JP6920669B2 - Pharmacokinetic analysis method, pharmacokinetic analyzer and program - Google Patents

Description

The present invention relates to a pharmacokinetic analysis method, a pharmacokinetic analyzer and a program, and more particularly to a pharmacokinetic analysis method for analyzing changes in a patient's pharmacokinetics.

Pharmacokinetics are generally analyzed on the assumption that physiology is constant. Specifically, there is known a method of obtaining a final solution (fixed value) by the Bayesian method on the assumption that the input physiological function is in a constant state.

For example, in infectious disease patients, physiology is not constant. Non-Patent Document 1 discloses a method that allows a change in physiological function. Specifically, the input physiological function includes a change point, the change in the physiological function is divided at the change point, the blood concentration transition in the section before the change point is calculated, and the change point is calculated. The transition of blood concentration in the section is described, and the final solution (fixed value) is obtained by the Bayesian method.

FIG. 7 shows an example of analysis of pharmacokinetics by the background technique. (A) is an analysis example assuming that the pharmacokinetics is constant, and (b) is an analysis example according to Non-Patent Document 1. The goal is to have a daily minimum daily blood concentration of 10 μg / mL or more and 20 μg / mL or less when antibiotics are administered. Physiological function is assumed to change as follows. Serum creatinine was 1.0 mg / dL at the beginning of administration, but increased to 1.5 mg / dL in the middle. After that, continuous renal replacement therapy was started.

YAMAMOTO, 3 other authors, "Bayesian Equation of Pharmacokinetic Parameters of Vancomycin in Patients with Decreasing Renal Function," Journal of Pharmaceutical Sciences, Vol.101, No.8, 2012.

However, under the assumption that the physiology is simply constant, it was not possible to depict changes in the patient's physiology as shown in FIG. 7 (a).

Further, in the method described in Non-Patent Document 1, as shown in FIG. 7B, it can be described that the blood concentration increases as the amount of serum creatinine increases. However, it has not been described that continuous renal replacement therapy is started and the blood concentration decreases. As described above, complicated calculations are required for each section, and it is difficult to describe the complicated changes in the pharmacokinetics of the patient, and it is difficult to describe the transition of the true blood concentration.

Therefore, an object of the present invention is to propose a pharmacokinetic analysis method or the like capable of easily analyzing a change in the pharmacokinetics of a patient in response to a change in the physiological function of the patient.

The first aspect of the present invention is a pharmacokinetic analysis method for analyzing changes in the pharmacokinetics of a patient, in which an individual pharmacokinetic calculation means and a group pharmacokinetic acquisition means included in the analyzer are input to the analyzer. A pharmacokinetic acquisition step for calculating an individual pharmacokinetic value of the patient and obtaining a collective pharmacokinetic value in the population to which the patient belongs, respectively, according to the physiological function of the patient, and a variation coefficient provided in the analyzer. The calculation means inputs the fluctuation coefficient calculation step for calculating the fluctuation coefficient indicating the correspondence relationship between the individual pharmacokinetic value and the group pharmacokinetic value, and the group change pharmacokinetic acquisition means included in the analysis device is input to the analysis device. The group change pharmacokinetic acquisition step for obtaining the change in the population pharmacokinetic value in the population corresponding to the change in the physiological function and the patient change pharmacokinetic acquisition means provided in the analyzer are the fluctuation coefficient and the group pharmacokinetic value. It comprises a patient change pharmacokinetic calculation step of calculating a change in the individual pharmacokinetic value of the patient corresponding to the change in physiological function using the change in.

The second aspect of the present invention is the pharmacokinetic analysis method of the first aspect, wherein the analysis apparatus includes a mathematical model storage means for storing a mathematical model including a physiological function as a coefficient, and the pharmacokinetic acquisition step. In the individual pharmacokinetic calculation means, the individual pharmacokinetic value of the patient is calculated using the mathematical model on the assumption that the physiological function of the patient is constant.

The third aspect of the present invention is the pharmacokinetic analysis method of the first or second aspect, and the mathematical model storage means includes a biomarker mathematical model having the individual pharmacokinetic value as a coefficient, and the analysis. The biomarker transition calculation means provided in the apparatus includes a biomarker transition calculation step for calculating the biomarker transition by solving the biomarker mathematical model using the change in the personal dynamic value of the patient.

A fourth aspect of the present invention is the pharmacokinetic analysis method of any one of the first to third aspects, wherein the group pharmacokinetic updating means included in the analyzer calculates the coefficient of variation, and then the individual. It includes a population pharmacokinetic update step of updating the population pharmacokinetic value corresponding to the physiological function using the pharmacokinetic value.

A fifth aspect of the present invention is a pharmacokinetic analyzer that analyzes changes in the pharmacokinetics of a patient, the input means for inputting the pharmacokinetic function of the patient and the changes in the pharmacokinetics, and the input. An individual pharmacokinetic calculation means for calculating the individual pharmacokinetic value of the patient corresponding to the physiological function of the patient, and a pharmacokinetic value for obtaining the input group pharmacokinetic value corresponding to the input physiological function of the patient in the group to which the patient belongs. The acquisition means, the fluctuation coefficient calculation means for calculating the fluctuation coefficient indicating the correspondence between the individual pharmacokinetic value and the group pharmacokinetic value, and the population pharmacokinetic value in the group corresponding to the input change in the physiological function. The change individual drug for calculating the change in the individual pharmacokinetic value of the patient corresponding to the change in the physiological function by using the change group pharmacokinetic acquisition means for obtaining the change in It is provided with a dynamic calculation means.

The sixth aspect of the present invention is a program for realizing the pharmacokinetic analysis method of any one of the first to fourth aspects in a computer.

The invention of the present application may be regarded as a computer-readable recording medium that constantly stores the program of the sixth aspect.

The present invention utilizes the fact that the coefficient of variation is constant even if the patient's physiology changes, and obtains the change in the patient's individual pharmacokinetic value corresponding to the change in the patient's physiology by a simple calculation process. be able to.

Specifically, the individual pharmacokinetic value for a specific physiological function of the patient is obtained. Here, the individual pharmacokinetic value for a specific physiological function can be obtained on the assumption that the physiological function is constant, as in the second viewpoint. Then, the coefficient of variation is obtained by comparing the individual pharmacokinetic value with the collective pharmacokinetic value of the population. This can be obtained by a simple calculation such as four arithmetic operations.

Subsequently, the change in the population pharmacokinetic value of the population corresponding to the change in physiological function is obtained. By utilizing the change and coefficient of variation of the population pharmacokinetic value of the population, it is possible to obtain the change of the individual pharmacokinetic value of the patient by a simple calculation such as four arithmetic operations.

In particular, as in the second aspect of the present invention, the coefficient of variation can be calculated assuming that the patient's physiology is constant. In the technique described in Non-Patent Document 1, since the patient's individual pharmacokinetic value is directly changed in response to the change in physiological function, it is necessary to divide the technique into sections before and after the change point, which is a complicated calculation. Was needed. In the present invention, it is possible to avoid such complicated calculations and use simple calculations such as four arithmetic operations.

Furthermore, as in the third aspect of the present invention, it is possible to estimate the amount of biomarker. Such a calculation is difficult to realize by a method such as dividing into sections as in Non-Patent Document 1.

Furthermore, in the past, the individual pharmacokinetic values of a plurality of patients were also independent, and were also independent of the population pharmacokinetic values. On the other hand, according to the fourth aspect of the present invention, the accuracy of the population pharmacokinetic value can be improved by reflecting the new individual pharmacokinetic value, and the change in the individual pharmacokinetic value due to the change in the population pharmacokinetic value. The accuracy of the simulation can be improved. Then, it leads to use in new patients, and new individual pharmacokinetic values are obtained to further improve the accuracy of population pharmacokinetic values. In this way, it is possible to realize a true personalized administration design by realizing a cycle between the individual pharmacokinetic value and the population pharmacokinetic value.

It is a block diagram which shows an example of the structure of the pharmacokinetic analysis apparatus which concerns on embodiment of this invention. It is a flow chart which shows an example of the operation of the pharmacokinetic analysis apparatus 1 of FIG. An example of the mathematical model stored in the mathematical model storage unit 7 of FIG. 1 is shown. An example of the transition of the blood concentration corresponding to the change of the physiological function obtained by the present invention is shown. The blood concentration curve of the antibiotic and the transition of the amount of microorganisms as a biomarker when the dose of the antibiotic is changed are shown. It is a figure which shows the effect of improving the predictability of the blood concentration of vancomycin. An example of analysis of pharmacokinetics by background technology is shown.

Hereinafter, examples of the present invention will be described with reference to the drawings. The invention of the present application is not limited to this embodiment.

FIG. 1 is a block diagram showing an example of the configuration of the pharmacokinetic analysis device according to the embodiment of the present invention.

The pharmacokinetic analyzer 1 of FIG. 1 analyzes changes in the pharmacokinetics of a patient. The pharmacokinetic analyzer 1 includes a population pharmacokinetic database 3, an input means 5, a mathematical model storage unit 7, a patient physiological function storage unit 9, a physiological function change storage unit 11, and an individual pharmacokinetic calculation unit 21 (the present application). An example of the "individual pharmacokinetic calculation means" of the claim), an individual pharmacokinetic memory unit 23, a group pharmacokinetic acquisition unit 25 (an example of the "group pharmacokinetic acquisition means" of the present claim), and a collective pharmacokinetic memory. Part 27, the fluctuation coefficient calculation unit 29 (an example of the “variation coefficient calculation means” of the claim of the present application), the fluctuation coefficient storage unit 31, and the population change pharmacokinetic acquisition unit 41 (the “group change pharmacokinetics acquisition” of the claim of the present application). An example of "means"), a population change pharmacokinetic memory unit 43, a patient change pharmacokinetic calculation unit 45 (an example of "patient change pharmacokinetic acquisition means" according to the claim of the present application), a patient change pharmacokinetic memory unit 47, It includes a biomarker transition calculation unit 49 (an example of the “biomarker transition calculation means” of the present application claim) and a group pharmacokinetic update unit 51 (an example of the “group pharmacokinetic update means” of the present application claim).

The input means 5 can be realized by an input device such as a keyboard or a mouse. Group pharmacokinetic database 3, mathematical model memory 7, patient physiological function memory 9, physiological function change memory 11, individual pharmacokinetic memory 23, group pharmacokinetic memory 27, fluctuation coefficient memory 31, population change pharmacokinetics The storage unit 43 and the patient change pharmacokinetic storage unit 47 can be realized by a storage device such as a memory or a hard disk. Individual pharmacokinetic calculation unit 21, group pharmacokinetic acquisition unit 25, fluctuation coefficient calculation unit 29, population change pharmacokinetic acquisition unit 41, patient change pharmacokinetic calculation unit 45, biomarker transition calculation unit 49, and group pharmacokinetic update unit. 51 can be realized by a computing device such as a CPU that operates by a program (spreadsheet program or the like).

FIG. 2 is a flow chart showing an example of the operation of the pharmacokinetic analysis device 1 of FIG. An example of the operation of the pharmacokinetic analysis device 1 of FIG. 1 will be described with reference to FIG. In the following, the effects of antibiotics on bacteria will be specifically described as an example. The biomarker is the amount of microorganisms.

The input means 5 is, for example, a keyboard or a mouse. The user of the pharmacokinetic analysis device 1 operates the input means 5 to input a mathematical model (step ST1). The mathematical model storage unit 7 stores the input mathematical model.

FIG. 3 shows an example of a mathematical model stored in the mathematical model storage unit 7 of FIG. Here, a mathematical model of pharmacokinetics (PK) and a mathematical model of changes in bacterial mass (pharmacodynamics, PD) are included.

First, PK modeling will be described. The PK model shall be a 2-compartment model. Antibiotics are administered centrally. Let Cc be the amount of antibiotic in the center. Antibiotics are exchanged between the center and the periphery and are excreted from the center to the outside of the body. Let Cp be the amount of antibiotic in the periphery. The differential equation of PK is represented by Equation 1. Here, k ₂₁ indicates the rate constant of antibiotic transfer from the periphery to the center _{, k 12} indicates the rate constant of transfer of antibiotic from the center to the _{periphery, and k 10} indicates the rate of transfer of antibiotic from the center to the outside of the body. (Disappearance) Indicates the rate constant.

Next, PD modeling will be described. The amount of bacteria basically increases, but it is sterilized by antibiotics. Let B be the amount of bacteria. The differential equation of PD is represented by Equation 2. Here, k _growth indicates the growth rate constant of the bacterium, and k _death indicates the sterilization rate constant.

Such a mathematical model can be easily calculated in an ideal state. Therefore, it has been used as being effective in setting the target blood concentration in drug development and the like. However, in clinical practice, it has not been used in individual dosing regimens due to the complexity of calculations required.

Next, the user of the pharmacokinetic analysis device 1 operates the input means 5 to input the physiological function of the patient (step ST2). The patient physiological function storage unit 9 stores the input physiological function of the patient. Physiological functions, for example, in the case of antibiotics, relate to the functioning of the kidneys, such as age, body weight, serum creatinine levels, and gender. In the following, for the sake of simplicity, a case where the user inputs the physiological function separately (step ST2) and inputs the change in the physiological function (step ST5) will be described as an example. ing. However, for example, both may be input at once so that the user inputs the change in the physiological function and the initial value thereof is the input of the physiological function.

The individual pharmacokinetic calculation unit 21 calculates the individual pharmacokinetic value of the patient according to the input physiological function of the patient, and the population pharmacokinetic acquisition unit 25 uses the population pharmacokinetics database 3 in the population to which the patient belongs. Obtain the population pharmacokinetic value (step ST3). The group is, for example, a group of people who share some or all of the physiological functions. The individual pharmacokinetic memory unit 23 and the group pharmacokinetic storage unit 27 store individual pharmacokinetic values and group pharmacokinetic values, respectively. The processing of the individual pharmacokinetic calculation unit 21 and the collective pharmacokinetic acquisition unit 25 may be performed in parallel, whichever comes first.

The personal pharmacokinetic calculation unit 21 directly solves the differential equation using, for example, the Runge-Kutta-Gill method for the mathematical model of Equation 1, and obtains the final solution by the Bayesian method. Here, the Runge-Kutta-Gill method, if visually explained in this case, is to describe the transition of blood concentration while continuously and positively predicting the inclination over time. A fixed value can be obtained as the final solution.

The population pharmacokinetic database 3 identifies the population to which the patient belongs according to the physiological function of the patient, and stores the population pharmacokinetic value in the population (for example, RAIN). A population can be identified, for example, as a population that shares some or all of its physiological functions. The population pharmacokinetic value is, for example, the average value of the pharmacokinetics in the population. In the following, the average value is used as an example, and the population pharmacokinetic value is also referred to as the average pharmacokinetic value. The mean pharmacokinetic value of the population can be identified (estimated, estimated) by the patient's physiology.

The coefficient of variation calculation unit 29 obtains the coefficient of variation from the individual pharmacokinetic value of the patient and the average pharmacokinetic value of the population (step ST4). The coefficient of variation storage unit 31 stores the coefficient of variation.

Here, the coefficient of variation expresses individual differences based on the average pharmacokinetic value of the population. As shown in Equation 3, there are cases where the average pharmacokinetic value is multiplied by the coefficient of variation, and cases where the average pharmacokinetic value and the coefficient of variation are added. When calculating the coefficient of variation, in the former case, the individual pharmacokinetic value may be divided by the average pharmacokinetic value, and in the latter case, the average pharmacokinetic value may be subtracted from the individual pharmacokinetic value. In this way, the coefficient of variation can be obtained by a simple operation such as four arithmetic operations.

The mean pharmacokinetic value of the population depends on the patient's physiology. Traditionally, only the patient's personal pharmacokinetic values have been calculated directly using the patient's physiology. Therefore, when the patient's physiological function changes, it is necessary to specifically obtain the patient's individual pharmacokinetic value in response to the complicated change in the physiological function.

On the other hand, the coefficient of variation does not change depending on the physiological function of the patient and is constant. The present invention utilizes the fact that the coefficient of variation is constant. Specifically, as will be described later, the change in the mean pharmacokinetic value of the population is obtained in response to the change in physiological function, and the change in the mean pharmacokinetic value of the population and the coefficient of variation are used to obtain the individual pharmacokinetic value of the patient. Get a change in. The patient's personal pharmacokinetic value may be calculated to obtain the coefficient of variation. Furthermore, it can be assumed that the patient's physiology is constant. Therefore, unlike Non-Patent Document 1, it is not necessary to divide into sections, and complicated calculations can be avoided.

Next, the user of the pharmacokinetic analysis device 1 operates the input means 5 to input the change in the physiological function (step ST5). The physiological function change storage unit 11 stores the input change in the physiological function. Changes in physiological function were, for example, a serum creatinine level of 1.0 mg / dL at the start of dosing, but changed to 1.5 mg / dL at the 5th dosing, and continuous renal replacement at the 11th dosing. The therapy was started.

The population change pharmacokinetic acquisition unit 41 uses the population pharmacokinetics database 3 to obtain a change in the average pharmacokinetics value of the population to which the patient belongs in response to a change in physiological function (step ST6). The population change pharmacokinetic memory unit 43 stores changes in the population pharmacokinetic value.

The patient change pharmacokinetic calculation unit 45 uses the change in the mean pharmacokinetic value of the population and the coefficient of variation to obtain the change in the individual pharmacokinetic value of the patient corresponding to the change in physiological function (step ST7). Specifically, in the former case of Equation 3, the change in the mean pharmacokinetic value of the population and the coefficient of variation may be multiplied, and in the latter case of Equation 3, the change and the coefficient of variation of the mean pharmacokinetic value of the population. Should be added.

FIG. 4 shows an example of the transition of blood concentration corresponding to the change in physiological function obtained by the present invention. The target value of blood concentration is that the daily minimum value should be within the range of 10 μg / ml or more and 20 μg / ml. In the first half, the blood concentration increased in response to the increase in serum creatinine, and in the second half, the blood concentration decreased due to the start of continuous renal replacement therapy, which is the target value of the blood concentration. It has been shown to be within the range of 10 μg / ml or more and 20 μg / ml.

As described above, according to the present invention, even if there is a change in physiological function, the individual pharmacokinetic value corresponding to the change in physiological function is used by using a simple and clear calculation process of converting the ratio to the average pharmacokinetic value or the deviation. It has become possible to easily obtain changes in.

The biomarker transition calculation unit 49 calculates the transition of the biomarker, if necessary (step ST8). For example, in the case of the example of FIG. 3, k _death can be expressed as the equation 4. Here, E _max is the maximum sterilization rate (log colony number), C is the blood concentration, Hill is the Hill coefficient, and EC ₅₀ is half the concentration at which _{E max is obtained. Regarding} k growth, B-k _death , and B in Equation 2, the former term indicates the growth of the bacterial number, and the latter term indicates the decrease in the bacterial number. As shown in FIG. 3, k _growth is a growth rate constant and can take a constant value (depending on the bacterium). However, _{it can be understood that K death} is a sterilization rate constant, and although it is a constant, it is governed by the drug concentration at the center (Cc). However, although k _death changes with increasing concentration, it does not increase proportionally or non-linearly endlessly. The maximum sterilization rate (E _max ) is reached at a certain concentration. Then, as the concentration increases, the sterilization rate gradually increases in the low concentration range (gradient slope), the slope is maximized near _{EC 50} ( _{concentration that obtains half the speed of E max} ), and the slope is tilted near _{E max.} Becomes gradual. The model that can centrally explain the shape of this graph is the Sigmoid Emax model, which is represented by Equation 4. _{The biomarker transition calculation unit 49 can calculate the K death} by the equation 4 using the obtained blood concentration, and can calculate the biomarker transition by the equation 2.

FIG. 5 shows a blood concentration curve of an antibiotic when the dose of the antibiotic is changed, and a transition of the amount of microorganisms as a biomarker. The horizontal axis indicates the number of days of administration. The vertical axis shows the blood concentration and the amount of microorganisms. The dose of antibiotic is lowest in (a), increases in alphabetical order, and is highest in (f). It can be seen that as the dose increases, the amount of bacteria decreases sharply. Thus, according to the present invention, it is possible to show the transition of biomarkers in individual patients whose dynamics are unstable. This has been difficult to achieve with conventional ones.

FIG. 6 is a diagram showing verification of the effect of improving the predictability of blood concentration of vancomycin in 15 target patients. The vertical axis is the measured concentration and the horizontal axis is the predicted concentration. The units are both μg / mL. The closer to the graph of y = x, the higher the prediction accuracy. FIG. 6 (b) shows a conventional simulation prediction that does not take into account fluctuations in serum creatinine levels. The ◯ mark indicates the predicted value by the conventional simulation. The solid line shows the regression line. The dashed line indicates the 95% confidence interval of the regression line. FIG. 6 (c) shows a simulation prediction of this example, taking into account fluctuations in serum creatinine levels. The ● mark indicates the predicted value by the simulation of this embodiment. The solid line shows the regression line. The dashed line indicates the 95% confidence interval of the regression line. FIG. 6A shows the predicted value 〇 and the regression line (broken line) obtained by the conventional simulation, and the predicted value ● and the regression line (solid line) obtained by the simulation of this embodiment. According to this example, the prediction accuracy was improved in all cases (100%).

In addition, it can be applied as long as it is dependent on pharmacokinetics, such as when the concentration dependence such as the amount of target protein has been clarified.

In addition, the population pharmacokinetic update unit 51 updates the population pharmacokinetics database using the individual pharmacokinetic values (step ST9). This makes it possible to realize and rotate a cycle in which the accumulation of individual data is accumulated in the population pharmacokinetic database to improve the accuracy of individual decisions. The renewal process may be performed at any time after obtaining the individual pharmacokinetic value.

1 Pharmacokinetic analyzer, 3 Group pharmacokinetic database, 5 Input means, 7 Mathematical model storage unit, 9 Patient physiological function memory unit, 11 Physiological function change memory unit, 21 Individual pharmacokinetic calculation unit, 23 Individual pharmacokinetic memory unit, 25 Group Pharmacokinetics Acquisition Department, 27 Group Pharmacokinetics Memory Department, 29 Fluctuation Factor Calculation Department, 31 Fluctuation Coefficient Memory Department, 41 Group Change Pharmacokinetics Acquisition Department, 43 Group Change Pharmacokinetics Memory Department, 45 Patient Change Pharmacokinetics Calculation Department, 47 Patient Change Pharmacokinetic Memory Department, 49 Biomarker Transition Calculation Department, 51 Group Pharmacokinetic Update Department

Claims (6)

A pharmacokinetic analysis method that analyzes changes in the pharmacokinetics of a patient.
The individual pharmacokinetic calculation means and the group pharmacokinetic acquisition means included in the analyzer calculate the individual pharmacokinetic value of the patient in response to the physiological function of the patient input to the analyzer, respectively, and The pharmacokinetic acquisition step to obtain the population pharmacokinetic value in the population to which the patient belongs,
The coefficient of variation calculation means provided in the analyzer includes a coefficient of variation calculation step for calculating a coefficient of variation indicating a correspondence between the individual pharmacokinetic value and the group pharmacokinetic value.
A population change pharmacokinetic acquisition step in which the population change pharmacokinetic acquisition means included in the analysis device obtains a change in the population pharmacokinetic value in the population corresponding to a change in physiological function input to the analysis device,
The patient change pharmacokinetic acquisition means included in the analyzer is obtained by using the fluctuation coefficient calculated by using the physiological function of the patient input to the analyzer and the change of the physiological function input to the analyzer. A pharmacokinetic analysis method comprising a patient change pharmacokinetic calculation step of calculating a change in an individual pharmacokinetic value of the patient corresponding to the change in physiological function using the change in the population pharmacokinetic value obtained. The analysis device includes a mathematical model storage means for storing a mathematical model including a physiological function as a coefficient.
The personal pharmacokinetic value according to claim 1, wherein in the pharmacokinetic acquisition step, the individual pharmacokinetic calculation means calculates the individual pharmacokinetic value of the patient using the mathematical model assuming that the physiological function of the patient is constant. Pharmacokinetic analysis method. The mathematical model storage means includes a biomarker mathematical model having the individual pharmacokinetic value as a coefficient.
A claim including a biomarker transition calculation step in which the biomarker transition calculation means included in the analyzer calculates the biomarker transition by solving the biomarker mathematical model by utilizing the change in the personal dynamic value of the patient. 2. The pharmacokinetic analysis method according to 2. A claim including a group pharmacokinetic update step in which the group pharmacokinetic update means included in the analyzer updates the group pharmacokinetics value corresponding to the physiological function by using the individual pharmacokinetic value after calculating the coefficient of variation. Item 4. The pharmacokinetic analysis method according to any one of Items 1 to 3. It is a pharmacokinetic analyzer that analyzes changes in the pharmacokinetics of patients.
An input means for inputting the physiological function of the patient and
An individual pharmacokinetic calculation means for calculating an individual pharmacokinetic value of the patient corresponding to the input physiological function of the patient, and
A pharmacokinetic acquisition means for obtaining a population pharmacokinetic value corresponding to an input physiological function of the patient in the population to which the patient belongs.
A coefficient of variation calculation means for calculating a coefficient of variation indicating a correspondence between the individual pharmacokinetic value and the group pharmacokinetic value is provided, and further.
Equipped with a means for acquiring population change pharmacokinetics and a means for calculating individual change pharmacokinetics,
Changes in physiological functions are input to the input means.
The population change pharmacokinetic acquisition means obtains a change in the population pharmacokinetic value in the population corresponding to the input change in the physiological function.
The individual change pharmacokinetic calculation means uses the fluctuation coefficient calculated by using the physiological function of the patient and the change of the population pharmacokinetic value obtained by using the change in the physiological function, and uses the physiological. A pharmacokinetic analyzer comprising a change personal pharmacokinetic calculation means for calculating a change in a personal pharmacokinetic value of the patient corresponding to a change in function. A program for realizing the pharmacokinetic analysis method according to any one of claims 1 to 4 in a computer.

Images