32 results about "Pharmacokinetic modeling" patented technology

To provide a method and device for predicting plasma concentration transition in combination of multiple drugs, by easily determining Ki, fm in vivo, and making them into a database, since, there has been dynamic prediction using a physiologic medicine speed theory (PBPK) model, as a prediction method of a drug interaction for medical products, however, prediction using an inhibition constant (Ki) of in vitro data and a contribution ratio (fm) of a metabolic enzyme, do not always match increase of clinical AUC.SOLUTION: There is provided a method for determining a parameter of a compartment model from a pharmacokinetic parameter of an interacted drug and interaction drug, then converting the compartment parameter into a PBPK model parameter, then determining by simulation using the PBPK model, a Ki value and fm being AUC increase ratios observed clinically. The acquired parameter is made into a database, and simulation of blood concentration transition of the interacted drug and interaction drug is performed.SELECTED DRAWING: Figure 1

The invention discloses a method for adjusting administration dosage according to adverse reaction risk based on a joint model, and is applied to the field of intelligent medical treatment. In the prior art, the relationship between the sirolimus steady-state valley concentration and the specific adverse reaction cannot be quantified, so that the defect of lack of accurate guidance for clinical medication is caused. Through systematic research, the method determines the individual target valley concentration range through a BKMR model for the first time, obtains individual pharmacokinetic parameters of a patient through a group pharmacokinetic model, and finally determines the medication time and the medication dosage of the patient through Bayesian negative feedback simulation, and the medication time and the medication dosage are used for adjusting a medication scheme. According to the invention, prospective risk prediction of adverse reactions of sirolimus is realized, the treatment safety of children with vascular diseases is remarkably improved, and long-term prognosis of patients is improved.

According to the remazolam target control infusion analysis method and system, a three-chamber model is adopted to construct an individualized pharmacokinetic model, and plasma concentration response of unit dose of remazolam is characterized through the individualized pharmacokinetic model; constructing an infusion pump control model, wherein the infusion pump control model characterizes the plasma drug concentration through a time function; defining a state variable for the infusion pump control model, and calculating the plasma drug concentration state variable at the moment t + delta t according to the plasma drug concentration state variable at the current moment t and the drug infusion amount within the interval from the current moment t to the moment t + delta t; and the infusion pump control model determines the medicine infusion rate required for the plasma medicine concentration to reach the target concentration CT according to the plasma medicine concentration state variable at the current moment t and the plasma medicine concentration state variable at the moment t + delta t. Individual medication can be achieved, cardiovascular adverse events of a patient are reduced, and a comfortable and safe infusion scheme is provided for the patient.

This invention discloses a dosage analysis method for anesthetic drugs during surgery in patients with hypertension, hyperlipidemia, and hyperglycemia. This method integrates multi-source heterogeneous health data from the patient over the past five years to construct a personalized, structured health record. During anesthesia induction, based on the patient's individualized pharmacokinetic model and assessed drug interaction risks, it calculates and recommends precisely adjusted induction doses. During anesthesia maintenance, it innovatively decodes specific EEG signals to establish a dynamic mapping relationship between neurophysiological characteristics and pharmacokinetics. Based on EEG characteristics reflecting drug sensitivity and pain perception, it automatically and in real-time adjusts the predicted concentration of the target drug, achieving closed-loop precise control based on direct feedback from the central nervous system. The system automatically identifies all medications used by the patient, generates a drug interaction risk assessment report using a massive knowledge base, and dynamically updates the risk level and sends warnings during surgery based on real-time vital signs.

The invention provides an oxytocin injection precise administration method based on physiological pharmacokinetics (PBPK) model optimization, and relates to the technical field of individualized drug administration. According to the method, firstly, individual physiological parameter information of a target receptor is collected, an individual PBPK model is constructed, simulation operation is carried out in combination with drug characteristics of oxytocin, and in-vivo drug distribution under different drug administration schemes is predicted. By evaluating the drug concentration, the drug administration duration and the uterine contraction reaction predicted value of each scheme in the uterine tissue and system circulation, the optimal drug administration parameter combination meeting the drug efficacy and safety requirements at the same time is screened from the drug concentration, the drug administration duration and the uterine contraction reaction predicted value. An injection control command is generated based on the combination, and precise infusion is performed through an injector system with a program control function. According to the method, individualized precise regulation and control based on model driving are achieved, and the safety and clinical effect of oxytocin medication are effectively improved.

The invention discloses a cardiovascular drug dose regulation and control system based on reinforcement learning, and relates to the technical field of medical artificial intelligence, and the system comprises a data acquisition module which collects physiological data, metabolic parameters, a drug interaction matrix and a current drug combination of a patient in real time; the adjustment factor calculation module calculates a drug metabolism rate adjustment factor through a pharmacokinetic model; the interaction modulation module adjusts the interaction intensity according to the adjustment factor; the risk score calculation module calculates the adverse reaction risk of the patient based on the adjusted interaction matrix, the drug combination and the physiological data; the first processing module judges whether the risk score exceeds the standard or not, and if yes, the risk is reduced through medicine replacement or dosage reduction; if the risk score is lower than a second threshold value, the second processing module maintains the current dose, otherwise, a dose adjustment strategy is optimized based on reinforcement learning; according to the method, the medicine dosage adjustment can be more fit with the actual metabolic characteristics of the patient, and the accuracy and individualization degree of treatment are improved.

The invention discloses a medical aid decision-making method and system fusing multiple modes and a deep network, and relates to the technical field of intelligent medical treatment.The method comprises the steps that first data of a user is collected and analyzed, and a physiological stage is judged; and calling a sub-library corresponding to the dynamic feature library, and matching and screening out a first physiological feature with high correlation. Second data are collected, the first physiological features are fused to construct individualized feature vectors, and a feature incidence matrix is generated. Third data are collected in real time, the incidence matrix is combined, the medicine in-vivo process is simulated through a physiological pharmacokinetic model, and an individualized treatment scheme is generated. According to the invention, through the full-process design of dynamic stage determination, multi-mode association mining, real-time dose optimization and continuous evolution iteration, core pain points in early-maturing diagnosis and treatment of children and adolescents are accurately broken, and transformation from an empirical general scheme to a data-driven individualized decision is realized; safer, more accurate and more timely medical support is provided for premature individuals.

The application discloses a drug interaction prediction and dose reference information generation method, system and device, and the method comprises the following steps: acquiring parameters of a Bruton's tyrosine kinase inhibitor as a target drug and an antiretroviral drug as an interaction drug; constructing and verifying a basic physiological pharmacokinetic model of each drug; integrating inhibition or induction parameters of the interaction drug on corresponding enzymes, constructing and verifying an interaction prediction model; running the verified model to simulate and calculate predicted exposure data of the target drug when the drugs are used in combination; and generating dose reference information meeting preset bioequivalence conditions by iteratively adjusting dose parameters of the target drug and re-simulating. The application realizes automatic, quantitative prediction and personalized dose scheme generation of specific drug combination interactions, and can provide key decision support for clinical combination drug use.

Provided are a method and system for determining a population pharmacokinetic model of propofol and derivatives thereof. The method comprises determining a formula as a population pharmacokinetic model of a compound of formula (I) or propofol: wherein the formula of the pharmacokinetic parameters in the population pharmacokinetic model of the compound of formula (I) comprises: and the formula of the pharmacokinetic parameters in the population pharmacokinetic model of propofol comprises: formula (I).

The invention discloses an insulin pump control method and system based on blood glucose data, and relates to the technical field of insulin infusion control, and the method comprises the steps: obtaining real-time blood glucose sequence data and historical infusion record data, and carrying out the preprocessing; constructing an insulin pharmacokinetic attenuation model to calculate an in-transit insulin residual activity value; inputting the standardized blood glucose time series data into a Bayesian neural network prediction model to generate prediction uncertainty distribution parameters; calculating a prediction confidence index according to the prediction variance and comparing the prediction confidence index with a threshold value to obtain a confidence judgment result; selecting a dose optimization strategy or a conservative control strategy according to a confidence coefficient judgment result to calculate an insulin infusion dose; infusion is executed, and historical records are updated; the prediction uncertainty is quantified through the Bayesian neural network, the in-transit insulin activity is accurately modeled in combination with the pharmacokinetic model, and the control strategy is dynamically switched according to the confidence, so that the control error amplification caused by prediction misjudgment is effectively avoided, and the delayed hypoglycemia caused by the insulin superposition effect is prevented.

Provided herein are systems and methods for optimizing a biological therapy regimen for a subject. The subject may be a patient diagnosed with an immune mediated inflammatory disease. In some embodiments, the systems and methods may involve inputting patient data into a model to forecast a drug concentration level in a patient and establish a dosing regimen for maintaining a pre-specified threshold drug concentration level in the patient. The pre-specified threshold may be a target concentration level for effective treatment of the immune mediated inflammatory disease in the patient.

The invention relates to the technical field of medical control, and discloses an anesthesia assisting method for promoting postoperative rapid recovery, which comprises the following steps: constructing a basic pharmacokinetic model for a selected anesthetic; intraoperative multi-modal data of a patient are collected in real time; calculating a temperature-metabolism correction coefficient based on the thermodynamic data, and calculating a volume-dilution correction coefficient based on the hemodynamic data; correcting the basic plasma clearance rate in real time by using the temperature-metabolism correction coefficient, and correcting the basic central ventricle distribution volume in real time by using the volume-dilution correction coefficient; the corrected plasma clearance rate and the corrected central ventricle distribution volume are substituted into the basic pharmacokinetic model, and the medicine infusion rate needed for maintaining the preset target concentration is calculated. According to the method, the parameters of the pharmacokinetic model are dynamically corrected according to the real-time thermal dynamics and hemodynamics data, medicine accumulation is effectively avoided, and the postoperative awakening time is shortened.

A computer implemented method for determining a dosage of Cagrisema for administering to a patient for the treatment of obesity, the method comprising: receiving patient data relating to a patient, wherein the patient data includes patient weight data; processing the patient data including the patient weight data with a dosage calculator to determine the dosage of Cagrisema for administering to the patient wherein the dosage calculator is derived from a pharmacokinetic, PK, model; and indicating the dosage.

This invention relates to the field of vancomycin pharmacokinetic technology, specifically to the design of a vancomycin clearance prediction scheme based on a VPB ensemble model. This method, based on an ensemble learning strategy, constructs a blending ensemble model to predict vancomycin clearance in adult Chinese patients. First, a variational autoencoder is used to amplify the original sample data to increase data diversity. Then, a particle swarm optimization algorithm is introduced to optimize the parameters of multiple base learners, and the prediction results of the optimized base learners are used as new feature inputs. Finally, a support vector regression machine is used as a meta-learner to integrate and model the above features, forming the final vancomycin clearance prediction model. The VPB model constructed in this invention achieves a determination coefficient R² exceeding 0.9 on both the test and training sets, demonstrating superior prediction accuracy compared to population pharmacokinetic models.

Provided are a method and system for determining a population pharmacokinetic model of propofol and a derivative thereof. The method comprises determining a population pharmacokinetic model of a compound of formula (I) or propofol, wherein an equation of pharmacokinetic parameters in the population pharmacokinetic model of the compound of formula (I) comprises: CLi=exp(4.20÷0.349·log(WT / 63.9)−0.749·log(TP / 72.4)+0.238·SITE+ηCL,i); an equation of pharmacokinetic parameters in the population pharmacokinetic model of propofol comprises: CLi=exp(4.56÷ηCL,i).

This invention relates to the field of medical information technology, specifically disclosing an intelligent follow-up and re-examination reminder system for outpatients throughout their entire treatment cycle. Targeting the management of chronic brain diseases, this invention integrates personalized pharmacokinetic modeling and a cross-validation mechanism of subjective and objective data. By continuously collecting and analyzing patients' micro-movement and vital sign data, combined with an individualized drug concentration theoretical model, it achieves accurate assessment of changes in the patient's condition. Simultaneously, it utilizes subjective and objective cross-validation to ensure data reliability and adopts an event-driven model, transforming traditional periodic follow-up into real-time response management based on key pathological events. The system also features an intelligent re-examination scheduling function, which can automatically adjust the re-examination plan and generate emergency re-examination reminders based on changes in the patient's condition and drug efficacy assessment results, forming a fully automated closed-loop management process. This invention improves the accuracy, timeliness, and patient satisfaction of chronic brain disease management, bringing a new breakthrough to the field of medical information technology.

The invention relates to the technical field of biological medicine, discloses a method and a system for determining the dose of an exogenous blood coagulation factor, and aims to solve the problems of large individual dose prediction deviation and adjustment lag caused by dependence on a population pharmacokinetic mean value and lack of dynamic feedback and multi-source data fusion capability in the prior art. The method comprises the following steps: collecting physiological parameters and gene information of a patient, and inputting an individualized pharmacokinetic model to generate a metabolic curve; in combination with a clinical treatment target, an initial dose is calculated through a multi-constraint numerical solution algorithm. According to the application, by integrating individual physiological and genetic characteristics, dynamic clinical feedback and an international guide knowledge graph, a closed-loop optimization system with self-learning ability is constructed, accurate and intelligent recommendation of the blood coagulation factor dose is realized, and the treatment safety and individualized level are improved.

The application discloses a genetic polymorphism-based oxcarbazepine quantitative pharmacological model and a construction method thereof, and belongs to the field of pharmacokinetics. The model construction method is characterized by the following steps: collecting steady-state blood drug concentration data and clinical data of epilepsy children patients, adopting a nonlinear mixed effect model method to establish a population pharmacokinetic model of an active metabolite of oxcarbazepine, and quantitatively investigating the influence of physiological factors, combined medication and genetic factors on pharmacokinetic characteristic parameters of the drug; and finally, the final model is internally verified and evaluated by using a goodness-of-fit diagram, a bootstrap method and an intuitive prediction test. The quantitative pharmacological model constructed by the application has stable and accurate prediction performance; the body weight and the genetic polymorphism ABCC2rs2273697 are determined as effective covariates influencing MHD CL / F; and an oxcarbazepine dose optimization scheme is established based on the body weight and the genotype of the patients, so that the treatment effect of the oxcarbazepine is improved, and the occurrence of adverse reactions is avoided.

The present application belongs to the technical field of pharmaceutical analysis, and particularly relates to a saccharide drug in-vivo pharmacokinetic model, a construction method thereof and application. The present application uses ion chromatography, high performance liquid chromatography and other detection technologies to realize in-vivo pharmacokinetic application analysis of saccharide drugs, quantitatively detects the content change of monosaccharide in tissue samples after administration, analyzes the monosaccharide change of serum and corresponding disease tissue, takes the monosaccharide with the maximum content change as an index to perform pharmacokinetic analysis, and constructs a corresponding pharmacokinetic model. The determination method used for constructing the model has good specificity, precision and high accuracy, a wide linear range and high sensitivity. The pharmacokinetic model constructed by the present application is suitable for non-preclinical pharmacokinetic research of saccharide drugs, provides theoretical and technical support for saccharide drug development, and solves the bottleneck problem of saccharide drug research.

The invention relates to the technical field of dosimetry, in particular to a method for constructing a pharmacokinetic model of a targeted alpha radionuclide drug and a device for predicting the administration dosage of radionuclide.The method comprises the steps that nuclear medicine imaging results of alternative radionuclides are obtained for multiple subjects at multiple time points, and on the basis of the nuclear medicine imaging results, the targeted alpha radionuclide drug is obtained; constructing a parent nuclide pharmacokinetic model for the parent nuclide including a plurality of compartments and transfer coefficients between the compartments, constructing a daughter pharmacokinetic model for the plurality of decay daughters including daughter transfer coefficients between the plurality of compartments and the compartments of the given daughter nuclide, and coupling the parent nuclide pharmacokinetic model with the daughter pharmacokinetic model to obtain a complete pharmacokinetic model of the targeted alpha radionuclide drug. Therefore, the problems that nuclear medicine imaging of the targeted alpha radionuclide medicine is difficult, daughters are distributed in an off-target mode after parent nuclides decay, and the parent nuclides and daughters are different in pharmacokinetic model are solved, and the complete pharmacokinetic model of the targeted alpha radionuclide medicine is constructed. And accurate evaluation of the dosage caused by targeted alpha radionuclide drug treatment is realized.

This invention provides a precise dosing method for oxytocin injection based on a physiological pharmacokinetic (PBPK) model, belonging to the field of personalized drug delivery technology. The method first collects individual physiological parameter information of the target receptor, constructs a personalized PBPK model, and performs simulation calculations based on the drug characteristics of oxytocin to predict the in vivo drug distribution under different dosing regimens. By evaluating the drug concentration, dosing duration, and predicted uterine contraction response of each regimen in uterine tissue and systemic circulation, the optimal combination of dosing parameters that simultaneously meets the requirements of efficacy and safety is selected. Based on this combination, injection control instructions are generated, and precise infusion is implemented through a syringe system with programmable control functionality. This method achieves model-driven personalized precision control, effectively improving the safety and clinical efficacy of oxytocin administration.

The invention discloses a precise medication mode construction method and system based on pharmacokinetics and artificial intelligence, and the method comprises the steps: carrying out the multi-stage preprocessing of an obtained drug concentration monitoring data file, and obtaining a structured data set for pharmacokinetics analysis; a drug metabolism concentration prediction model is constructed, a neural network is utilized to learn from the structured data set, pharmacokinetic parameters are generated, prediction concentration is calculated through the pharmacokinetic model, and the drug metabolism concentration prediction model is trained based on the structured data set; drawing a drug concentration metabolism curve of an individual patient based on the constructed drug metabolism concentration prediction model, realizing individual drug administration decision, and realizing drug concentration monitoring; and visually deploying all the algorithms and data to a front-end interface. According to the invention, individualized precise medication can be realized, the safety and effectiveness of drug treatment can be improved, clinical availability and reliability are realized, and the clinical working efficiency and decision scientificity can be improved.

The invention relates to a PET data simulation method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring an input function of radionuclide and a parameter diagram corresponding to a pharmacokinetic model, and determining radiation information corresponding to each voxel in a simulation time period according to the input function and the parameter diagram corresponding to the pharmacokinetic model, so as to determine the radiation information corresponding to each voxel in a preset time period according to the radiation information and attenuation information corresponding to each voxel in the preset time period. And simulating each photon corresponding to the radionuclide in the to-be-simulated object to obtain PET raw data in the preset time period. Wherein the input function is used for representing the change condition of the concentration of the radionuclide in the plasma along with time, and the parameter diagram is used for representing the condition of metabolizing the radionuclide by each voxel. By adopting the method, the simulation accuracy can be improved.

The present application relates to a kind of laparoscopic postoperative shoulder pain syndrome prevention and control and comfort degree promotion method and device, wherein the method comprises: obtaining the multi-modal medical data of patient including individual physiological characteristics, intraoperative real-time monitoring parameter and pain sensitivity related gene group composition;Multi-modal medical data is input into pre-trained timing-multiscale pain risk model, and the probability of moderate shoulder pain within 24 hours after operation and risk classification are output;The probability of shoulder pain and risk classification, patient real-time feedback and situational information are input into pre-trained individualized analgesia and comfort degree model, and the optimal individualized analgesia and comfort degree intervention scheme combination is output, for medical staff to carry out personalized intervention according to patient.The present application predicts pain risk according to timing-multiscale model and optimizes drug dose by pharmacokinetic model, significantly improves the postoperative experience and rehabilitation quality of patient in the prevention and control and comfort degree promotion of laparoscopic postoperative shoulder pain.

The invention provides a formula determination method of a long-acting injection, electronic equipment and a storage medium, and relates to the technical field of pharmaceutical preparations. The method comprises the following steps: constructing a physiological pharmacokinetic model of a reference pharmaceutical preparation, and constructing a pharmacodynamic model of the reference pharmaceutical preparation; reversely deducing an in-vitro target release curve of the reference pharmaceutical preparation according to the physiological pharmacokinetic model and the pharmacodynamic model; according to a property prediction model obtained through pre-training, obtaining an in-vitro predicted release curve of the at least one alternative preparation formula; and screening out a target preparation formula from at least one alternative preparation formula according to the in-vitro predicted release curve and the in-vitro target release curve of each alternative preparation formula. According to the application, the physiological pharmacokinetic model, the pharmacodynamic model and the property prediction model are integrated, the design and screening of a new preparation formula are guided, an intelligent and closed-loop long-acting injection development process is realized, the development period is greatly shortened, the success rate is improved, and the problem of long development period of the long-acting injection is solved.

The present disclosure relates to the establishment and use of physiology-based pharmacokinetic models for predicting the absorption / distribution of nasal administered compounds of interest.

The invention relates to the technical field of anesthetic medicine, in particular to a patient vital sign linkage early warning system in the anesthesia process, which comprises an injection passage sign sensing unit, a pharmacokinetic-sign correlation analysis unit, an injection safety dynamic early warning unit and an infusion equipment linkage control unit, wherein the injection channel sign sensing unit is used for collecting mechanical, electrical and hemodynamic signals in the puncture and injection process in real time through a multi-mode physiological sensing assembly integrated in an injection device in the biomedical engineering industry and converting the mechanical, electrical and hemodynamic signals into digital physiological parameters capable of being used for risk identification. According to the method, the individual pharmacokinetic model is constructed in combination with the individual physiological parameters of the patient, multi-source data are fused, and the machine learning and transfer learning technology is utilized, so that risk characteristic modes such as drug extravasation, tissue accumulation and toxic reaction are accurately detected, and the safety and intelligent level of anesthesia are effectively improved.

A computer-implemented method determines an optimal drug dosing regimen to treat a patient against a drug-sensitive pathogenic agent responsible for infection. The method obtains patient data and drug data, including the drug, administration route, adaptation type (priori or posteriori), and a drug treatment regimen. The method includes determining values for pharmacokinetic parameters for efficacy (PPE) and pharmacokinetic parameters for toxicity (PPT) from the patient and drug data using population pharmacokinetic models when PPE and PPT are not obtained directly from measurement concentration(s). The method includes obtaining infection data, including parameters selected from the documentation of infection, localization, infection type, identified pathogen, susceptibility, and minimum inhibitory concentration. The method includes determining an efficacy target for the drug from the infection data and a target at risk of toxicity. The method includes determining an optimal dosing regimen by comparing PPE with the efficacy target and PPT with the risk of toxicity target.

The application relates to a dynamic PET-CT pharmacokinetics fusion modeling method combining mechanism and data, and belongs to the technical field of pharmacokinetic modeling, wherein the method comprises the following steps: acquiring original image data of historical cases, and constructing a training data set according to the original image data; constructing a dynamic pharmacokinetic model based on a mechanism model and a data-driven network, and training the dynamic pharmacokinetic model according to the training data set; collecting actual image data of a current subject for a period of time; inputting the actual image data into the trained dynamic pharmacokinetic model to obtain a prediction result. The application fuses a mechanism model and a data-driven network, can accurately predict a complete pharmacokinetic process only by using image data for a short period of time in the early stage, solves problems that a traditional model depends on long-time scanning and is easily disturbed by motion artifacts, greatly improves parameter analysis efficiency and accuracy, and is suitable for clinical rapid diagnosis.