Real-time analysis method for intestinal permeability facing antihypertensive drug candidates

Abstract

The application relates to a real-time intestinal permeability analysis method for anti-hypertensive drug candidates, and relates to the technical field of drug permeation analysis, and comprises the following steps: acquiring the drug concentration and volume flow rate of the entrance and exit of a target intestinal segment in real time under different pH perfusion fluids; calculating the absorption flux at each moment and generating a waveform; identifying the absorption plateau period and the decay period to obtain the platform absorption flux and decay kinetic parameters; calculating the absorption clearance coefficient; inputting the aforementioned parameters into a nonlinear correction mapping relationship to obtain a dynamic permeability coefficient; when the adjacent decay amounts of the coefficient under three consecutive pH gradients are all greater than a threshold value, it is determined that there is carrier-mediated saturated absorption. The application solves the technical problem that traditional intestinal absorption experiments rely on manual operation and offline detection, leading to sparse data collection, difficulty in reflecting real-time dynamic changes of the absorption process, and influence on accurate identification of carrier transport mechanisms.

Description

Technical Field

[0001] This application relates to the field of drug permeation analysis technology, and in particular to a method for real-time measurement and analysis of intestinal permeability for antihypertensive drug candidates. Background Technology

[0002] In the preclinical stage of antihypertensive drug development, the intestinal absorption characteristics of candidate compounds directly determine their oral bioavailability and drug potential. Intestinal permeability, as a key indicator of a drug's ability to cross the intestinal wall, needs to be systematically evaluated under different pH conditions to reveal the drug absorption mechanism—whether it is dominated by passive diffusion or mediated by carrier proteins. Traditional intestinal permeability assessment mainly uses an in vivo one-way intestinal perfusion model. Cannulas are inserted at both ends of the target intestinal segment in experimental animals such as rats, and the intestinal tract is perfused with drug-containing perfusion fluid. The effluent is manually collected at fixed time intervals, and the drug concentration is then measured offline using methods such as high-performance liquid chromatography or mass spectrometry. Absorption parameters are calculated based on the concentration difference between the inlet and outlet.

[0003] However, this manual offline detection method has significant shortcomings. On the one hand, the time interval for sampling is typically 5 to 15 minutes, resulting in low sampling density. This makes it impossible to capture the instantaneous fluctuations and dynamic changes in drug absorption rates on minute or even second-scale timescales, easily missing key inflection points in the absorption process. On the other hand, multiple steps from sample collection and processing to offline detection rely on manual operation. It is difficult to ensure consistency in operation between different experimenters or between different batches by the same person, and the introduced human error directly affects the accuracy and reproducibility of the data. Furthermore, traditional data analysis methods are based on the assumption of steady state and lack sophisticated analytical tools for the complete dynamic process of absorption from the initial plateau to gradual decay, making it difficult to effectively distinguish the contributions of passive diffusion and carrier-mediated transport to drug absorption. Summary of the Invention

[0004] This application provides a real-time measurement and analysis method for intestinal permeability of antihypertensive drug candidates, which improves the technical problem that the intestinal permeability assessment method in the prior art is limited by the manual timed sampling mode, resulting in poor time continuity and low operational consistency, which leads to the easy omission or misjudgment of instantaneous absorption characteristics.

[0005] This application discloses the following technical solution:

[0006] This application provides a method for real-time measurement and analysis of intestinal permeability for antihypertensive drug candidates, the method comprising:

[0007] Under different pH perfusion fluid environments, the first real-time drug concentration of the candidate compound at the inlet of the target intestinal segment and the second real-time drug concentration at the outlet were obtained, and the real-time volumetric flow rate of the perfusion fluid through the target intestinal segment was recorded simultaneously.

[0008] Based on the real-time volumetric flow rate, the first real-time drug concentration, and the second real-time drug concentration at each sampling time, the real-time absorption flux at the corresponding time is calculated, and the absorption flux waveform is generated by arranging them in chronological order.

[0009] Based on the absorption flux waveform, the absorption plateau period and absorption decay period are identified, the plateau absorption flux is calculated, and the absorption decay kinetic parameters are extracted. The absorption decay kinetic parameters include at least the absorption flux value at the decay start time, the fast decay time constant, and the slow decay time constant.

[0010] The absorption and clearance coefficient is calculated based on the platform absorption flux, the first real-time drug concentration, and the effective length of the target intestinal segment.

[0011] The platform absorption flux, the absorption attenuation kinetic parameters, and the absorption scavenging coefficient are input into a pre-calibrated nonlinear correction mapping relationship to output the dynamic permeability coefficient.

[0012] When the dynamic permeability coefficient decreases beyond the dynamic decay threshold in three consecutive pH gradients, the candidate compound is determined to have carrier-mediated saturation absorption characteristics within the current pH range.

[0013] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0014] The technical solution of this application provides a real-time measurement and analysis method for intestinal permeability of antihypertensive drug candidates. First, by synchronously acquiring the drug concentration and perfusion volume flow rate of the candidate compound at the inlet and outlet of the target intestinal segment in real time under different pH perfusion fluid environments, and aligning the outlet signal by time shift, it realizes second-level continuous monitoring of the drug absorption process across the intestinal wall. This provides a high-time-resolution synchronous data foundation for subsequent absorption kinetic analysis and solves the problem of sparse data acquisition and inability to capture instantaneous changes in absorption rate in traditional manual timed sampling methods.

[0015] Furthermore, by calculating the instantaneous concentration difference between the inlet and outlet at each sampling time and combining it with the real-time volumetric flow rate to generate an absorption flux waveform, and then through outlier removal and interpolation filling based on overall statistical characteristics, discrete concentration data is transformed into a continuous and smooth dynamic curve of absorption flux. This solves the problem that traditional methods can only obtain static concentration values ​​at discrete time points and are difficult to reflect the overall dynamic trend of the absorption process.

[0016] Furthermore, by automatically identifying the plateau and decay periods on the absorption flux waveform using a fluctuation tolerance threshold, extracting the plateau absorption flux, and calculating two time constants for rapid and slow decay, a quantitative description of the complete dynamic characteristics of the absorption process from steady-state maintenance to gradual decay is achieved. This solves the problem that traditional methods, based on the steady-state assumption, cannot accurately analyze the phased changes in the absorption process.

[0017] Furthermore, by calculating the absorption and clearance coefficient based on the plateau absorption flux, inlet steady-state concentration, and effective intestinal length, the steady-state absorption capacity during the plateau phase is transformed into a standardized absorption and clearance index that can be compared across compounds. This solves the problem of poor comparability between different experiments caused by the traditional single-point estimation method neglecting concentration and length factors.

[0018] Furthermore, by inputting the platform's absorption flux, absorption attenuation kinetic parameters, and absorption scavenging coefficient into a pre-calibrated nonlinear correction mapping relationship, the dynamic permeability coefficient is obtained as the output. This achieves attenuation distortion correction for the traditional steady-state permeability coefficient calculation and solves the problem of biased estimation of the absorption scavenging coefficient under the steady-state assumption when significant absorption attenuation occurs.

[0019] Finally, by comparing the adjacent decay amounts of the dynamic permeability coefficient with the dynamic decay threshold under multiple pH gradients, it was determined whether the candidate compound had a carrier-mediated saturation absorption characteristic. Furthermore, the carrier type was inferred based on the change trend of the decay distortion coefficient with pH. This enabled the automatic identification and differentiation of passive diffusion and several common carrier-mediated transport mechanisms from absorption kinetic data, solving the problem that traditional methods are difficult to effectively distinguish different absorption mechanisms and that the judgment of carrier participation depends on additional inhibitor experiments.

[0020] In summary, the technical solution of this application achieves real-time monitoring and time-shift alignment of intestinal permeability of candidate drugs at multiple pH levels, generation and anomaly handling of absorption flux waveforms, automatic identification of plateau / decay periods and extraction of dual time constants, and determination of multi-pH gradient characteristics for decay distortion correction. Through synchronous acquisition of fiber optic spectra and linear conversion of absorbance to concentration, elimination of flux waveform anomalies and median interpolation, first-order differential fluctuation tolerance scanning and logarithmic decay piecewise linear regression, steady-state permeability coefficient calculation and nonlinear correction mapping, and comparison of adjacent decay thresholds at multiple pH levels, it effectively improves the technical problems in the prior art where intestinal permeability assessment relies on manual timed sampling and offline analysis, resulting in sparse acquisition, difficulty in capturing instantaneous changes in absorption, and inability to automatically distinguish between passive diffusion and carrier-mediated transport characteristics. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart illustrating the real-time measurement and analysis method for intestinal permeability of antihypertensive drug candidates provided in this application embodiment;

[0023] Figure 2 A flowchart illustrating the identification of the absorption plateau and decay phases in a real-time measurement and analysis method for intestinal permeability of antihypertensive drug candidates provided in this application embodiment;

[0024] Figure 3 The diagram shows the relationship between the absorption attenuation distortion coefficient and the correction factor in the real-time measurement and analysis method for intestinal permeability of antihypertensive drug candidates provided in the embodiments of this application. Detailed Implementation

[0025] This application provides a real-time measurement and analysis method for intestinal permeability of antihypertensive drug candidates, which addresses the technical problem that existing technologies lack automated real-time analysis methods for measuring intestinal permeability of candidate drugs, making it difficult to perform detailed analysis of the dynamic changes in the absorption process and the transport mechanism.

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. It should be noted that the numerical values ​​in the embodiments are for illustrative purposes only and do not constitute a limitation on this application.

[0027] Examples, as shown in the appendix Figure 1 As shown, this application provides a method for real-time measurement and analysis of intestinal permeability for antihypertensive drug candidates, the method comprising the following steps:

[0028] S100: Under different pH perfusion fluid environments, obtain the first real-time drug concentration of the candidate compound at the inlet of the target intestinal segment and the second real-time drug concentration at the outlet, and simultaneously record the real-time volumetric flow rate of the perfusion fluid through the target intestinal segment.

[0029] In this embodiment, this step, as a data acquisition component of the overall method, requires real-time monitoring of the absorption process of candidate compounds in the intestine under different pH conditions. A fiber optic spectral probe is used to achieve second-level real-time online monitoring of inlet and outlet drug concentrations, and the outlet data is time-shifted and aligned to provide a precise and synchronized time-series data foundation for subsequent absorption flux calculations.

[0030] Step S100 in the method provided in this application embodiment includes:

[0031] After inserting cannulas into both ends of the target intestinal segment, the perfusion fluid with multiple pH gradients is injected sequentially. Each pH gradient is maintained for an independent measurement cycle, and the real-time volumetric flow rate of the perfusion fluid is kept constant within each measurement cycle. Within each measurement cycle, the first fiber optic spectral probe is immersed in the inlet reservoir, and the second fiber optic spectral probe is connected to the outlet flow cell. The absorbance time-series signals at the inlet and outlet at the characteristic absorption wavelength of the candidate compound are collected synchronously at the same sampling frequency.

[0032] The absorbance time-series signal at the inlet is converted into a first real-time drug concentration time-series sequence, and the absorbance time-series signal at the outlet is converted into a second real-time drug concentration time-series sequence. The real-time volumetric flow rate in the pipeline between the inlet reservoir and the target intestinal segment is continuously recorded using a flow meter, and the real-time volumetric flow rate is aligned with the first and second real-time drug concentration time-series sequences according to the sampling time. After one measurement cycle, the process switches to the perfusion solution for the next pH gradient, repeating the operations of simultaneous absorbance acquisition, concentration conversion, and flow rate recording until all pH gradients to be measured are covered. Detailed explanation follows:

[0033] In this embodiment, the target intestinal segment refers to a specific segment of the small intestine of an experimental animal selected for perfusion experiments. After surgical cannulation at both ends, independent perfusion chambers are formed, which are the sites of drug absorption. The multi-pH gradient perfusion solution refers to a perfusion solution containing candidate compounds with pH values ​​varying sequentially according to a preset gradient. This is used to simulate different acid-base environments encountered by the drug in different regions of the gastrointestinal tract, with each pH gradient maintained for an independent measurement cycle.

[0034] The first real-time drug concentration refers to the drug concentration value obtained by real-time monitoring of absorbance at the inlet of the target intestinal segment using a fiber optic spectral probe and conversion, reflecting the drug content in the perfusion fluid before entering the intestinal segment. The second real-time drug concentration refers to the drug concentration value obtained by synchronous monitoring and conversion at the outlet of the target intestinal segment, reflecting the residual drug content in the perfusion fluid after absorption by the intestinal segment. Real-time volumetric flow rate refers to the volume of perfusion fluid passing through the target intestinal segment per unit time, continuously recorded by a flow meter, used to calculate the absolute flux of drug absorption.

[0035] In this step, firstly, in order to simulate the acid-base environment encountered by the drug in different regions of the gastrointestinal tract, the target intestinal segment is inserted into both ends and perfused sequentially with perfusion fluid of multiple pH gradients. Each pH gradient is maintained in an independent measurement cycle, and the perfusion fluid flow rate is kept constant within each cycle.

[0036] Furthermore, in order to obtain drug concentration signals at the intestinal inlet and outlet in real time, two fiber optic spectral probes need to be immersed in the inlet reservoir and the outlet flow cell, respectively, and the absorbance time-series signal of the candidate compound at the characteristic absorption wavelength is collected synchronously at the same sampling frequency, so that the absorbance change can be continuously recorded without sampling.

[0037] Furthermore, in order to convert the absorbance signal into a drug concentration value, the absorbance value at each sampling time needs to be substituted into the conversion formula through a pre-established absorbance-concentration conversion curve to calculate the corresponding drug concentration value, thus forming a real-time drug concentration time sequence at the inlet and outlet.

[0038] Furthermore, to eliminate the pipeline delay effect of the perfusion fluid flowing from the inlet to the outlet, the delay time needs to be calculated based on the dead volume of the perfusion pipeline and the real-time flow velocity. The outlet concentration sequence is then shifted forward by the corresponding delay time and aligned with the inlet concentration sequence in time. Simultaneously, the real-time volumetric flow velocity is recorded using a flow meter, and the flow velocity data is aligned with the concentration sequence according to the sampling time.

[0039] Finally, after one measurement cycle is completed, switch to the perfusion solution for the next pH gradient and repeat the above operation until all pH gradients to be measured are covered.

[0040] Step S100 in the method provided in this application embodiment further includes:

[0041] Prepare at least three standard solutions of candidate compounds with different concentrations, and measure the absorbance of each standard solution at the characteristic absorption wavelength to obtain a standard data point set corresponding to absorbance and concentration. Perform linear regression on the standard data point set to fit the slope and intercept parameters of the absorbance-concentration conversion curve. Substitute the absorbance value of each sampling moment in the absorbance time-series signal at the inlet into the absorbance-concentration conversion curve to calculate the first real-time drug concentration at the corresponding moment, and arrange them in chronological order to form a first real-time drug concentration time-series sequence. Substitute the absorbance value of each sampling moment in the absorbance time-series signal at the outlet into the same absorbance-concentration conversion curve to calculate the second real-time drug concentration at the corresponding moment, and arrange them in chronological order to form a second real-time drug concentration time-series sequence. Detailed explanation is as follows:

[0042] In this embodiment, the absorbance-concentration conversion curve refers to the linear relationship curve between absorbance and drug concentration established using standard solutions based on the Lambert-Beer law, used to convert real-time spectral signals into quantitative concentration values. The standard data set refers to the set of data on the correspondence between concentration and absorbance obtained by measuring the absorbance of multiple sets of standard solutions of known concentrations at characteristic absorption wavelengths; it forms the basis for linear regression fitting of the conversion curve. The characteristic absorption wavelength refers to the specific wavelength position in the ultraviolet or visible light band where the candidate compound has maximum absorption; measuring absorbance at this wavelength yields the highest detection sensitivity and quantitative accuracy.

[0043] In this step, firstly, in order to establish a quantitative conversion relationship between absorbance and concentration, at least three standard solutions of candidate compounds with different concentrations need to be prepared, and the absorbance of each standard solution is measured at the characteristic absorption wavelength to obtain a standard data point set.

[0044] Furthermore, in order to obtain accurate conversion parameters, linear regression needs to be performed on the standard data point set to fit the slope and intercept parameters of the absorbance-concentration conversion curve.

[0045] Furthermore, in order to convert the real-time absorbance into concentration values, the absorbance values ​​at each sampling time at the inlet need to be substituted into the conversion curve to calculate the first real-time drug concentration at the corresponding time and arrange them in chronological order; the same conversion curve is used at the outlet for concentration conversion.

[0046] Finally, to ensure the validity of the concentration data, the concentration values ​​corresponding to abnormal sampling times with absorbance values ​​exceeding the calibration range need to be removed. The removed values ​​are then filled in by linear interpolation using the concentration values ​​of adjacent valid times to obtain the effective concentration time series.

[0047] In summary, this step achieves real-time online monitoring of inlet and outlet drug concentrations using a fiber optic spectral probe. Through absorbance-concentration standard curve conversion and outlet time shift alignment, three sets of synchronized time-series data—inlet concentration, outlet concentration, and volumetric flow rate—under multiple pH gradients were obtained, providing a high-temporal-resolution and precisely synchronized data foundation for the subsequent construction of absorption flux waveforms.

[0048] S200: Calculate the real-time absorption flux at each sampling time based on the real-time volumetric flow rate, the first real-time drug concentration, and the second real-time drug concentration, and generate the absorption flux waveform in chronological order.

[0049] In this embodiment, after obtaining the real-time drug concentration and volumetric flow rate at the inlet and outlet, the discrete concentration difference and flow rate data need to be converted into a continuous and smooth absorption flux waveform to fully reflect the dynamic process of drug absorption. This step generates a smooth and continuous absorption flux waveform by calculating the flux at each sampling time and detecting and interpolating outliers based on statistical characteristics, providing a reliable data foundation for the subsequent identification of plateau and decay phases.

[0050] Step S200 in the method provided in this application embodiment includes:

[0051] For each sampling time, the difference between the first real-time drug concentration and the second real-time drug concentration is calculated to obtain the instantaneous concentration difference at the corresponding sampling time; the instantaneous concentration difference is multiplied by the real-time volumetric flow rate at the corresponding sampling time to obtain the real-time total absorption flux at the corresponding sampling time; the real-time total absorption flux calculated at all sampling times is arranged sequentially according to the sampling time to form an initial absorption flux waveform; the overall average value and overall standard deviation of the real-time total absorption flux at all sampling points in the initial absorption flux waveform are calculated;

[0052] For each sampling time, the real-time total absorbed flux of the current sampling time and the same number of adjacent sampling times are taken to form an odd number of flux values. The median of these odd number of flux values ​​is calculated. When the absolute deviation of the real-time total absorbed flux from the median exceeds a preset multiple of the overall standard deviation, the corresponding sampling time is marked as an outlier. The real-time total absorbed flux corresponding to all outliers is removed from the initial absorbed flux waveform. For the missing time caused by the removal, the real-time total absorbed flux of the two adjacent non-removed times is taken, and linear interpolation is performed according to the sampling time interval to fill in the gap and obtain the interpolated absorbed flux value. The initial absorbed flux waveform after removal and interpolation is taken as the final absorbed flux waveform. Detailed explanation is as follows:

[0053] In this embodiment, the instantaneous concentration difference refers to the difference between the inlet drug concentration and the outlet drug concentration at a single sampling moment, representing the total amount of drug absorbed by the intestinal wall as the perfusion fluid flows through the target intestinal segment at that moment. Real-time total absorption flux refers to the total amount of drug absorbed through the intestinal wall into the systemic circulation per unit time, obtained by multiplying the instantaneous concentration difference by the real-time volumetric flow rate, and is a core dynamic indicator for measuring drug absorption rate. The absorption flux waveform refers to a continuous dynamic curve formed by arranging the real-time total absorption flux at all sampling moments in chronological order, visually displaying the overall trend of drug absorption rate changes over time.

[0054] The overall standard deviation refers to the standard deviation of flux values ​​at all sampling points in the initial absorbed flux waveform, reflecting the overall fluctuation level of the flux data. Outlier removal refers to the operation of removing isolated sampling points whose flux values ​​significantly deviate from the local neighborhood median and whose deviation exceeds the limit, based on a dual judgment of median filtering and standard deviation threshold. Linear interpolation filling refers to filling the gaps left by the removed sampling points by calculating interpolation estimates at a linear ratio of the two nearest valid flux values ​​before and after them, using the two nearest valid flux values ​​before and after them as endpoints.

[0055] In this step, firstly, in order to convert the inlet and outlet concentration data into dynamic indicators of drug absorption, it is necessary to calculate the difference between the first real-time drug concentration and the second real-time drug concentration at each sampling time to obtain the instantaneous concentration difference, and then multiply it by the real-time volumetric flow rate to obtain the real-time total absorption flux at that time. The flux values ​​at all times are arranged in time to form the initial absorption flux waveform.

[0056] Furthermore, in order to assess the overall fluctuation level of the waveform from a statistical perspective, it is necessary to calculate the overall average and overall standard deviation of the flux values ​​of all sampling points in the initial absorbed flux waveform, as a benchmark for subsequent outlier determination.

[0057] Furthermore, in order to identify and eliminate isolated outliers caused by instantaneous sensor noise or bubble interference, it is necessary to take several adjacent sampling times before and after each sampling time to form a local neighborhood, calculate the median of flux values ​​within the neighborhood, and mark it as an outlier when the absolute deviation of the flux value at that sampling time from the median exceeds three times the overall standard deviation.

[0058] Finally, all outliers are removed from the waveform. For the missing time points, the two adjacent valid flux values ​​that were not removed are taken and linearly interpolated according to the sampling time interval to fill the gap, resulting in a smooth and continuous final absorption flux waveform for subsequent plateau and decay period identification.

[0059] In summary, this step generates a smooth and continuous absorption flux waveform by calculating flux at each sampling time and using dual outlier detection and interpolation based on median filtering and standard deviation threshold, providing a reliable data foundation for the subsequent identification of the plateau and decay periods.

[0060] S300: Based on the absorption flux waveform, identify the absorption plateau period and the absorption decay period, calculate the plateau absorption flux and extract the absorption decay kinetic parameters, wherein the absorption decay kinetic parameters include at least the absorption flux value at the decay start time, the fast decay time constant and the slow decay time constant;

[0061] In this embodiment, after obtaining a smooth absorption flux waveform, it is necessary to automatically identify the plateau and decay phases in the waveform, extract the plateau absorption flux and decay kinetic parameters, and provide quantitative input for subsequent absorption scavenging coefficient calculation and dynamic permeability coefficient correction. This step achieves a fine characterization of the dual time constants of the plateau maintenance and decay phases during absorption through adaptive plateau detection using first-order difference and fluctuation tolerance threshold, and piecewise linear regression based on logarithmic decay rate.

[0062] Step S300 in the method provided in this application embodiment includes:

[0063] Calculate the first-order difference of the real-time absorbed flux at adjacent sampling times on the absorbed flux waveform to obtain a difference sequence; calculate the upper quartile of the absolute value of the difference sequence and use the upper quartile as the fluctuation tolerance threshold; scan backward from the starting sampling time of the absorbed flux waveform, and mark the sampling intervals that continuously satisfy the condition that the absolute value of the first-order difference is less than the fluctuation tolerance threshold as candidate plateau intervals; determine the interval with the longest duration among the candidate plateau intervals as the absorption plateau period, record the median of all real-time absorbed fluxes within the absorption plateau period as the plateau absorbed flux, and use the ending sampling time of the absorption plateau period as the attenuation start reference point;

[0064] Starting from the attenuation initiation reference point, the scan proceeds backward to find the first sampling point where the first-order difference is negative and the absolute value of the first-order difference continuously exceeds the fluctuation tolerance threshold. This sampling point is taken as the attenuation initiation time. The scan continues backward from the attenuation initiation time until the absolute value of the first-order difference is continuously below the fluctuation tolerance threshold or the waveform end is reached. The interval covered by the scan is defined as the absorption attenuation period. A detailed explanation follows:

[0065] In this embodiment, the first-order difference sequence refers to the difference sequence obtained by subtracting the flux values ​​at two adjacent sampling times on the absorbed flux waveform, used to characterize the instantaneous rate of flux change on the time axis. The fluctuation tolerance threshold refers to the upper limit of the acceptable range of flux fluctuations calculated based on the upper quartile of the absolute value of the difference sequence, used to distinguish between normal fluctuations and significant decay trends. The candidate plateau period interval refers to the sampling interval on the absorbed flux waveform where the absolute value of the first-order difference is less than the fluctuation tolerance threshold, reflecting the time period during which the flux remains at a relatively stable level. When three or more consecutive sampling times meet this condition, it is marked as a candidate interval.

[0066] The plateau absorption flux refers to the median of all real-time absorption fluxes during the absorption plateau period, representing the maximum absorption rate of the drug under steady-state conditions. The decay onset time refers to the moment when, during the scan from the decay onset reference point, the first sampling point where the first-order difference becomes negative and its absolute value continuously exceeds the fluctuation tolerance threshold, marking the transition of the absorption process from steady state to decay.

[0067] In this step, the process for identifying the absorption plateau phase and the decay phase is as follows: Figure 2 As shown.

[0068] In this step, firstly, in order to distinguish the small random fluctuations of the flux waveform from the significant decay trend, it is necessary to calculate the first-order difference sequence of the absorbed flux waveform and take the upper quartile of the absolute value sequence of the difference as the adaptive fluctuation tolerance threshold. This threshold can be automatically adjusted according to the fluctuation characteristics of the waveform itself.

[0069] Furthermore, in order to identify the steady-state phase of the absorption process, it is necessary to scan backward from the starting position of the waveform, mark the sampling interval where the absolute value of the first difference is continuously less than the fluctuation tolerance threshold as the candidate plateau period, select the interval with the longest duration as the absorption plateau period, and record the median flux value in the interval as the plateau absorption flux.

[0070] Finally, to pinpoint the start of the decay process, we need to scan backward from the end of the plateau period to find the first sampling point where the first-order difference is negative and its absolute value continuously exceeds the fluctuation tolerance threshold. This point is marked as the start of decay. From this point, we continue scanning backward until the absolute value of the difference is continuously below the threshold or the waveform ends, thus determining the complete range of the absorption decay period.

[0071] Step S300 in the method provided in this application embodiment further includes:

[0072] Arrange the real-time absorbed flux at all sampling times within the absorption decay period in chronological order to form a decay flux sequence; take the natural logarithm of each real-time absorbed flux in the decay flux sequence to generate a logarithmic decay flux sequence; starting from the second sampling time of the logarithmic decay flux sequence, calculate the difference in logarithmic decay flux between each sampling time and the previous sampling time to obtain a logarithmic decay rate sequence; in the logarithmic decay rate sequence, find the sampling time with the largest absolute value of the logarithmic decay rate, and use the found sampling time as the dividing point between the fast decay segment and the slow decay segment;

[0073] The interval from the attenuation start time to the boundary point is defined as the fast attenuation segment, and the interval from the boundary point to the last sampling time of the absorption attenuation period is defined as the slow attenuation segment. Linear regressions are performed on the logarithmic attenuation flux and sampling time in both the fast and slow attenuation segments. The absolute value of the slope of the regression line for the fast attenuation segment is taken as the fast attenuation rate, and the absolute value of the slope of the regression line for the slow attenuation segment is taken as the slow attenuation rate. The reciprocal of the fast attenuation rate is taken as the fast attenuation time constant, and the reciprocal of the slow attenuation rate is taken as the slow attenuation time constant. The real-time absorbed flux corresponding to the attenuation start time is taken as the absorbed flux value at the attenuation start time. Detailed explanation follows:

[0074] In this embodiment, the rapid decay phase and the slow decay phase are two stages within the absorption decay period, divided according to the sampling moment when the absolute value of the logarithmic decay rate is the largest. The rapid decay phase refers to the interval from the start of decay to the moment when the absolute value of the logarithmic decay rate is the largest, corresponding to the stage of rapid drug release from the intestinal wall or carrier dissociation. The slow decay phase refers to the interval from the moment when the absolute value of the logarithmic decay rate is the largest to the end of the decay period, corresponding to the residual slow passive diffusion process. The logarithmic decay flux sequence refers to the new sequence obtained by taking the natural logarithm of each flux value in the decay flux sequence, which can transform the exponential decay process into an approximately linear change to facilitate linear regression. The rapid decay time constant refers to the reciprocal of the absolute value of the linear regression slope of the logarithmic decay flux over time within the rapid decay phase, reflecting the time required for the absorption flux in this stage to decay to approximately 37% of its initial value.

[0075] In this step, in order to decompose the decay process into different dynamic stages, it is necessary to extract the decay flux sequence and take the natural logarithm, calculate the logarithmic decay rate sequence, and take the moment when the absolute value of the logarithmic decay rate is the largest as the dividing point to divide the decay period into a fast decay segment and a slow decay segment.

[0076] Furthermore, to obtain the quantitative kinetic parameters for each stage, linear regressions need to be performed on the logarithmic decay flux and sampling time in both the rapid and slow decay stages. The absolute value of the regression slope represents the decay rate for each stage, and its reciprocal is the corresponding time constant. Through these steps, three core kinetic parameters can be extracted: the absorbed flux at the start of decay, the rapid decay time constant, and the slow decay time constant.

[0077] For example, taking the real-time measurement of a novel antihypertensive candidate compound 221s(2,9) in a rat in vivo one-way intestinal perfusion model as an example, under the perfusion fluid conditions of pH 6.5, the absolute value of the first-order difference in the absorption flux waveform remained below the fluctuation tolerance threshold for a period of 8 minutes from 0 to 8 minutes, which was identified as the absorption plateau period. The median flux during the plateau period was 24.6 μg / min. The decay started at 8.2 minutes, corresponding to an absorption flux of 24.1 μg / min. The decay period lasted until 22.5 min, with a cutoff point at 13.6 min. The rapid decay phase lasted from 8.2 min to 13.6 min, with an absolute value of the log-linear regression slope of 0.215 min⁻¹ and a rapid decay time constant of 1 ÷ 0.215 ≈ 4.65 min. The slow decay phase lasted from 13.6 min to 22.5 min, with an absolute value of the regression slope of 0.043 min⁻¹ and a slow decay time constant of 1 ÷ 0.043 ≈ 23.3 min.

[0078] In summary, this step achieves automatic identification and dynamic parameter extraction of the plateau and decay phases in the absorption flux waveform through adaptive plateau detection with first-order differential fluctuation tolerance and piecewise regression based on logarithmic decay rate with dual time constants. This provides quantitative input for subsequent calculation of absorption scavenging coefficient and dynamic permeability coefficient correction.

[0079] S400: Calculate the absorption clearance coefficient based on the platform absorption flux, the first real-time drug concentration, and the effective length of the target intestinal segment;

[0080] In this embodiment, after obtaining the platform absorption flux, it is necessary to combine it with the inlet steady-state concentration and the effective length of the intestinal segment to calculate the absorption clearance coefficient, a standardized index, so as to make cross-comparisons between different compounds and different experimental conditions. This step eliminates the influence of differences in experimental conditions on absorption evaluation by normalizing the absorption rate to an absorption clearance coefficient that is independent of concentration and length.

[0081] Step S400 in the method provided in this application embodiment includes:

[0082] The effective length of the target intestinal segment is a known fixed value. The absorption and clearance coefficient is directly proportional to the platform absorption flux and inversely proportional to the first real-time drug concentration and the effective length of the target intestinal segment. A detailed explanation follows:

[0083] In this embodiment, the absorption clearance coefficient refers to a standardized absorption capacity index that normalizes the drug absorption rate across the intestinal wall based on the steady-state assumption. Physically, it represents the volume of perfusion fluid that completely removes the drug per unit intestinal length per unit time, used to eliminate the influence of differences in perfusion fluid concentration and intestinal length on the comparability between different compounds or experiments. Effective length refers to the intestinal length measured by ultrasound imaging along the target intestinal segment during the measurement period, between the tips of the inlet and outlet cannula, as the actual length of the intestinal segment involved in drug absorption. Inlet steady-state concentration refers to the average real-time drug concentration during the absorption plateau period, representing the drug concentration level in the perfusion fluid on the inlet side when drug absorption reaches steady state.

[0084] In this step, firstly, in order to convert the absorption rate into a standardized indicator independent of concentration and intestinal segment size, the average inlet drug concentration during the absorption plateau period needs to be obtained as the inlet steady-state concentration. The inlet steady-state concentration is taken as the arithmetic mean of the first real-time drug concentration at all sampling times during the plateau period, which can reduce the random error of single-point measurements.

[0085] Furthermore, to determine the actual length of the intestinal segment involved in drug absorption, an ultrasound imaging device is used to scan along the target intestinal segment and measure the length of the intestinal tube between the tip of the inlet cannula and the tip of the outlet cannula. This length is taken as the effective length of the target intestinal segment. Ultrasound measurement can be performed before or after the perfusion experiment. It is a non-invasive measurement method and does not interfere with the perfusion process.

[0086] Finally, the absorption flux of the platform is divided by the product of the inlet steady-state concentration and the effective length to obtain the absorption and removal coefficient, calculated using the following formula: J / ( ×L), where Let J be the absorption and removal coefficient, and J be the platform absorption flux. The inlet steady-state concentration is given by , and L is the effective length of the target intestinal segment. The absorption-clearance coefficient per unit intestinal segment length is expressed in milliliters per centimeter per minute, representing the drug clearance capacity per unit intestinal segment length per unit time.

[0087] In summary, this step, by combining the platform absorption flux with the inlet steady-state concentration and the effective length of the intestinal segment, calculates a standardized absorption and clearance coefficient, eliminating the influence of differences in experimental conditions on drug absorption assessment and providing a basic parameter for subsequent correction of the dynamic permeability coefficient.

[0088] S500: Input the platform absorption flux, the absorption attenuation kinetic parameters and the absorption scavenging coefficient into a pre-calibrated nonlinear correction mapping relationship, and output the dynamic permeability coefficient;

[0089] In this embodiment, after obtaining the absorption and scavenging coefficient and attenuation kinetic parameters, it is necessary to correct the attenuation distortion. The absorption and scavenging coefficient is calculated based on the steady-state assumption. When significant attenuation occurs in the absorption process, the apparent value calculated solely based on the steady-state plateau period is too high and needs to be corrected downwards. This step uses an offline calibrated nonlinear mapping relationship to convert the attenuation distortion parameters into a correction factor, thereby correcting the absorption and scavenging coefficient and obtaining a more accurate dynamic permeability coefficient.

[0090] Step S500 in the method provided in this application embodiment includes:

[0091] Data from multiple historical intestinal perfusion experiments were collected. Each experiment corresponded to candidate compounds with different degrees of saturated absorption mediated by known carriers and various pH perfusion fluid environments. From each experiment, the plateau absorption flux, the absorption flux value at the onset of decay, the rapid decay time constant, the slow decay time constant, and the absorption clearance coefficient were extracted. Based on the ratio of the plateau absorption flux to the absorption flux value at the onset of decay, combined with the rapid decay time constant and the slow decay time constant, the absorption decay distortion coefficient was calculated. The absorption decay distortion coefficient has a value of 1 when there is no decay, and a larger value when the decay is more severe.

[0092] For each historical experiment, the dynamic permeability coefficient was determined through a carrier inhibitor control experiment. The ratio of the dynamic permeability coefficient to the absorption and clearance coefficient was calculated and used as a correction factor. Using the absorption attenuation distortion coefficient as the abscissa and the correction factor as the ordinate, all corresponding point pairs obtained from historical experiments were plotted on a plane. A monotonically decreasing curve passing through the point pair and the coordinate point (1,1) was fitted using a nonlinear regression method, yielding a mapping function of the correction factor as a function of the absorption attenuation distortion coefficient. This mapping function and its corresponding fitting parameters were determined as the nonlinear correction mapping relationship, converting the input absorption and clearance coefficient and absorption attenuation kinetic parameters into a dynamic permeability coefficient. A detailed explanation follows:

[0093] In this embodiment, the nonlinear correction mapping relationship refers to a monotonically decreasing function curve obtained by calibration using historical experimental data, with the absorption attenuation distortion coefficient as input and the correction factor as output. This curve is used to correct the absorption scavenging coefficient of new compounds to the dynamic permeability coefficient during the online prediction stage. The absorption attenuation distortion coefficient is a dimensionless index calculated jointly by the platform's absorption flux and attenuation kinetic parameters. It takes a value of 1 when there is no attenuation, and a larger value when the attenuation is more severe, comprehensively reflecting the degree to which the absorption process deviates from the ideal steady state.

[0094] The correction factor, the ratio of the dynamic permeability coefficient to the absorption and clearance coefficient, is calculated during the online calibration phase after determining the true dynamic permeability coefficient through a carrier inhibitor control experiment. It measures the impact of attenuation distortion on the absorption and clearance coefficient. The dynamic permeability coefficient, after attenuation correction, is a standardized absorption and clearance index that more closely approximates the actual transmembrane transport capacity of the drug. It is an intestinal permeability evaluation index expressed in the dimensions of the absorption and clearance coefficient, obtained by transforming the absorption and clearance coefficient through a nonlinear correction mapping relationship. The carrier inhibitor control experiment involves repeated perfusion experiments after adding a specific carrier protein inhibitor to the perfusion fluid. By comparing the absorption differences with and without the inhibitor, the contribution of carrier-mediated transport to drug absorption is determined. This is an independent pharmacological method for obtaining the true dynamic permeability coefficient.

[0095] In this step, firstly, to accumulate the training data needed to establish the mapping relationship, it is necessary to collect data from multiple historical intestinal perfusion experiments. These experiments cover a variety of candidate compounds with different degrees of saturation uptake mediated by known carriers, and are conducted under various pH perfusion fluid conditions. From each experiment, the platform uptake flux, the uptake flux value at the onset of decay, the rapid decay time constant, the slow decay time constant, and the uptake clearance coefficient are extracted.

[0096] Furthermore, to quantify the degree of absorption attenuation distortion in each experiment, the absorption attenuation distortion coefficient needs to be calculated based on the extracted kinetic parameters. The calculation formula is: the absorption attenuation distortion coefficient equals the plateau absorption flux divided by the absorption flux value at the start of attenuation, multiplied by the difference between the plateau absorption flux and the absorption flux value at the start of attenuation divided by the plateau absorption flux, and then multiplied by the ratio of the fast attenuation time constant to the slow attenuation time constant. This formula satisfies the relationship that "it takes a value of 1 when there is no attenuation, and the value increases as the attenuation becomes more severe."

[0097] Furthermore, to obtain the true value of the correction factor for each experiment, the dynamic permeability coefficient of the compound under the same conditions needs to be determined through a carrier inhibitor control experiment. The ratio of the dynamic permeability coefficient to the absorption and clearance coefficient is then calculated as the correction factor. It should be noted that the carrier-mediated saturation absorption of the compounds in the calibration dataset is confirmed through independent pharmacological experiments using a control perfusion experiment with a specific carrier inhibitor, rather than relying on the method itself for determination. The calibration phase and the online prediction phase are separated in time.

[0098] Furthermore, to establish a mapping function from the distortion coefficient to the correction factor, the corresponding point pairs obtained from all historical experiments are plotted on a plane with the absorption attenuation distortion coefficients as the x-axis and the correction factor as the y-axis. A monotonically decreasing curve passing through the point pair and forced to pass through the coordinate point (1,1) is fitted using a nonlinear regression method. The forced passage through the (1,1) point reflects the physical intuition that no correction is needed when there is no attenuation distortion. When the absorption process is completely attenuated, the absorption scavenging coefficient equals the dynamic permeability coefficient, and the correction factor is 1.

[0099] Finally, the fitted mapping function and fitting parameters are determined as a nonlinear correction mapping relationship. In the online prediction stage, the absorption scavenging coefficient and absorption attenuation kinetic parameters of the new compound are input into this mapping relationship. First, the absorption attenuation distortion coefficient is calculated, then the corresponding correction factor is obtained through the mapping function, and finally, the dynamic permeability coefficient is obtained by multiplying the correction factor by the absorption scavenging coefficient.

[0100] For example, using real-time measurements of a novel antihypertensive candidate compound 221s(2,9) in a rat in vivo one-way intestinal perfusion model, continuing with the S300 example data. Under perfusion conditions of pH 6.5, the platform uptake flux J = 24.6 μg / min, and the inlet steady-state concentration... 18.2 μg / mL, effective intestinal segment length L = 10.5 cm. Absorption clearance coefficient. =J / ( ×L)=24.6 / (18.2×10.5)≈0.129mL / (min (cm). Absorbed flux value at the onset of decay. =24.1 μg / min, rapid decay time constant 4.65 min, slow decay time constant 23.3 min. The absorption attenuation distortion coefficient D is calculated as follows: D = (24.6 / 24.1) × [1 + ((24.6 - 24.1) / 24.6) × (4.65 / 23.3)] ≈ 1.021 × [1 + 0.0203 × 0.200] ≈ 1.021 × 1.0041 ≈ 1.025.

[0101] Data on candidate compounds with different carrier-mediated saturation absorption degrees were collected from historical enteral perfusion experiments. The absorption attenuation distortion coefficient and correction factor for each group were calculated. Figure 3 As shown:

[0102] from Figure 3It can be seen that the correction factor exhibits a monotonically decreasing trend as the absorption attenuation distortion coefficient increases. A nonlinear regression method was used to fit the above data points, while constraining the fitted curve to pass through the coordinate point (1,1). The resulting mapping function was used to correct the absorption scavenging coefficient of subsequent new compounds to the dynamic permeability coefficient. The mapping function used for fitting is as follows: ,in Let 'a' be the correction factor, 'D' be the absorption attenuation distortion coefficient, and 'a' be the attenuation sensitivity coefficient obtained through nonlinear regression fitting. Substituting the current distortion coefficient of 1.025 into the mapping function, the correction factor is obtained from the fitted curve. ≈0.996, dynamic permeability coefficient = ≈0.996 0.129≈0.128mL / (min m).

[0103] In summary, this step transforms the absorption attenuation distortion coefficient into a correction factor through an offline calibrated nonlinear mapping relationship, and obtains the dynamic permeability coefficient after attenuation correction of the absorption scavenging coefficient. Compared with existing technologies, this step has the following advantages: First, it utilizes attenuation kinetic parameters to construct the distortion coefficient, quantifying the attenuation degree into a single comparable index, and then converts it into a correction factor through a pre-calibrated mapping relationship, eliminating the overestimation effect of attenuation distortion on permeability assessment; second, the calibration of the mapping relationship is based on the true value obtained from independent carrier inhibitor experiments, and the forced passage through the (1,1) point ensures a normalized benchmark that does not require correction when there is no attenuation, improving the physical rationality and reliability of the correction.

[0104] S600: When the dynamic permeability coefficient decreases by more than the dynamic decay threshold in three consecutive pH gradients, the candidate compound is determined to have carrier-mediated saturation absorption characteristics in the current pH range.

[0105] In this embodiment, after obtaining the dynamic permeability coefficients under multiple pH gradients, it is necessary to determine whether the candidate compound exhibits carrier-mediated saturation absorption characteristics and its carrier type. Traditional methods require additional inhibitor experiments for this determination. This step automatically identifies carrier participation and carrier type by analyzing the dynamic permeability coefficient variation with pH.

[0106] Step S600 in the method provided in this application embodiment includes:

[0107] The dynamic permeability coefficients calculated under each pH gradient are arranged in ascending order of pH value to construct a pH-dynamic permeability coefficient sequence. The absolute value of the difference between the dynamic permeability coefficients corresponding to two adjacent pH gradients in the pH-dynamic permeability coefficient sequence is calculated sequentially to obtain each adjacent attenuation amount. Three consecutive adjacent attenuation amounts are extracted from this sequence. The three adjacent attenuation amounts are compared with the dynamic attenuation threshold. If each adjacent attenuation amount is greater than the dynamic attenuation threshold, it is determined that the candidate compound has carrier-mediated saturation absorption characteristics within the pH range covered by the three pH gradients.

[0108] The step of determining the dynamic decay threshold includes: selecting at least one known negative control compound that does not have carrier-mediated saturation absorption characteristics, analyzing it in perfusion fluid environments with multiple pH gradients similar to the candidate compound, and obtaining the dynamic permeability coefficient of the negative control compound at each pH gradient; arranging the dynamic permeability coefficients of the negative control compounds in ascending order of pH value, and sequentially calculating the absolute value of the difference between the dynamic permeability coefficients corresponding to two adjacent pH gradients to obtain multiple adjacent decay amounts of the negative control compound; calculating the average and standard deviation of all adjacent decay amounts of the negative control compound, and determining the upper limit of fluctuation based on the average and standard deviation; and determining the upper limit of fluctuation as the dynamic decay threshold. A detailed explanation follows:

[0109] In this embodiment, the pH-dynamic permeability coefficient sequence refers to a sequence formed by arranging the dynamic permeability coefficients calculated at each pH gradient in ascending order of pH value, used to analyze the trend of drug permeability changes with pH value. Adjacent decay refers to the absolute value of the difference in dynamic permeability coefficients between two adjacent pH gradients in the pH-dynamic permeability coefficient sequence, reflecting the magnitude of the decrease in permeability coefficient within adjacent pH ranges. The dynamic decay threshold refers to the upper limit of fluctuation determined based on the statistical characteristics of adjacent decay of negative control compounds, used to distinguish between normal fluctuations and significant decay caused by carrier saturation effects. Negative control compounds refer to compounds known to primarily undergo transmembrane transport via passive diffusion and lack carrier-mediated saturation absorption characteristics, such as atenolol or mannitol, which are low-permeability references; their dynamic permeability coefficient changes with pH only reflect the pH dependence of passive diffusion.

[0110] In this step, firstly, to analyze the variation of dynamic permeability coefficient with pH, ​​the dynamic permeability coefficients at each pH gradient need to be arranged in ascending order of pH to construct a sequence. From the sequence, the adjacent decay amounts between adjacent pH values ​​are calculated sequentially, and three consecutive adjacent decay amounts are selected as the judgment interval.

[0111] Furthermore, to distinguish between carrier-mediated saturation absorption and normal fluctuations, a dynamic decay threshold needs to be pre-determined. A negative control compound known to lack carrier-mediated saturation absorption characteristics is selected, and its dynamic permeability coefficient is obtained using the same pH gradient and analytical procedure as the candidate compound. The average and standard deviation of adjacent decay values ​​are calculated, and the average value plus twice the standard deviation is used as the upper limit of fluctuation, which is then set as the dynamic decay threshold.

[0112] Finally, the three consecutive adjacent decay values ​​of the candidate compound were compared with the dynamic decay threshold one by one. If each adjacent decay value exceeded the threshold, it indicates that the decrease in the permeability coefficient has exceeded the normal fluctuation range of passive diffusion, and there is a carrier-mediated saturation absorption characteristic within these three consecutive pH intervals.

[0113] Step S600 in the method provided in this application embodiment further includes:

[0114] After determining the presence of carrier-mediated saturation absorption characteristics, the absorption attenuation distortion coefficients of the candidate compounds at the three consecutive pH gradients are extracted. These coefficients are then arranged in ascending order of pH value, and the trend of the absorption attenuation distortion coefficients with increasing pH value is determined. When the absorption attenuation distortion coefficient shows a monotonically increasing trend with increasing pH value, the saturation absorption characteristic is confirmed to be mediated by a proton-coupled carrier. When the absorption attenuation distortion coefficient shows a monotonically decreasing trend with increasing pH value, the saturation absorption characteristic is confirmed to be mediated by anion-exchange carrier. When the absorption attenuation distortion coefficient shows no obvious monotonically increasing trend with increasing pH value, the candidate compound is classified as requiring further investigation of the carrier type. Detailed explanation follows:

[0115] In this application, proton-coupled carriers refer to carrier proteins that utilize proton gradients to drive transmembrane transport of substrates, such as the oligopeptide transporter PepT1. These carriers exhibit high activity in slightly acidic environments, with activity decreasing as pH increases, and the corresponding absorption attenuation distortion coefficient increases with increasing pH. Anion-exchange carriers refer to carrier proteins that mediate substrate uptake through anion exchange mechanisms, such as some members of the organic anion transport polypeptide OATP family. These carriers exhibit high activity in slightly alkaline environments, with activity increasing as pH increases, and the corresponding absorption attenuation distortion coefficient decreases with increasing pH. A monotonic trend refers to a directional change in the absorption attenuation distortion coefficient that consistently increases or decreases with increasing pH across three consecutive pH gradients.

[0116] In this step, to further infer the type of carrier involved in drug transport, the absorption attenuation distortion coefficients corresponding to three consecutive pH gradients within the determination interval need to be extracted and arranged in ascending pH order. The trend of these coefficients with pH is then observed. If the distortion coefficients monotonically increase with increasing pH, it indicates that the carrier activity is stronger at low pH, consistent with proton-coupled carrier characteristics; if they monotonically decrease, it indicates that the carrier activity is stronger at high pH, ​​consistent with anion-exchange carrier characteristics; if there is no obvious monotonic trend, it indicates that three points alone are insufficient to determine the type, and the pH gradient range needs to be expanded or other experimental methods should be used for further investigation. It should be noted that three pH gradients are the minimum condition for making a determination. In practical applications, the number of pH gradients can be increased to 5 to 7 as needed to improve the reliability and statistical confidence of carrier type inference.

[0117] For example, taking the real-time measurement of a novel antihypertensive candidate compound 221s(2,9) in a rat in vivo one-way intestinal perfusion model as an example, the dynamic permeability coefficient was obtained by performing this method at five pH gradients. The dynamic permeability coefficients at each pH gradient are as follows: 0.215 mL / (min·cm) at pH 5.0, 0.198 mL / (min·cm) at pH 5.5, 0.164 mL / (min·cm) at pH 6.0, 0.128 mL / (min·cm) at pH 6.5, and 0.105 mL / (min·cm) at pH 7.0. After sorting in ascending order of pH, the adjacent decay was calculated: the decay from pH 5.0 to 5.5 was |0.215|. 0.198|=0.017, 0.034 for pH 5.5 to 6.0, 0.036 for pH 6.0 to 6.5, and 0.023 for pH 6.5 to 7.0.

[0118] Atenolol was selected as the negative control compound and analyzed at the same five pH gradients. The adjacent decay values ​​were 0.005, 0.006, 0.008, and 0.007, respectively. The mean was calculated to be 0.0065, the standard deviation was 0.0013, and the upper limit of fluctuation, i.e. the dynamic decay threshold, was 0.0065 + 2 × 0.0013 = 0.0091.

[0119] The candidate compound was selected from three consecutive adjacent decay values ​​of 0.034, 0.036 and 0.023 in the pH range of 5.5 to 6.5. All three values ​​were greater than the dynamic decay threshold of 0.0091. Therefore, it was determined that the candidate compound had carrier-mediated saturation absorption characteristics in the pH range of 5.5 to 6.5.

[0120] The absorption attenuation distortion coefficients for the three pH gradients within this range were extracted: 1.025 at pH 5.5, 1.082 at pH 6.0, and 1.156 at pH 6.5. Arranged in ascending order of pH, the absorption attenuation distortion coefficients showed a monotonically increasing trend with increasing pH, confirming that this saturated absorption characteristic is mediated by a proton-coupled transporter, consistent with the known biological characteristics of the oligopeptide transporter PepT1, which exhibits high activity in the slightly acidic environment of the gut.

[0121] In summary, this step, through comparison of adjacent attenuation amounts of dynamic permeability coefficients with dynamic attenuation thresholds under multiple pH gradients and statistical comparison with negative control compounds, enables the automatic determination of carrier-mediated saturated absorption characteristics. Furthermore, it infers the carrier type by the trend of absorption attenuation distortion coefficient with pH, ​​providing an alternative analytical method for identifying the transport mechanism of candidate compounds without the need for additional inhibitor experiments.

[0122] The embodiments of this application, through the specific implementation methods described above, achieve the following technical effects:

[0123] This application proposes a real-time measurement and analysis method for intestinal permeability of antihypertensive drug candidates. First, the drug concentrations of candidate compounds at the inlet and outlet of the target intestinal segment are acquired in real time under different pH perfusion fluid environments, with simultaneous recording of the perfusion fluid volumetric flow rate and time-shifted alignment. Then, at each sampling time, the product of the instantaneous concentration difference and volumetric flow rate is calculated to generate an absorption flux waveform, which is smoothed by outlier removal and interpolation. Next, a first-order differential scan is performed on the waveform to adaptively identify the absorption plateau and decay phases, extracting the plateau absorption flux and calculating dual time constants for rapid and slow decay. Based on the plateau absorption flux, the inlet steady-state concentration, and the effective intestinal segment length, the absorption clearance coefficient is calculated. Finally, the plateau absorption flux, decay kinetic parameters, and absorption clearance coefficient are input into a pre-calibrated nonlinear correction mapping relationship to output a dynamic permeability coefficient.

[0124] When the dynamic permeability coefficient exceeds the dynamic decay threshold in three consecutive pH gradients, the candidate compound is determined to exhibit carrier-mediated saturation absorption characteristics within that pH range. Furthermore, the carrier type is inferred based on the trend of the absorption decay distortion coefficient with pH. This method achieves dynamic and continuous monitoring of the intestinal permeability of candidate drugs and automatic identification of carrier-mediated transport mechanisms through real-time analysis of the absorption flux waveform and nonlinear correction of decay distortion.

[0125] It should be noted that the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some implementations, multitasking and parallel processing are possible or may be advantageous.

Claims

1. A method for real-time measurement and analysis of intestinal permeability for antihypertensive drug candidates, characterized in that, The method includes: Under different pH perfusion fluid environments, the first real-time drug concentration of the candidate compound at the inlet of the target intestinal segment and the second real-time drug concentration at the outlet were obtained, and the real-time volumetric flow rate of the perfusion fluid through the target intestinal segment was recorded simultaneously. Based on the real-time volumetric flow rate, the first real-time drug concentration, and the second real-time drug concentration at each sampling time, the real-time absorption flux at the corresponding time is calculated, and the absorption flux waveform is generated by arranging them in chronological order. Based on the absorption flux waveform, the absorption plateau period and absorption decay period are identified, the plateau absorption flux is calculated, and the absorption decay kinetic parameters are extracted. The absorption decay kinetic parameters include at least the absorption flux value at the decay start time, the fast decay time constant, and the slow decay time constant. The absorption and clearance coefficient is calculated based on the platform absorption flux, the first real-time drug concentration, and the effective length of the target intestinal segment. The platform absorption flux, the absorption attenuation kinetic parameters, and the absorption scavenging coefficient are input into a pre-calibrated nonlinear correction mapping relationship to output the dynamic permeability coefficient. When the dynamic permeability coefficient decreases beyond the dynamic decay threshold in three consecutive pH gradients, the candidate compound is determined to have carrier-mediated saturation absorption characteristics within the current pH range.

2. The method for real-time measurement and analysis of intestinal permeability for antihypertensive drug candidates according to claim 1, characterized in that, Under different pH perfusion fluid environments, the first real-time drug concentration of the candidate compound at the inlet of the target intestinal segment and the second real-time drug concentration at the outlet were obtained, and the real-time volumetric flow rate of the perfusion fluid through the target intestinal segment was recorded simultaneously, including: After inserting cannulas into both ends of the target intestinal segment, the perfusion fluid with multiple pH gradients is injected sequentially. Each pH gradient is maintained for an independent measurement cycle, and the real-time volumetric flow rate of the perfusion fluid is kept constant within each measurement cycle. During each measurement cycle, the first fiber optic spectral probe is immersed in the inlet reservoir, and the second fiber optic spectral probe is connected to the outlet flow cell. The absorbance time-series signals at the inlet and outlet at the characteristic absorption wavelength of the candidate compound are collected synchronously at the same sampling frequency. The absorbance time-series signal at the inlet is converted into a first real-time drug concentration time-series sequence, and the absorbance time-series signal at the outlet is converted into a second real-time drug concentration time-series sequence. The real-time volumetric flow rate in the pipeline between the inlet storage tank and the target intestinal segment is continuously recorded by a flow meter, and the real-time volumetric flow rate is aligned with the first real-time drug concentration time series and the second real-time drug concentration time series according to the sampling time. After one measurement cycle is completed, switch to the perfusion solution of the next pH gradient and repeat the operation of simultaneous absorbance acquisition, concentration conversion and flow rate recording until all pH gradients to be measured are covered.

3. The method for real-time measurement and analysis of intestinal permeability for antihypertensive drug candidates according to claim 2, characterized in that, Converting the absorbance time-series signal at the inlet into a first real-time drug concentration time-series sequence, and converting the absorbance time-series signal at the outlet into a second real-time drug concentration time-series sequence, includes: Prepare at least three standard solutions of candidate compounds with different concentrations, measure the absorbance of each standard solution at the characteristic absorption wavelength, and obtain a set of standard data points corresponding to absorbance and concentration. Linear regression was performed on the standard data point set to fit the slope and intercept parameters of the absorbance-concentration conversion curve; Substitute the absorbance value of each sampling moment in the absorbance time-series signal at the entrance into the absorbance-concentration conversion curve to calculate the first real-time drug concentration at the corresponding moment, and arrange them in chronological order to form the first real-time drug concentration time-series sequence. Substitute the absorbance value of each sampling moment in the absorbance time-series signal at the outlet into the same absorbance-concentration conversion curve to calculate the second real-time drug concentration at the corresponding moment, and arrange them in chronological order to form the second real-time drug concentration time-series sequence.

4. The method for real-time measurement and analysis of intestinal permeability for antihypertensive drug candidates according to claim 1, characterized in that, Based on the real-time volumetric flow rate, the first real-time drug concentration, and the second real-time drug concentration at each sampling time, the real-time absorption flux at the corresponding time is calculated, and the absorption flux waveform is generated by arranging them in chronological order, including: For each sampling time, the difference between the first real-time drug concentration and the second real-time drug concentration is calculated to obtain the instantaneous concentration difference at the corresponding sampling time; Multiply the instantaneous concentration difference by the real-time volumetric flow rate at the corresponding sampling time to obtain the real-time total absorption flux at the corresponding sampling time; The total real-time absorption flux calculated at all sampling times is arranged sequentially according to the sampling time to form the initial absorption flux waveform; Calculate the overall average and overall standard deviation of the real-time total absorbed flux at all sampling points in the initial absorbed flux waveform; For each sampling time, the real-time total absorbed flux of the current sampling time and the same number of sampling times before and after it is taken to form an odd number of flux values; Calculate the median of the odd number of flux values. When the absolute deviation between the real-time total absorbed flux and the median exceeds a preset multiple of the overall standard deviation, mark the corresponding sampling time as an outlier. Remove the real-time total absorbed flux corresponding to all anomalies from the initial absorbed flux waveform. For the missing moments caused by the removal, take the two adjacent real-time total absorbed fluxes that were not removed, and perform linear interpolation according to the sampling time interval to fill in the gaps and obtain the interpolated absorbed flux values. The initial absorption flux waveform after elimination and interpolation is used as the final absorption flux waveform.

5. The method for real-time measurement and analysis of intestinal permeability for antihypertensive drug candidates according to claim 1, characterized in that, Identifying the absorption plateau and absorption decay periods based on the absorption flux waveform includes: Calculate the first-order difference of the real-time absorption flux at adjacent sampling times on the absorption flux waveform to obtain the difference sequence; Calculate the upper quartile of the absolute value of the difference sequence, and use the upper quartile as the fluctuation tolerance threshold; Starting from the initial sampling time of the absorption flux waveform, the sampling intervals in which the absolute value of the first-order difference is continuously less than the fluctuation tolerance threshold are marked as candidate plateau intervals. The longest duration among the candidate plateau periods is determined as the absorption plateau period. The median of all real-time absorption fluxes within the absorption plateau period is recorded as the plateau absorption flux, and the end sampling time of the absorption plateau period is used as the attenuation start reference point. Starting from the attenuation start reference point, scan backwards to find the first sampling point that satisfies the condition that the first-order difference is negative and the absolute value of the first-order difference continuously exceeds the fluctuation tolerance threshold, and take the corresponding sampling point as the attenuation start time. Continue scanning from the attenuation start time until the absolute value of the first-order difference is continuously lower than the fluctuation tolerance threshold or the waveform ends, and the interval covered by the scan is determined as the absorption attenuation period.

6. The method for real-time measurement and analysis of intestinal permeability for antihypertensive drug candidates according to claim 1, characterized in that, The platform absorbs flux and extracts absorption attenuation kinetic parameters, including: The real-time absorption flux at all sampling times during the absorption decay period is arranged in chronological order of sampling time to form an attenuation flux sequence. Take the natural logarithm of each real-time absorbed flux in the decay flux sequence to generate a logarithmic decay flux sequence. Starting from the second sampling time of the logarithmic decay flux sequence, the logarithmic decay flux difference between each sampling time and the previous sampling time is calculated sequentially to obtain the logarithmic decay rate sequence. In the logarithmic decay rate sequence, find the sampling moment with the largest absolute value of the logarithmic decay rate, and use the found sampling moment as the dividing point between the fast decay segment and the slow decay segment. The interval from the attenuation start time to the boundary point is defined as the fast attenuation segment, and the interval from the boundary point to the last sampling time of the absorption attenuation period is defined as the slow attenuation segment. Linear regressions were performed on the logarithmic decay flux and sampling time in the fast decay segment and the slow decay segment, respectively. The absolute value of the slope of the regression line in the fast decay segment was taken as the fast decay rate, and the absolute value of the slope of the regression line in the slow decay segment was taken as the slow decay rate. The reciprocal of the rapid decay rate is used as the rapid decay time constant, the reciprocal of the slow decay rate is used as the slow decay time constant, and the real-time absorbed flux corresponding to the decay start time is used as the absorbed flux value at the decay start time.

7. The method for real-time measurement and analysis of intestinal permeability for antihypertensive drug candidates according to claim 1, characterized in that, The effective length of the target intestinal segment is a known fixed value, and the absorption and clearance coefficient is directly proportional to the absorption flux of the platform and inversely proportional to the first real-time drug concentration and the effective length of the target intestinal segment.

8. The method for real-time measurement and analysis of intestinal permeability for antihypertensive drug candidates according to claim 1, characterized in that, The pre-calibration steps for the nonlinear correction mapping relationship include: Data from multiple historical intestinal perfusion experiments were collected. Each experiment corresponded to candidate compounds with different degrees of saturated absorption mediated by known carriers and various pH perfusion fluid environments. The platform absorption flux, the absorption flux value at the onset of decay, the rapid decay time constant, the slow decay time constant, and the absorption and clearance coefficient were extracted from each experiment. Based on the ratio of the platform's absorbed flux to the absorbed flux value at the start of attenuation, and combined with the fast attenuation time constant and the slow attenuation time constant, the absorption attenuation distortion coefficient is calculated. The absorption attenuation distortion coefficient takes a value of 1 when there is no attenuation, and takes a larger value when the attenuation is more severe. For each historical experiment, the dynamic permeability coefficient was determined by a carrier inhibitor control experiment, and the ratio of the dynamic permeability coefficient to the absorption and clearance coefficient was calculated as a correction factor. Using the absorption attenuation distortion coefficient as the abscissa and the correction factor as the ordinate, all corresponding point pairs obtained from historical experiments are plotted on a plane. A monotonically decreasing curve passing through the point pair and the coordinate point (1,1) is fitted using a nonlinear regression method to obtain the mapping function of the correction factor as a function of the absorption attenuation distortion coefficient. The mapping function and its corresponding fitting parameters are determined as the nonlinear correction mapping relationship, and the input absorption scavenging coefficient and absorption attenuation kinetic parameters are converted into dynamic permeability coefficients.

9. The method for real-time measurement and analysis of intestinal permeability for antihypertensive drug candidates according to claim 1, characterized in that, When the dynamic permeability coefficient decreases beyond the dynamic decay threshold at three consecutive pH gradients, the candidate compound is determined to possess carrier-mediated saturation absorption characteristics within the current pH range, including: The dynamic permeability coefficients calculated under each pH gradient are arranged in ascending order of pH value to construct a pH-dynamic permeability coefficient sequence. Calculate the absolute value of the difference between the dynamic permeability coefficients corresponding to two adjacent pH gradients in the pH-dynamic permeability coefficient sequence in turn to obtain each adjacent attenuation amount, and extract three consecutive adjacent attenuation amounts from them. The three adjacent decay amounts are compared with the dynamic decay threshold. If each adjacent decay amount is greater than the dynamic decay threshold, the candidate compound is determined to have carrier-mediated saturation absorption characteristics within the pH range covered by the three pH gradients. The step of determining the dynamic attenuation threshold includes: At least one known negative control compound that does not have carrier-mediated saturated absorption characteristics was selected and analyzed in the same perfusion fluid environment with multiple pH gradients as the candidate compound to obtain the dynamic permeability coefficient of the negative control compound at each pH gradient. The dynamic permeability coefficients of the negative control compounds are arranged in ascending order of pH value. The absolute value of the difference between the dynamic permeability coefficients corresponding to two adjacent pH gradients is calculated sequentially to obtain multiple adjacent attenuation amounts of the negative control compounds. Calculate the average and standard deviation of all adjacent attenuations of the negative control compound, and determine the upper limit of fluctuation based on the average and standard deviation; The upper limit of fluctuation is determined as the dynamic decay threshold.

10. The method for real-time measurement and analysis of intestinal permeability for antihypertensive drug candidates according to claim 1, characterized in that, When the dynamic permeability coefficient decreases beyond the dynamic decay threshold at three consecutive pH gradients, after determining that the candidate compound exhibits carrier-mediated saturation absorption characteristics within the current pH range, the process further includes: After determining the presence of carrier-mediated saturated absorption characteristics, the absorption attenuation distortion coefficients of the candidate compounds at the three consecutive pH gradients were extracted. The absorption attenuation distortion coefficients under the three consecutive pH gradients are arranged in ascending order of pH value to determine the trend of the absorption attenuation distortion coefficients as the pH value increases. When the absorption attenuation distortion coefficient shows a monotonically increasing trend with increasing pH value, it is confirmed that the saturated absorption characteristic is mediated by a proton-coupled carrier. When the absorption attenuation distortion coefficient shows a monotonically decreasing trend with increasing pH value, it is confirmed that the saturated absorption characteristic is mediated by anion exchange carrier. When the absorption attenuation distortion coefficient does not show a significant monotonic trend with increasing pH, the candidate compound is classified as requiring further investigation of the carrier type.

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