

[0246] Figure 3 is a schematic diagram of a computer device provided by an embodiment of the present application, such as Figure 3As 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.

[0247] 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.

[0248] 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.

[0249] 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.

[0250] This invention also includes a computer-readable storage medium, such as... Figure 5As shown, a schematic diagram of the storage medium 4000 provided by the embodiment of the present application is shown, and the computer storage medium 4020 stores computer readable instructions 4010. When the computer readable instructions 4010 are run by the processor, the method according to the embodiment of the present application can be executed, which is described with reference to the above drawings. The computer readable storage medium in the embodiment of the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. The non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM) used as an external cache. By way of example, and not limitation, many forms of RAM can be used, 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 (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct Rambus dynamic random access memory (DR RAM). It should be noted that the memory of the method described herein is intended to include, but not be limited to, these and any other suitable types of memory. It should be noted that the memory of the method described herein is intended to include, but not be limited to, these and any other suitable types of memory.

[0251] The embodiment of the present application also provides a computer program product or computer program, which, when executed by a processor, implements the steps of the above method. Figure 2 As shown, the computer program product or computer program includes:

[0252] The first acquisition module 201 is configured to acquire the age of the PID child.

[0253] The first output module 202 is configured to determine the dosage of the combination of Volikangsu and sirolimus based on the age of the PID child. If the age of the PID child is in the interval of [1, 6), the dosage is [0.100, 0.330] mg / QD; if the age is in the interval of [6, 13), the dosage is [0.280, 0.540] mg / QD.

[0254] It should be noted that the flowchart and block diagrams in the drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and

[0255] In general, various example embodiments of the present disclosure can be implemented in hardware or special-purpose circuits, software, firmware, logic, or any combination thereof. Some aspects can be implemented in hardware, while other aspects can be implemented in firmware or software which can be executed by a controller, microprocessor or other computing device, Although various example embodiments of the disclosure can be illustrated and described as block diagrams, flow charts, or using some other pictorial representation, it is well understood that these blocks, apparatus, systems, techniques or methods described herein can be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controler or other computing devices, or some combination thereof.

[0256] The specific operations disclosed herein can be implemented as one or more computer programs or software modules being executed by one or more processors. Each computer program or software module can include an operating system that is executed by the one or more processors. The operating system can be stored in any apparatus's memory that is accessible by the processor(s), including the above mentioned memories. The computer programs or software modules and / or the operating system can be stored on any non-transitory computer-readable medium, including the above mentioned memories. The computer programs or software modules and / or the operating system can also include computer-readable instructions that, when executed, cause the apparatus to perform each of the respective processes, steps, calculations, and / or comparisons described herein. As such, the memory that can include the computer program or software modules, and / or the operating system can be used to store data that is manipulated by the one or more processors when executing the computer program or software modules, and / or the operating system. When referring to the memory, computer program, software modules, and / or the operating system, the processor(s) can be said to execute the computer program, software modules, and / or the operating system to perform each of the respective processes, steps, calculations, and / or comparisons described herein.

[0257] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described device embodiments are only schematic. The division of the units is only a logical function division. In actual implementation, there can be another division manner. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or components shown or discussed can be indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other form.

[0258] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0259] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0260] The example embodiments of the present disclosure described in detail above are only illustrative, not limiting. Those skilled in the art should understand that various modifications and combinations of these embodiments or their features can be made without departing from the principles and spirits of the present disclosure, and such modifications should fall within the scope of the present disclosure.

Claims

1. A personalized dosing method for treating children with PID using sirolimus in combination with voriconazole, characterized in that, The method includes: Obtain the age of the child with PID; 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 [6, 13) years, the dosage is [0.280, 0.540] mg / QD.

2. The individualized dosing method for treating children with PID using sirolimus combined with voriconazole according to claim 1, characterized in that, The dosage of sirolimus in combination with voriconazole is determined based on the child's age, including: 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; Optionally, 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 / day. 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 / QD. 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. Optionally, the method further includes: determining the dosage of sirolimus without voriconazole 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 [0.500, 1.500] mg / QD; if the age is in the range of [6, 13) years, the dosage is [1.200, 2.400] mg / QD; optionally, determining the dosage of sirolimus without voriconazole based on the age of the child with PID includes: 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.

3. A method for constructing a PPK model of sirolimus in children with PID treated with voriconazole, 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 was obtained by deriving the model parameters of the PPK model based on the dataset of PID patients.

4. The method for constructing the PPK model of sirolimus in children with PID when combined with voriconazole according to claim 3, characterized in that, The PPK model is represented as follows: Wherein, CL represents clearance rate, Age represents age, ALB represents albumin, and VRC indicates whether voriconazole is used concurrently; θ CL The median is the population-typical value of the clearance rate. age f is the median age CL-Age The age term is the exponential term in the clearance rate model; median ALB f is the median of ALB. CL-ALB f is the exponential term of ALB in the clearance rate model; CL-VRC η is the exponential term of VRC in the clearance rate model when VRC is used in conjunction with VRC. CL Indicates the inter-individual variability in clearance rate; This represents the absorption rate constant; Optional, θ CL median age f CL-Age median ALB f CL-ALB f CL-VRC θ V f Vd-VRC To determine the PPK model to be built based on the training set data; Optional, Fixed at 0.7521 / h; Optionally, when voriconazole is used in combination, the VRC is 1; when voriconazole is not used in combination, the VRC is 0.

5. A method for constructing a dosage calculation model for sirolimus in children with PID when combined 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 3-4; A dosage calculation model was constructed based on different age groups, whether voriconazole was used in combination, and the corresponding sirolimus dosage. Optionally, the medication 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; Optionally, when using the Monte Carlo simulation method to simulate different age groups, each age group can be divided into 0.5-year segments.

6. A personalized dosing method for sirolimus combined with voriconazole in the treatment of children with PID, characterized in that, The method includes: Obtain the age of the child with PID and whether voriconazole is being used in combination; Based on the age group to which the PID patient belongs and whether voriconazole is being used in combination, the dosage calculation model obtained by the method constructed in claim 5 is used to obtain the first dosage, which is then used as the dosage for individualized administration.

7. The individualized dosing method for treating children with PID using sirolimus in combination with voriconazole according to claim 6, characterized in that, The method further includes: adjusting the first drug dose based on posterior Bayesian methods to obtain a second drug dose, and using the second drug dose as the individualized dosing dose. The posterior Bayesian methods include: 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 volume of distribution 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; 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: 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, a second PPK model is obtained. Based on the second PPK model, the volume of distribution and clearance rate of the individual child are obtained.

8. A computer device, characterized in that, 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 1-7.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the method as described in any one of claims 1-7.

10. 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 1-7.

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