Method and device for characterizing the quantity-time relationship of pharmacokinetics of a single component of traditional chinese medicine

Abstract

This application relates to a method and apparatus for characterizing the quantity-time relationship of pharmacokinetics of monomeric components of traditional Chinese medicine (TCM). Addressing the problem that traditional methods, which directly utilize noisy experimental data to invert pharmacokinetic parameters of TCM monomeric components, suffer from significant errors, making it difficult to reliably reveal metabolic patterns in different experimental individuals, this application proposes a method using modern statistical parameter estimation techniques to dynamically track and analyze the ADME process of TCM monomeric components. Specifically, the metabolic patterns of TCM monomeric components in vivo are modeled as state variables of a dynamic system, and the measured random experimental data are modeled as noisy observation data. Bayesian filtering techniques are then used to dynamically track and analyze the ADME process of TCM monomeric components, achieving accurate, robust, and reliable characterization of the pharmacokinetic behavior of TCM monomeric components.

Description

Technical Field

[0001] This application relates to the field of pharmacokinetic technology of monomeric components of traditional Chinese medicine, and in particular to a method and apparatus for characterizing the quantity-time relationship of pharmacokinetic properties of monomeric components of traditional Chinese medicine. Background Technology

[0002] Pharmacokinetic studies of traditional Chinese medicine (TCM) focus on the dynamic changes in the absorption, distribution, metabolism, and excretion (ADME) of active components, components, single-herb formulas, and compound TCM preparations, as well as their time-dose-time-effect relationships. This is a crucial approach to revealing the material basis of TCM formulation effects and their in vivo processes. However, due to the genetic polymorphism of TCM components and individual differences in experimental animals, the types and contents of TCM components in vivo are constantly changing. Furthermore, unavoidable errors in animal administration, sampling, and instrument detection introduce random noise into the experimental data. These factors combined reduce the accuracy and reliability of ADME process parameters. Summary of the Invention

[0003] Therefore, it is necessary to provide a method and apparatus for characterizing the quantity-time relationship of pharmacokinetic behavior of monomeric components of traditional Chinese medicine, in order to achieve accurate, robust and reliable characterization of the pharmacokinetic behavior of monomeric components of traditional Chinese medicine.

[0004] A method for characterizing the dose-time relationship of pharmacokinetic properties of monomeric components of traditional Chinese medicine, comprising:

[0005] Prior information is needed to perform Bayesian filtering estimation on pharmacokinetic measurement data of single components of traditional Chinese medicine; the prior information is obtained by administering the drug to a pre-experimental animal model and taking samples.

[0006] By administering the drug to the current formal experimental animal model and taking samples, the initial fingerprint spectrum area measurement value of each Chinese medicine monomer component at each sampling time was obtained. Combined with prior information, Bayesian filtering was performed on the initial fingerprint spectrum area measurement value to obtain the denoised fingerprint spectrum area estimate value and the corresponding denoising estimation error value.

[0007] By integrating the area estimates of the denoised fingerprint spectrum of each Chinese herbal monomer component in different formal experimental animal models and the corresponding denoised estimation error values, the average fingerprint spectrum area estimate and the corresponding average estimation error value are obtained to characterize the pharmacokinetic quantity-time relationship of the corresponding Chinese herbal monomer component.

[0008] A device for characterizing the pharmacokinetic properties of monomeric components of traditional Chinese medicine, comprising:

[0009] The prior information acquisition module is used to acquire the prior information required for Bayesian filtering estimation of pharmacokinetic measurement data of single components of traditional Chinese medicine; the prior information is obtained by administering drugs to and sampling pre-experimental animal models.

[0010] The Bayesian filtering module is used to obtain the initial fingerprint spectrum area measurement value of each Chinese herbal single component at each sampling time by administering drugs and taking samples to the current formal experimental animal model. The initial fingerprint spectrum area measurement value is then subjected to Bayesian filtering in combination with prior information to obtain the denoised fingerprint spectrum area estimate value and the corresponding denoising estimation error value.

[0011] The fusion module is used to fuse the denoised fingerprint area estimates and corresponding denoised estimation error values ​​of each Chinese herbal monomer component in different formal experimental animal models to obtain the average fingerprint area estimate and the corresponding average estimation error value, so as to characterize the pharmacokinetic quantity-time relationship of the corresponding Chinese herbal monomer component.

[0012] The aforementioned method and apparatus for characterizing the quantity-time relationship of pharmacokinetic components of traditional Chinese medicine (TCM) addresses the problem that traditional methods, which directly utilize noisy experimental data to invert pharmacokinetic parameters of TCM components, suffer from significant errors, making it difficult to reliably reveal metabolic patterns in different experimental individuals. This method proposes a dynamic tracking and analysis of the ADME process of TCM component metabolism using modern statistical parameter estimation techniques. Specifically, it models the metabolic patterns of TCM component metabolism in vivo as state variables of a dynamic system, models the measured random experimental data as noisy observation data, and uses Bayesian filtering techniques to dynamically track and analyze the ADME process of TCM component metabolism. This achieves accurate, robust, and reliable characterization of the pharmacokinetic behavior of TCM component metabolism.

[0013] Compared with the prior art, the technical effects of the present invention are as follows:

[0014] First, it exhibits strong robustness. Compared to traditional methods for characterizing the dose-time relationship of pharmacokinetic components of Chinese herbal medicines, it employs Bayesian filtering technology to remove various measurement noise sources, including individual differences in experimental animals, measurement instrument and sampling personnel operation errors, and other influencing factors. Furthermore, it uses fusion estimation of multiple animal models to further enhance the robustness of the dose-time relationship characterization.

[0015] Second, it has good scalability. This method provides clear measurement and calculation methods for various types of parameters required by the Bayesian filtering technique, and is applicable to the dose-time relationship characterization of pharmacokinetics of monomeric components in various compound preparations. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the dose-time relationship characterization method for the pharmacokinetics of a single component of traditional Chinese medicine in one embodiment;

[0017] Figure 2 This is a schematic diagram of the structure of a computer device in one embodiment. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] In one embodiment, such as Figure 1 As shown, a method for characterizing the dose-time relationship of pharmacokinetic components of traditional Chinese medicine is provided, including the following steps:

[0020] Step 102: Obtain the prior information required for Bayesian filtering estimation of the pharmacokinetic measurement data of the single components of traditional Chinese medicine.

[0021] The prior information was obtained by administering drugs to and sampling pre-experimental animal models.

[0022] Step 104: By administering the drug to the current formal experimental animal model and taking samples, the initial fingerprint spectrum area measurement value of each Chinese medicine monomer component at each sampling time is obtained. Combined with prior information, Bayesian filtering is performed on the initial fingerprint spectrum area measurement value to obtain the denoised fingerprint spectrum area estimate value and the corresponding denoising estimation error value.

[0023] The sampling time when administering drugs and taking samples to the pre-experimental animal model may be the same as or different from the sampling time when administering drugs and taking samples to the formal experimental animal model.

[0024] Step 106: Combine the denoised fingerprint area estimates and corresponding denoised estimation error values ​​of each Chinese herbal monomer component in different formal experimental animal models to obtain the average fingerprint area estimate and the corresponding average estimation error value to characterize the pharmacokinetic quantity-time relationship of the corresponding Chinese herbal monomer component.

[0025] The aforementioned method for characterizing the quantity-time relationship of pharmacokinetic components of traditional Chinese medicine (TCM) first extracts prior information needed for high-precision quantity-time relationship characterization through preliminary pharmacokinetic experiments on TCM components. Then, Bayesian filtering is applied to the pharmacokinetic measurement data of TCM components through formal experiments. Finally, a high-precision quantity-time relationship characterization model is obtained by fusing different formal experimental animal models. This invention fully utilizes the advantages of modern statistical parameter estimation techniques in noise reduction, possesses robust pharmacokinetic characterization capabilities for TCM components, and can provide a high-precision model foundation for subsequent multi-component pharmacokinetic studies of TCM.

[0026] In one embodiment, the prior information includes the average measurement error and the average maximum acceleration of change of the fingerprint area of ​​each Chinese herbal monomer component;

[0027] Prior information required for Bayesian filtering estimation of pharmacokinetic data of single components of traditional Chinese medicine obtained by administering drugs to and sampling pre-experimental animal models includes:

[0028] Select prepared traditional Chinese medicine and pre-experimental animal models a∈{1,2,…,A}; there are a total of A pre-experimental animal models;

[0029] By administering drugs regularly to a pre-experimental animal model and sampling at intervals of high and low density, different monomeric components of traditional Chinese medicine (TCM) were extracted at different sampling times t∈{t1,t2,…,t}, c∈{1,2,…,C}. T fingerprint area value Where t1 < t2 < ... < t T The sampling time is indicated by a total of C kinds of herbal monomers. The principle of sampling from dense to sparse areas is based on the general rule (the changes of drugs in the body are initially rapid and then slow). For each herbal monomer c∈{1,2,…C, and the pre-experimental animal model a∈{1,2,…A,}, an Nth-order polynomial is used. fingerprint area sequence Least squares fitting was performed to obtain the average measurement error of each herbal monomer c. in:

[0030]

[0031] Where σ c,a This represents the measurement error of the c-th herbal monomer component in the a-th pre-experimental animal model;

[0032] And the average maximum change acceleration of the fingerprint area extracted for each monomeric component c. in:

[0033]

[0034] Among them, g c,a This represents the maximum acceleration of change of the c-th herbal monomer component in the a-th pre-experimental animal model.

[0035] The polynomial order N is typically configured to be 5, but an optimal balance can be struck between computational complexity and accuracy. Generally, a higher order leads to higher accuracy, but excessively high orders can result in overfitting.

[0036] In one embodiment, Bayesian filtering is applied to the initial fingerprint area measurement based on prior information to obtain a denoised fingerprint area estimate and a corresponding denoising estimation error, including:

[0037] By combining the average measurement error and the average maximum acceleration change, a Bayesian filter is applied to the initial fingerprint area measurement to obtain the denoised fingerprint area estimate and the corresponding denoising estimation error:

[0038]

[0039]

[0040] in, This indicates that the c-th herbal monomer component is in the a-th... f The fingerprint area at time k in a formal experimental animal model, K k Represents the gain matrix. This indicates that the c-th herbal monomer component is in the a-th... f The predicted fingerprint area at time k in a formal experimental animal model. This indicates that the c-th herbal monomer component is in the a-th... f The area estimate of the denoised fingerprint at time k in a formal experimental animal model. This indicates that the c-th herbal monomer component is in the a-th... f The prediction error value of the fingerprint area at time k in a formal experimental animal model. This indicates that the c-th herbal monomer component is in the a-th... f The denoising estimation error value at time k in a formal experimental animal model;

[0041] in:

[0042]

[0043]

[0044]

[0045]

[0046] in, This represents the rate of change of the c-th herbal monomer component in the a-th pre-experimental animal model at time k-1. p is a hyperparameter, p∈(0,1], which controls the magnitude of noise influence in the Bayesian filtering process. It is usually configured to 0.5, but can also be configured empirically. Generally, the larger the p value, the higher the weight of the measured value, and the more the filtering result depends on the measured value.

[0047] In one embodiment, the denoised fingerprint area estimate and the corresponding denoised estimation error value of each traditional Chinese medicine monomer component in different formal experimental animal models are fused to obtain the average fingerprint area estimate and the corresponding average estimation error value, including:

[0048] For each individual component of traditional Chinese medicine in different formal experimental animal models, the estimated area of ​​the denoised fingerprint spectrum and the corresponding denoising estimation error are fused using the variance convex combination method to obtain the average fingerprint spectrum area estimate. and its corresponding average estimation error value

[0049]

[0050]

[0051] The variance convex combination method is a weighted fusion method in which the weight coefficients are inversely proportional to the estimation errors of different formal experimental animal models and the coefficients can be normalized.

[0052] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but may be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but may be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0053] In one embodiment, a device for characterizing the dose-time relationship of pharmacokinetic properties of monomeric components of traditional Chinese medicine is provided, comprising: a prior information acquisition module, a Bayesian filtering module, and a fusion module, wherein:

[0054] The prior information acquisition module is used to acquire the prior information required for Bayesian filtering estimation of pharmacokinetic measurement data of single components of traditional Chinese medicine; the prior information is obtained by administering drugs and taking samples from a pre-experimental animal model.

[0055] The Bayesian filtering module is used to obtain the initial fingerprint spectrum area measurement value of each Chinese herbal single component at each sampling time by administering drugs and taking samples to the current formal experimental animal model. The initial fingerprint spectrum area measurement value is then subjected to Bayesian filtering in combination with the prior information to obtain the denoised fingerprint spectrum area estimate value and the corresponding denoising estimation error value.

[0056] The fusion module is used to fuse the denoised fingerprint area estimates and corresponding denoised estimation error values ​​of each Chinese herbal monomer component in different formal experimental animal models to obtain the average fingerprint area estimate and the corresponding average estimation error value, so as to characterize the pharmacokinetic quantity-time relationship of the corresponding Chinese herbal monomer component.

[0057] Specific limitations regarding the dose-time characterization device for the pharmacokinetic properties of individual components of traditional Chinese medicine (TCM) can be found in the above-mentioned limitations on the dose-time characterization method for the pharmacokinetic properties of individual components of TCM, and will not be repeated here. Each module in the aforementioned dose-time characterization device for the pharmacokinetic properties of individual components of TCM can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0058] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 2 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and the database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores fingerprint data. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a method for testing the vacuum leak rate of a diamond growth device.

[0059] Those skilled in the art will understand that Figure 2 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0060] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method described above.

[0061] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0062] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0063] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0064] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for characterizing the dose-time relationship of pharmacokinetic properties of monomeric components in traditional Chinese medicine, characterized in that, The method includes: Prior information is required to perform Bayesian filtering estimation on pharmacokinetic measurement data of single components of traditional Chinese medicine; the prior information is obtained by administering the drug to a pre-experimental animal model and taking samples. By administering the drug to the current formal experimental animal model and taking samples, the initial fingerprint spectrum area measurement value of each Chinese medicine monomer component at each sampling time is obtained. The initial fingerprint spectrum area measurement value is then subjected to Bayesian filtering in combination with the prior information to obtain the denoised fingerprint spectrum area estimate value and the corresponding denoising estimation error value. By integrating the area estimates of the denoised fingerprint spectrum of each Chinese herbal monomer component in different formal experimental animal models and the corresponding denoised estimation error values, the average fingerprint spectrum area estimate and the corresponding average estimation error value are obtained to characterize the pharmacokinetic quantity-time relationship of the corresponding Chinese herbal monomer component. The prior information includes the average measurement error and average maximum acceleration of change of the fingerprint spectrum area of ​​each Chinese herbal medicine monomer component; Prior information required for Bayesian filtering estimation of pharmacokinetic data of single components of traditional Chinese medicine obtained by administering drugs to and sampling pre-experimental animal models includes: Select prepared traditional Chinese medicine and pre-experimental animal models ; of which a total A preliminary experimental animal model; By administering drugs regularly to a pre-experimental animal model and sampling at intervals of high and low frequency, different monomeric components of traditional Chinese medicine were extracted. Different sampling times fingerprint area value ;in, Indicates the sampling time, total Monomeric components of traditional Chinese medicine; For each single component of traditional Chinese medicine and pre-experimental animal models ,use polynomial of order For the fingerprint area sequence Least squares fitting was performed to obtain the monomeric components of each traditional Chinese medicine. Average measurement error ,in: ； in Indicates the first The first pre-experimental animal model Measurement error of individual components of traditional Chinese medicine; and extraction of each monomer component The average maximum change acceleration of the fingerprint area ,in: ； in, Indicates the first The first pre-experimental animal model The maximum acceleration of change in individual components of traditional Chinese medicine.

2. The method according to claim 1, characterized in that, By combining the prior information with the initial fingerprint area measurement value, Bayesian filtering is performed to obtain a denoised fingerprint area estimate and a corresponding denoising estimation error value, including: By combining the average measurement error and the average maximum acceleration change, a Bayesian filter is applied to the initial fingerprint area measurement to obtain a denoised fingerprint area estimate and a corresponding denoising estimation error: ； ； in, Indicates the first The single component of traditional Chinese medicine in the first The first formal experimental animal model The area of ​​the fingerprint at each moment. Represents the gain matrix. Indicates the first The single component of traditional Chinese medicine in the first The first formal experimental animal model The predicted area of ​​the fingerprint at each time point. Indicates the first The single component of traditional Chinese medicine in the first The first formal experimental animal model The estimated area of ​​the denoised fingerprint at each time step. Indicates the first The single component of traditional Chinese medicine in the first The first formal experimental animal model The prediction error value of the fingerprint area at each time point. Indicates the first The single component of traditional Chinese medicine in the first The first formal experimental animal model The denoising estimation error value at each time step; in: ； ； ； ； in, Indicates the first The first pre-experimental animal model The single component of traditional Chinese medicine in the first The rate of change at each moment For hyperparameters, .

3. The method according to claim 2, characterized in that, By integrating the denoised fingerprint area estimates and corresponding denoising estimation errors of each herbal monomer component in different formal experimental animal models, the average fingerprint area estimate and corresponding average estimation error are obtained, including: For each individual component of traditional Chinese medicine in different formal experimental animal models, the estimated area of ​​the denoised fingerprint spectrum and the corresponding denoising estimation error are fused using the variance convex combination method to obtain the average fingerprint spectrum area estimate. and its corresponding average estimation error value : ； 。 4. The method according to claim 1, characterized in that, The sampling time when administering drugs and taking samples to the pre-experimental animal model may be the same as or different from the sampling time when administering drugs and taking samples to the formal experimental animal model.

5. The method according to claim 1, characterized in that, The degree of the polynomial is 5.

6. The method according to claim 2, characterized in that, The hyperparameter is set to 0.

5.

7. A device for characterizing the dose-time relationship of pharmacokinetic properties of monomeric components of traditional Chinese medicine, characterized in that, The device includes: The prior information acquisition module is used to acquire the prior information required for Bayesian filtering estimation of pharmacokinetic measurement data of single components of traditional Chinese medicine; the prior information is obtained by administering drugs and taking samples from a pre-experimental animal model. The Bayesian filtering module is used to obtain the initial fingerprint spectrum area measurement value of each Chinese herbal single component at each sampling time by administering drugs and taking samples to the current formal experimental animal model. The initial fingerprint spectrum area measurement value is then subjected to Bayesian filtering in combination with the prior information to obtain the denoised fingerprint spectrum area estimate value and the corresponding denoising estimation error value. The fusion module is used to fuse the denoised fingerprint area estimates and the corresponding denoised estimation error values ​​of each Chinese herbal monomer component in different formal experimental animal models to obtain the average fingerprint area estimate and the corresponding average estimation error value, so as to characterize the pharmacokinetic quantity-time relationship of the corresponding Chinese herbal monomer component. The prior information includes the average measurement error and average maximum acceleration of change of the fingerprint spectrum area of ​​each Chinese herbal medicine monomer component; the prior information acquisition module is also used to select the prepared Chinese herbal medicine and the pre-experimental animal model. ; of which a total A preliminary experimental animal model; By administering drugs regularly to a pre-experimental animal model and sampling at intervals of high and low frequency, different monomeric components of traditional Chinese medicine were extracted. Different sampling times fingerprint area value ;in, Indicates the sampling time, total Monomeric components of traditional Chinese medicine; For each single component of traditional Chinese medicine and pre-experimental animal models ,use polynomial of order For the fingerprint area sequence Least squares fitting was performed to obtain the monomeric components of each traditional Chinese medicine. Average measurement error ,in: ； in Indicates the first The first pre-experimental animal model Measurement error of individual components of traditional Chinese medicine; and extraction of each monomer component The average maximum change acceleration of the fingerprint area ,in: ； in, Indicates the first The first pre-experimental animal model The maximum acceleration of change in individual components of traditional Chinese medicine.

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