JP2019121020A - Method and system for temporarily predicting market infiltration of generic medicine - Google Patents

Abstract

To provide a prediction system of market infiltration of a generic medicine at a specific time point.SOLUTION: A prediction system includes: an input processing unit which accepts input at a specific time point; a generic medicine data recording unit which records data on date when the generic medicine is firstly marketed and a peak year sales of an original medicine of the same active ingredient as the generic medicine data; an output processing unit which outputs the market infiltration of the generic medicine at the specific time point; and a cumulative distribution function recording unit which records a parameter of a cumulative distribution function of Gamma distribution indicating the market infiltration in association with the generic medicine data as cumulative distribution function data. The cumulative distribution index corresponding to the data on the date when the generic medicine is firstly marketed and the peak year sales of the original medicine of the same active ingredient is read out from the cumulative distribution function recording unit, and the market infiltration corresponding to a future time point is calculated and output based on the read-out cumulative distribution index.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a method and a system for predicting the market penetration of generic drugs over time.

  When the defined protection period expires, whichever is the later of the drug patent's lifetime or reexamination period, generic drugs enter the market and penetrate the market. Therefore, after the expiration of the protection period, the sales volume of pharmaceuticals sold by the original drug manufacturer and protected by the patent or reexamination period will inevitably decrease.

  Since the leading drug manufacturer will continue to sell the drug under the circumstances as described above, it is important to accurately predict future market penetration of the generic drug as much as possible overtime production of the drug. It is important for the planning of production plans and distribution budgets to prevent stock shortages.

  Also, for generic drug makers, it is important to predict the future market penetration of generic drugs as accurately as possible over time, as with the original drug makers, for planning the production plan and equipment budget of the drug concerned. is there.

  The prediction of market penetration of generic drugs is often made by taking into account recent trends and relying on the experience of the person making the prediction. However, such predictions are not always accurate.

  On the other hand, as technology relating to a method for performing sales forecasting until the end of the drug protection period, a system has been reported that evaluates the investment profitability of drug R & D using the real option method.

  Until now, various studies have been conducted on the market penetration of generic drugs, and there is Non-Patent Document 1 as a representative one, but the market penetration of generic drugs is greatly different depending on the efficacy area. We find that "price difference" and "dosage form coverage" are factors.

  On the other hand, Non-Patent Document 2 states that the market penetration of generic drugs is due to the price and the advanced drug Goodwill, and is effective for the government's generic drug spread promotion measures.

  However, after these analyzes were conducted, further measures were taken to promote the spread of generic drugs in the government, and the penetration of generic drugs rapidly, and the influence and factors of factors affecting market penetration of generic drugs themselves Is also considered to be changing.

"Use promotion of generic drugs and its impact on the market" Kiritani Nishimura Institute for Pharmaceutical Industry Policy June 2012 "The market share determinants of generic drugs and the effect of the policy to promote the spread" Ebara and the south Economic Shirin 81 (2-4), 83-108, 2014-03 Hosei University of Economics Society of Japan

As health insurance related expenditures are increasing significantly due to factors such as the progress of the elderly society, MHLW has stated that after the expiration of the patent or reexamination protection period for the original drug, the generic drug is relatively inexpensive. The policy is to encourage the switch to Due to this, the rate of generic drug penetration into the market is increasing year by year.
On the other hand, the penetration of generic drugs into the market may be relatively slow, such as in the case of small-scale drugs with characteristics or sales value due to the indication of the medicine.

  That is, it is becoming more difficult to predict the market penetration of generics taking into account recent trends and relying on the experience of those who make predictions. The method for predicting the market penetration of future generics based on the most recent typical cases is not always accurate. A major misunderstanding of future generic drug market penetration has a significant negative impact on production planning and budget planning.

  Under the background as described above, the present inventor has described the above-mentioned problems which have not been found up to now, that is, there is no technique for predicting market penetration of generic drugs over time and accurately. It was an issue to find a method and system to predict the market penetration of generic drugs more accurately with the passage of time.

  In order to solve the above problems, the inventor uses the market penetration of a large number of generic drugs, that is, the number of tablets sold, the number of prescription days, or the number of prescriptions of generic drugs as indicators. An analysis of the ratio of generic drug volume to total volume of the drug revealed that the annual change in penetrance may have a specific pattern. More specifically, the inventor does not approach from the influence of each factor, but analyzes it from the market penetration results of generic drugs, and it is possible that there is a specific pattern in the annual change in the penetration. As a result of further finding and further research, the present invention has been completed.

That is, the present invention relates at least to each of the following inventions:
[1] In the market penetration forecasting system for generic drugs at a specific time,
An input processing unit for inputting a specific point in time;
A generic drug data recording unit that records data on the time when the generic drug is first launched and the peak year sales amount of the generic drug of the same active ingredient as the generic drug as generic drug data;
An output processing unit that outputs market penetration of generic drugs at a specific time as market penetration,
A cumulative distribution function recording unit of a gamma distribution in which a parameter of a cumulative distribution function of the gamma distribution indicating the market penetration degree is linked with the generic drug data and recorded as a cumulative distribution function data of the gamma distribution;
Equipped with
From the cumulative distribution function recording section of the gamma distribution, the parameters of the cumulative distribution function of the gamma distribution corresponding to the peak year sales of the generic drug and the time of its first launch and the generic drug of the same active ingredient as the generic drug reading,
The prediction system, wherein the market penetration degree corresponding to the specific time point is calculated based on the read cumulative distribution function of the gamma distribution, and is output to an output processing unit.

[2] [1] The prediction system according to [1], further comprising
A measured data storage unit that records measured data on at least two market penetration rates;
A penetration period multiple regression analysis calculation unit that calculates multiple regression analysis on the at least two market penetration degrees based on the actual measurement data;
Equipped with
The cumulative distribution function of the gamma distribution is a function for calculating the market penetrability based on the peak year sales of the generic drug of the same active ingredient as the period from when the generic drug is launched, and the parameters. The prediction system characterized by being.

  [3] The prediction system according to [1], wherein, when the disease to which the generic drug is applied is a general disease having a large number of patients, that effect is displayed.

  [4] The common diseases having a large number of patients are hypertension, thrombosis, dyslipidemia, diabetes, insomnia, dementia, migraine, depression, schizophrenia, osteoporosis, acid related diseases, asthma, The prediction system according to [3], which is allergic rhinitis or prostate hypertrophy.

  [5] The prediction system according to [1], wherein when the disease to which the generic drug is applied is a disease with a small number of patients, a message to that effect is displayed.

  [6] The prediction system according to [5], wherein the disease with a small number of patients is epilepsy, Parkinson's disease or a disease to which an orphan drug is applied.

  [7] Further comprising means for predicting the production amount of the advanced drug to be supplied based on the prediction result of the market penetration degree, and presenting the procurement amount of each raw material necessary for producing the advanced drug. [1] The prediction system according to [1].

  [8] The slope of the rising edge of the graph showing the penetrance of the cumulative distribution function of the gamma distribution is the later the point when the generic drug is first marketed, and the peak year sales of the original drug of the same active ingredient as the generic drug The prediction system according to [1], characterized in that the higher the number, the higher.

[9] In the market penetration forecasting program for generic drugs at specific points in time,
A means for entering the peak year sales amount of the generic drug of the same active ingredient as the generic drug at a specific time point and the generic drug;
Means for recording data on the time when the generic drug is first marketed and the peak year sales of the generic drug of the same active ingredient as the generic drug as generic drug data;
A means for outputting the market penetration of generic drugs at a specific time as market penetration,
A parameter of the cumulative distribution function of the gamma distribution indicating the market penetration degree linked with the generic drug data and recorded as cumulative distribution function data of the gamma distribution;
Equipped with
Read out the cumulative distribution function of the gamma distribution corresponding to the peak year sales value of the generic drug and the time of first launch and the generic drug of the same active ingredient as the generic drug.
A forecasting program characterized by calculating and outputting the market penetration degree corresponding to the specific time point based on the read cumulative distribution function of the gamma distribution.

[10] In a method for predicting the market penetration of generic drugs at a specific time,
Entering a specific point in the future;
Recording data as to when the generic drug is first marketed and the peak year sales of the generic drug of the same active ingredient as the generic drug as generic drug data;
Outputting the market penetration of generic drugs at a specific time in the future as the market penetration,
Recording the parameter of the cumulative distribution function of the gamma distribution indicating the market penetration degree as the cumulative distribution function data of the gamma distribution in association with the generic drug data;
Equipped with
Read out the cumulative distribution function of the gamma distribution corresponding to the peak year sales value of the generic drug and the time of first launch and the generic drug of the same active ingredient as the generic drug.
The market penetration degree corresponding to the future time point is calculated and output based on the read cumulative distribution function of the gamma distribution.

FIG. 6 shows a functional block diagram of a system for predicting the market penetration of generic drugs over time according to one embodiment of the present invention. The data structure used with the time-lapse | temporary prediction system of the market penetration degree of the generic drug by one Example of this invention is shown, (a1) is an example of generic drug data, and (a2) is a modification. The data structure used in the temporal prediction system for market penetration of generic drug according to one embodiment of the present invention is shown, and (a) an example of actual measurement data of market penetration of generic drug and (b) the actual measurement data The example of the comparison with the actual value and forecast value about the number of months until it reaches 50% of market penetration created based on it is shown. (A) shows the cumulative distribution function data of the gamma distribution recorded in the cumulative distribution function recording part (1) and the same (2) of the gamma distribution, (b) shows the market of generic medicine according to one embodiment of the present invention The comparison of the time-lapse prediction result of the degree of penetration is shown. It is a flowchart which shows the flow of the process which calculates the time-lapse | temporal prediction result of the market penetration degree of the generic drug by one Example of this invention. It is a flowchart which shows the flow of a process which produces | generates a data required for the time-lapse | temporal prediction of market penetration of a generic drug by one Example of this invention. The modification of the output processing part of the time-lapse | temporal prediction system of the market penetration degree of the generic drug by one Example of this invention is shown. 7 shows the structure of pharmaceutical production data according to an embodiment of the present invention. It is a comparative example with a present Example, and is a figure which shows the difference of the predicted value and actual value by a Gompertz curve.

  FIG. 1 shows a functional block diagram of a system for predicting the market penetration of generic drugs over time according to an embodiment of the present invention.

  The temporal prediction system 1000 according to this embodiment includes a data generation unit 1100, a data calculation unit 1200, an interface unit 1310, and a control unit 1320.

  The data generation unit 1100 includes an actual measurement value input processing unit 1110, an actual measurement data recording unit 1120, a parameter calculation unit 1130, a generic drug data input processing unit 1140, a generic drug data recording unit 1150, and a data linking unit 1160. The gamma distribution cumulative distribution function recording unit (1) 1170, the permeation period multiple regression analysis calculation unit 1180, and the permeation period parameter recording unit 1190.

  Data calculation unit 1200 includes future permeation period calculation unit 1210, cumulative distribution function calculation unit 1220 of gamma distribution, cumulative distribution function recording unit (2) 1230 of gamma distribution, and cumulative distribution function selection unit 1240 of gamma distribution. A specific time point input processing unit 1250 for inputting a specific time point, a penetration rate calculation unit 1260, and a calculation result output processing unit 1270.

  The actual measurement value input processing unit 1110 is to input data on generic drug data, and in particular, to input a time point after the generic drug is marketed and a market penetration rate.

  The actual measurement data recording unit 1120 is a database for recording the time point after the launch of the generic drug and the market penetration rate.

  The parameter calculator 1130 determines, based on the input data, a parameter (k, θ) of the cumulative distribution function of the gamma distribution, and a period until reaching a specific market penetration obtained from the cumulative distribution function of the gamma distribution. It is a unit to calculate.

  The generic drug data input processing unit 1140 is for inputting data regarding the generic drug, and in particular, is for inputting an attribute of the generic drug data.

  The generic drug data recording unit 1150 is a unit for storing data input from the generic drug data input processing unit 1140.

  The data linking unit 1160 is a unit that links generic drug data and actual measurement data, and transmits the linked data to the cumulative distribution function recording unit (1) of the gamma distribution.

  The gamma distribution cumulative distribution function recording unit (1) 1170 is a database for recording the cumulative distribution function of the gamma distribution.

  The penetration period multiple regression analysis calculation unit 1180 is a multiple regression for calculating the period until reaching a specific market penetration degree of generic drug from the release time of the generic drug and the peak year sales of the generic drug of the same active ingredient. It is a unit to obtain analysis parameters. The period from the point of sale of generic drugs until reaching each of two or more previously set market penetrance levels (for example, 50%, 70%), as well as the point of release of generic drugs, as well as of original drugs of the same active ingredient As the peak year sales amount, the one recorded in the cumulative distribution function recording unit (1) 1170 of the gamma distribution is used. Peak year sales should be expressed as fixed price over the target period to avoid the effects of price change. In addition, if the original manufacturer sells a combination agent containing another active ingredient in addition to a single agent consisting only of a specific active ingredient, the total sales of the advanced pharmaceutical containing the active ingredient It is also possible to use the peak year sales amount based on the amount. The calculation method is as follows: X1 when generic drug is marketed, X2 the peak year sales of advanced drug with the same component as X, number of months when market penetration of generic drug reaches 50% is Y50, Log (Y50) The partial regression coefficients m1 and m2 and the intercept b of the multiple regression equation of = m1 * X1 + m2 * X2 + b are determined by the method of least squares. In order to verify the calculated formula, it demonstrates, showing FIG.3 (b). The left figure in Fig. 3 (b) shows the actual number of months until the generic drug reaches 50% market penetration, and the right figure in Fig. 3 (b) shows the generic drug at 50% market penetration. The predicted value of the number of months until it reaches (the predicted value calculated by the prediction system of the present embodiment) is shown.

  In addition, although the relationship between the time of launch and 50% market penetration is shown in FIG. 3 (b), using the same method, it is different from the peak year sales amount of the advanced drug at the time of launch and its component. The partial regression coefficients m1 and m2 and the intercept b of the multiple regression equation showing the relationship of market penetration (eg, 70%) of

  The penetration period parameter storage unit 1190 records each parameter calculated by the penetration period multiple regression analysis calculation unit 1180. Here, the parameters are the partial regression coefficients m1 and m2 and the intercept b at each market penetration degree. Record the calculated partial regression coefficient (m1, m2) as well as the intercept b. In addition, it becomes one set for each of 50% and 70% market penetration.

  The future penetration period calculation unit 1210 uses the partial regression coefficient m1 and m2 and the intercept b at two or more market penetration levels (for example, 50% and 70%), respectively, to introduce future generic medicines And calculate the period until reaching each market penetration in the combination of the peak year sales value of the same drug as the active ingredient of the same active ingredient. We will substitute various combinations of the time of launch of generic drugs in the future, and the peak sales value of generic drugs of the same active ingredient in the partial regression coefficient (m1, m2) and the intercept b, and market penetration in each Calculate 50% and 70%.

  The gamma distribution cumulative distribution function calculation unit 1220 generates parameters k, θ of the cumulative distribution function of the gamma distribution (to be described later, for example, by the Monte Carlo method) so as to satisfy the period until reaching each of these market penetration levels. . The cumulative distribution function of the gamma distribution is calculated by Monte Carlo simulation for each set of the calculated 50% and 70% market penetration. The generic drugs are released at discrete times, but since the peak sales of advanced drugs are continuous, we will construct a database with a predetermined step size (any width such as ¥ 10 billion or ¥ 100 million).

  Cumulative distribution function recording part (2) 1230 of gamma distribution is the cumulative distribution of gamma distribution in the combination of (i) future generic drug launch time, (ii) and the peak annual sales value of the same drug of the same active ingredient Record the parameters k, θ of the function.

  The gamma distribution cumulative distribution function selection unit 1240 is a unit for selecting the cumulative distribution function of the gamma distribution recorded in the gamma distribution cumulative distribution function recording unit. The keywords for selection are, for example, (1) future generic drug launch time, and (2) peak year sales of the original drug of the same active ingredient. Here, the sales forecast value separately implemented by each user may be substituted for the peak year sales amount of the first active medicine of the same active ingredient.

  The specific point input processing unit 1250 for inputting a specific point is a unit for inputting a point (a date such as a date or a year) to know the market penetration ratio of the generic drug.

  The penetration rate calculation unit 1260 is a unit that calculates the penetration rate of the generic drug corresponding to the specific time point input by the user.

  The calculation result output processing unit 1270 is a unit that outputs the penetration rate of the generic drug calculated by the penetration rate calculation unit 1260 in correspondence with a specific time point.

  The interface unit 1310 transmits and receives data and signals to and from devices and systems external to the temporal prediction system 1000. As another example, each interface is composed of a plurality of interfaces (for example, generic drug data input processor 1140, actual value input processor 1110, gamma distribution cumulative distribution function selector 1240, specific time) It may be provided in the input processing unit 1250 and the calculation result output processing unit 1270).

  The control unit 1320 includes a processor, and controls each unit in the temporal prediction system 1000. As another example, the control unit 1320 may be configured of a plurality of processors, and each processor individually controls each unit.

  The respective units described above can be realized by hardware, software, or a combination of hardware and software.

  The cumulative distribution function of the gamma distribution used in the present embodiment will be described.

  The cumulative distribution function F (x) of the gamma distribution is represented by the following equation using a probability density function of shape parameter k> 0 and scale parameter θ> 0.

Is a distribution defined by

  Here, Γ (k) is a gamma function, and γ (k, x / θ) is an incomplete gamma function. At this time, the probability density function of the gamma distribution is expressed by the following equation.

  FIG. 2 shows a data structure used in a system for temporally predicting market penetration of generic drugs according to an embodiment of the present invention, (a1) is an example of generic drug data, and (a2) is It is a modification of the data structure of a1).

  Figure 2 (a1) is an example of generic drug data, and generic drug (such as active ingredient name), when the drug is launched first (date of market launch), peak year sales of the same active ingredient pioneer drug It consists of items of forehead. Here, the date of first launch (date of launch) means the date of release and the date of approval for each additional indication described in the (pharmaceutical) interview form for each product announced by the generic drug manufacturer. It is. An interview form is a comprehensive information provision provided by a company to make up for insufficient information in a prescription drug package, and it is an independent administrative corporation Pharmaceuticals and Medical Devices Agency (PMDA) You may provide the means to acquire from the website (https://www.pmda.go.jp/PmdaSearch/iyakuSearch/) etc. In addition, the peak year sales amount of the same active ingredient generic drug is the amount in the year in which the sales amount was largest (in Figure 2) for the advanced drug with the same active ingredient (A, B ... in Figure 2). Displayed in yen units). The peak year sales amount indicates the market size of a drug having a certain active ingredient, and the larger the peak year sales amount, the larger the market size. Products with a large market size (pharmaceuticals) have a large scope for entry into the generic drug, so the generic drug manufacturer will expire when the protection period prescribed by the patent drug's term of the original drug or the reexamination period, whichever is later, expires. It is empirically known that the market is likely to be eroded (ie, the market penetration is faster).

  FIG. 2 (a2) has a data structure in which a remarks column is added in addition to the data structure of FIG. 2 (a1). For remarks, record information on indications and diseases. For example, generic drugs may not be compatible with all indications of the original drug. That is, even generic drugs of the same active ingredient may not have been approved for all indications of the generic drug. The data structure is configured to be able to record in the remarks column for this difference. For example, in the remarks column of FIG. 2 (a2), information on indications and diseases is described in a descriptive format, but for example, when the user inputs generic drug data, the target indications or specific diseases The list of (Epileptic, Parkinson's disease, diseases to which orphan drugs are applied) is in the form of a check box etc., and if there is a corresponding disease etc., generic medicine can be selected by checking the check box. The data remarks column may be configured to be generated.

  The disease to which the present invention is applied is not particularly limited. As the applicable disease, diseases other than diseases in which entry of generic drugs into the market is generally relatively small are preferable. More preferable diseases are general diseases having a large number of patients, including, but not limited to, hypertension, thrombosis, dyslipidemia, diabetes, insomnia, dementia, migraine headache, depression, schizophrenia And osteoporosis, acid related diseases, asthma, allergic rhinitis, prostate hypertrophy and the like.

  The accuracy of the prediction by the system of the present invention when the penetration of a generic drug is usually extremely small compared to the general case, such as epilepsy, Parkinson's disease or orphan drug application disease, is as described above. It may be worse than expected in the case of common disease.

  The system of the present invention displays "unpredictable (unpredictable)" when there is no cost burden in patients such as medicine for growth hormone deficiency.

  FIG. 4 (b) shows the time-lapse prediction result of market penetration of generic drugs according to one embodiment of the present invention.

  The horizontal axis (x-axis) indicates the number of months since the launch of generic drugs, and the vertical axis (y-axis) indicates the market penetration degree (market penetration rate) of generic drugs in the range of 0 to 1.0 .

  For example, it is assumed that the release date (marketing date) of the generic drug is June 2017, and the cumulative distribution function of the gamma distribution as shown by the solid line (pattern D) in FIG. 4 (b) is recorded. At this time, the user designates June 2020 as a specific point in time, and inputs the system of this embodiment so as to calculate the market penetration of the generic drug.

  Since the generic drug in Figure 4 (b) has passed 36 months from the release date in June 2020, according to Figure 4 (b), the generic drug accounts for about 0.78 (78%) Indicated.

  In FIG. 4B, the dashed line (pattern C) is earlier than the solid line (pattern D) at the time of market launch, and the two-dot chain line (pattern B) is marketed than the dashed line (pattern C). The time point is earlier, and the dotted line (pattern A) indicates that it is earlier than the two-dot chain line (pattern B).

  The market penetration rate comparison (1) shows that the generic drug market is different when the generic drug peak sales year is the same (37.2 billion yen in the figure) and when the generic drug's sales year and month (year when it was launched) differ. Indicates the difference in penetration. According to the market penetration comparison (1), if the sales amount of the original drug peak year is the same (or may be the same size in another example), the later the month of generic medicine sales, the market penetration of the generic drug Can be found to be faster.

  Comparison of market penetration (2) shows the difference in market penetration of generic medicines when the sales amount of the original pharmaceutical peak year is different when the generic medicine sales date is the same. According to market penetration comparison (2), if the sales date of the original drug is the same point (or another example may be the same period), the larger the peak year sales amount of the original drug, the generic drug market It can be seen that the penetration rate is faster.

  FIG. 5 is a flowchart showing a flow of processing for calculating a temporally predicted result of market penetration of a generic drug according to an embodiment of the present invention, which corresponds to processing in the data calculation unit 1200 of FIG.

  In S4010, the gamma distribution cumulative distribution function selection unit 1240 designates generic drug data for which the penetrance is to be estimated. For example, when the user inputs a release date of a generic drug or inputs the peak year sales amount of the generic drug, the generic drug data corresponding to the input is a gamma distribution cumulative distribution function recording unit (2) It may be configured to be selected from 1230.

  Here, when there is no generic medicine data corresponding to the cumulative distribution function recording unit (2) 1230 of the gamma distribution, an error message may be displayed and the processing may be ended, but the corresponding generic medicine data is acquired It may be configured to In such a case, for example, the parameter is acquired from the permeation period multiple regression analysis calculation unit 1180, and the result calculated by the future permeation period calculation unit 1210 and the cumulative distribution function calculation unit 1220 of the gamma distribution is the cumulative distribution function of the gamma distribution After the step of recording in the recording unit (2) 1230, the corresponding generic drug data may be newly obtained from the cumulative distribution function recording unit (2) 1230 of the gamma distribution.

  In S4020, the cumulative distribution function of the gamma distribution corresponding to the designated generic drug is selected from among the cumulative distribution functions of the gamma distribution recorded in the cumulative distribution function recording section (2) 1230 of the gamma distribution, and is taken out . Here, the cumulative distribution function of the gamma distribution is selected by the user using an arbitrary terminal, but generic drugs A, B,... Are displayed on the terminal used by the user. At this time, by selecting any generic drug, the parameter used in the cumulative distribution function of the gamma distribution (that is, the cumulative distribution function of the gamma distribution) will be selected. Here, it is configured to display the information (for example, information related to the indication, disease, etc.) described in the remarks column of the data structure of FIG. 4A on the terminal used by the user to draw the user's attention. It may be done.

  At S4030, the user inputs a (future) specific time point that he wishes to make a prediction.

  In S4040, the market penetration of the generic drug at a specific time point entered by the user is calculated using the cumulative distribution function of the selected gamma distribution.

  Furthermore, in S4050, the result based on the calculation in S4040 is output. Specifically, the cumulative distribution function of the generic drug and the gamma distribution corresponding to the first market (date and year) is read out, and the market penetration corresponding to the specific time is read out. Calculate based on the cumulative distribution function of the distribution and output to the output processing unit. Furthermore, information associated with the degree of market penetration (for example, information on social factors such as FIG. 8 and the amount of pharmaceutical production) may be presented.

  FIG. 6 is a flowchart showing a flow of processing for generating data necessary for temporally predicting market penetration of generic drugs according to an embodiment of the present invention, and processing in the data generation unit 1100 of FIG. Corresponds to

  In S5010, generic drug data (at the time of first market launch) is input, and the entered data is recorded in the generic drug data recording unit. Furthermore, if the list of target indications or specific diseases (epileptic, Parkinson's disease, orphan drug applicable diseases) is in the form of a check box etc., and there is a corresponding disease etc, check the check box. The input field may be configured to enter the information, and the information input to this check box may also be recorded as generic drug data (see FIG. 2 (a1) (a2)).

  In S5020, the actual measurement value regarding the degree of market penetration and the time (date) are input, and the input data is recorded in the actual measurement data recording unit (see FIG. 3A). The actual measurement value can be calculated, for example, from the sales volume of generic medicine / the sales volume of the generic medicine and all generic medicines. Here, the sales volume can be grasped on a daily basis, a weekly basis, a monthly basis, or the like. In this embodiment, the sales volume (that is, the degree of market penetration) is grasped on a monthly basis, and the date shown in FIG. 3A is configured to record the date of the end of the month.

  Also, it is required by the system of the present embodiment to input at least two different actual values. For example, one is data when the market penetration is 50%, and the other is data when the market penetration is 70%. Here, the time to market penetration of 50% and up to 70% may be calculated, for example, from the inverse function of the cumulative distribution function of the gamma distribution. Also, numerical values such as 50% and 70% are examples. Also, when it is thought that the market penetration level has reached 50% and 70%, it may be entered as a date, or only a year and month may be entered. In another embodiment, the monthly or weekly generic medicine sales volume, the number of prescription days, or the number of prescriptions can be automatically and / or manually acquired along with these data of the original pharmaceutical of the same active ingredient. If so, it is possible to automatically determine the market penetration of generic drugs at each time point, and to allow the control unit 1320 or any unit to automatically determine that the market penetration has reached 50% etc. It may be configured. In yet another embodiment, the daily generic drug sales volume, the number of prescription days, or the number of prescriptions can be automatically and / or manually acquired together with these data of the original drug of the same active ingredient. Also, in such a configuration, if an error occurs such that an unreachable day comes out on the next day of 50% arrival, it may be configured to be able to perform predetermined error processing.

  In the prediction system of the present embodiment, it is preferable that the actual market penetration degree be at least two, but in order to improve the accuracy of the prediction, it is preferable that the number of actual values be large. This is because the parameter used for the cumulative distribution function of the gamma distribution described later is a more optimal parameter value. Thus, for example, in S5020, the user may be configured to be able to enter as many actual values as they wish to enter, in which case in addition to the actual values regarding market penetration and the time (date) The degree of market penetration reached may be configured to be entered in units of percent.

  In S5030, parameters of multiple regression analysis for at least two market penetration levels (for example, 50% and 70% market penetration levels) are respectively calculated.

  In S5030, parameters (two) of the cumulative distribution function of the gamma distribution indicating the market penetration (which is determined monthly in this embodiment) are generated. The cumulative distribution function of the gamma distribution is a function for calculating the market penetrance based on the period from when the generic drug was launched. Here, the parameters are a shape parameter and a scale parameter, and these parameters may be determined by, for example, the Monte Carlo method, an optimal combination of two. Here, the Monte Carlo method in the present embodiment is a method of generating a combination of numerical values using random numbers to obtain a cumulative distribution function of a gamma distribution passing at least two measured values. Also, based on the obtained parameters, it is possible to automatically calculate the period from the time of launch until the desired market penetration degree is reached.

  In S5040, a data structure (see FIG. 4A) in which generic drug data and parameters are linked is generated and recorded.

  In S5050, using generic generic drug data in the past, X1 when generic drug is marketed (which may be the number of months from when the proprietary drug was launched), and peak year sales of generic drug of the same active ingredient The amount of partial regression coefficient m1 and m2 and the intercept b of the multiple regression equation consisting of Log (Y50) = m1 * X1 + m2 * X2 + b, where the amount of X2 and the number of months the market penetration of generic drug has reached 50% is Y50 Find by multiplication. The same applies to the number of months for which generic drug penetration has reached 70%.

  At S5060, combinations of partial regression coefficients (m1, m2) and intercept b of the multiple regression analysis calculated at S5050 are recorded for each of the market penetration rates (50% and 70%).

  FIG. 7 shows a modified example of the calculation result output processing unit 1270 of the system for temporally predicting the market penetration degree of generic medicine according to one embodiment of the present invention.

  The calculation result output processing unit 1270 of this embodiment includes a medicine production amount calculation unit 6030 and a medicine production data recording unit 6040. Here, the calculation result output processing unit 1270 may not include the social factor identifying unit 6010 and the social factor recording unit 6020.

  The pharmaceutical production data recording unit 6040 is a database that records the relationship between the amount of production of the leading drug and the amount of procurement of each raw material. An example of the data structure is shown in FIG. The data structure of FIG. 8 shows the relationship between the drug name, the name of the raw material used for the drug, and the ratio of the raw material. For example, it shows that 50% of the raw material a and 50% of the raw material b are required to produce the pharmaceutical A. Furthermore, information on raw materials (minimum procurement amount, procurement period, etc.) may also be recorded.

  The medicine production amount calculation unit 6030 calculates the production amount of the advanced medicine to be supplied from the market penetration degree. Then, based on the data obtained from the pharmaceutical production data recording unit 6040, the pharmaceutical production calculation unit 6030 procures the amounts of raw materials required to produce the advanced pharmaceutical based on the calculated production of the advanced pharmaceutical. Are calculated and output to the output processing unit 6050.

  Although not shown, the calculation result output processing unit 1270 of this embodiment may include a social factor identification unit and a social factor recording unit. Here, the social factor identification unit records social factors such as medical treatment fee revisions related to the promotion of the use of generic drugs in advance in a database such as the social factor recording unit, and when the predicted value and the measured value differ. And present social factors corresponding to different points in time.

  In addition, the social factor identification unit is based on the time when there is a difference between the market penetration based on the measured (entered) data and the market penetration based on the cumulative distribution function of the gamma distribution, or based on the measured data. It is a unit that identifies when a singular point occurs, such as when market penetration suddenly rises. When the social factor identifying unit identifies a point when the singularity occurs, a predetermined period including the point corresponding to the social factor recording unit (for example, from three months ago to three months after the point of the singularity) Period inquired about social facts that occurred. Output the result inquired from the Social Factors Identification Department. If there is no social fact in the corresponding period, it is output to the output processing unit 6050 that there is nothing.

  The social factor recording unit is a database that records social facts such as medical service fee revisions for promoting the use of generic drugs, along with the time when the social facts occur. For example, prepare a data structure that shows the relationship between a point in time (date) and the social factor that occurred at that point, and that social factor occurred on xx, xx, xx, or Information on the details of the contents of the medical care fee revision may also be recorded.

  Although the Monte Carlo method is used to determine the parameters of the cumulative distribution function of the gamma distribution, combinations in which the range of combinations of parameters is limited or prioritized may be determined in order to reduce calculation time. For example, in another embodiment, the penetrability of the cumulative distribution function of the gamma distribution (the slope of the rising of the graph) generally tends to be faster (increase) as the release point is later. By narrowing down to a combination of parameters satisfying this tendency and using the Monte Carlo method, the time required for parameter determination can be reduced. In addition, what the above-mentioned tendency means is that the initial market penetration degree is quicker as a generic drug newer on sale date.

  As another example, a data adjustment unit (not shown) for adjusting each data is provided in the prediction system of the present embodiment, for example, multiple regression parameters required for the prediction in FIG. The cumulative distribution function data (including parameters) of the gamma distribution in FIG. 4A may be adjusted to improve the accuracy. In addition, depending on the type of original drug and the sales scale, the predicted value of market penetration may fluctuate. In such a case, the original drug having a small sales scale (which may be determined by the sales scale being smaller than a predetermined amount) or the relatively narrow (special) initial drug (predetermined For example, the prediction system may be configured to correct partial regression coefficient or intercept of multiple regression analysis for the name of the generic drug, etc. The prediction system may be configured to be displayed along with the prediction result that there is a possibility of significant swing, or the prediction system may be configured to display a swing width along with the predicted value.

  By using the prediction system of this embodiment, a generic drug maker can predict the market size of a new product over time, and can provide information for determining entry opportunities. In addition, the leading drug manufacturer can more accurately predict the decline in sales volume after the end of the protection period, and can provide information contributing to a lean manufacturing plan.

  Comparative Example FIG. 9 is a comparative example to the present example and is a view showing the difference between the predicted value and the actual measured value by the Gompertz curve. In the present embodiment, the cumulative distribution function of the gamma distribution is used, but generally, when estimating the number of products sold from the time of release to the end of the protection period, the growth curves such as Gompertz curve and logistic curve It may be used. Using these growth curves, a curve is obtained in which the slope is small initially at the time of launch, but the slope increases midway and then decreases again. However, looking at the market penetration rate of generic drugs in practice, such growth curves were not predictable. In particular, the real statistics and the growth curve did not match for a while after being launched. On the other hand, the prediction system of this embodiment is excellent in that it matches the real statistics.

  While the embodiments of the present invention have been described above, various alternatives, modifications and variations are possible for those skilled in the art based on the above description, and the present invention is not limited to the various modifications described above without departing from the scope of the invention. It is intended to cover alternatives, modifications or variations.

1000 Temporal prediction system 1100 Data generation unit 1110 Measured value input processing unit 1120 Measured data recording unit 1130 Parameter calculation unit 1140 Generic drug data input processing unit 1150 Generic medicine data recording unit 1160 Data linking unit 1170 Gamma distribution cumulative distribution function recording Part (1)
1180 permeation period multiple regression analysis calculation unit 1190 permeation period parameter recording unit 1200 data calculation unit 1210 future permeation period calculation unit 1220 gamma distribution cumulative distribution function calculation unit 1230 gamma distribution cumulative distribution function recording unit (2)
1240 Gamma distribution cumulative distribution function selection unit 1250 specific time point input processing unit 1260 penetration ratio calculation unit 1270 calculation result output processing unit 1310 interface unit 1320 control unit 6010 social factor identification unit 6020 social factor recording unit 6030 medicine production amount calculation unit 6040 Pharmaceutical production data recording unit 6050 Output processing unit

Claims (10)

In the market penetration forecasting system for generic drugs at a specific time,
An input processing unit for inputting a specific point in time;
A generic drug data recording unit that records data on the time when the generic drug is first marketed and the peak year sales of the generic drug of the same active ingredient as generic drug data;
An output processing unit that outputs the market penetration of the generic drug at the specific time as the market penetration;
A cumulative distribution function recording unit of a gamma distribution in which a parameter of a cumulative distribution function of the gamma distribution indicating the market penetration degree is linked with the generic drug data and recorded as a cumulative distribution function data of the gamma distribution;
Equipped with
Reading out the generic drug and the cumulative distribution function of the gamma distribution corresponding to the time of the first launch from the cumulative distribution function recording part of the gamma distribution;
The prediction system, wherein the market penetration degree corresponding to the specific time point is calculated based on the read cumulative distribution function of the gamma distribution, and is output to an output processing unit. The prediction system according to claim 1, further comprising
A measured data storage unit that records measured data on at least two market penetration rates;
A penetration period multiple regression analysis calculation unit that calculates multiple regression analysis on the at least two market penetration degrees based on the actual measurement data;
Equipped with
The cumulative distribution function of the gamma distribution is a function for calculating the market penetrability based on the peak year sales of the generic drug of the same active ingredient as the period from when the generic drug is launched, and the parameters. The prediction system characterized by being.   The prediction system according to claim 1, wherein, when the disease to which the generic drug is applied is a general disease having a large number of patients, that effect is displayed.   Common diseases with a large number of patients include hypertension, thrombosis, dyslipidemia, diabetes, insomnia, dementia, migraine, depression, schizophrenia, osteoporosis, acid related diseases, asthma, allergic rhinitis The prognostic system according to claim 3, characterized in that it is prostatic hyperplasia.   The prediction system according to claim 1, wherein, when the disease to which the generic drug is applied is a disease with a small number of patients, a message to that effect is displayed.   The prediction system according to claim 5, wherein the disease with a small number of patients is epilepsy, Parkinson's disease or an orphan drug.   The method is characterized by further comprising means for predicting the production amount of the advanced drug to be supplied based on the prediction result of the market penetration degree, and presenting the procured amount of each raw material necessary for producing the advanced drug. The prediction system according to claim 1.   The rising slope of the graph showing the penetrance of the cumulative distribution function of the gamma distribution is higher the later the generic drug is first marketed and the higher the peak year sales of the generic drug of the same active ingredient. The prediction system according to claim 1, characterized in that: In the market penetration forecast program for generics at specific times,
A means for entering a specific point in time;
Means for recording data on the time when the generic drug is first marketed and the peak year sales of the generic drug of the same active ingredient as generic drug data,
A means for outputting the market penetration of the generic drug at the specific time as the market penetration,
A parameter of the cumulative distribution function of the gamma distribution indicating the market penetration degree linked with the generic drug data and recorded as cumulative distribution function data of the gamma distribution;
Equipped with
Read out the cumulative distribution function of the generic drug and the gamma distribution corresponding to the time of the first launch,
A forecasting program characterized by calculating and outputting the market penetration degree corresponding to the specific time point based on the read cumulative distribution function of the gamma distribution. In the method of predicting the market penetration of generics at a specific time,
Entering a specific point in time;
Recording data as to when the generic drug is first marketed and the peak year sales of the generic drug of the same active ingredient as generic drug data;
Outputting the market penetration of the generic drug at the specific point in time as the market penetration;
Recording the parameter of the cumulative distribution function of the gamma distribution indicating the market penetration degree as the cumulative distribution function data of the gamma distribution in association with the generic drug data;
Equipped with
Read out the cumulative distribution function of the generic drug and the gamma distribution corresponding to the time of the first launch,
And calculating the market penetration degree corresponding to the specific time point based on the read cumulative distribution function of the gamma distribution.