Drug targeted delivery device and method

Abstract

The invention discloses a drug targeted delivery device and method, and belongs to the technical field of intelligent medical treatment. The device comprises the following modules: an intelligent targeted recognition module for accurately recognizing a gastric cancer specific molecular marker and improving the selectivity and specificity of targeted recognition in combination with a dynamic surface modification strategy; the microenvironment real-time monitoring module is used for monitoring key physiological parameters of the gastric cancer microenvironment in real time; the intelligent path planning module is used for dynamically calculating and optimizing a drug delivery path and realizing accurate drug positioning; the personalized drug administration regulation and control module is used for accurately predicting the drug dosage demand of an individual and dynamically and finely adjusting the drug administration amount in real time; the drug resistance risk management module is used for early warning a drug resistance risk in advance and automatically generating a personalized alternative drug administration strategy through multi-dimensional drug resistance analysis and real-time early warning; and the intelligent drug release module adopts a temperature-sensitive / pH-sensitive polymer shell layer and a nano valve to control drug release, so that precise gradient release of drug concentration is realized.

Description

Technical Field

[0001] This application relates to the field of smart healthcare technology, and more specifically, to a drug targeted delivery device and method. Background Technology

[0002] Gastric cancer is one of the deadliest malignant tumors worldwide, and its early diagnosis and precision treatment have always been a focus of medical research. While current diagnostic and treatment methods for gastric cancer have improved, some shortcomings remain. For example, traditional imaging examinations and biomarker detection methods have low sensitivity and specificity in the early diagnosis of gastric cancer, leading to many patients being diagnosed at an advanced stage. On the other hand, conventional chemotherapy and radiotherapy, due to their lack of targeted therapy, often cause significant damage to surrounding normal tissues, resulting in severe adverse reactions for patients.

[0003] In recent years, with the rapid development of molecular biology and nanotechnology, researchers have developed a series of novel molecular markers and nanocarriers for the precision diagnosis and treatment of gastric cancer. The core of these technologies lies in recognizing gastric cancer-specific molecular markers and combining them with intelligent targeted delivery systems to precisely deliver therapeutic drugs to the tumor site, maximizing treatment efficacy while minimizing damage to normal tissues.

[0004] However, despite the enormous theoretical potential of intelligent targeted identification technology, it still faces many challenges in practical application. First, the significant differences in tumor characteristics among patients make personalized targeted therapy a difficult problem. Second, the complexity of the gastric cancer microenvironment makes real-time monitoring and dynamic adjustment of treatment strategies particularly important. Furthermore, drug resistance significantly impacts treatment outcomes, necessitating a system capable of providing real-time warnings of drug resistance risks and developing alternative strategies.

[0005] In summary, how to utilize intelligent targeted recognition technology to accurately identify molecular markers of gastric cancer, monitor in real time, and dynamically adjust treatment strategies to address personalized treatment and drug resistance issues has become an urgent technical problem to be solved. Summary of the Invention

[0006] In order to overcome a series of defects in the existing technology, the purpose of this application is to provide a drug targeted delivery device, which includes the following modules:

[0007] The intelligent targeted recognition module accurately identifies gastric cancer-specific molecular markers and combines dynamic surface modification strategies to improve the selectivity and specificity of targeted recognition.

[0008] The microenvironment real-time monitoring module monitors key physiological parameters of the gastric cancer microenvironment in real time and enables real-time data transmission and remote monitoring.

[0009] The intelligent path planning module dynamically calculates and optimizes the drug delivery path to achieve precise drug positioning, maximize therapeutic efficacy, and minimize damage to surrounding normal tissues.

[0010] The personalized drug administration control module accurately predicts an individual's drug dosage requirements and dynamically adjusts the dosage in real time.

[0011] The drug resistance risk management module provides early warnings of drug resistance risks and automatically generates personalized alternative drug administration strategies through multi-dimensional drug resistance analysis and real-time early warning.

[0012] The intelligent drug release module uses a temperature-sensitive / pH-sensitive polymer shell and nanovalve to control drug release, achieving precise gradient release of drug concentration.

[0013] Preferably, the intelligent target recognition module includes the following components:

[0014] The molecular recognition unit is responsible for real-time detection and analysis of the characterization features and expression profiles of gastric cancer-specific molecular markers;

[0015] The dynamic modification unit is responsible for intelligently regulating the peptide conformation on the surface of the nanocarrier, enabling precise docking and dynamic optimization with different molecular markers;

[0016] The structural adaptation unit automatically calculates and evaluates the three-dimensional spatial conformational matching degree between the nanocarrier and the target molecule;

[0017] The signal feedback unit continuously collects real-time data on intermolecular interactions and continuously optimizes the performance parameters of the recognition algorithm through iterative learning.

[0018] Anomaly monitoring unit identifies and screens non-specific binding events in real time.

[0019] Preferably, the real-time microenvironment monitoring module includes the following components:

[0020] A multi-parameter sensing unit integrates a multimodal micro physiological sensor array to monitor key physiological parameters in the gastric cancer microenvironment in real time.

[0021] The signal conditioning unit performs real-time digital filtering, signal amplification, and normalization on the acquired raw sensor signals;

[0022] The wireless transmission unit adopts a low-power, high-reliability wireless communication protocol to achieve real-time, secure, and low-latency transmission of microenvironment monitoring data;

[0023] The data parsing unit uses intelligent algorithms to perform real-time parsing and noise reduction of transmitted data, and extracts characteristic indicators of dynamic changes in the microenvironment through multi-dimensional correlation analysis.

[0024] The remote visualization unit transforms complex microenvironment monitoring data into an intuitive and easy-to-understand graphical interface.

[0025] Preferably, the intelligent path planning module includes the following components:

[0026] The path evaluation unit constructs a dynamic path evaluation model through reinforcement learning algorithms to analyze and weigh the optimal path selection for drug delivery in real time.

[0027] The obstacle recognition unit performs real-time identification and feature extraction of anatomical structures, physiological barriers, and high-risk areas in human tissues;

[0028] The dynamic optimization unit, based on a closed-loop feedback mechanism, continuously optimizes path parameters and delivery strategies to achieve real-time control of the drug delivery process.

[0029] The carrier navigation unit, combined with multi-source sensor data, enables the nanocarrier to achieve autonomous navigation and precise positioning in complex physiological environments;

[0030] The dose mapping unit establishes a precise mapping system between drug concentration and therapeutic efficacy, ensuring optimal drug distribution and absorption in the target area;

[0031] Physiological constraint units establish a multi-dimensional constraint mechanism that includes tissue elasticity, hemodynamics, and physiological barriers, providing comprehensive physiological boundary conditions for path planning;

[0032] The side effect prediction unit predicts and quantifies potential side effects and tissue damage during drug delivery.

[0033] Preferably, the personalized drug delivery control module includes the following components:

[0034] The gene analysis unit utilizes high-throughput gene sequencing technology to accurately map an individual's genome and identify key gene variations related to drug metabolism and sensitivity.

[0035] The dose prediction unit accurately calculates the optimal drug dosage based on gene characteristics, metabolic indicators, and tumor characteristics;

[0036] The dynamic control unit monitors the patient's physiological response and drug metabolism in real time, and dynamically fine-tunes the dosage to ensure maximum therapeutic effect and minimum adverse reaction.

[0037] The drug interaction unit analyzes an individual's medication history and potential drug interactions to predict and mitigate possible drug conflicts and side effects.

[0038] The metabolic monitoring unit integrates multidimensional physiological indicator sensors to track the absorption, distribution, metabolism, and excretion of drugs in the body in real time.

[0039] The personalized feedback unit establishes a database of individual patient drug responses and continuously optimizes personalized dosing strategies through continuous learning and iteration.

[0040] Preferably, the drug resistance risk management module includes the following components:

[0041] The drug resistance analysis unit uses deep learning algorithms to comprehensively analyze gene mutations, protein expression, and molecular markers in tumor cells to accurately identify potential drug resistance mechanisms and risks.

[0042] The dynamic monitoring unit monitors drug response and changes in tumor cells in real time, and promptly captures drug resistance signals and trends.

[0043] The strategy generation unit rapidly generates personalized alternative dosing strategies, including drug combinations, dosage adjustments, and treatment pathway optimization.

[0044] Molecular interaction units are used to deeply analyze the molecular interaction mechanisms between drugs and tumor cells, and to predict and simulate potential drug resistance development pathways and molecular-level changes.

[0045] The drug resistance database unit builds a continuously updated knowledge base of drug resistance information, integrates the latest clinical research and genomics data, and supports accurate drug resistance risk assessment.

[0046] The precision intervention unit intelligently recommends and formulates targeted early intervention measures based on real-time monitoring data to reduce the risk of drug resistance.

[0047] Preferably, the intelligent drug delivery module includes the following components:

[0048] The intelligent shell unit is designed with a smart polymer shell that has temperature-sensitive and pH-responsive characteristics, so as to realize intelligent opening and closing and drug release regulation according to changes in the microenvironment.

[0049] The nano-valve control unit adjusts the opening and closing of the drug release channel in real time according to environmental signals to achieve precise drug concentration gradient release;

[0050] A dose gradient unit is used to construct a drug concentration gradient release system based on a multilayer microfluidic structure to ensure the precise distribution of drugs at different spatiotemporal scales.

[0051] The release kinetics unit uses machine learning algorithms to predict and optimize the release rate and release pattern of drugs under different conditions;

[0052] The real-time feedback unit collects and analyzes real-time data during the drug release process, dynamically adjusts the release strategy, and improves the accuracy and effectiveness of the release.

[0053] The purpose of this application is also to provide a drug targeted delivery method, including the following steps:

[0054] Real-time monitoring of key physiological parameters in the gastric cancer microenvironment, and extraction of characteristic indicators of dynamic changes in the microenvironment through multidimensional correlation analysis;

[0055] By real-time detection and analysis of the expression characteristics of gastric cancer-specific molecular markers, the conformation of peptides on the surface of nanocarriers can be dynamically controlled, thereby achieving precise docking with different molecular markers.

[0056] To achieve autonomous navigation of nanocarriers in complex physiological environments, and to establish a multidimensional constraint mechanism including tissue elasticity, hemodynamics and physiological barriers, ensuring the safety and precision of drug delivery;

[0057] Predict and avoid potential drug interactions, and continuously learn and iteratively optimize personalized dosing strategies to ensure maximum drug efficacy;

[0058] By constructing a drug concentration gradient release system using a multilayer microfluidic structure, the precise distribution of drugs at different spatiotemporal scales can be ensured, thereby maximizing the therapeutic effect.

[0059] Compared with the prior art, this application has the following beneficial effects:

[0060] This application can optimize drug delivery pathways, personalize drug dosage, provide early warning of drug resistance risks, and ensure optimal drug distribution and absorption in the target area through intelligent control of drug release, thereby maximizing therapeutic effects and minimizing damage to normal tissues. Attached Figure Description

[0061] Figure 1 This is a schematic diagram of the structure of a drug targeted delivery device disclosed in an embodiment of this application.

[0062] Figure 2 This is a schematic flowchart of a drug targeted delivery method disclosed in an embodiment of this application. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be described in more detail below with reference to the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some embodiments of this invention, but not all embodiments.

[0064] Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0065] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0066] like Figure 1 As shown, a targeted drug delivery device includes the following modules:

[0067] The intelligent targeted recognition module accurately identifies gastric cancer-specific molecular markers and combines dynamic surface modification strategies to improve the selectivity and specificity of targeted recognition.

[0068] The microenvironment real-time monitoring module monitors key physiological parameters of the gastric cancer microenvironment in real time and enables real-time data transmission and remote monitoring.

[0069] The intelligent path planning module dynamically calculates and optimizes the drug delivery path to achieve precise drug positioning, maximize therapeutic efficacy, and minimize damage to surrounding normal tissues.

[0070] The personalized drug administration control module accurately predicts an individual's drug dosage requirements and dynamically adjusts the dosage in real time.

[0071] The drug resistance risk management module provides early warnings of drug resistance risks and automatically generates personalized alternative drug administration strategies through multi-dimensional drug resistance analysis and real-time early warning.

[0072] The intelligent drug release module uses a temperature-sensitive / pH-sensitive polymer shell and nanovalve to control drug release, achieving precise gradient release of drug concentration and improving therapeutic targeting.

[0073] In this embodiment, the drug-targeted delivery device significantly improves the accuracy of diagnosis and treatment of gastric cancer through the synergistic effect of multiple modules. The intelligent target recognition module, combined with a dynamic surface modification strategy, enhances the selectivity and specificity of targeted molecular markers, facilitating accurate identification of gastric cancer-related molecules and reducing misidentification. The real-time microenvironment monitoring module monitors key physiological parameters in the gastric cancer microenvironment, ensuring real-time data transmission and remote monitoring, thus improving patient monitoring accuracy and treatment timeliness. The intelligent pathway planning module dynamically calculates the drug delivery path, optimizing precise drug positioning, maximizing therapeutic efficacy, and minimizing damage to healthy tissues. The personalized dosing control module predicts and adjusts individual drug dosage requirements, achieving personalized treatment. The drug resistance risk management module provides early warnings of drug resistance risks through multi-dimensional analysis and automatically generates personalized alternatives, enhancing treatment sustainability. The intelligent drug release module controls drug release using temperature-sensitive / pH-sensitive polymers and nanovalves, achieving precise drug concentration gradient release and ensuring targeted treatment. Overall, this device significantly improves the efficacy and safety of gastric cancer treatment through precise identification, dynamic monitoring, and personalized adjustment, promoting the development of personalized medicine.

[0074] Furthermore, the intelligent target recognition module includes the following components:

[0075] The molecular recognition unit is responsible for real-time detection and analysis of the characterization features and expression profiles of gastric cancer-specific molecular markers, and ensures the specificity of recognition through multiple screening and cross-validation.

[0076] The dynamic modification unit is responsible for intelligently regulating the peptide conformation on the surface of the nanocarrier, enabling precise docking and dynamic optimization with different molecular markers;

[0077] The structural adaptation unit automatically calculates and evaluates the three-dimensional spatial conformation matching degree between the nanocarrier and the target molecule, optimizing the spatial accuracy of target recognition.

[0078] The signal feedback unit continuously collects real-time data on intermolecular interactions and continuously optimizes the performance parameters of the recognition algorithm through iterative learning.

[0079] The anomaly monitoring unit can identify and screen non-specific binding events in real time, effectively reducing the false positive rate and improving the accuracy of identification.

[0080] In this embodiment, the intelligent targeted recognition module significantly improves the accuracy and reliability of identifying gastric cancer-specific molecular markers through the synergistic effect of multiple high-precision components. The molecular recognition unit can detect gastric cancer-related molecular markers in real time and ensures high specificity of marker identification through multiple screenings and cross-validation, effectively avoiding false identification. The dynamic modification unit achieves precise docking with target molecules by regulating the conformation of peptides on the surface of nanocarriers and improves the adaptability of targeted recognition through dynamic optimization. The structure adaptation unit further improves the pairing accuracy between target molecules and carriers by evaluating the conformational matching degree in three-dimensional space, ensuring spatial accuracy of recognition. The signal feedback unit continuously collects intermolecular interaction data to continuously optimize the performance of the recognition algorithm, improving its adaptability and accuracy. The anomaly monitoring unit screens non-specific binding events in real time, reducing the false positive rate and ensuring high accuracy of recognition. Overall, this module improves the recognition efficiency and accuracy of gastric cancer molecular markers through a sophisticated molecular recognition and optimization mechanism, providing strong technical support for precision medicine.

[0081] Furthermore, the three-dimensional spatial conformational matching degree between the nanocarrier and the target molecule is automatically calculated and evaluated to optimize the spatial accuracy of target recognition, including the following steps:

[0082] Detailed three-dimensional models of the target molecule and nanocarrier were performed, and the matching degree score between them was calculated using a molecular docking algorithm, expressed by the formula: Among them, S matchA matching score is given between the nanocarrier and the target molecule, representing the degree of spatial conformational matching between the two; E desolvation E represents the dissolution energy, indicating the energy change of molecular interactions due to the influence of the environmental solvent when the nanocarrier comes into contact with the target molecule; electrostatic (i) represents the energy of the i-th molecule in terms of charge interaction; λ is a weighting factor used to adjust the influence of dissolution energy on the matching degree calculation; E vdw (i) represents the energy of the i-th molecule under the action of van der Waals forces;

[0083] The spatial matching degree is evaluated by calculating the contact area between the nanocarrier surface and the target molecule, and is expressed by the formula: Among them, M match The spatial matching degree between the nanocarrier and the target molecule; A contact A represents the contact area generated during the actual docking process between the nanocarrier and the target molecule; max This represents the theoretically maximum contact area that nanocarriers and targeting molecules can achieve.

[0084] By evaluating the difference between the current matching degree and the target matching degree in real time, the parameters of the matching algorithm are adjusted to further optimize the accuracy of the conformation. The feedback formula is as follows: Among them, S feedback The matching score is used for feedback optimization; N is the total number of molecules in the molecular docking process; S current (i) represents the matching score of the i-th molecule in the current matching results; S target (i) represents the target matching score, indicating the ideal or expected matching score; α is a moderating factor used to adjust the impact of the difference between the current matching result and the target matching result.

[0085] Real-time monitoring of non-specific binding events and adjustment of the matching algorithm to reduce false matches can be expressed by the following formula: Where, ΔS abnormal is the matching degree adjustment amount for nonspecific binding monitoring results; errorsignal(i) is the error signal detected by the i-th molecule during the matching process, used to evaluate the impact of nonspecific binding; β is a factor used to adjust the impact of nonspecific binding on the matching degree calculation;

[0086] The final assessment of the match degree is obtained by weighted average: M overall =w1·S match +w2·M match +w3·S feedback +w4·ΔS abnormal , of which M overallThe final overall matching score is calculated using w1, w2, w3, and w4 as weighting factors, which are used to weight different evaluation items to determine their influence on the final score.

[0087] Furthermore, the real-time microenvironment monitoring module includes the following components:

[0088] The multi-parameter sensing unit integrates a multimodal micro physiological sensor array to monitor key physiological parameters in the gastric cancer microenvironment in real time, including pH, oxygen partial pressure, temperature, and the concentration distribution of specific biomolecules.

[0089] The signal conditioning unit performs real-time digital filtering, signal amplification, and normalization on the acquired raw sensor signals to improve the signal-to-noise ratio and stability of the data.

[0090] The wireless transmission unit adopts a low-power, high-reliability wireless communication protocol to achieve real-time, secure, and low-latency transmission of microenvironment monitoring data;

[0091] The data parsing unit uses intelligent algorithms to perform real-time parsing and noise reduction of transmitted data, and extracts characteristic indicators of dynamic changes in the microenvironment through multi-dimensional correlation analysis.

[0092] The remote visualization unit constructs an interactive data visualization platform, transforming complex microenvironment monitoring data into an intuitive and easy-to-understand graphical interface.

[0093] In this embodiment, the real-time microenvironment monitoring module significantly improves the real-time monitoring capability and data analysis accuracy of the gastric cancer microenvironment by integrating multiple advanced technologies. The multi-parameter sensing unit utilizes a multimodal physiological sensor array to comprehensively monitor key physiological parameters in the gastric cancer microenvironment, such as pH, oxygen partial pressure, temperature, and the concentration of specific biomolecules, ensuring comprehensive data coverage and providing real-time and accurate microenvironment information. The signal conditioning unit optimizes data quality, enhances the signal-to-noise ratio and stability, and ensures more reliable monitoring results through real-time digital filtering, signal amplification, and normalization. The wireless transmission unit achieves real-time and secure transmission of monitoring data through low-power, high-reliability communication protocols, reducing latency and enhancing response speed. The data analysis unit uses intelligent algorithms for real-time analysis and noise reduction, deeply analyzing the dynamic changes of the microenvironment, extracting key features, and helping to assess cancer progress and treatment effectiveness. The remote visualization unit transforms complex monitoring data into an intuitive and easy-to-understand graphical interface, enhancing user interactivity and data visualization, and making the decision-making process more efficient. Overall, this module improves the monitoring accuracy and decision support capabilities of the gastric cancer microenvironment through comprehensive and real-time data acquisition, transmission, and analysis, providing strong data support for precision medicine.

[0094] Furthermore, the intelligent path planning module includes the following components:

[0095] The path evaluation unit constructs a dynamic path evaluation model through reinforcement learning algorithms to analyze and weigh the optimal path selection for drug delivery in real time.

[0096] The obstacle recognition unit performs real-time identification and feature extraction of anatomical structures, physiological barriers and high-risk areas in human tissues, providing accurate spatial references for path planning.

[0097] The dynamic optimization unit, based on a closed-loop feedback mechanism, continuously optimizes path parameters and delivery strategies to achieve real-time control of the drug delivery process.

[0098] The carrier navigation unit, combined with multi-source sensor data, enables the nanocarrier to achieve autonomous navigation and precise positioning in complex physiological environments;

[0099] The dose mapping unit establishes a precise mapping system between drug concentration and therapeutic efficacy, ensuring optimal drug distribution and absorption in the target area;

[0100] Physiological constraint units establish a multi-dimensional constraint mechanism that includes tissue elasticity, hemodynamics, and physiological barriers, providing comprehensive physiological boundary conditions for path planning;

[0101] The side effect prediction unit predicts and quantifies potential side effects and tissue damage during drug delivery.

[0102] In this embodiment, the intelligent path planning module significantly improves the accuracy of drug delivery and therapeutic efficacy through the close collaboration of multiple components. The path evaluation unit employs reinforcement learning algorithms to analyze and evaluate the optimal drug delivery path in real time, effectively balancing multiple delivery schemes to ensure the drug reaches the target area quickly and accurately. The obstacle recognition unit identifies anatomical structures, anatomical barriers, and high-risk areas in real time, providing precise spatial references for path planning and ensuring avoidance of obstacles within the body. The dynamic optimization unit continuously adjusts path parameters and delivery strategies through a closed-loop feedback mechanism, achieving real-time control during drug delivery and optimizing therapeutic effects. The carrier navigation unit combines multi-source sensor data to achieve autonomous navigation and precise positioning of the nanocarrier in complex physiological environments, improving the accuracy of drug delivery. The dose mapping unit establishes a precise mapping system between drug concentration and therapeutic efficacy, ensuring optimal drug distribution and absorption in the target area and enhancing therapeutic effects. The physiological constraint unit combines physiological characteristics and hemodynamics to provide multi-dimensional physiological boundary conditions, ensuring that path planning conforms to physiological laws. The side effect prediction unit predicts and quantifies potential side effects and tissue damage during drug delivery, reducing the risk of adverse reactions. Overall, this module improves treatment outcomes and reduces the risk of side effects by optimizing drug delivery pathways and precisely controlling drug distribution, thus promoting the development of precision medicine.

[0103] Furthermore, the personalized drug delivery modulation module includes the following components:

[0104] The gene analysis unit utilizes high-throughput gene sequencing technology to accurately map an individual's genome and identify key gene variations related to drug metabolism and sensitivity.

[0105] The dose prediction unit accurately calculates the optimal drug dosage based on gene characteristics, metabolic indicators, and tumor characteristics;

[0106] The dynamic control unit monitors the patient's physiological response and drug metabolism in real time, and dynamically fine-tunes the dosage to ensure maximum therapeutic effect and minimum adverse reaction.

[0107] The drug interaction unit analyzes an individual's medication history and potential drug interactions to predict and mitigate possible drug conflicts and side effects.

[0108] The metabolic monitoring unit integrates multidimensional physiological indicator sensors to track the absorption, distribution, metabolism, and excretion of drugs in the body in real time, providing accurate metabolic kinetic data.

[0109] The personalized feedback unit establishes a database of individual patient drug responses and continuously optimizes personalized dosing strategies through continuous learning and iteration.

[0110] In this embodiment, the personalized drug delivery control module significantly improves the efficacy of drug therapy and reduces the risk of side effects and adverse drug reactions through precise individualized treatment strategies. The gene analysis unit utilizes high-throughput gene sequencing technology to map an individual's genome, identifying gene variations related to drug metabolism and sensitivity, providing a precise basis for personalized drug delivery. The dose prediction unit integrates gene characteristics, metabolic indicators, and tumor characteristics to calculate the optimal dosage, ensuring maximum therapeutic effect. The dynamic control unit dynamically adjusts the dosage by monitoring the patient's physiological response and drug metabolism in real time, ensuring continuous optimization and precise control during treatment and minimizing side effects. The drug interaction unit analyzes an individual's medication history, predicts potential drug conflicts, avoids adverse drug interactions, and reduces risks. The metabolic monitoring unit tracks the drug's metabolic process in the body in real time using multidimensional physiological sensors, providing precise metabolic kinetic data to ensure efficient drug delivery and absorption. The personalized feedback unit continuously updates the patient's drug response database, continuously learning and optimizing the drug delivery strategy to form a closed loop of personalized treatment. Overall, this module, by comprehensively considering various factors such as an individual's genes, metabolism, and medication history, precisely adjusts drug dosages and treatment strategies, achieving true precision medicine, improving treatment outcomes, and reducing adverse reactions.

[0111] Furthermore, dynamic fine-tuning of the dosage can be achieved through the following formula: Where D(t) is the drug dose at the current time t; D(t+1) is the drug dose at the next time t+1; γ is a regulating factor used to control the sensitivity of drug dose adjustment; and δ is used to adjust the therapeutic effect E. eff (t) Weighting coefficients for relative importance in the objective function; μ is used to adjust for adverse reactions R. AE (t) is the weighting coefficient of the relative importance of t in the objective function; E eff (t) represents the therapeutic effect at time t; R AE (t) represents the adverse reaction at time t, reflecting the side effects produced by the drug; D represents the drug dosage.

[0112] Furthermore, based on genetic characteristics, metabolic indicators, and tumor characteristics, the optimal drug dosage is precisely calculated, including the following steps:

[0113] The patient's genomic data, metabolomics indicators, and tumor phenotypic characteristics are input into a validated population pharmacokinetic-pharmacodynamic model for analysis to obtain a quantitative assessment of the initial dose range.

[0114] By taking into account factors such as the patient's age, weight, previous medication history, and complications, the model prediction results are individually corrected to develop a dosing regimen that balances efficacy and safety.

[0115] Based on drug concentration monitoring data and clinical efficacy feedback during treatment, the dosage is adjusted in real time to achieve dynamic optimization of the treatment plan, thereby achieving the goal of individualized precision medication.

[0116] Furthermore, the drug resistance risk management module includes the following components:

[0117] The drug resistance analysis unit uses deep learning algorithms to comprehensively analyze gene mutations, protein expression, and molecular markers in tumor cells to accurately identify potential drug resistance mechanisms and risks.

[0118] The dynamic monitoring unit monitors drug response and changes in tumor cells in real time, and promptly captures drug resistance signals and trends.

[0119] The strategy generation unit rapidly generates personalized alternative dosing strategies, including drug combinations, dosage adjustments, and treatment pathway optimization.

[0120] Molecular interaction units are used to deeply analyze the molecular interaction mechanisms between drugs and tumor cells, and to predict and simulate potential drug resistance development pathways and molecular-level changes.

[0121] The drug resistance database unit builds a continuously updated knowledge base of drug resistance information, integrates the latest clinical research and genomics data, and supports accurate drug resistance risk assessment.

[0122] The precision intervention unit intelligently recommends and formulates targeted early intervention measures based on real-time monitoring data to reduce the risk of drug resistance.

[0123] In this embodiment, the drug resistance risk management module significantly improves drug resistance management capabilities in anti-tumor treatment through multi-level monitoring and analysis, and effectively slows down the occurrence of drug resistance. The drug resistance analysis unit uses deep learning algorithms to comprehensively analyze gene mutations, protein expression, and molecular markers in tumor cells, accurately identifying potential drug resistance mechanisms and providing early warnings of drug resistance risks. The dynamic monitoring unit monitors drug response and changes in tumor cells in real time, enabling timely capture of drug resistance signals and trends for early intervention. The strategy generation unit quickly generates personalized alternative treatment plans based on monitoring results, including drug combinations, dosage adjustments, and treatment pathway optimization, ensuring the flexibility and adaptability of treatment plans. The molecular interaction unit deeply analyzes the molecular interactions between drugs and tumor cells, predicts drug resistance development pathways, and helps identify molecular-level changes in drug resistance mechanisms. The drug resistance database unit continuously updates and maintains the drug resistance information knowledge base, integrating the latest clinical research and genomics data to provide strong data support for accurate assessment of drug resistance risks. The precise intervention unit intelligently recommends early intervention measures based on real-time data to reduce drug resistance risks in advance. Overall, this module provides stronger drug resistance management capabilities for anti-tumor treatment through comprehensive monitoring, precise analysis, and personalized intervention, optimizes treatment outcomes, and reduces the treatment challenges caused by drug resistance.

[0124] Furthermore, the intelligent drug delivery module includes the following components:

[0125] The intelligent shell unit is designed with a smart polymer shell that has temperature-sensitive and pH-responsive characteristics, so as to realize intelligent opening and closing and drug release regulation according to changes in the microenvironment.

[0126] The nano-valve control unit adjusts the opening and closing of the drug release channel in real time according to environmental signals to achieve precise drug concentration gradient release;

[0127] A dose gradient unit is used to construct a drug concentration gradient release system based on a multilayer microfluidic structure to ensure the precise distribution of drugs at different spatiotemporal scales.

[0128] The release kinetics unit uses machine learning algorithms to predict and optimize the release rate and release pattern of drugs under different conditions;

[0129] The real-time feedback unit collects and analyzes real-time data during the drug release process, dynamically adjusts the release strategy, and improves the accuracy and effectiveness of the release.

[0130] In this embodiment, the intelligent drug release module significantly improves the accuracy and therapeutic effect of drug release through precise drug control and an intelligent response mechanism. The intelligent shell unit uses a thermosensitive and pH-responsive intelligent polymer material, enabling automatic adjustment of drug release based on changes in the microenvironment, ensuring the release channel is opened or closed under specific conditions. The nanovalve control unit adjusts the opening degree of the drug release channel in real time, precisely controlling the amount of drug released, thereby achieving precise drug concentration gradient release and avoiding over- or under-dose. The dose gradient unit utilizes a multi-layer microfluidic structure to construct a drug concentration gradient system, ensuring precise drug distribution in different regions and at different time scales, improving targeting and absorption. The release kinetics unit uses machine learning algorithms to predict and optimize the drug release rate and pattern under different conditions, making drug release more stable and controllable. The real-time feedback unit monitors real-time data during the drug release process, dynamically adjusting the release strategy to ensure the accuracy of drug release and therapeutic effect. Overall, this module provides personalized and precise treatment plans through intelligent regulation of drug release, improving drug targeting and effectiveness, and reducing the occurrence of side effects.

[0131] like Figure 2 As shown, a targeted drug delivery method includes the following steps:

[0132] Real-time monitoring of key physiological parameters in the gastric cancer microenvironment, and extraction of characteristic indicators of dynamic changes in the microenvironment through multidimensional correlation analysis;

[0133] By real-time detection and analysis of the expression characteristics of gastric cancer-specific molecular markers, the conformation of peptides on the surface of nanocarriers can be dynamically controlled, thereby achieving precise docking with different molecular markers.

[0134] To achieve autonomous navigation of nanocarriers in complex physiological environments, and to establish a multidimensional constraint mechanism including tissue elasticity, hemodynamics and physiological barriers, ensuring the safety and precision of drug delivery;

[0135] Predict and avoid potential drug interactions, and continuously learn and iteratively optimize personalized dosing strategies to ensure maximum drug efficacy;

[0136] By constructing a drug concentration gradient release system using a multilayer microfluidic structure, the precise distribution of drugs at different spatiotemporal scales can be ensured, thereby maximizing the therapeutic effect.

[0137] Overall, this targeted drug delivery method, by combining multiple advanced technologies, systematically improves the precision and efficacy of drug treatment for gastric cancer. Real-time monitoring of key physiological parameters of the gastric cancer microenvironment and extraction of characteristic indicators enables dynamic adaptation to changes in the microenvironment, providing precise data for subsequent drug delivery. Real-time detection of molecular markers and conformational changes of peptides on the nanocarrier surface achieve precise docking with the markers. Autonomous navigation technology combined with physiological constraint mechanisms ensures the safety of drug delivery. Continuous learning and iterative optimization of personalized dosing strategies significantly enhance therapeutic efficacy. A multilayer microfluidic structure drug concentration gradient release system precisely controls drug distribution, further enhancing therapeutic effects. This comprehensive application not only opens new avenues for personalized precision treatment of gastric cancer but also provides a technical reference for targeted delivery of other cancer types.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A drug targeted delivery device, characterized in that, Includes the following modules: The intelligent targeted recognition module accurately identifies gastric cancer-specific molecular markers and combines dynamic surface modification strategies to improve the selectivity and specificity of targeted recognition. The microenvironment real-time monitoring module monitors key physiological parameters of the gastric cancer microenvironment in real time and enables real-time data transmission and remote monitoring. The intelligent path planning module dynamically calculates and optimizes the drug delivery path to achieve precise drug positioning, maximize therapeutic efficacy, and minimize damage to surrounding normal tissues. The personalized drug administration control module accurately predicts an individual's drug dosage requirements and dynamically adjusts the dosage in real time. The drug resistance risk management module provides early warnings of drug resistance risks and automatically generates personalized alternative drug administration strategies through multi-dimensional drug resistance analysis and real-time early warning. The intelligent drug release module uses a temperature-sensitive / pH-sensitive polymer shell and nanovalve to control drug release, achieving precise gradient release of drug concentration.

2. The drug targeted delivery device according to claim 1, characterized in that, The intelligent target recognition module includes the following components: The molecular recognition unit is responsible for real-time detection and analysis of the characterization features and expression profiles of gastric cancer-specific molecular markers; The dynamic modification unit is responsible for intelligently regulating the peptide conformation on the surface of the nanocarrier, enabling precise docking and dynamic optimization with different molecular markers; The structural adaptation unit automatically calculates and evaluates the three-dimensional spatial conformational matching degree between the nanocarrier and the target molecule; The signal feedback unit continuously collects real-time data on intermolecular interactions and continuously optimizes the performance parameters of the recognition algorithm through iterative learning. Anomaly monitoring unit identifies and screens non-specific binding events in real time.

3. The drug targeted delivery device according to claim 1, characterized in that, The real-time microenvironment monitoring module includes the following components: A multi-parameter sensing unit integrates a multimodal micro physiological sensor array to monitor key physiological parameters in the gastric cancer microenvironment in real time. The signal conditioning unit performs real-time digital filtering, signal amplification, and normalization on the acquired raw sensor signals; The wireless transmission unit adopts a low-power, high-reliability wireless communication protocol to achieve real-time, secure, and low-latency transmission of microenvironment monitoring data; The data parsing unit uses intelligent algorithms to perform real-time parsing and noise reduction of transmitted data, and extracts characteristic indicators of dynamic changes in the microenvironment through multi-dimensional correlation analysis. The remote visualization unit transforms complex microenvironment monitoring data into an intuitive and easy-to-understand graphical interface.

4. The drug targeted delivery device according to claim 1, characterized in that, The intelligent route planning module includes the following components: The path evaluation unit constructs a dynamic path evaluation model through reinforcement learning algorithms to analyze and weigh the optimal path selection for drug delivery in real time. The obstacle recognition unit performs real-time identification and feature extraction of anatomical structures, physiological barriers, and high-risk areas in human tissues; The dynamic optimization unit, based on a closed-loop feedback mechanism, continuously optimizes path parameters and delivery strategies to achieve real-time control of the drug delivery process. The carrier navigation unit, combined with multi-source sensor data, enables the nanocarrier to achieve autonomous navigation and precise positioning in complex physiological environments; The dose mapping unit establishes a precise mapping system between drug concentration and therapeutic efficacy, ensuring optimal drug distribution and absorption in the target area; Physiological constraint units establish a multi-dimensional constraint mechanism that includes tissue elasticity, hemodynamics, and physiological barriers, providing comprehensive physiological boundary conditions for path planning; The side effect prediction unit predicts and quantifies potential side effects and tissue damage during drug delivery.

5. The drug targeted delivery device according to claim 1, characterized in that, The personalized drug delivery control module includes the following components: The gene analysis unit utilizes high-throughput gene sequencing technology to accurately map an individual's genome and identify key gene variations related to drug metabolism and sensitivity. The dose prediction unit accurately calculates the optimal drug dosage based on gene characteristics, metabolic indicators, and tumor characteristics; The dynamic control unit monitors the patient's physiological response and drug metabolism in real time, and dynamically fine-tunes the dosage to ensure maximum therapeutic effect and minimum adverse reaction. The drug interaction unit analyzes an individual's medication history and potential drug interactions to predict and mitigate possible drug conflicts and side effects. The metabolic monitoring unit integrates multidimensional physiological indicator sensors to track the absorption, distribution, metabolism, and excretion of drugs in the body in real time. The personalized feedback unit establishes a database of individual patient drug responses and continuously optimizes personalized dosing strategies through continuous learning and iteration.

6. The drug targeted delivery device according to claim 1, characterized in that, The drug resistance risk management module includes the following components: The drug resistance analysis unit uses deep learning algorithms to comprehensively analyze gene mutations, protein expression, and molecular markers in tumor cells to accurately identify potential drug resistance mechanisms and risks. The dynamic monitoring unit monitors drug response and changes in tumor cells in real time, and promptly captures drug resistance signals and trends. The strategy generation unit rapidly generates personalized alternative dosing strategies, including drug combinations, dosage adjustments, and treatment pathway optimization. Molecular interaction units are used to deeply analyze the molecular interaction mechanisms between drugs and tumor cells, and to predict and simulate potential drug resistance development pathways and molecular-level changes. The drug resistance database unit builds a continuously updated knowledge base of drug resistance information, integrates the latest clinical research and genomics data, and supports accurate drug resistance risk assessment. The precision intervention unit intelligently recommends and formulates targeted early intervention measures based on real-time monitoring data to reduce the risk of drug resistance.

7. The drug targeted delivery device according to claim 1, characterized in that, The intelligent drug delivery module includes the following components: The intelligent shell unit is designed with a smart polymer shell that has temperature-sensitive and pH-responsive characteristics, so as to realize intelligent opening and closing and drug release regulation according to changes in the microenvironment. The nano-valve control unit adjusts the opening and closing of the drug release channel in real time according to environmental signals to achieve precise drug concentration gradient release; A dose gradient unit is used to construct a drug concentration gradient release system based on a multilayer microfluidic structure to ensure the precise distribution of drugs at different spatiotemporal scales. The release kinetics unit uses machine learning algorithms to predict and optimize the release rate and release pattern of drugs under different conditions; The real-time feedback unit collects and analyzes real-time data during the drug release process, dynamically adjusts the release strategy, and improves the accuracy and effectiveness of the release.

8. A drug targeted delivery method, implemented based on a drug targeted delivery device according to any one of claims 1-7, characterized in that, Includes the following steps: Real-time monitoring of key physiological parameters in the gastric cancer microenvironment, and extraction of characteristic indicators of dynamic changes in the microenvironment through multidimensional correlation analysis; By real-time detection and analysis of the expression characteristics of gastric cancer-specific molecular markers, the conformation of peptides on the surface of nanocarriers can be dynamically controlled, thereby achieving precise docking with different molecular markers. To achieve autonomous navigation of nanocarriers in complex physiological environments, and to establish a multidimensional constraint mechanism including tissue elasticity, hemodynamics and physiological barriers, ensuring the safety and precision of drug delivery; Predict and avoid potential drug interactions, and continuously learn and iteratively optimize personalized dosing strategies to ensure maximum drug efficacy; By constructing a drug concentration gradient release system using a multilayer microfluidic structure, the precise distribution of drugs at different spatiotemporal scales can be ensured, thereby maximizing the therapeutic effect.

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