15 results about "Pharmacokinetic modeling" patented technology

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 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 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.

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.

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

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.

A computer system, computer program product and method for determining a probability of attaining a PK-PD target associated with efficacy for a patient that includes a processor obtaining information identifying an infection and based on the information, generating and displaying, by the processor, a list comprising one or more pathogens consistent with the information, the processor then obtaining a first indication designating at least one pathogen from the list comprising one or more pathogens and based on at the obtaining of the least one pathogen, generating a list comprising one or more drug therapies utilized to treat the at least one pathogen. The method also includes the processor obtaining, descriptive information relating to a patient and based on the one or more drug therapies, selecting a pharmacokinetic model and the processor applying the pharmacokinetic model and utilizing the information relating to the patient to determine, for each of the one or more drug therapies, a probability of attaining a PK-PD target associated with efficacy for the patient with the infection.