A method for determining the efficacy and quality of a veterinary traditional Chinese medicine material characteristic map

Abstract

The application provides a veterinary traditional Chinese medicinal material characteristic map efficacy quality correlation determination method, relates to the fields of traditional Chinese medicine analysis and detection and intelligent sensing technology, and the application converts static chemical components in the characteristic map into active node networks with inherent metabolic properties and definite dynamic interaction rules by constructing a chemical component characteristic space based on pharmacological mechanisms, quantifies synergistic or antagonistic inhibitory relationships between key nodes by using data driving and model simulation methods, thereby establishing a dynamic correlation evaluation system from material basis to biological effects, and realizing scientific determination of internal quality and actual efficacy of the veterinary traditional Chinese medicinal material. The application introduces metabolic response disorder degree and physiological stress load index as dynamic feedback variables, and improves the accuracy of the evaluation model in responding to real-time biological state of the body.

Description

Technical Field

[0001] This invention relates to the fields of traditional Chinese medicine analysis and detection and intelligent sensing technology, specifically a method for determining the efficacy and quality correlation of characteristic spectra of veterinary Chinese medicinal materials. Background Technology

[0002] In modern animal husbandry, the application of traditional Chinese medicinal materials for veterinary use is becoming increasingly widespread in order to reduce the use of chemically synthesized drugs, address drug resistance issues, and improve animal welfare. However, as complex natural products, the quality and final efficacy of traditional Chinese medicinal materials are affected by various factors such as seed source, origin, harvesting time, and processing techniques, exhibiting significant batch-to-batch variations. Therefore, how to scientifically and accurately assess and predict the actual efficacy of a specific batch of medicinal materials in animals is a core technical bottleneck to ensure their safe and effective application and achieve industrial standardization.

[0003] Currently, the mainstream technical method for evaluating the quality of veterinary Chinese medicinal materials in the industry is to generate a "chemical fingerprint spectrum" containing multiple chemical component peaks using analytical techniques such as high-performance liquid chromatography (HPLC) and gas chromatography (GC). The core logic of quality control is to compare the spectrum of the batch of medicinal materials to be tested with a "standard spectrum" (usually from a proven, high-quality batch). By calculating the similarity coefficient between the spectra or quantifying the content of a few key indicator components, the quality of the batch to be tested is determined to be "qualified". For example, when evaluating Astragalus membranaceus used to enhance the immunity of poultry, the traditional method is to first prepare a batch of Astragalus membranaceus that has been proven in animal experiments to significantly increase the antibody level in chickens as a "standard" and determine its chemical fingerprint spectrum. Subsequent batches of Astragalus membranaceus are also measured in the same way. If the retention time and peak area of ​​several marker components such as astragaloside in the new batch spectrum deviate from the standard spectrum within a preset range (e.g., ±5%), then the batch of Astragalus membranaceus is determined to be "qualified".

[0004] This method has certain limitations, specifically in that it only establishes a static, one-dimensional correlation between the "chemical components of medicinal materials" and the "final experimental results." It fails to reveal the dynamic process by which medicinal components are absorbed, distributed, metabolized, and ultimately produce pharmacological effects after entering the animal's body. Furthermore, factors such as animal breed, health status, gut microbiota, and the environment and feed of the farm all influence the in vivo processes and final efficacy of the medicinal materials. This static chemical mapping completely ignores these crucial interactions. Summary of the Invention

[0005] The purpose of this invention is to provide a method for determining the efficacy and quality correlation of characteristic spectra of veterinary Chinese medicinal materials, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for determining the efficacy and quality correlation of characteristic spectra of veterinary Chinese medicinal materials, the specific steps of which include:

[0008] S1. Construct the chemical composition feature space of the medicinal material to be tested, identify the active ingredient nodes in the initial chemical spectrum of the medicinal material; collect and fuse the metabolic response time-series spectrum and behavioral physiological data stream of the target animal after drug administration, and calculate the initial efficacy potential value of each active ingredient node.

[0009] S2. Define the synergistic effect of the relationships between nodes in the chemical component feature space. Based on pharmacological mechanisms and historical data, assign synergistic or antagonistic inhibitory association attributes to active ingredient node pairs, and determine the initial metabolic transformation damping coefficient of each node.

[0010] S3. Execute the efficacy evolution evaluation process within the dynamic time window. This process includes the following steps at each time step: For each active ingredient node, calculate the dynamic efficacy characterization index, which includes the metabolic response disorder degree, which characterizes the degree of deviation of the in vivo metabolic process from homeostasis, and the physiological stress load index, which characterizes the intensity of abnormal external physiological signs; Calculate the efficacy contribution evolution vector for each active ingredient node based on the dynamic efficacy characterization index and the preset efficacy transfer model; Update the cumulative efficacy potential of each active ingredient node in the chemical composition feature space according to the efficacy contribution evolution vector; Until the evaluation process meets the preset period termination conditions, a dynamic comprehensive pharmacodynamic spectrum of the overall quality of the medicinal material is generated.

[0011] Compared with the prior art, the beneficial effects of the present invention are:

[0012] This invention improves the accuracy of the assessment model in responding to the body's real-time biological state by introducing metabolic response disorder and physiological stress load index as dynamic feedback variables.

[0013] This invention improves the correlation between the efficacy evaluation results of traditional Chinese medicine and the actual biological effects by simulating the dynamic evolution and synergistic effects of multiple components in organisms.

[0014] This invention improves the scientific rigor of veterinary drug quality control and clinical medication regimen formulation by generating dynamic comprehensive pharmacodynamic maps that display the onset speed, peak intensity, and duration of action. Attached Figure Description

[0015] Fig. 1 This is a schematic diagram illustrating the overall method flow principle and step structure of the present invention.

[0016] Fig. 2 A schematic diagram of a dynamic comprehensive pharmacodynamic spectrum is generated by connecting the global cumulative efficacy potential at each time point of the present invention into a time series curve.

[0017] Fig. 3 This is a schematic diagram illustrating the execution of each method flow of the present invention. Detailed Implementation

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0019] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0020] Example 1:

[0021] Please see Figs. 1-3 This invention provides a technical solution: a method for determining the efficacy and quality correlation of characteristic spectra of veterinary Chinese medicinal materials, the specific steps of which include:

[0022] S1. Construct the chemical composition feature space of the medicinal material to be tested, identify the active ingredient nodes in the initial chemical spectrum of the medicinal material; collect and fuse the metabolic response time-series spectrum and behavioral physiological data stream of the target animal after drug administration, and calculate the initial efficacy potential value of each active ingredient node.

[0023] In step S1, when constructing the chemical composition feature space, the initial chemical spectrum is obtained by high performance liquid chromatography; the metabolic response time-series spectrum is obtained by non-destructive acquisition of blood or saliva samples from the target animal using portable near-infrared / Raman spectroscopy; and the behavioral physiological data stream is acquired by a smart collar integrating heart rate, body temperature and activity monitoring functions or a sensor array based on video and sound analysis.

[0024] Initial chromatograms were obtained using standardized high-performance liquid chromatography (HPLC) with a diode array detector (HPLC-DAD). The process comprises three main stages: sample preparation, chromatographic separation, and data acquisition. The specific operating procedures are as follows:

[0025] S111. The sample preparation procedure is as follows: Accurately weigh 1.000 g of the veterinary traditional Chinese medicine compound powder sample to be tested using an analytical balance (accuracy 0.1 mg) and place it in a 50 mL Erlenmeyer flask. Add 25.0 mL of 70% (v / v) methanol aqueous solution as the extraction solvent. Place the Erlenmeyer flask in a constant temperature ultrasonic cleaner for extraction. Set the ultrasonic frequency to 40 kHz, power to 250 W, and temperature to 40°C, and extract continuously for 30 minutes. This step aims to utilize the cavitation effect of ultrasound to efficiently extract the chemical components in the medicinal material into the solvent. After extraction, remove the Erlenmeyer flask and cool it to room temperature. Replenish the weight loss caused by evaporation with 70% methanol aqueous solution and shake well. Centrifuge the extract at 4000 r / min for 10 minutes. Take the supernatant and filter it using a 0.22 μm microporous membrane. The filtrate is the sample solution to be tested and collect it in a sample vial for later use.

[0026] S112. The chromatographic analysis procedure is as follows: The sample solution to be tested is injected into the high-performance liquid chromatography system via an autosampler. This system is equipped with a quaternary gradient pump, a column oven, and a diode array detector. Chromatographic separation is performed on a C18 reversed-phase column (250 mm × 4.6 mm, 5 μm) at a column temperature maintained at 30°C. The mobile phase is a binary system consisting of 0.1% phosphoric acid aqueous solution (A) and acetonitrile (B), and the following linear gradient elution program is executed at a flow rate of 1.0 mL / min: within 0-10 minutes, phase B increases from 5% to 20%; within 10-35 minutes, phase B increases from 20% to 60%; within 35-50 minutes, phase B increases from 60% to 95%; after maintaining phase B at 95% for 5 minutes, it is reduced to 5% over 0.1 minutes and equilibrated for 5.9 minutes. The total runtime for a single analysis is 60 minutes, and the injection volume is set to 10 μL.

[0027] S113. The data acquisition procedure is as follows: a diode array detector is used to perform a full wavelength scan in the wavelength range of 200-400nm, while simultaneously focusing on monitoring the absorbance signals at three characteristic wavelengths of 254nm, 280nm, and 330nm; the chromatography workstation software is responsible for acquiring and recording two-dimensional data of the signal intensity change with retention time throughout the entire 60-minute analysis cycle. The final generated file is the initial chemical spectrum, which provides complete raw data for subsequent characteristic peak identification and integration calculation.

[0028] S121. The method for acquiring the metabolic response time series spectrum is as follows: a portable Raman spectrometer equipped with a non-invasive fiber optic probe is used. At preset time points after drug administration (e.g., 1, 3, and 6 hours), a technician gently touches the surface skin of the ear vein of the sample pig with the fiber optic probe. The device completes non-destructive irradiation within seconds and quickly acquires the Raman spectral data of the blood. The sequence of changes in the shift and intensity of characteristic peaks related to key metabolites such as lactic acid and glucose over time constitutes the metabolic response time series spectrum reflecting the biochemical processes in the body.

[0029] S131. The method for collecting behavioral and physiological data streams involves two collaborative systems. The first is the collection of individual physiological indicators, where each sample pig is fitted with a smart ear tag or collar integrating a body temperature sensor, heart rate sensor, and a six-axis inertial measurement unit (IMU). This device is responsible for continuously collecting and uploading the pig's body temperature and heart rate data, and simultaneously analyzing the IMU data through built-in algorithms to quantify behavioral indicators such as activity level, posture changes, gait abnormalities, and lying down time. The second is the collection of group behavior and specific symptoms, where a non-contact monitoring system consisting of a 3D depth camera and a MEMS microphone array is deployed in the pigsty. This system uses computer vision and acoustic analysis algorithms to quantitatively monitor the pigs' social behavior and feeding and drinking patterns, and to locate the source and assess the severity of specific sounds such as coughing. Finally, the data streams with unified timestamps generated by the two systems are aligned and fused to form the behavioral and physiological data stream.

[0030] The method for obtaining the initial efficacy potential value includes: Step 1, the characteristic peak identification process, which involves baseline correction and noise filtering of the initial chromatogram, and the use of the watershed algorithm to identify and segment independent chromatographic peaks as active ingredient nodes; Step 2, the historical data mapping process, which involves comparing the characteristic parameters (retention time, peak area) of each active ingredient node with the pharmacodynamic material basis database, matching known components and assigning them a benchmark bioactivity score based on literature or experimental data; Step 3, the potential value normalization process, which involves weighted calculation and normalization of the benchmark bioactivity score based on the relative content of each component node to obtain the initial efficacy potential value.

[0031] The practical application scenario of this embodiment is as follows: A large-scale integrated pig farm with 50,000 pigs recently experienced a group outbreak of respiratory infection symptoms (such as coughing and wheezing) in some pig houses. To avoid drug resistance caused by large-scale use of antibiotics, it was decided to use a batch of compound traditional Chinese medicine preparations (main ingredients include honeysuckle, forsythia, and tangerine peel) for treatment. However, due to the large differences between batches of traditional Chinese medicine, the manager needs a scientific method to quickly and accurately assess the expected efficacy of this batch of medicines in the current pig herd in order to determine the medication plan and predict the treatment effect. This embodiment aims to construct a dynamic and interpretable drug efficacy prediction model by collecting the chemical fingerprint of the medicines and real-time metabolic and behavioral data of the pigs after medication, so as to accurately assess the overall efficacy quality of this batch of medicines within a few hours after medication.

[0032] S1. Construct the chemical composition feature space of the medicinal material to be tested, identify the active ingredient nodes in the initial chemical map of the medicinal material; collect and fuse the time series map of metabolic response and behavioral physiological data stream after drug administration to the target animal, and calculate the initial efficacy potential value of each active ingredient node.

[0033] This step is a crucial one in building a digital foundation for drug efficacy evaluation, and its internal implementation is as follows:

[0034] S11. In-depth mining of active ingredient nodes and multimodal data stream access. Unlike traditional methods that focus only on the content of a few key indicators, this embodiment first, based on pharmacopoeia standards and previous research, creates a deep "profile" of the entire chemical composition of the compound preparation, identifying multiple key active ingredients as nodes in the feature space:

[0035] The first category of nodes (core antiviral / antibacterial components): High-performance liquid chromatography (HPLC) analysis of the herbal extracts precisely located and identified chlorogenic acid in honeysuckle and forsythoside in forsythia in the chromatogram. These are recognized core components with direct inhibitory effects on pathogens.

[0036] The second category of nodes (immunomodulatory / anti-inflammatory components): Also identified in the initial chemical map are hesperidin from dried tangerine peel and glycyrrhizic acid from licorice. These components primarily function by regulating the body's immune response and reducing inflammation.

[0037] The third category of nodes (absorption-enhancing / bioavailability-improving ingredients): identifies some auxiliary ingredients that can affect the absorption or metabolism of other ingredients, such as certain volatile oil components.

[0038] Meanwhile, multimodal sensing devices were deployed on a sample of pigs (e.g., 30 pigs) randomly selected from the pigsty.

[0039] Metabolic response time-series mapping acquisition: At 1 hour, 3 hours, and 6 hours after drug administration, technicians used a portable Raman spectroscopy probe to non-destructively irradiate the marginal ear vein of pigs to rapidly acquire Raman spectral data of the blood. Specific peak shifts and intensity changes in the spectrum (such as characteristic peaks of metabolites like lactic acid and glucose) constitute the "metabolic response time-series mapping".

[0040] Behavioral and physiological data stream collection: Each sample pig was fitted with a smart ear tag integrating a body temperature sensor and a triaxial accelerometer, which uploaded body temperature and activity level data in real time. Simultaneously, an acoustic sensor array was installed above the pigpen, using AI algorithms to identify and count the frequency and intensity of coughs. This data constituted the "behavioral and physiological data stream."

[0041] S12. Feature Extraction and Initial Potential Value Calculation of Multimodal Data. After storing the collected initial chemical maps, metabolic response time-series maps, and physiological data streams, the initial potential value of any active ingredient node i (e.g., "chlorogenic acid") is calculated to quantify the theoretical pharmacodynamic potential of the ingredient before it enters the animal body. This embodiment introduces a normalization method based on historical data mapping to calculate this value.

[0042] The method for obtaining the initial performance potential value includes the following steps:

[0043] S12. The procedure for feature extraction and initial efficacy potential calculation of multimodal data is as follows: After structuring and storing the initial physiological map obtained in step S11, the metabolic response time-series map obtained in step S12, and the behavioral physiological data stream obtained in step S13, the following sub-steps are executed to calculate the initial efficacy potential value of each active ingredient node:

[0044] S121, the chromatographic peak segmentation and nodeization procedure is as follows: baseline correction and Gaussian smoothing filtering are performed on the original initial chromatogram obtained in S113 to eliminate noise interference; the watershed algorithm is used to perform peak detection and segmentation on the processed chromatogram. This algorithm can effectively separate overlapping and tailed chromatographic peaks, ensuring that each accurately segmented independent chromatographic peak is defined as a unique and quantifiable active ingredient node.

[0045] S122, the procedure for feature parameter mapping and baseline activity score acquisition, involves extracting the feature parameters of each active ingredient node generated in S121, mainly including its retention time (tR) and peak area (Ac), and automatically comparing these parameters with the internally constructed "Veterinary Pharmacological Active Ingredient Database (VPAID)". When the retention time of a certain node matches the feature retention time of a known compound in the database within a preset threshold (e.g., ±0.1 minutes), the "Baseline Bioactivity Score (BBS)" of that compound in a specific pathological model (e.g., a porcine respiratory infection model) is retrieved from the database. This BBS score is a standardized potency score obtained through meta-analysis and expert evaluation based on historical experimental data and literature, with a value range of 0-100. The BBS is a standardized score (0-100 points) obtained based on a large amount of literature and experimental data, after expert scoring and meta-analysis, representing the theoretical potency of the ingredient in a specific pathological model (e.g., a respiratory virus infection model). For example, by matching retention time, the peak area was identified as chlorogenic acid, and a BBS score of 92 (highly potent antiviral) was found in VPAID in a porcine respiratory infection model. See Table 1 for an example:

[0046] Table 1: Example chart of baseline bioactivity score (BBS)

[0047]

[0048] S123. The procedure for quantifying the initial efficacy potential value is as follows: After mapping is completed in S122, for each successfully matched active ingredient node i, hereinafter referred to as the i-th node, active ingredient node = node, its BBS score is weighted and calculated based on its relative abundance in the current batch of samples, so as to obtain the final initial efficacy potential value, denoted as IEP.

[0049] The initial efficacy potential value of the i-th active ingredient node Calculated using the following formula:

[0050]

[0051] in, Let be the baseline bioactivity score for the i-th node. The relative peak area of ​​active ingredient node i is represented by N, where N is the total number of active ingredient nodes. This represents the sum of the relative peak areas of all N active ingredient nodes. j represents the index of the temporary variable used to iterate through all active ingredient nodes to calculate the sum of the relative peak areas.

[0052] The physical meaning of Initial Potential (IEP) is as follows: a high IEP value indicates that the component not only possesses strong theoretical biological activity (high BBS), but also has a relatively high content in this batch of medicinal materials (high relative peak area). It is the "main force" and "core contributor" that determines the efficacy. A low IEP value may be due to the component's weak activity (low BBS), or it may be because although its activity is strong, its content in this batch is extremely low.

[0053] The core logic of step S1 is to elevate the quality assessment of medicinal materials from a simple "content determination" to a "comprehensive assessment of efficacy and content." Traditional methods only concern themselves with whether the content of astragaloside A in Astragalus membranaceus meets the standard, but cannot answer the question of "how much efficacy does astragaloside A, even with a standard content, actually contribute?" This method introduces BBS (Bioassay Body Scale) to assign a "potential weight" to each component, making the assessment closer to the pharmacological reality. For example, even if a component has a high content, but an extremely low BBS, its IEP (Influenced Efficacy Preservation) value will still be very small, and it will not be assigned an excessively high weight in the subsequent model.

[0054] As shown in Table 2 below, through the above steps, a set of quantitative initial efficacy parameter systems were established for the active ingredient nodes of this batch of compound traditional Chinese medicine.

[0055] Table 2: Examples of Initial Potential Values ​​of Active Ingredient Nodes in Compound Traditional Chinese Medicine

[0056]

[0057] As shown in Table 2, the first category of nodes (N-001, N-002) are defined as components that directly inhibit pathogens in the target pathological model. The second category of nodes (N-003, N-004, N-005) are defined as components that mainly achieve therapeutic effects by regulating physiological responses. The magnitude of the IEEP value is directly related to the theoretical contribution of the component to key pharmacological processes such as inhibiting inflammatory responses and promoting tissue repair. In particular, N-003 (hesperidin), although its BBS is not the highest, achieved the highest IEP value due to its high content and key function, highlighting its important role in improving clinical symptoms. The third category of nodes (N-006, synergistic and absorption-enhancing): defined as auxiliary components that can affect the in vivo processes or efficacy of other components. This classification step structures chemical components according to their pharmacological effects, providing a logical basis for subsequent weighted evaluation and interaction analysis. This method uses the Initial Potential Value (IEP) to quantify the theoretical contribution of each node in the formulation. The IEP value is determined by both the baseline bioactivity score (BBS) and the relative content. This calculation method yields the following technical conclusions: N-001 (chlorogenic acid) and N-002 (forsythoside) have high BBS values, and combined with their content, their calculated IEP values ​​(7.82, 6.34) are also correspondingly high. This numerically identifies them as major contributors to the first category of functions. The case of N-003 (hesperidin) illustrates another technical characteristic of this method. Its BBS (75) is lower than that of N-001 and N-002, but because its relative content (12.1%) is the highest in the table, its IEP value (9.08) is also the highest. This result indicates that, for this specific batch of medicinal material, the second category of functions (immunomodulation and anti-inflammation) represented by N-003 has the greatest theoretical potential efficacy. This conclusion cannot be directly derived by traditional methods based solely on content or a single indicator component. The IEP values ​​of N-004 and N-005 are relatively low, and this method classifies them as minor contributing nodes, thus reducing their weight in the overall evaluation model and allowing for a more focused evaluation. This method can effectively identify components with low IEP values ​​but special functional roles. For example, N-006 (d-limonene) has the lowest IEP value (0.38) among all nodes; based solely on this value, this component might be overlooked. However, this method pre-classifies it as "Category III (synergistic and absorption-enhancing)," assigning it a functional label. This label instructs subsequent processing modules to treat this node as a moderating rather than a direct-effect variable. In subsequent dynamic evolution calculations, the presence of this node will serve as a parameter to adjust the absorption or metabolic coefficients of other related nodes (such as other lipid-soluble components), thereby reflecting its synergistic effect on overall efficacy.

[0058] In this embodiment, the core principle is to establish a quantitative correlation between chemical components and theoretical bioactivity through digital means. A complete chemical spectrum of the medicinal material is obtained using standardized HPLC-DAD technology, and signal processing algorithms (such as the watershed algorithm) are applied to segment the continuous chromatographic signal into independent active ingredient nodes, thereby achieving a comprehensive digital characterization of the chemical composition of the medicinal material. The core technical step is the introduction of the concepts of "Baseline Bioactivity Score (BBS)" and "Initial Potential Potential (IEP)". By comparing the identified node characteristic parameters (retention time) with a pre-set pharmacodynamic material basis database, the component identity is determined. Based on historical literature and experimental data of specific pathological models (such as swine respiratory tract infection), a standardized BBS score is assigned to each identified component, quantifying its theoretical pharmacological activity intensity. On this basis, combined with the relative content (peak area ratio) of each component in the current batch to be tested, the IEP value of each node is calculated through weighted average. The IEP value comprehensively reflects the "quality" (theoretical activity) and "quantity" (relative abundance) of the component, constituting a quantitative indicator of the component's contribution to the overall theoretical pharmacodynamic efficacy. Simultaneously, this embodiment integrates portable Raman spectroscopy technology for non-destructive acquisition of animal blood metabolic data, as well as smart wearable devices and environmental sensors for monitoring animal behavioral and physiological indicators, thereby establishing a multimodal data foundation encompassing chemical, metabolic, and behavioral physiological aspects.

[0059] The beneficial effects of this embodiment are reflected in its departure from the traditional model that relies solely on the content of a few indicator components to determine whether a drug is qualified. It achieves a quantitative and graded assessment of the theoretical efficacy of each component of the medicinal material through the IEP value. This method can effectively distinguish the actual contribution differences between high-content, low-activity components and low-content, high-activity components. For example, it can identify components with high content but moderate activity (such as hesperidin) that may have greater overall efficacy potential than components with low content but extremely high activity. It can also identify components with low direct activity but with auxiliary synergistic effects (such as d-limonene). By classifying components by function (such as antipathogenicity, immune regulation, and auxiliary synergistic effects), a structured basis is provided for understanding the multi-target synergistic mechanism of compound traditional Chinese medicine. This step establishes a comprehensive, quantitative, and theoretically activity-linked data foundation, laying the necessary foundation for subsequent accurate efficacy prediction by combining in vivo dynamic metabolism and behavioral response data, and helping to improve the scientific rigor and predictability of veterinary drug use protocols. The technical principle of this embodiment lies in constructing a digital assessment foundation that connects the chemical components of medicinal materials with their theoretical biological activities. The initial chromatograms of the veterinary Chinese medicinal materials were obtained using standardized HPLC-DAD technology. Baseline correction, noise filtering, and a watershed algorithm were then used to precisely segment the continuous chromatographic data into independent active ingredient nodes. The core of this approach lies in the introduction of the "Veterinary Pharmacological Active Ingredient Database (VPAID)," constructed based on historical experimental meta-analysis and expert evaluation of literature. By comparing characteristic parameters (such as retention time), the identified chemical component nodes were mapped to known compounds and assigned a standardized "Baseline Bioactivity Score (BBS)" representing the theoretical potency under specific pathological models (such as swine respiratory infections). Combining the relative content (i.e., relative peak area percentage) of each component node in the current batch of samples, a weighted calculation yielded the "Initial Potential Value (IEP)" for each node. The IEP value is a comprehensive indicator that combines the material basis "quantity" of the component with its theoretical pharmacological "quality," quantifying the potential contribution of the component to the theoretical efficacy before it enters the animal body. Simultaneously, based on pharmacological mechanisms of action, nodes are structurally categorized into different functional classes, such as core anti-pathogen activity, immunomodulatory and anti-inflammatory effects, and absorption-enhancing effects. Its beneficial effects lie in overcoming the limitations of traditional chemical fingerprinting techniques, which focus solely on component content comparison while neglecting their specific biological significance. Through IEP value calculation, it achieves a shift in the quality assessment of medicinal materials from qualitative and quantitative analysis of single chemical components to a comprehensive correlation between quantity and efficacy, enabling a more objective evaluation of the theoretical contributions of different components to overall efficacy. For example, it can highlight key active ingredients that, while not having the highest content, possess high BBS or high overall IEP; simultaneously, for components with low IEP values ​​but specific auxiliary functions (such as promoting absorption), pre-defined functional classification labels ensure that their synergistic value in the compound is reflected in subsequent analyses, avoiding being overlooked simply based on numerical values.This step lays a crucial structured data foundation for the subsequent integration of metabolic response time-series and behavioral physiological data streams within animals to construct a dynamic drug efficacy prediction model.

[0060] Example 2:

[0061] S2. Define the synergistic effect of the relationships between nodes in the chemical component feature space. Based on pharmacological mechanisms and historical data, assign synergistic or antagonistic inhibitory association attributes to active ingredient node pairs, and determine the initial metabolic transformation damping coefficient of each node.

[0062] In step S2, association attributes are assigned to active ingredient node pairs, specifically including: based on pharmacokinetic simulation, the node pair relationships are classified as follows: absorption-enhancing synergy, used to describe the effect of active ingredient node A in increasing the bioavailability of active ingredient node B, and assigned a bioavailability gain factor; target-binding synergy, used to describe the superadditive effect produced when active ingredient node A and active ingredient node B act together on the same receptor, and assigned a pharmacodynamic synergy index; metabolic antagonism and inhibition, used to describe how active ingredient node C accelerates the metabolic inactivation of active ingredient node D in vivo, and assigned a metabolic acceleration factor.

[0063] This step transforms qualitative pharmacological knowledge into quantitative interaction parameters using data-driven and model simulation methods. The core principle of parameter design is that all interaction coefficients are greater than 1, and are applied as direct multipliers to the target process so that their values ​​can intuitively reflect the pharmacological effects.

[0064] Based on this principle, the attributes of the active ingredient node pairs in Example 1 were calculated and assigned values, and the results are detailed in Table 3.

[0065] Table 3 shows the attribute calculation and assignment for key node pairs.

[0066]

[0067] The specific calculation process for the associated attributes is as follows:

[0068] S211. The bioavailability gain factor γ quantifies the absorption-promoting effect of active ingredient node A on active ingredient node B, and its calculation is based on historical pharmacokinetic data. Specifically, the bioavailability gain factor γ is obtained by retrieving pharmacokinetic data of the target animals from the VPAID database, including the area under the baseline drug-time curve after administration of active ingredient node B alone, denoted as AUC. base The area under the curve (AUC) for the enhanced pharmacokinetic effect of active ingredient node B after combined administration of active ingredient nodes A and B is denoted as AUC. enhanced .

[0069] The specific formula for the bioavailability gain factor γ is:

[0070]

[0071] Specifically, the area under the curve (AUC) for enhanced drug-time effect of active ingredient node B after combined administration of active ingredient nodes A and B is calculated as the measured plasma drug concentration and the area under the curve for active ingredient node B, expressed as plasma drug concentration × time. base , representing the area under the baseline plasma concentration-time curve when the active ingredient at node B is administered alone, in units of AUC. enhanced Same. Data example: Data shows that after pigs were administered hesperidin (N-003) by gavage alone, the AUC base The concentration was 500 μg·h / L. When co-administered with d-limonene (N-006), the AUC of hesperidin was... enhanced Increase to 700 μg·h / L. Then γ N-006→N-003 =700 / 500=1.40.

[0072] S212. The synergistic effect index α is used to evaluate the superadditive effect of active ingredients node A and B at a specific pharmacological target. Its calculation is based on the joint index theory. The synergistic effect index α is obtained by acquiring dose-response data for a specific pharmacological target, including: the half-maximal effective concentration (EC50) of each ingredient acting alone. 50A EC 50B ), and the concentration combination that achieves the half-maximal effect (C) A C B ).

[0073] The specific formula for the synergistic effect index α is:

[0074]

[0075] This represents the joint index. An example of data is shown below: Calculate the joint index CI value: CI = (C...) A / EC 50A )+(C B / EC 50B ). Specific example: The EC of chlorogenic acid (N-001) was experimentally measured. 50A Forsythoside (N-002) EC at 10 μM 50B The concentration is 8 μM. When used in combination, C A =4μM,C B =3.2μM. Therefore, CI = (4 / 10) + (3.2 / 8) = 0.4 + 0.4 = 0.8. Calculate α. N-001→N-002 =1 / 0.8=1.25. For a conservative estimate, we take the value as 1.20.

[0076] S213. The metabolic accelerator μ is used to quantify the accelerating effect of active ingredient node C on the in vivo metabolism of active ingredient node D. Its calculation is based on in vitro liver microsomal experiments. Specifically, the metabolic accelerator μ is obtained by measuring the baseline metabolic rate v of substrate active ingredient node D in the presence and absence of the inducer active ingredient node C using liver microsomes of the target animal. base and post-induced metabolic rate v induced .

[0077] Parameter calculation: The metabolic acceleration factor μ is calculated using the following formula:

[0078]

[0079] Example: The v of rutin (N-005) was measured experimentally. base The concentration was 10 pmol / min / mg. After pre-incubation with glycyrrhizic acid (N-004), v induced It is 15 pmol / min / mg. Therefore, μ N-004→N-005 =15 / 10=1.50.

[0080] S22. This step assigns an intrinsic, unaffected initial metabolic transformation damping coefficient λ to each node. The initial metabolic transformation damping coefficient λ reflects the inherent clearance rate of that component in vivo and is calculated from its biological half-life (T½). The initial metabolic transformation damping coefficient λ of the i-th active ingredient node... Calculated using the following formula:

[0081]

[0082] in, Let be the biological half-life (h) of the i-th active ingredient node, which is the time required for its blood drug concentration to decrease by half. Its unit is time. ln(2): represents the natural logarithm 2, which is approximately equal to 0.693 and is a dimensionless constant. The calculation results are shown in Table 4.

[0083] Table 4: Example of initial metabolic transformation damping coefficient λ for active ingredient nodes

[0084]

[0085] Through the complete procedure of this embodiment, the present invention successfully upgrades the Chinese medicinal materials to be tested from a "static component list" (Example 1) into a "dynamic blueprint" with intrinsic decay characteristics and quantified interaction rules. It not only defines the types of interactions, but more importantly, provides specific computational methodologies for obtaining each interaction parameter (γ, α, μ) and the intrinsic metabolic parameter λ. This ensures that every step of the entire evaluation system is based on science, objectivity, and repeatability, providing all the necessary, calculated initial conditions and evolutionary rules for the next step of constructing a dynamic evolutionary model based on differential equations or intelligent agents.

[0086] The technical principle of this embodiment lies in constructing a dynamic correlation system and intrinsic metabolic attributes among nodes in the characteristic space of chemical components of traditional Chinese medicine. Its core lies in clearly defining and quantifying the synergistic or antagonistic inhibitory relationships between active ingredient node pairs based on pharmacological mechanisms and historical data. This process, based on pharmacokinetic simulation, subdivides the relationships between nodes into three categories and establishes calculation procedures for each: For absorption-promoting synergy, a bioavailability gain factor γ is introduced, quantifying the ability of one component to improve the bioavailability of another by retrieving the area under the curve (AUC) ratio after combined administration and single administration from the database; for target-binding synergy, a pharmacodynamic synergy index α is introduced, calculated based on the synergy index theory, combining the concentration data of each component acting alone and in combination to achieve the half-maximal effect, to assess the superadditive effect when acting together on the same receptor; for metabolic antagonism and inhibition, a metabolic acceleration factor μ is introduced, measuring the ratio of substrate metabolic rates in the presence or absence of an inducer through in vitro liver microsomal experiments to describe the process by which one component accelerates the metabolic inactivation of another. To intuitively reflect the pharmacological effects, all interaction coefficients are designed as direct multipliers greater than 1. Furthermore, the principle also includes assigning each active ingredient node an initial metabolic transformation damping coefficient λ, unaffected by other components. This coefficient is directly calculated from the biological half-life of each component using a logarithmic formula, reflecting its inherent clearance rate in vivo. The beneficial effect of this embodiment lies in using a data-driven and model simulation approach to transform qualitative pharmacological knowledge into quantitative interaction parameters, thereby upgrading the tested Chinese medicinal materials from a static list of components into a dynamic system blueprint with inherent decay characteristics and quantified interaction rules. This parameter calculation methodology based on objective experimental data and classical theory ensures that the evaluation system is established on a scientific, objective, and repeatable basis, providing all the necessary calculated initial conditions and evolutionary rules to support the next step of constructing a dynamic evolutionary model based on differential equations or intelligent agents. The technical principle of Embodiment Two lies in constructing a quantitative dynamic interaction network based on pharmacological mechanisms and measured data, aiming to transform a static list of chemical components into a system blueprint with evolutionary rules. Its core lies in using data-driven and model simulation methods to transform qualitative pharmacological interactions into quantitative parameter indicators, and combining this with the inherent pharmacokinetic characteristics of the components for system setting. Specifically, this step first classifies and quantifies the synergistic or antagonistic relationships between active ingredient nodes based on pharmacokinetic simulation and joint index theory.For absorption-enhancing synergistic effects, the principle is based on the increase in the area under the curve of combined drug administration compared to monotherapy, calculating the bioavailability gain factor γ to quantify the degree to which one component enhances the transmembrane absorption of another component. For target-binding synergistic effects, the principle is to calculate the synergistic index CI using the half-maximal effect concentration data of each component and the combined drug, and take its reciprocal to obtain the pharmacodynamic synergistic index α to characterize the superadditive effect produced by the components on the same receptor. For metabolic antagonistic inhibition effects, the principle is to determine the metabolic acceleration factor μ by measuring the ratio of substrate metabolic rates before and after the presence of an inducer through in vitro liver microsomal experiments to describe the process by which one component accelerates the metabolic inactivation of another component. All these interaction parameters are designed to be values ​​greater than 1, and are applied as direct multipliers to the target process to intuitively reflect the enhancement of pharmacological effects or the acceleration of rates. At the same time, the principle of this technology also includes determining the intrinsic initial metabolic transformation damping coefficient λ for each node, which is based on the inherent biological half-life (T½) of each component, calculated by the formula ln(2) / T½, reflecting the natural clearance rate of the component without external influence. The beneficial effect of this embodiment is that it provides a scientific, objective, and repeatable computational foundation and necessary initial conditions for the subsequent construction of complex dynamic evolution models. By establishing specific parameter calculation procedures, this method avoids the subjectivity of qualitative descriptions based solely on experience, ensuring that every interaction and attenuation process is based on evidence. Designing the interaction parameters as multipliers greater than 1 simplifies the mathematical expression of synergistic or accelerating processes in the model. Clearly distinguishing between the inherent metabolic properties of components and the interaction properties (γ, α, μ) between components enables the model to more realistically simulate the complex dynamic changes of Chinese medicinal materials in the body, thus achieving a key leap from component identification to system behavior prediction, and providing a quantitative tool for in-depth research on the material basis of the efficacy of Chinese medicine.

[0087] Example 3:

[0088] S3. Execute the efficacy evolution evaluation process within the dynamic time window. This process includes the following steps at each time step: For each active ingredient node, calculate the dynamic efficacy characterization index, which includes the metabolic response disorder degree, which characterizes the degree of deviation of the in vivo metabolic process from homeostasis, and the physiological stress load index, which characterizes the intensity of abnormal external physiological signs; Calculate the efficacy contribution evolution vector for each active ingredient node based on the dynamic efficacy characterization index and the preset efficacy transfer model; Update the cumulative efficacy potential of each active ingredient node in the chemical composition feature space according to the efficacy contribution evolution vector; Until the evaluation process meets the preset period termination conditions, a dynamic comprehensive pharmacodynamic spectrum of the overall quality of the medicinal material is generated.

[0089] The metabolic response disorder is calculated based on at least two spectral feature entropies pre-calculated on the metabolic response time-series map, including: one is the Shannon entropy change rate of the peak distribution that quantifies the change in spectral profile complexity, and the other is the residual dispersion of the characteristic spectral absorption intensity that quantifies the deviation of metabolite concentration from the normal fluctuation range; the metabolic response disorder is obtained by weighted fusion calculation of the Shannon entropy change rate of the peak distribution and the residual dispersion of the characteristic spectral absorption intensity of the i-th active ingredient node.

[0090] The physiological stress load index is calculated as follows: multidimensional physiological indicators (such as heart rate variability, food intake, and activity frequency) in the behavioral physiological data stream are normalized; a baseline model of physiological indicators under healthy conditions is constructed; the Mahalanobis distance between the real-time physiological indicators and the predicted values ​​of the baseline model is calculated, and this distance value is the physiological stress negative GH index, the magnitude of which reflects the overall stress level of the animal.

[0091] The model used in the performance evolution assessment process is trained and optimized through a federated learning framework. When applied to new farms or animal species, the method also includes: using a global model trained based on multi-field data aggregation as a pre-trained model, and fine-tuning the pre-trained model using a small amount of labeled data from the new field or species through transfer learning to generate a high-precision assessment model with field specificity.

[0092] The efficacy contribution evolution vector includes an endogenous activation contribution component and a co-regulatory contribution component; the endogenous activation contribution component and the co-regulatory contribution component are vector-superimposed to form the final efficacy contribution evolution vector acting on the current active ingredient node.

[0093] The endogenous activation contribution component is obtained as follows: the magnitude of this component is positively correlated with the concentration of the current active ingredient node and its initial efficacy potential value, and is used to characterize the direct pharmacological effect produced independently by the component. The synergistic regulation contribution component is obtained as follows: based on the metabolic response disorder of the current node, and combined with the cumulative efficacy potential of other nodes with related attributes, it is calculated through a preset synergistic function. The magnitude and direction of this component characterize the dynamic influence of other components on the enhancement or inhibition of the efficacy of the current component.

[0094] The steps for generating a dynamic comprehensive pharmacodynamic spectrum include: updating the efficacy potential step: updating the cumulative efficacy potential of each active ingredient node using the state transition equation based on the efficacy contribution evolution vector; calculating the comprehensive evaluation value characterizing the endpoint efficacy of the medicinal material at the end of the evaluation period; and connecting the global cumulative efficacy potential of each time step into a time series to form a dynamic curve reflecting the onset speed, peak intensity, and duration of action of the drug. The evaluation value and the dynamic curve together constitute the dynamic comprehensive pharmacodynamic spectrum.

[0095] Fig. 1In this process, step S1, which constructs the feature space of the chemical components of the medicinal material to be tested, corresponds to the feature space construction. Step S2, which defines the synergistic effects between nodes in the chemical component feature space, corresponds to the synergistic definition. Step S3, which executes the efficacy evolution evaluation process within a dynamic time window, corresponds to the dynamic evaluation process. The efficacy spectrum generation is the step of generating a dynamic comprehensive pharmacodynamic spectrum after the update.

[0096] This embodiment aims to detail the core step S3 of the method of the present invention. Its fundamental objective is to utilize the initial parameters (IEP, γ, α, μ, λ) calculated in Examples 1 and 2, within a preset dynamic time window, combined with real-time collected animal physiological and metabolic data, to simulate and deduce the dynamic evolution process of the efficacy of each active ingredient at each node, ultimately generating a comprehensive index that can fully and dynamically characterize the efficacy and quality of this batch of veterinary Chinese medicinal materials. In this embodiment, the evaluation time window is set to 6 hours after medication, and the time step Δt is set to 0.5 hours.

[0097] S31. Before the dynamic evaluation process begins, based on the calculation results of Examples 1 and 2, set the initial state of each active ingredient node at time t=0.

[0098] The initial cumulative efficacy potential, denoted as AEP, is set at time t=0, before the drug has exerted its complex effects in the body. The cumulative efficacy potential at each node is then set to the initial efficacy potential (IEP) calculated in Example 1. That is, AEP. i (0)=IEP i ;

[0099] Setting the initial effective concentration Cc: At time t=0, although the drug has not yet been fully absorbed, an initial concentration is needed to start the model. The most direct and reasonable assumption is that the relative concentration distribution at the initial time is consistent with the relative content distribution of the chemical components of the medicinal material itself. Therefore, the relative peak area calculated in Example 1 is used as a proxy for its initial value. The initial effective concentration of each node at time t=0 is set to be proportional to its relative peak area in the initial chemical spectrum. i (0) = relative peak area i (%). Based on the above rules, the initial state variables of the dynamic evolution model are obtained, as detailed in Table 5.

[0100] Table 5: Initial state variables of the dynamic evolution model (t=0)

[0101]

[0102] S32. The dynamic evolution iteration process within the execution time window (t=0.5h, 1.0h, ..., 6.0h) will be executed once in each time step Δt. The internal calculation process will be explained in detail below using the time point t=3.0h as an example.

[0103] S321. The dynamic performance characterization index is obtained by collecting multimodal data streams at t=3.0h and calculating two key dynamic characterization indices, including metabolic response disorder and residual discrepancy of characteristic spectral absorption intensity.

[0104] The determination of metabolic response disorder includes:

[0105] S3211. Blood spectra of the sample pig herd were collected using a portable Raman spectrometer. Compared with the baseline spectrum before drug administration (t=0), the rate of change of Shannon entropy in the peak distribution was calculated, denoted as ΔHspec, reflecting the overall complexity of metabolite changes. An example calculated value of 0.15 was obtained. Shannon entropy is an indicator in information theory that measures uncertainty or complexity. In spectral analysis, a complex, disordered spectrum (corresponding to the abnormal occurrence of multiple metabolites and chaotic concentration distribution) will have a higher entropy value. The focus is on the rate of change of entropy relative to the healthy baseline.

[0106] S3212. Construct the residual dispersion of characteristic spectral absorption intensity, denoted as Dres. This index focuses on a few key metabolites strongly correlated with disease states (such as lactate, representing enhanced anaerobic respiration, and glucose, representing disordered energy metabolism). It measures the degree to which the concentrations of these key metabolites (represented by their characteristic peak intensities) deviate from their normal healthy fluctuation range.

[0107] Locating lactic acid (52 cm⁻¹) in the spectrum -1 ), glucose (1125cm) -1 Characteristic peaks of m key metabolites were identified, and the average intensity of each characteristic peak f was obtained through data analysis of a large number of healthy animals. and standard deviation For example, the healthy range for lactate peak intensity is 150 ± 2015 (au);

[0108] For the intensity of each characteristic peak in the current spectrum S(3.0) Calculate the standardized residual of the f-th characteristic peak. The specific formula is as follows:

[0109]

[0110] Assuming the lactic acid peak intensity is measured to be 200 a.u. at t=3.0h, then its Z... f =(200-150) / 20=2.5; Assuming the Z-value of the glucose peak is... f =-1.8. The standardized residuals of all characteristic peaks. We obtain the characteristic spectral absorption intensity residual dispersion Dres by weighted summation of the squares:

[0111]

[0112] Where, q f The weight of each metabolite, i.e., the f-th characteristic peak, is given, and the sum of the weights equals 1; m is the total number of characteristic peaks.

[0113] By weighted fusion, the Shannon entropy change rate of the spectral peak distribution and the dispersion of the residual absorption intensity of the characteristic spectrum are combined, and the metabolic response disorder is calculated using the following formula, denoted as MRD, with weights wH=0.4 and wD=0.6:

[0114]

[0115] S322. Integrating data from smart ear tags and environmental sensors, and comparing it with a health benchmark model, a physiological stress load index, denoted as PSLI, is constructed. The Mahalanobis distance between the real-time vector and the health benchmark model is calculated. The technical principle of Mahalanobis distance calculation is that when calculating the distance between a data point and the center of the data distribution, the covariance between variables is considered. It first performs a coordinate transformation on the data to eliminate the correlation between variables, and then calculates the Euclidean distance. Therefore, even if a point deviates from the mean in all dimensions, if it deviates along the direction of the main data distribution (e.g., activity level and heart rate increase simultaneously), its Mahalanobis distance may not be large. Conversely, even if a point deviates little in each dimension, but its combination is anomalous (e.g., very low activity level but extremely high heart rate), its Mahalanobis distance will be very large. This allows it to effectively capture "pattern anomalies." The specific calculation process includes:

[0116] S3221. At t=3.0h, collect multidimensional physiological data to form an observation vector. ,include:

[0117]

[0118] in, Indicates body temperature. Indicates heart rate, Indicates cough frequency; Example: x1 Body temperature (°C) = 39.8; x2 Heart rate (bpm) = 110; x3 Cough frequency (times / minute) = 8; Observation vector =[39.8,110,8] T ;

[0119] A health baseline model is constructed by statistically analyzing data from a large-scale healthy pig herd and establishing a multidimensional normal distribution model. The health baseline model consists of a mean vector. Defined by a covariance matrix G, specifically the mean vector This represents the average condition of healthy pigs, specifically: =[Body temperature, heart rate, cough]T =[38.8,85,1] T ;T represents the transpose term.

[0120] The covariance matrix G (representing the volatility and interrelationships of each indicator) is shown in the following example:

[0121]

[0122] The diagonal elements represent variances: body temperature variance σ = 0.5, and heart rate variance σ = 100. The off-diagonal elements represent covariances: σ¹² = 5.0 indicates a positive correlation between body temperature and heart rate.

[0123] Calculate the observation vector With mean vector The Mahalanobis distance is used to obtain the Physiological Stress Liability Index (PSLI); the example PSLI value is 4.45, which is a dimensionless distance value. The higher the value, the further the animal's physiological state deviates from the healthy pattern, and the higher the stress load it is experiencing. This value integrates three abnormal indicators—high fever, tachycardia, and severe cough—and takes into account the intrinsic correlation between them, resulting in a more comprehensive assessment than looking at any one indicator alone.

[0124] S323. After obtaining the state indices MRD and PSLI, the next step is to calculate how the efficacy of each active component node will change within the current time step Δt, i.e., the efficacy contribution evolution vector, denoted as... .

[0125] It is composed of two superimposed vectors, representing the two driving forces of efficiency change.

[0126] ;in, This represents the endogenous activation contribution component of the i-th active ingredient node. This represents the synergistic regulatory contribution component of the i-th active ingredient node;

[0127] The calculation formula is: ;

[0128] in, The calibration parameter is set to 0.01; Let be the effective concentration of the i-th active ingredient node at time t; Let be the initial efficacy potential value of the i-th active ingredient node;

[0129] The calculation formula is: ;

[0130] in, The stress response function transforms macroscopic state indicators into amplification factors for synergistic effects, specifically: ;in, and These are the weight values ​​of metabolic response disorder and physiological stress load index at time t, respectively; wMRD=0.5, wPSLI=1.5 (indicating that physiological stress has a greater triggering weight for synergistic effects). Let represent the interaction term, where j is a target-functional synergy with i. j→i =α j→i (Drug efficacy synergy index).

[0131] If j is an absorption-promoting synergist for i, then Interaction j→i =γ j→i (Bioavailability gain factor).

[0132] If j is a metabolic antagonist to i, this term does not directly affect the increase of AEP, but is reflected in the change of concentration.

[0133] If j and i are not directly related, then Interaction j→i =0.

[0134] Updates to S324, initial effective concentration Cc, and cumulative effective potential AEP;

[0135] Concentration changes are primarily influenced by metabolic clearance, following a first-order kinetic elimination model, i.e., exponential decay. Simultaneously, the metabolic antagonistic effects of other components need to be considered. The update formula for the initial effective concentration Cc is:

[0136] ;

[0137] in, The effective damping coefficient, ;

[0138] The initial damping coefficient λ of the node itself i (From Table 4) and metabolic acceleration factors μ for all nodes that have a metabolic acceleration effect on them. j→i The product determination (from Table 3).

[0139] Calculation example (N-005, rutin): From Table 4, λ5 = 0.315h -1 Table 3 shows that N-004 accelerates the metabolism of N-005, μ 4→5 =1.5. λ 5,eff =0.315 × 1.50 = 0.4725h -1Assuming 5(3.0) = 1.8C5(3.0) = 1.8ru, then C5(3.5) = 1.8•exp(−0.4725×0.5)≈1.42ru.

[0140] The rate of change of the cumulative potential AEP is The AEP value at the next time step can be obtained by numerical integration using a simple Euler method. The updated formula is as follows:

[0141] ;

[0142] S325. By repeating S321 to S324 at each time point from t=0.5h to t=6.0h, the complete trajectory of the evolution of all node state variables over time can be obtained. Table 6 shows some evolution data fragments of key nodes.

[0143] Table 6: Data Trajectory Fragments of Dynamic Evolution Process

[0144]

[0145] S326. When the simulation reaches the end time T final =6.0h, the data from the entire process will be collected and summarized to obtain the comprehensive evaluation value, denoted as DCEI. The specific formula is:

[0146]

[0147] Where N is the total number of active ingredient nodes, and i and j are the indices of the active ingredient nodes, respectively. This represents the normalized biological activity weight. This term calculates the proportion of the intrinsic activity of node i in the total activity of all nodes, and uses it as a weighting factor when calculating the total score; The i-th active ingredient node at the endpoint time T final The final calculated value of the cumulative effectiveness potential.

[0148] Connecting the global cumulative efficacy potential at each time point into a time series curve generates a dynamic comprehensive pharmacodynamic spectrum, such as... Fig. 2 Interpretation of the dynamic comprehensive pharmacodynamic spectrum curve shown: This spectrum visually demonstrates the pharmacodynamic characteristics of this batch of medicinal materials: Onset of action: Significant effect begins approximately 1.5 hours after administration. Peak intensity: Peak efficacy is reached at approximately 4 hours, with a cumulative efficacy potential of approximately 115. Duration of action: A high efficacy plateau is maintained for 4-6 hours.

[0149] The technical principle of this embodiment lies in establishing a time-step-based dynamic simulation framework to deduce the efficacy evolution of veterinary Chinese medicinal materials in animals. This method initializes the cumulative efficacy potential and effective concentration of each active ingredient node based on static chemical and bioactivity analysis results. Within a set dynamic time window, the system advances the evaluation process through iterative calculations. At each time step, data is collected using spectral technology and multidimensional physiological sensors to calculate the metabolic response disorder, characterizing the complexity of metabolite type changes and the deviation of key metabolite concentrations, as well as the physiological stress load index, which measures the deviation of multidimensional physiological indicators from the healthy baseline model based on Mahalanobis distance. This allows for real-time quantification of the body's physiological and metabolic state. Subsequently, the efficacy contribution evolution vector of each active ingredient node is calculated. This vector integrates the endogenous activation contribution determined by the current concentration and inherent potential, as well as the synergistic regulatory contribution triggered by the body's stress state and transmitted through the inter-component interaction network. Based on this evolution vector, the cumulative efficacy potential of each node is updated using numerical integration methods, while the effective concentration is updated according to a first-order kinetic model modified by metabolic antagonism. At the end of the simulation, the dynamic comprehensive evaluation value is obtained by performing a bioactivity-weighted summation of the final cumulative potential of all nodes, and the time-series change of the global cumulative potential is plotted as a dynamic curve.

[0150] The beneficial effect of this method lies in its ability to more realistically reflect the synergistic effects of multiple components of traditional Chinese medicine and their pharmacodynamic characteristics over time by simulating the dynamic action of drugs in vivo. Introducing metabolic response disorder and physiological stress load index as dynamic feedback variables allows the assessment model to respond to the real-time state of the organism, improving the correlation between the assessment results and actual biological effects. The resulting dynamic comprehensive pharmacodynamic spectrum not only provides a quantitative endpoint efficacy evaluation but also visually displays key kinetic parameters such as onset rate, peak intensity, and duration of action, providing a more comprehensive and scientifically sound basis for veterinary drug quality control and clinical application.

[0151] It should be noted that all calculation formulas in this application employ regression analysis, including but not limited to machine learning algorithms, to deeply analyze the collected parameters and identify their natural trends and interrelationships. Specialized software, such as Python's Scikit-learn library or the R language, is used to automatically generate mathematical models that match the data. Then, cross-validation and other methods are used to objectively evaluate the model performance, and continuous feedback and optimization are combined to ensure that the created formulas truly reflect the inherent laws of the data, thereby guaranteeing their effectiveness and accuracy. In all calculation formulas in this application, the parameters in each formula undergo dimensionless processing within a consistent range to ensure that different physical quantities are compared on the same scale; dimensionless processing techniques include, but are not limited to, min-max-normalization and Z-score standardization.

[0152] The algorithm of this invention is implemented as a Python script. Before executing the core logic, the program first executes a data loading module (e.g., using the widely used pandas library in Python) configured to read the aforementioned spreadsheet file and load its contents into the program's working memory (e.g., a DataFrame data structure). Subsequent algorithm steps will directly query and retrieve the required configuration parameters from this in-memory data structure.

[0153] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for determining the efficacy and quality correlation of characteristic spectra of veterinary Chinese medicinal materials, characterized in that, The specific steps include: S1. Construct the chemical composition feature space of the medicinal material to be tested, identify the active ingredient nodes in the initial chemical spectrum of the medicinal material; collect and fuse the metabolic response time-series spectrum and behavioral physiological data stream of the target animal after drug administration, and calculate the initial efficacy potential value of each active ingredient node. S2. Define the synergistic effect of the relationships between nodes in the chemical component feature space. Based on pharmacological mechanisms and historical data, assign synergistic or antagonistic inhibitory association attributes to active ingredient node pairs, and determine the initial metabolic transformation damping coefficient of each node. S3. Execute the efficacy evolution assessment process within the dynamic time window. This process includes the following in each time step: For each active ingredient node, calculate the dynamic efficacy characterization index. The dynamic efficacy characterization index includes the metabolic response disorder degree, which characterizes the degree of deviation of the metabolic process from homeostasis in the body, and the physiological stress load index, which characterizes the intensity of abnormal manifestations of external physiological signs. Based on dynamic efficacy characterization indicators and a preset efficacy transfer model, the efficacy contribution evolution vector is calculated for each active ingredient node; the cumulative efficacy potential of each active ingredient node in the chemical composition feature space is updated according to the efficacy contribution evolution vector; until the evaluation process meets the preset cycle termination conditions, so as to generate a dynamic comprehensive efficacy map that quantifies the overall quality of medicinal materials.

2. The method for determining the efficacy and quality correlation of characteristic maps of veterinary Chinese medicinal materials according to claim 1, characterized in that: In step S1, when constructing the chemical composition feature space, the initial chemical spectrum was obtained by high performance liquid chromatography; the metabolic response time-series spectrum was obtained by non-destructive acquisition of blood or saliva samples from the target animal using portable near-infrared / Raman spectroscopy. Behavioral physiological data streams are acquired through smart collars that integrate heart rate, body temperature, and activity level monitoring functions, or through sensor arrays based on video and sound analysis.

3. The method for determining the efficacy and quality correlation of characteristic spectra of veterinary Chinese medicinal materials according to claim 1, characterized in that: The method for obtaining the initial efficacy potential value includes: baseline correction and noise filtering of the initial chemical spectrum; identification and segmentation of independent chromatographic peaks using the watershed algorithm as active ingredient nodes; comparison of the characteristic parameters of each active ingredient node with the pharmacodynamic material basis database, matching known components and assigning them a benchmark bioactivity score based on literature or experimental data; and weighted calculation and normalization of the benchmark bioactivity score based on the relative content of each component node to obtain the initial efficacy potential value.

4. The method for determining the efficacy and quality correlation of characteristic maps of veterinary Chinese medicinal materials according to claim 1, characterized in that: Step S2 assigns association attributes to active ingredient node pairs, specifically including: classifying node pair relationships based on pharmacokinetic simulations as follows: The term "absorption-enhancing synergy" is used to describe the effect of active ingredient node A in improving the bioavailability of active ingredient node B, thus assigning it a bioavailability gain factor. Target binding synergy is used to describe the superadditive effect produced when active ingredient node A and active ingredient node B act together on the same receptor, and is given a pharmacodynamic synergy index. Metabolic antagonism and inhibition are used to describe how active ingredient node C accelerates the metabolic inactivation of active ingredient node D in vivo, thus giving it a metabolic acceleration factor.

5. The method for determining the efficacy and quality correlation of characteristic maps of veterinary Chinese medicinal materials according to claim 1, characterized in that: The metabolic response disorder is calculated based on at least two spectral feature entropies pre-calculated on the metabolic response time-series map, including: one is the Shannon entropy change rate of the peak distribution that quantifies the change in spectral profile complexity, and the other is the residual dispersion of the characteristic spectral absorption intensity that quantifies the deviation of metabolite concentration from the normal fluctuation range; the metabolic response disorder is obtained by weighted fusion calculation of the Shannon entropy change rate of the peak distribution and the residual dispersion of the characteristic spectral absorption intensity of the i-th active ingredient node.

6. The method for determining the efficacy and quality correlation of characteristic maps of veterinary Chinese medicinal materials according to claim 1, characterized in that: The physiological stress load index is calculated as follows: the multidimensional physiological indicators in the behavioral physiological data stream are normalized; a baseline model of physiological indicators in a healthy state is constructed; the Mahalanobis distance between the real-time physiological indicators and the predicted values ​​of the baseline model is calculated. The Mahalanobis distance is the physiological stress load index, and its value reflects the overall stress level of the animal.

7. The method for determining the efficacy and quality correlation of characteristic maps of veterinary Chinese medicinal materials according to claim 1, characterized in that: The model used in the performance evolution assessment process is trained and optimized through a federated learning framework. When applied to new farms or animal species, the method also includes: using a global model trained based on multi-field data aggregation as a pre-trained model, and fine-tuning the pre-trained model using a small amount of labeled data from the new field or species through transfer learning to generate a high-precision assessment model with field specificity.

8. The method for determining the efficacy and quality correlation of characteristic spectra of veterinary Chinese medicinal materials according to claim 1, characterized in that: The efficacy contribution evolution vector includes an endogenous activation contribution component and a co-regulatory contribution component; the endogenous activation contribution component and the co-regulatory contribution component are vector-superimposed to form the final efficacy contribution evolution vector acting on the current active ingredient node.

9. The method for determining the efficacy and quality correlation of characteristic maps of veterinary Chinese medicinal materials according to claim 1, characterized in that: The endogenous activation contribution component is obtained by the following method: the magnitude of this component is positively correlated with the concentration of the current active ingredient node and its initial efficacy potential value, and is used to characterize the direct pharmacological effect produced independently by the component. The collaborative regulatory contribution component is obtained by calculating it based on the metabolic response disorder of the current node and the cumulative efficacy potential of other nodes with related attributes through a preset collaborative function. The magnitude and direction of the collaborative regulatory contribution component characterize the dynamic influence of other components on the enhancement or inhibition of the efficacy of the current component.

10. The method for determining the efficacy and quality correlation of characteristic maps of veterinary Chinese medicinal materials according to claim 1, characterized in that: The steps for generating a dynamic comprehensive pharmacodynamic profile include: Update efficacy potential steps: Based on the efficacy contribution evolution vector, update the cumulative efficacy potential of each active ingredient node using the state transition equation; at the end of the evaluation period, calculate the comprehensive evaluation value characterizing the endpoint efficacy of the medicinal material; at the same time, connect the global cumulative efficacy potential of each time step into a time series to form a dynamic curve reflecting the onset speed, peak intensity and duration of action of the drug effect. The evaluation value and the dynamic curve together constitute a dynamic comprehensive efficacy map.

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