Busulfan clinical drug decision support system

Abstract

This invention discloses a decision support system for the precise use of busulfan, comprising modules for information input, pharmacokinetic analysis, graphical display, report generation, and query. The information input module collects accurate raw data; the pharmacokinetic analysis module, relying on a dedicated model and algorithm, calculates key pharmacokinetic parameters, supports dual-guided dose adjustment, and generates individualized recommended doses; the graphical display module generates superimposed blood drug concentration-time curves, intuitively presenting the effect of dose adjustment; the report generation module outputs standardized reports, supporting printing, export, and storage; and the query module allows for multi-condition retrieval and tracing of historical treatment data. This system addresses the problems of busulfan's narrow therapeutic window, significant individual variability, and fragmented traditional monitoring processes by achieving fully automated closed-loop management, improving monitoring efficiency and dose accuracy, reducing medication risks, adapting to individualized dosing in hematology and transplantation departments, solving pain points in traditional medication decision-making, and demonstrating significant clinical application value.

Description

Technical Field

[0001] This invention relates to the field of medication support systems, and more specifically to a clinical medication decision support system. Background Technology

[0002] Bai Xiaoan ( BU is a bifunctional alkylating agent of dimethyl sulfonate. As a cell cycle nonspecific drug, it is widely used in the preconditioning regimen for hematopoietic stem cell transplantation (HSCT) in patients with hematologic malignancies such as chronic myeloid leukemia and acute myeloid leukemia. Its core function is to clear the myeloablative follicle and reduce the cell load, creating conditions for hematopoietic stem cell engraftment.

[0003] However, the clinical application of busulfan presents significant technical challenges: firstly, its therapeutic window is narrow, and excessively high blood drug concentrations can easily trigger hepatic sinusoidal obstruction syndrome. Fatal adverse reactions such as occlusive disease (HVOD) can occur, and excessively low doses can lead to transplant failure or disease relapse. The usual dosage of busulfan is 0.8 mg / kg, with the initial dose determined according to AIBW. With a treatment regimen of busulfan administered four times daily, the US FDA recommends a target BU AUC range of 900-1350 per dose. The European EMA recommends a range of 900-1500. Secondly, individual differences are significant. Drug metabolism is influenced by various factors such as weight, body surface area, liver and kidney function, and genetic polymorphism. Conventional dosing regimens based solely on weight or body surface area cannot achieve the target treatment range for nearly half of patients. Thirdly, some tools only support a single pharmacokinetic model and are not optimized for the pharmacokinetic characteristics of busulfan, resulting in insufficient accuracy in dosage recommendations; a few tools lack visualization capabilities, making it difficult for clinicians to intuitively assess trends in blood drug concentration changes.

[0004] While existing personalized drug administration tools involve pharmacokinetic analysis, they lack algorithmic optimization for features such as busulfan's one-compartment model and first-order elimination rate, or they focus solely on data storage and retrieval, lacking automated pharmacokinetic analysis and dosage recommendation capabilities. Furthermore, traditional therapeutic drug monitoring relies on manual AUC calculations in laboratories, and the linear trapezoidal method is prone to errors due to inconsistent sampling times, further impacting the accuracy of dosing decisions.

[0005] Therefore, developing an integrated, automated, and algorithm-specific busulfan medication decision-making system to creatively solve the technical bottlenecks of low efficiency in traditional drug monitoring and analysis, unintuitive decision-making, and inaccurate dosage recommendations has become a key technical problem that urgently needs to be solved in clinical practice. Summary of the Invention

[0006] To address the technical shortcomings of busulfan, such as its narrow therapeutic window, significant individual variability, fragmented traditional drug monitoring processes, data redundancy, insufficient accuracy in dosage recommendations, and lack of data traceability, this invention aims to provide a busulfan precision medication decision support system. This system achieves fully automated closed-loop management of the entire process, including data collection, analysis, decision-making, display, and archiving, thereby improving medication safety and clinical efficiency.

[0007] To achieve the objectives of this invention, the present invention provides the following technical solution: a precision medication decision support system, characterized in that it includes an information input module, a pharmacokinetic analysis module, a graphical display module, a report generation module, and a query module. These modules are interconnected and data flows seamlessly. Each module provides a unique data / result source for the next module, and the next module professionally processes the output of the previous module, collaboratively completing the entire process of individualized busulfan medication decision-making. The specific structure and functions are as follows:

[0008] Information entry module: includes patient information sub-module, drug administration information sub-module, and blood collection information sub-module. It adopts a dual mode of "manual entry and automatic calculation" to complete the collection, automatic calculation and validity verification of full-dimensional data, and provide standardized and non-redundant structured data for the pharmacokinetic analysis module.

[0009] Pharmacokinetic Analysis Module: This module receives structured data from the information input module and employs a one-compartment pharmacokinetic model of busulfan and a linear trapezoidal algorithm for optimization. It includes submodules for parameter calculation, concentration prediction, and dosage recommendation, and supports the area under the concentration-time curve at steady state. A dual-guided dose adjustment strategy based on either steady-state drug concentration (Css) or concentration at steady state (Css) is used to generate individualized recommended doses through formulas, providing accurate analysis and prediction results for the graphical display module.

[0010] Graphical display module: Based on the analysis and prediction results of the pharmacokinetic analysis module, it generates blood drug concentration-time curves with labeled measured data points and simulated curves of initial and recommended doses, intuitively presenting the effect of dose adjustment and providing core visualization materials for the report generation module;

[0011] The report generation module integrates the structured data from the information entry module, the analysis and prediction results from the pharmacokinetic analysis module, and the core visualization materials from the graphical display module to automatically generate standardized busulfan precision dosing reports guided by pharmacokinetic (PK). It supports printing, exporting, and storing, enabling standardized output and clinical traceability of medication decision results.

[0012] Query module: Supports multi-condition combination retrieval of medication decision-related data, enabling rapid retrieval of historical medical records, detailed viewing, and data traceability, meeting the needs of clinical medication management and scientific research data organization.

[0013] Furthermore, in the patient information submodule of the information entry module, after manually entering the patient's name, medical record number, gender, date of birth, height, weight, admission date, department, and basic admission diagnosis information, the built-in height-weight correlation algorithm calculates the age and corrected standard weight. The formula for the height-weight correlation algorithm is as follows:

[0014] IBW represents ideal body weight. If actual body weight is greater than ideal body weight... In the formula: HT is height, BW is actual weight, and AIBW is the adjusted ideal weight, i.e., the corrected standard weight; if actual weight ≤ ideal weight, The calculation results, based on the height-weight correlation algorithm formula, will be synchronously transmitted to the pharmacokinetic analysis module for individual pharmacokinetic parameter calculation.

[0015] Furthermore, after manually entering the dosing time, dosing interval, dosage, drug volume, extension tube volume, and dosing rate in the drug administration information submodule of the information input module, the following calculation formula is used:

[0016] , , ,

[0017] It supports setting the starting dose and number of days for dose adjustment, and pushes the calculation results according to the extended tube infusion time formula, drug infusion start time formula, drug infusion time formula, and drug infusion end time formula to the pharmacokinetic analysis module in real time, providing basic dosing parameters for calculating blood drug concentration and elimination rate constant.

[0018] Furthermore, after manually entering the estimated blood collection time, infusion start time, and busulfan concentration in the blood collection information submodule of the information entry module, the following linear trapezoidal formula is used to calculate... , extrapolation , , In the formula, Let K be the area under the drug-time curve from time 0 to time t, and K be the elimination rate constant. and Represents two different time points within the drug elimination phase. and The measured drug concentration, Let be the area under the drug-time curve from time t to infinity. It represents the drug concentration at time t. The area under the drug-time curve from time 0 to infinity. To achieve the steady-state drug concentration, and to estimate the peak time based on the end time of drug infusion, the peak concentration of busulfan was derived. , In the formula, INTERCEPT represents the phase elimination. The intercept of the linear regression line. For all blood sampling points i in the elimination phase participating in the regression, the blood drug concentration Ci is summed after taking the natural logarithm, and slope is the elimination phase. The slope of the linear regression, under first-order elimination kinetics, is slope = -K. For all blood collection points i involved in the regression elimination phase, the corresponding blood collection time t is... i Summation, where n is the total number of blood collection points that eliminated the phase involved in the linear regression. Where K is the peak concentration of the drug and K is the elimination rate constant; the blood collection information submodule has built-in data validity verification rules, which require that there be no fewer than 3 sampling points where the sampling time is longer than the drug infusion time, to ensure that the blood collection data transmitted to the pharmacokinetic analysis module meets the calculation accuracy requirements.

[0019] Furthermore, the parameter calculation submodule of the pharmacokinetic analysis module adopts the busulfan one-compartment model multi-dose formula to calculate the blood drug concentration using the following formula: This is the formula for the infusion period; This is the formula after the infusion is completed, where: The drug concentration at the nth administration. Concentration during infusion. Here, k is the concentration after infusion, k0 is the infusion rate, K is the elimination rate constant, V is the apparent volume of distribution, T is the duration of a single infusion, n is the number of doses administered, τ is the dosing interval, and t is the time elapsed since the start of the nth infusion, i.e., the time elapsed since the current infusion began at time t. The time elapsed after the nth infusion, i.e., the time started from the end of the infusion; automated derivation of clearance rate CL and steady-state blood drug concentration. Key pharmacokinetic parameters, calculated using the following formula: Where DOSE is the dosage. The area under the drug-time curve from time point 0 to infinity; in the concentration prediction submodule, the first dose uses real patient test data from the information entry module, and based on individual pharmacokinetic parameters, simulates the blood drug concentration-time curves for the remaining 15 doses (i.e., the second to sixteenth doses) using the multi-dose formula of the busulfan one-compartment model; the dose recommendation submodule supports... Dual-guided dose adjustment, with a treatment regimen of busulfan administered four times a day, incorporating the FDA-recommended dose of 900-1350 mg / dL. The AUC range is 900-1500 The EMA recommended range, while also supporting custom targets. or target Value, calculated using a formula That is, the adjusted AUC:

[0020] ,

[0021] Generate an individualized recommended dose, where n is the current number of doses administered. The adjusted AUC is... For the current AUC, For the set target AUC, DOSE represents the current dosage, and all calculation and prediction results are simultaneously pushed to the graphical display module.

[0022] Furthermore, the blood drug concentration-time curve of the graphical display module supports the overlay display of measured data points from the information entry module and the initial dose simulation curve and recommended dose simulation curve generated by the pharmacokinetic analysis module. The visualized curve results can be directly called to the report generation module to realize the display of decision results combining text and graphics.

[0023] Furthermore, the PK-guided busulfan precision dosing report generated by the report generation module includes patient baseline data, dosing regimen details, and original blood sampling results provided by the information entry module; pharmacokinetic parameters, dosage adjustment basis, and individualized recommended dosage provided by the pharmacokinetic analysis module; a visualized drug-time curve provided by the graphical display module; and information on the reporter, reviewer, and report time. The report has data storage capabilities, supporting subsequent clinical traceability and research data archiving.

[0024] Furthermore, the query module supports querying by patient name, medical record number, administration time, and dual-lead dosage adjustment method. The system supports multi-condition combined search, real-time display of key information summaries of search results, and allows for detailed viewing and data tracing of historical records, meeting the dual needs of clinical drug management and scientific research data organization.

[0025] Specifically, the precision medication decision support system of this invention includes an information input module, a pharmacokinetic analysis module, a graphical display module, a report generation module, and a query module. These modules are interconnected and data flows seamlessly. Each module provides a unique data / result source for the next, and the next module professionally processes the output of the previous module, collaboratively completing the entire process of individualized busulfan medication decision-making. The specific technical solution is as follows:

[0026] The information entry module, serving as the core foundation for system data acquisition, employs a dual-mode approach of "manual entry + automatic calculation" to provide standardized, non-redundant, and accurate raw data for subsequent analysis. This module comprises three sub-modules: patient information, medication information, and blood collection information. The patient information sub-module collects basic information such as name, medical record number, gender, date of birth, height, and weight, automatically calculating age and correcting for standard weight to meet individual pharmacokinetic parameter calculation needs. The medication information sub-module inputs parameters such as dosing time, interval, dosage, and volume, automatically deriving extended infusion time and the start and end times of drug infusion, providing basic medication data for concentration calculation. The blood collection information sub-module records the estimated blood collection time, actual blood collection time, and busulfan concentration, with built-in data validity verification rules to ensure that sampling time meets calculation requirements, while automatically processing blood collection data formats to prepare data for pharmacokinetic analysis.

[0027] Pharmacokinetic Analysis Module: As the core decision-making unit of the system, it connects to the structured data in the information entry module. Based on the busulfan pharmacokinetic characteristic optimization algorithm, it achieves automated parameter calculation, concentration prediction, and dose recommendation. The module incorporates a busulfan one-compartment model and linear trapezoidal optimization logic, automatically calculating key pharmacokinetic parameters such as the area under the drug-time curve, clearance rate, and elimination rate constant. It simulates the blood drug concentration change trend after multiple doses using individual parameters, generating concentration curves at the initial dose and potential adjustment dose. It supports AUCss / Css dual-guided dose adjustment strategies, combining clinically recommended target concentration ranges to generate individualized recommended doses, ensuring that the adjusted blood drug concentration accurately falls within the safe and effective range, and providing core analytical results for the graphical display module.

[0028] The graphical display module, serving as the system's visual interaction unit, generates intuitive blood drug concentration-time curves based on the calculation and prediction results from the pharmacokinetic analysis module. The curves are overlaid with labeled measured data points, the initial dose simulation curve, and the recommended dose simulation curve, clearly presenting the differences in concentration changes before and after dose adjustment. This allows clinicians to quickly grasp the effects of dose adjustment, intuitively judge the rationality of their decisions, and provides core visual materials for the report generation module, lowering the threshold for clinical decision-making.

[0029] Report Generation Module: As the system's output unit, this module integrates raw data from the information entry module, core parameters and recommended regimens from the pharmacokinetic analysis module, and visualization curves from the graphical display module to automatically generate a standardized "PK-Guided Busulfan Precision Dosing Report." The report includes patient baseline information, dosing regimen details, raw blood sampling data, key pharmacokinetic parameters, dosage adjustment rationale, and visualization curves. It supports printing, exporting, and local storage, enabling standardized output and clinical traceability of medication decision-making results, and providing comprehensive data support for subsequent diagnosis and treatment.

[0030] The query module, serving as the system's data traceability unit, supports multi-condition retrieval of medication decision-related data. Search dimensions include patient name, medical record number, administration time, and dosage adjustment method. Search results display key information summaries in real time, supporting detailed viewing of historical records and data traceability. This meets the dual needs of clinical medication management, efficacy review, and research data organization, forming a complete closed loop of "data collection-analysis-decision-archiving-traceability."

[0031] The beneficial effects of this invention are as follows: Through the coordinated linkage of various modules, this system creatively solves the technical bottlenecks of traditional busulfan medication decision-making and clinical workflow, such as low analysis efficiency, unintuitive decision-making, and disordered data management. It significantly improves the efficiency of therapeutic drug monitoring and the accuracy of dosage recommendations, reduces the risk of adverse drug reactions and treatment failure caused by inappropriate dosage, and is suitable for individualized busulfan dosing decisions in hematology and transplantation departments, with outstanding clinical application value. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following is a brief description of the drawings used in the embodiments:

[0033] Figure 1 is a flowchart of the method of the present invention;

[0034] Figure 2 is a functional logic diagram of the data operation module of the present invention;

[0035] Figure 3 is a schematic diagram of the interface of the information entry module of the present invention (patient information + medication information);

[0036] Figure 4 is a schematic diagram of the blood collection information entry and AUC calculation logic of the present invention;

[0037] Figure 5 is a schematic diagram of the recommended dose calculation logic of the present invention;

[0038] Figure 6 is a schematic diagram showing the blood drug concentration-time curve of the present invention (including comparison before and after adjustment);

[0039] Figure 7 is a schematic diagram of the precise drug administration report form of the present invention;

[0040] Figure 8 is a schematic diagram of the data query and historical record management interface of the present invention. Detailed Implementation

[0041] The specific embodiments of the present invention will be described in further detail below to clearly present the linkage logic and operation flow of each module:

[0042] Example

[0043] The precision medication decision support system described in this invention includes an information input module, a pharmacokinetic analysis module, a graphical display module, a report generation module, and a query module. These modules are interconnected and data flows seamlessly. Each module provides a unique data / result source for the next, and the next module professionally processes the output of the previous module, collaboratively completing the entire process of individualized busulfan medication decision-making. The specific structure and functions are as follows:

[0044] I. Data Collection and Entry Stage (i.e., Information Entry Module)

[0045] After logging into the system, clinicians / pharmacists can access the information entry interface through "Data Operations - Add," see below. Figure 2 Complete full-dimensional data collection and automatic processing:

[0046] 1) Enter patient information through the patient information submodule, see... Figure 3 Manually enter the following information: Name "Wang Moumou", Medical Record Number "1075406", Gender "Male", Date of Birth "1994-03-21", Height "180cm", Weight "74kg", Admission Date "2021-03-23", Department "Hematology", Admission Diagnosis "Acute Myeloid Leukemia". Then, use the following height-weight correlation algorithm formula:

[0047] IBW is the ideal weight. If the actual weight is greater than the ideal weight, HT is the height. AIBW is the adjusted ideal weight, and BW is the actual weight; if the actual weight is less than or equal to the ideal weight, The system automatically calculates the age as "27 years old" and derives the ideal weight using the height-weight correlation algorithm formula. Then, based on the relationship between the actual weight and the ideal weight, it automatically generates a corrected standard weight, providing individualized basic data for subsequent pharmacokinetic parameter calculations.

[0048] 2) Enter "Drug Administration Information" through the drug administration information submodule, see... Figure 3 Enter the following parameters: infusion pump start time "2021-03-23 ​​10:08", dosing interval "6h", dosage "52mg", drug volume "48.67ml", extension tubing volume "7.5ml", and infusion rate "25ml / h". Use the following formula:

[0049] , , , The system will automatically calculate the extended tube infusion time, drug infusion start time, drug infusion start time, and drug infusion end time according to the formulas for extended tube infusion time, drug infusion start time, drug infusion start time, and drug infusion end time. It also supports setting the initial dose for dose adjustment to "3" and the number of days of administration to "4". All calculation results are synchronized to the pharmacokinetic analysis module in real time.

[0050] 3) Enter blood collection information through the blood collection information submodule, see... Figure 4 Enter the estimated blood collection time ("0 min", "120 min", "150 min", "180 min", "240 min", "360 min"), and the corresponding actual infusion start time ("2021-03-23 ​​10:26", "2021-03-23 ​​12:20", "2021-03-23 ​​12:46", "2021-03-23 ​​13:12", "2021-03-23 ​​14:17", "2021-03-23 ​​15:52") and the measured busulfan concentration ("0 ng / ml", "979 ng / ml", "892 ng / ml", "890 ng / ml", "687 ng / ml", "466 ng / ml"). The process will be completed using the following formula:

[0051] Automatic calculation using the linear trapezoidal method , extrapolation , , In the formula, Let K be the area under the drug-time curve from time 0 to time t, and K be the elimination rate constant. and Represents two different time points within the drug elimination phase ( and The measured drug concentration, Let Ct be the area under the drug-time curve from time t to infinity, and Ct be the drug concentration at time t. The area under the drug-time curve from time 0 to infinity. To achieve the steady-state drug concentration, and to estimate the peak time based on the end time of drug infusion, the peak concentration of busulfan was derived. , In the formula, INTERCEPT represents the phase elimination. The intercept of the linear regression line. For all blood sampling points i in the elimination phase participating in the regression, the blood drug concentration Ci is summed after taking the natural logarithm, and slope is the elimination phase. The slope of the linear regression, under first-order elimination kinetics, is slope = -K. For all blood collection points i involved in the regression elimination phase, the corresponding blood collection time is... Summation, where n is the total number of blood collection points that eliminated the phase involved in the linear regression. Let K be the peak drug concentration and K be the elimination rate constant. The actual blood sampling time (the difference between the time of blood collection and the time of drug infusion start) and the natural logarithm of the concentration are calculated using the above formula. The AUC is initially calculated using the linear trapezoidal rule, and a "Adjust" row of supplementary concentration data is generated. Simultaneously, the system has built-in data validity verification rules that automatically detect the number of sampling points (at least 3, to meet the calculation accuracy requirements) where the sampling time is longer than the drug infusion time, ensuring that the data meets the analysis standards.

[0052] II. Pharmacokinetic Analysis Stage (Pharmacokinetic Analysis Module)

[0053] After the information is entered through the information entry module, the structured data transmitted from the information entry module is sent to the pharmacokinetic analysis module for processing. (See...) Figure 5 This enables full automation of parameter calculation, concentration prediction, and dosage recommendation.

[0054] The parameter calculation submodule in the pharmacokinetic analysis module uses a multi-dose formula based on a one-compartment model of busulfan to calculate blood drug concentration: This is the formula for the infusion period; This is the formula after the infusion is completed, where: The drug concentration at the nth administration. Concentration during infusion. Here, k0 is the infusion rate, K is the elimination rate constant, V is the apparent volume of distribution, T is the duration of a single infusion, n is the number of doses, τ is the dosing interval, t is the time elapsed since the start of the nth infusion (i.e., time t is the current infusion up to time t), and t′ is the time elapsed since the end of the nth infusion (i.e., the time started from the end of the infusion). The clearance rate CL and steady-state plasma concentration are automatically derived. Key pharmacokinetic parameters, calculated using the following formula: Where DOSE is the dosage. The area under the drug-time curve from time 0 to infinity;

[0055] In the concentration prediction submodule of the pharmacokinetic analysis module, the first dose uses the patient's real test data from the information entry module. Based on individual pharmacokinetic parameters, the blood drug concentration-time curves for the remaining 15 doses (i.e., the second to sixteenth doses) are simulated using the multi-dose formula of the busulfan one-compartment model.

[0056] The dosage recommendation submodule in the pharmacokinetic analysis module supports... Dual-guided dose adjustment, with a treatment regimen of busulfan administered four times a day, incorporating the FDA-recommended dose of 900-1350 mg / dL. The AUC range is 900-1500 The EMA recommended range, while also supporting custom targets. or target Value, calculated using a formula That is, the adjusted AUC:

[0057] ,

[0058] Generate an individualized recommended dose, where n is the current number of doses administered. The adjusted AUC is... For the current AUC, For the set target AUC, DOSE represents the current dosage, and all calculation and prediction results are simultaneously pushed to the graphical display module.

[0059] The pharmacokinetic analysis module specifically includes:

[0060] "Parameter Calculation": Based on the busulfan one-compartment model and first-order elimination characteristics, the system automatically calculates key pharmacokinetic parameters such as clearance rate, elimination rate constant, and steady-state blood drug concentration using the entered blood sampling data (selecting the terminal elimination phase data after the Adjust row), providing a core basis for dose adjustment.

[0061] "Concentration Prediction": The first dose uses real patient test data and is based on the calculated individual pharmacokinetic parameters to simulate the blood drug concentration change curves of the second to sixteenth doses at the initial dose (52mg), clearly showing the concentration fluctuation trend over time.

[0062] "Recommended Dosage": Physician Selection "Guided dosage adjustment method, setting targets" 1169 The system combines current... Based on clearance rate data, an individualized recommended dose is automatically generated and adjusted. The medication falls within the clinically recommended treatment range, ensuring its safety and effectiveness. Simultaneously, the system supports switching between "..." "Adjustment of guidance model to achieve..." A dual-guided dose adjustment strategy to adapt to different clinical decision-making needs.

[0063] III. Graphical Display Stage (Graphical Display Module)

[0064] After the pharmacokinetic analysis is completed, the system automatically generates a blood drug concentration-time curve, see below. Figure 6 The system employs an overlay display mode: labeled actual data points (including supplementary data in the Adjust row), initial dose simulation curve, and recommended dose simulation curve. Different curves are distinguished by different colors, intuitively presenting the concentration difference before and after dose adjustment. Physicians can quickly determine the rationality of dose adjustment through the curves, clarifying whether the concentration falls within the target range, thus lowering the threshold for clinical decision-making.

[0065] IV. Report Generation Stage (Report Generation Module)

[0066] Click "View Report" to see Figure 7 The system automatically integrates data from various modules (structured data from the information entry module, analysis and prediction results from the pharmacokinetic analysis module, and core visualization materials from the graphical display module) to generate a standardized, pharmacokinetic-guided precision dosing report for busulfan. This report includes patient baseline information, dosing regimen details, raw blood sampling data, key pharmacokinetic parameters, basis for dose adjustments, a visualized pharmacokinetic curve, and information such as the reporter, reviewer, and report date. The report can be printed, exported as a PDF, or stored locally, enabling standardized output and clinical traceability of medication decisions.

[0067] V. Data Query and Management Phase (Query Module)

[0068] In subsequent clinical work, physicians can use the "Data Operation - Query" function to see Figure 8 Enter patient name, medical record number, administration time, dosage adjustment method, and other search criteria to quickly retrieve historical records. Search results display key information summaries in real time, allowing users to click to view details (including complete data and curves), modify abnormal data such as blood sample concentrations (the system automatically recalculates parameters and recommended dosages after modification), and delete invalid records, ensuring data consistency and traceability and meeting the needs of clinical medication management and research data organization.

[0069] This embodiment verifies the system's practicality and accuracy through a complete operation process: the entire process from data acquisition to report generation takes only a few minutes, significantly improving efficiency compared to traditional manual calculations; after the recommended dosage is adjusted, the patient... Precisely targeting the target range significantly reduces the risk of adverse reactions and treatment failure caused by inappropriate dosage; visualized curves and standardized reports make clinical decision-making more intuitive and standardized, solving the technical bottlenecks of the traditional treatment drug monitoring process being scattered and data management being disordered, fully demonstrating the outstanding application value of this invention in the individualized dosing decision of busulfan in hematology.

[0070] Key technological innovations

[0071] The pharmacokinetic algorithm for busulfan: The algorithm optimizes the parameter calculation logic for the one-compartment model and first-order elimination rate characteristics of busulfan, and combines the linear trapezoidal method to accurately calculate AUC, thus solving the problems of insufficient adaptability and large calculation error of general algorithms.

[0072] Visualized decision support function: The overlay display of drug-time curves and parameter color markings intuitively present the effect of dosage adjustment, reducing the difficulty of decision-making for clinicians;

[0073] Dual-guided dose adjustment strategy: Support Two adjustment methods are available to adapt to different clinical scenarios and improve system applicability;

[0074] Intelligent data validity verification: Built-in rules for verifying the number of sampling points and concentration anomalies ensure the accuracy of data collection and provide a reliable basis for dosage recommendations. Specific Implementation

[0075] To further verify the practical effects of this invention, a specific implementation and effect evaluation were conducted using the "clinical application of busulfan" in a clinical laboratory of a medical center as an example:

[0076] A male patient, born on January 30, 1958, with a height of 174.8 cm and a weight of 68.4 kg, was receiving busulfan treatment, administered every 24 hours. The first dose was given on March 20, 2019, at 06:21, with an initial dose of 240 mg. Due to the patient's previous night's use of metronidazole, which reduced the clearance of busulfan, the AUC after the first dose was higher than expected. Therefore, the clinicians needed to dynamically adjust the dosage for the second, third, and fourth doses based on blood drug concentration measurements at multiple time points. Three pharmacokinetic studies were subsequently performed.

[0077] For this patient, the system of this invention was used to make individualized medication decisions. The specific operation and results are as follows:

[0078] Patient's name, date of birth, height, weight, and other baseline information were manually entered. The system automatically calculated and verified the AIBW to be 68.4 kg, with no data redundancy or error. Core dosing information (initial dose 240 mg, dosing interval 24 h, first dose administration time 2019-03-20 06:21) was entered. Clinical blood sampling data was also entered, specifically the busulfan concentrations at 0 min, 176 min, 191 min, 240 min, 300 min, 360 min, and 480 min after infusion start: 0 ng / ml, 4956 ng / ml, 4533 ng / ml, 3819 ng / ml, 3044 ng / ml, 2521 ng / ml, and 1680 ng / ml, respectively. Using a one-compartment busulfan pharmacokinetic model and a linear trapezoidal algorithm, the system automatically calculated lnC. After calculating the basic data, we get =1336819.50 The unit is automatically converted to 5427.61 Automatic extrapolation =480000 The units are automatically converted to =7376.45 CL=132.10ml / min, k=0.0035 This provides precise parameters for dose adjustment.

[0079] Based on system A dual-guided dose adjustment strategy was employed, adjusting the dose on the second dose, administering the medication over four days, and setting a four-dose average steady state. The target value is 4800 (Total target AUC for 4 doses = 19200) The system automatically calculated the recommended dose for doses 2-4 using a built-in individualized dosage recommendation formula, resulting in a dose of 128.23 mg. Considering the practicalities of clinical use, the dose was adjusted to 128 mg. After the patient received the second dose of 128 mg, the measured AUC was 3920. This indicates that the drug effect of metronidazole on busulfan has not yet disappeared; after the third dose of 128 mg, the measured AUC was 3184. This indicates that the drug effects of metronidazole are beginning to subside. This is based on a 4-dose average steady-state setting. The target value is 4800 (Total target AUC for 4 doses = 19200) ), combined with the first dose 7372, the second dose 3920, and the third dose 3184 The test results were entered into the system sequentially. The system then re-invoked the algorithm to calculate the pharmacokinetic parameters and recommended a fourth dose of 190 mg (assuming the patient's clearance rate the following day was the same as on the first day). In this case, graphical display and blood drug concentration simulation were not implemented; only the core individualized dose adjustment process was completed.

[0080] At the same time, we also used traditional manual calculation methods for comparison. Two senior clinical pharmacists performed calculations using the linear trapezoidal method, manually organizing patient baseline, medication and blood collection data to complete a series of pharmacokinetic parameter calculations and report production. Due to the large amount of data, the manual calculation process required multiple checks to avoid calculation errors. This not only took a long time overall, but was also very prone to calculation errors due to human factors.

[0081] This embodiment is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any technical solution obtained by conventional modification or equivalent substitution based on the technical solution of the present invention should fall within the scope of protection of the present invention.

Claims

1. A precision medicine decision support system, characterized by: It includes modules for information entry, pharmacokinetic analysis, graphical display, report generation, and query. These modules are interconnected and data flows seamlessly. Each module provides a unique data / result source for the next, and the next module professionally processes the output of the previous module, collaboratively completing the entire process of individualized Busulfan medication decision-making. The specific structure and functions are as follows: Information entry module: includes patient information sub-module, drug administration information sub-module, and blood collection information sub-module. It adopts a dual mode of "manual entry and automatic calculation" to complete the collection, automatic calculation and validity verification of full-dimensional data, and provide standardized and non-redundant structured data for the pharmacokinetic analysis module. Pharmacokinetic Analysis Module: This module receives structured data from the information input module and uses a one-compartment pharmacokinetic model of busulfan and a linear trapezoidal algorithm for optimization. It includes a parameter calculation submodule, a concentration prediction submodule, and a dose recommendation submodule. It supports a dual-guided dose adjustment strategy based on either the area under the concentration-time curve (AUCss) or the concentration at steady state (Css). It generates individualized recommended doses through formulas, providing accurate analysis and prediction results for the graphical display module. Graphical display module: Based on the analysis and prediction results of the pharmacokinetic analysis module, it generates blood drug concentration-time curves with labeled measured data points and simulated curves of initial and recommended doses, intuitively presenting the effect of dose adjustment and providing core visualization materials for the report generation module; The report generation module integrates the structured data from the information entry module, the analysis and prediction results from the pharmacokinetic analysis module, and the core visualization materials from the graphical display module to automatically generate standardized busulfan precision dosing reports guided by pharmacokinetic (PK). It supports printing, exporting, and storing, enabling standardized output and clinical traceability of medication decision results. Query module: Supports multi-condition combination retrieval of medication decision-related data, enabling rapid retrieval of historical medical records, detailed viewing, and data traceability, meeting the needs of clinical medication management and scientific research data organization.

2. The precision medication decision support system according to claim 1, characterized in that: In the patient information submodule of the information entry module, after manually entering the patient's name, medical record number, gender, date of birth, height, weight, date of admission, department, and basic admission diagnosis, the built-in height-weight correlation algorithm calculates the age and corrected standard weight. The formula for the height-weight correlation algorithm is as follows: IBW represents ideal body weight. If actual body weight is greater than ideal body weight... In the formula: HT is height, BW is actual weight, and AIBW is the adjusted ideal weight, i.e., the corrected standard weight; if actual weight ≤ ideal weight, The calculation results, based on the height-weight correlation algorithm formula, will be synchronously transmitted to the pharmacokinetic analysis module for individual pharmacokinetic parameter calculation.

3. The precision medication decision support system according to claim 1, characterized in that: After manually entering the dosing information submodule of the information entry module, the dosing time, dosing interval, dosing dose, drug volume, extension tube volume, and dosing rate, the following calculation formula is used: , , , It supports setting the starting dose and number of days for dose adjustment, and pushes the calculation results according to the extended tube infusion time formula, drug infusion start time formula, drug infusion time formula, and drug infusion end time formula to the pharmacokinetic analysis module in real time, providing basic dosing parameters for calculating blood drug concentration and elimination rate constant.

4. The precision medication decision support system according to claim 1, characterized in that: After manually entering the estimated blood collection time, infusion start time, and busulfan concentration in the blood collection information submodule of the information entry module, the AUC is calculated using the following linear trapezoidal rule formula. 0-t , extrapolation , , In the formula, AUC 0-t Let be the area under the drug-time curve from time point 0 to time t, K be the elimination rate constant, and C1 and C2 represent the drug concentrations measured at two different time points t1 and t2 within the drug elimination phase. Let Ct be the area under the drug-time curve from time t to infinity, and Ct be the drug concentration at time t. Let Css be the area under the drug-time curve from time 0 to infinity, and Css be the drug concentration at steady state. The peak time is estimated based on the end time of drug infusion, i.e., the drug infusion time, and the peak concentration of busulfan is derived. , In the formula, INTERCEPT represents the phase elimination. The intercept of the linear regression line. For all blood sampling points i in the elimination phase participating in the regression, the blood drug concentration Ci is summed after taking the natural logarithm, and slope is the elimination phase. The slope of the linear regression, under first-order elimination kinetics, is slope = -K. For all blood collection points i involved in the regression elimination phase, the corresponding blood collection time t is... i The summation is performed, where n is the total number of blood sampling points participating in the elimination phase in the linear regression, Cmax is the peak drug concentration, and K is the elimination rate constant. The blood sampling information submodule has built-in data validity verification rules, which require that there be no fewer than 3 sampling points with a sampling time longer than the drug infusion time, to ensure that the blood sampling data transmitted to the pharmacokinetic analysis module meets the calculation accuracy requirements.

5. The precision medication decision support system according to claim 1, characterized in that: The parameter calculation submodule of the pharmacokinetic analysis module uses the busulfan one-compartment model multi-dose formula to calculate blood drug concentration using the following formula: This is the formula for the infusion period; This is the formula after the infusion is completed, where: Cn represents the drug concentration at the nth administration, and Cn represents the concentration during the infusion period. Here, k is the concentration after infusion, k0 is the infusion rate, K is the elimination rate constant, V is the apparent volume of distribution, T is the duration of a single infusion, n is the number of doses administered, τ is the dosing interval, and t is the time elapsed since the start of the nth infusion, i.e., the time elapsed since the current infusion began at time t. This is the time elapsed after the nth infusion, that is, the time counted from the moment the infusion ended; The clearance rate (CL) and steady-state plasma concentration (Css) are key pharmacokinetic parameters derived automatically using the following formula: Where DOSE is the dosage. The area under the drug-time curve from time point 0 to infinity; in the concentration prediction submodule, the first dose uses real patient test data from the information entry module, and based on individual pharmacokinetic parameters, simulates the blood drug concentration-time curves for the remaining 15 doses (i.e., the second to sixteenth doses) using the multi-dose formula of the busulfan one-compartment model; the dose recommendation submodule supports... Dual-targeted dose adjustment, with built-in FDA-recommended AUC range of 900-1350 μmol·min / L and EMA-recommended range of 900-1500 μmol·min / L for each dose in a treatment regimen of busulfan administered four times a day, while also supporting custom target doses. Or the target CSS value, calculated using a formula. That is, the adjusted AUC: Generate an individualized recommended dose, where n is the current number of doses administered. The adjusted AUC is... For the current AUC, For the set target AUC, DOSE represents the current dosage, and all calculation and prediction results are simultaneously pushed to the graphical display module.

6. The precision medication decision support system according to claim 1, characterized in that: The graphical display module's blood drug concentration-time curve supports the overlay display of measured data points from the information entry module with the initial dose simulation curve and recommended dose simulation curve generated by the pharmacokinetic analysis module. The visualized curve results can be directly called to the report generation module, realizing a combined text and graphic display of decision results.

7. The precision medication decision support system according to claim 1, characterized in that: The PK-guided precision dosing report generated by the report generation module includes patient baseline data, dosing regimen details, and original blood sampling results provided by the information entry module; pharmacokinetic parameters, dosage adjustment basis, and individualized recommended dosage provided by the pharmacokinetic analysis module; a visualized drug-time curve provided by the graphical display module; and information on the reporter, reviewer, and report time. The report has data storage capabilities, supporting subsequent clinical traceability and research data archiving.

8. The precision medication decision support system according to claim 1, characterized in that: The query module supports multiple conditions for searching, including patient name, medical record number, administration time, and dual-lead dose adjustment method (AUCss / Css). The search results display key information summaries in real time and support detailed viewing and data tracing of retrieved historical records, meeting the dual needs of clinical medication management and scientific research data organization.

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