A method for calculating blood drug concentration

Abstract

The present application relates to a kind of medicine blood concentration calculation method, S1: the body information of patient is counted, including age, height, weight, sex, serum creatinine concentration, predict creatinine clearance rate;S2: according to the clearance rate of drug calculated according to formula of S1 estimation;S3: according to formula three, and according to the apparent distribution volume of patient is estimated;S4: according to formula four, the drug concentration prediction value change in patient's body is calculated according to pharmacokinetics;S5: the actual measured value of drug concentration in patient's body is collected, and corresponding time and dose are recorded;S6: define minimum loss function;S7: execute MCMC sampling;S8: regression model is established using system learning method, constructs the individualized model of the calculation parameter of drug concentration in patient's body, first constructs (X,Y) data set, obtains the predictor of patient;S9: using the predictor obtained by S8, obtains corresponding and, obtains the future blood concentration change value in patient's body, assists doctor to use medicine.

Description

Technical Field

[0001] This invention relates to the field of pharmaceutical monitoring technology, and more specifically, to a method for calculating drug blood concentration. Background Technology

[0002] In July 2022, the National Health Commission and the State Administration of Traditional Chinese Medicine jointly issued the "Notice on Further Strengthening Medication Safety Management and Improving the Level of Rational Drug Use" (hereinafter referred to as the "Notice"). The Notice requires further strengthening of medication safety management, improving the level of rational drug use, and strengthening the management of key monitored drugs for rational use, including antimicrobial drugs, antitumor drugs, proton pump inhibitors, glucocorticoids, narcotic, psychotropic, and radioactive drugs, and traditional Chinese medicine injections. It also mandates the identification of medication risks through blood drug concentration monitoring and gene testing, the development of individualized medication plans, optimization of drug selection, and precise determination of dosage.

[0003] Due to differences in gender, age, comorbidities, concomitant medications, and genetic characteristics, there are significant differences in pharmacokinetic dynamics. Significant differences also exist in the pharmacokinetic processes of drugs in special populations such as infants, pregnant women, the elderly, and those with hepatic or renal impairment. Differences also exist between different dosage forms (e.g., injections, oral formulations) or within the same individual under different physiological states. Thermodynamic monitoring (TDM) effectively reflects the drug's status in the body, controlling drug concentrations within the therapeutic range, thereby reducing toxic side effects and improving drug efficacy.

[0004] Drug concentration monitoring (TDM) is a method of measuring drug concentration in the blood using methods such as chromatography or immunoassay, guided by pharmacokinetic principles. It is used to evaluate efficacy or determine dosing regimens and is currently widely used in clinical practice to detect drugs with significant toxic side effects and narrow therapeutic ranges (such as antibacterial drugs, antiepileptic drugs, immunosuppressants, and antitumor drugs).

[0005] Some drugs have a narrow therapeutic window, with therapeutic concentrations close to toxic concentrations, making them highly susceptible to poisoning. Other drugs are difficult to control in terms of blood concentration; for example, phenytoin sodium, after a certain dose, exhibits a non-linear and rapid increase in blood concentration, posing a risk of poisoning. Long-term use of phenobarbital can lead to decreased bodily reactivity and reduced efficacy, requiring gradual dose increases to achieve the original therapeutic effect. Therefore, these drugs all require adjustments based on blood drug concentration monitoring.

[0006] Currently, only some departments in top-tier hospitals in China monitor the blood concentration of certain drugs, and the drugs currently used... The formula monitors and predicts blood drug concentrations, but its parameters are population averages, making it unsuitable for patients with different disease groups. Willems, JM, found that the Cockcroft-Gault method is inferior to MDRD and CKD-EPI when patients are over 70 years old. Pierrat A also demonstrated that Cockcroft-Gault is not suitable for patients under 12 years old. Experiments show that, based on comparisons between foreign open-source mimic databases and domestic hospital patient data, Cockcroft-Gault has a larger error in predicting blood drug concentrations in domestic patients than in foreign patients, with mean absolute percentage error (MAPE) distributions of 0.47 and 0.41, indicating that direct application to Chinese patients has a large error problem. This invention proposes a new solution to address these issues. Summary of the Invention

[0007] In view of the problems existing in the prior art, the purpose of this invention is to provide a method for calculating drug blood concentration, so as to solve the technical problems mentioned in the background art.

[0008] To solve the above problems, the present invention adopts the following technical solution.

[0009] A method for calculating drug blood concentration, comprising:

[0010] S1: Collect patient information, including age, height, weight, gender, and serum creatinine concentration, with the most important factor being... Representing age, in Representing height, in represents body weight, Representing gender, with Represents serum creatinine concentration, using Formula, based on , , , , Predicted creatinine clearance rate:

[0011] (Formula 1)

[0012] In the formula, This indicates the predicted creatinine clearance rate for patients. The unit is , The unit is When the patient is male Take 1, when the patient is female. Take 0.85;

[0013] S2: Estimated based on S1 The clearance rate of the drug is calculated according to the formula. ),

[0014] (Formula 2)

[0015] In the formula represent Its unit and same, The clearance rate estimated for the drug;

[0016] S3: According to Formula 3, and based on , Estimate the apparent volume of distribution of patients

[0017] (Formula 3);

[0018] S4: According to Formula 4, calculate the predicted drug concentration in the patient's body based on pharmacokinetics. ) Changes

[0019] (Formula 4);

[0020] In the formula This represents the initial drug concentration in the patient's body.

[0021] S5: Collect actual measurements of drug concentration in the patient's body. And record the corresponding time and dosage;

[0022] S6: Define the minimum damage function ,

[0023] (Formula 5);

[0024] S7: Perform MCMC sampling to obtain the optimal result. and ;

[0025] S8: Utilize systems learning methods to establish a regression model and construct a personalized model for calculating drug concentration parameters in patients. First, construct a (X, Y) dataset, where X includes age, height, weight, creatinine level, heart rate, blood pressure, and body temperature, among other parameters. Select the maximum value of the medication measurement point 24 hours prior, where Y is the optimal value of the parameter at the corresponding measurement point. and and with and Construct two different datasets; then analyze the patients' data. and Regression analysis modeling is performed based on the dataset, and then... The minimum value, used as the final predictor, ultimately yields the patient's result. and Predictor;

[0026] S9: Using the predictor obtained in S8, based on the entered patient's basic information, obtain the corresponding... and Based on pharmacokinetic formula four, the patient's blood drug concentration is calculated to assist the doctor in administering the medication.

[0027] Furthermore, the MCMC is an approximate probability calculation method that uses sampling to reject and accept data, thereby obtaining the posterior distribution of the parameters. The PyMc software package is used to model the model, constructing a likelihood function by comparing measured values ​​with true values, and then performing MCMC.

[0028] Furthermore, during the MCMC sampling process, 2000 samples were taken, with 1000 samples taken during the pre-burning period. The mean of the posterior distribution of the parameters was used as the optimal value for each patient, i.e., the optimal clearance rate for the patient at each medication time point. With apparent distribution container Dataset.

[0029] Furthermore, in the (X, Y) dataset, X also includes whether the patient has sepsis, whether CRRT is used, albumin, blood urea nitrogen, and white blood cell characteristics.

[0030] Furthermore, the regression analysis employs... Modeling.

[0031] Compared with the prior art, the advantages of this invention are:

[0032] 1. Based on clinical data of Chinese patients, a model consistent with Chinese physical characteristics was established, and the method proposed in this plan has better predictive effect.

[0033] 2. It uses more patient parameters and takes into account the patient's condition more than traditional methods.

[0034] 3. This solution proposes a standardized modeling method. By constructing an intermediate variable dataset and a prediction model, it solves the problem of not having training data in the past. It is applicable to the calculation of blood drug concentrations for various drugs and has a wider range of applications. Attached Figure Description

[0035] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0036] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0037] Example:

[0038] A method for calculating drug blood concentration.

[0039] S1: Collect patient information, including age, height, weight, gender, and serum creatinine concentration, with the most important factor being... Representing age, in Representing height, in represents body weight, Representing gender, with Represents serum creatinine concentration, using Formula, based on , , , , Predicted creatinine clearance rate:

[0040] (Formula 1)

[0041] In the formula, This indicates the predicted creatinine clearance rate for patients. The unit is , The unit is When the patient is male Take 1, when the patient is female. Take 0.85;

[0042] S2: Estimated based on S1 The clearance rate of the drug is calculated according to the formula. ),

[0043] (Formula 2)

[0044] In the formula represent Its unit and same, The clearance rate estimated for the drug;

[0045] S3: According to Formula 3, and based on , Estimate the apparent volume of distribution of patients

[0046] (Formula 3);

[0047] S4: According to Formula 4, calculate the predicted drug concentration in the patient's body based on pharmacokinetics. ) Changes

[0048] (Formula 4);

[0049] In the formula This represents the initial drug concentration in the patient's body.

[0050] S5: Collect actual measurements of drug concentration in the patient's body. And record the corresponding time and dosage;

[0051] S6: Define the minimum damage function ,

[0052] (Formula 5);

[0053] S7: Perform MCMC sampling to obtain the optimal result. and MCMC is an approximate probability calculation method that uses sampling to reject and accept data, obtaining the posterior distribution of parameters. The PyMc software package was used to model the system, constructing a likelihood function by comparing measured and true values, followed by MCMC. During MCMC sampling, 2000 samples were taken, with 1000 samples taken during the pre-treatment period. The mean of the posterior distribution of parameters was used as the optimal value for each patient, representing the optimal clearance rate at each medication time point. With apparent distribution container Dataset;

[0054] S8: Utilize systems learning methods to establish a regression model and construct a personalized model for calculating drug concentration parameters in patients. First, construct an (X, Y) dataset. X includes not only age, height, weight, creatinine level, heart rate, blood pressure, and body temperature, but also whether the patient has sepsis, whether CRRT was used, and albumin features. Select the maximum value of the medication measurement point 24 hours prior, where Y is the optimal value of the parameter at the corresponding measurement point. and and with and Construct two different datasets; then analyze the patients' data. and Regression analysis modeling is performed based on the dataset, and the regression analysis adopts... Model, then select The minimum value, used as the final predictor, ultimately yields the patient's result. and Predictor;

[0055] S9: Using the predictor obtained in S8, based on the entered patient's basic information, obtain the corresponding... and Based on pharmacokinetic formula four, the patient's blood drug concentration is calculated to assist the doctor in administering the medication.

[0056] The above description is merely a preferred embodiment of the present invention; however, the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and its improved concepts, should be covered within the scope of protection of the present invention.

Claims

1. A method for calculating drug blood concentration, characterized in that: include: S1: Collect patient information, including age, height, weight, gender, and serum creatinine concentration, with the most important factor being... Representing age, in Representing height, in represents body weight, Representing gender, with Represents serum creatinine concentration, using Formula, based on , , , , Predicted creatinine clearance rate: (Formula 1) In the formula, This indicates the predicted creatinine clearance rate for patients. The unit is , The unit is When the patient is male Take 1, when the patient is female. Take 0.85; S2: Estimated based on S1 The clearance rate of the drug is calculated according to the formula. , (Formula 2) In the formula represent Its unit and same, The clearance rate estimated for the drug; S3: According to Formula 3, and based on , Estimate the apparent volume of distribution of patients , (Formula 3); S4: Calculate the predicted drug concentration in the patient's body based on pharmacokinetics, according to Formula 4. Changes (Formula 4) In the formula This represents the initial drug concentration in the patient's body. , This refers to the dosage of the medication. S5: Collect actual measurements of drug concentration in the patient's body. And record the corresponding time and dosage; S6: Define the minimum loss function , (Formula 5); S7: Perform MCMC sampling to obtain the optimal result. and MCMC is an approximate probability calculation method that uses sampling to reject and accept data, obtaining the posterior distribution of parameters. The PyMc software package was used to model the system, constructing a likelihood function by comparing measured and true values, followed by MCMC. During MCMC sampling, 2000 samples were taken, with 1000 samples taken during the pre-treatment period. The mean of the posterior distribution of the parameters was used as the optimal value for each patient, representing the optimal clearance rate at each medication time point. With apparent distribution volume Dataset; S8: Utilize systems learning methods to establish a regression model and construct a personalized model for calculating drug concentration parameters in patients. First, construct an (X,Y) dataset. X includes not only age, height, weight, creatinine level, heart rate, blood pressure, and body temperature, but also whether the patient has sepsis, whether CRRT was used, and albumin features. Select the maximum value of the medication measurement point 24 hours prior, where Y is the optimal value of the parameter at the corresponding measurement point. and and with and Construct two different datasets; then analyze the patients' data. and Regression analysis modeling is performed based on the dataset, and the regression analysis adopts... Model, then select The minimum value, used as the final predictor, ultimately yields the patient's result. and Predictor; S9: Using the predictor obtained in S8, based on the entered patient's basic information, obtain the corresponding... and Calculate the patient's blood drug concentration according to pharmacokinetic formula four. Used to assist doctors in administering medication.

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