Reasonably-used liquid medicine metering method and metering device

Abstract

The invention provides a reasonable medication liquid medicine metering method and metering device.The metering method comprises the following steps that patient related data and target medicine information are obtained, dosage scheme calculation is conducted through a pharmacokinetics related model on the basis of the patient related data and the target medicine information, individualized dosage parameters are obtained, and the individualized dosage parameters are calculated according to the patient related data and the target medicine information. The dosage scheme is executed through a metering execution device, the actual dosage is detected in real time and compared with the individualized dosage parameter, feedback adjustment is conducted according to the comparison result, the safety threshold value of the target medicine is preset, and the blood concentration of the patient is monitored in real time; according to the method, individual pharmacokinetic differences are fully adapted, key factors, such as genes and diets, influencing drug metabolism are comprehensively taken into consideration, a dosage scheme better fits actual metabolism characteristics of patients, and dosage deviation caused by individual differences is avoided.

Description

Technical Field

[0001] This invention relates to the field of pharmaceutical liquid metering technology, and in particular to a rational method and metering device for measuring pharmaceutical liquid. Background Technology

[0002] With the development of medical technology, rational drug use has become a core element in improving treatment outcomes and ensuring patient safety, and precise control of drug dosage is a key prerequisite for achieving rational drug use. In clinical treatment, patients need to obtain individualized dosage plans based on their own physiological conditions, disease progression, and drug characteristics. Especially in special scenarios such as severe infections, tumor chemotherapy, and liver and kidney dysfunction, dosage deviations may directly lead to treatment failure, aggravated adverse reactions, or even endanger life.

[0003] Currently, commonly used drug dosing methods in clinical practice mainly include the fixed-dose method and the experience-based adjustment method. The fixed-dose method uses a standardized dosing regimen, which does not fully consider individual differences in patients such as age, weight, genetic polymorphism, and liver and kidney function. This leads to the risk of some patients experiencing insufficient dosage (e.g., in critically ill patients, the drug's efficacy is not as expected) or excessive dosage (e.g., in elderly patients, drug accumulation and toxicity). The experience-based adjustment method relies on the physician's clinical experience and subjective judgment, which is easily affected by factors such as diagnostic and treatment level and cognitive bias, making it difficult to guarantee the accuracy and consistency of dosage adjustments. Although some existing technologies attempt to introduce simple data models to assist in dosage calculation, significant limitations still exist: First, there is insufficient adaptation to individual differences in pharmacokinetics, and key factors affecting drug metabolism, such as genes and diet, are not fully integrated. Second, the interaction assessment in multi-drug combination scenarios is not comprehensive and it is difficult to quantify the cumulative toxicity effect; Third, the dose adjustment response is delayed, making it impossible to track changes in the patient's condition and fluctuations in blood drug concentration in real time; Fourth, the execution accuracy is limited, making it difficult to meet the micro-dose requirements of special populations such as newborns and those in intensive care.

[0004] These problems result in a high rate of non-compliance with treatment goals and an incidence of adverse reactions in clinical drug use, which not only affects treatment efficacy but also increases medical costs and safety risks. Summary of the Invention

[0005] In view of this, the present invention aims to provide a rational drug liquid metering method and metering device to solve or alleviate the technical problems existing in the prior art, and at least provide a beneficial option.

[0006] To solve the above-mentioned technical problems, one technical solution adopted in this application is: to provide a rational drug dosage method, comprising the following steps: Step S1: Obtain patient-related data and target drug information. The patient-related data includes physiological indicators, gene-related data, diet-related data, and organ function-related data. The target drug information includes drug type, dosage form, and route of administration. Step S2: Based on the patient-related data and target drug information, the dosage regimen is calculated using a pharmacokinetic model to obtain individualized dosage parameters; Step S3: Execute the dosage plan through the metering execution device, detect the actual dosage in real time and compare it with the individualized dosage parameters, and make feedback adjustments based on the comparison results; Step S4: Preset the safety threshold of the target drug, monitor the patient's blood drug concentration in real time, generate an assessment result by combining the changes in the blood drug concentration and the patient's relevant data, and provide early warning or dosage adjustment suggestions based on the assessment result.

[0007] As a further preferred embodiment of this technical solution, acquiring patient-related data includes: Step S11: Standardize the original data for each dimension using the following formula:

[0008] in, This represents the historical mean of the data in this dimension. This represents the historical standard deviation of the data in this dimension. Step S12: Calculate the comprehensive data using a weighted fusion algorithm. The formula is:

[0009] in, The initial weights are respectively ; Step S13: Adjust the weights using the bias feedback algorithm, the formula is as follows: ,in This is the weighting adjustment coefficient. Data standardized for a single dimension; Step S14: Use the 3σ criterion to remove outliers. When this happens, the data for that period is recollected and recalculated, where... The average of the combined data. To determine the standard deviation of the aggregated data, the data update cycle is set to three minutes.

[0010] As a further preferred embodiment of this technical solution, the calculation of the individualized dosage parameters includes: Step S21: Calculate the initial dose based on the population pharmacokinetic model, using the following formula:

[0011] in, For group correction coefficients, To achieve the target blood drug concentration, For drug distribution volume, For bioavailability, This refers to the dosing interval; Step S22: Calculate the individual patient correction coefficient using the following formula:

[0012] in For the patient's creatinine clearance rate, , , This is the physiological parameter adjustment coefficient. It is a gene correction factor (CYP450 enzyme subtype adaptation value, range 0.7-1.3). Step S23: Calculate the degree of disease fluctuation, using the following formula:

[0013] in For current physiological indicators, These are physiological indicators from the previous cycle; Step S24: Adjust the fine-tuning step size according to the volatility, using the following formula: ,in Let be an indicator function, where, Take 1 if >20%, otherwise take 0, for the final individualized dose. .

[0014] As a further preferred embodiment of this technical solution, the feedback adjustment based on the comparison results includes: Step S31: Calculate the deviation between the actual dose and the target dose, using the formula: ,in for Actual dosage at any given time; Step S32: Calculate the adjustment amount using the proportional-integral control algorithm, the formula is as follows: ,in This is the proportionality coefficient. The integral coefficient; Step S33: Set the adjustment threshold, the formula is as follows ,when When this happens, the proportional-integral adjustment algorithm is activated for adjustment; Step S34: Calculate the valve adjustment range using the following formula: ,in This is the initial valve opening. The valve adjustment coefficient is used to ensure that the adjustment response time does not exceed 200 milliseconds, and the final deviation is controlled within... .

[0015] As a further preferred embodiment of this technical solution, the provision of early warning or dosage adjustment suggestions based on the evaluation results includes: Step S41: Calculate the safety threshold, using the following formula: ,in This is the standard safety threshold for drugs. For the patient's creatinine clearance rate, Gene correction factor; Step S42: Predict blood drug concentration using the sliding window method, the formula is:

[0016] in This is the drug elimination rate constant. =30 minutes is the prediction time window; Step S43: Calculate the warning trigger factor, using the following formula:

[0017] when An alert is triggered when the value is ≥0.8; Step S44: Determine the warning level, using the following formula: Where L takes values ​​from 1 to 3, corresponding to three levels of early warning, and adjustment suggestions are pushed according to the level. The formula is as follows: The early warning push response time shall not exceed three minutes.

[0018] As a further preferred embodiment of this technical solution: when the target drug information includes combination information of multiple combined drugs, the dosage scheme calculation further includes: In response to the clear metabolic pathway interactions or toxicity additive risks of combination drug use, a special assessment has been initiated. The high-risk combinations include the combination of paclitaxel and cisplatin, paclitaxel and carboplatin, and vancomycin and aminoglycoside drugs. Based on a pre-defined drug interaction database, the cumulative toxicity effect at different dosing intervals is quantified, and the toxicity synergy coefficient is determined. In response to a toxicity synergy coefficient exceeding a preset threshold, the dosage or dosing interval of one or more drugs is adjusted to ensure that the incidence of toxicity from combined drug use is controlled within a preset safe range.

[0019] As a further preferred embodiment of this technical solution: when the patient is a special population, dosage adjustment also includes: For newborns with a weight ≥1.5kg and a gestational age ≥34 weeks, a specific dose correction factor is determined based on their weight and gestational age, and the dose prediction error is controlled within 10%. In response to patients with hepatic and renal insufficiency, the baseline adjustment ratio is determined according to the stage of chronic kidney disease and the Child-Pugh classification of liver function. For patients with end-stage renal disease (CKD stage 5), the initial dose shall not exceed 50% of the conventional dose, and for patients with Child-Pugh C, the initial dose shall not exceed 40% of the conventional dose. Based on the blood drug concentration monitoring results after patient medication, the dose correction coefficient is dynamically adjusted to ensure that the blood drug concentration is maintained within the target range.

[0020] As a further preferred embodiment of this technical solution, it also includes data storage and traceability steps: Record all information related to data collection, dosage calculation, protocol execution, feedback adjustment, and safety assessment, and retain it in an immutable storage method for at least two years after the patient stops taking the medication; Access is restricted to clinicians, pharmacists, and medical quality control personnel only; In response to the need for adverse reaction tracing, it links the patient's unique identifier and medication course number, and simultaneously provides the trajectory of key parameter changes during the dosage calculation process, supporting traceability queries by time and drug type.

[0021] As a further preferred embodiment of this technical solution, a dosage optimization step based on efficacy feedback is also included. In response to three consecutive courses of treatment, efficacy evaluation data of patients after medication is obtained, including at least two clinical core symptom scores and laboratory test results related to drug action targets; To determine whether the efficacy evaluation data has achieved the expected treatment effect, if the expected effect has not been achieved and the blood drug concentration is within 60% of the safe threshold, dose optimization is initiated. Individualized dosage parameters are iteratively optimized based on pharmacokinetic models, with each optimization not exceeding 15% of the original dosage. Blood drug concentration and adverse reactions are continuously monitored during the iteration process. If the interval between two dose optimizations is less than one dosing course, the optimization operation is paused and restarted once the interval requirement is met.

[0022] To solve the above-mentioned technical problems, another technical solution adopted in this application is: a rational drug liquid metering method and metering device.

[0023] To solve the above-mentioned technical problems, another technical solution adopted in this application is: a computer device, the computer device including a processor and a memory coupled to the processor, the memory storing program instructions, when the program instructions are executed by the processor, causing the processor to perform the steps of the rational drug dosage method and dosage device as described above.

[0024] To solve the above-mentioned technical problems, another technical solution adopted in this application is: a storage medium storing program instructions capable of implementing a rational drug liquid metering method and metering device as described above.

[0025] The embodiments of the present invention have the following advantages due to the adoption of the above technical solutions: This invention fully adapts to individual pharmacokinetic differences, taking into account key factors affecting drug metabolism such as genes and diet, so that the dosage regimen is more in line with the actual metabolic characteristics of patients and avoids dosage deviations caused by individual differences. This invention improves the interaction assessment in multi-drug combination scenarios, effectively defines the risks of different drug combinations, accurately controls the cumulative toxicity effect, and enhances the safety of combined drug use. This invention can respond promptly to changes in the patient's condition and fluctuations in blood drug concentration, enabling rapid adjustment of the dosage regimen and avoiding treatment risks or insufficient efficacy caused by delayed adjustments. This invention improves the accuracy of dosage execution, and can reliably meet the stringent requirements of special populations such as newborns and those in intensive care for micro-doses, ensuring that the medication dosage for these high-risk groups is accurate and controllable.

[0026] The above overview is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the invention will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram comparing key indicators of the technical solution used in this invention with those of traditional methods, provided for an embodiment of the invention. Detailed Implementation

[0029] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0030] It should be understood that the following specific examples illustrate the implementation of this disclosure, and those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. This disclosure can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0031] Figure 1 This is a flowchart illustrating a rational drug dosage method according to an embodiment of the present invention. It should be noted that if substantially the same result is obtained, the method of this application is not necessarily identical. Figure 1 The illustrated process sequence is limited. For example... Figure 1 As shown: A method for rational drug dosage, comprising the following steps: Step S1: Obtain patient-related data and target drug information. Patient-related data includes physiological indicators, gene-related data, diet-related data and organ function-related data. Target drug information includes drug type, dosage form and route of administration. Obtaining patient-related data includes: Step S11: Standardize the original data for each dimension using the following formula:

[0032] in, This represents the historical mean of the data in this dimension. This represents the historical standard deviation of the data in this dimension. It is understandable that, based on step S11, the differences in the dimensions and numerical spans of the original data of different dimensions are eliminated, making different types of data such as physiological indicators and genetic data comparable and compatible, and avoiding subsequent calculation deviations caused by inconsistent data scales. Step S12: Calculate the comprehensive data using a weighted fusion algorithm. The formula is:

[0033] in, The initial weights are respectively By pre-setting a scientific initial weight allocation, priority is given to highlighting core factors that have a more significant impact on drug metabolism, such as physiological indicators and genetic data, while also taking into account auxiliary factors such as diet and organ function, to ensure the relevance and rationality of data fusion. Step S13: Adjust the weights using the bias feedback algorithm, the formula is as follows: ,in This is the weighting adjustment coefficient. It provides standardized data for a single dimension; it can dynamically adjust the weight of each dimension. When the data for a single dimension deviates significantly from the overall data (such as a sudden abnormality in liver and kidney function indicators), it automatically increases the weight of that dimension, making the data fusion results more consistent with the patient's real-time physiological state and changes in condition. Step S14: Use the 3σ criterion to remove outliers. When this happens, the data for that period is recollected and recalculated, where... The average of the combined data. To optimize the standard deviation of the comprehensive data, the data update cycle is set to three minutes. This effectively eliminates outliers caused by equipment errors and accidental factors during data collection, preventing abnormal data from interfering with the accuracy of dosage calculation. The three-minute data update cycle also ensures the real-time nature of the data, enabling timely capture of dynamic changes in patients' physiological indicators, dietary status, and other factors.

[0034] In summary, through multi-step collaborative processing, the comprehensiveness and completeness of patient-related data are ensured, while the accuracy and timeliness of the data are improved. This effectively solves the problems of single dimension, fixed weight, and weak anti-interference ability in traditional data processing, providing reliable data support for subsequent pharmacokinetic model calculations and risk assessment of combined drug use, thereby ensuring the individualization and precision of drug dosage.

[0035] Specifically, when the target drug information includes combination information of multiple drugs, the dosage regimen calculation also includes: In response to the clear metabolic pathway interactions or toxicity accumulation risks associated with combination drug use, a special assessment has been initiated. High-risk combinations include the use of paclitaxel with cisplatin, paclitaxel with carboplatin, and vancomycin with aminoglycosides. Based on a pre-defined drug interaction database, the cumulative toxicity effect at different dosing intervals is quantified, and the toxicity synergy coefficient is determined. In response to a toxicity synergy coefficient exceeding a preset threshold, the dosage or dosing interval of one or more drugs is adjusted to ensure that the incidence of toxicity from combined drug use is controlled within a preset safe range.

[0036] First, by clearly defining high-risk combination drug pairings (paclitaxel and cisplatin, paclitaxel and carboplatin, vancomycin and aminoglycosides, etc.), "precise focusing" of risk assessment is achieved. This avoids the problems of underestimating high-risk combinations or overestimating low-risk combinations caused by "indiscriminate assessment" in traditional combination drug dosage, significantly improving the efficiency and specificity of risk assessment. Taking the combination of paclitaxel and cisplatin as an example, the two have a metabolic competition relationship through the CYP450 enzyme system, which is a typical high-risk combination with superimposed toxicities. The initiation of a special assessment can directly identify the core risk points of this type of combination.

[0037] Secondly, by quantifying the toxicity synergy coefficient based on a pre-set drug interaction database, this approach overcomes the limitation of traditional combination therapy, which can only qualitatively determine the existence of an interaction, and achieves a quantitative characterization of toxicity risk. The database pre-stores metabolic interference patterns and toxicity superposition data of different drug combinations at different dosing intervals. Through this database, the toxicity synergy coefficient of a specific combination can be accurately calculated. For example, the toxicity synergy coefficient of paclitaxel and cisplatin is 0.6 at a 24-hour dosing interval, while it drops to 0.3 at a 48-hour dosing interval, providing a precise numerical basis for subsequent adjustments.

[0038] Finally, the dosage or dosing interval was dynamically adjusted based on whether the toxicity synergy coefficient exceeded a preset threshold, ensuring that dosage adjustments were "scientifically controllable." When the toxicity synergy coefficient exceeded the threshold, the dosage of one of the drugs was reduced (e.g., cisplatin dosage from 75 mg / m²). 2 Reduced to 60 mg / m 2 Extending the dosing interval (e.g., extending the paclitaxel infusion time from 3 hours to 24 hours) can effectively reduce the cumulative toxicity effect. Practice has shown that this adjustment can reduce the incidence of neurotoxicity when paclitaxel is used in combination with cisplatin from 38% to below 25%, while maintaining tumor cell killing activity, thus resolving the core contradiction of "difficulty in balancing efficacy and toxicity" in traditional combination therapy.

[0039] Step S2: Based on patient-related data and target drug information, calculate the dosage regimen using a pharmacokinetic model to obtain individualized dosage parameters; Calculation of individualized dosing parameters includes: Step S21: Calculate the initial dose based on the population pharmacokinetic model, using the following formula:

[0040] in, For group correction coefficients, To achieve the target blood drug concentration, For drug distribution volume, For bioavailability, This refers to the dosing interval; Step S22: Calculate the individual patient correction coefficient using the following formula:

[0041] in For the patient's creatinine clearance rate, , , This is the physiological parameter adjustment coefficient. It is a gene correction factor (CYP450 enzyme subtype adaptation value, range 0.7-1.3). Step S23: Calculate the degree of disease fluctuation, using the following formula:

[0042] in For current physiological indicators, These are physiological indicators from the previous cycle; Step S24: Adjust the fine-tuning step size according to the volatility, using the following formula: ,in Let be an indicator function, where, Take 1 if >20%, otherwise take 0, for the final individualized dose. .

[0043] The above four-step coordinated dosage calculation scheme deeply binds the pharmacokinetic model with individual patient characteristics and dynamic changes in the condition, enabling the precise derivation of individualized dosage parameters. This completely breaks through the limitations of traditional group-based standardization and static assessment in dosage calculation. It ensures the scientific nature of the dosage (based on the pharmacokinetic model) while taking into account individual adaptability (integrating genes, liver and kidney function, etc.) and disease responsiveness (dynamically adjusting step size). It effectively solves the core problems of insufficient accuracy, poor individual adaptability, and delayed disease response in traditional dosage calculation, providing accurate and reliable parameter basis for subsequent dosage execution.

[0044] It is particularly important to note that dosage adjustments for patients from special populations also include: For newborns with a weight ≥1.5kg and a gestational age ≥34 weeks, a specific dose correction factor is determined based on their weight and gestational age, and the dose prediction error is controlled within 10%. In response to patients with hepatic and renal insufficiency, the baseline adjustment ratio is determined according to the stage of chronic kidney disease and the Child-Pugh classification of liver function. For patients with end-stage renal disease (CKD stage 5), the initial dose shall not exceed 50% of the conventional dose, and for patients with Child-Pugh C, the initial dose shall not exceed 40% of the conventional dose. Based on the blood drug concentration monitoring results after patient medication, the dose correction coefficient is dynamically adjusted to ensure that the blood drug concentration is maintained within the target range.

[0045] For special populations such as newborns and those with hepatic or renal insufficiency, the treatment plan employs a closed-loop design of "defining applicable conditions + quantitative grading adjustment + dynamic feedback optimization" to precisely mitigate medication risks. Newborns must meet the following criteria: weight ≥1.5kg and gestational age ≥34 weeks. A specific correction coefficient is determined based on weight and gestational age, with a dose prediction error ≤10%. For those with hepatic or renal insufficiency, adjustments are made according to chronic kidney disease (CKD) staging and Child-Pugh classification. For CKD stage 5, the initial dose is ≤50% of the conventional dose; for Child-Pugh C, ≤40%. Subsequent monitoring of blood drug concentrations after administration dynamically adjusts the correction coefficient to ensure that blood drug concentrations remain within the target range. This completely resolves the pain points of traditional, experience-based, and vague dose adjustments for special populations, achieving a balance between safety and precision in medication use.

[0046] Step S3: Execute the dosage plan through the metering execution device, detect the actual dosage in real time and compare it with the individualized dosage parameters, and make feedback adjustments based on the comparison results; Feedback adjustments based on the comparison results include: Step S31: Calculate the deviation between the actual dose and the target dose, using the formula: ,in for Actual dosage at any given time; Step S32: Calculate the adjustment amount using the proportional-integral control algorithm, the formula is as follows: ,in This is the proportionality coefficient. The integral coefficient; Step S33: Set the adjustment threshold, the formula is as follows ,when When this happens, the proportional-integral adjustment algorithm is activated for adjustment; Step S34: Calculate the valve adjustment range using the following formula: ,in This is the initial valve opening. The valve adjustment coefficient is used to ensure that the adjustment response time does not exceed 200 milliseconds, and the final deviation is controlled within... .

[0047] First, calculate the deviation between the actual dose and the target dose at time t. ,set up When the deviation exceeds the threshold, the proportional-integral control algorithm (Kp=0.8, Ki=0.05) is activated to calculate the adjustment amount. Then through ( =0.1) Adjust the valve opening. The entire adjustment response time does not exceed two hundred milliseconds, ultimately strictly controlling the deviation within... It solves the problem of insufficient accuracy of traditional metrology and the cumulative error of dosage caused by the lack of real-time feedback, and accurately adapts to the stringent requirements of newborns, critically ill patients and other patients for micro-doses, ensuring the accurate implementation of dosage plans.

[0048] Step S4: Preset the safety threshold of the target drug, monitor the patient's blood drug concentration in real time, generate an assessment result by combining changes in blood drug concentration and relevant patient data, and provide early warning or dosage adjustment suggestions based on the assessment result.

[0049] Based on the assessment results, early warnings or dosage adjustment recommendations include: Step S41: Calculate the safety threshold, using the following formula: ,in This is the standard safety threshold for drugs. For the patient's creatinine clearance rate, Gene correction factor; Step S42: Predict blood drug concentration using the sliding window method, the formula is:

[0050] in This is the drug elimination rate constant. =30 minutes is the prediction time window; Step S43: Calculate the warning trigger factor, using the following formula:

[0051] when An alert is triggered when the value is ≥0.8; Step S44: Determine the warning level, using the following formula: Where L takes values ​​from 1 to 3, corresponding to three levels of early warning, and adjustment suggestions are pushed according to the level. The formula is as follows: The early warning push response time shall not exceed three minutes.

[0052] First, the safety threshold C is dynamically calculated by combining the patient's creatinine clearance rate and gene correction factors, avoiding the rigidity and limitations of conventional fixed thresholds; then, the sliding window method is used to predict blood drug concentration 30 minutes in advance, accurately predicting concentration change trends; when the warning trigger factor... When the value is ≥0.8, a Level 3 early warning system (L=1-3) is activated, and the following steps are followed: The system pushes out dosage adjustment plans with an early warning response time of no more than three minutes. This system upgrades safety monitoring from passive feedback to proactive prediction, addressing the pain points of traditional monitoring such as lag, thresholds not matching individuals, and vague recommendations. It provides precise safety assurance for drugs with narrow therapeutic windows and medications for special populations.

[0053] Based on the above solutions, the following are also included: S5. Data storage and traceability steps: S51. Record all information related to data collection, dosage calculation, protocol execution, feedback adjustment, and safety assessment, and retain it in an immutable storage manner for a period of not less than two years after the patient stops taking the medication; S52. Restrict access permissions, authorizing only clinicians, pharmacists, and medical quality control personnel to access relevant data; S53. In response to the need for adverse reaction tracing, it associates the patient's unique identifier and medication course number, and simultaneously provides the trajectory of key parameter changes during the dosage calculation process, supporting traceability queries by time and drug type. It retains all data in an immutable manner (retention period ≥ two years after drug discontinuation), restricts authorized access permissions, and when responding to adverse reaction tracing needs, it associates the patient identifier and treatment course number, supporting the tracing of key parameter changes by time and drug type, solving the problems of incomplete data retention and difficulty in tracing traditional methods.

[0054] S6. Dosage optimization steps based on efficacy feedback: S61. In response to three consecutive courses of treatment, obtain efficacy evaluation data of patients after medication. The efficacy evaluation data includes at least two core clinical symptom scores and laboratory test results related to drug action targets. S62. Determine whether the efficacy evaluation data has achieved the expected treatment effect. If the expected effect has not been achieved and the blood drug concentration is in the middle 60% range of the safe threshold, initiate dose optimization. S63. Iteratively optimize individualized dosage parameters based on pharmacokinetic models, with each optimization not exceeding 15% of the original dosage, and continuously monitor blood drug concentration and adverse reactions during the iteration process; S64. If the interval between two dose optimizations is less than one treatment cycle, the optimization operation is paused and restarted once the interval requirement is met. After three consecutive treatment cycles, efficacy is evaluated based on at least two core clinical symptom scores and target-related examination results. Optimization is only initiated when the efficacy does not meet expectations and the blood drug concentration is within 60% of the safe threshold. The dose is iteratively adjusted in increments not exceeding 15%, with an interval of at least one treatment cycle between optimizations. Blood drug concentration and adverse reactions are continuously monitored to avoid safety risks caused by blind adjustments and to achieve a dynamic balance between efficacy and safety.

[0055] like Figure 2 As shown, the present invention also provides an embodiment of the method according to the present invention: An example is a lung cancer patient with Child-Pugh B liver dysfunction in the oncology department of a tertiary hospital (treated with paclitaxel + cisplatin combination chemotherapy): When the patient initially received conventional medication, inflammatory markers suddenly fluctuated during chemotherapy, and the dose adjustment response time was 180 minutes (corresponding to...). Figure 2The traditional Chinese method of "dose adjustment response time" ultimately resulted in a chemotherapy efficacy achievement rate of only about 70% (corresponding to...). Figure 2 Traditional Chinese medicine methods ("drug efficacy target rate") are ineffective, and adverse reactions such as neurotoxicity occur, with an adverse reaction rate of approximately 20% (corresponding to...). Figure 2 The traditional Chinese method is "adverse reaction rate".

[0056] After adopting this technical solution, patient-related data (physiological indicators, liver function classification, drug combination information) are standardized and fused. When inflammatory markers fluctuate, the dose adjustment response time is only 30 minutes (corresponding to...). Figure 2 The technical solution includes "dose adjustment response time"; individualized doses are calculated using pharmacokinetic models and the dosing interval is adjusted in conjunction with the toxicity synergy coefficient of combined drug use, ultimately increasing the chemotherapy efficacy target achievement rate to 90% (corresponding to...). Figure 2 The technical solution achieved a "drug efficacy target rate"; simultaneously, relying on real-time blood drug concentration monitoring and feedback adjustment, the patient did not experience significant adverse reactions, and the adverse reaction rate approached 0 (corresponding to...). Figure 2 (Adverse reaction rate in the technical solution).

[0057] This embodiment verifies that the method of the present invention is more efficient, accurate and safer than traditional methods in the context of combined drug use in special populations.

[0058] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A method for rationally measuring liquid medication, characterized in that, Includes the following steps: Step S1: Obtain patient-related data and target drug information. The patient-related data includes physiological indicators, gene-related data, diet-related data, and organ function-related data. The target drug information includes drug type, dosage form, and route of administration. Step S2: Based on the patient-related data and target drug information, the dosage regimen is calculated using a pharmacokinetic model to obtain individualized dosage parameters; Step S3: Execute the dosage plan through the metering execution device, detect the actual dosage in real time and compare it with the individualized dosage parameters, and make feedback adjustments based on the comparison results; Step S4: Preset the safety threshold of the target drug, monitor the patient's blood drug concentration in real time, generate an assessment result by combining the changes in the blood drug concentration and the patient's relevant data, and provide early warning or dosage adjustment suggestions based on the assessment result.

2. The method for rational drug dosage according to claim 1, characterized in that: The acquisition of patient-related data includes: Step S11: Standardize the original data for each dimension using the following formula: ； in, This represents the historical mean of the data in this dimension. This represents the historical standard deviation of the data in this dimension; Step S12: Calculate the comprehensive data using a weighted fusion algorithm. The formula is: ； in, The initial weights are respectively ; Step S13: Adjust the weights using the bias feedback algorithm, the formula is as follows: ,in This is the weighting adjustment coefficient. Data standardized for a single dimension; Step S14: Use the 3σ criterion to remove outliers. When this happens, the data for that period is recollected and recalculated, where... The average of the combined data. The data update cycle is set to three minutes to represent the standard deviation of the aggregated data.

3. The method for rational drug dosage according to claim 1, characterized in that: The calculation of the individualized dosage parameters includes: Step S21: Calculate the initial dose based on the population pharmacokinetic model, using the following formula: ； in, For group correction coefficients, To achieve the target blood drug concentration, This is the drug distribution volume. For bioavailability, This refers to the dosing interval; Step S22: Calculate the individual patient correction coefficient using the following formula: ； in For the patient's creatinine clearance rate, , , This is the physiological parameter adjustment coefficient. Gene correction factor; Step S23: Calculate the degree of disease fluctuation, using the following formula: ； in For current physiological indicators, These are physiological indicators from the previous cycle; Step S24: Adjust the fine-tuning step size according to the volatility, using the following formula: ,in Let be an indicator function, where, Take 1 if >20%, otherwise take 0, for the final individualized dose. .

4. The method for rational drug dosage according to claim 1, characterized in that: The feedback adjustment based on the comparison results includes: Step S31: Calculate the deviation between the actual dose and the target dose, using the formula: ,in for Actual dosage at any given time; Step S32: Calculate the adjustment amount using the proportional-integral control algorithm, the formula is as follows: ,in This is the proportionality coefficient. The integral coefficient; Step S33: Set the adjustment threshold, the formula is as follows ,when When this happens, the proportional-integral adjustment algorithm is activated for adjustment; Step S34: Calculate the valve adjustment range using the following formula: ,in This is the initial valve opening. The valve adjustment coefficient is used to ensure that the adjustment response time does not exceed 200 milliseconds, and the final deviation is controlled within... .

5. The method for rational drug dosage according to claim 1, characterized in that: The provision of early warnings or dosage adjustment recommendations based on the assessment results includes: Step S41: Calculate the safety threshold, using the following formula: ,in This is the standard safety threshold for drugs. For the patient's creatinine clearance rate, Gene correction factor; Step S42: Predict blood drug concentration using the sliding window method, the formula is: ； in This is the drug elimination rate constant. =30 minutes is the prediction time window; Step S43: Calculate the warning trigger factor, using the following formula: ； when An alert is triggered when the value is ≥0.8; Step S44: Determine the warning level, using the following formula: Where L takes values ​​from 1 to 3, corresponding to three levels of early warning, and adjustment suggestions are pushed according to the level. The formula is as follows: The early warning push response time shall not exceed three minutes.

6. The method for rational drug dosage according to claim 1, characterized in that: When the target drug information includes combination information of multiple combination drugs, the dosage regimen calculation also includes: In response to the clear metabolic pathway interactions or toxicity additive risks of combination drug use, a special assessment has been initiated. The high-risk combinations include the combination of paclitaxel and cisplatin, paclitaxel and carboplatin, and vancomycin and aminoglycoside drugs. Based on a pre-defined drug interaction database, the cumulative toxicity effect at different dosing intervals is quantified, and the toxicity synergy coefficient is determined. In response to a toxicity synergy coefficient exceeding a preset threshold, the dosage or dosing interval of one or more drugs is adjusted to ensure that the incidence of toxicity from combined drug use is controlled within a preset safe range.

7. The method for rational drug dosage according to claim 1, characterized in that: When the patient is from a special population, dosage adjustments also include: For newborns with a weight ≥1.5kg and a gestational age ≥34 weeks, a specific dose correction factor is determined based on their weight and gestational age, and the dose prediction error is controlled within 10%. In response to patients with hepatic and renal insufficiency, the baseline adjustment ratio is determined according to the stage of chronic kidney disease and the Child-Pugh classification of liver function. For patients with end-stage renal disease (CKD stage 5), the initial dose shall not exceed 50% of the conventional dose, and for patients with Child-Pugh C, the initial dose shall not exceed 40% of the conventional dose. Based on the blood drug concentration monitoring results after patient medication, the dose correction coefficient is dynamically adjusted to ensure that the blood drug concentration is maintained within the target range.

8. The method for rational drug dosage according to claim 1, characterized in that: It also includes data storage and traceability steps: Record all information related to data collection, dosage calculation, protocol execution, feedback adjustment, and safety assessment, and retain it in an immutable storage manner for at least two years after the patient stops taking the medication; Access is restricted to clinicians, pharmacists, and medical quality control personnel only; In response to the need for adverse reaction tracing, it links the patient's unique identifier and medication course number, and simultaneously provides the trajectory of key parameter changes during the dosage calculation process, supporting traceability queries by time and drug type.

9. The method for rational drug dosage according to claim 1, characterized in that: It also includes a dosage optimization step based on efficacy feedback: In response to three consecutive courses of treatment, efficacy evaluation data of patients after medication is obtained, including at least two clinical core symptom scores and laboratory test results related to drug action targets; To determine whether the efficacy evaluation data has achieved the expected treatment effect, if the expected effect has not been achieved and the blood drug concentration is within 60% of the safe threshold, dose optimization is initiated. Individualized dosage parameters are iteratively optimized based on pharmacokinetic models, with each optimization not exceeding 15% of the original dosage. Blood drug concentration and adverse reactions are continuously monitored during the iteration process. If the interval between two dose optimizations is less than one dosing course, the optimization operation is paused and restarted once the interval requirement is met.

10. A device for metering liquid medicine for rational drug use, characterized in that, include: Memory is used to store computer-readable instructions in a non-transitory manner. as well as A processor for executing the computer-readable instructions, wherein the computer-readable instructions, when executed by the processor, perform the liquid drug metering method according to any one of claims 1-9.

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