Drug effect simulation system

Abstract

The invention relates to the field of drug effect simulation, in particular to a drug effect simulation system, which comprises a data input module for receiving drug information and patient information and carrying out structured storage on the information; the data preprocessing module is used for performing cleaning, standardization and feature extraction on the input data; the intelligent model selection module automatically selects the most suitable PK and PD models through a machine learning algorithm in combination with historical data; the personalized parameter adjusting module is used for dynamically adjusting simulation parameters according to the specific data of the patient; the drug effect simulation module is used for simulating the action process of the drug in the virtual environment and adjusting the simulation in real time through the real-time feedback module; the calculation module is used for executing simulation calculation and generating a drug effect prediction result; and the result analysis module is used for carrying out statistical analysis on the simulation result and generating a drug effect prediction curve and a detailed report.

Description

Technical Field

[0001] The invention relates to the field of drug effect simulation, in particular to a drug effect simulation system. Background Art

[0002] Existing pharmacodynamic simulation technologies mainly focus on the application of pharmacokinetic (PK) and pharmacodynamic (PD) models of traditional drugs; these models simulate the absorption, distribution, metabolism and excretion processes of drugs in the body through mathematical equations to predict the concentration-time curve and effect-time curve of drugs; however, existing technologies are insufficient in dealing with complex drug interactions, multi-dimensional patient data integration and real-time feedback regulation.

[0003] In view of the shortcomings of existing technologies, it is imperative to develop an efficient pharmacodynamic simulation system, especially in the field of rare disease drug development and multi-drug combination. Due to limited clinical samples, it is difficult for traditional clinical trials to obtain sufficient data. The pharmacodynamic simulation system proposed in this article integrates advanced PK / PD models, personalized parameter adjustment mechanisms and real-time feedback modules, which can dynamically simulate the drug action process in a virtual environment, provide highly reliable pharmacodynamic predictions, and significantly improve the efficiency and success rate of drug development. Summary of the invention

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: a drug efficacy simulation system, comprising:

[0005] The data input module is used to obtain drug-related information and patient physiological and genetic information uploaded by the user in the user interface module through RPC, and convert it into a structured data format and store it in the system database;

[0006] The data preprocessing module is used to obtain the data in the data input module, and is responsible for performing data cleaning, standardization and feature extraction, and then transmitting the processed data to the intelligent model selection module;

[0007] The intelligent model selection module is used to receive data provided by the data preprocessing module, and use the built-in machine learning algorithm and historical data to evaluate the performance of different models, automatically select the most suitable PK / PD model, and transmit it to the personalized parameter adjustment module through RPC;

[0008] The personalized parameter adjustment module is used to obtain the model parameters transmitted by the intelligent model selection module, dynamically adjust the parameters based on patient-specific data and user feedback, and then transmit them to the drug efficacy simulation module;

[0009] The drug efficacy simulation module is used to obtain the model parameters of the personalized parameter adjustment module and simulate the drug action process under specific human conditions in a virtual environment. During the simulation process, the simulation data is transmitted to the real-time feedback module and the user interface module in real time;

[0010] The real-time feedback module analyzes the simulation data of the drug efficacy simulation module through the real-time data processing engine, dynamically adjusts the model parameters, and feeds back the adjustment results to the calculation module;

[0011] A calculation module, used to obtain the parameters transmitted by the real-time feedback module, solve the differential equations in the PK / PD model using the Euler method numerical solver, and transmit the calculation results to the result analysis module;

[0012] The result analysis module obtains the calculation results of the calculation module through RPC, performs statistical analysis and generates graphics on the results, generates drug efficacy prediction curves and reports, and then transmits them to the user interface module through RPC;

[0013] Preferably, in the data input module, the drug-related information includes drug composition, solubility, chemical structure, dosage, route of administration and metabolic pathway; the patient's physiological and genetic information includes the patient's age, gender, weight, height, genetic information and medical history; data is obtained through manual input, interface import and database retrieval, and the interface import is based on the interface of the hospital information system and the electronic health record.

[0014] Preferably, the virtual environment supports multi-scenario simulation and Monte Carlo simulation, wherein the multi-scenario simulation is for different doses, routes of administration or physiological states of patients; the Monte Carlo simulation is a simulation based on equation solving and a Monte Carlo simulation, wherein the simulation based on equation solving establishes a mathematical model describing drug absorption, distribution, metabolism and excretion according to the pharmacokinetic and pharmacodynamic principles of the drug, and converts the model into a set of differential equations or difference equations, which can describe the change of drug concentration over time, and then uses the Euler method numerical method to solve these equations to obtain the predicted value of drug concentration over time and then analyze and solve it. As a result, the efficacy and safety of the drug are evaluated, and the time and duration for the drug concentration to reach an effective therapeutic level are predicted; the Monte Carlo simulation defines a probability distribution for the random variables in the model, the random variables are the absorption rate and distribution volume of the drug, and a large number of possible scenarios are generated by random sampling, each scenario represents a possible state of drug behavior, and then each scenario is simulated using an equation-solving method to calculate the corresponding drug concentration and effect, and the simulation results of all scenarios are statistically analyzed, the statistical analysis is to calculate the mean, standard deviation and confidence interval, and finally the statistical analysis results are explained.

[0015] Preferably, the data input module uses a form and drag interface provided by the user interface module, and the user can input the drug chemical structure and dosage information by keyboard input or file upload, wherein the chemical structure information is imported in a standardized format file of SMILES or InCh I and is parsed and converted into an internal data structure by the system's built-in chemical structure parser and then stored in the database, the dosage information is input through a digital input field and the consistency of the dosage data is ensured by a unit conversion tool provided by the system, and the data verification and standardization submodule uses preset data verification rules and standardization algorithms to perform format verification, range verification and normalization on the input data.

[0016] Preferably, the historical data are drug use records, patient treatment results and drug reaction data, the model evaluation indicators are accuracy, recall rate and F1 score, the pharmacokinetic (PK) model is used to describe the absorption, distribution, metabolism and excretion process of the drug in the body; the pharmacodynamic (PD) model is used to describe the relationship between drug concentration and efficacy; the differential equation is to convert the pharmacokinetic (PK) and pharmacodynamic (PD) models into a set of differential equations, which are used to express the changes in drug concentration over time and the manifestation of drug efficacy.

[0017] Preferably, the data cleaning is to delete duplicate records and process missing values, and the missing value processing is to fill the missing values ​​with the mean, median or mode. The missing value processing unit adopts a linear regression model to predict the missing values ​​according to other data points; the data standardization unit uses the Zscore standardization method to convert the data into a standard normal distribution by calculating the mean and standard deviation of the input data to ensure the consistency of different feature scales; the feature extraction is to select the most informative features from the original data to reduce the complexity of the model and improve the performance, and then create new features through mathematical transformation or combination of existing features, and then use principal component analysis technology to reduce the number of features while retaining the information of the original data as much as possible.

[0018] Preferably, the real-time data processing engine realizes real-time communication with the computing module through the WebSocket protocol to monitor the key performance indicators and drug response data during the simulation process; and the operation of dynamically adjusting the model parameters is realized through a real-time feedback mechanism, firstly collecting and analyzing the data generated by the pharmacodynamic simulation module during the simulation process and identifying the key parameters, and then adjusting these parameters using a genetic algorithm to improve the prediction accuracy of the model; the adjusted parameters are transmitted to the pharmacodynamic simulation module via RPC and trigger the pharmacodynamic simulation module to re-run the simulation to verify the improvement effect; this iterative process is automated, allowing the system to continuously optimize itself based on new data and feedback, while transmitting the data to the user interface for professionals to perform manual fine-tuning and result analysis, and after obtaining the fine-tuning information, transmitting it to the pharmacodynamic simulation module for continued iteration, ensuring that the model can adapt to individual differences and provide accurate pharmacodynamic predictions.

[0019] Preferably, the real-time data processing engine is used to process and analyze a large amount of real-time data collected during the simulation process, wherein the real-time data is the output of the real-time monitoring drug efficacy simulation module, and collects key performance indicators and drug response data during the simulation process, analyzes the collected data to identify trends and abnormal conditions, and dynamically adjusts the model parameters using a genetic algorithm based on the analysis results, and feeds the adjusted parameters back to the calculation module in real time for the next simulation calculation.

[0020] Preferably, the Euler method numerical solver used for differential equation solving solves ordinary differential equations by numerical integration to simulate the change of drug concentration over time; the Monte Carlo simulation simulates the uncertainty of drug response by generating random samples, and performs random number generation and statistical analysis through Python's NumPy library.

[0021] Preferably, the statistical analysis uses ANOVA and covariance analysis to evaluate the differences in drug efficacy between different drug doses or patient groups; the visualization tool uses the Plotly library to input simulation result data to generate interactive charts, which are line charts and scatter plots to show the changes in drug efficacy over time.

[0022] Preferably, it includes an interactive chart display interface, a report generator and a data export function; the interactive chart display interface is implemented through HTML5 and JavaScript, and provides chart zooming, hover prompts and data point exploration functions; the report generator uses the Mustache template engine to dynamically generate a report containing charts, data and conclusions according to the parameters and analysis results selected by the user; the data export function exports the results in PDF, Excel, BMP, CSV and Docx formats through the server-side file generation library, and the file generation library is composed of Python's Report Lab library, openpyx l library, Pi l low library, python-docx library, and csv module.

[0023] The present invention provides a drug efficacy simulation system, which has the following beneficial effects:

[0024] 1. Improve prediction accuracy: This drug efficacy simulation system ensures the quality and consistency of input data through advanced data preprocessing and feature extraction technology, thereby improving the accuracy of drug efficacy prediction; the Zscore standardization method and principal component analysis technology adopted by the system enhance the model's sensitivity to key drug properties and patient physiological parameters, making the prediction results closer to actual drug reactions.

[0025] 2. Optimize personalized medical plans: The system is equipped with a personalized parameter adjustment module that can dynamically adjust model parameters according to the patient's specific data to achieve precision medicine; this personalized simulation method provides customized treatment plans for patients with rare diseases and special physical conditions, helping doctors to make treatment decisions that are more in line with individual patient differences.

[0026] 3. Enhanced evaluation of multi-drug combination therapy: For multi-drug combination therapy, this system can simulate the interactions between drugs and predict the efficacy and potential side effects of different drug combinations; the application of this multi-dimensional model provides doctors with comprehensive drug interaction information, which helps to optimize the combination drug regimen, reduce adverse reactions and improve treatment effects.

[0027] 4. Improve R&D efficiency and reduce costs: By applying this drug efficacy simulation system in the early stages of drug development, the resource consumption and time delays in the drug screening process can be significantly reduced; the system's high degree of automation and real-time feedback mechanism allow researchers to quickly iterate and optimize the model, thereby shortening the drug cycle from laboratory to clinical application and reducing R&D costs; at the same time, by reducing dependence on animal experiments, the system also helps promote the practice of green chemistry and ethical research. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] Embodiment 1:

[0030] The embodiment of the present invention provides a drug efficacy simulation system, which is used to help researchers simulate the absorption, distribution, metabolism and excretion process of drugs in the human body to predict the efficacy of drugs;

[0031] First, researchers input relevant information about the drug through the user interface module, including drug ingredients, dosage, route of administration, and the patient's physiological and genetic information, such as age, gender, weight, gene expression data, etc.; after receiving this information, the user interface module transmits the data to the data input module through a remote procedure call (RPC); the data input module is responsible for converting this information into a structured data format and storing it in the system's database.

[0032] The data preprocessing module then processes the input data; this module cleans the data, deletes duplicate records and handles missing values, which are usually filled by the mean, median or mode; all data are converted to a uniform scale through data standardization, and the Z-score standardization method is used to scale the values ​​to the standard normal distribution range; the data preprocessing module also performs feature extraction, selects the most informative features from the original data, and reduces the dimensionality of the data through principal component analysis technology, while maintaining as much original data information as possible.

[0033] The preprocessed data will be transmitted to the intelligent model selection module; the intelligent model selection module uses a machine learning algorithm and combines historical data in the system to evaluate different pharmacokinetic (PK) and pharmacodynamic (PD) models; the evaluation process involves multiple indicators, including the model's accuracy, recall rate and F1 score; after the model evaluation is completed, the system automatically selects the best PK / PD model suitable for the current data, and transmits the selected model parameters to the personalized parameter adjustment module through RPC.

[0034] After receiving the model parameters, the personalized parameter adjustment module dynamically adjusts the model parameters based on the patient's specific data and user feedback to ensure that the drug efficacy simulation process is more personalized; the adjusted parameters will be transmitted to the drug efficacy simulation module to simulate the drug's action process under specific human conditions in a virtual environment; the drug efficacy simulation module supports a variety of simulation scenarios, such as different routes of administration, dosages, and patient physiological states, and performs multi-dimensional drug efficacy simulation through simulation technology based on equation solving and Monte Carlo simulation technology.

[0035] During the simulation process, the pharmacodynamic simulation module transmits the simulation data generated in real time to the real-time feedback module and the user interface module; the real-time feedback module analyzes the simulation data through the real-time data processing engine, identifies the trends or anomalies, and dynamically adjusts the model parameters according to the analysis results; the adjusted parameters are transmitted to the calculation module through RPC to trigger new simulation calculations; the calculation module uses the Euler method numerical solver to solve the differential equations of the PK / PD model and output new simulation results. The calculation results are then transmitted to the result analysis module.

[0036] Finally, the result analysis module performs statistical analysis on the calculation results to generate drug efficacy prediction curves and detailed drug efficacy analysis reports; these results are transmitted to the user interface module via RPC for researchers to view, fine-tune data, and export analysis reports; the entire system automatically performs data processing, model optimization, and simulation results display, greatly improving the efficiency and accuracy of drug efficacy prediction.

[0037] Embodiment 2:

[0038] This embodiment is an extension of the first embodiment and is particularly suitable for scenarios of multi-drug combination therapy and drug development for rare diseases; multi-drug combination therapy is common in treatment plans for complex diseases, while the development of drugs for rare diseases faces huge challenges due to the scarcity of clinical samples. The pharmacodynamic simulation system of the present invention provides strong support for these two types of application scenarios through advanced PK / PD models and data processing mechanisms.

[0039] In the application of multi-drug combination therapy, researchers first input detailed information about the drugs involved in the combination therapy through the user interface module, including the chemical structure, dosage and administration route of each drug, as well as physiological data of the combination therapy patient population, such as age, weight and gender, and genetic data, such as gene expression, metabolic capacity, etc.; the data input module converts these data into a structured format and stores them in the system database; the data preprocessing module identifies interactions between the input multiple drugs and extracts key features related to the multi-drug combination to ensure that the model can fully consider the interactions between drugs.

[0040] When selecting a PK / PD model, the intelligent model selection module will give special consideration to the interactions in the combined use of multiple drugs; this module selects complex PK / PD models that can describe the synergistic or antagonistic effects of drugs and transmits them to the personalized parameter adjustment module; the personalized parameter adjustment module will dynamically adjust the model parameters according to the combined effects of the drugs to accurately reflect these complex drug interaction processes; the pharmacodynamic simulation module uses Monte Carlo simulation technology to further evaluate the efficacy and uncertainty under drug interactions.

[0041] In the development of drugs for rare diseases, the system can effectively deal with the problem of insufficient clinical samples; since there are fewer patients with rare diseases and clinical data are extremely limited, the system simulates the patients' physiological and genetic information and combines it with sophisticated model parameter adjustments to predict the actual response of drugs in these patients; researchers can input the patients' detailed genetic information, and the system automatically adjusts the simulation model based on this data to make drug efficacy predictions more in line with individual needs; the drug efficacy simulation module simulates the rare disease patient group in a virtual environment, and the real-time feedback module dynamically adjusts the model based on the feedback data to ensure the reliability of the prediction results.

[0042] Finally, the result analysis module conducts a detailed analysis of the simulation results of multi-drug combination therapy and rare disease drugs, and generates visual efficacy prediction curves and analysis reports; the user interface module provides interactive visualization tools to help researchers deeply understand the efficacy and potential risks of multi-drug combination therapy, or the clinical manifestations of rare disease drugs; in addition, the analysis results will be exported as a report to facilitate subsequent clinical research and application.

[0043] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A drug efficacy simulation system, comprising a data input module, a data preprocessing module, an intelligent model selection module, a personalized parameter adjustment module, a drug efficacy simulation module, a real-time feedback module, a calculation module, a result analysis module, and a user interface module, characterized in that: Each module communicates through remote procedure calls (RPC); The data input module is used to obtain drug-related information and patient physiological and genetic information uploaded by the user in the user interface module through RPC, and convert it into a structured data format and store it in the system database; The data preprocessing module is used to obtain the data in the data input module, and is responsible for performing data cleaning, standardization and feature extraction, and then transmitting the processed data to the intelligent model selection module; The intelligent model selection module is used to receive data provided by the data preprocessing module, and use the built-in machine learning algorithm and historical data to evaluate the performance of different models, automatically select the most suitable PK / PD model, and transmit it to the personalized parameter adjustment module through RPC; The personalized parameter adjustment module is used to obtain the model parameters transmitted by the intelligent model selection module, dynamically adjust the parameters based on patient-specific data and user feedback, and then transmit them to the drug efficacy simulation module; The drug efficacy simulation module is used to obtain the model parameters of the personalized parameter adjustment module and simulate the drug action process under specific human conditions in a virtual environment. During the simulation process, the simulation data is transmitted to the real-time feedback module and the user interface module in real time; The real-time feedback module analyzes the simulation data of the drug efficacy simulation module through the real-time data processing engine, dynamically adjusts the model parameters, and feeds back the adjustment results to the calculation module; A calculation module, used to obtain the parameters transmitted by the real-time feedback module, solve the differential equations in the PK / PD model using the Euler method numerical solver, and transmit the calculation results to the result analysis module; The result analysis module obtains the calculation results of the calculation module through RPC, performs statistical analysis and graphics on the results, generates drug efficacy prediction curves and reports, and then transmits them to the user interface module through RPC.

2. A drug efficacy simulation system according to claim 1, characterized in that: The data input module receives drug chemical structure and dosage information through a user interface. The chemical structure is uploaded and parsed and stored in SMILES or InChI format. The dosage information is input digitally and a unit conversion tool is used to ensure consistency. The data verification and standardization submodule is responsible for format verification, range verification and normalization processing.

3. A drug efficacy simulation system according to claim 1, characterized in that: The virtual environment supports multi-scenario simulation and Monte Carlo simulation, wherein the multi-scenario simulation technology uses a simulation method based on equation solving to establish a mathematical model of drug absorption, distribution, metabolism and excretion, which is converted into a differential equation and then numerically solved using the Euler method to solve the change of drug concentration over time. In combination with Monte Carlo simulation, random variables such as absorption rate and distribution volume are randomly sampled to generate a variety of possible drug behavior scenarios, which are then simulated and the mean, standard deviation and confidence interval of drug concentration and effect are calculated through statistical analysis.

4. A drug efficacy simulation system according to claim 1, characterized in that: The real-time data processing engine communicates with the computing module in real time through the WebSocket protocol to monitor key performance indicators and drug response data. The dynamic adjustment of model parameters is achieved through a real-time feedback mechanism. First, the pharmacodynamic simulation data is collected and analyzed, and the key parameters are adjusted using a genetic algorithm to improve prediction accuracy. The adjusted parameters are transmitted to the pharmacodynamic simulation module via RPC to trigger re-simulation, and are continuously optimized during the automated iterative process. At the same time, the data is transmitted to the user interface for professionals to perform manual fine-tuning. After obtaining the fine-tuning information, iteration continues to ensure that the model adapts to individual differences and provides accurate predictions.

5. A drug efficacy simulation system according to claim 1, characterized in that: The real-time data processing engine processes and analyzes a large amount of real-time data collected during the simulation process, including the output of the efficacy simulation module, key performance indicators and drug response data. It identifies trends and abnormal situations through analysis, and then uses genetic algorithms to dynamically adjust model parameters and feed back the adjusted parameters to the calculation module in real time.

6. A drug efficacy simulation system according to claim 1, characterized in that: The differential equations are solved using the Euler method numerical solver, which solves ordinary differential equations through numerical integration to simulate the changes in drug concentration over time; the Monte Carlo simulation simulates the uncertainty of drug reactions by generating random samples, and performs random number generation and statistical analysis through the NumPy library in Python.

7. A drug efficacy simulation system according to claim 1, characterized in that: The statistical analysis uses ANOVA and covariance analysis to evaluate the differences in efficacy between different drug doses or patient groups; the visualization tool uses the Plotly library to input simulation result data to generate interactive charts.

8. A drug efficacy simulation system according to claim 1, characterized in that: It also includes a user interface module, which obtains the efficacy simulation data of the real-time feedback module and the efficacy prediction curve and report of the result analysis module through RPC, and displays them on the user interface for users to view and fine-tune the data. The fine-tuned data is fed back to the personalized parameter adjustment module through RPC, and the function of exporting reports is also provided.