JP7374425B2 - Drug identification equipment and methods - Google Patents

Description

The present invention relates to a drug identification device and method using means for identifying and verifying drugs using spectroscopy.

In recent years, demands for ensuring the quality of pharmaceuticals have been increasing, and international guidelines have been established, including standards for conformity confirmation related to the manufacture of pharmaceuticals and quasi-drugs, and compliance requirements for manufacturers.

Further, at a pharmacy, a pharmacist may need to confirm whether the medicine given to the customer is the medicine prescribed by the doctor. In addition, labels on containers may be peeled off, forgotten, or incorrectly labeled. In such cases, before handing over the drug to the customer, the pharmacist needs to identify, without bias, whether the material at hand is the desired drug. There is a need to accurately identify drugs in this way to formulate and carry out safer and more effective drug treatment plans.

Furthermore, from the viewpoint of pharmacy management, it is desirable to manage customers and the drugs prescribed to them in terms of demand forecasting. By recording the history of pharmaceuticals prescribed to customers and predicting demand from those records, it is possible to minimize the amount of inventory and optimize cash flow.

Furthermore, there is a need to analyze the characteristics of local medical care and use medicines efficiently.

Patent Document 1 discloses a system and method for rapidly identifying and verifying pharmaceutical products using spectroscopic techniques, such as Raman spectroscopy. In the illustrated system, laser light excites Raman active modes within a drug product contained in a drug container, and Raman spectral characteristics are determined from the light scattered by the Raman active modes using, for example, a multimode multiple sampling spectrometer. obtain. The Raman spectral characteristics obtained from the drug are then compared with the Raman spectral characteristics stored in the database, and the one closest to the Raman spectral characteristics obtained from the drug is displayed on the screen.

Special Publication No. 2009-517631

However, the device and method for quickly identifying and verifying drugs using the spectroscopic techniques described in Patent Document 1, such as Raman spectroscopy, has a problem in that it may not be possible to accurately identify the type and amount of drugs. .

Furthermore, the system and method described in Patent Document 1 only performs the identification of medicines, so there is a problem in that it is not possible to issue a warning when a wrong medicine is prescribed to a customer.

Furthermore, with the device and method described in Patent Document 1, the sales history of pharmaceuticals must be recorded in a separate device, so there is a problem in that pharmacies are required to separately prepare demand forecasts for pharmaceuticals.

For some patients, pharmacies may need to identify the patient's usual medications when creating a new drug treatment plan for the patient to prevent duplicate dosing or administration of contraindicated medications. There are times when you have to. Although such drug identification is sometimes referred to as "bringing drug identification," the device and method described in Patent Document 1 has a problem in that it cannot support "bringing drug identification."

Therefore, there is a primary need to use spectroscopic techniques to more accurately identify and verify the type and amount of drugs prescribed to customers, and to formulate and implement safer and more effective drug treatment plans. Furthermore, there is a need for a drug identification device and method that is useful for inventory management such as demand forecasting, and that can analyze the characteristics of local medical care.

To achieve the above objectives, an apparatus for identifying and verifying and associating a medication prescribed to a patient according to one embodiment of the present invention comprises a first light wave transmitted through the medication or scattered or reflected by the medication. and a receiver for receiving information about a second light wave having a different wavelength than the first light wave transmitted through or scattered or reflected by the drug; a processor that sets characteristics, identifies prescription data including the type and amount of medicine prescribed to the patient based on the characteristics and preset medicine data, associates the prescription data and information about the patient, and stores it in the memory; It is characterized by including the following.

To achieve the above objective, a method for identifying and verifying a drug prescribed to a patient and storing it in association with the patient according to one embodiment of the present invention provides a method for identifying and verifying a drug prescribed to a patient and storing it in association with the patient. detecting a first light wave; detecting a second light wave having a different wavelength than the first light wave transmitted through or scattered or reflected by the drug; a step of setting characteristics for the drug based on the characteristics, a step of identifying prescription data including the type and amount of the drug prescribed to the patient based on the characteristics and the drug data, and associating and accumulating information about the prescription data and the patient. It is characterized by comprising the following steps.

To achieve the above object, a system for identifying and verifying medicines prescribed to a patient and storing them in association with the patient according to one embodiment of the present invention includes a memory for storing medicine data corresponding to known medicines. , a first detector for detecting a first light wave transmitted through or scattered or reflected by the drug product, and having a wavelength different from the first light wave transmitted through the drug product or scattered or reflected by the drug product. a second detector that detects a second light wave; and a drug that sets characteristics for the drug based on the first and second light waves, and that is prescribed to a patient based on the characteristics and the drug data stored in the memory. and a processor that identifies prescription data including the type and amount of the patient, associates the prescription data with information regarding the patient, and stores the associated information in a memory.

According to the present invention, it is possible to use spectroscopic techniques to more accurately identify and verify the type and amount of drugs prescribed to a customer, and to formulate and carry out a safer and more effective drug treatment plan. Data useful for inventory management, such as demand forecasting, can be obtained.

FIG. 1 is a diagram illustrating the drug identification system of this embodiment. FIG. 2 is a diagram illustrating the drug identification method of this embodiment. FIG. 3 is a diagram showing the results of measuring the Raman spectrum of "Laxoberone Tablets 2.5 mg" and comparing it with pharmaceutical data. FIG. 4 is a diagram showing the results of measuring the near-infrared spectrum of "Laxoberone Tablets 2.5 mg" and comparing it with pharmaceutical data. FIG. 5 is a diagram showing a comparison between the results obtained by the drug identification device of this embodiment and the conventional example for "Laxoberone Tablets 2.5 mg." FIG. 6 is a diagram showing the results of measuring the Raman spectrum of "domperidone tablets 10 mg" and comparing it with pharmaceutical data. FIG. 7 is a diagram showing the results of measuring the near-infrared spectrum of "domperidone tablets 10 mg" and comparing it with pharmaceutical data. FIG. 8 is a diagram showing a comparison of the results obtained by the drug identification device of this embodiment with the conventional example regarding "domperidone tablets 10 mg". FIG. 9 is a diagram showing a comparison of results obtained by the drug identification device of this embodiment with a conventional example for 46 types of drug samples. FIG. 10 is a diagram illustrating a drug identification system using a powdered drug weight/component inspection device, which is one of the embodiments.

An apparatus for identifying and verifying a drug prescribed to a patient and storing it in association with the patient according to one embodiment of the present invention includes a memory for storing drug data corresponding to known drugs and a memory that stores drug data corresponding to known drugs; a first detector that detects a first light wave that is scattered or reflected; and a second light wave that detects a second light wave that has a different wavelength than the first light wave that is transmitted through or is scattered or reflected by the drug product. a second detector, and setting characteristics for the drug based on the first and second light waves, and prescription data including the type and amount of the drug prescribed to the patient based on the characteristics and the drug data stored in the memory. and a processor that associates prescription data and information about the patient and stores them in a memory.

Thus, the "apparatus for identifying, verifying, and storing medications prescribed to a patient in association with the patient" includes a detector and a processor. It may include one or two light sources. It may also include memory. This configuration makes it possible to more accurately identify and verify the types and quantities of drugs prescribed to customers, design and implement safer and more effective drug treatment plans, and further improve demand forecasting, etc. Data useful for inventory management can be obtained. Furthermore, the device can be downsized, and drugs can be easily identified and verified and stored in association with patients, regardless of location.

A system for identifying and verifying medications prescribed to a patient and storing them in association with the patient according to one embodiment of the present invention includes a memory that stores pharmaceutical data corresponding to known medications and a first detector that detects a first light wave that is scattered or reflected; and a second light wave that detects a second light wave that has a different wavelength than the first light wave that is transmitted through or is scattered or reflected by the drug product. a second detector, and setting characteristics for the drug based on the first and second light waves, and prescription data including the type and amount of the drug prescribed to the patient based on the characteristics and the drug data stored in the memory. and a processor that associates prescription data and information about the patient and stores them in a memory.

A method of identifying and verifying a medication prescribed to a patient and storing it in association with the patient according to one embodiment of the present invention includes the steps of: detecting a first light wave transmitted through, or scattered or reflected by, the medication; and detecting a second light wave having a different wavelength than the first light wave transmitted through or scattered or reflected by the drug, and establishing a property for the drug based on the first and second light waves. identifying prescription data including the type and amount of medicine prescribed to the patient based on the characteristics and the medicine data; and correlating and accumulating information about the prescription data and the patient. shall be.

Here, the parts constituting the "system that identifies and verifies medicines prescribed to a patient and stores them in association with the patient" do not need to be housed in one housing. For example, a first detector that detects a first light wave that is transmitted through, or scattered or reflected by, a pharmaceutical product and a first detector that detects a first light wave that is transmitted through, or scattered or reflected by, a pharmaceutical product, and a first detector that detects a first light wave that is transmitted through, or scattered or reflected by, a pharmaceutical product; The second detector that detects the second light wave may be housed in a housing that is separate from the housing that stores the memory and processor. In such cases, it is possible to downsize the device that houses the two detectors, and the medicine prescribed to the patient can be identified and verified using a handy device.

Even if two detectors, memory, and processor are housed in one housing, it is possible to downsize the equipment by using a single light source and using a demultiplexer to generate two lights of different wavelengths. It is possible to identify and verify medicines prescribed to patients using handheld devices.
In this way, by configuring part or all of the "system that identifies and verifies medicines prescribed to patients, and stores them in association with the patient" as a handy device, it can be used in a limited number of places such as counters in pharmacies where customers face-to-face. Using spectroscopic techniques, users can quickly and easily use spectroscopic techniques to more accurately identify and verify the type and quantity of drugs prescribed to customers, as well as obtain data useful for inventory management, such as demand forecasting.

Here, the "first light wave" may be Raman scattered light, and the "second light wave" may be infrared light including near infrared, mid-infrared, and far infrared.

Furthermore, "characteristics for medicines" may be a spectrum obtained by Fourier transforming a part or all of the waveform of the first light wave and the waveform of the second light wave.

Spectroscopic analysis methods include near-infrared (NIR, Near InfraRed), infrared (IR, InfraRed) spectroscopy (near-infrared, mid-infrared spectroscopy), Raman, ultraviolet (UV), and nuclear magnetic resonance (NIR). Examples include resonance (NMR), mass spectrum (MS) analysis, X-ray spectroscopy, and fluorescence analysis.

Near-infrared (NIR) spectroscopy and Raman spectroscopy are qualitative analysis methods that can obtain spectra based on molecular vibrations.
Near-infrared light is light with a wavelength of 800 nm to 2500 nm (wave number 12500 to 4000 cm-1). Absorption that appears in the near-infrared region is more complex than absorption in the mid-infrared region, as harmonics and combination tones of the reference vibration overlap, and a broad peak is generally observed. Additionally, since the absorption intensity is weaker than in the mid-infrared region, samples can be directly measured. Furthermore, since scientifically stable glass and quartz cells show almost no absorption in the near-infrared region, it is also possible to perform measurements using these cells.
Raman spectroscopy uses visible or near-infrared laser light to measure the light scattered by a sample when it is irradiated with light of a certain wavelength. The Raman spectrum is plotted with the vertical axis representing the scattering intensity and the horizontal axis representing the Raman shift, that is, the wave number difference between the incident light and the scattered light. Compared to near-infrared spectroscopy, the spectral shape of Raman spectroscopy has sharper peaks and can be distinguished even from samples with similar structures. The wavelength of the light used as the incident wave in Raman spectroscopy can be, for example, between 250 nm and 2500 nm.

Regarding samples that are unsuitable for measurement in both near-infrared spectroscopy and Raman spectroscopy, in near-infrared spectroscopy, samples with weak near-infrared absorption, such as inorganic compounds, are unsuitable for measurement. Furthermore, in Raman spectroscopy, samples with weak Raman scattering and samples with strong fluorescence effects are unsuitable for measurement. Furthermore, samples that are easily decomposed and burned by visible light or near-infrared laser light are also unsuitable for measurement using near-infrared spectroscopy.

Regarding near-infrared spectroscopy and Raman spectroscopy, whether there is an effect of the particle size of the sample is that in near-infrared spectroscopy, separate reference data are required when the particle sizes are different. On the other hand, Raman spectroscopy is not affected by the particle size of the sample.

Also, in near-infrared spectroscopy and Raman spectroscopy, when the sample is placed in a packaging container and measured, is there an effect of the packaging container? In near-infrared spectroscopy, if the packaging containers are different, the type and thickness Separate reference data is required for each. On the other hand, in Raman spectroscopy, if visible light or near-infrared laser light is transmitted, the packaging container does not have much of an effect.

In this way, near-infrared spectroscopy and Raman spectroscopy contain complementary data. Raman spectroscopy gives an additional spectrum to the spectrum obtained by near-infrared spectroscopy for a specific substance, and the spectra obtained by near-infrared spectroscopy and the spectrum obtained by Raman spectroscopy are independent of each other. . Therefore, by combining the two methods, it is possible to perform verification with higher accuracy than when each method is used alone.

"Drug data" is stored in a database and includes preset reference spectra of medical products.
The degree of similarity is calculated from the difference between the spectrum obtained from part or all of the waveform of the first light wave and the waveform of the second light wave and the reference spectrum stored in the database, and "prescription data" is derived from the degree of similarity. do. "Prescription data" includes the type and amount of drug to be tested spectroscopically.

In order to identify a drug from its spectrum, the difference between the measured spectrum and the spectrum included in the "drug data" stored in the database is quantified as a "degree of similarity." The smaller this difference is, the higher the similarity.
In the ``dispensing audit,'' which determines whether the medicine is packaged in the same package as prescribed, the spectrum of ``drug data'' corresponding to the medicine written on the prescription and the actual measurement data (``drug If the difference is below a certain level (threshold), it is determined to be correct, and if it exceeds that value, it is determined to be suspicious.

For some patients, pharmacies may need to identify the patient's usual medications when creating a new drug treatment plan for the patient to prevent duplicate dosing or administration of contraindicated medications. There are times when you have to. This type of drug identification is sometimes referred to as ``bring drug identification.''
In this case, the measured data is compared with the spectra of all drugs stored in the "drug data", and the difference between the measured spectrum and the spectrum included in the "drug data" stored in the database is set to a certain level (threshold value). The following drugs may be set as a "hit list" and the results may be displayed in descending order of similarity (the difference between the measured spectrum and the spectrum included in the "drug data" stored in the database is small). If there are no drugs for which the difference between the measured spectrum and the spectrum included in the "drug data" stored in the database is less than a certain level (threshold), the result will be displayed as "unidentifiable" and the pharmacist conducting the test will be confused. You can avoid it.

The light used in Raman spectroscopy or near-infrared spectroscopy may be a continuous wave or a discrete wave that allows time-resolved analysis. As time-resolved analysis methods, methods using time-resolved Fourier analysis transform, wavelet transform, etc. are known.

In general, with spectroscopy, measurement accuracy can be improved by time-resolved analysis, so pharmacists at pharmacies check whether the medicines given to customers are the medicines prescribed by the doctor. If necessary, it can also be determined whether a drug is a genuine product.

In addition, by ``associating prescription data and patient information and storing them in memory,'' pharmacies can determine the types and amounts of drugs to purchase based on time-series data on patients and the types and amounts of drugs prescribed to them. It is possible to make predictions and streamline pharmacy management.

Storing "drug data" may require a large memory size. However, in the case of pharmaceutical products, certification is required in each country, and the number of types of pharmaceutical products on the market in each country is limited. The "drug data", that is, the spectra, of drugs in circulation are measured, managed and stored appropriately, and when a new drug is released or the ingredients of an existing drug are changed, the information is immediately available. By obtaining the drug and updating the "drug data," it is possible to avoid the measured spectrum from being a "totally unknown drug."

(Description of the illustrated embodiments)
A drug identification device and method according to one embodiment of the present invention will be described with reference to FIGS. 1 to 10.

FIG. 1 is a diagram illustrating the drug identification system of this embodiment.

The drug identification system 1 includes a processor 10, a memory 20, a Raman spectrometer 30 with a built-in light source, a short-pass filter 40, a near-infrared light source 50, a near-infrared spectrometer 60, an optical fiber 70, a long-pass filter 80, and a sample holder 90. I'm here.

The processor 10 and the memory 20 are housed in separate housings and are electrically connected to each other via a wireless network.
In the illustrated device, processor 10 and memory 20 are housed in separate housings, but they may be integrated.

The sample holder 90 holds a drug (a tablet may be used) (sample 100) to be measured. The sample 100 held in the sample holder 90 is designed to be irradiated with light from the Raman spectrometer 30 and the near-infrared light source 50.

A short pass filter 40 is installed between the optical path from the Raman spectrometer 30 to the sample 100, and cuts light having a wavelength shorter than 939 nm. An example of Raman spectrometer 30 is Mira M-3 manufactured by Metronome.

The Raman spectrometer 30 irradiates the sample 100 with a laser beam having a wavelength of 785 nm. Additionally, Raman scattered light (first light wave) from the sample 100 is detected. The wave number of the Raman scattered light to be detected was from 400 cm-1 to 2300 cm-1.

The processor 10 derives a Raman spectrum (characteristics for the drug) from the Raman scattered light (first light wave). Further, the processor 10 collates the Raman spectrum with the "medicine data" stored in the memory 20 in the range of 400 cm-1 to 1800 cm-1, and calculates a Raman Quality Index value (Raman QI value). This Raman QI value is related to the difference between the measured spectrum and the spectrum included in the "drug data" stored in the database, and the smaller the value, the more similar the two spectra are. The processor 10 may perform processing using, for example, commercially available database matching software "Spectral ID (ThermoFischer").

Furthermore, the processor 10 lists up to 20 drugs with the highest Raman QI values based on the measurement data of the "medicines" to be measured.

The near-infrared light source 50 is a tungsten lamp, and the light emitted from the tungsten lamp has a continuous spectrum from 350 nm to around 3000 nm. The near-infrared light source 50 is connected to a long-pass filter 80 via an optical fiber 70. The long pass filter 80 cuts light with wavelengths longer than 990 nm.

The near-infrared spectrometer 60 detects the reflected wave (second light wave) from the sample 100 via the optical fiber 70 with a wavelength of 900 nm to 1700 nm. An example of the near-infrared spectrometer 60 is the NIR Meter manufactured by SpectraCorp.

The processor 10 acquires a near-infrared spectrum from the reflected waves detected by the near-infrared spectrometer 60. At this time, second-order differential processing is performed on the obtained near-infrared spectrum (characteristics for pharmaceuticals).

The processor 10 collates the obtained second-order differential of the near-infrared spectrum with "drug data" stored in the memory 20 in the range of 1000 nm to 1600 nm, and obtains a near-infrared Quality Index value (near-infrared QI value). ) is calculated. This near-infrared QI value is related to the difference between the measured spectrum and the spectrum included in the "drug data" stored in the database, and the smaller the value, the more similar the two spectra are. The processor 10 may perform processing using, for example, commercially available database matching software "Spectral ID (ThermoFischer").

Furthermore, the processor 10 lists up to 20 drugs with near-infrared QI values based on the measurement data of the "medicines" to be measured.

Furthermore, the processor 10 calculates Raman QI values and near-infrared rays for the measurement data of the "medicinal products" to be measured, for the drugs with the top 20 Raman QI values and the top 20 near infrared QI values. "Similarity" is calculated by multiplying the QI values.

Further, the processor 10 lists the corresponding drugs in descending order of similarity, and displays the list on a display (not shown) as necessary. The processor 10 uses the top drug on the list and its amount as prescription data.

Further, the processor 10 stores prescription data and patient information in the memory 20 in association with each other.

FIG. 2 is a flowchart illustrating the drug identification method of this embodiment.

When the process starts, in S10, the processor 10 causes the Raman spectrometer 30 to irradiate the sample 100 with a laser beam having a wavelength of 785 nm. Additionally, Raman scattered light (first light wave) from the sample 100 is detected. The wave number of the Raman scattered light to be detected was from 400 cm-1 to 2300 cm-1.

In the next step S20, the processor 10 derives a Raman spectrum (characteristics for the drug) from the Raman scattered light (first light wave). Further, the processor 10 collates the Raman spectrum with the "medicine data" stored in the memory 20 in the range of 400 cm-1 to 1800 cm-1, and calculates a Raman Quality Index value (Raman QI value). This Raman QI value is related to the difference between the measured spectrum and the spectrum included in the "drug data" stored in the database, and the smaller the value, the more similar the two spectra are.

Furthermore, in S20, the processor 10 lists up to 20 drugs with Raman QI values based on the measurement data of the "medicines" to be measured.

In S30, the near-infrared spectrometer 60 detects the reflected wave (second light wave) from the sample 100 via the optical fiber 70 with a wavelength of 900 nm to 1700 nm.

In S40, the processor 10 acquires a near-infrared spectrum from the reflected wave detected by the near-infrared spectrometer 60. At this time, second-order differential processing is performed on the obtained near-infrared spectrum (characteristics for pharmaceuticals).

In S40, the processor 10 further collates the obtained second-order differential of the near-infrared spectrum with "drug data" stored in the memory 20 in the range of 1000 nm to 1600 nm, and calculates the near-infrared Quality Index value (near-infrared outside QI value). This near-infrared QI value is related to the difference between the measured spectrum and the spectrum included in the "drug data" stored in the database, and the smaller the value, the more similar the two spectra are.

In S40, the processor 10 further lists the top 20 drugs with near-infrared QI values based on the measurement data of the "medicines" to be measured. Further, the processor 10 uses the highest drug on the list and its amount as prescription data.

In S50, the processor 10 calculates the Raman QI values and near-infrared QI values for the measurement data of the "medicinal products" to be measured, for the drugs with the top 20 Raman QI values and the top 20 near-infrared QI values. "Similarity" is calculated by multiplying the outer QI values.

In S50, the processor 10 further lists the corresponding drugs in descending order of similarity, and displays them on a display (not shown) as necessary.

In S60, the processor 10 associates the prescription data and information regarding the patient and stores them in the memory 20, and the process ends.

Here, the processing in S10 and S30 may be performed at the same time. In this case, the processes of S20 and S40 may also be performed in parallel.

By utilizing the devices and methods described above, spectroscopic techniques can be used to more accurately identify and verify the types and quantities of pharmaceutical products prescribed to customers, as well as to obtain data useful for inventory management, such as demand forecasting. I can do it.

3 to 8 are analysis examples.

FIG. 3 is a diagram showing the results of measuring the Raman spectrum of "Laxoberone Tablets 2.5 mg" and comparing it with pharmaceutical data.
At this time, the "medical product data" includes Raman spectra of 210 types of pharmaceuticals, including "Laxoberone Tablets 2.5 mg." Among the spectral data, L1 is the Raman spectrum of the sample, and L2 and L3 are the two most similar spectral data among the "medical product data". "Quality" in the list is a Raman QI value, and it is determined that the closer this value is to 0, the higher the similarity. In this case, "Laxoberone Tablets 2.5 mg" is correctly ranked in first place.

FIG. 4 is a diagram showing the results of measuring the near-infrared spectrum (secondary differential) of "Laxoberone Tablets 2.5 mg" and comparing it with pharmaceutical data.
At this time, the "medical product data" includes near-infrared spectra (secondary derivatives thereof) of 210 types of pharmaceuticals including "Laxoberone Tablets 2.5 mg." Among the spectral data, L3 is the near-infrared spectrum of the sample (the second derivative), and L4 and L5 are the two most similar spectral data (the second derivative) of the "medical product data", respectively. It is. "Quality" in the list is a near-infrared QI value, and it is determined that the closer this value is to 0, the higher the similarity. In this case, "Rispedrin Tablets 1 mg Sawai" is ranked first, and "Laxoberon Tablets 2.5 mg" is ranked third.

FIG. 5 is a diagram showing a comparison between the results obtained by the drug identification device of this embodiment and the conventional example for "Laxoberone Tablets 2.5 mg."
When the similarity is calculated by multiplying the Raman QI value and the near-infrared QI value and ranked based on the similarity, "Laxoberone Tablets 2.5 mg" ranks first, indicating a correct result.

FIG. 6 is a diagram showing the results of measuring the Raman spectrum of "Domperidone Tablets 10 mg TYK" and comparing it with pharmaceutical data.
At this time, the "medical product data" includes Raman spectra of 210 types of pharmaceuticals, including "domperidone tablets 10 mg TYK." Among the spectral data, L7 is the Raman spectrum of the sample, and L8 and L9 are the two most similar spectral data among the "medical product data". "Quality" in the list is a Raman QI value, and it is determined that the closer this value is to 0, the higher the similarity. In this case, "Domperidone Tablets 10mg TYK" is ranked 4th.

FIG. 7 is a diagram showing the results of measuring the near-infrared spectrum of "Domperidone Tablets 10 mg TYK" and comparing it with pharmaceutical data.
At this time, the "medical product data" includes near-infrared spectra (secondary derivatives thereof) of 210 types of pharmaceuticals including "domperidone tablets 10 mg TYK." Among the spectral data, L10 is the near-infrared spectrum (secondary differential thereof) of the sample, and L11 and L12 are the two most similar spectral data among the "medical product data". In this case, "Domperidone Tablets 10mg TYK" is ranked first.

FIG. 8 is a diagram showing a comparison of the results obtained by the drug identification device of this embodiment with the conventional example for "Domperidone Tablets 10 mg TYK".
When the similarity is calculated by multiplying the Raman QI value and the near-infrared QI value and ranked based on the similarity, "Domperidone Tablets 10mg TYK" is ranked first, indicating a correct result.

FIG. 9 is a diagram showing a comparison of results obtained by the drug identification device of this embodiment with a conventional example for 46 types of drug samples.
For Raman spectroscopy alone, 40 out of 46 samples (87%) were ranked within the top 5, and 6 samples (13%) were ranked 6th or lower. However, when we calculated the similarity by multiplying the Raman QI value and the near-infrared QI value and ranked it based on the similarity, 45 out of 46 samples (98%) were ranked within the top 5, and one Only one sample (2%) ranked 6th.

FIG. 10 is a diagram illustrating a drug identification system (powder weight and component inspection device) 1A, which is one of the present embodiments.

The processor 10 and the memory 20 are housed in separate housings and are electrically connected to each other via a wireless network.
In the illustrated device, processor 10 and memory 20 are housed in separate housings, but they may be integrated.

The sample holder 91 is a dish-shaped holder that holds a drug (packaged drug) (sample 100) to be measured. The sample holder 91 is attached to a weighing scale 111 and holds the sample 100, and at the same time, the weighing scale 111 measures the weight of the sample 100 and displays it on the weighing scale 111.

The sample 100 held in the sample holder 91 is designed to be irradiated with light from the built-in near-infrared light source 50.

The near-infrared light source 50 is a tungsten lamp, and the light emitted from the tungsten lamp has a continuous spectrum from 350 nm to around 3000 nm. The near-infrared light source 50 is connected to a long-pass filter 80 via an optical fiber 70. The long pass filter 80 cuts light with wavelengths longer than 990 nm.

The near-infrared spectrometer 60 detects the reflected wave (second light wave) from the sample 100 via the optical fiber 70 with a wavelength of 900 nm to 1700 nm. An example of the near-infrared spectrometer 60 is the NIR Meter manufactured by SpectraCorp.

The processor 10 acquires a near-infrared spectrum from the reflected waves detected by the near-infrared spectrometer 60. At this time, second-order differential processing is performed on the obtained near-infrared spectrum (characteristics for pharmaceuticals).

The processor 10 collates the obtained second-order differential of the near-infrared spectrum with "drug data" stored in the memory 20 in the range of 1000 nm to 1600 nm, and obtains a near-infrared Quality Index value (near-infrared QI value). ) is calculated. This near-infrared QI value is related to the difference between the measured spectrum and the spectrum included in the "drug data" stored in the database, and the smaller the value, the more similar the two spectra are. The processor 10 may perform processing using, for example, commercially available database matching software "Spectral ID (ThermoFischer").

Furthermore, the processor 10 lists up to 20 drugs with near-infrared QI values based on the measurement data of the "medicines" to be measured.

Furthermore, the processor 10 calculates Raman QI values and near-infrared rays for the measurement data of the "medicinal products" to be measured, for the drugs with the top 20 Raman QI values and the top 20 near infrared QI values. "Similarity" is calculated by multiplying the QI values.

Further, the processor 10 lists the corresponding drugs in descending order of similarity, and displays the list on a display (not shown) as necessary. The processor 10 uses the top drug on the list and its amount as prescription data.

Further, the processor 10 stores prescription data and patient information in the memory 20 in association with each other.

This is a drug identification system (powder weight/component inspection device) 1A that measures the weight of a packaged sample 100 and at the same time identifies internal components using a spectroscopic analysis method. Note that the sample 100 is not limited to powder medicines, but can also be applied to liquid medicines, injection medicines, ointment creams, crushed tablets, and capsules.

Using spectroscopic techniques, it is now possible to more accurately identify and verify the type and amount of drugs prescribed to customers, develop and implement safer and more effective drug treatment plans, and improve inventory management such as demand forecasting. You can obtain useful data.

1 Pharmaceutical identification system 1A Pharmaceutical identification system (powder weight and ingredient inspection device)
111 Weight scale 10 Processor 20 Memory 30 Raman spectrometer 40 Short-pass filter 50 Near-infrared light source 60 Near-infrared spectrometer 70 Optical fiber 80 Long-pass filter 90 Sample holder 91 Sample holder (dish-shaped)

Claims (15)

A device for identifying and verifying and associating medications prescribed to a patient with said patient, the device comprising:
a holder that holds the pharmaceutical;
Light irradiated from a Raman spectrometer with a built-in light source hits the drug held by the holder from a first direction via a short pass filter, and a first light wave scattered or reflected by the drug is detected. a first detector to detect;
Light including near-infrared, mid-infrared, and far-infrared rays irradiated from an infrared light source from a second direction different from the first direction hits the medicine held by the holder through a long-pass filter . , a second detector that detects a second light wave having a different wavelength than the first light wave that is scattered or reflected by the pharmaceutical;
A spectral spectrum of the drug is set based on the first and second light waves, and the drug is prescribed to the patient with reference to the spectral spectrum and a preset reference spectrum of the drug stored in a database. Specify the data on the type and amount of the drug to be tested by spectroscopy, associate the data on the type and amount of the drug to be tested by spectrometry, and the time-series information of the drug prescribed to the patient and store it in memory. a processor that
equipment containing. 2. The apparatus according to claim 1, wherein the spectroscopic spectrum includes a spectrum obtained by Fourier transforming a part or all of the waveform of the first light wave and the waveform of the second light wave. 3. The apparatus of claim 2, wherein the spectroscopic spectrum includes a time-resolved spectrum of some or all of the first light wave and the second light wave. 4. The apparatus according to claim 3, wherein the optical spectrum includes a spectrum obtained by wavelet transforming a part or all of the waveform of the first light wave and the waveform of the second light wave. 5. The apparatus of claim 4, wherein the processor includes the step of determining whether the pharmaceutical product is genuine based on the optical spectrum. The processor stores data on the type and amount of the drug prescribed to the patient to be tested by spectroscopy in the memory in association with time-series information of the drug prescribed to the patient; 6. The device of any one of claims 1 to 5, wherein the device predicts the purchase of the drug to be sold or a drug other than the drug. The processor calculates a degree of similarity from the difference between the spectrum obtained from part or all of the waveform of the first light wave and the waveform of the second light wave and the reference spectrum, and calculates the degree of similarity by spectroscopy based on the degree of similarity. Identifying data on the type and amount of the drug to be tested, and deriving the memory by correlating the data on the type and amount of the drug to be tested by spectroscopy and time-series information on drugs prescribed to the patient. , an apparatus according to any one of claims 1 to 6. A system for identifying and verifying medicines prescribed to a patient and storing them in association with the patient, the system comprising:
a memory for accumulating preset reference spectra of said medicines stored in a database corresponding to known medicines;
Light emitted from a Raman spectrometer with a built-in light source hits the drug held by the holder from a first direction via a short-pass filter, and a first light wave is scattered or reflected by the drug. a first detector that detects the
Light including near-infrared, mid-infrared, and far-infrared rays irradiated from an infrared light source from a second direction different from the first direction hits the medicine held by the holder through a long-pass filter . , a second detector that detects a second light wave having a different wavelength than the first light wave that is scattered or reflected by the pharmaceutical;
A spectral spectrum of the drug is set based on the first and second light waves, and a spectral spectrum is prescribed to the patient based on the spectral spectrum and a preset reference spectrum of the drug stored in the memory. specifying data on the type and amount of the drug to be tested by spectrometry, and storing the data in the memory in association with data on the type and amount of the drug to be tested by spectrometry and time-series information on drugs prescribed to the patient. a processor that accumulates;
system containing. A method for identifying and verifying a medicine prescribed to a patient and storing it in association with the patient, the method comprising:
Light irradiated from a Raman spectrometer with a built-in light source hits the drug held by the holder from a first direction via a short pass filter, and a first light wave scattered or reflected by the drug is detected. a step of detecting;
Light including near-infrared, mid-infrared, and far-infrared rays emitted from an infrared light source is applied to the medicine held by the holder from a second direction different from the first direction through a long-pass filter, detecting a second light wave having a different wavelength than the first light wave scattered or reflected by the pharmaceutical;
setting a spectral spectrum of the drug based on the first and second light waves;
a step of identifying data on the type and amount of the drug to be tested by spectroscopy prescribed to the patient based on the spectroscopic spectrum and a preset reference spectrum of the drug;
correlating and accumulating data on the type and amount of the drug tested by spectroscopy and time-series information on the drug prescribed to the patient;
method including. 10. The method according to claim 9, wherein the spectroscopic spectrum includes a spectrum obtained by Fourier transforming a part or all of the waveform of the first light wave and the waveform of the second light wave. 11. The method of claim 10, wherein the spectroscopic spectrum includes a time-resolved spectrum of some or all of the first light wave and the second light wave. 12. The method according to claim 11, wherein the spectroscopic spectrum includes a spectrum obtained by wavelet transforming a part or all of the waveform of the first light wave and the waveform of the second light wave. The method according to claim 11 or 12, further comprising the step of determining whether or not the pharmaceutical product is a genuine product based on the optical spectrum. Furthermore, based on the data on the type and amount of the drug prescribed to the patient and examined by spectrometry and the time series information of the drug prescribed to the patient, the drug to be sold to the patient or the 14. A method according to any one of claims 9 to 13, comprising the step of predicting the purchase of a medicinal product other than the medicinal product. Further, a degree of similarity is calculated from the difference between the spectrum obtained from part or all of the waveform of the first light wave and the waveform of the second light wave, and the reference spectrum, and a degree of similarity is calculated by spectroscopy based on the degree of similarity. 15. A method according to any one of claims 10 to 14, comprising the step of deriving data on the type and amount of said pharmaceutical product.

Images