Endoplasmic reticulum stress mitigation inhibitor administration regimen recommendation method and system

Abstract

The present application relates to the technical field of biological medicine, in particular to an inhibitor administration scheme recommendation method and system for endoplasmic reticulum stress mitigation, comprising: obtaining multi-dimensional physiological data of a target object in a specific time period and cleaning and aligning to form a time-consistent object state data sequence; extracting key biomarkers based on an endoplasmic reticulum stress signal pathway model to obtain a core index set; analyzing the conditional dependence relationship between the core index and external intervention factors by using a causal inference network to build an endoplasmic reticulum stress evolution dynamic graph; simulating the expected change trajectory of the core index after administration of different inhibitors; combining the genomic characteristics of the target object and historical drug reaction records to correct the trajectory; calculating the comprehensive stability score of each scheme to generate a recommendation report. The method improves the accuracy and pertinence of the inhibitor administration scheme and adapts to the needs of personalized treatment.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and in particular to a method and system for recommending inhibitor administration regimens for relieving endoplasmic reticulum stress. Background Technology

[0002] Endoplasmic reticulum stress (ERS) is a stress response that occurs when cells experience disruption of the endoplasmic reticulum's function in response to external stimuli. Persistent ERS can lead to cell damage and even apoptosis, and is closely related to the development of various diseases. Currently, the primary method for alleviating ERS stress is through the administration of ERS stress inhibitors. The recommended approach for conventional inhibitor administration typically involves obtaining basic physiological data of the target individual, screening for ERS-related biomarkers, combining this data with existing drug databases, simulating the effects of different ERS stress inhibitors, and then generating a recommended regimen.

[0003] Conventional technical approaches only perform simple screening of biomarkers without in-depth analysis of the intrinsic relationship between core indicators and external intervention factors. This fails to clarify the core drivers of endoplasmic reticulum stress evolution, leading to discrepancies between simulated changes in core indicators after the action of endoplasmic reticulum stress inhibitors and actual conditions. Furthermore, conventional approaches do not consider individual differences among target subjects, neglecting to incorporate their genomic characteristics and historical drug response records. The use of a generalized simulation approach results in recommended administration regimens that lack specificity, are difficult to adapt to the physiological states of different target subjects, and cannot accurately meet personalized treatment needs. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a recommended method and system for administering inhibitors to alleviate endoplasmic reticulum stress.

[0005] To achieve the above objectives, the present invention employs the following technical solution: a recommended method for administering inhibitors to alleviate endoplasmic reticulum stress, comprising the following steps:

[0006] Acquire multidimensional physiological data of the target object within a specific time period, clean and align the multidimensional physiological data to form a temporally consistent object state data sequence; Based on the endoplasmic reticulum stress signaling pathway model, key biomarkers were extracted from the time-consistent object state data sequence to identify a set of core indicators related to endoplasmic reticulum stress levels. Using causal inference networks, the conditional dependencies between each indicator in the core indicator set and external intervention factors are analyzed, and an endoplasmic reticulum stress evolution dynamic diagram is constructed. Based on the endoplasmic reticulum stress evolution causation diagram, the expected change trajectory of the core indicator set after the application of different types and doses of endoplasmic reticulum stress inhibitors is simulated. Obtain the genomic characteristics and historical drug response records of the target object, and combine the genomic characteristics and historical drug response records to make personalized corrections to the expected change trajectory; Based on the revised expected change trajectory, the overall stability score of different endoplasmic reticulum stress inhibitor administration regimens was calculated within the preset evaluation period; Based on the comprehensive stability score, a recommended administration schedule for endoplasmic reticulum stress inhibitors is generated.

[0007] As a further aspect of the present invention, the step of cleaning and aligning the multidimensional physiological data to form a temporally consistent object state data sequence specifically includes: The multidimensional physiological data are obtained from monitoring devices and databases from different sources, including proteomics data, metabolomics data, and cell imaging data. The proteomics data, metabolomics data, and cell imaging data that have undergone anomaly processing and missing data filling are uniformly mapped onto the same time axis to obtain time-aligned multi-dimensional data. Standardize the time-aligned multi-dimensional data to eliminate the dimensional differences between different data sources and generate a fused data matrix with a unified scale. The fused data matrix is ​​organized in chronological order to form the time-consistent object state data sequence.

[0008] As a further aspect of the present invention, the extraction of key biomarkers from the time-consistent object state data sequence based on the endoplasmic reticulum stress signaling pathway model specifically includes: The steps for constructing the endoplasmic reticulum stress signaling pathway model include: We obtained experimentally validated data on endoplasmic reticulum stress-related signaling pathways from multiple publicly available biomedical databases. The acquired endoplasmic reticulum stress-related signaling pathway data were integrated and conflict resolved, redundant interactions were removed, and missing key regulatory steps were supplemented to form a unified endoplasmic reticulum stress signaling pathway data set. Based on the endoplasmic reticulum stress signaling pathway data set, a directed weighted network model is constructed, where nodes represent biomolecular entities in the pathway, and directed edges represent regulatory or reactive relationships between biomolecular entities. The directed weighted network model is associated with a prior organelle function annotation database to identify and label the core sub-networks directly related to the endoplasmic reticulum lumen, endoplasmic reticulum membrane, and transmembrane signal transduction. The core sub-network is initialized with dynamic parameters, and the node state changes and edge signal transmission efficiency in the weighted network model are assigned benchmark parameter values ​​based on literature reports, thereby completing the construction of the endoplasmic reticulum stress signal pathway model. Load a predefined endoplasmic reticulum stress signaling pathway knowledge graph, which includes the interaction relationships between endoplasmic reticulum stress-related proteins, genes and metabolites; Each data item in the time-consistent object state data sequence is matched with an entity in the endoplasmic reticulum stress signal pathway knowledge graph, and the data items corresponding to the matching entities are selected. The consistency of the change magnitude and trend of the selected data items in the time-consistent object state data sequence is calculated, and data items with change magnitudes higher than a preset magnitude threshold and consistent change trends are selected as initial biomarkers. Co-expression network analysis was performed on the initially selected biomarkers to identify key biomarkers that occupy core positions in the co-expression network.

[0009] As a further aspect of the present invention, the step of utilizing a causal inference network to analyze the conditional dependencies between each indicator in the core indicator set and external intervention factors, and constructing an endoplasmic reticulum stress evolution causal diagram, specifically includes: Collect records of external intervention factors experienced by the target subjects during the specified time period, including records of environmental toxin exposure, dietary composition, and past medication use. The external intervention factors are recorded and associated with the core indicator set in a time series to construct an initial relationship network containing intervention factor nodes and biomarker nodes; On the initial relation network, a constraint-based causal discovery algorithm is applied to test the conditional independence between intervention factor nodes and biomarker nodes under different sets of conditions. Based on the test results of conditional independence, corresponding edges are added or deleted in the initial relation network to form an undirected dependency graph structure; For the edges in the undirected dependency graph structure, the directionality is determined by combining temporal sequence and domain prior knowledge, and each edge is assigned a direction from cause to effect, thus generating the endoplasmic reticulum stress evolution causal graph.

[0010] As a further aspect of the present invention, the step of simulating the expected change trajectory of the core indicator set after the application of different types and doses of endoplasmic reticulum stress inhibitors based on the endoplasmic reticulum stress evolution causal diagram specifically includes: Construct a drug knowledge base containing multiple endoplasmic reticulum stress inhibitors, wherein the drug knowledge base includes the known targets, pharmacokinetic parameters and common dose ranges of each endoplasmic reticulum stress inhibitor; For each endoplasmic reticulum stress inhibitor in the drug knowledge base, locate the biomarker node corresponding to its target point in the endoplasmic reticulum stress evolution causation diagram. Using the biomarker nodes as the starting point of intervention, the propagation process of the pharmacological effect of endoplasmic reticulum stress inhibitors in the endoplasmic reticulum stress evolution causal diagram is simulated based on the pharmacokinetic parameters of the endoplasmic reticulum stress inhibitors. For different set doses, the perturbation intensity and time delay of the pharmacological effect propagation on each indicator in the core indicator set are calculated respectively; By integrating the perturbation intensity and time delay generated by each indicator in the core indicator set, the expected change trajectory of each indicator in the core indicator set over time is generated at a specific dose of the endoplasmic reticulum stress inhibitor.

[0011] As a further aspect of the present invention, the step of obtaining the genomic characteristics and historical drug response records of the target object, and combining the genomic characteristics and historical drug response records to perform personalized correction on the expected change trajectory, specifically includes: Analyze the whole genome sequencing data of the target object and extract single nucleotide polymorphism information related to genes of drug-metabolizing enzymes, drug transporters and endoplasmic reticulum stress-related pathways; By querying pharmacogenomics databases and obtaining the predictive influence coefficients of the single nucleotide polymorphism information on the metabolic rate and potency of different endoplasmic reticulum stress inhibitors; Extract historical drug response records of target subjects from medical records, convert their specific manifestations in terms of efficacy into the magnitude of changes in biomarkers, and quantify their specific manifestations in terms of adverse reactions into severity levels; The predicted influence coefficient is applied to the expected change trajectory of the corresponding endoplasmic reticulum stress inhibitor, and the perturbation intensity and peak time parameters of the indicators in the expected change trajectory are adjusted. Individual-specific response patterns reflected in historical drug response records are incorporated as a regularization term into the adjustment process of the expected change trajectory, and the adjusted expected change trajectory is smoothed and personalized.

[0012] As a further aspect of the present invention, the step of calculating the comprehensive stability score of different endoplasmic reticulum stress inhibitor administration regimens within a preset evaluation period based on the corrected expected change trajectory specifically includes: For each core indicator in the revised expected change trajectory, set a target steady-state range and an acceptable fluctuation threshold; Within a preset evaluation period, the corrected expected change trajectory is sampled at fixed time intervals to obtain the predicted value of each core indicator at the sampling time point. The steady-state retention rate is calculated by statistically analyzing the frequency proportion of predicted values ​​for each core indicator falling within the target steady-state range. The magnitude and duration of the predicted value of each core indicator exceeding the acceptable fluctuation threshold are statistically analyzed, and a fluctuation over-limit penalty score is calculated. The overall stability score for each endoplasmic reticulum stress inhibitor administration regimen is calculated by weighting and combining the steady-state retention rate and fluctuation violation penalty score of all core indicators, and introducing scheme complexity as an adjustment factor.

[0013] As a further aspect of the present invention, the step of generating a recommended administration report of endoplasmic reticulum stress inhibitors based on the comprehensive stability score includes: All endoplasmic reticulum stress inhibitor administration regimens were ranked based on the comprehensive stability score, and a set of candidate regimens with scores higher than a predetermined threshold was selected. By integrating the detailed parameters, correction criteria, and expected trajectory of each scheme in the candidate scheme set, a structured endoplasmic reticulum stress inhibitor administration scheme recommendation report is generated; The step of ranking all feasible endoplasmic reticulum stress inhibitor administration regimens based on the comprehensive stability score and selecting a set of candidate regimens with scores higher than a predetermined threshold specifically includes: All feasible endoplasmic reticulum stress inhibitor administration regimens are sorted in descending order of their corresponding comprehensive stability scores to generate a regimen ranking list. In the list of ranked options, starting with the option with the highest score, the drug interaction risks and conflicts with known contraindications of each option are checked in turn; Eliminate application schemes with high-risk conflicts or contraindications, and reorder the remaining schemes; In the reordered list, select all schemes that rank highly and whose overall stability scores are all higher than the predetermined threshold. All selected schemes, along with their detailed application parameters, simulated trajectory identifiers, and comprehensive stability scores, are collectively formed into the candidate scheme set.

[0014] As a further aspect of the present invention, the step of using the biomarker node as the starting point for intervention and simulating the propagation process of the pharmacological effect of the endoplasmic reticulum stress inhibitor in the endoplasmic reticulum stress evolution causal diagram based on the pharmacokinetic parameters of the endoplasmic reticulum stress inhibitor specifically includes: In the endoplasmic reticulum stress evolution causation diagram, starting from the biomarker node, a breadth-first traversal is performed along the directed edges to identify all downstream biomarker nodes affected by the biomarker node. Based on the peak time and half-life parameters of endoplasmic reticulum stress inhibitors, a pharmacokinetic time curve model describing the change of their concentration in vivo over time was constructed. Based on the drug-time curve model, the inhibitory intensity on the biomarker nodes at different time points after drug administration was calculated; The calculated inhibition strength is used as the initial perturbation value and propagated attenuated along the directed edges in the endoplasmic reticulum stress evolution causality diagram according to the causal effect strength marked on the edges. For each downstream biomarker node in the propagation path, the sum of perturbation values ​​from all its upstream nodes is received, and its state change is calculated based on the dynamic characteristics of the downstream biomarker node itself, thus completing the simulation of the propagation process of the pharmacological effect.

[0015] As a further aspect of the present invention, the present invention also includes a system for recommending inhibitor administration regimens for endoplasmic reticulum stress relief, the system including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor, when executing the computer program, implements the steps of the method for recommending inhibitor administration regimens for endoplasmic reticulum stress relief as described above.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: By using causal inference network analysis to analyze the conditional dependencies between each indicator in the core indicator set and external intervention factors, and constructing a dynamic diagram of endoplasmic reticulum stress evolution, we can clearly identify the intrinsic relationship between core indicators and external intervention factors, clarify the core driving factors of endoplasmic reticulum stress state evolution, and overcome the limitations of conventional techniques that simply screen biomarkers without clarifying the driving factors of stress evolution. This makes the simulated expected change trajectory of core indicators after the administration of different endoplasmic reticulum stress inhibitors more closely match the actual evolution law of endoplasmic reticulum stress, reduce the simulation bias caused by unclear driving factors in conventional techniques, and make the expected change trajectory more accurate.

[0017] By combining the genomic characteristics of the target subjects with historical drug response records, the expected change trajectory of the core indicators obtained from the simulation is personalized. This can fully take into account the individual physiological differences of the target subjects and make up for the shortcomings of conventional techniques that use generalized simulations and ignore individual differences. The modified expected change trajectory is more in line with the specific physiological state of the target subjects. The comprehensive stability score calculated based on this trajectory is more targeted. In this way, the generated endoplasmic reticulum stress inhibitor administration regimen can be adapted to the individual characteristics of the target subjects, avoiding the problem of poor adaptability of conventional generalized regimens, and making the administration regimen more in line with actual treatment needs. Attached Figure Description

[0018] Figure 1 The flowchart shows the recommended method for administering inhibitors to relieve endoplasmic reticulum stress as described in this invention. Figure 2 A flowchart for constructing a dynamic diagram of endoplasmic reticulum stress evolution using a causal inference network; Figure 3 Graphs showing the pharmacokinetic time curves of different doses of endoplasmic reticulum stress inhibitors; Figure 4 A time-series diagram of key biomarkers in the endoplasmic reticulum stress pathway; Figure 5 A distribution chart of the overall stability scores for all feasible solutions. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0020] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0021] See Figure 1This study acquires multidimensional physiological data of target subjects over a specific time period, cleans and aligns the data to form a time-consistent sequence of subject state data. Based on an endoplasmic reticulum (ER) stress signaling pathway model, key biomarkers are extracted from the time-consistent subject state data sequence to identify a set of core indicators related to ER stress levels. Using a causal inference network, the conditional dependencies between each indicator in the core indicator set and external intervention factors are analyzed, constructing an ER stress evolution causal diagram. Based on the ER stress evolution causal diagram, the expected change trajectory of the core indicator set after the application of different types and doses of ER stress inhibitors is simulated. Genomic characteristics and historical drug response records of the target subjects are acquired, and the expected change trajectory is personalized by combining these characteristics with the historical drug response records. Based on the corrected expected change trajectory, the comprehensive stability score of different ER stress inhibitor administration regimens is calculated within a preset evaluation period. Based on the comprehensive stability score, an ER stress inhibitor administration regimen recommendation report is generated.

[0022] In one embodiment of the present invention, the multidimensional physiological data of the target object are obtained from multiple monitoring devices and databases. For example, proteomics data are obtained from a mass spectrometry platform, metabolomics data from a nuclear magnetic resonance platform, and cell imaging data from a high-content imaging system. Outlier detection and removal are performed on the proteomics, metabolomics, and cell imaging data respectively. Specifically, an interquartile range-based method is used to identify and remove outliers in each data dimension. For missing data points caused by equipment calibration or sample loss, time-series linear interpolation is used to supplement them. The proteomics, metabolomics, and cell imaging data, after anomaly processing and missing data supplementation, are uniformly mapped to the same time axis with seconds as the smallest unit, based on their precise timestamps at the time of acquisition. Through timestamp alignment and resampling, it is ensured that each aligned timestamp contains complete data entries from all three dimensions. The time-aligned multidimensional data were standardized using Z-score normalization to eliminate differences in magnitude and dimensions between proteomics, metabolomics, and cell imaging data caused by different detection principles. This resulted in a fused data matrix with a uniform scale, where rows correspond to time points and columns correspond to data dimensions. The fused data matrix was then organized chronologically, from the earliest to the latest time point, forming a temporally consistent sequence of object state data, which serves as the input for subsequent analyses.

[0023] In some embodiments, key biomarkers are extracted from time-consistent object state data sequences based on an endoplasmic reticulum (ER) stress signaling pathway model. A predefined ER stress signaling pathway knowledge graph is loaded. This knowledge graph, stored in a graph database, contains the interactions between ER stress-related proteins, genes, and metabolites. For example, nodes include proteins such as BIP, CHOP, and XBP1s, and edges represent regulatory relationships such as phosphorylation and ubiquitination. Each data point in the time-consistent object state data sequence is precisely matched with the entity names and identifiers in the ER stress signaling pathway knowledge graph. Data items corresponding to entities present in the knowledge graph are selected; unmatched data items are not further filtered. The magnitude and trend consistency of the selected data items in the time-consistent object state data sequence are calculated. The magnitude is obtained by calculating the coefficient of variation of values ​​within the entire time window, and the trend consistency is evaluated by comparing the proportion of consistent change directions between adjacent time points. Data items with magnitudes exceeding a preset threshold and consistent change trends are selected as initial biomarkers. Co-expression network analysis was performed on the initially selected biomarkers. Based on their time-series data, pairwise correlations were calculated to construct an undirected weighted network. Nodes represent biomolecular entities in the pathway, and directed edges represent regulatory or response relationships between these entities. The weight of each edge is the absolute value of the correlation coefficient between the biomarkers. Nodes with the highest betweenness centrality and tight centrality were identified, and these nodes were designated as key biomarkers at core hub positions. The key biomarkers at core hub positions and their directly connected neighboring biomarkers in the co-expression network were collectively identified as the core indicator set, representing the core characteristics of the endoplasmic reticulum stress state of the target organism.

[0024] The construction steps of the endoplasmic reticulum stress signaling pathway model include: acquiring experimentally validated endoplasmic reticulum stress-related signaling pathway data from multiple publicly available biomedical databases. This data includes protein-protein interaction pairs from the KEGG and Reactome databases, gene regulatory relationships from the STRING database, and metabolic reaction chains from the MetaCyc database. The acquired endoplasmic reticulum stress-related signaling pathway data are integrated and conflict-resolved. Redundant relationships describing the same interactions from different sources are removed through entity unification and relationship merging. Undocumented transmembrane signal transduction steps (i.e., key regulatory steps) are supplemented based on literature evidence, forming a unified endoplasmic reticulum stress signaling pathway data set. Based on this dataset, a directed weighted network model is constructed, where nodes represent biomolecular entities in the pathway, directed edges represent regulatory or reactive relationships between biomolecular entities, and edge weights represent the confidence or strength of the interaction. The confidence level is derived from joint scores from multiple databases. A directed weighted network model was associated with a prior organelle functional annotation database. Nodes located in the endoplasmic reticulum membrane and lumen, along with their interaction edges, were identified using node annotation information. Core subnetworks directly related to the endoplasmic reticulum lumen, membrane, and transmembrane signal transduction were then identified and labeled. Dynamic parameters of the core subnetworks were initialized, assigning baseline parameter values ​​based on literature reports to node state changes and edge signal transduction efficiency, such as the rate constant of protein phosphorylation and the activation threshold of transcription factors. This completed the construction of an endoplasmic reticulum stress signaling pathway model, which can be used to dynamically simulate signal transduction processes.

[0025] In practical implementation, a robust statistic is used to calculate the correlation between biomarkers. It can be understood that calculating the pairwise association strength between initially selected biomarkers in a temporally consistent object state data sequence is the foundation for constructing a co-expression network. Optionally, a correlation calculation formula based on rank correlation can be introduced:

[0026] Where: symbol The Spearman rank correlation coefficient represents the correlation between biomarker X and biomarker Y, with the symbol […]. The symbol represents the total number of time points in a time-consistent object state data sequence. and Represent the measured values ​​of biomarker X and biomarker Y at time point i, respectively, with symbols... and They represent and Rank, symbol among all time point measurements and These represent the average values ​​of biomarker X and biomarker Y across all time points, respectively. This formula is used to quantify the similarity of monotonic trends among initially selected biomarkers, and calculates... The absolute value is used as the weight of the connecting edges in the co-expression network.

[0027] In one embodiment of the present invention, see [reference] Figure 2 For the target subjects, records of external intervention factors experienced by them within a specific time period were collected. These records included environmental toxin exposure records, dietary composition records, and past medication records. Environmental toxin exposure records were obtained from environmental monitoring data, such as daily concentrations of specific volatile organic compounds in the air. Dietary composition records were obtained from cross-referencing the subjects' dietary logs and food composition databases, recording the daily intake of macronutrients and micronutrients. Past medication records were obtained from the subjects' electronic health records, recording the names, dosages, routes of administration, and times of medications used within the specific time period. External intervention factor records were stored in time-series format, with each record entry including a timestamp, intervention factor type, and quantified value.

[0028] External intervention factor records are time-series correlated with the core indicator set to construct an initial relationship network containing intervention factor nodes and biomarker nodes. In implementation, a graph structure is initialized, with the node set including nodes corresponding to all external intervention factor variables and nodes corresponding to all biomarker variables in the core indicator set. For each pair of nodes—an intervention factor node and a biomarker node—the statistical correlation between their data sequences at all overlapping time points is calculated, for example, by calculating the Pearson correlation coefficient. It can be understood that in the initial relationship network, if the absolute value of the correlation calculated between two node variables exceeds a preset threshold, an undirected edge is added between the two nodes, with the edge weight set to the absolute value of the correlation, thus forming an initial relationship network representing potential associations between variables.

[0029] On the initial relational network, a constraint-based causal discovery algorithm is applied to test the conditional independence between intervention factor nodes and biomarker nodes under different condition sets. For any two adjacent node variables X and Y in the initial relational network, the algorithm systematically selects one or more other node variables to form a condition set Z, and then tests whether X and Y are independent given the condition set Z. Optionally, the conditional independence test can use a partial correlation coefficient test based on a Gaussian distribution, or a nonparametric test based on a resampling method. Each test generates a p-value to determine whether the two variables are conditionally independent under a specific condition set. Based on the conditional independence test results, corresponding edges are added or deleted in the initial relational network. If the algorithm finds that two nodes that were originally connected by an edge are conditionally independent under a certain condition set, the edge between these two nodes is deleted; if the algorithm still cannot determine that the two nodes are conditionally independent after traversing all possible condition sets, the edge is retained. After iterative verification and edge removal, a large number of spurious edges were removed from the initial relational network, forming an undirected dependency graph structure. This graph structure more concisely reflects the direct dependencies between variables.

[0030] For edges in an undirected dependency graph structure, directionality is determined by combining temporal sequence and domain prior knowledge, assigning each edge a direction from cause to effect. In some embodiments, the rules for directionality determination include: if an edge connects an intervention factor node and a biomarker node, and the occurrence time of the intervention factor is earlier than the change time of the biomarker in the time record, then the direction of the edge is directed from the intervention factor node to the biomarker node. If an edge connects two biomarker nodes, the direction is determined based on prior knowledge provided by the endoplasmic reticulum stress signaling pathway model. For example, if it is known that protein A activates protein B in the pathway, then the direction of the edge is directed from the node representing protein A to the node representing protein B. It can be understood that in the absence of explicit temporal sequence or prior knowledge, for the remaining undirected edges, the direction of the edge can be inferred by applying the direction propagation rules in causal discovery algorithms. For example, if there exists a structure X—Y—Z, and X and Z are not connected, but X and Z are independent given Y, then the edge can be directed as X->Y<-Z (collision structure). Finally, the direction of each edge in the undirected dependency graph is determined, generating a directed acyclic graph, namely the endoplasmic reticulum stress evolution causality graph. In the endoplasmic reticulum stress evolution causality graph, nodes represent variables, and directed edges represent direct influence relationships from causal variables to outcome variables.

[0031] In practice, the conditional independence test involves the calculation of a core statistic. Optionally, when the variables follow a multivariate Gaussian distribution, the partial correlation coefficient can be used to determine independence. The formula for calculating the partial correlation coefficient is as follows:

[0032] Where: symbol This represents the partial correlation coefficient between variables X and Y, given a set of control variables Z. The sign is... The Pearson correlation coefficient between variables X and Y is represented by the symbol […]. This represents the overall correlation characteristic obtained by calculating the correlation coefficients between variable X and all variables in the variable set Z, using a recursive formula or matrix inversion. The symbol is... This represents the overall correlation characterization obtained by calculating the correlation coefficients between variable Y and all variables in the variable set Z, using a recursive formula or matrix inversion. Then, based on the partial correlation coefficients... Calculate the test statistic to determine whether X and Y are independent given Z. This statistic guides the algorithm in deciding whether to remove the edge between variables X and Y.

[0033] In one embodiment of the present invention, a drug knowledge base containing multiple endoplasmic reticulum stress inhibitors is constructed. The drug knowledge base is stored in the form of a relational database table, with each record corresponding to one endoplasmic reticulum stress inhibitor. The drug knowledge base contains the known targets, pharmacokinetic parameters, and common dose ranges for each endoplasmic reticulum stress inhibitor. The pharmacokinetic parameters include oral bioavailability, time to peak concentration, half-life, and clearance rate. The common dose ranges are derived from preclinical studies and phase I clinical trial data. For each endoplasmic reticulum stress inhibitor in the drug knowledge base, the biomarker node corresponding to its target is located in the endoplasmic reticulum stress evolution causal diagram. Specifically, the gene or protein name of the known target of the endoplasmic reticulum stress inhibitor is matched with the identifiers of all nodes in the endoplasmic reticulum stress evolution causal diagram. If the node identifier is completely identical to or synonymous with the target name, the node is determined as the intervention starting point.

[0034] Using the matched biomarker nodes as the starting point for intervention, the propagation process of the pharmacological effect of endoplasmic reticulum stress inhibitors in the endoplasmic reticulum stress evolution causal graph is simulated based on the pharmacokinetic parameters of the inhibitors. In the endoplasmic reticulum stress evolution causal graph, a breadth-first traversal is performed along directed edges starting from the biomarker node to identify all downstream biomarker nodes affected by the biomarker node. This traversal continues until a terminal node with no outgoing edges is reached. Based on the peak time and half-life parameters of the endoplasmic reticulum stress inhibitors, a pharmacokinetic-time curve model describing the change in their concentration in vivo over time is constructed. Optionally, a one-compartment model can be used for intravenous administration, and a one-compartment model with absorption can be used for oral administration. Based on the pharmacokinetic-time curve model, the inhibitory intensity on the biomarker nodes at different time points after administration is calculated. The inhibitory intensity is usually related to the drug concentration at the site of action. The calculated inhibition intensity is used as the initial perturbation value and propagated along the directed edges in the endoplasmic reticulum stress evolution causality diagram, decaying according to the causal effect intensity marked on the edges. The causal effect intensity marked on the edges is a decay coefficient between 0 and 1. For each downstream biomarker node in the propagation path, the sum of perturbation values ​​from all its upstream nodes is received, and its state change is calculated based on the downstream biomarker node's own kinetic characteristics, including its activation threshold and response function, ultimately completing the simulation of pharmacological effect propagation.

[0035] In some embodiments, the different doses include multiple gradients selected from common dose ranges in the drug knowledge base. For the same endoplasmic reticulum stress inhibitor, different dosages directly affect the peak concentration parameter in its pharmacokinetic curve model, thereby changing the initial inhibitory intensity on the initiating biomarker node. During the simulated propagation process, the simulation time point at which the state of each indicator in the core indicator set first undergoes a significant change is recorded. The difference between this time point and the dosing time point is recorded as the time delay, and the final amplitude of the indicator state change is recorded as the perturbation intensity. Integrating the perturbation intensity and the time delay, the expected change trajectory of each indicator in the core indicator set under a specific dose of the endoplasmic reticulum stress inhibitor over time is generated. The expected change trajectory is stored in the form of time series data, containing a series of time points and the predicted state value of each indicator at each time point.

[0036] In some embodiments, the relationship between inhibitory intensity and drug concentration can be described by a saturation function. The inhibitory intensity at time t on the target is calculated using the following formula. :

[0037] Where: symbol This indicates the inhibitory intensity of the endoplasmic reticulum stress inhibitor on the target biomarker at time t after drug administration, and is a value between 0 and... Dimensionless values ​​between; symbols This indicates the maximum inhibitory strength that the endoplasmic reticulum stress inhibitor can produce; symbol This represents the predicted concentration of the endoplasmic reticulum stress inhibitor at its site of action at time t, calculated using a drug-time curve model; symbol Indicates the drug concentration required to produce half of the maximum inhibitory intensity; symbol The Hill coefficient describes the steepness of the concentration-response curve. This formula links the pharmacokinetic model to the pharmacodynamic model, and its calculation results... This will serve as the initial perturbation value for subsequent propagation in the network. It can be understood that the parameters... , and Data is derived from drug knowledge bases or in vitro experimental data.

[0038] Simulating the propagation of pharmacological effects within the endoplasmic reticulum stress evolution causal diagram involves calculating network dynamics. In practice, a state variable is defined for each node in the endoplasmic reticulum stress evolution causal diagram. Starting from the biomarker node, which serves as the intervention initiation point, its state change is determined by the intensity of inhibition. Direct drive. For any downstream biomarker node, its rate of state change at time t depends on the current states of all upstream nodes pointing to it, the strength of the causal effect of the connecting edges, and the node's own dynamic parameters. This process is achieved by solving a set of coupled differential equations or using an iterative update algorithm with discrete time steps, thereby simulating the process by which the perturbation signal spreads throughout the causal network and affects each core indicator. Finally, it outputs the continuous change curve of each core indicator within the simulated time range, i.e., the expected change trajectory.

[0039] See Figure 3 This is a pharmacokinetic analysis chart of different doses of endoplasmic reticulum stress inhibitors, showing the changes in drug concentration in vivo over 24 hours for three doses. The peak concentration is highest at 40 mg, followed by 20 mg, and lowest at 10 mg. The peak concentrations of the three doses show an approximately 2:1 ratio, consistent with the pharmacokinetic characteristics of a linear correlation between dose and peak concentration. The peak time of the three curves is consistent, and the elimination trend is synchronized, indicating that the dose only changes the concentration amplitude and does not affect the time characteristics of drug absorption and elimination. The drug concentration rises rapidly from 0 to 5 hours, reflecting the process of drug absorption from the gastrointestinal tract into the blood after oral administration. After 5 hours, the concentration gradually decreases, reflecting the comprehensive process of drug distribution, metabolism, and excretion in the body, with the curve showing an exponential decay trend. Higher doses result in greater drug exposure and stronger pharmacological potential, but may also increase the risk of adverse reactions. The fixed peak time suggests that clinicians can monitor the peak blood drug concentration approximately 5 hours after administration to assess efficacy or toxicity.

[0040] In one embodiment of the present invention, the genomic characteristics and historical drug response records of the target object are obtained, and the expected change trajectory is personalized by combining the genomic characteristics and historical drug response records. The whole-genome sequencing data of the target object is analyzed to extract single nucleotide polymorphism (SNP) information related to drug-metabolizing enzymes, drug transporters, and endoplasmic reticulum stress-related pathway genes. For example, from the VCF format sequencing file of target object "Object A", the genotypes at the rs4244285 site on the CYP2C19 gene, the genotypes at the rs1045642 site on the ABCB1 gene, and related site information on the XBP1 gene are extracted. A pharmacogenomics database is queried, and based on the SNP information, the predictive influence coefficients on the metabolic rate and potency of different endoplasmic reticulum stress inhibitors are obtained. For example, the PharmGKB database is queried, based on the CYP2C19... The genotype of the second allele (corresponding to the rs4244285 locus) yielded a metabolic rate adjustment coefficient of 0.5 for the endoplasmic reticulum stress inhibitor "drug M," indicating a slowed metabolism. Historical drug response records of the target subjects were extracted from medical records, and their specific manifestations in terms of efficacy were converted into changes in biomarkers. Their specific manifestations in terms of adverse reactions were quantified into severity levels. For example, from subject A's electronic medical record, records of a 30% reduction in LDL cholesterol during statin use, coupled with a slight increase in creatine kinase levels, were extracted. The efficacy was quantified as "30% reduction," and the adverse reaction as "Crease kinase elevation level 1."

[0041] The predictive impact coefficient is applied to the expected trajectory of the corresponding endoplasmic reticulum stress inhibitor, adjusting the perturbation intensity and peak time parameters of the indicators in the trajectory. In practice, if the predictive impact coefficient indicates that the metabolic rate of the endoplasmic reticulum stress inhibitor "drug M" in subject A is slowed down, then the peak time parameter of the core indicator state change in the expected trajectory of drug M is multiplied by a delay factor greater than 1. For example, if the original peak time is 4 hours, it is adjusted to 6 hours. If the predictive impact coefficient indicates that the potency is enhanced, then the expected perturbation amplitude of the core indicator in the trajectory is multiplied by an enhancement factor greater than 1. The individual-specific response pattern reflected in the historical drug response record is incorporated as a regularization term into the trajectory adjustment process. For example, if the historical record of subject A shows that its response to drugs is usually mild, that is, the magnitude of efficacy and adverse reactions is lower than the population average, then a global attenuation coefficient is applied to the predicted change amplitude of all core indicators on the adjusted trajectory to smooth and correct the adjusted trajectory, forming a personalized expected trajectory.

[0042] Based on the revised expected change trajectory, the comprehensive stability score of different endoplasmic reticulum stress inhibitor administration regimens was calculated within a preset evaluation period. For each core indicator in the revised expected change trajectory, an ideal target steady-state range and an acceptable fluctuation threshold were set. The target steady-state range is a numerical interval; for example, the target steady-state range for the core indicator "protein X phosphorylation level" was set to [0.2, 0.4] (relative units). The acceptable fluctuation threshold was set to extend 20% outward from the boundary of the target steady-state range, i.e., a lower limit of 0.16 and an upper limit of 0.48. Within the preset evaluation period, the revised expected change trajectory was sampled at fixed time intervals to obtain the predicted value of each core indicator at the sampling time point. For example, if the evaluation period was 7 days, samples were taken every 6 hours, resulting in 28 predicted values ​​at a time point. The proportion of predicted values ​​for each core indicator falling within the ideal target steady-state range was calculated, and its steady-state retention rate was calculated. The steady-state retention rate is equal to the number of predicted values ​​falling within the target steady-state range divided by the total number of samplings. The magnitude and duration of predicted values ​​for each core indicator exceeding the acceptable fluctuation threshold were calculated, and its fluctuation violation penalty score was calculated.

[0043] The formula for calculating the cumulative confidence probability is:

[0044] in, This represents the cumulative confidence probability that the current state of the vacuum circuit breaker belongs to the i-th lifetime state level. This represents the confidence distribution of the life state of the fused vacuum circuit breaker, and is considered as the probability density function of the remaining life random variable t in the calculation. and These represent the lower and upper limits of the preset lifespan range for the i-th lifespan state level, respectively. Integration operation. That is, in the probability density function Above, for variable t from arrive The definite integral yields the probability that the state belongs to that interval; by iterating through all the preset lifetime state levels and calculating them individually, a set of cumulative confidence probabilities corresponding to each level can be obtained. And satisfy .

[0045] The overall stability score for each endoplasmic reticulum stress inhibitor administration regimen is calculated by weighting the steady-state retention rate and fluctuation violation penalty scores of all core indicators, and introducing protocol complexity as a moderating factor. Each indicator in the core indicator set is assigned a clinical importance weight, determined based on the correlation strength between the core indicator and the pathological outcome of endoplasmic reticulum stress. The steady-state retention rate of each indicator is multiplied by its clinical importance weight, and the sum is obtained to obtain the weighted total steady-state retention score. Similarly, the fluctuation violation penalty score of each indicator is multiplied by its clinical importance weight, and the sum is obtained to obtain the weighted total violation penalty score. The complexity of the endoplasmic reticulum stress inhibitor administration regimen is calculated, quantified based on the number of inhibitor types involved, dosing frequency, and dose adjustment frequency. The weighted total steady-state retention score is subtracted from the weighted total violation penalty score, and then divided by the protocol complexity to obtain the final overall stability score. In some embodiments, each indicator in the core indicator set is assigned a clinical importance weight, determined based on the correlation strength between the core indicator and the pathological outcome of endoplasmic reticulum stress. The correlation strength can be derived from hazard ratios in the literature or expert ratings. The weighted steady-state maintenance score is obtained by multiplying the steady-state maintenance rate of each indicator by its clinical importance weight and summing the results. The weighted out-of-bounds penalty score is obtained by multiplying the fluctuation penalty score of each indicator by its clinical importance weight and summing the results. The complexity of the endoplasmic reticulum stress inhibitor administration regimen is calculated. The complexity is quantified based on the number of endoplasmic reticulum stress inhibitors involved in the regimen, the dosing frequency, and the dose adjustment frequency. Optionally, the complexity calculation formula is: Complexity = Number of endoplasmic reticulum stress inhibitors × Daily dosing frequency × (1 + Weekly dose adjustment frequency). It can be understood that the final comprehensive stability score S of the endoplasmic reticulum stress inhibitor administration regimen can be calculated using a comprehensive formula. Optionally, the comprehensive stability score calculation formula is defined as:

[0046] Where: symbol Indicates the overall stability score of the endoplasmic reticulum stress inhibitor administration regimen; symbol Indicates the total number of core indicators; symbol Represents the clinical importance weight of the i-th core indicator; symbol Represents the steady-state retention rate of the i-th core indicator; symbol This represents the penalty score for fluctuations exceeding the limit for the i-th core indicator; symbol This represents the complexity of the endoplasmic reticulum stress inhibitor administration regimen. The denominator is incremented by 1 to prevent calculation errors when the complexity is zero. This formula quantifies the effectiveness of the regimen in maintaining endoplasmic reticulum homeostasis while penalizing the regimen's complexity and instability.

[0047] In practice, the penalty score for exceeding the limit is fluctuating. The calculation relies on quantifying the predicted trajectory exceeding a threshold. The fluctuation out-of-bounds penalty score... It can be defined as a function of all sampling points exceeding the amplitude and duration, for example ,in It is the absolute distance by which the predicted value of the i-th indicator at time t exceeds the acceptable fluctuation threshold. This is the time weighting for the duration of the out-of-bounds state. Refer to Table 1, which shows an example for target object "Object A", including the core indicators of the three hypotheses, their clinical importance weights, the target steady-state range, the acceptable fluctuation threshold, and the steady-state retention rate and fluctuation out-of-bounds penalty score calculated after evaluating a candidate endoplasmic reticulum stress inhibitor administration regimen.

[0048] Table 1: Example Table of Steady-State Assessment of Core Indicators for Object A

[0049] Based on the data in Table 1, the weighted steady-state retention total score is... The weighted total penalty for exceeding the boundary is... Assume the complexity C of the currently evaluated endoplasmic reticulum stress inhibitor administration regimen is 2 (e.g., involving one endoplasmic reticulum stress inhibitor, administered twice daily, with no dose adjustment weekly). It is understood that, substituting into the overall stability score calculation formula, this regimen scores as follows: This score can be compared and ranked with the scores of other schemes.

[0050] See Figure 4 This is a time-series graph showing the changes in signal intensity of key biomarkers in the endoplasmic reticulum stress (ERstress) pathway over time, reflecting the dynamic response of the pathway after intervention with ERstress inhibitors. PERK, a pathway with an early and rapid response, reaches its peak at 0 hours and then rapidly decays, serving as an early warning signal of ERstress. ATF6 is initiated synchronously with PERK, with a slightly delayed peak and a slightly slower decay rate, participating in early stress regulation. IRE1 initially has a strong signal, briefly decreases, and then forms a secondary peak around 12 hours, making it a key molecule in the regulation of sustained stress and apoptosis. XBP1, a downstream molecule of IRE1, reaches its peak signal around 4 hours, followed by fluctuating decline, reflecting the sustained activation of the unfolded protein response (UPR). CHOP, an apoptosis-related biomarker, initiates latest and lasts the longest, serving as a core indicator of ERstress-induced apoptosis.

[0051] In one embodiment of the present invention, an endoplasmic reticulum stress inhibitor (ERI) administration regimen recommendation report is generated based on the comprehensive stability score. All feasible ERI administration regimens are ranked according to their comprehensive stability scores, and a set of candidate regimens with scores above a predetermined threshold is selected. All feasible ERI administration regimens are then sorted in descending order of their corresponding comprehensive stability scores to generate a regimen ranking list. The regimen ranking list is an ordered data structure, such as a table or array, where each record contains a regimen identifier, detailed administration parameters (e.g., drug name, dosage, dosing frequency, treatment duration), and a calculated comprehensive stability score. The list is arranged from highest to lowest comprehensive stability score. Starting with the regimen with the highest score, the drug interaction risk and known contraindications of each regimen are checked sequentially. This check is performed by querying a drug interaction database and the target individual's personal health record. For example, for the top-ranked regimen "Rule Alpha: Endoplasmic reticulum stress inhibitor A 50 mg orally once daily," the database is queried to confirm whether ERI is contraindicated or has serious interactions with other drugs currently being taken by the target individual, and the target individual's medical history is checked for contraindications to this regimen.

[0052] After removing administration regimens with high-risk conflicts or contraindications, the remaining regimens are re-ranked. In practice, if a regimen is identified as having a serious risk of drug interaction or violating a clear contraindication of the target subject (e.g., the target subject has severe hepatic impairment and the drug in the regimen is primarily metabolized by the liver), this regimen is permanently removed from the ranking list. After removing high-risk regimens, the remaining regimens are arranged in descending order based on their original comprehensive stability scores, forming a new, filtered regimen ranking list. In the re-ranked list, all regimens with the highest ranking and comprehensive stability scores above a predetermined threshold are selected. The predetermined threshold is a configurable value; starting from the top of the new ranking list, all regimens with a comprehensive stability score greater than 0.5 are selected sequentially. All selected regimens, along with their detailed administration parameters, simulation trajectory identifiers, and comprehensive stability scores, form a candidate regimen set, which serves as the direct data source for subsequent report generation.

[0053] The system integrates detailed parameters, correction rationale, and expected trajectories of each candidate regimen from the candidate regimen set to generate a structured endoplasmic reticulum stress inhibitor administration regimen recommendation report. In some embodiments, the structured report uses a predefined template and includes the following sections: report summary, target identifier, regimen generation timestamp, candidate regimen list, and detailed description. The report summary briefly describes the number of candidate regimens generated and the highest score. The candidate regimen list presents the core information of each candidate regimen in tabular form. It is understood that the table includes at least the following: regimen ranking, regimen identifier, drug composition, dosing regimen, overall stability score, and primary target. The detailed description provides a comprehensive description of each candidate regimen, including its detailed administration parameters, the personalized correction rationale upon which the regimen simulation is based, and a summary of the expected change trajectory of key indicators. Optionally, the report may include a link or identifier to a detailed simulation trajectory data file for decision-makers to review in depth.

[0054] In practice, setting a predetermined threshold requires balancing the performance and safety of the proposed solutions. The predetermined threshold can be a fixed empirical value or a dynamic value based on the score distribution of all feasible solutions. In some embodiments, a quantile-based dynamic threshold setting method is used. Optionally, the dynamic threshold... The calculation formula is as follows:

[0055] Where: symbol Indicates the dynamically predetermined threshold used to filter candidate solutions; symbol Indicates the first quartile of the overall stability score for all feasible endoplasmic reticulum stress inhibitor administration regimens; symbol This represents the third quartile of the overall stability score for all feasible endoplasmic reticulum stress inhibitor administration regimens; symbol This is an adjustable coefficient used to control the threshold level, with a typical value between 1.0 and 1.5. This formula utilizes the distribution of solution scores to set the threshold at a level higher than the scores of most solutions. The interquartile range (IQR) is used to measure the dispersion of scores. Using this method, a predetermined threshold can adapt to the overall score level of different target objects or different sets of solutions; only solutions whose overall stability score is significantly higher than the median will be included in the candidate solution set.

[0056] The generated endoplasmic reticulum stress inhibitor administration regimen recommendation report is output in electronic document format, such as PDF or structured JSON file. The report's content directly supports clinical decision-making. The regimen ranking in the report provides decision-makers with an intuitive reference for prioritization, while detailed parameters and the rationale for correction enhance the transparency and interpretability of the regimen, ultimately achieving personalized treatment recommendations based on quantitative assessment.

[0057] See Figure 5 This is a comprehensive stability score distribution chart of all feasible options, used to visually demonstrate the score distribution and threshold selection logic of all feasible options. The option scores exhibit a multi-peak distribution, mainly concentrated in two high-score ranges: 0.25–0.35 and 0.65–0.90, with fewer options in the middle range (0.4–0.6). A fixed threshold (0.5) retains a large number of low-to-medium-scoring options, indicating a relatively lenient selection criterion. A dynamic threshold (0.878) retains only high-quality options with scores significantly higher than the overall level, with a stricter selection criterion, adapting to the score distribution of different datasets. The dynamic threshold excludes most options, retaining only high-stability options with scores ≥0.878, better aligning with the clinical decision-making requirement of "prioritizing highly reliable options." Comparing the differences between fixed and dynamic thresholds balances the strictness and adaptability of the selection process.

[0058] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A recommended method for administering inhibitors to alleviate endoplasmic reticulum stress, characterized in that, Includes the following steps: Acquire multidimensional physiological data of the target object within a specific time period, clean and align the multidimensional physiological data to form a temporally consistent object state data sequence; Based on the endoplasmic reticulum stress signaling pathway model, key biomarkers were extracted from the time-consistent object state data sequence to identify a set of core indicators related to endoplasmic reticulum stress levels. Using causal inference networks, the conditional dependencies between each indicator in the core indicator set and external intervention factors are analyzed, and an endoplasmic reticulum stress evolution dynamic diagram is constructed. Based on the endoplasmic reticulum stress evolution causation diagram, the expected change trajectory of the core indicator set after the application of different types and doses of endoplasmic reticulum stress inhibitors is simulated. Obtain the genomic characteristics and historical drug response records of the target object, and combine the genomic characteristics and historical drug response records to make personalized corrections to the expected change trajectory; Based on the revised expected change trajectory, the overall stability score of different endoplasmic reticulum stress inhibitor administration regimens was calculated within the preset evaluation period; Based on the comprehensive stability score, a recommended administration schedule for endoplasmic reticulum stress inhibitors is generated.

2. The method for recommending an inhibitor administration regimen for relieving endoplasmic reticulum stress according to claim 1, characterized in that, The step of cleaning and aligning the multidimensional physiological data to form a temporally consistent object state data sequence specifically includes: The multidimensional physiological data are obtained from monitoring devices and databases from different sources, including proteomics data, metabolomics data, and cell imaging data. The proteomics data, metabolomics data, and cell imaging data that have undergone anomaly processing and missing data filling are uniformly mapped onto the same time axis to obtain time-aligned multi-dimensional data. Standardize the time-aligned multi-dimensional data to eliminate the dimensional differences between different data sources and generate a fused data matrix with a unified scale. The fused data matrix is ​​organized in chronological order to form the time-consistent object state data sequence.

3. The method for recommending an inhibitor administration regimen for relieving endoplasmic reticulum stress according to claim 2, characterized in that, The extraction of key biomarkers from the time-consistent object state data sequence based on the endoplasmic reticulum stress signaling pathway model specifically includes: The steps for constructing the endoplasmic reticulum stress signaling pathway model include: We obtained experimentally validated data on endoplasmic reticulum stress-related signaling pathways from multiple publicly available biomedical databases. The acquired endoplasmic reticulum stress-related signaling pathway data were integrated and conflict resolved, redundant interactions were removed, and missing key regulatory steps were supplemented to form a unified endoplasmic reticulum stress signaling pathway data set. Based on the endoplasmic reticulum stress signaling pathway data set, a directed weighted network model is constructed, where nodes represent biomolecular entities in the pathway, and directed edges represent regulatory or reactive relationships between biomolecular entities. The directed weighted network model is associated with a prior organelle function annotation database to identify and label the core sub-networks directly related to the endoplasmic reticulum lumen, endoplasmic reticulum membrane, and transmembrane signal transduction. The core sub-network is initialized with dynamic parameters, and the node state changes and edge signal transmission efficiency in the weighted network model are assigned benchmark parameter values ​​based on literature reports, thereby completing the construction of the endoplasmic reticulum stress signal pathway model. Load a predefined endoplasmic reticulum stress signaling pathway knowledge graph, which includes the interaction relationships between endoplasmic reticulum stress-related proteins, genes and metabolites; Each data item in the time-consistent object state data sequence is matched with an entity in the endoplasmic reticulum stress signal pathway knowledge graph, and the data items corresponding to the matching entities are selected. The consistency of the change magnitude and trend of the selected data items in the time-consistent object state data sequence is calculated, and data items with change magnitudes higher than a preset magnitude threshold and consistent change trends are selected as initial biomarkers. Co-expression network analysis was performed on the initially selected biomarkers to identify key biomarkers that occupy core positions in the co-expression network.

4. The method for recommending an inhibitor administration regimen for relieving endoplasmic reticulum stress according to claim 3, characterized in that, The method of utilizing causal inference networks to analyze the conditional dependencies between each indicator in the core indicator set and external intervention factors, and constructing an endoplasmic reticulum stress evolution dynamics diagram, specifically includes: Collect records of external intervention factors experienced by the target subjects during the specified time period, including records of environmental toxin exposure, dietary composition, and past medication use. The external intervention factors are recorded and associated with the core indicator set in a time series to construct an initial relationship network containing intervention factor nodes and biomarker nodes; On the initial relation network, a constraint-based causal discovery algorithm is applied to test the conditional independence between intervention factor nodes and biomarker nodes under different sets of conditions. Based on the test results of conditional independence, corresponding edges are added or deleted in the initial relation network to form an undirected dependency graph structure; For the edges in the undirected dependency graph structure, the directionality is determined by combining temporal sequence and domain prior knowledge, and each edge is assigned a direction from cause to effect, thus generating the endoplasmic reticulum stress evolution causal graph.

5. The method for recommending an inhibitor administration regimen for relieving endoplasmic reticulum stress according to claim 1, characterized in that, The step of simulating the expected change trajectory of the core indicator set after the application of different types and doses of endoplasmic reticulum stress inhibitors, based on the endoplasmic reticulum stress evolution causal diagram, specifically includes: Construct a drug knowledge base containing multiple endoplasmic reticulum stress inhibitors, wherein the drug knowledge base includes the known targets, pharmacokinetic parameters and common dose ranges of each endoplasmic reticulum stress inhibitor; For each endoplasmic reticulum stress inhibitor in the drug knowledge base, locate the biomarker node corresponding to its target point in the endoplasmic reticulum stress evolution causation diagram. Using the biomarker nodes as the starting point of intervention, the propagation process of the pharmacological effect of endoplasmic reticulum stress inhibitors in the endoplasmic reticulum stress evolution causal diagram is simulated based on the pharmacokinetic parameters of the endoplasmic reticulum stress inhibitors. For different set doses, the perturbation intensity and time delay of the pharmacological effect propagation on each indicator in the core indicator set are calculated respectively; By integrating the perturbation intensity and time delay generated by each indicator in the core indicator set, the expected change trajectory of each indicator in the core indicator set over time is generated at a specific dose of the endoplasmic reticulum stress inhibitor.

6. The method for recommending an inhibitor administration regimen for relieving endoplasmic reticulum stress according to claim 5, characterized in that, The process of acquiring the genomic characteristics and historical drug response records of the target object, and then combining these characteristics with the historical drug response records to personalize the expected change trajectory, specifically includes: Analyze the whole genome sequencing data of the target object and extract single nucleotide polymorphism information related to genes of drug-metabolizing enzymes, drug transporters and endoplasmic reticulum stress-related pathways; By querying pharmacogenomics databases and obtaining the predictive influence coefficients of the single nucleotide polymorphism information on the metabolic rate and potency of different endoplasmic reticulum stress inhibitors; Extract historical drug response records of target subjects from medical records, convert their specific manifestations in terms of efficacy into the magnitude of changes in biomarkers, and quantify their specific manifestations in terms of adverse reactions into severity levels; The predicted influence coefficient is applied to the expected change trajectory of the corresponding endoplasmic reticulum stress inhibitor, and the perturbation intensity and peak time parameters of the indicators in the expected change trajectory are adjusted. Individual-specific response patterns reflected in historical drug response records are incorporated as a regularization term into the adjustment process of the expected change trajectory, and the adjusted expected change trajectory is smoothed and personalized.

7. The method for recommending an inhibitor administration regimen for relieving endoplasmic reticulum stress according to claim 6, characterized in that, Based on the revised expected change trajectory, the comprehensive stability score of different endoplasmic reticulum stress inhibitor administration regimens is calculated within a preset evaluation period, specifically including: For each core indicator in the revised expected change trajectory, set a target steady-state range and an acceptable fluctuation threshold; Within a preset evaluation period, the corrected expected change trajectory is sampled at fixed time intervals to obtain the predicted value of each core indicator at the sampling time point. The steady-state retention rate is calculated by statistically analyzing the frequency proportion of predicted values ​​for each core indicator falling within the target steady-state range. The magnitude and duration of the predicted value of each core indicator exceeding the acceptable fluctuation threshold are statistically analyzed, and a fluctuation over-limit penalty score is calculated. The overall stability score for each endoplasmic reticulum stress inhibitor administration regimen is calculated by weighting and combining the steady-state retention rate and fluctuation violation penalty score of all core indicators, and introducing scheme complexity as an adjustment factor.

8. The method for recommending an inhibitor administration regimen for relieving endoplasmic reticulum stress according to claim 7, characterized in that, The process of generating a recommended administration schedule for endoplasmic reticulum stress inhibitors based on the comprehensive stability score includes: All endoplasmic reticulum stress inhibitor administration regimens were ranked based on the comprehensive stability score, and a set of candidate regimens with scores higher than a predetermined threshold was selected. By integrating the detailed parameters, correction criteria, and expected trajectory of each scheme in the candidate scheme set, a structured endoplasmic reticulum stress inhibitor administration scheme recommendation report is generated; The step of ranking all feasible endoplasmic reticulum stress inhibitor administration regimens based on the comprehensive stability score and selecting a set of candidate regimens with scores higher than a predetermined threshold specifically includes: All feasible endoplasmic reticulum stress inhibitor administration regimens are sorted in descending order of their corresponding comprehensive stability scores to generate a regimen ranking list. In the list of ranked options, starting with the option with the highest score, the drug interaction risks and conflicts with known contraindications of each option are checked in turn; Eliminate application schemes with high-risk conflicts or contraindications, and reorder the remaining schemes; In the reordered list, select all schemes that rank highly and whose overall stability scores are all higher than the predetermined threshold. All selected schemes, along with their detailed application parameters, simulated trajectory identifiers, and comprehensive stability scores, are collectively formed into the candidate scheme set.

9. The method for recommending an inhibitor administration regimen for relieving endoplasmic reticulum stress according to claim 5, characterized in that, The process of simulating the propagation of the pharmacological effect of endoplasmic reticulum stress inhibitors in the endoplasmic reticulum stress evolution causal diagram, using the biomarker nodes as the starting point of intervention and based on the pharmacokinetic parameters of the endoplasmic reticulum stress inhibitors, specifically includes: In the endoplasmic reticulum stress evolution causation diagram, starting from the biomarker node, a breadth-first traversal is performed along the directed edges to identify all downstream biomarker nodes affected by the biomarker node. Based on the peak time and half-life parameters of endoplasmic reticulum stress inhibitors, a pharmacokinetic time curve model describing the change of their concentration in vivo over time was constructed. Based on the drug-time curve model, the inhibitory intensity on the biomarker nodes at different time points after drug administration was calculated; The calculated inhibition strength is used as the initial perturbation value and propagated attenuated along the directed edges in the endoplasmic reticulum stress evolution causality diagram according to the causal effect strength marked on the edges. For each downstream biomarker node in the propagation path, the sum of perturbation values ​​from all its upstream nodes is received, and its state change is calculated based on the dynamic characteristics of the downstream biomarker node itself, thus completing the simulation of the propagation process of the pharmacological effect.

10. A system for recommending inhibitor administration regimens for relieving endoplasmic reticulum stress, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for recommending an inhibitor administration regimen for relieving endoplasmic reticulum stress as described in any one of claims 1 to 9.

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