Method and program for predicting change in plasma concentration from pharmacokinetic parameter

Abstract

To provide a method for predicting a change in plasma concentration described by a primary absorption two-compartment model with lag time because it was impossible to predict changes other than changes that can be described by the primary absorption two-compartment mode from a pharmacokinetic parameter even though the parameter of a medicine is described in attachment documents of a pharmaceutical goods or scientific essays.SOLUTION: A parameter of a primary absorption one-compartment model from dosage of an inputted medicine and a pharmacokinetic parameter (S-1) is acquired (S-2), a prediction model is selected from comparison of predicted maximum plasma concentration with actual concentration (S-3), a macro parameter of an optimum medicine is estimated from information of the pharmacokinetic parameter and the macro parameter analyzed by a primary absorption two-compartment model with lag time of an existing medicine when the primary absorption two-compartment model is selected (S-4), and a change in the plasma concentration using the selected model is predicted (S-5).SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a method and apparatus for predicting (simulating) the progression of the plasma concentration of a compound, including a drug, using a compartmental model when the compound is orally administered to an animal, including a human. [Background technology]

[0002] After administration, drugs are absorbed from the digestive tract, move to the site of action, and exert their medicinal or toxic effects. Because the concentration of a drug at the site of action and the duration of its action depend on the plasma concentration, understanding the relationship between efficacy / toxicity and plasma concentration trends is important for the appropriate use of pharmaceuticals. Pharmacokinetic analysis is a method for analyzing the time course of a drug's plasma concentration.

[0003] There are three methods of pharmacokinetic analysis: compartmental model analysis, non-compartmental analysis, and physiological pharmacokinetic analysis. Generally, the pharmacokinetic parameters listed in drug package inserts and academic papers are the results of non-compartmental analysis. Non-compartmental analysis cannot predict plasma concentration trends. Compartmental model analysis and physiological pharmacokinetic analysis are used to predict blood and plasma concentration trends. Physiological pharmacokinetic models are complex models that require many parameters. Furthermore, physiological pharmacokinetic model analysis is limited in its use because it requires dedicated software. Compartmental model analysis is widely used for dosage design, etc., because analytical solutions exist and calculations are simple.

[0004] Predicting plasma concentration trends using compartment models is effective for appropriate drug administration planning. Conventional compartment model analysis applies the obtained plasma concentration to 1-, 2-, or 3-compartment models, and the obtained analysis results can be used to easily predict plasma concentration trends under different administration conditions, such as repeated administration.

[0005] The pharmacokinetic parameters listed in the package insert are the maximum plasma concentration (Cmax), the time to reach the maximum plasma concentration (Tmax), the area under the plasma concentration-time curve (AUC), and the half-life (T1 / 2). From these four parameters, the three parameters required for the first-order absorption one-compartment model - the apparent volume of distribution (Vd / F), the elimination rate constant (Ke), and the absorption rate constant (Ka) - can be calculated, and the plasma concentration profile can be predicted. However, in predictions using this method, the actual Cmax is on average 2 (0.998-5.97) times higher than the predicted value, resulting in problems with predictability (Non-Patent Document 1).

[0006] The first-order absorption two-compartment model consists of two compartments: compartment 1 (the central compartment), which represents the systemic circulation containing plasma, and compartment 2, which takes time to equilibrate with plasma. Drugs absorbed from the gastrointestinal tract enter compartment 1. The two-compartment model requires five microparameters: the distribution volume of compartment 1 (V1), two intercompartment rate constants (K12, K21), the elimination rate constant (Ke), and Ka. Because the analytical solution for the plasma concentration profile would be complex if expressed using microparameters, it is expressed using four macroparameters: A and B, which represent concentration, and α and β, which represent rate constants, plus Ka. Analysis and prediction are performed using the analytical solution. Furthermore, if there is a lag in absorption, the lag time (Tlag) must also be calculated, requiring six parameters. In this specification, all six of these parameters are defined as macroparameters. Because only four pharmacokinetic parameters are listed in the package insert, there is insufficient information to calculate the necessary parameters using the two-compartment model.

[0007] Although not directly related to the present invention, an example of an analytical method that compensates for a lack of information is shown below. Population analysis is a method for predicting the plasma concentration over time of a patient based on limited blood sampling from that patient. Although compartmental analysis is not possible with only a few plasma concentrations per patient, data from many patients, even a few samples per patient, can be analyzed as a group to evaluate the mean and variance of parameters. These mean and variance data can then be used as prior information for Bayesian estimation in combination with the blood sampling from several samples, thereby estimating the individual's parameters and predicting the plasma concentration over time (Non-Patent Document 2).

[0008] Here is another example. In physiological pharmacokinetic models, there are too many parameters to identify from observable data, so it can be difficult to obtain the parameters. One solution to this problem is the cluster Gauss-Newton method. This method efficiently searches for unknown parameters from a wide parameter space (Non-Patent Document 3). Just as in population analysis, where the parameter range is specified by the mean and variance, this method also specifies the parameter range and then estimates it.

[0009] Currently, it is not easy to obtain a compartment model or its parameters for predicting the progression of drug blood or plasma concentrations. Calculations are performed using pharmacokinetic parameters and assuming a first-order absorption one-compartment model, but this often leads to predictions of Cmax that are lower than the actual measured value (Non-Patent Document 1). [Prior art documents] [Non-patent literature]

[0010] [Non-Patent Document 1] Motohiro Kato P-067 "Prediction of plasma concentration trends using a one-compartment model with pharmacokinetic parameters" The 36th Annual Meeting of the Japanese Society of Pharmaceutical Sciences, p.231(2021). [Non-patent document 2] Introduction to Population Pharmacokinetics for Drug Blood Concentration Monitoring, Yakugyo Jihosha, 1988. [Non-patent document 3] CPT Pharmacometrics Syst Pharmacol.2023;00:1-14. Summary of the Invention [Problem to be solved by the invention]

[0011] In view of the above problems, we provide a method and program for predicting the plasma concentration transition of a drug after oral administration using a first-order absorption two-compartment model from the pharmacokinetic parameters listed in the package insert of a drug, which could not be predicted using only the conventional first-order absorption one-compartment model. [Means for solving the problem]

[0012] The present inventors have devised the following method for predicting the plasma concentration profile of a drug using a first-order absorption one- or two-compartment model, based on pharmacokinetic parameters described in drug package inserts and academic papers, and parameter information obtained from a first-order absorption two-compartment model analysis with lag time for existing drugs.

[0013] In a first exemplary embodiment of the present invention, there is provided the following method for predicting the plasma concentration profile of a drug using a computer. The prediction method includes the steps of calculating first-order absorption 1-compartment model parameters from four pharmacokinetic parameters of the drug to be predicted, namely AUC, Cmax, T1 / 2, and Tmax, and selecting a model by comparing the predicted Cmax with the actually measured Cmax; if the selected model is a 2-compartment model, estimating the optimal 2-compartment model macroparameters for the target drug from the pharmacokinetic parameters of the drug to be predicted and prior information on existing drugs; and predicting the plasma concentration profile using the compartment model.

[0014] In a second exemplary aspect of the present invention, there is provided a program for executing the prediction method of the first aspect.

[0015] In a third exemplary aspect of the present invention, there is provided a simulation device comprising a memory storing the program of the first aspect and a control circuit which is a computer, and which executes the computer program to estimate parameters in a compartment model and predict the plasma concentration transition using the parameters. [Effects of the Invention]

[0016] The plasma concentration transition can be predicted from the pharmacokinetic parameters described in the package inserts of pharmaceuticals, academic papers, etc. [Brief explanation of the drawings]

[0017] [Figure 1] FIG. 1 is a flowchart showing the procedure of a system for predicting the progression of plasma drug concentration according to an embodiment of the present invention. [Figure 2] FIG. 1 is a diagram showing an example of a system for predicting the transition of plasma concentration of a drug according to an embodiment of the present invention. [Figure 3] FIG. 1 shows the number of drugs analyzed based on the plasma concentration profiles using a one-compartment model with first-order absorption and a lag time or a two-compartment model with first-order absorption and a lag time. [Figure 4] FIG. 1 is a graph comparing the pharmacokinetic parameters described in the package insert with those obtained from the fitting results of the graph data. [Figure 5] FIG. 1 shows the correlation of macro parameters obtained by first-order absorption two-compartment model analysis with lag time for existing drugs. [Figure 6] FIG. 1 is a diagram showing a comparison of the plasma concentration time courses of afatinib, alogliptin, dapagliflozin, and mirabegron predicted by the conventional method (method A) and the methods of the present invention (methods B, C, and D). [Figure 7] FIG. 1 shows a comparison of the plasma concentration trends of mirabegron using methods that provide macroparameters from microparameters. [Figure 8]FIG. 1 shows the plasma concentration transition predicted by the present invention from the pharmacokinetic parameters of epigallocatechin gallate (EGCG). [Figure 9] FIG. 1 shows the plasma concentration time course of mirabegron in rats predicted by the present invention from the pharmacokinetic parameters. DETAILED DESCRIPTION OF THE INVENTION

[0018] The prediction method and apparatus of this embodiment will be described below with reference to the accompanying drawings as appropriate.

[0019] <Parameter definition> The parameters used in the following disclosure are defined as follows: D: Dosage Cmax: maximum plasma concentration Tmax: Time to reach maximum plasma concentration AUC: Area under the plasma concentration time curve T1 / 2: Half-life Vd: Volume of distribution in a one-compartment model F: Bioavailability Vd / F: apparent volume of distribution Tlag: Absorption lag time Ke: Disappearance rate constant Ka: absorption rate constant V1: Volume of distribution of compartment 1 in the 2-compartment K12: Rate constant for transfer from compartment 1 to compartment 2 K21: Rate constant for transfer from compartment 2 to compartment 1 A / D: Dose-normalized concentration constant in the α phase. B / D: Dose-normalized concentration constant in the beta phase. α: Disappearance rate constant in the α phase β: Disappearance rate constant in the β phase

[0020] The flowchart and system for the prediction method of the present invention are shown in Figures 1 and 2. The data input unit 10 inputs the dosage, the pharmacokinetic parameters at that dosage, and the simulation conditions (S-1). The simulation conditions include the dosage, the time for calculating the concentration, and, in the case of repeated administration, the administration frequency and administration interval.

[0021] The model determination calculation unit 20 calculates the parameters of a first-order absorption one-compartment model from the pharmacokinetic parameters (S-2).

[0022] The model is judged from the ratio of the measured Cmax to the Cmax predicted from the calculated parameters of the first-order absorption one-compartment model (S-3).

[0023] The equation for the first-order absorption one-compartment model that describes the plasma concentration profile is shown in equation (1). Usually, the plasma concentration profile is fitted to these equations to estimate the optimal parameters. Furthermore, the plasma concentration profile can be predicted from the obtained parameters.

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[0024] Conventional method (Method A) The method for directly determining the pharmacokinetic parameters without fitting them to a model is shown below. The one-compartment model parameters (Ke, Vd / F) are calculated using equations (3), (4), and (5). Since Ka cannot be determined analytically, the Ka that satisfies the measured value of Tmax is determined using equation (6) and a nonlinear equation solution such as the Newton-Raphson method.

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[0025] To determine whether the plasma concentration profile can be predicted using a first-order absorption one-compartment model, the predicted Cmax is calculated using equation (6) and compared with the measured Cmax. The ratio of the measured Cmax to the predicted Cmax is defined as CmaxR. For example, if CmaxR is 1.5 times or less, it is determined that the profile can be predicted using a one-compartment model.

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[0026] If it is determined to be a two-compartment model, the two-compartment model calculation unit 30 estimates macro parameters (S-4).

[0027] The equation for the first-order absorption two-compartment model that describes the plasma concentration profile is shown in equation (7). Usually, the plasma concentration profile is fitted to these equations to estimate the optimal parameters. Furthermore, the plasma concentration profile can be predicted from the obtained parameters.

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[0028] The pharmacokinetic parameter range control section specifies the possible difference (error) between the measured pharmacokinetic parameters and the estimated pharmacokinetic parameters. Using equation (7), the plasma concentration profile of existing drugs is applied to determine the optimal macroparameters (A, B, α, β, Ka, Tlag) for each drug. The predicted pharmacokinetic parameters are calculated using the obtained macroparameters using equations (8)-(11) below, and the ratios to the measured pharmacokinetic parameters are calculated. The mean and standard deviation of the ratios are used as the range parameters for the pharmacokinetic parameter range control section. Note that equation (9) assumes that Cmax in the 2-compartment model can be approximated by Tmax, the α-phase term.

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[0029] A and B are standardized by the dosage. The parameters are logarithmically transformed, and the range, mean value, standard deviation, and correlation coefficient between parameters for each logarithmically transformed macroparameter are calculated. The macroparameter range control section associates the obtained macroparameter information using an estimation method (multiple regression analysis, neural network, etc.) that takes correlation into account and has a high probability of existence of parameters, thereby limiting the parameter range.

[0030] The macro parameter calculation unit estimates parameters for predicting plasma concentrations from the range information provided by the macro parameter range control unit and the pharmacokinetic parameters of the target drug. The Bayesian method is shown as an example of a parameter estimation method. The optimal parameters are estimated using equation (12). Let Y be the parameter listed in the package insert. The square of the difference between the estimated macro parameter and the value calculated using equations (8)-(11) is taken as the residual square. This is calculated for all parameters and the sum is taken as the residual sum of squares. Let P be the logarithmically transformed macro parameter of the existing drug. The square of the difference between the estimated logarithmically transformed parameter and the residual sum of squares is taken as the residual sum. This is calculated for all parameters and the sum is taken as the residual sum of squares. The optimal macro parameters are estimated so that the sum of squares of the pharmacokinetic parameter term and the sum of squares of the logarithmically transformed macro parameter term (SS) are minimized.

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[0031] If the model determination calculation unit 20 determines that the model is a first-order absorption 1-compartment model, the plasma concentration calculation unit 40 uses the parameters of the 1-compartment model calculated by the model determination calculation unit 20, and if the model is determined to be a 2-compartment model, it uses the parameters estimated by the 2-compartment model calculation unit 30 to predict the plasma concentration transition under the set simulation conditions (dosage, administration frequency). [Example]

[0032] The present invention will be specifically described below with reference to examples, but the present invention is not limited to these examples.

[0033] Plasma concentration profiles after oral administration of oral medications (excluding enteric-coated drugs) approved as new active ingredients in Japan between 2010 and 2016 were read from diagrams in the package inserts or interview forms. Pharmacokinetic parameters were determined using values ​​listed in the package inserts or interview forms. Plasma concentration profiles were analyzed using a one-compartment model and a two-compartment model, and the models were judged using Akaike's information criterion. The one-compartment model was optimal for 18 drugs, and the two-compartment model was optimal for 68 drugs.

[0034] The parameters of the first-order absorption 1-compartment model were obtained from the drug's pharmacokinetic parameters using conventional methods, and CmaxR was calculated. Figure 3 shows a boxplot of CmaxR for drugs for which the 1-compartment model and 2-compartment model were determined to be the optimal models. The mean and standard deviation of CmaxR for drugs for which the 1-compartment model was determined to be the optimal model were 1.26 and 0.21, respectively. The mean and standard deviation of CmaxR for drugs for which the 2-compartment model was determined to be the optimal model were 2.85 and 1.79, respectively. CmaxR is used to select the model. The CmaxR value used for the judgment is set practically or statistically.

[0035] The data ranges of the pharmacokinetic parameter range control section are shown as examples of analysis results for 68 drugs. Figure 4 shows the relationship between the measured values ​​of the pharmacokinetic parameters AUC, T1 / 2, Cmax, and Tmax and the predicted values ​​from the parameters obtained using a two-compartment model. The ratios of the measured and predicted values ​​of the pharmacokinetic parameters, AUC, T1 / 2R, CmaxR, and TmaxR, are shown in Table 1 as the mean, standard deviation, maximum, and minimum values. The values ​​listed in the figures and tables in the package insert are mean values ​​and do not necessarily match. The mean and standard deviation of the ratio of the Cmax listed as a pharmacokinetic parameter to the Cmax read from the figure was 1.13 ± 0.16 (N = 86), with the listed Cmax being higher. The variance calculated from the standard deviations of the parameters listed in Table 1 was used as the variance of the relative error of the pharmacokinetic parameters. [Table 1]

[0036] The data range of the macro parameter range control section is shown as an example of the analysis results for 68 drugs. The mean values, standard deviations, maximum values, and minimum values ​​of the logarithmically transformed macro parameters are shown in Table 2. The correlation coefficients between parameters are shown in Table 3. The relationships between log(A / D) and log(B / D), and log(α) and log(β), for which the correlation coefficient was 0.6 or higher, are shown in Figure 5. [Table 2] The units of A / D, B / D, α, β, Ka, and Tlag are ng / mL / mg, ng / mL / mg, / h, / h, / h, and h, respectively. [Table 3]

[0037] Three methods were compared as methods for providing macro parameters for the macro parameter range control unit. The prediction accuracy was compared among the following: a method in which all parameters are uncorrelated and mean values ​​and SDs are provided (Method B); a method in which log(A / D) and log(B / D) are correlated using log(B / A) (Method C); and a method in addition to Method C that further correlates log(α) and log(β) using the regression equation log(α) = 0.4917 x log(β) + 0.2041 (Method D). The values ​​in Table 2 were used for the mean values ​​and standard deviations. The standard deviation of log(α) in Method D was 0.293, based on the variance of the regression equation. While mean values ​​and regression equations are used to provide macro parameters, this is not limited to these methods.

[0038] <Example of plasma concentration transition prediction> The pharmacokinetic parameters of afatinib, alogliptin, dapagliflozin, and mirabegron obtained from the literature are shown in Table 4. [Table 4] Afatinib:Cancer Chemother Pharmacol(2014)74:267-275 Alogliptin:Eur J Clin Pharmacol(2017)73:279.288 Dapagliflozin:Drug Design, Development and Therapy(2023)17:1203-1210 Mirabegron: Clin Drug Investig(2014)34:27-35

[0039] The CmaxR values ​​calculated in the model evaluation section for afatinib, alogliptin, dapagliflozin, and mirabegron were 2.95, 2.40, 5.75, and 6.70, respectively. If a value of 1.5 or higher is considered a 2-compartment model, the 2-compartment model is selected. Macroparameters were estimated in the 2-compartment model calculation section. Methods B, C, and D were used to provide macroparameters in the macroparameter range control section. The results of predicting the plasma concentration transitions from the obtained parameters of the four drugs and the actual measured values ​​read from the figures published in the paper are shown in Figure 6. Method A is a conventional method based on a one-compartment model. <Comparison of prediction methods>

[0040] The prediction method using the 2-compartment model of the present invention and conventional method A were compared with the values ​​in the attached document. The method of the present invention gave better results than the conventional method. When comparing the methods of providing 2-compartment model parameters, prediction by method B, which has no correlation between the parameters, tended to show lower values ​​in the final phase than methods C and D.

[0041] The prediction accuracy of the conventional method A and the two-compartment model parameter providing methods B, C, and D was compared using absolute average fold error (AAFE) and root mean squared error (RMSE).

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[0042] Cpred and Cobs are the predicted and measured values, respectively. Table 4 shows the AAFE for the four drugs, and Table 5 shows the RMSE. Method A, a conventional method using a one-compartment model, had the lowest accuracy, followed by Method B, which uses a two-compartment model with no correlation between parameters, and Methods C and D, which take correlation into account. [Table 5] [Table 6]

[0043] <Prediction using micro parameters> The method of providing macroparameters is not limited to methods based on macroparameter information from the results of two-compartment model analysis of existing drugs. It can also be provided from information on microparameters (V1, K12, K21, Ke) that form the macroparameters.

[0044] Table 7 shows the mean values ​​and standard deviations of the logarithmically transformed microparameters calculated from the macroparameters of the 68 drugs, and Table 8 shows the correlation coefficients between the parameters. [Table 7] The units of V1, K21, Ke, and K12 are L, / h, / h, and / h, respectively. [Table 8]

[0045] The conversion formula from micro parameters to macro parameters is shown below.

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[0046] One method for providing macro parameters for the macro parameter range control unit is to provide them from micro parameters. We compared the prediction accuracy of two methods: (1) a method in which all parameters are uncorrelated and provide mean values ​​and SDs (M1 method); and (2) a method in which log(K21) and log(Ke) and log(K21) and log(K12) are correlated using the regression equations log(Ke) = 0.409 x log(K21) - 0.357 and log(K12) = 0.826 x log(K21) - 0.237 (M2 method). The mean values ​​and standard deviations used were those in Table 7. The standard deviations of log(Ke) and log(K12) in the M2 method were 0.409 and 0.359, respectively, based on the variance of the regression equations. While mean values ​​and regression equations are used to provide micro parameters, these methods are not limited to these.

[0047] The pharmacokinetic parameters to be estimated are calculated from the macro parameters using the micro parameters and the formulas (8), (9), (10), and (11).

[0048] Using Equation 12, the optimal microparameters are estimated so that the sum of squares of the residuals of the pharmacokinetic parameter terms and the sum of squares of the residuals (SS) of the logarithmically transformed microparameter terms are minimized. Let Y1 = AUC, Y2 = Cmax, Y3 = T1 / 2, and Y4 = Tmax. The uppercase Y represents the pharmacokinetic parameter of the target drug entered, and the lowercase y represents the estimated pharmacokinetic parameter. Let P1 = log(V1), P2 = log(K21), P3 = log(Ke), P4 = log(K12), P5 = log(Ka), and P6 = log(Tlag). The uppercase P represents the mean value of the parameter of the existing drug, and the lowercase p represents the estimated value of the corresponding parameter of the target drug. The weight of parameter Y is the variance of parameter Y (the square of the mean multiplied by the standard deviation σ), and the weight of parameter P is the variance of parameter P.

[0049] The plasma concentration transition was predicted using macro parameters calculated from the estimated micro parameters. Figure 7 shows the results of the plasma concentration transition predicted from the obtained parameters and the actual measured values ​​read from the figure published in the paper.

[0050] <Predictions Other than Drugs> The present invention is not limited to drugs. The case of conducting with pharmacokinetic parameters of food ingredients will be exemplified.

[0051] <Example with Catechin> Pharmacokinetic parameters upon oral administration of epigallocatechin-gallate (EGCG), which is a component of tea, were obtained from literature information (J Int Med Res. (2003) 31(2):88-101). With a dosage of 1600 mg, AUC = 10262.7 ng h / mL, Cmax = 3391.6 ng / mL, T1 / 2 = 4.58 h, and Tmax = 1.31 h, parameters were calculated using a one-compartment model. From Vd / F = 1030 L, Ke = 0.151 / h, and Ka = 2.19 / h, Cmax = 1274 ng / mL and CmaxR = 2.66 were calculated. Since CmaxR is 1.5 or more, macroparameters were estimated by methods B, C, and D in the two-compartment model calculation unit. The result of predicting the plasma concentration change from the obtained parameters is shown in FIG. 8. Method A is a conventional method using a one-compartment model. The measured values shown in FIG. 8 were read from the figure published in the literature.

[0052] <Predictions in Rats> The present invention is not limited to humans. The case of conducting with pharmacokinetic parameters of rats will be exemplified.

[0053] The data range of the macroparameter range control unit in rats was exemplified as the analysis results of 11 drugs. The average value, standard deviation, maximum value, and minimum value of the logarithmically transformed macroparameters are shown in Table 9. The correlation coefficients between the parameters are shown in Table 10.

Table 9

Table 10

[0054] <Examples in mirabegron> Pharmacokinetic parameters when mirabegron was orally administered to rats were obtained from the literature information (REV ASSOC MED BRAS (2019) 65(2):141-148). The dose = 20 mg / kg, AUC = 4243.65 ng h / mL, Cmax = 829.06 ng / mL, T1 / 2 = 3.207 h, Tmax = 3 h. Parameters were calculated using a one-compartment model. From Vd / F = 21.8 L / kg, Ke = 0.216 / h, Ka = 0.487 / h, Cmax = 479.5 ng / mL and CmaxR = 1.73 were calculated. Since CmaxR is 1.5 or more, macroparameters were estimated by methods B, C, and D in the two-compartment model calculation unit. The predicted plasma concentration changes in rats from the obtained parameters are shown in Fig. 9 as the measured values read from the figures published in the paper. Method A is the conventional method using a one-compartment model.

Industrial Applicability

[0055] According to the present invention, it has become possible to associate plasma concentration changes with pharmacokinetic parameters and to handle them on a computer as compartment model parameters. It is considered to play a major role in optimal dosing design and prediction of drug-drug interactions in clinical practice. Analyzing the relationship between the efficacy of existing drugs and plasma concentration changes is useful for the development of more effective new drugs.

Explanation of Signs

[0056] 10 Data input unit 20 Model judgment calculation unit 30 Two-compartment model calculation unit 40 Plasma concentration calculation unit

Claims

1. A method for predicting the plasma concentration profile of a compound after oral administration using a computer, comprising: determining a prediction model from the pharmacokinetic parameters of the target compound; providing compartmental model parameters for the compound of interest; and calculating the plasma concentration profile of the target compound using a compartmental model.

2. The method according to claim 1, wherein three parameters of a first-order absorption one-compartment model, i.e., an apparent distribution volume, an elimination rate constant, and an absorption rate constant, are determined from the pharmacokinetic parameters, and the maximum plasma concentration is predicted using said parameters, and the prediction model is selected by comparing the predicted maximum plasma concentration with the actually measured value.

3. The method for providing model parameters according to claim 1, wherein optimal parameters of a first-order absorption two-compartment model with lag time are estimated from the ranges of parameters of multiple drugs analyzed using the first-order absorption two-compartment model with lag time and the error ranges of the pharmacokinetic parameters of the target compound for predicting the plasma concentration transition after oral administration. The parameter ranges here specify the possible ranges of parameters such as the mean value and standard deviation of the parameters as prior information, maximum value, initial value, and estimated values ​​obtained by regression analysis or machine learning.

4. A method for providing model parameters that estimates optimal parameters of a first-order absorption two-compartment model with lag time by Bayesian estimation from the ranges of parameters of multiple drugs analyzed using the first-order absorption two-compartment model with lag time and the error ranges of the pharmacokinetic parameters of the target compound for predicting plasma concentration transitions after oral administration. The parameter ranges here specify the possible ranges of parameters such as the mean value and standard deviation of the parameters as prior information, maximum value, initial value, and estimated values ​​obtained by regression analysis and machine learning.

5. A database of macro- and micro-parameters of a first-order absorption one-compartment model and a first-order absorption two-compartment model with lag time for compounds obtained by the method of claims 2-4.

6. A simulation device that executes the prediction method according to any one of claims 1 to 4.

7. A computer program for carrying out the prediction method according to any one of claims 1 to 4.

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