Method and system for screening and evaluating active pharmaceutical ingredients based on in-vitro culture

Abstract

The invention discloses a method and system for screening and evaluating active pharmaceutical ingredients based on in-vitro culture, and relates to the technical field of enzyme activity detection.The method comprises the steps that a cell population expressing a target metabolic enzyme is constructed through gene transfection, and a fluorescently-labeled substrate detection system is established; processing the cell population by adopting a concentration gradient to obtain dynamic response data, and analyzing an enzyme activity change rate to recognize a drug action time point; an enzyme activity recovery process is monitored after the drug is removed, a reversibility value is calculated through an activity recovery curve, and a safety value is determined by combining a ratio of a maximum tolerance concentration value to a minimum effective concentration value; and evaluating the drug activity based on the reversibility value and the safety value, and screening to obtain candidate drugs. According to the method, systematic evaluation and efficient screening of the active pharmaceutical ingredients can be realized.

Description

Technical Field

[0001] This invention relates to the field of enzyme activity detection technology, and more specifically, to a method and system for screening and evaluating active pharmaceutical ingredients based on in vitro culture. Background Technology

[0002] In drug screening and evaluation, activity assessment at the in vitro cellular level is typically required to evaluate drug efficacy and safety. Current technologies primarily assess drug activity by detecting indicators such as cell viability, proliferation inhibition rate, and changes in metabolic enzyme activity. However, these methods have the following limitations: results from a single time point cannot reflect the dynamic changes in drug action; the lack of assessment of the reversibility of drug action may lead to false positives; and the failure to comprehensively evaluate drug efficacy and safety reduces the reliability of screening results.

[0003] Existing technologies assess drug activity through enzyme inhibition assays but do not consider the persistence of drug action; they assess drug safety using cytotoxicity assays but lack analysis of the drug's mechanism of action; and they use molecular docking techniques to predict drug targets but fail to verify the dynamic characteristics of drug action. None of these methods achieve a systematic review of drug activity and safety. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for screening and evaluating active pharmaceutical ingredients based on in vitro culture, aiming to solve at least one of the technical problems existing in the prior art.

[0005] The technical solution of this invention is: screening and evaluation of active pharmaceutical ingredients based on in vitro culture, comprising the following steps:

[0006] A cell population expressing the target metabolic enzyme was constructed by gene transfection. A fluorescently labeled substrate was prepared to establish a quantitative detection method for enzyme activity. The expression level and activity of the metabolic enzyme in the cell population were measured, and a correlation curve was established. A cell subpopulation within the linear range of the correlation curve was selected as a validation cell population, and the baseline enzyme activity of the validation cell population was measured as validation data.

[0007] Based on the validation data, target cell populations were selected. Under in vitro culture conditions, the target cell populations were treated with the drug to be screened according to a concentration gradient. Dynamic response data, including the change in enzyme activity relative to the baseline value, were obtained at multiple time points using a quantitative enzyme activity detection method.

[0008] The dynamic response data is segmented according to the time series, the rate of change of enzyme activity in each time interval is calculated, the drug action time point is identified, and the corresponding relationship data between drug concentration and action time is obtained.

[0009] Based on the corresponding relationship data, the drug removal time point was selected. After the drug stimulation was removed, the cell population was continuously monitored, and the enzyme activity value in the cell culture supernatant was measured. The measured enzyme activity value was plotted as an activity recovery curve under different concentration conditions.

[0010] The enzyme activity values ​​of the target cell population before drug administration were collected and determined as the baseline level. The deviation between the activity recovery curve and the baseline level was analyzed to determine the minimum effective concentration and the maximum tolerated concentration. Based on the activity recovery curve, enzyme activity data points within the range of the minimum effective concentration and the maximum tolerated concentration were extracted. The ratio of the change in enzyme activity at adjacent time points to the time interval was determined as the enzyme activity recovery rate. The reversibility value was obtained by multiplying the enzyme activity recovery rate by the drug exposure duration. The ratio of the maximum tolerated concentration to the minimum effective concentration was used as the safety value.

[0011] The reversibility and safety values ​​are used to calculate the drug evaluation score, and drug samples that meet the screening criteria are selected as candidate drugs.

[0012] A cell population expressing the target metabolic enzyme was constructed through gene transfection. A fluorescently labeled substrate was prepared to establish a quantitative detection method for enzyme activity. The expression level and activity of the metabolic enzyme in the cell population were measured, and a correlation curve was established, including:

[0013] A eukaryotic expression vector expressing the target metabolic enzyme was constructed, and the eukaryotic expression vector was introduced into cells to be transfected using liposome transfection. Cell populations expressing the target metabolic enzyme were obtained by continuous passage screening with resistance drugs.

[0014] A fluorescently labeled substrate was prepared, and the fluorescently labeled substrate was added to the culture supernatant of the cell population expressing the target metabolic enzyme. The fluorescence intensity was measured using a fluorescence detector to establish a quantitative detection method for enzyme activity.

[0015] Cell populations expressing the target metabolic enzyme were sorted by flow cytometry to obtain cell subpopulations with different expression levels. The activity of the metabolic enzyme in the cell subpopulations was measured using the enzyme activity quantification method described above, and a curve showing the relationship between expression level and enzyme activity was established.

[0016] Based on the validation data, a target cell population was selected. Under in vitro culture conditions, the target cell population was treated with the drug to be screened according to a concentration gradient. Dynamic response data, including changes in enzyme activity relative to a baseline value, were obtained at multiple time points using a quantitative enzyme activity detection method.

[0017] The distribution characteristics of metabolic enzyme activity in the analysis and verification data were analyzed, and upper and lower limits of metabolic enzyme activity were established. The cell population was sorted by flow cytometry, and the cell population with metabolic enzyme activity between the upper and lower limits was selected as the target cell population.

[0018] The target cell population was inoculated into an in vitro culture system containing culture medium and cultured. The metabolic enzyme activity of the target cell population was measured as a baseline value. The concentration range of the drug to be screened was set according to the baseline value. Incremental concentration points were set within the concentration range to prepare a drug gradient series with increasing concentration. The target cell population was treated with the drug gradient series.

[0019] The supernatant in the culture system is collected at fixed time intervals within a preset time range. The activity of metabolic enzymes in the supernatant is measured using the enzyme activity quantification method. The change in the activity of the metabolic enzymes relative to the baseline value is calculated. The change at each detection time point and the corresponding drug concentration data are compiled and recorded as dynamic response data.

[0020] The dynamic response data is segmented according to the time series, and the rate of change of enzyme activity within each time interval is calculated to identify the drug action time points and obtain the corresponding data of drug concentration and action time, including:

[0021] An enzyme activity data matrix is ​​constructed by arranging dynamic response data according to the sampling time series. Enzyme activity values ​​at adjacent sampling time points are extracted from the enzyme activity data matrix to construct time-enzyme activity data pairs. The rate of change matrix is ​​obtained by calculating the ratio of the change in enzyme activity to the time interval in the time-enzyme activity data pairs.

[0022] The rate of change matrix is ​​differentiated to obtain the acceleration matrix. The time position corresponding to the maximum value of the acceleration is marked in the acceleration matrix. The time position is determined as the drug action time point, and a correspondence table between drug concentration and the drug action time point is established.

[0023] Based on the corresponding table, a drug concentration-time curve is plotted, and the slope distribution of the curve is calculated. The slope distribution is used as the data relating drug concentration and time of action.

[0024] Based on the corresponding relationship data, the drug removal time point was selected. After the drug stimulation was removed, the cell population was continuously monitored, and the enzyme activity value in the cell culture supernatant was measured. The measured enzyme activity value was plotted as an activity recovery curve under different concentration conditions over time, including:

[0025] Drug concentration information and action time point information are extracted from the corresponding relationship data. The rate of change of enzyme activity corresponding to the drug concentration information is calculated to construct drug action intensity data. The drug treatment duration is calculated based on the drug action intensity data, and the end of the drug treatment duration is determined as the drug removal time point.

[0026] The drug removal operation is performed according to the drug removal time point. Before removal, the enzyme activity value of the cell culture supernatant is measured as the enzyme activity benchmark value. After removal, the cell culture supernatant is continuously collected at the set time interval, the sampling time point information is recorded, and the enzyme activity value in the cell culture supernatant is measured.

[0027] The measured enzyme activity values, enzyme activity baseline values, and sampling time point information were compiled into enzyme activity recovery record data.

[0028] The enzyme activity recovery record data is grouped according to drug concentration information. In each drug concentration group, the ratio of the change in enzyme activity value between adjacent sampling time points to the time interval is calculated as the enzyme activity recovery rate.

[0029] The difference between the final enzyme activity value and the enzyme activity baseline value is calculated as the enzyme activity recovery range. The enzyme activity recovery rate and enzyme activity recovery range are plotted as a curve of enzyme activity value changing over time, forming activity recovery curves under different concentration conditions.

[0030] Analyze the deviation of the activity recovery curve from the baseline level to determine the minimum effective concentration and the maximum tolerated concentration, including:

[0031] The enzyme activity measurements at each concentration gradient in the activity recovery curve are recorded as recovery data. The deviation value is calculated by comparing the recovery data with the baseline level, and a concentration-deviation value distribution sequence is generated.

[0032] Based on the concentration-deviation value distribution sequence, concentration points that exceed the baseline fluctuation range are selected as the minimum effective concentration value, and concentration points that cause cell viability to begin to decline are selected as the maximum tolerable concentration value.

[0033] The reversibility and safety scores are used to calculate a drug evaluation score. Drug samples that meet the screening criteria are selected as candidate drugs, including:

[0034] The reversibility and safety values ​​are normalized to obtain the reversibility weight coefficient and the safety weight coefficient, respectively. The reversibility weight coefficient and the safety weight coefficient are weighted and summed to obtain the preliminary drug evaluation score.

[0035] The preliminary drug evaluation scores under different concentration conditions are sorted, the distribution inflection point of the preliminary drug evaluation scores is identified, and the preliminary drug evaluation score value corresponding to the distribution inflection point is determined as the effectiveness threshold.

[0036] Extract the concentration points corresponding to the preliminary drug evaluation scores that are higher than the effectiveness threshold, calculate the span of the concentration point range as the effective concentration window width, and weight the effective concentration window width and the maximum value of the preliminary drug evaluation scores to obtain the final drug evaluation score.

[0037] When the final drug evaluation score is higher than the preset screening criterion threshold, the drug to be screened is identified as a candidate drug.

[0038] This invention provides a drug active ingredient screening and evaluation system based on in vitro culture, the system comprising:

[0039] The transfection construction module is used to construct a cell population expressing the target metabolic enzyme through gene transfection, prepare fluorescently labeled substrates to establish a quantitative detection method for enzyme activity, measure the expression level and activity of metabolic enzymes in the cell population, establish a correlation curve, select a cell subpopulation within the linear range of the correlation curve as a validation cell population, and measure the baseline enzyme activity of the validation cell population as validation data.

[0040] The drug processing module is used to select target cell populations based on validation data, treat the target cell populations with the drugs to be screened according to a concentration gradient under in vitro culture conditions, and acquire dynamic response data including the change in enzyme activity relative to the baseline value at multiple time points using enzyme activity quantification detection methods.

[0041] The time series analysis module is used to segment dynamic response data according to the time series, calculate the rate of change of enzyme activity within each time interval, identify the drug action time point, and obtain the corresponding data of drug concentration and action time.

[0042] The activity monitoring module is used to select the drug removal time point based on the corresponding relationship data, continuously monitor the cell population after the drug stimulation is removed, measure the enzyme activity value in the cell culture supernatant, and plot the measured enzyme activity value over time as an activity recovery curve under different concentration conditions.

[0043] The numerical calculation module is used to collect the enzyme activity measurement values ​​of the target cell population before drug administration to determine the baseline level, analyze the deviation value between the activity recovery curve and the baseline level, determine the minimum effective concentration value and the maximum tolerated concentration value, extract enzyme activity data points within the range of the minimum effective concentration value and the maximum tolerated concentration value according to the activity recovery curve, determine the enzyme activity recovery rate by the ratio of the enzyme activity change at adjacent time points to the time interval, obtain the reversibility value by multiplying the enzyme activity recovery rate by the drug exposure duration, and use the ratio of the maximum tolerated concentration value to the minimum effective concentration value as the safety value.

[0044] The scoring and screening module is used to calculate drug evaluation scores based on reversibility and safety values, and select drug samples that meet the screening criteria as candidate drugs.

[0045] One technical solution provided in this embodiment of the invention is an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in any of the aforementioned methods.

[0046] One technical solution provided in this embodiment of the invention is a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the steps in any of the aforementioned methods.

[0047] This invention achieves precise quantitative assessment of drug activity by constructing a cell population expressing the target metabolic enzyme and establishing a fluorescence detection method; it obtains response data by dynamically monitoring the drug action process, accurately identifies the drug action time points, and establishes the correspondence between drug concentration and action time; it determines the reversibility characteristics of drug action based on enzyme activity recovery curve analysis after drug removal; it assesses the safety range of the drug by combining the determination of minimum effective concentration and maximum tolerated concentration; and it establishes objective drug screening criteria through a comprehensive score of reversibility and safety values. Attached Figure Description

[0048] Figure 1 A flowchart illustrating a method for screening and evaluating active pharmaceutical ingredients based on in vitro culture, provided in an embodiment of the present invention;

[0049] Figure 2 This is a schematic diagram comparing the enzyme activities of cell subsets obtained by different sorting methods in embodiments of the present invention;

[0050] Figure 3 This is a schematic diagram illustrating the minimum effective concentration analysis in an embodiment of the present invention;

[0051] Figure 4 This is a schematic diagram illustrating the maximum tolerated concentration analysis of an embodiment of the present invention. Detailed Implementation

[0052] like Figure 1 As shown, Figure 1 This is a flowchart of a method for screening and evaluating active pharmaceutical ingredients based on in vitro culture, provided in an embodiment of the present invention. The method includes the following steps:

[0053] A cell population expressing the target metabolic enzyme was constructed by gene transfection. A fluorescently labeled substrate was prepared to establish a quantitative detection method for enzyme activity. The expression level and activity of the metabolic enzyme in the cell population were measured, and a correlation curve was established. A cell subpopulation within the linear range of the correlation curve was selected as a validation cell population, and the baseline enzyme activity of the validation cell population was measured as validation data.

[0054] Based on the validation data, target cell populations were selected. Under in vitro culture conditions, the target cell populations were treated with the drug to be screened according to a concentration gradient. Dynamic response data, including the change in enzyme activity relative to the baseline value, were obtained at multiple time points using a quantitative enzyme activity detection method.

[0055] The dynamic response data is segmented according to the time series, the rate of change of enzyme activity in each time interval is calculated, the drug action time point is identified, and the corresponding relationship data between drug concentration and action time is obtained.

[0056] Based on the corresponding relationship data, the drug removal time point was selected. After the drug stimulation was removed, the cell population was continuously monitored, and the enzyme activity value in the cell culture supernatant was measured. The measured enzyme activity value was plotted as an activity recovery curve under different concentration conditions.

[0057] The enzyme activity values ​​of the target cell population before drug administration were collected and determined as the baseline level. The deviation between the activity recovery curve and the baseline level was analyzed to determine the minimum effective concentration and the maximum tolerated concentration. Based on the activity recovery curve, enzyme activity data points within the range of the minimum effective concentration and the maximum tolerated concentration were extracted. The ratio of the change in enzyme activity at adjacent time points to the time interval was determined as the enzyme activity recovery rate. The reversibility value was obtained by multiplying the enzyme activity recovery rate by the drug exposure duration. The ratio of the maximum tolerated concentration to the minimum effective concentration was used as the safety value.

[0058] The reversibility and safety values ​​are used to calculate the drug evaluation score, and drug samples that meet the screening criteria are selected as candidate drugs.

[0059] A cell population expressing the target metabolic enzyme was constructed through gene transfection. A fluorescently labeled substrate was prepared to establish a quantitative detection method for enzyme activity. The expression level and activity of the metabolic enzyme in the cell population were measured, and a correlation curve was established, including:

[0060] A eukaryotic expression vector expressing the target metabolic enzyme was constructed, and the eukaryotic expression vector was introduced into cells to be transfected using liposome transfection. Cell populations expressing the target metabolic enzyme were obtained by continuous passage screening with resistance drugs.

[0061] A fluorescently labeled substrate was prepared, and the fluorescently labeled substrate was added to the culture supernatant of the cell population expressing the target metabolic enzyme. The fluorescence intensity was measured using a fluorescence detector to establish a quantitative detection method for enzyme activity.

[0062] Cell populations expressing the target metabolic enzyme were sorted by flow cytometry to obtain cell subpopulations with different expression levels. The activity of the metabolic enzyme in the cell subpopulations was measured using the enzyme activity quantification method described above, and a curve showing the relationship between expression level and enzyme activity was established.

[0063] First, a eukaryotic expression vector for expressing the target metabolic enzyme is constructed. This vector contains the coding sequence of the target metabolic enzyme, the promoter sequence, the multiple cloning site, and the resistance gene. Molecular cloning techniques are used to insert the target metabolic enzyme gene into the multiple cloning site of the vector, enabling its expression under promoter control. The eukaryotic expression vector can be selected from pcDNA3.1, pEGFP-N1, or pET series vectors. These vectors all contain the promoter and terminator sequences required for efficient intracellular expression, as well as resistance genes such as neomycin or ampicillin resistance genes for selection.

[0064] After constructing the expression vector, it was introduced into the cells to be transfected using liposome transfection. Liposome transfection utilizes the formation of a complex between positively charged lipid molecules and negatively charged DNA, promoting DNA translocation across the cell membrane into the cytoplasm. Specifically, the target cells were transfected at a rate of 5 × 10⁶ cells / mL. 5 Seed cells at a density of 100 cells / well in 6-well plates and cultured for 24 hours until cell confluence reaches 70-80%. Prepare a transfection mixture containing 2 μg plasmid DNA and 6 μL transfection reagent, mix in serum-free medium, and incubate at room temperature for 15 minutes to allow the liposomes to form a complex with the DNA. Add the transfection mixture dropwise to the cell culture wells, gently shake to mix, and incubate at 37°C and 5% CO2 for 4-6 hours. Then replace with complete medium containing 10% fetal bovine serum and continue incubation for 18 hours.

[0065] Forty-eight hours after transfection, selection drugs are added to the culture medium to begin screening for stable transfected cells. Based on the resistance gene carried by the vector, appropriate antibiotics are selected, such as G418 (400-800 μg / mL) or puromycin (1-10 μg / mL) for selection. The antibiotic-containing culture medium is changed every 3-4 days, and selection continues for 2-3 weeks until the untransfected control group cells die completely, while resistant clones form in the transfected group. These resistant clones are collected and expanded, and cultured continuously (at least 3-5 generations) in antibiotic-containing medium to ensure a stable cell population expressing the target metabolic enzyme.

[0066] Select a suitable substrate based on the characteristics of the target metabolic enzyme and covalently couple it with a fluorescent group such as FITC, rhodamine, coumarin, or nitrobenzoxadiazole (NBD). A specific preparation method can be the active ester method, where the carboxyl group in the substrate molecule reacts with the amino group on the fluorescent group to form an amide bond. The reaction is carried out in an organic solvent (such as dimethyl sulfoxide or dimethylformamide), with condensing agents such as EDC (1-ethyl-3-(3-dimethylaminopropyl)carbodiimide) and HOBt (1-hydroxybenzotriazole) added to promote the reaction. The reaction mixture is stirred at room temperature for 4-6 hours, and the labeled substrate is purified by high-performance liquid chromatography. The molecular weight of the purified product is confirmed by mass spectrometry, verifying successful labeling. The fluorescently labeled substrate should be stored in the dark at -20°C, and the working solution should be freshly prepared before use.

[0067] Fluorescently labeled substrate was added to the culture supernatant of a cell population expressing the target metabolic enzyme for enzyme activity assay. The culture supernatant of stably transfected cells was collected after 48 hours, and cell debris was removed by centrifugation. 100 μL of supernatant and 100 μL of buffer solution containing the fluorescent substrate (final concentration 0.5–10 μM) were added to a 96-well black flat-bottom microplate. The pH of the buffer solution should be adjusted according to the optimal pH of the target enzyme, typically within the range of 6.5–8.0. The plates were incubated at a suitable temperature (typically 37°C) for 0–60 minutes, and the change in fluorescence intensity was detected using a fluorescence detector at specific excitation and emission wavelengths. Enzyme activity was calculated by measuring the rate of change in fluorescence value at different time points. A graph of fluorescence value versus reaction time was plotted, and the slope within the linear range was calculated to represent enzyme activity.

[0068] Flow cytometry was used to sort the cell population expressing the target metabolic enzyme to obtain cell subpopulations with different expression levels. The stably transfected cell suspension was adjusted to 1×10⁻⁶ cells / mL. 6 Cells were expressed at a density of [number] cells / mL. Cells were treated with a cell permeability reagent such as 0.1% Triton X-100 for 5 minutes to permeate the cell membrane. A fluorescently labeled antibody specifically recognizing the target enzyme (e.g., FITC or PE-labeled antibody, diluted 1:100-1:500) was added, and the cells were incubated at 4°C for 30 minutes. Cells were washed three times with PBS to remove unbound antibodies and resuspended in PBS containing 2% fetal bovine serum. Flow cytometry was used to analyze the distribution of cell fluorescence intensity, and cells were divided into 3-5 subpopulations based on fluorescence intensity, such as a low-expression group (bottom 10-20%), a medium-expression group (middle 40-60%), and a high-expression group (top 10-20%). Cells from each subpopulation were collected and expanded.

[0069] Using the aforementioned established method for quantitative detection of enzyme activity, the activity of target metabolic enzymes in each cell subpopulation was determined. Culture supernatants from each cell subpopulation were collected, and their enzyme activities were measured. The expression level (expressed as fluorescence intensity) and corresponding enzyme activity values ​​for each cell group were recorded. A curve was plotted with expression level on the x-axis and enzyme activity on the y-axis. The curve shape was analyzed to determine the linear range, i.e., the interval in which enzyme expression level and activity are directly proportional. In this embodiment, the middle expression group (middle 40-60%) of the cell subpopulation was located within the linear range of the curve, within which enzyme expression level and activity showed a good linear positive correlation (R²>0.95). Cell subpopulations within this linear range were selected as validation cell populations, and their baseline enzyme activities were measured under standard culture conditions (37℃, 5% CO2, 48 hours). These enzyme activity values ​​were recorded as validation data. Validation data should include complete parameters such as cell density, culture conditions, supernatant collection time, substrate concentration, reaction time, and fluorescence readings to ensure the reproducibility of experimental results.

[0070] Figure 2 This diagram illustrates the comparison of enzyme activities in cell subsets obtained using different sorting methods according to embodiments of the present invention. It compares the enzyme activities of cell subsets obtained using three different sorting methods. The multi-parameter flow cytometry sorting method used in this technical solution showed the highest enzyme activity in all expression level groups, especially reaching 81.9 nmol / min / mg protein in the high-expression group, significantly higher than the density gradient separation method (68.2 nmol / min / mg protein) and the traditional limiting dilution cloning method (52.4 nmol / min / mg protein). From the low-expression group to the high-expression group, the enzyme activity measured by this technical solution showed a linear increasing trend, rising from 16.8 nmol / min / mg protein to 81.9 nmol / min / mg protein, fully demonstrating its advantages in maintaining cell viability and enzyme functional integrity, providing a reliable tool for high-precision research on metabolic enzyme activity.

[0071] This invention successfully constructed a stable cell population expressing target metabolic enzymes using gene transfection combined with flow cytometry sorting technology, and established a quantitative detection system for enzyme activity based on fluorescently labeled substrates. This system accurately reflects the relationship between the expression level and activity of metabolic enzymes within the cell population, providing a reliable cellular tool and detection platform for drug screening, metabolic engineering, and biocatalysis. It avoids the problems of complex sample processing and significant enzyme purification losses in traditional enzyme activity detection, achieving in-situ quantitative analysis of enzyme activity under live cell conditions, effectively improving measurement accuracy and biological relevance.

[0072] Based on the validation data, a target cell population was selected. Under in vitro culture conditions, the target cell population was treated with the drug to be screened according to a concentration gradient. Dynamic response data, including changes in enzyme activity relative to a baseline value, were obtained at multiple time points using a quantitative enzyme activity detection method.

[0073] The distribution characteristics of metabolic enzyme activity in the analysis and verification data were analyzed, and upper and lower limits of metabolic enzyme activity were established. The cell population was sorted by flow cytometry, and the cell population with metabolic enzyme activity between the upper and lower limits was selected as the target cell population.

[0074] The target cell population was inoculated into an in vitro culture system containing culture medium and cultured. The metabolic enzyme activity of the target cell population was measured as a baseline value. The concentration range of the drug to be screened was set according to the baseline value. Incremental concentration points were set within the concentration range to prepare a drug gradient series with increasing concentration. The target cell population was treated with the drug gradient series.

[0075] The supernatant in the culture system is collected at fixed time intervals within a preset time range. The activity of metabolic enzymes in the supernatant is measured using the enzyme activity quantification method. The change in the activity of the metabolic enzymes relative to the baseline value is calculated. The change at each detection time point and the corresponding drug concentration data are compiled and recorded as dynamic response data.

[0076] To analyze the metabolic enzyme activity in the validation data, a sufficient number of cell population samples must be collected, typically no fewer than 30 independent sampling points. The metabolic enzyme activity of each sample is determined using a fluorescent substrate method, and the obtained data are plotted as a frequency distribution to observe its distribution characteristics. Metabolic enzyme activity usually follows a normal or skewed distribution; therefore, the mean and standard deviation of the data need to be calculated. An upper limit is set as the sum of the mean and 1.5 times the standard deviation, and a lower limit is set as the difference between the mean and 1.5 times the standard deviation. This range typically covers approximately 85% of the cell population, excluding extreme outliers while maintaining a sufficient sample size to ensure the representativeness of the screening results.

[0077] When the distribution of metabolic enzyme activity deviates from a normal distribution, the interquartile range method can be used to determine the upper and lower limits, taking the 25th percentile as the lower limit and the 75th percentile as the upper limit. If the sample size is small, the median can be used as the central value, with the upper limit set as the sum of the median and 0.5 times the median, and the lower limit set as the difference between the median and 0.5 times the median. Setting reasonable upper and lower limits is crucial for the stability and reproducibility of the screening results. Too narrow a range will result in insufficient target cell population, while too wide a range will introduce too much variation and reduce data reliability.

[0078] After determining the upper and lower limits of metabolic enzyme activity, the cell population was sorted by flow cytometry. The cell suspension concentration was adjusted to 1×10⁻⁶. 6Cells are labeled with fluorescent antibodies or substrates that specifically recognize the target metabolic enzyme at a concentration of 10 cells / ml. The labeling method depends on the location and characteristics of the target enzyme. For intracellular enzymes, cell permeability is achieved by treating the cell membrane with a cell permeability agent such as 0.05% saponin before adding the fluorescent antibody. For secretory or membrane-bound enzymes, the fluorescent antibody or substrate can be added directly for labeling. After labeling, flow cytometry is used to analyze and sort the cells, setting sorting thresholds that correspond precisely to the calculated upper and lower limits of enzyme activity. The cell population falling between these limits is the target cell population.

[0079] The selected target cell population was seeded into an in vitro culture system for expansion. The culture system included culture dishes or multi-well plates, suitable cell growth medium, and appropriate culture conditions. The culture medium was typically DMEM or RPMI-1640 basal medium, supplemented with 10% fetal bovine serum, 2 mM glutamine, 100 U / ml penicillin, and 100 μg / ml streptomycin. The cell seeding density was 2 × 10⁶ cells / ml. 4 pcs / cm 2 The cells were cultured in an incubator at 37°C, 5% CO2, and saturated humidity. Once the cells adhered and grew to the logarithmic growth phase (usually 24-48 hours), the culture supernatant was collected, and the metabolic enzyme activity of the target cell population was measured as a baseline value.

[0080] The baseline value was determined using a previously established method for quantitative enzyme activity detection, such as the fluorescent substrate method. Culture supernatant was collected, centrifuged to remove cell debris, and 100 μl of the supernatant was mixed with 100 μl of reaction buffer containing the fluorescently labeled substrate. The mixture was then incubated under suitable conditions (e.g., 37°C) for 30 minutes, and the fluorescence value was measured using a fluorescence detector. To ensure accuracy, 3-6 replicates were performed for each sample, and the average value was used as the baseline value. If enzyme activity varied significantly, enzyme activity could be measured continuously for 3 days, and the average value was used as a more stable baseline value.

[0081] The concentration range of the drug to be screened should be set based on the measured baseline value. Generally, a concentration range that causes a fluctuation of approximately 50% above or below the baseline value should be selected; that is, the lowest concentration should cause approximately 10% activity change, and the highest concentration should cause approximately 90% activity change. If there is no prior knowledge of the drug's activity, a wide concentration gradient can be set using logarithmic serial dilution, such as from 10 nM to 100 μM, with 8-10 concentration points, and the concentration ratio between adjacent points should be 3-10 times. A suitable solvent should be selected based on the drug's solubility and stability. Commonly used solvents include dimethyl sulfoxide, ethanol, or phosphate buffer. The final solvent concentration should be controlled at 0.1-0.5% to avoid solvent effect interference.

[0082] After preparing a gradient series of drugs with increasing concentrations, divide the culture plates into groups, with 3-6 replicate wells in each group, including a drug treatment group and a negative control group (with only an equal volume of solvent added). Add the appropriate concentration of drug solution to each group's wells to achieve the preset drug concentration. The amount of drug added should not exceed 1 / 10 of the culture medium volume to avoid over-diluting the medium. Gently shake the culture plate to evenly disperse the drug, then return it to the incubator for further incubation.

[0083] Collect the supernatant from the culture system at fixed time intervals within a preset time range. The time range is generally set to 0-72 hours, and the fixed time interval is 4-12 hours, with the specific interval depending on the metabolic turnover rate of the target enzyme. During sampling, carefully aspirate 50-200 μl of culture supernatant into a centrifuge tube, centrifuge at 12000×g for 10 minutes at 4°C to remove cell debris, and use the supernatant for enzyme activity assay or store it at -80°C for later use. The sampling volume should not be too large to avoid significantly altering the culture environment; if culture medium needs to be added, an equal volume of fresh culture medium should be added to maintain consistent culture conditions.

[0084] The activity of metabolic enzymes in the supernatant at each time point was determined using a previously established quantitative enzyme activity detection method. The supernatant was reacted with a fluorescent substrate, and the fluorescence intensity was measured. The results were then converted to enzyme activity values ​​based on a pre-established standard curve. The change in metabolic enzyme activity at each time point and concentration group relative to the baseline value was calculated, expressed as a relative percentage (with the baseline value set to 100%) or an absolute change. The data on enzyme activity changes at each time point and concentration group were compiled and recorded along with the corresponding drug concentration information as dynamic response data.

[0085] Dynamic response data includes two key variables: time and concentration. These can be constructed into a three-dimensional data matrix, where rows represent time points, columns represent drug concentrations, and matrix elements represent changes in enzyme activity under corresponding conditions. This data can be used to analyze the time- and concentration-dependent effects of drug action, calculate the half-maximal effective concentration (MCC) and maximum effect size, and assess drug efficacy and potency. By analyzing dose-response curves at different time points, the kinetic characteristics of drug action can be obtained, such as key parameters like onset time, peak time, and duration of action.

[0086] This invention achieves efficient screening and precise evaluation of active drug components by precisely selecting target cell populations with stable metabolic enzyme activity and systematically measuring the dynamic changes in metabolic enzyme activity after drug treatment under in vitro culture conditions. It overcomes the problems of high cell heterogeneity and poor stability in traditional drug screening methods, significantly improving the reliability and reproducibility of screening results. By acquiring dynamic response data at multiple time points, it not only evaluates the efficacy of drugs but also reveals the temporal characteristics and modes of action, providing comprehensive and systematic data support for drug mechanism research and pharmacodynamic evaluation.

[0087] The dynamic response data is segmented according to the time series, and the rate of change of enzyme activity within each time interval is calculated to identify the drug action time points and obtain the corresponding data of drug concentration and action time, including:

[0088] An enzyme activity data matrix is ​​constructed by arranging dynamic response data according to the sampling time series. Enzyme activity values ​​at adjacent sampling time points are extracted from the enzyme activity data matrix to construct time-enzyme activity data pairs. The rate of change matrix is ​​obtained by calculating the ratio of the change in enzyme activity to the time interval in the time-enzyme activity data pairs.

[0089] The rate of change matrix is ​​differentiated to obtain the acceleration matrix. The time position corresponding to the maximum value of the acceleration is marked in the acceleration matrix. The time position is determined as the drug action time point, and a correspondence table between drug concentration and the drug action time point is established.

[0090] Based on the corresponding table, a drug concentration-time curve is plotted, and the slope distribution of the curve is calculated. The slope distribution is used as the data relating drug concentration and time of action.

[0091] First, it is crucial to strictly adhere to the pre-defined sampling time points during the experiment, typically using 0 hours as the baseline, followed by sampling every 4 to 6 hours until 72 or 96 hours. After collection, the enzyme activity in each sample is measured, and the data from different concentration groups are arranged chronologically to construct an enzyme activity data matrix. In this matrix, rows represent different drug concentrations, columns represent different time points, and matrix elements represent the enzyme activity values ​​measured under the corresponding conditions. When constructing the matrix, attention should be paid to the completeness and consistency of the data. If data for certain time points are missing, linear interpolation of nearby time points can be used to supplement them, ensuring the integrity of the matrix structure.

[0092] Based on the constructed enzyme activity data matrix, enzyme activity values ​​at adjacent time points are extracted to form time-enzyme activity data pairs. For each row in the matrix (i.e., each drug concentration group), two adjacent time points t1 and t2 and their corresponding enzyme activity values ​​a1 and a2 are taken to form a data pair in the form (t1, a1, t2, a2). For n time points, each concentration group can obtain n-1 such data pairs. Next, the rate of change of enzyme activity is calculated, which is the ratio of the change in enzyme activity to the time interval. For the data pair (t1, a1, t2, a2), the rate of change is equal to (a2-a1) / (t2-t1). The rates of change of all data pairs are arranged according to the original matrix structure to obtain the rate of change matrix. This matrix has one less column than the original enzyme activity data matrix because only n-1 rate of change values ​​can be generated for n time points.

[0093] The rate of change matrix is ​​differentiated to obtain the acceleration matrix. This differentiation is performed by analyzing the relationships between adjacent rates of change and their corresponding time parameters. For each adjacent rate value in the rate of change matrix (corresponding to different time intervals), the acceleration is calculated in conjunction with the time factor. The midpoint of the time interval is used as a reference for calculation, which more accurately reflects the trend of acceleration change. This operation is performed on all adjacent rate values ​​to construct the acceleration matrix. This matrix has one less column than the rate of change matrix; that is, for n time points, n-2 acceleration values ​​are ultimately obtained.

[0094] In the acceleration matrix, the acceleration values ​​in each row (i.e., each drug concentration group) are traversed, and the point with the largest absolute value is identified. The time position corresponding to this point is determined as the drug's action time point at that concentration. The method for determining the time position is as follows: if the maximum acceleration value occurs at the k-th position, the corresponding time point is the (k+1)-th time point in the original time series. This is because the k-th acceleration value is calculated based on the k-th and (k+1)-th rates of change, and these rates correspond to the corresponding positions in the original time series. The drug action time points for each identified concentration group are paired with the corresponding drug concentration values ​​to establish a correspondence table between drug concentration and action time points.

[0095] Based on a table of drug concentrations and time points of action, a drug concentration-time curve is plotted. The horizontal axis represents drug concentration (usually using a logarithmic scale to facilitate observation of a wide range of concentration changes), and the vertical axis represents the corresponding time point of action. The curve can be plotted using point-to-point straight lines or spline interpolation to smooth the curve. Slope analysis is performed on the plotted curve to calculate the slope distribution across different concentration intervals. The slope characterizes the relationship between concentration changes and time-of-action changes. Considering that concentration is usually expressed logarithmically, the slope calculation needs to incorporate logarithmic values. The calculated slope values ​​for each concentration interval are sorted to form slope distribution data. This slope distribution data represents the correspondence between drug concentration and time of action, reflecting the degree to which changes in drug concentration affect time of action.

[0096] In an in vitro culture experiment, five concentration gradients (1 μM, 3 μM, 10 μM, 30 μM, 100 μM) were set for a certain enzyme inhibitor. Samples were collected at 0, 4, 8, 12, 24, 36, and 48 hours to measure enzyme activity. After constructing an enzyme activity data matrix, the rate of change for each time interval was calculated, and the acceleration value was obtained. For the 10 μM concentration group, if the absolute value of the acceleration value was found to be the largest in the 8-12 hour interval, then 12 hours was determined as the drug action time point at that concentration. This process was repeated for all concentration groups to obtain a concentration-action time correspondence table, and curves were plotted and the slope distribution was calculated. The analysis results may show that the slope of the curve is larger in the low concentration interval (1-10 μM), indicating that the concentration change has a significant impact on the action time in this interval; while the slope tends to be flat in the high concentration interval (30-100 μM), indicating that after exceeding a certain concentration threshold, further increasing the drug concentration has limited effect on accelerating the action time.

[0097] This invention achieves a deeper understanding of the characteristics of drug activity by precisely analyzing the time-kinetic properties of drug action. Traditional drug screening often focuses on dose-response relationships, neglecting the important dimension of drug action time. This invention incorporates the time dimension into the evaluation system, accurately identifying key time points of drug action through rate of change and acceleration analysis, revealing the intrinsic correlation between drug concentration and time of action. This multidimensional evaluation system can distinguish drugs with similar mechanisms of action but different kinetic properties, providing a more comprehensive basis for drug selection and improving the accuracy and value of screening.

[0098] Based on the corresponding relationship data, the drug removal time point was selected. After the drug stimulation was removed, the cell population was continuously monitored, and the enzyme activity value in the cell culture supernatant was measured. The measured enzyme activity value was plotted as an activity recovery curve under different concentration conditions over time, including:

[0099] Drug concentration information and action time point information are extracted from the corresponding relationship data. The rate of change of enzyme activity corresponding to the drug concentration information is calculated to construct drug action intensity data. The drug treatment duration is calculated based on the drug action intensity data, and the end of the drug treatment duration is determined as the drug removal time point.

[0100] The drug removal operation is performed according to the drug removal time point. Before removal, the enzyme activity value of the cell culture supernatant is measured as the enzyme activity benchmark value. After removal, the cell culture supernatant is continuously collected at the set time interval, the sampling time point information is recorded, and the enzyme activity value in the cell culture supernatant is measured.

[0101] The measured enzyme activity values, enzyme activity baseline values, and sampling time point information were compiled into enzyme activity recovery record data.

[0102] The enzyme activity recovery record data is grouped according to drug concentration information. In each drug concentration group, the ratio of the change in enzyme activity value between adjacent sampling time points to the time interval is calculated as the enzyme activity recovery rate.

[0103] The difference between the final enzyme activity value and the enzyme activity baseline value is calculated as the enzyme activity recovery range. The enzyme activity recovery rate and enzyme activity recovery range are plotted as a curve of enzyme activity value changing over time, forming activity recovery curves under different concentration conditions.

[0104] From the obtained correspondence data, key information including drug concentration and action time point information is extracted. Drug concentration information records all drug concentration gradients; action time point information identifies the time point at which the drug begins to exert a significant effect at each concentration. Based on this extracted information, the rate of change of enzyme activity under each concentration condition is calculated, reflecting how quickly enzyme activity changes over time. Drug activity intensity data is constructed from the rate of change data; a higher intensity value indicates a more significant effect of the drug on the enzyme. The drug treatment duration is calculated based on the intensity data by combining the intensity value with a preset duration coefficient, typically set between 1.5 and 2.0. The end of the treatment duration is the drug removal time point, which is the precise moment to perform the drug withdrawal operation.

[0105] Upon reaching the calculated drug removal time point, the drug removal procedure is performed. Prior to the procedure, cell culture supernatant is collected as a baseline sample. Carefully aspirate 100-200 μl of supernatant from the cell culture dish using a sterile pipette, avoiding disturbance to the adherent cells at the bottom. Immediately after collection, enzyme activity is measured in the supernatant; the measured value serves as the baseline enzyme activity, representing the enzyme activity level before drug removal. Drug removal is accomplished by replacing the culture medium. The original medium is aspirated, and the cells are gently rinsed twice with phosphate-buffered saline (PBSS) pre-warmed to 37°C to thoroughly remove residual drug. Fresh, drug-free culture medium is then added. After the removal procedure, cell culture supernatant is continuously collected at predetermined time intervals. The sampling intervals are typically set as follows: every hour in the initial stage (0-6 hours), every 3 hours in the middle stage (6-24 hours), and every 6 hours in the later stage (after 24 hours). The total monitoring duration can be set from 48 to 96 hours depending on the cell type and research needs. For each sampling, the precise sampling time point information must be recorded, and the enzyme activity value in the collected supernatant must be measured immediately.

[0106] The enzyme activity values ​​obtained from each measurement, the initial enzyme activity baseline value, and the corresponding sampling time point information are paired and organized to form a complete enzyme activity recovery record data. The data structure includes four basic fields: drug concentration, sampling time point, enzyme activity value, and enzyme activity baseline value. Outliers must be checked during data processing. If the enzyme activity value at a certain measurement point changes by more than 50% compared to adjacent points, it should be marked as suspicious data and remeasurement or interpolation using adjacent points should be considered. The completed record data is saved in a structured format for easy subsequent analysis and processing.

[0107] The compiled enzyme activity recovery data were grouped according to drug concentration information. Each concentration group contained enzyme activity data for all sampling time points under that concentration condition. Within each drug concentration group, the relationship between the change in enzyme activity value between adjacent sampling time points and the corresponding time interval was calculated to obtain the enzyme activity recovery rate. A positive recovery rate indicates that enzyme activity is recovering, while a negative value indicates that it is still decreasing. When adjacent time points are too close (interval less than 30 minutes), measurement errors may cause fluctuations in the recovery rate. In this case, a sliding window averaging method can be considered to smooth the data. The window size is usually 3 time points.

[0108] The difference between the enzyme activity value at the endpoint and the initial baseline enzyme activity value is calculated; this difference represents the enzyme activity recovery amplitude. A positive recovery amplitude indicates that the enzyme activity has exceeded the baseline level, while a negative value indicates that it has not fully recovered. The enzyme activity values ​​at each time point are plotted against time to form a group of activity recovery curves under different concentration conditions. The curves are plotted with time on the x-axis and enzyme activity value on the y-axis, with different labels or colors used to distinguish different concentration groups. A three-parameter exponential model can be used for curve fitting to better characterize the dynamic features of the recovery process. The recovery rate and final recovery degree of each concentration group can be visually observed from the curves, providing a quantitative basis for evaluating the reversibility of drug effects.

[0109] For example, a protease inhibitor was used to treat cultured hepatocytes at five concentration gradients (0.5, 1, 5, 10, 50). Based on previously obtained correlation data, the drug removal time points were calculated to be 18, 24, 36, 48, and 72 hours. After removing the drug at these time points, enzyme activity changes were continuously monitored for 72 hours. The results showed that the enzyme activity in the low concentration group (0.5, 1) recovered to more than 90% of the baseline value within 12 hours after drug withdrawal; the medium concentration group (5, 10) required 24-36 hours to recover to a similar level; while the high concentration group (50) only recovered to about 70% of the baseline value even at the monitoring endpoint. This indicates that the inhibitor has good reversibility in the low and medium concentration range, while long-term treatment at high concentrations may lead to some irreversible damage.

[0110] This invention provides a new dimension for evaluating drug action mechanisms by monitoring the recovery process of enzyme activity after drug withdrawal. Traditional drug screening mainly focuses on inhibitory or activating effects, neglecting the persistence and reversibility of drug action, which are crucial for clinical medication regimens and safety assessments. Based on precise time-point control, quantitative analysis of the drug effect decline process is achieved, enabling the identification of kinetic differences between different drugs and the differentiation of compounds with similar effects but vastly different recovery patterns. Screening strategies based on the reversibility of pharmacodynamics can identify ideal, controllable drugs, avoid the risk of persistent side effects, improve the therapeutic window, and provide a reference for personalized precision medicine.

[0111] Analyze the deviation of the activity recovery curve from the baseline level to determine the minimum effective concentration and the maximum tolerated concentration, including:

[0112] The enzyme activity values ​​of the target cell population before drug administration were collected and determined as the baseline level. The enzyme activity values ​​at each concentration gradient in the activity recovery curve were recorded as recovery data. The deviation value was calculated by comparing the recovery data with the baseline level, and a concentration-deviation value distribution sequence was generated.

[0113] Based on the concentration-deviation value distribution sequence, concentration points that exceed the baseline fluctuation range are selected as the minimum effective concentration value, and concentration points that cause cell viability to begin to decline are selected as the maximum tolerable concentration value.

[0114] Ten to fifteen samples were randomly selected from the target cell population, and their enzyme activity values ​​were measured. The measurement method employed either a standard colorimetric method or a fluorescence method, the specific choice depending on the characteristics of the target enzyme. The average value was used as the baseline, and the standard deviation was calculated to determine the significance of subsequent enzyme activity changes. Baseline measurement should be performed when the cells are in a stationary phase, typically 24-48 hours after passage, to ensure stable and representative cell conditions. After baseline measurement, the cells were treated with a pre-set concentration gradient, typically at 5-8 points, covering the nM to μM range, and exhibiting a logarithmic distribution.

[0115] The activity recovery curves were plotted by continuously monitoring changes in cellular enzyme activity after drug withdrawal, with a corresponding recovery curve for each concentration gradient. Enzyme activity measurements at each time point were extracted from these curves as recovery data. Measurements were taken every hour for 0-6 hours after drug withdrawal, every 3 hours for 6-24 hours, and every 6-12 hours after 24 hours, until enzyme activity stabilized. These recovery data were compared with a previously established baseline level to calculate the deviation value. The deviation value was calculated as the percentage difference between the recovery data and the baseline level; a positive value indicated above the baseline, and a negative value indicated below the baseline. Deviation values ​​were calculated for all time points at each concentration gradient, forming a complete concentration-deviation value distribution sequence. This sequence reflects the dynamic process of enzyme activity recovery after treatment with different concentrations of drug and the degree of difference from normal levels.

[0116] Key concentration indicators were determined based on the concentration-deviation value distribution sequence. The minimum effective concentration (MPC) is an important indicator for evaluating the starting point of drug activity, and the screening process was based on statistical principles. The deviation value was compared with the baseline fluctuation range, which is usually defined as twice the baseline standard deviation. When the deviation value at a certain concentration point exceeds the baseline fluctuation range, and this phenomenon is confirmed at three consecutive measurement time points, this concentration is determined as the MPC. For cases where the deviation is not significant, analysis of variance can be used to further verify the significance of the difference, with a p-value threshold set at 0.05. The determination of the maximum tolerated concentration focuses on cell viability, assessed through cell viability indicators. Cell viability was measured using trypan blue staining. The concentration point where the viability decreased by more than 10% compared to the control group was the maximum tolerated concentration. If the decrease in viability was not significant, cell morphology observations, such as cell shrinkage and shedding, could be used to assist in the judgment, and concentration points exhibiting these phenomena could also be considered as the maximum tolerated concentration.

[0117] Enzyme activity data points within the range of minimum effective concentration and maximum tolerated concentration are extracted from the activity recovery curve. These data points reflect the reversibility of the drug within an effective and safe concentration range. For each concentration point, the changes in enzyme activity between adjacent time points are analyzed. The ratio of the difference in enzyme activity values ​​between any two adjacent time points to the corresponding time interval is calculated; the result is the enzyme activity recovery rate within that time period. A positive recovery rate indicates that enzyme activity is recovering, while a negative value indicates that it is still decreasing. For the entire monitoring period, a curve showing the recovery rate versus time can be plotted, typically exhibiting a trend of first increasing and then stabilizing. Outlier handling is crucial in recovery rate calculations. When the recovery rate at a certain point differs from that of adjacent points by more than 50%, the original data should be examined, or the average value of adjacent points should be considered as a substitute.

[0118] Reversibility value R=k r ×T, where R is the reversibility value, k rThe average rate of enzyme activity recovery during the recovery period is represented by T, where T is the total duration of drug exposure. The ratio of the reversibility value to the treatment window index is used to calculate the reversibility value. This ratio, also known as the therapeutic window index, reflects the speed and extent of drug effect decline; a higher value indicates that the drug effect is more easily reversed. The safety value, expressed as the maximum tolerated concentration (MTC) to the minimum effective concentration (EPC), reflects the size of the interval between effective and toxic drug concentrations. A higher ratio indicates a wider safety margin and lower clinical application risk. Ideally, a drug candidate should possess both high reversibility and high safety values.

[0119] For example, in evaluating a protease inhibitor on liver cancer cell lines, concentration gradients of 0.01, 0.1, 1, 10, and 100 μM were set. The baseline enzyme activity level was measured at 1.24 activity units, with a standard deviation of 0.15 activity units. After 72 hours of drug treatment, the drug was withdrawn, and enzyme activity recovery was continuously monitored over 96 hours. Data analysis showed that the deviation in the 0.01 μM group was within the baseline fluctuation range, while the deviation in the 0.1 μM group exceeded the baseline fluctuation range and was confirmed at three consecutive time points; therefore, 0.1 μM was determined as the minimum effective concentration. Cell viability measurements showed that the survival rate in the 10 μM group was 92% of the control group, while the survival rate in the 100 μM group decreased to 78%, significantly lower than the 90% threshold; therefore, 10 μM was determined as the maximum tolerated concentration. The enzyme activity recovery rate was calculated within the range of 0.1–10 μM, with an average value of 0.04 activity units per hour. The reversibility value was calculated to be 2.88 after 72 hours of drug exposure. The safety value is the ratio of the maximum tolerated concentration to the minimum effective concentration, which is 100 times, indicating that the drug has a wide therapeutic window.

[0120] like Figure 3 The diagram illustrates the minimum effective concentration analysis in this embodiment, based on the deviation of enzyme activity from the baseline level. The horizontal axis represents the drug concentration on a logarithmic scale, and the vertical axis represents the maximum percentage deviation of enzyme activity below the baseline. Based on a baseline standard deviation of 0.15 and a mean of 1.24, the baseline fluctuation threshold (2 times the standard deviation) is approximately -24.2%, indicated by a dashed line in the diagram. It can be seen that the maximum deviation at a concentration of 0.01 μM is only -8%, not reaching the threshold; while the maximum deviation at a concentration of 0.1 μM reaches -35.5%, significantly exceeding the threshold for the first time, and is therefore determined as the minimum effective concentration.

[0121] like Figure 4The diagram illustrates the maximum tolerated concentration analysis in this embodiment. When cell viability decreased by more than 10% relative to the control group (i.e., below 90%), the drug was considered to have begun to produce unacceptable toxicity. The diagram shows that at a concentration of 10 μM, the cell viability was 92%, still above the 90% threshold, but showing a downward trend; while at 100 μM, the viability plummeted to 78%, significantly below the threshold. According to the protocol description, the 10 μM concentration at which the viability began to decline was determined as the maximum tolerated concentration.

[0122] This invention expands and deepens the dimensions of drug screening and evaluation through precise analysis of the drug activity recovery process. Traditional drug screening mainly focuses on immediate inhibitory or activating effects, neglecting the persistence and reversibility of drug action, which have a significant impact on the safety and efficacy of clinical medication. This method establishes a quantitative evaluation system for drug reversibility and safety based on the cellular level, which can simultaneously obtain key indicators such as minimum effective concentration, maximum tolerated concentration, reversibility value, and safety value, comprehensively characterizing the dynamic action characteristics of drugs.

[0123] The reversibility and safety scores are used to calculate a drug evaluation score. Drug samples that meet the screening criteria are selected as candidate drugs, including:

[0124] The reversibility and safety values ​​are normalized to obtain the reversibility weight coefficient and the safety weight coefficient, respectively. The reversibility weight coefficient and the safety weight coefficient are weighted and summed to obtain the preliminary drug evaluation score.

[0125] The preliminary drug evaluation scores under different concentration conditions are sorted, the distribution inflection point of the preliminary drug evaluation scores is identified, and the preliminary drug evaluation score value corresponding to the distribution inflection point is determined as the effectiveness threshold.

[0126] Extract the concentration points corresponding to the preliminary drug evaluation scores that are higher than the effectiveness threshold, calculate the span of the concentration point range as the effective concentration window width, and weight the effective concentration window width and the maximum value of the preliminary drug evaluation scores to obtain the final drug evaluation score.

[0127] When the final drug evaluation score is higher than the preset screening criterion threshold, the drug to be screened is identified as a candidate drug.

[0128] After obtaining the reversibility and safety values ​​for the drug under evaluation, these two parameters are normalized to eliminate the influence of dimensions. The normalization process uses a maximum-minimum standardization method to transform the original values ​​into the 0-1 range. The reversibility weight coefficient is obtained through normalization calculation, which references the maximum and minimum values ​​in the sample set as standardization benchmarks. The safety values ​​are normalized in the same way as the reversibility values, resulting in the safety weight coefficient. An outlier handling mechanism should be included in the normalization process; when the difference between the maximum and minimum values ​​is too small, the weight coefficient can be set to a fixed value of 0.5 to avoid numerical instability.

[0129] After normalization, weight allocation and composite calculations are performed to obtain a preliminary drug evaluation score. The preliminary drug evaluation score is composed of two weighted coefficients in a specific ratio, with each accounting for 50% of the weight by default. The weight allocation can be adjusted for specific drug types; for example, for targeted drugs, the safety weight coefficient can be set to 60%-70%, and the reversibility weight coefficient to 30%-40%; while for short-acting drugs, the reversibility weight coefficient can be set to 60%-70%, and the safety weight coefficient to 30%-40%. During the weight allocation process, the sum of the two coefficients should be maintained at 100%. The preliminary drug evaluation score is obtained through the weighted combination calculation, with a score range between 0 and 1.

[0130] After the preliminary drug evaluation scores are calculated, a ranking analysis is required to determine the validity threshold. The ranking process should cover the preliminary drug evaluation scores of all drugs under different concentration conditions, arranged from highest to lowest score. The ranking results are used to identify distribution inflection points, which are identified using curvature change analysis. The score difference between adjacent ranking positions is calculated, and the change in difference is then calculated. When the change exceeds a preset threshold, it is identified as an inflection point. The preset threshold is typically set to 1.5 to 2 times the average rate of change; 1.5 times is used for larger sample sizes, and 2 times for smaller sample sizes. If multiple inflection points meet the criteria, the inflection point with the highest score is selected as the validity inflection point. The preliminary drug evaluation score corresponding to the inflection point is determined as the validity threshold, which typically falls within the range of 0.6-0.8, with the specific value depending on the distribution characteristics of the set of drugs to be evaluated.

[0131] After determining the effectiveness threshold, extract the concentration points corresponding to all preliminary drug evaluation scores above the threshold and calculate the effective concentration window width. The effective concentration window width is defined as the logarithmic difference between the highest and lowest concentration points above the effectiveness threshold. Concentrations are usually expressed in logarithmic form; when calculating the window width, the concentration values ​​must first be converted to logarithmic form, and then the range between the maximum and minimum logarithmic concentrations is obtained. If only a single concentration point is above the effectiveness threshold, the window width is defined as the test accuracy range for that concentration point, typically 5% of that concentration value. The calculated window width reflects the drug's ability to maintain a high evaluation score at different concentrations; a larger window width indicates a wider applicable concentration range for the drug.

[0132] The final drug evaluation score is obtained by combining the effective concentration window width with the maximum preliminary drug evaluation score in a specific ratio. The combination method integrates the normalized effective concentration window width and the maximum preliminary drug evaluation score according to a specific ratio. The normalization process for the effective concentration window width is the same as the aforementioned normalization method, converting the original window width to a range of 0-1. The default ratio allocation is 40% for the window width and 60% for the maximum score. For drugs with special purposes, the ratio can be adjusted; for example, for drugs for chronic diseases requiring long-term administration, the window width ratio can be increased to 50%-60%, while for drugs for acute diseases, the maximum score ratio can be increased to 70%-80%. The final drug evaluation score ranges from 0 to 1, with a value closer to 1 indicating a better overall drug evaluation effect.

[0133] The setting of the screening criterion threshold directly affects the screening results of candidate drugs. The default threshold is set to 0.75, which is suitable for routine drug screening scenarios. For drugs with special uses, the threshold can be adjusted; for high-risk indications, it can be increased to 0.85, while for drugs for routine chronic diseases, it can be decreased to 0.7. When the final drug evaluation score is higher than the set screening criterion threshold, the drug is identified as a candidate drug. If multiple drugs meet the screening criteria simultaneously, the highest-scoring drug can be selected based on its final evaluation score, or, depending on the specific research needs, the top 2-3 samples can be selected for subsequent studies.

[0134] In the screening application, 10 protease inhibitors were evaluated, with five concentration gradients set for each inhibitor: 0.01, 0.1, 1, 10, and 100 μM. Cellular experiments were used to obtain the reversibility and safety values ​​of each drug at different concentrations. The reversibility values ​​ranged from 0.5 to 4.2, and the safety values ​​ranged from 10 to 200. After normalization, preliminary drug evaluation scores were calculated, ranging from 0.2 to 0.95. Ranking analysis showed a clear inflection point at 0.8, which was determined as the effectiveness threshold. Concentrations above the threshold were extracted, and the effective concentration window width was calculated. The final drug evaluation score was calculated by combining the window width and the maximum score, with a screening threshold set at 0.75. The evaluation results showed that the final evaluation scores of three drugs were 0.82, 0.78, and 0.76, respectively, all above the threshold, and they were identified as candidate drugs.

[0135] This invention establishes a drug evaluation scoring system, enabling comprehensive evaluation and efficient screening of active pharmaceutical ingredients. By normalizing and rationally proportioning the two key indicators of reversibility and safety, the influence of dimensions is eliminated while retaining the inherent importance of the indicators. A distribution inflection point analysis method is introduced to adaptively determine the effectiveness threshold, avoiding the subjectivity of manually setting thresholds. Through effective concentration window width analysis, the stability performance of the drug at different concentrations is further considered, compensating for the limitations of single-point evaluation.

[0136] The present invention provides a drug active ingredient screening and evaluation system based on in vitro culture, the system comprising:

[0137] The transfection construction module is used to construct a cell population expressing the target metabolic enzyme through gene transfection, prepare fluorescently labeled substrates to establish a quantitative detection method for enzyme activity, measure the expression level and activity of metabolic enzymes in the cell population, establish a correlation curve, select a cell subpopulation within the linear range of the correlation curve as a validation cell population, and measure the baseline enzyme activity of the validation cell population as validation data.

[0138] The drug processing module is used to select target cell populations based on validation data, treat the target cell populations with the drugs to be screened according to a concentration gradient under in vitro culture conditions, and acquire dynamic response data including the change in enzyme activity relative to the baseline value at multiple time points using enzyme activity quantification detection methods.

[0139] The time series analysis module is used to segment dynamic response data according to the time series, calculate the rate of change of enzyme activity within each time interval, identify the drug action time point, and obtain the corresponding data of drug concentration and action time.

[0140] The activity monitoring module is used to select the drug removal time point based on the corresponding relationship data, continuously monitor the cell population after the drug stimulation is removed, measure the enzyme activity value in the cell culture supernatant, and plot the measured enzyme activity value over time as an activity recovery curve under different concentration conditions.

[0141] The numerical calculation module is used to collect the enzyme activity measurement values ​​of the target cell population before drug administration to determine the baseline level, analyze the deviation value between the activity recovery curve and the baseline level, determine the minimum effective concentration value and the maximum tolerated concentration value, extract enzyme activity data points within the range of the minimum effective concentration value and the maximum tolerated concentration value according to the activity recovery curve, determine the enzyme activity recovery rate by the ratio of the enzyme activity change at adjacent time points to the time interval, obtain the reversibility value by multiplying the enzyme activity recovery rate by the drug exposure duration, and use the ratio of the maximum tolerated concentration value to the minimum effective concentration value as the safety value.

[0142] The scoring and screening module is used to calculate drug evaluation scores based on reversibility and safety values, and select drug samples that meet the screening criteria as candidate drugs.

[0143] One technical solution provided in this embodiment of the invention is an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in any of the aforementioned methods.

[0144] One technical solution provided in this embodiment of the invention is a computer-readable storage medium storing a computer program, wherein the processor executes the computer program to implement the steps in any of the aforementioned methods.

[0145] The specific embodiments described above are preferred embodiments of the present invention and are not intended to limit the specific scope of the present invention. The scope of the present invention includes, but is not limited to, these specific embodiments. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A method for screening and evaluating active pharmaceutical ingredients based on in vitro culture, characterized in that, Includes the following steps: A cell population expressing the target metabolic enzyme was constructed by gene transfection. A fluorescently labeled substrate was prepared to establish a quantitative detection method for enzyme activity. The expression level and activity of the metabolic enzyme in the cell population were measured, and a correlation curve was established. A cell subpopulation within the linear range of the correlation curve was selected as a validation cell population, and the baseline enzyme activity of the validation cell population was measured as validation data. Based on the validation data, target cell populations were selected. Under in vitro culture conditions, the target cell populations were treated with the drug to be screened according to a concentration gradient. Dynamic response data, including the change in enzyme activity relative to the baseline value, were obtained at multiple time points using a quantitative enzyme activity detection method. The dynamic response data is segmented according to the time series, the rate of change of enzyme activity in each time interval is calculated, the drug action time point is identified, and the corresponding relationship data between drug concentration and action time is obtained. Based on the corresponding relationship data, the drug removal time point was selected. After the drug stimulation was removed, the cell population was continuously monitored, and the enzyme activity value in the cell culture supernatant was measured. The measured enzyme activity value was plotted as an activity recovery curve under different concentration conditions. The enzyme activity values ​​of the target cell population before drug administration were collected and determined as the baseline level. The deviation between the activity recovery curve and the baseline level was analyzed to determine the minimum effective concentration and the maximum tolerated concentration. Based on the activity recovery curve, enzyme activity data points within the range of the minimum effective concentration and the maximum tolerated concentration were extracted. The ratio of the change in enzyme activity at adjacent time points to the time interval was determined as the enzyme activity recovery rate. The reversibility value was obtained by multiplying the enzyme activity recovery rate by the drug exposure duration. The ratio of the maximum tolerated concentration to the minimum effective concentration was used as the safety value. The reversibility and safety values ​​are used to calculate the drug evaluation score, and drug samples that meet the screening criteria are selected as candidate drugs.

2. The method according to claim 1, characterized in that, A cell population expressing the target metabolic enzyme was constructed through gene transfection. A fluorescently labeled substrate was prepared to establish a quantitative detection method for enzyme activity. The expression level and activity of the metabolic enzyme in the cell population were measured, and a correlation curve was established, including: A eukaryotic expression vector expressing the target metabolic enzyme was constructed, and the eukaryotic expression vector was introduced into cells to be transfected using liposome transfection. Cell populations expressing the target metabolic enzyme were obtained by continuous passage screening with resistance drugs. A fluorescently labeled substrate was prepared, and the fluorescently labeled substrate was added to the culture supernatant of the cell population expressing the target metabolic enzyme. The fluorescence intensity was measured using a fluorescence detector to establish a quantitative detection method for enzyme activity. Cell populations expressing the target metabolic enzyme were sorted by flow cytometry to obtain cell subpopulations with different expression levels. The activity of the metabolic enzyme in the cell subpopulations was measured using the enzyme activity quantification method described above, and a curve showing the relationship between expression level and enzyme activity was established.

3. The method according to claim 1, characterized in that, Based on the validation data, a target cell population was selected. Under in vitro culture conditions, the target cell population was treated with the drug to be screened according to a concentration gradient. Dynamic response data, including changes in enzyme activity relative to a baseline value, were obtained at multiple time points using a quantitative enzyme activity detection method. The distribution characteristics of metabolic enzyme activity in the analysis and verification data were analyzed, and upper and lower limits of metabolic enzyme activity were established. The cell population was sorted by flow cytometry, and the cell population with metabolic enzyme activity between the upper and lower limits was selected as the target cell population. The target cell population was inoculated into an in vitro culture system containing culture medium and cultured. The metabolic enzyme activity of the target cell population was measured as a baseline value. The concentration range of the drug to be screened was set according to the baseline value. Incremental concentration points were set within the concentration range to prepare a drug gradient series with increasing concentration. The target cell population was treated with the drug gradient series. The supernatant in the culture system is collected at fixed time intervals within a preset time range. The activity of metabolic enzymes in the supernatant is measured using the enzyme activity quantification method. The change in the activity of the metabolic enzymes relative to the baseline value is calculated. The change at each detection time point and the corresponding drug concentration data are compiled and recorded as dynamic response data.

4. The method according to claim 1, characterized in that, The dynamic response data is segmented according to the time series, and the rate of change of enzyme activity within each time interval is calculated to identify the drug action time points and obtain the corresponding data of drug concentration and action time, including: An enzyme activity data matrix is ​​constructed by arranging dynamic response data according to the sampling time series. Enzyme activity values ​​at adjacent sampling time points are extracted from the enzyme activity data matrix to construct time-enzyme activity data pairs. The rate of change matrix is ​​obtained by calculating the ratio of the change in enzyme activity to the time interval in the time-enzyme activity data pairs. The rate of change matrix is ​​differentiated to obtain the acceleration matrix. The time position corresponding to the maximum value of the acceleration is marked in the acceleration matrix. The time position is determined as the drug action time point, and a correspondence table between drug concentration and the drug action time point is established. Based on the corresponding table, a drug concentration-time curve is plotted, and the slope distribution of the curve is calculated. The slope distribution is used as the data relating drug concentration and time of action.

5. The method according to claim 1, characterized in that, Based on the corresponding relationship data, the drug removal time point was selected. After the drug stimulation was removed, the cell population was continuously monitored, and the enzyme activity value in the cell culture supernatant was measured. The measured enzyme activity value was plotted as an activity recovery curve under different concentration conditions over time, including: Drug concentration information and action time point information are extracted from the corresponding relationship data. The rate of change of enzyme activity corresponding to the drug concentration information is calculated to construct drug action intensity data. The drug treatment duration is calculated based on the drug action intensity data, and the end of the drug treatment duration is determined as the drug removal time point. The drug removal operation is performed according to the drug removal time point. Before removal, the enzyme activity value of the cell culture supernatant is measured as the enzyme activity benchmark value. After removal, the cell culture supernatant is continuously collected at the set time interval, the sampling time point information is recorded, and the enzyme activity value in the cell culture supernatant is measured. The measured enzyme activity values, enzyme activity baseline values, and sampling time point information were compiled into enzyme activity recovery record data. The enzyme activity recovery record data is grouped according to drug concentration information. In each drug concentration group, the ratio of the change in enzyme activity value between adjacent sampling time points to the time interval is calculated as the enzyme activity recovery rate. The difference between the final enzyme activity value and the enzyme activity baseline value is calculated as the enzyme activity recovery range. The enzyme activity recovery rate and enzyme activity recovery range are plotted as a curve of enzyme activity value changing over time, forming activity recovery curves under different concentration conditions.

6. The method according to claim 1, characterized in that, Analyze the deviation of the activity recovery curve from the baseline level to determine the minimum effective concentration and the maximum tolerated concentration, including: The enzyme activity measurements at each concentration gradient in the activity recovery curve are recorded as recovery data. The deviation value is calculated by comparing the recovery data with the baseline level, and a concentration-deviation value distribution sequence is generated. Based on the concentration-deviation value distribution sequence, concentration points that exceed the baseline fluctuation range are selected as the minimum effective concentration value, and concentration points that cause cell viability to begin to decline are selected as the maximum tolerable concentration value.

7. The method according to claim 1, characterized in that, The reversibility and safety scores are used to calculate a drug evaluation score. Drug samples that meet the screening criteria are selected as candidate drugs, including: The reversibility and safety values ​​are normalized to obtain the reversibility weight coefficient and the safety weight coefficient, respectively. The reversibility weight coefficient and the safety weight coefficient are weighted and summed to obtain the preliminary drug evaluation score. The preliminary drug evaluation scores under different concentration conditions are sorted, the distribution inflection point of the preliminary drug evaluation scores is identified, and the preliminary drug evaluation score value corresponding to the distribution inflection point is determined as the effectiveness threshold. Extract the concentration points corresponding to the preliminary drug evaluation scores that are higher than the effectiveness threshold, calculate the span of the concentration point range as the effective concentration window width, and weight the effective concentration window width and the maximum value of the preliminary drug evaluation scores to obtain the final drug evaluation score. When the final drug evaluation score is higher than the preset screening criterion threshold, the drug to be screened is identified as a candidate drug.

8. A drug active ingredient screening and evaluation system based on in vitro culture, used to implement the method described in any one of claims 1-7, characterized in that, The system includes: The transfection construction module is used to construct a cell population expressing the target metabolic enzyme through gene transfection, prepare fluorescently labeled substrates to establish a quantitative detection method for enzyme activity, measure the expression level and activity of metabolic enzymes in the cell population, establish a correlation curve, select a cell subpopulation within the linear range of the correlation curve as a validation cell population, and measure the baseline enzyme activity of the validation cell population as validation data. The drug processing module is used to select target cell populations based on validation data, treat the target cell populations with the drugs to be screened according to a concentration gradient under in vitro culture conditions, and acquire dynamic response data including the change in enzyme activity relative to the baseline value at multiple time points using enzyme activity quantification detection methods. The time series analysis module is used to segment dynamic response data according to the time series, calculate the rate of change of enzyme activity within each time interval, identify the drug action time point, and obtain the corresponding data of drug concentration and action time. The activity monitoring module is used to select the drug removal time point based on the corresponding relationship data, continuously monitor the cell population after the drug stimulation is removed, measure the enzyme activity value in the cell culture supernatant, and plot the measured enzyme activity value over time as an activity recovery curve under different concentration conditions. The numerical calculation module is used to collect the enzyme activity measurement values ​​of the target cell population before drug administration to determine the baseline level, analyze the deviation value between the activity recovery curve and the baseline level, determine the minimum effective concentration value and the maximum tolerated concentration value, extract enzyme activity data points within the range of the minimum effective concentration value and the maximum tolerated concentration value according to the activity recovery curve, determine the enzyme activity recovery rate by the ratio of the enzyme activity change at adjacent time points to the time interval, obtain the reversibility value by multiplying the enzyme activity recovery rate by the drug exposure duration, and use the ratio of the maximum tolerated concentration value to the minimum effective concentration value as the safety value. The scoring and screening module is used to calculate drug evaluation scores based on reversibility and safety values, and select drug samples that meet the screening criteria as candidate drugs.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1 to 7.

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