Prediction of amount of in vivo dopamine etc., and application thereof

Abstract

The present disclosure provides a method for assessing a therapeutic drug or prophylactic drug, or other medical techniques for a Parkinson's disease patient who is being treated with an L-DOPA or L-DOPA-related compound, or a dopamine agonist. Specifically, the present invention provides a method that is for assessing a therapeutic drug or prophylactic drug, or other medical techniques for a Parkinson's disease patient who is being treated with an L-DOPA or L-DOPA-related compound, or a dopamine agonist, and that comprises: A) a step for obtaining eyeball information of the patient; and B) a step for calculating an estimated effective amount or effective level of the therapeutic drug or prophylactic drug, or other medical techniques from the eyeball information.

Description

Prediction of in vivo dopamine levels and their applications

[0001] The present disclosure relates to a method for predicting the amount of dopamine or a substance bioequivalent to dopamine in a living body based on ocular information, etc. The present disclosure also relates to a method for predicting the condition of a Parkinson's disease patient being treated with L-DOPA, an L-DOPA-related compound, or a dopamine agonist, etc.

[0002] Parkinson's disease is a progressive neurodegenerative disease characterized by abnormalities in extrapyramidal function. Pathologically, dopaminergic neuron loss and alpha-synuclein deposition are observed in the substantia nigra pars compacta. Clinically, the disease presents with various motor symptoms such as akinesia, resting tremor, muscle rigidity, and loss of postural reflexes.

[0003] The basis of Parkinson's disease treatment is pharmacotherapy aimed at replenishing dopamine in the brain, and drugs containing the dopamine precursor levodopa (L-DOPA) are used as the first-line drug for the initial treatment of Parkinson's disease. However, as the disease progresses, almost all patients undergoing levodopa treatment develop motor complications such as diurnal fluctuations of Parkinson's symptoms (motor fluctuations) and levodopa-induced dyskinesia in Parkinson's disease (hereinafter sometimes referred to as "PD-LID" (Parkinson's Disease Levodopa-induced dyskinesia)).

[0004] Known representative symptoms of circadian variation include wearing-off, on-off phenomenon, no-on phenomenon, delayed on phenomenon, etc. Among these, wearing-off is a symptom in which, as described above, when the ability to retain dopamine in the synaptic cleft decreases as the pathology progresses, the brain dopamine concentration fluctuates according to the blood concentration of levodopa, resulting in the levodopa blood concentration falling below the safe therapeutic range and a shortened duration of the effect of levodopa.

[0005] The incidence of PD-LID is 30-50% five years after the start of levodopa treatment, and increases as the disease progresses, reaching 50-100% 10 years after the start of treatment. Peak-dose dyskinesia is known as a typical symptom of PD-LID, and is an involuntary movement that appears in the face, tongue, neck, limbs, trunk, etc. when levodopa blood concentrations are high.

[0006] The condition of Parkinson's disease patients undergoing treatment with L-DOPA is often unknown until dyskinesia actually occurs. On the other hand, although it is known that the amount of L-DOPA in the brain correlates with the onset of dyskinesia, it is not practical to measure the amount of L-DOPA in the brain in real time, and therefore no practical method for estimating the condition of Parkinson's disease in real time has been provided.

[0007] The present disclosure was made possible by the discovery that information on eye movement can be used as an indicator for estimating the amount of dopamine or a substance biologically equivalent to dopamine in the body and the condition of Parkinson's disease patients.

[0008] Accordingly, the present disclosure provides the following: (Item 1) A method for estimating or predicting the presence, absence, amount, or level of dopamine or a substance bioequivalent to dopamine, or fluctuations thereof, in the living body of a subject based on ocular information from the subject. (Item 2) The method of item 1, wherein the in-vivo dopamine or a substance bioequivalent to dopamine includes dopamine or a substance bioequivalent to dopamine in the brain. (Item 3) The method of item 1 or 2, wherein the ocular information includes at least a parameter related to eyeblinks. (Item 4) A method for evaluating a therapeutic or prophylactic agent or other medical technology for a patient with Parkinson's disease being treated with L-DOPA, an L-DOPA-related compound, or a dopamine agonist, comprising the steps of: A) acquiring ocular information from the patient; and B) calculating an estimated effective amount or effective level of the therapeutic or prophylactic agent or other medical technology from the ocular information. (Item 5) The method of item 4, wherein step B) comprises: inputting the ocular information into a trained model, the trained model having learned the relationship between the ocular information and an estimated effective amount or effectiveness level of the therapeutic or prophylactic drug or other medical technology; and obtaining an output from the trained model. (Item 6) The method of item 4, wherein step B) comprises: a) calculating an estimated presence or absence, amount, or level of dopamine or a substance bioequivalent to dopamine in the patient's living body based on the ocular information, and b) calculating an estimated effective amount or effectiveness level of the therapeutic or prophylactic drug or other medical technology from the presence or absence, amount, or level, or fluctuations thereof. (Item 7) The method of item 6, wherein step a) comprises: inputting the ocular information into a trained model, the trained model having learned the relationship between the ocular information and the presence or absence, amount, or level of dopamine or a substance bioequivalent to dopamine in the living body; and obtaining an output from the trained model.(Item 7A) The method according to Item 7, wherein the ocular information includes at least one of blink duration, blink frequency classified based on blink duration, frequency of long blinks and frequency of short blinks, and a ratio of the frequency of long blinks to the frequency of short blinks, and the in vivo dopamine or a substance biologically equivalent to dopamine is L-DOPA in peripheral blood. (Item 7B) The method according to Item 7, wherein the ocular information includes a ratio of short blinks to long blinks, a proportion of blinks whose blink duration is shorter than its first quartile, a median blink duration, a proportion of blinks whose blink duration is between its first quartile and its third quartile, the number of blinks whose blink duration is between its first quartile and its third quartile, a maximum blink duration, a standard deviation of the blink duration, the number of blinks whose blink duration is shorter than its first quartile, a square root of the number of blinks, an average blink duration, a logarithm of the average blink duration, and a square of the average blink duration, and wherein the in vivo dopamine or a substance biologically equivalent to dopamine is L-DOPA in peripheral blood. (Item 8) The method of any one of Items 4 to 7, wherein the therapeutic or prophylactic agent or other medical technology comprises L-DOPA, an L-DOPA-related compound, or a dopamine agonist, an L-DOPA adjunct, a dopamine nerve function restoring agent, a dopamine-producing cell pharmaceutical, a dopamine-producing gene therapy, or surgical therapy. (Item 9) The method of Item 8, wherein the surgical therapy comprises deep brain stimulation or stereotactic ablation. (Item 10) The method of any one of Items 4 to 9, further comprising the step of: C) estimating the effect of a drug having an improving effect on L-DOPA, an L-DOPA-related compound, or a dopamine agonist in a Parkinson's disease patient being treated with L-DOPA, an L-DOPA-related compound, or a dopamine agonist, based on the estimated effective amount or effective level. (Item 11) The method according to Item 10, wherein the effect of the medicament includes suppression of rapid fluctuations or time-dependent decreases in the amount of dopamine or dopamine agonist in the synaptic cleft of the striatum upon administration of L-DOPA, an L-DOPA-related compound, or a dopamine agonist.(Item 12) A method for estimating or predicting the condition of a patient with Parkinson's disease being treated with L-DOPA, an L-DOPA-related compound, or a dopamine agonist, comprising: A) acquiring ocular information of the patient; and B) estimating or predicting the patient's condition based on the ocular information. (Item 13) The method of item 12, wherein step B) comprises: inputting the ocular information into a trained model, the trained model having learned a relationship between the ocular information and the patient's condition; and obtaining an output from the trained model. (Item 13A) The method of item 13, wherein the ocular information includes at least one of blink duration, blink frequency classified based on blink duration, long blink frequency and short blink frequency, and a ratio of the long blink frequency to the short blink frequency, and the patient's condition is a dyskinesia score. (Item 13B) The method of Item 13, wherein the eyeball information includes the ratio of short blinks to long blinks, the median blink duration, the square of the average blink duration, the proportion of blinks whose blink duration is between the first and third quartiles, the standard deviation of the blink duration, the square root of the number of blinks, the logarithm of the average blink duration, the square of the number of blinks, the average blink duration, and the number of blinks, and the patient's condition is a dyskinesia score. (Item 13C) The method of Item 13, wherein the eyeball information includes at least one of blink duration, blink frequency classified based on blink duration, the frequency of long blinks and short blinks, and the ratio of the frequency of long blinks to the frequency of short blinks, and the patient's condition is a state in which dyskinesia occurs in four or more of six areas: the face, right arm, left arm, trunk, right leg, and left leg.(Item 13D) The method according to Item 13, wherein the eyeball information includes a ratio of blinks whose blink duration is shorter than the first quartile, a ratio of short blinks to long blinks, the number of blinks whose blink duration is between the first quartile and the third quartile, a ratio of blinks whose blink duration is between the first quartile and the third quartile, the number of blinks whose blink duration is shorter than the first quartile, a square root value of the number of blinks, a median blink duration, and a logarithm of the mean blink duration, and the patient's condition is a state in which dyskinesia occurs in four or more of six areas: the face, right arm, left arm, trunk, right leg, and left leg. (Item 14) The method of item 12, wherein step B) comprises: a) calculating an estimated presence / absence, amount, or level of dopamine or a substance bioequivalent to dopamine in the patient's living body based on the ocular information; and b) estimating or predicting the patient's condition from the presence / absence, amount, or level, or fluctuations thereof. (Item 15) The method of item 14, wherein step a) comprises inputting the ocular information into a trained model, the trained model having learned the relationship between the ocular information and the presence / absence, amount, or level of dopamine or a substance bioequivalent to dopamine in the living body; and obtaining an output from the trained model. (Item 16) The method of any one of Items 12 to 15, wherein the condition includes the presence or absence, degree, or score of at least one selected from the group consisting of dyskinesia, wearing off, ON-OFF, no-on phenomenon, delayed on phenomenon, akinesia, resting tremor, muscular rigidity, postural instability, forward leaning posture, freezing phenomenon, sleep disorder, mental, cognitive, or behavioral disorder, autonomic nervous disorder, and sensory disorder. (Item 17) The method of Item 16, wherein step B) comprises estimating or predicting the presence or absence, degree, or score of Parkinson's disease symptoms or the presence or absence, degree, or score of dyskinesia of the patient based on the ocular information. (Item 18) The method of Item 15, wherein step b) comprises estimating or predicting the condition of the patient by comparing the output from the model with one or more thresholds set for the patient.(Item 19) The method of item 18, further comprising calculating one or more thresholds customized for the patient. (Item 20) The method of item 19, wherein calculating one or more thresholds customized for the patient comprises, prior to step A), acquiring ocular information of the patient in a baseline state, and calculating thresholds from the ocular information in the baseline state. (Item 21) The method of any one of items 4 to 20, wherein step A) comprises acquiring at least one ocular information source and extracting the ocular information from the ocular information source. (Item 22) The method of item 21, wherein the at least one ocular information source comprises optical information, physical information, or electrical information of the patient's eye or muscles involved in eye movement or surrounding skin, or a combination of two or more thereof. (Item 23) The method of item 22, wherein acquiring the at least one ocular information source comprises acquiring the at least one ocular information source using at least one method selected from the group consisting of an image analysis method, a reflected light method, a distance measurement method, an electro-oculography method, a search coil method, and a probe method. 24. The method of claim 22, wherein the extracting the eye information from the eye information source comprises: deriving a first parameter related to the eye of the patient from the eye information source; and deriving a second parameter related to the eye of the patient by processing the first parameter, wherein the eye information comprises the first parameter and / or the second parameter. 25. The method of claim 24, wherein the first parameter comprises at least one selected from the group consisting of blink frequency or number of blinks for spontaneous, voluntary, or reflex blinks, blink duration, inter-blink time, eye closure time, eye opening velocity, eye closing velocity, eyelid movement amplitude, eyelid opening degree, and movement distance, movement direction, velocity, acceleration, angular velocity, saccades, smooth eye movement, vestibulo-ocular reflex, convergence / divergence, and fixational eye movements (ocular tremor, drift, microsaccades).(Item 26) The method of item 24 or item 25, wherein the second parameter includes at least one selected from the group consisting of a function output using the first parameter as an input variable, an intra-section calculated value which is a calculated value within each section when data of the first parameter is divided into multiple sections using a predetermined threshold, and an inter-section calculated value which is a calculated value of the intra-section calculated values ​​for the multiple sections. (Item 27) The method of item 26, wherein the intra-section calculated value includes various statistical quantities such as a maximum value, a minimum value, an average value, a median value, a variance, a 25% percentile value, a 75% percentile value, a frequency analysis spectrum, etc. (Item 28) The method of item 26 or item 27, wherein the inter-section calculated value includes a weighted sum, a difference, a time derivative, a time integral, a ratio, a correlation coefficient, and a covariance. (Item 29) The method of item 24, wherein the first parameter includes a blink duration, and the second parameter includes a blink frequency classified based on the blink duration, or a frequency of long blinks and a frequency of short blinks, or a ratio between the frequency of long blinks and the frequency of short blinks. (Item 30) The method of item 29, further comprising acquiring a criterion for classifying the blink duration from the ocular information source. (Item 31) The method of item 30, wherein acquiring the criterion for classifying the blink duration from the ocular information source includes: deriving time series data of blink frequency and the blink duration of each blink from an ocular information source acquired from the patient or another patient, classifying the time series data of blink frequency into a plurality of data according to the length of the blink duration, calculating a similarity between each of the plurality of classified data and other data among the plurality of classified data adjacent in terms of the blink duration, and setting a threshold for classifying the blink duration based on the similarity. (Item 32) A system for evaluating a therapeutic or prophylactic drug or other medical technology for a Parkinson's disease patient being treated with L-DOPA, an L-DOPA-related compound, or a dopamine agonist, comprising: means for acquiring ocular information of the patient; and means for calculating an estimated effective amount or level of the therapeutic or prophylactic drug or other medical technology from the ocular information.(Item 33) A system for estimating or predicting the condition of a patient with Parkinson's disease undergoing treatment with L-DOPA, an L-DOPA-related compound, a dopamine agonist, or a dopaminergic agent, comprising: means for acquiring ocular information of the patient; and means for estimating or predicting the patient's condition based on the ocular information. (Item 34) The system of items 32 or 33, wherein the system is a user device. (Item 35) The system of item 34, wherein the user device is an information processing device selected from the group consisting of a smartphone, a tablet computer, smart glasses, a smart watch, a laptop computer, and a desktop computer. (Item 36) The system of items 34 or 35, wherein the user device includes a portion that implements a function for measuring optical information of the eyeball or muscles or surrounding skin involved in eye movement and / or electrooculography. (Item 37) The system according to item 32 or 33, comprising a user device and a server device, wherein the user device comprises the information acquisition means, a transmission means for transmitting the eyeball information to the server device, and a receiving means for receiving the result estimated by the estimation means from the server device, and the server device comprises a receiving means for receiving the eyeball information from the user device, the calculation means, the estimation means, and a transmission means for transmitting the result estimated by the estimation means to the user device. (Item 38) The system according to item 32 or 33, comprising a user device and a server device, wherein the user device comprises the information acquisition means, the calculation means, a transmission means for transmitting the calculated presence / absence, amount or level to the server device, and a reception means for receiving a result estimated by the estimation means from the server device, and the server device comprises a reception means for receiving the calculated presence / absence, amount or level from the user device, the estimation means, and a transmission means for transmitting the result estimated by the estimation means to the user device. (Item 38A) The system according to item 32 or 33, including the features described in any one or more of the preceding items.(Item 39) A program for evaluating a therapeutic or prophylactic agent or other medical technology for a Parkinson's disease patient being treated with L-DOPA, an L-DOPA-related compound, or a dopamine agonist, the program being executed on a computer system including a processor and causing the processor to perform processes including: A) acquiring ocular information of the patient, and B) calculating an estimated effective amount or level of the therapeutic or prophylactic agent or other medical technology from the ocular information. (Item 40) A program for estimating or predicting the condition of a Parkinson's disease patient being treated with L-DOPA, an L-DOPA-related compound, or a dopamine agonist, the program being executed on a computer system including a processor and causing the processor to perform processes including: A) acquiring ocular information of the patient, and B) estimating or predicting the condition of the patient based on the ocular information. (Item 41) The program of items 39 or 40, wherein the computer system is a user device. (Item 42) The program according to item 39 or 40, wherein the computer system is a server device. (Item 42A) The program according to item 39 or 40, including the features described in any one or more of the above items. (Item 42B) A storage medium for storing the program according to any one of items 39 to 42A. (Item 43) A health management method for a patient with Parkinson's disease, comprising: performing the method according to item 1, 4 or 12; and if it is determined by the method that additional treatment is required for the Parkinson's disease patient, administering the treatment to the Parkinson's disease patient. (Item 43A) The method according to item 43, including the features described in any one or more of the above items.(Item 44) A health management method for a patient with Parkinson's disease, comprising: performing the method of items 1, 4, or 12; and if it is determined by the method that the patient with Parkinson's disease should be administered L-DOPA, an L-DOPA-related compound, or a dopamine agonist, an L-DOPA adjunct, a dopamine nerve function restoring agent, a dopamine-producing cell pharmaceutical product, or dopamine-producing gene therapy, or surgical therapy, administering the L-DOPA, an L-DOPA-related compound, or a dopamine agonist, an L-DOPA adjunct, a dopamine nerve function restoring agent, a dopamine-producing cell pharmaceutical product, or dopamine-producing gene therapy, or surgical therapy to the patient with Parkinson's disease. (Item 45) The method of item 44, wherein the surgical therapy includes deep brain stimulation or stereotactic ablation. (Item 45A) The method of item 44, including the features of any one or more of the preceding items. (Item 46) A method for managing the health of a patient with Parkinson's disease, comprising: performing the method described in item 1, 4 or 12; and if the method determines that additional treatment should be administered to the Parkinson's disease patient, issuing the treatment and an alert regarding the treatment. (Item 46A) The method of item 46, including the features described in any one or more of the preceding items. (Item 47) A system for managing the health of a patient with Parkinson's disease, comprising: an estimation or prediction system configured to be able to perform the method described in item 1, 4 or 12; and if the estimation or prediction system determines that additional treatment should be administered to the Parkinson's disease patient, means for administering the treatment to the Parkinson's disease patient. (Item 47A) The system of item 47, including the features described in any one or more of the preceding items.(Item 48) A system for health management of a patient with Parkinson's disease, comprising: an estimation or prediction system configured to be able to execute the method of items 1, 4, or 12; and, when the estimation or prediction system determines that the patient with Parkinson's disease should be administered L-DOPA or an L-DOPA-related compound or a dopamine agonist, an L-DOPA adjunct, a dopamine nerve function restoring agent, a dopamine-producing cell pharmaceutical product, or dopamine-producing gene therapy, or surgical therapy, a means for administering the L-DOPA or an L-DOPA-related compound or a dopamine agonist, an L-DOPA adjunct, a dopamine nerve function restoring agent, a dopamine-producing cell pharmaceutical product, or dopamine-producing gene therapy, or surgical therapy to the patient with Parkinson's disease. (Item 49) The system according to item 48, wherein the surgical therapy includes deep brain stimulation or stereotactic ablation. (Item 49A) The system according to item 48, including the features described in any one or more of the preceding items. (Item 50) A system for health management of a patient with Parkinson's disease, comprising: an estimation or prediction system configured to be able to execute the method described in items 1, 4 or 12; and, if the estimation or prediction system determines that additional treatment should be administered to the Parkinson's disease patient, means for issuing the treatment and an alert regarding the treatment. (Item 50A) The system according to item 50, including the features described in any one or more of the preceding items. (Item 51) A program for health management of a patient with Parkinson's disease, the program being executed on a computer system including a processor, the program causing the processor to perform processes including: performing the method described in items 1, 4 or 12; and, if the method determines that additional treatment should be administered to the Parkinson's disease patient, issuing instructions to administer the treatment to the Parkinson's disease patient. (Item 51A) A program according to Item 51, including the features according to any one or more of the above items.(Item 52) A program for health management of a patient with Parkinson's disease, the program being executed on a computer system having a processor, the program causing the processor to perform processes including: carrying out the method according to Item 1, 4 or 12; and if it is determined by the method that the patient with Parkinson's disease should be administered L-DOPA or an L-DOPA-related compound or a dopamine agonist, an L-DOPA adjunct, a dopamine nerve function restoring agent, a dopamine-producing cell pharmaceutical product, or dopamine-producing gene therapy, or surgical therapy, issuing instructions to administer the L-DOPA or an L-DOPA-related compound or a dopamine agonist, an L-DOPA adjunct, a dopamine nerve function restoring agent, a dopamine-producing cell pharmaceutical product, or dopamine-producing gene therapy, or surgical therapy to the patient with Parkinson's disease. (Item 53) The program according to item 52, wherein the surgical therapy includes deep brain stimulation or stereotactic ablation. (Item 53A) The program according to item 52, including the features described in any one or more of the above items. (Item 54) A program for health management of a patient with Parkinson's disease, the program being executed on a computer system having a processor, the program causing the processor to perform processing including: performing the method described in item 1, 4 or 12; and, if the method determines that additional treatment should be performed on the patient with Parkinson's disease, performing the treatment and issuing an alert regarding the treatment. (Item 54A) The program according to item 54, including the features described in any one or more of the above items. (Item 54B) A storage medium storing the program according to any one of items 51 to 54A.

[0009] It is contemplated that one or more of the above features may be provided in combinations other than those explicitly stated. Still further embodiments and advantages of the present disclosure will be recognized by those skilled in the art upon reading and understanding the following detailed description, if necessary.

[0010] The present disclosure provides a practically feasible method for estimating the condition of Parkinson's disease patients in real time, which can easily estimate the presence or absence, amount or level of dopamine or a substance biologically equivalent to dopamine in the brain, thereby providing a technology that contributes to improving the quality of life of Parkinson's disease patients.

[0011] FIG. 1 shows an example of the configuration of a system 10 according to one embodiment of the present disclosure. FIG. 2 shows an example of the structure of a neural network 20 for constructing a trained model that can be used by the calculation means 12. FIG. 3 shows an example of the configuration of a system 10′ according to another embodiment of the present disclosure. FIG. 4 is a diagram showing an example of the configuration of a system 1000 according to the present disclosure. FIG. 5A shows an example of the configuration of a user device 100. FIG. 5B shows an example of the configuration of a server device 200. FIG. 6 is a data flow diagram showing data exchange between the user device 100 and the server device 200 according to one embodiment. FIG. 7 is a data flow diagram showing data exchange between the user device 100 and the server device 200 according to another embodiment. FIG. 8 is a data flow diagram showing data exchange between the user device 100 and the server device 200 according to yet another embodiment. Figure 9A shows the results of measuring the time-dependent change in dopamine levels in the striatum using microdialysis after intraperitoneal administration of L-dopa to PD-LID model rats, followed by application of tandospirone or placebo tape. Figure 9B shows the calculated time required for a change in dopamine release of 0.2 pg or more. Application of tandospirone tape extended the duration of striatal dopamine release compared to the placebo group. Figure 9C shows the results of the total dopamine release after levodopa administration. No significant difference was observed in total dopamine release. Figure 10 shows blink count histograms in an MPTP-induced PD-LID model rhesus monkey. (A) shows the number of blinks every 3 minutes when the model monkey was orally administered vehicle (0.5% methylcellulose 400 aqueous solution). (B) shows the number of blinks every 3 minutes when the model monkey was orally administered L-DOPA / Benserazide suspended in vehicle (L-DOPA 22 mg / kg, Benserazide 1 / 4 weight of L-DOPA). (C) shows the total number of blinks from 30 to 120 minutes after vehicle administration and administration of the L-DOPA / Benserazide suspension. In (A), no increase in blinks was observed. In (B), blinks increased approximately 30 minutes after administration of the L-DOPA suspension, peaked at 60 minutes, then decreased, and then began to increase again from 120 minutes. In (C), the number of blinks tended to increase when L-DOPA was administered.Figure 11 shows the number of blinks in an MPTP-induced PD-LID model rhesus monkey after L-DOPA administration as a histogram for each blink duration every 3 minutes. The blink duration is T (s), where T is (A) T<0.14, (B) 0.14≦T<0.16, (C) 0.16≦T<0.18, (D) 0.18≦T<0.20, (E) 0.20≦T<0.25, and (F) 0.25≦T. The results show that the pattern of increased blinking induced by L-DOPA differs depending on the blink duration. Figure 12 shows the relationship between peak blinks and peak dyskinesia scores upon L-DOPA administration in an MPTP-induced PD-LID model rhesus monkey. (A) shows a histogram of the number of blinks upon L-DOPA administration in the model monkey, (B) shows the dyskinesia score in the same test, and (C) shows the generalized dyskinesia score in the same test. In (A), the number of blinks increased approximately 30 minutes after administration of the L-DOPA suspension, peaked at 60 minutes, and then attenuated, but began to increase again from 120 minutes. In (B), the dyskinesia score increased approximately 20 minutes after L-DOPA administration, peaked at 60 minutes, and did not attenuate until 180 minutes. In (C), the generalized dyskinesia score increased from 60 minutes after administration, peaked around 90 minutes, and then attenuated from around 150 minutes. These results show that the peak in the number of blinks occurs before the peak in dyskinesia. The results of Example 1 are shown. In Example 1, a linear regression model was trained to predict dyskinesia scores using the number of blinks per blink duration in an MPTP-induced PD-LID model rhesus monkey as a feature. The training data and test data were divided 8:2. (A) Changes in dyskinesia scores in the model monkey upon administration of L-DOPA are shown every 3 minutes. The blink duration is shown as T (s), and the weighted parameter is shown as θ. The results were as follows: (B) θ = 0.30 for T < 0.14; (C) θ = 0.87 for 0.14 ≦ T < 0.16; (D) θ = 0.20 for 0.16 ≦ T < 0.18; (E) θ = -0.066 for 0.18 ≦ T < 0.20; (F) θ = -0.33 for 0.20 ≦ T < 0.25; (G) θ = -0.33 for 0.25 ≦ T.The feature quantity that best predicted dyskinesia was the number of blinks for the blink duration where 0.14≦T<0.16. (H) The predicted dyskinesia score is plotted on the vertical axis, and the true dyskinesia score on the horizontal axis. The correlation coefficient between the predicted value and the true value in the test data was r=0.97, indicating a very strong correlation. The results of Example 1 are shown. In Example 1, a linear regression model was trained to predict the generalized dyskinesia score (A) in an MPTP-induced PD-LID model rhesus monkey from various feature quantities related to blink duration. The training data and test data were divided 8:2. As newly adopted features, a certain blink duration T (s) and its ratio were selected, and the weighted parameter θ was defined as (B) θ = 0.012 for short blinks (T < 0.16), (C) θ = -0.16 for long blinks (T ≥ 0.16), and (D) θ = 0.26 for the ratio of short blinks to long blinks. The feature that best predicted generalized dyskinesia was the ratio of short blinks to long blinks. (H) The predicted generalized dyskinesia score was plotted on the vertical axis and the true generalized dyskinesia score on the horizontal axis. The correlation coefficient r = 0.77 between the predicted and true values ​​in the test data, indicating a correlation. The results of Example 1 are shown below. In Example 1, a linear regression model was trained from all data to predict the dyskinesia score in an MPTP-induced PD-LID model rhesus monkey from the blink duration parameter when only L-DOPA was administered (see Figure 13). Using this trained model, (B) the dyskinesia score was predicted using the blink count parameter for each blink duration during tandospirone administration as a feature. (A) The predicted dyskinesia score (gray line) and the true dyskinesia score (black line) are shown. The correlation coefficient between them was r = 0.84, indicating a strong correlation. The results of Example 3 are shown. In Example 3, a linear regression model was trained to predict plasma L-DOPA concentrations using the blink count for each blink duration as a feature in an MPTP-induced PD-LID model rhesus monkey. The training data and test data were split 8:2. (A) Plasma L-DOPA concentrations during L-DOPA administration in the model monkey were measured every 30 minutes and linearly approximated. Blink duration is denoted as T (s), and the weighted parameter is denoted as θ.(B) For T<0.14, θ=966; (C) For 0.14≦T<0.16, θ=-1310; (D) For 0.16≦T<0.18, θ=399; (E) For 0.18≦T<0.20, θ=3744; (F) For 0.20≦T<0.25, θ=759; (G) For 0.25≦T, θ=-2448. The feature that best predicted plasma L-DOPA concentration was the number of blinks for the blink duration of 0.18≦T<0.20. (H) The predicted plasma L-DOPA concentration was plotted on the vertical axis, and the true plasma L-DOPA concentration on the horizontal axis. The correlation coefficient r=0.83 between the predicted and true values ​​in the test data, indicating a strong correlation. An example of a test flow for exploring objective parameters of ocular information correlated with Parkinson's symptoms. FIG. 1 shows an example of a decision tree for constructing a random forest model constructed to predict a dyskinesia score in Example 1'. FIG. 2 shows another example of a decision tree for constructing a random forest model constructed to predict a dyskinesia score in Example 1'. FIG. 3 shows the results of prediction by the random forest model constructed to predict a dyskinesia score in Example 1'. FIG. 4 shows an example of a decision tree for constructing a random forest model constructed to predict a generalized dyskinesia score in Example 1'. FIG. 5 shows another example of a decision tree for constructing a random forest model constructed to predict a generalized dyskinesia score in Example 1'. FIG. 6 shows the results of prediction by the random forest model constructed to predict a generalized dyskinesia score in Example 1'. FIG. 7 shows an example of a decision tree for constructing a random forest model constructed to predict an L-DOPA concentration in peripheral blood in Example 3'. FIG. 8 shows another example of a decision tree for constructing a random forest model constructed to predict an L-DOPA concentration in peripheral blood in Example 3'. FIG. 9 shows the results of prediction by the random forest model constructed to predict an L-DOPA concentration in peripheral blood in Example 3'.

[0012] The present disclosure will be described below. Throughout this specification, singular expressions should be understood to include the plural concept unless otherwise specified. Therefore, singular articles (e.g., "a," "an," "the," etc. in English) should be understood to include the plural concept unless otherwise specified. Furthermore, it should be understood that the terms used in this specification are used in the sense commonly used in the relevant field unless otherwise specified. Therefore, unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. In case of conflict, the present specification (including definitions) will prevail.

[0013] Definitions The following explains terms and general techniques used in this disclosure.

[0014] As used herein, "about" means ±10% of the preceding numerical value.

[0015] As used herein, the term "subject" is used in the same sense as "subject" or "test subject," and refers to an organism that is the target of treatment, diagnosis, or testing according to the present disclosure. When the subject is suffering from a disease, the term is used synonymously with "patient." The subject may be any animal, but is preferably a mammal (Rodentia such as mice and rats; Carnivora such as dogs; Artiodactyla such as cattle, pigs, and goats; Perissodactyla such as horses; or Primates), more preferably a primate, and even more preferably a human.

[0016] As used herein, "dopamine or a substance biologically equivalent to dopamine" refers to dopamine or any substance that has a function similar to that of dopamine in vivo. One such function is as a neurotransmitter. Dopamine functions mainly through three neural pathways: the nigrostriatal pathway, the mesolimbic pathway, and the mesocortical pathway. Since the nigrostriatal pathway is associated with Parkinson's disease, in this specification, a substance that can be said to be biologically equivalent to dopamine in relation to the nigrostriatal pathway is considered to be biologically equivalent to dopamine in the present disclosure.

[0017] In this specification, examples of substances bioequivalent to dopamine include, but are not limited to, levodopa (L-3,4-dihydroxyphenylalanine (IUPAC name: (S)-2-amino-3-(3,4-dihydroxyphenyl)propanoic acid), also known as L-dopa), as well as L-3,4-dihydroxyphenylalanine, bromocriptine, pergolide, talipexole, cabergoline, pramipexole, ropinirole, rotigotine, apomorphine, and the like.

[0018] As used herein, "L-DOPA or an L-DOPA-related compound or a dopamine agonist" refers to a substance that, when administered to a living body (e.g., a patient with Parkinson's disease), exhibits the pharmacological function of L-DOPA after being metabolized in the living body, for example, a substance that exhibits the biological function of "dopamine or a substance biologically equivalent to dopamine," and includes L-DOPA as well as L-DOPA-related compounds and dopamine agonists.

[0019] As used herein, the term "dopamine agonist" includes, but is not limited to, L-3,4-dihydroxyphenylalanine, bromocriptine, pergolide, talipexole, cabergoline, pramipexole, ropinirole, rotigotine, apomorphine and the like.

[0020] In this disclosure, "L-DOPA" or "levodopa" are synonymous.

[0021] In the present disclosure, the term "L-DOPA-related compound" refers to a compound that differs from L-DOPA but has biological functions equivalent to those of L-DOPA (particularly, for example, activity related to the treatment of Parkinson's disease). For example, L-DOPA-related compounds include, but are not limited to, esters of L-3,4-dihydroxyphenylalanine and salts thereof. Examples of L-3,4-dihydroxyphenylalanine esters include levodopa ethyl ester (LDEE; ethyl(2S)-2-amino-3-(3,4-dihydroxyphenyl)propanoate), levodopa propyl ester; levodopa propyl ester (propyl(2S)-2-amino-3-(3,4-dihydroxyphenyl)propanoate), and levodopa methyl ester (methyl(2S)-2-amino-3-(3,4-dihydroxyphenyl)propanoate). L-3,4-dihydroxyphenylalanine esters may be salts, including hydrated salts. Salts of levodopa esters may include, but are not limited to, octanoate, myristate, succinate, succinate dihydrate, fumarate, fumarate dihydrate, mesylate, tartrate, and hydrochloride. For example, the succinate or succinate dihydrate of an L-3,4-dihydroxyphenylalanine ester may be levodopa ethyl ester succinate (LDEE-S) or levodopa ethyl ester succinate dihydrate (LDEE-S-dihydrate or LDEE-S(d)). L-DOPA-related compounds also include L-DOPA prodrugs and depot preparations. L-DOPA-related compounds may overlap with dopamine agonists, and in this specification, they may be classified into either category.

[0022] As used herein, the term "adjunct" refers to a drug other than the drug that has the primary effect. In the present disclosure, if levodopa is the primary drug, then tandospirone and the like can be considered adjuncts. As used herein, the term "L-DOPA adjunct" refers to a drug that inhibits the metabolism or degradation of L-DOPA or regulates the release of L-DOPA-derived dopamine into the synaptic cleft. The daily dose of the primary drug, levodopa, is the usual dose for levodopa treatment set forth in the 2018 version of the Guidelines for the Treatment of Parkinson's Disease or corresponding guidelines in the United States and Europe. Generally, the usual daily dose of levodopa, when used in combination with or as a compounded drug with a peripheral dopa decarboxylase inhibitor (DCI), is 50 to 1200 mg / day, preferably 100 to 600 mg / day. For example, FDA-approved SINEMET® (Carbidopa-Levodopa Combination Tablets) (New Drug Application (NDA) #017555) is available as a 1:4 ratio combination tablet (25 mg Carbidopa-100 mg Levodopa) and a 1:10 ratio combination tablet (10 mg Carbidopa-100 mg Levodopa, 25 mg Carbidopa-250 mg Levodopa). The daily maintenance dose of SINEMET® is 70 mg to 100 mg of carbidopa, with a maximum daily dose of SINEMET® of 200 mg of carbidopa.

[0023] Examples of adjuvants for levodopa include levodopa metabolic enzyme inhibitors and dopamine release regulators.

[0024] As used herein, the term "levodopa metabolic enzyme inhibitor" refers to any drug having the effect of inhibiting the metabolism of levodopa in a broad sense so as to enhance the effect of levodopa, and examples thereof include dopa decarboxylase inhibitors (DCIs) (examples of which include carbidopa, α-methyldopa, benzerazide (Ro4-4602), α-difluoromethyl-DOPA (DFMD) or salts thereof) that prevent levodopa from being converted to dopamine in the intestines, liver, or blood vessels; catecholamine-O-methyltransferase inhibitors (COMT-Is) (examples of which include entacapone) that similarly prevent levodopa from being broken down before it enters the brain; and monoamine oxidase inhibitors (MAO-Is) (examples of which include selegiline) that prevent dopamine from being broken down in the brain. "Dopamine release controller" refers to a drug that has the effect of controlling the release of dopamine from dopamine neurons, and examples include zonisamide, amantadine, tandospirone, buspirone, and istradefylline.

[0025] As used herein, the term "agent for recovering dopaminergic neuron function" refers to any drug or treatment intended to restore the function of dopaminergic neurons and / or prevent or suppress degeneration or loss of dopaminergic neurons, and examples thereof include substances that have a dopaminergic neuron degenerative effect, such as vaccines or antibody drugs against alpha-synuclein, or nucleic acid drugs that affect the expression of alpha-synuclein.

[0026] As used herein, the term "dopamine-producing cell pharmaceutical" refers to a pharmaceutical product for treating Parkinson's disease by transplantation of dopaminergic neurons. Examples include cell transplantation of fetal midbrain tissue and cell transplantation of dopaminergic neuronal progenitor cells derived from human induced pluripotent stem cells (iPS cells).

[0027] As used herein, the term "dopamine-producing gene therapy" refers to a therapy in which an enzyme gene required for dopamine synthesis is introduced into the putamen using a viral vector. An example of such a therapy is a gene therapy in which an aromatic amino acid decarboxylase (AADC) gene is introduced into an adeno-associated virus (AAV).

[0028] As used herein, "surgical therapy" refers to surgical treatments such as deep brain stimulation (DBS) of the subthalamic nucleus, the internal segment of the globus pallidus, or the thalamus, stereotactic destruction of the thalamus or globus pallidus, and focused ultrasound therapy (FUS).

[0029] In the present disclosure, when a certain "amount" is mentioned, it may include not only an "absolute amount" in the narrow sense, but also a "relative amount" in the broad sense. A "relative amount" includes, for example, a change (e.g., an increase or decrease).

[0030] In this disclosure, the term "level" refers to an approximate degree, even if the amount cannot be specified. These levels refer to the degree of effect (action), and are based on the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS), UPDRS, Unified Dyskinesia Rating Scale (UDysRS), Clinical Dyskinesia Rating Scale (CDRS), The Rush Dyskinesia Rating Scale (Rush DRS), Abnormal Involuntary Movement Scale (AIMS), and EuroQol 5. It can be expressed using clinical assessment scales such as Parkinson's Disease Questionnaire-39 (PDQ-39), Clinical Global Impressions (CGI), and Patient Global Impression (PGI), patient diaries, and scales calculated from movement information obtained by wearable devices such as accelerometers and / or gyroscopes.

[0031] In this disclosure, "fluctuation" refers to a change over time or a value that has changed over time. A value that has changed over time is also referred to as the amount of change.

[0032] In this disclosure, "in vivo dopamine or a substance biologically equivalent to dopamine" refers to dopamine or a substance biologically equivalent to dopamine present in a certain organ, tissue (including blood), or cell.

[0033] In the present disclosure, "dopamine in the brain or a substance biologically equivalent to dopamine" refers to dopamine or a substance biologically equivalent to dopamine present in the brain. Dopamine or a substance biologically equivalent to dopamine in the brain can be measured by measuring the amount of dopamine using microdialysis, mass spectrometry imaging (MSI), or functional brain imaging using a single photon emission computed tomography (SPECT) preparation of a drug that is metabolized in the same way as L-DOPA, or a drug that has affinity for dopamine transporters or dopamine receptors, or a positron emission tomography (PET) preparation.

[0034] In the present disclosure, "eye information" refers to information related to the eye. Eye information includes, for example, information related to eye movement. In the present disclosure, "eye movement" refers to all movement related to the eye. Eye movement may include, for example, eye movement, changes in eye state, changes in electrooculography, etc. Eye movement is also included when the eye is stationary. Examples of eye information include, for example, information related to blinking, information related to pupil coordinates or the relative position of the eye, and information related to pupil diameter. Blinking includes, for example, spontaneous blinking, voluntary blinking, and reflex blinking. Information related to blinking includes, for example, but is not limited to, blink frequency or number of blinks, blink duration, inter-blink time, eye closure time, eye opening speed, eye closure speed, eyelid movement width, and eyelid opening degree. For example, information regarding pupil coordinates or relative eye position includes, but is not limited to, the presence or absence, amount, or level of movement distance, movement direction, speed, acceleration, angular velocity, saccades, smooth eye movements, vestibulo-ocular reflex, convergence / divergence, and fixational eye movements (ocular tremor, drift, microsaccades).

[0035] The information regarding blinks and the information regarding pupil coordinates or the relative position of the eyeball may also include calculated values ​​of the information. The calculated values ​​may include, for example, function outputs (such as powers, logarithms, distributions, and frequency analysis values) using the information as input variables. The calculated values ​​may include, for example, calculated values ​​of values ​​within each section (intra-section calculated values) when data is divided by a predetermined threshold (for example, a time-related threshold (time window), an absolute value threshold, or a relative value threshold). The intra-section calculated values ​​include, but are not limited to, various statistical quantities such as maximum, minimum, average, median, variance, 25% percentile, 75% percentile, and frequency analysis spectrum. The calculated values ​​may include, for example, calculated values ​​of intra-section calculated values ​​of multiple sections (inter-section calculated values). The inter-section calculated values ​​include, but are not limited to, weighted sums, differences, time derivatives, time integrals, ratios, correlation coefficients, and covariances.

[0036] In the present disclosure, a "Parkinson's disease patient being treated with L-DOPA" refers to a Parkinson's disease patient receiving treatment with the administration of L-DOPA, and symptoms can be confirmed using clinical assessment scales such as MDS-UPDRS, UPDRS, UDysRS, CDRS, Rush DRS, AIMS, EQ-5D-5L, PDQ-39, CGI, PGI, patient diaries, scales calculated from movement information obtained by wearable devices such as accelerometers and / or gyroscopes, and the like.

[0037] In the present disclosure, the term "therapeutic drug," when applied to Parkinson's disease patients, refers to a drug for curing Parkinson's disease or alleviating symptoms. Clinically, symptom improvement can be confirmed using clinical assessment scales such as MDS-UPDRS, UPDRS, UDysRS, CDRS, Rush DRS, AIMS, EQ-5D-5L, PDQ-39, CGI, and PGI, patient diaries, and scales calculated from movement information acquired by wearable devices such as accelerometers and / or gyroscopes.

[0038] In the present disclosure, the term "prophylactic drug," when applied to Parkinson's disease patients, refers to a drug for preventing the worsening of Parkinson's disease or the onset of symptoms (e.g., dyskinesia, etc.). Clinically, the preventive effect can be confirmed using clinical assessment scales such as MDS-UPDRS, UPDRS, UDysRS, CDRS, Rush DRS, AIMS, EQ-5D-5L, PDQ-39, CGI, and PGI, patient diaries, and scales calculated from movement information acquired by wearable devices such as accelerometers and / or gyroscopes.

[0039] In this disclosure, "medical technology" refers to technology for performing some kind of medical treatment, and may be provided in any form, such as pharmaceuticals, medical devices, regenerative medicine products, surgical treatments, etc.

[0040] In the present disclosure, the terms "effective amount" or "effective level" refer to an amount or level effective for the treatment or prevention of Parkinson's disease. In this specification, the daily dose of the active ingredient, levodopa, is the usual dose for levodopa treatment set forth in the 2018 version of the Guidelines for the Treatment of Parkinson's Disease or corresponding guidelines in the United States and Europe. Generally, the usual daily dose of levodopa, when used in combination with or as a compounded drug with a peripheral dopa decarboxylase inhibitor (DCI), is 50 to 1200 mg / day, preferably 100 to 600 mg / day. For example, FDA-approved SINEMET® (Carbidopa-Levodopa Combination Tablets) (New Drug Application (NDA) #017555) is available as a 1:4 ratio combination tablet (25 mg Carbidopa-100 mg Levodopa) and a 1:10 ratio combination tablet (10 mg Carbidopa-100 mg Levodopa, 25 mg Carbidopa-250 mg Levodopa). The daily maintenance dose of SINEMET® is 70 mg to 100 mg of carbidopa, with a maximum daily dose of SINEMET® of 200 mg of carbidopa.

[0041] In the present disclosure, the "effect of a drug having an improving effect on L-DOPA, an L-DOPA-related compound, or a dopamine agonist" refers to the effect of a drug having an improving effect on L-DOPA, an L-DOPA-related compound, or a dopamine agonist, and can be measured by various techniques such as those described below.

[0042] That is, the ameliorative effect of the present disclosure on levodopa-induced dyskinesia in Parkinson's disease can be confirmed clinically using clinical rating scales such as the MDS-UPDRS, UPDRS, UDysRS, CDRS, Rush DRS, AIMS, EQ-5D-5L, PDQ-39, CGI, and PGI, patient diaries, and scales calculated from movement information acquired by wearable devices such as accelerometers and / or gyroscopes. Furthermore, in non-clinical PD-LID model rats or PD-LID model monkeys, the ameliorative effect on dyskinesia can be confirmed by evaluating dyskinesia-like abnormal involuntary movement behavior. Using this method, it is possible to measure the improvement, progression inhibition, or prevention of levodopa-induced dyskinesia (PD-LID) symptoms, as well as the shortening of the onset time of levodopa-induced dyskinesia (PD-LID).

[0043] Parkinson's disease conditions that can be measured by the present disclosure include major motor symptoms such as akinesia and bradykinesia, tremor, muscle rigidity, loss of postural reflexes, floxed posture, freezing, sleep disorders, mental, cognitive, and behavioral disorders, autonomic nervous system disorders, sensory disorders, and any motor complications.

[0044] As used herein, "motor complications" refers to any motor symptoms observed in patients with advanced Parkinson's disease that pose a problem for treatment, and examples thereof include diurnal fluctuations of Parkinson's symptoms and dyskinesia, an involuntary movement associated with levodopa treatment (levodopa-induced dyskinesia (PD-LID)). Motor complications are considered to be due to abnormal dopamine release in the brain, but the underlying mechanism has not been fully elucidated.

[0045] As used herein, "diurnal fluctuations of Parkinson's symptoms (motor fluctuations)" include known phenomena such as wearing-off, on-off phenomenon, no-on phenomenon, and delayed-on phenomenon. Among these, wearing-off is a symptom observed when the dopamine retention capacity in the synaptic cleft decreases as the pathology progresses, causing the brain dopamine concentration to fluctuate in response to the blood concentration of levodopa, resulting in the blood concentration falling below the safe therapeutic range and shortening the duration of the effect of levodopa.

[0046] As used herein, "levodopa-induced dyskinesia (PD-LID)" refers to involuntary movements (dyskinesias) induced by excessive use of levodopa, which occurs in more than half of Parkinson's disease patients 5 to 10 years after starting treatment with levodopa, and the percentage of patients affected by PD-LID increases over time (for a review, see, e.g., Encarnacion and Hauser, (2008), "Levodopa-induced dyskinesias in Parkinson's disease: etiology, impact on quality of life, and treatments." Eur Neurol, 60(2), pp. 57-66).

[0047] Dyskinesia refers to involuntary and unstoppable movements of parts of the body, lip biting, difficulty speaking, inability to stay still, and difficulty moving the limbs as desired, and is a movement disorder characterized by involuntary movements of the limbs and / or orofacial region and / or axial region of the body. Peak-dose dyskinesia is known as a typical symptom of PD-LID, and symptoms appear in the face, tongue, neck, limbs, trunk, etc., during periods when levodopa blood concentrations are high. It is known that PD-LID is more likely to occur when excessively large amounts of levodopa are taken from the early stages of the disease, and that once PD-LID has appeared, it is very difficult to control even with various adjustments to the levodopa dosage.

[0048] (Preferred Embodiments) Preferred embodiments of the present disclosure will be described below. The embodiments provided below are provided for a better understanding of the present disclosure, and it is understood that the scope of the present disclosure should not be limited to the following description. Therefore, it is clear that those skilled in the art can make appropriate modifications within the scope of the present disclosure by referring to the description in this specification. It is also understood that the following embodiments of the present disclosure can be used alone or in combination.

[0049] The embodiments described below are all comprehensive or specific examples. The numerical values, shapes, materials, components, component placement and connection configurations, steps, and step order shown in the following embodiments are merely examples and are not intended to limit the scope of the claims. Furthermore, among the components in the following embodiments, components that are not described in the independent claims that represent the highest concepts are described as optional components.

[0050] (Method for predicting the amount of dopamine or a substance bioequivalent to dopamine in a living body, etc.) In one aspect of the present disclosure, there are provided a method for predicting the amount of dopamine or a substance bioequivalent to dopamine in a living body based on ocular information in a subject, a computer program for implementing the method, a recording medium for storing the program, a system, or a user device constituting part of the system, etc. In this aspect, the present disclosure provides a method for predicting the presence, amount, or level of dopamine or a substance bioequivalent to dopamine in a living body of a subject, or fluctuations thereof. In-vivo dopamine or a substance bioequivalent to dopamine may include any part of a living body, but particularly includes dopamine or a substance bioequivalent to dopamine in the brain, etc. Although not wishing to be bound by theory, it has been believed in the past that the concentration of dopamine or a substance biologically equivalent to dopamine in the body is related to involuntary movements, but it was not known that it was precisely linked to eye movements. The present disclosure makes it possible to know the concentration of dopamine or a substance biologically equivalent to dopamine in the body in real time by examining eye information more accurately and in real time.

[0051] L-DOPA is used as a standard treatment for Parkinson's disease, and current treatments have focused on the ability to control and manage the amount of L-DOPA or similar dopamine agonists (and their converted forms, dopamine or substances bioequivalent to dopamine) in the body (particularly in the brain, where they act). The present disclosure provides a method for determining the level of dopamine or substances bioequivalent to dopamine in the body from ocular information without examining the inside of the body, thereby providing an indication of the condition, therapeutic effect, and / or further treatment of Parkinson's disease patients being treated with dopamine or substances bioequivalent to dopamine. Controlling the in vivo (brain) concentration of dopamine or substances bioequivalent to dopamine by appropriate dosages and administration methods of L-DOPA, L-DOPA-related compounds, or dopamine agonists (e.g., dopamine agonists with activity similar to dopamine) may potentially achieve both improvement of Parkinson's symptoms and prevention of motor complications.

[0052] In the present disclosure, by conducting an unprecedented detailed analysis of eye movement parameters including blinks, we have discovered a parameter that is thought to reflect dopamine fluctuations more sensitively than spontaneous blink frequency.

[0053] The present disclosure has shown that the amount of dopamine or a substance biologically equivalent to dopamine in a living body, including the brain, tends to correlate with the number of blinks. Therefore, in the present disclosure, for example, the correlation between the number of blinks and the amount of dopamine in a healthy individual is used as an index to measure the number of blinks in a Parkinson's disease patient, and the dopamine amount can be predicted from this measurement. In another embodiment, correlations between the increase in the number of blinks per blink duration (e.g., >0.25 s, 0.25 s, 0.20 s, 0.16 s, 0.14 s, <0.12 s, etc.) when L-DOPA is administered to a Parkinson's disease patient and the occurrence of Parkinson's symptoms, motor complications, or psychiatric symptoms, or increases or decreases in the amount of dopamine or a substance with a biological function similar to dopamine, or blood L-DOPA concentration or fluctuations, etc., can be obtained in advance, and the presence, amount, level, or fluctuations of L-DOPA can be predicted by applying the ocular information obtained from the patient.

[0054] The specific calculation method for in vivo dopamine or a substance biologically equivalent to dopamine is as follows.

[0055] (Method for Estimating Dopamine or a Substance Bioequivalent to Dopamine in a Parkinson's Disease Patient Being Treated with L-DOPA, an L-DOPA-Related Compound, or a Dopamine Agonist) In one embodiment, the eyeball information of a Parkinson's disease patient is measured using a device capable of acquiring eyeball information, such as a glasses-type device equipped with an image recording function, an eye-tracking function, a three-point electrooculography sensor, an acceleration sensor, and / or a gyro sensor, eye-tracking glasses, a smartphone, a personal computer, or an electrooculography measuring device. The eyeball information includes, for example, information about blinks, information about pupil coordinates or the relative position of the eyeball, and information about eyeball movement. Blinks include, for example, spontaneous blinks, voluntary blinks, and reflex blinks. The blink-related information includes, for example, blink frequency or number of blinks, blink duration, inter-blink time, eye-closing time, eye-opening speed, eye-closing speed, eyelid movement width, and eyelid opening degree, but is not limited to these. For example, information about eye movement may include parameters such as eye movement distance, eye movement direction, eye movement velocity, eye acceleration, eye angular velocity, saccades, smooth eye movement, vestibulo-ocular reflex, convergence / divergence, fixational eye movements (ocular tremor, drift, microsaccades), and pupil diameter, or function outputs (such as exponentiation, logarithm, distribution, and frequency analysis values) using these information as input variables. The calculated values ​​may include, for example, calculated values ​​of values ​​within each segment (intra-segment calculated values) when data is divided by a predetermined threshold (e.g., a time-related threshold (time window), an absolute value threshold, or a relative value threshold). The intra-segment calculated values ​​include, but are not limited to, various statistical quantities such as maximum, minimum, mean, median, variance, 25% percentile, 75% percentile, and frequency analysis spectrum. The calculated values ​​may include, for example, calculated values ​​of intra-segment calculated values ​​of multiple segments (inter-segment calculated values). The calculated values ​​between the segments may be, for example, a weighted sum, a difference, a time differential, a time integral, a ratio, a correlation coefficient, a covariance, or the like.

[0056] The measurement time per session is determined to be long enough to ensure relatively stable measurement of ocular information, and may range from 30 seconds to 16 hours per session. More preferably, it ranges from 5 minutes to 8 hours. During the measurement, the patient may engage in unrestricted daily activities or may perform a task to measure specific eye movements. Examples of tasks include tracking an indicator that suddenly disappears and reappears on a display as a visual stimulus to induce saccades, or tracking a smoothly moving indicator to induce smooth eye movements. In this case, an accelerometer attached to the arm or head may be used in combination to evaluate ocular information in conjunction with head or arm movements, or a device for immobilizing the head or jaw may be used in combination to eliminate head movement factors. The patient may also keep a patient diary to record subjective symptoms.

[0057] In some embodiments, time intervals or "time windows" of digital data are extracted from the time series data. The duration of each time window can be set to be long enough to allow relatively stable determination of each ocular data measurement and the amount of in vivo dopamine or a dopamine bioequivalent, but short enough to capture changes in the amount of in vivo dopamine or a dopamine bioequivalent over time. For example, the width of the time window can range from 10 seconds to 30 minutes, and more preferably from 1 minute to 10 minutes.

[0058] In one embodiment, data from the eye tracking device is filtered to remove noise and extract attention data. Regarding blink data, for example, abnormally long blink events that would not normally occur in human blinks can be excluded, such as blink durations exceeding 1000 milliseconds. However, if the device is intended for use by users with abnormal blinking, such as Parkinson's disease patients, the exclusion criteria can be looser, such as 2000 milliseconds. Regarding eye movement data, for example, when focusing on ocular tremor, eye movements with large movement distances, such as saccades, become noise that interferes with frequency analysis, so time window data including data points with large movement distances can be excluded in advance.

[0059] In some embodiments, eye movements are obtained as discrete pupil coordinate data, but the data points may be smoothly interpolated using a spline curve or the like and upsampled to obtain sufficient time resolution.

[0060] Additionally, in some embodiments, the user's eye measurements may be normalized or corrected based on the user's previous measurements or measurements from other users.

[0061] In another embodiment, the amount of dopamine or a substance biologically equivalent to dopamine in a living body can be predicted from the correlation between ocular information and the amount of dopamine or a substance biologically equivalent to dopamine in a living body. The amount of dopamine or a substance biologically equivalent to dopamine in a living body can be predicted from the correlation between the eye movement parameters and the amount of dopamine or a substance biologically equivalent to dopamine in a living body. Specifically, by inputting appropriately selected parameters from among these parameters as features into a trained model, it is possible to estimate the amount of dopamine or a substance biologically equivalent to dopamine in a living body at present or after a certain time period.

[0062] In one embodiment, blink frequency information for a certain range of blink durations can be used to estimate the amount or level of dopamine or a substance bioequivalent to dopamine in peripheral blood. The certain range, for example, is 0.18 s to 0.25 s in absolute values. This range may be corrected or standardized based on individual patient differences and day-to-day variability, or may be corrected or standardized based on historical patient population data. Furthermore, blink duration may be divided into certain time intervals (e.g., 0.02 s) and independently weighted according to the strength of correlation. The resulting weighted sum of the number and / or increase over a certain time window can be used as an estimated index of the concentration or level of dopamine or a substance bioequivalent to dopamine in peripheral blood.

[0063] In one embodiment, when estimating fluctuations in brain dopamine or a substance bioequivalent to dopamine, blink frequency information for a certain range of blink durations can be used. For example, the certain range is 0.18 s to 0.25 s in absolute values. This range may be corrected or standardized based on individual patient differences or day-to-day variations, or may be corrected or standardized based on historical patient population data. Furthermore, blink duration may be divided into certain time intervals (e.g., 0.02 s) and independently weighted according to the strength of correlation. The weighted sum of the number and / or increase over a certain time window obtained in this manner can be used as an estimated index of brain dopamine concentration or level.

[0064] In one aspect of the present disclosure, there are provided a method for estimating the amount or level of dopamine or a substance bioequivalent to dopamine in the body of a Parkinson's disease patient undergoing treatment with L-DOPA, an L-DOPA-related compound, or a dopamine agonist, a computer program for implementing the method, a recording medium for storing the program, a system, or a user device constituting part of the system, etc. The method includes the steps of A) acquiring ocular information of the patient, and B) calculating the estimated presence or absence, amount, or level of dopamine or a substance bioequivalent to dopamine in the body of the patient based on the ocular information.

[0065] In another embodiment, the estimating step can estimate a change in the amount of dopamine in the synaptic cleft of the striatum upon administration of L-DOPA.

[0066] (Method for estimating the amount of L-DOPA or an L-DOPA-related compound or a dopamine agonist in the living body of a Parkinson's disease patient being treated with L-DOPA or an L-DOPA-related compound or a dopamine agonist) In one embodiment, the eyeball information of a Parkinson's disease patient is measured using a device capable of acquiring eyeball information, such as a glasses-type device equipped with an image recording function, an eye-tracking function, a three-point electrooculography sensor, an acceleration sensor, and / or a gyro sensor, eye-tracking glasses, a smartphone, a personal computer, an electrooculography measuring device, etc. Eyeball information includes, for example, information about blinks, information about pupil coordinates or relative eyeball position, and information about eyeball movement. Blinks include, for example, spontaneous blinks, voluntary blinks, and reflex blinks. Blink-related information includes, for example, blink frequency or number of blinks, blink duration, inter-blink time, eye-closing time, eye-opening speed, eye-closing speed, eyelid movement width, and eyelid opening degree, but is not limited to these. For example, information about eye movement may include parameters such as eye movement distance, eye movement direction, eye movement velocity, eye acceleration, eye angular velocity, saccades, smooth eye movement, vestibulo-ocular reflex, convergence / divergence, fixational eye movements (ocular tremor, drift, microsaccades), and pupil diameter, or function outputs (such as exponentiation, logarithm, distribution, and frequency analysis values) using these information as input variables. The calculated values ​​may include, for example, calculated values ​​of values ​​within each segment (intra-segment calculated values) when data is divided by a predetermined threshold (e.g., a time-related threshold (time window), an absolute value threshold, or a relative value threshold). The intra-segment calculated values ​​include, but are not limited to, various statistical quantities such as maximum, minimum, mean, median, variance, 25% percentile, 75% percentile, and frequency analysis spectrum. The calculated values ​​may include, for example, calculated values ​​of intra-segment calculated values ​​of multiple segments (inter-segment calculated values). The calculated values ​​between the segments may be, for example, a weighted sum, a difference, a time differential, a time integral, a ratio, a correlation coefficient, a covariance, or the like.

[0067] The measurement time per session is determined to be long enough to ensure relatively stable measurement of ocular information, and may range from 30 seconds to 16 hours per session. More preferably, it ranges from 5 minutes to 8 hours. During the measurement, the patient may engage in unrestricted daily activities or may perform a task to measure specific eye movements. Examples of tasks include tracking an indicator that suddenly disappears and reappears on a display as a visual stimulus to induce saccades, or tracking a smoothly moving indicator to induce smooth eye movements. In this case, an accelerometer attached to the arm or head may be used in combination to evaluate ocular information in conjunction with head or arm movements, or a device for immobilizing the head or jaw may be used in combination to eliminate head movement factors. The patient may also keep a patient diary to record subjective symptoms.

[0068] In one embodiment, time intervals or "windows" of digital data are extracted from the time series data, and the duration of each window is determined to be long enough to allow relatively stable determination of each ocular data measurement and the amount of in vivo dopamine or a bioequivalent thereof, but short enough to capture changes in the amount of in vivo dopamine or a bioequivalent thereof over time. For example, the width of the window may range from 10 seconds to 30 minutes, and more preferably from 1 minute to 10 minutes.

[0069] In one embodiment, data from the eye tracking device is filtered to remove noise and extract attention data. Regarding blink data, for example, abnormally long blink events that would not normally occur in human blinks can be excluded, such as blink durations exceeding 1000 milliseconds. However, if the device is intended for use by users with abnormal blinking, such as Parkinson's disease patients, the exclusion criteria can be looser, such as 2000 milliseconds. Regarding eye movement data, for example, when focusing on ocular tremor, eye movements with large movement distances, such as saccades, become noise that interferes with frequency analysis, so time window data including data points with large movement distances can be excluded in advance.

[0070] In some embodiments, eye movements are obtained as discrete pupil coordinate data, but the data points may be smoothly interpolated using a spline curve or the like and upsampled to obtain sufficient time resolution.

[0071] In some embodiments, the user's eye measurements may be normalized or corrected based on the user's previous measurements or measurements from other users.

[0072] In another embodiment, the amount of L-DOPA or an L-DOPA-related compound or a dopamine agonist in a living body can be predicted from the correlation between ocular information and the amount of L-DOPA or an L-DOPA-related compound or a dopamine agonist in a living body. The amount of L-DOPA or an L-DOPA-related compound or a dopamine agonist in a living body can be predicted from the correlation between the eye movement parameters and the amount of L-DOPA or an L-DOPA-related compound or a dopamine agonist in a living body. Specifically, by inputting appropriately selected parameters from among these parameters as features into a trained model, it is possible to estimate the amount of L-DOPA or an L-DOPA-related compound or a dopamine agonist in a living body at present or after a certain time.

[0073] In one embodiment, blink frequency information for a certain range of blink durations can be used to estimate the amount or level of L-DOPA, L-DOPA-related compounds, or dopamine agonists in peripheral blood. The certain range, for example, is 0.18 s to 0.25 s in absolute values. This range may be corrected or standardized based on individual patient differences or day-to-day variability, or may be corrected or standardized based on historical patient population data. Furthermore, blink duration may be divided into certain time intervals (e.g., 0.02 s) and independently weighted according to the strength of correlation. The weighted sum of the number and / or increase in the number of blinks within a certain time window obtained in this manner can be used as an estimated index of the concentration or level of L-DOPA, L-DOPA-related compounds, or dopamine agonists in peripheral blood.

[0074] In one aspect of the present disclosure, there are provided a method for estimating the amount of L-DOPA or an L-DOPA-related compound or a dopamine agonist in the body of a Parkinson's disease patient being treated with L-DOPA or an L-DOPA-related compound or a dopamine agonist, a computer program for implementing the method or a recording medium for storing the program, a system, or a user device constituting part of the system, etc. The method includes the steps of A) acquiring ocular information of the patient, and B) calculating the estimated presence, amount, or level of L-DOPA or an L-DOPA-related compound or a dopamine agonist in the body of the patient based on the ocular information.

[0075] The L-DOPA or L-DOPA-related compound or dopamine agonist in the patient's body can preferably be L-DOPA or L-DOPA-related compound or dopamine agonist in the brain. The L-DOPA or L-DOPA-related compound or dopamine agonist can include, for example, L-DOPA. These calculated factors are preferably those whose amounts, levels, or presence or absence affect the symptoms of Parkinson's disease patients. For example, in the case of L-DOPA, the amount can indicate whether the patient is in a therapeutic state. Furthermore, depending on the progression of the disease, a correlation can be found between the increase in the concentration of L-DOPA or L-DOPA-related compound or dopamine agonist taken into the body and the level of dopamine or a substance biologically equivalent to dopamine, and the resulting therapeutic state.

[0076] (Method for Evaluating Therapeutic or Preventive Drugs or Other Medical Technologies for Parkinson's Disease Patients Undergoing Treatment with L-DOPA, L-DOPA-Related Compounds, or Dopamine Agonists) In one embodiment, a patient with Parkinson's disease has their eye information measured using a device capable of acquiring eye information, such as a glasses-type device equipped with an image recording function, an eye tracking function, a three-point electrooculography sensor, an acceleration sensor, and / or a gyro sensor, eye tracking glasses, a smartphone, a personal computer, or an electrooculography measuring device. The eye information includes, for example, information about blinks, information about pupil coordinates or the relative position of the eyeballs, and information about eye movement. Blinks include, for example, spontaneous blinks, voluntary blinks, and reflex blinks. The blink information includes, for example, blink frequency or number of blinks, blink duration, inter-blink time, eye closure time, eye opening speed, eye closure speed, eyelid movement width, and eyelid opening degree, but is not limited to these. For example, information about eye movement may include parameters such as eye movement distance, eye movement direction, eye movement velocity, eye acceleration, eye angular velocity, saccades, smooth eye movement, vestibulo-ocular reflex, convergence / divergence, fixational eye movements (ocular tremor, drift, microsaccades), and pupil diameter, or function outputs (such as exponentiation, logarithm, distribution, and frequency analysis values) using these information as input variables. The calculated values ​​may include, for example, calculated values ​​of values ​​within each segment (intra-segment calculated values) when data is divided by a predetermined threshold (e.g., a time-related threshold (time window), an absolute value threshold, or a relative value threshold). The intra-segment calculated values ​​include, but are not limited to, various statistical quantities such as maximum, minimum, mean, median, variance, 25% percentile, 75% percentile, and frequency analysis spectrum. The calculated values ​​may include, for example, calculated values ​​of intra-segment calculated values ​​of multiple segments (inter-segment calculated values). The calculated values ​​between the segments may be, for example, a weighted sum, a difference, a time differential, a time integral, a ratio, a correlation coefficient, a covariance, or the like.

[0077] The measurement time per session is determined to be long enough to ensure relatively stable measurement of ocular information, and may range from 30 seconds to 16 hours per session. More preferably, it ranges from 5 minutes to 8 hours. During the measurement, the patient may engage in unrestricted daily activities or may perform a task to measure specific eye movements. Examples of tasks include tracking an indicator that suddenly disappears and reappears on a display as a visual stimulus to induce saccades, or tracking a smoothly moving indicator to induce smooth eye movements. In this case, an accelerometer attached to the arm or head may be used in combination to evaluate ocular information in conjunction with head or arm movements, or a device for immobilizing the head or jaw may be used in combination to eliminate head movement factors. The patient may also keep a patient diary to record subjective symptoms.

[0078] In some embodiments, time intervals or "time windows" of digital data can be extracted from the time series data. The duration of each time window is set to be long enough to allow for a relatively stable determination of each ocular data measurement and the status of the Parkinson's disease patient being treated with L-DOPA or an L-DOPA-related compound or a dopamine agonist, but short enough to capture changes over time in the status of the Parkinson's disease patient being treated with L-DOPA or an L-DOPA-related compound or a dopamine agonist. For example, the width of the time window can range from 10 seconds to 30 minutes, and more preferably from 1 minute to 10 minutes.

[0079] In one embodiment, data from the eye tracking device is filtered to remove noise and extract attention data. Regarding blink data, for example, abnormally long blink events that would not normally occur in human blinks can be excluded, such as blink durations exceeding 1000 milliseconds. However, if the device is intended for use by users with abnormal blinking, such as Parkinson's disease patients, the exclusion criteria can be looser, such as 2000 milliseconds. Regarding eye movement data, for example, when focusing on ocular tremor, eye movements with large movement distances, such as saccades, become noise that interferes with frequency analysis, so time window data including data points with large movement distances can be excluded in advance.

[0080] In some embodiments, eye movements are obtained as discrete pupil coordinate data, but the data points may be smoothly interpolated using a spline curve or the like and upsampled to obtain sufficient time resolution.

[0081] In some embodiments, the user's eye measurements may be normalized or corrected based on the user's previous measurements or measurements from other users.

[0082] In some embodiments, the condition of a Parkinson's disease patient being treated with L-DOPA, an L-DOPA-related compound, or a dopamine agonist can be predicted based on the correlation between ocular information and clinical rating scales such as the MDS-UPDRS, UPDRS, UDysRS, CDRS, Rush DRS, AIMS, EQ-5D-5L, PDQ-39, CGI, and PGI, or patient diaries, or output from other wearable devices designed to evaluate the condition of Parkinson's disease patients. By inputting appropriately selected eye movement parameters described above as features into a trained model, it is possible to estimate the current or future condition of a Parkinson's disease patient being treated with L-DOPA, an L-DOPA-related compound, or a dopamine agonist, and evaluate therapeutic or prophylactic drugs or other medical technologies.

[0083] In one embodiment, the effectiveness of a therapeutic drug, prophylactic drug, or other medical technology can be evaluated by acquiring ocular information over a time range before and after a patient has started or taken the drug, and comparing the information with the patient's condition, for example, from 10 minutes to 1 year, and more preferably from 30 minutes to 1 month.

[0084] In certain embodiments, ocular information from Parkinson's disease patients currently being treated with L-DOPA, an L-DOPA-related compound, or a dopamine agonist can be used to assess the appropriateness of the current treatment and optimize the treatment. For example, because blink parameters are thought to correlate with symptoms such as dopamine release and dyskinesia, ocular information can be used to infer whether dopamine is being released in the striatum at levels exceeding the appropriate range for treatment. In such patients, suppressing dopamine release to a desirable level using, for example, an adjuvant that suppresses dopamine release (e.g., tandospirone) is expected to improve dyskinesia, thereby assisting physicians in selecting the most appropriate therapeutic or prophylactic medication or other medical technique for the patient.

[0085] In another embodiment, by examining in advance the correlation between the dosage or treatment amount of a therapeutic or prophylactic drug or other medical technology in a Parkinson's disease patient and changes in the condition of the Parkinson's disease patient, it is possible to estimate the optimal dosage or treatment amount of a therapeutic or prophylactic drug or other medical technology from the eye movement information.

[0086] In one aspect of the present disclosure, there are provided a method for evaluating a therapeutic or prophylactic drug or other medical technology for a Parkinson's disease patient being treated with L-DOPA, an L-DOPA-related compound, or a dopamine agonist, a computer program for implementing the method or a recording medium for storing the program, and a system or a user device constituting part of the system. The method includes the steps of: A) acquiring ocular information of the patient; B) calculating an estimated presence / absence, amount, or level of L-DOPA or an L-DOPA-related compound in the patient's body based on the ocular information; C) estimating or predicting the patient's condition based on the ocular information; and D) calculating an estimated effective amount or effective level of the therapeutic or prophylactic drug or other medical technology from the presence / absence, amount, or level, or fluctuations thereof, and the patient's condition.

[0087] In one embodiment, the therapeutic or prophylactic agent or other medical technique may be L-DOPA or an L-DOPA-related compound, an L-DOPA adjunct, a dopamine neuron function restorative, a dopamine-producing cell pharmaceutical or dopamine-producing gene therapy, or surgical therapy (deep brain stimulation, stereotactic ablation, etc.).

[0088] Therefore, in a preferred embodiment of the present disclosure, the method of the present disclosure may further comprise the step of: E) calculating an estimated effective amount or effective level of L-DOPA or an L-DOPA-related compound or a dopamine agonist, an L-DOPA adjunct, a dopamine neuron function restoring agent, a dopamine-producing cell pharmaceutical product, or a dopamine-producing gene therapy in the patient based on the estimated effective amount or effective level.

[0089] In one embodiment, the effect of the pharmaceutical agent may include suppressing the rapid fluctuation or time-dependent decrease in the amount of dopamine or dopamine agonist in the synaptic cleft of the striatum upon administration of L-DOPA, an L-DOPA-related compound, or a dopamine agonist.

[0090] In one embodiment, blink frequency information can be used to estimate the ON / OFF state of a Parkinson's disease patient. Because the blink frequency decreases during ON state, a blink frequency exceeding a certain threshold can be considered ON. The threshold may be set, for example, in the range of 10 to 30 blinks per minute. This threshold may be corrected or standardized based on individual patient differences and day-to-day variability, or may be corrected or standardized based on past patient population data.

[0091] In one embodiment, when estimating ON / OFF as the state of a Parkinson's disease patient, the ratio of the weighted sum of the number and / or increase per time window of short blinks with a blink duration of a certain level or less to the weighted sum of long blinks with a blink duration of a certain level or more (weighted sum of short blinks / weighted sum of long blinks) can be used based on blink duration. A moderate or higher ratio (e.g., 1.5 or higher) of this ratio is an indicator of dyskinesia, while a ratio below that range (e.g., 0.2 to 1.5) can be used as an indicator of ON, in which improvement in motor symptoms is observed without dyskinesia. The range used as the indicator may be corrected or standardized based on individual patient differences and day-to-day variation, or may be corrected or standardized based on past patient population data.

[0092] By estimating the patient's condition in this way, it is possible to predict the therapeutic effect or ameliorative effect of a drug administered to the patient. For example, it is possible to estimate whether or not the rapid fluctuation or time-dependent decrease in the amount of dopamine or a dopamine agonist in the synaptic cleft of the striatum upon administration of L-DOPA, an L-DOPA-related compound, or a dopamine agonist can be suppressed. Such information can be used to suppress the occurrence of Parkinson's symptoms, motor complications, and psychiatric symptoms in the patient.

[0093] (Method for predicting the presence or absence, severity, score, etc. of dyskinesia) In one embodiment, eye information of a Parkinson's disease patient is measured using a device capable of acquiring eye information, such as a glasses-type device equipped with an image recording function, an eye tracking function, a three-point electrooculography sensor, an acceleration sensor, and / or a gyro sensor, eye tracking glasses, a smartphone, a personal computer, an electrooculography measuring device, etc. Eye information includes, for example, information about blinking, information about pupil coordinates or relative eye position, and information about eye movement. Blinking includes, for example, spontaneous blinking, voluntary blinking, and reflex blinking. Blink information includes, for example, blink frequency or number of blinks, blink duration, blink interval, eye closure time, eye opening speed, eye closure speed, eyelid movement width, and eyelid opening degree, but is not limited to these. For example, information about eye movement may include parameters such as eye movement distance, eye movement direction, eye movement velocity, eye acceleration, eye angular velocity, saccades, smooth eye movement, vestibulo-ocular reflex, convergence / divergence, fixational eye movements (ocular tremor, drift, microsaccades), and pupil diameter, or function outputs (such as exponentiation, logarithm, distribution, and frequency analysis values) using these information as input variables. The calculated values ​​may include, for example, calculated values ​​of values ​​within each segment (intra-segment calculated values) when data is divided by a predetermined threshold (e.g., a time-related threshold (time window), an absolute value threshold, or a relative value threshold). The intra-segment calculated values ​​include, but are not limited to, various statistical quantities such as maximum, minimum, mean, median, variance, 25% percentile, 75% percentile, and frequency analysis spectrum. The calculated values ​​may include, for example, calculated values ​​of intra-segment calculated values ​​of multiple segments (inter-segment calculated values). The calculated values ​​between the segments may be, for example, a weighted sum, a difference, a time differential, a time integral, a ratio, a correlation coefficient, a covariance, or the like.

[0094] The measurement time per session is determined to be long enough to ensure relatively stable measurement of ocular information, and may range from 30 seconds to 16 hours per session. More preferably, it ranges from 5 minutes to 8 hours. During the measurement, the patient may engage in unrestricted daily activities or may perform a task to measure specific eye movements. Examples of tasks include tracking an indicator that suddenly disappears and reappears on a display as a visual stimulus to induce saccades, or tracking a smoothly moving indicator to induce smooth eye movements. In this case, an accelerometer attached to the arm or head may be used in combination to evaluate ocular information in conjunction with head or arm movements, or a device for immobilizing the head or jaw may be used in combination to eliminate head movement factors. The patient may also keep a patient diary to record subjective symptoms.

[0095] In one embodiment, time intervals of digital data, or "time windows," can be extracted from the time-series data. The duration of each time window is set to be long enough to allow relatively stable determination of the ocular data measurements and the presence, severity, score, etc. of dyskinesia in a Parkinson's disease patient, but short enough to capture changes over time in the presence, severity, score, etc. of dyskinesia. For example, the width of the time window may range from 10 seconds to 30 minutes, and more preferably from 1 minute to 10 minutes.

[0096] In one embodiment, data from the eye tracking device is filtered to remove noise and extract attention data. Regarding blink data, for example, abnormally long blink events that would not normally occur in human blinks can be excluded, such as blink durations exceeding 1000 milliseconds. However, if the device is intended for use by users with abnormal blinking, such as Parkinson's disease patients, the exclusion criteria can be looser, such as 2000 milliseconds. Regarding eye movement data, for example, when focusing on ocular tremor, eye movements with large movement distances, such as saccades, become noise that interferes with frequency analysis, so time window data including data points with large movement distances can be excluded in advance.

[0097] In some embodiments, eye movements are obtained as discrete pupil coordinate data, but the data points may be smoothly interpolated using a spline curve or the like and upsampled to obtain sufficient time resolution.

[0098] In some embodiments, the user's eye measurements may be normalized or corrected based on the user's previous measurements or measurements from other users.

[0099] In another embodiment, a specific method for estimating dyskinesia in a Parkinson's disease patient can be clinically predicted from the correlation between the eye movement parameters described above and clinical rating scales such as MDS-UPDRS, UPDRS, UDysRS, CDRS, Rush DRS, AIMS, EQ-5D-5L5D, PDQ-39, CGI, and PGI, patient diaries, and outputs from other wearable devices designed to evaluate dyskinesia in Parkinson's disease patients. Specifically, by inputting appropriately selected parameters from these as features into a trained model, it is possible to estimate the presence or absence, severity, score, etc. of dyskinesia in a Parkinson's disease patient, either now or at a certain time point.

[0100] In one embodiment, when predicting the onset of dyskinesia after administration of L-DOPA, an L-DOPA-related compound, or a dopamine agonist, the number of "long blinks" per time window, or the amount of change therein, whose blink duration is equal to or greater than a certain value, can be used. In the present disclosure, it has been found that long blinks rapidly increase approximately 15 to 45 minutes prior to the onset of dyskinesia after administration of L-DOPA, an L-DOPA-related compound, or a dopamine agonist. The absolute value of the long blink duration can be, for example, in the range of 0.16 s to 0.50 s, more preferably in the range of 0.18 s to 0.25 s. This range may be corrected or standardized based on individual patient differences and day-to-day variability, or may be corrected or standardized based on past patient population data. Furthermore, the long blink duration may be divided into fixed time intervals (e.g., 0.02 s) and independently weighted according to the strength of correlation. The weighted sum of the number and / or increase in the number of long blinks over a certain time window obtained in this way can be used as an index of the probability of dyskinesia occurring after a certain time period, for example, 10 minutes.

[0101] In one embodiment, the presence and severity of dyskinesia at the time of measurement can be estimated using the number of "short blinks" whose blink duration is equal to or shorter than a certain value. In the present disclosure, it has been found that short blinks correlate with the presence and severity of dyskinesia. The absolute value of short blink duration can be, for example, less than 0.18 seconds. This range may be corrected or standardized based on individual patient differences and day-to-day variability, or may be corrected or standardized based on historical patient population data. Furthermore, the short blink duration may be divided into fixed time intervals (e.g., 0.02 seconds) and independently weighted according to the strength of correlation. The weighted sum of the number and / or increase in the number of short blinks within a time window obtained in this manner can be used as a probability index of the presence and severity of dyskinesia.

[0102] In one embodiment, the severity of dyskinesia at the time of measurement can be estimated based on blink duration by using the ratio of the weighted sum of the number and / or increase per time window for short blinks with a blink duration of a certain level or less to the weighted sum of long blinks with a blink duration of a certain level or more (weighted sum of short blinks / weighted sum of long blinks). This ratio is an index positively correlated with the occurrence of severe dyskinesia, and a moderate value (e.g., 1.5 or more) is considered to indicate the occurrence of dyskinesia, while a high value (e.g., 3.0 or more) indicates the occurrence of very severe dyskinesia.

[0103] In one embodiment, information about eye movements can be used to estimate the severity of dyskinesia at the time of measurement. Since eye tremors increase when the severity of dyskinesia is high, frequency analysis of pupil coordinates, eye movement distance, and eye movement velocity produces characteristic spectral bands whose power correlates with the severity.

[0104] In one embodiment, information on eyelid opening can be used to estimate the severity of dyskinesia at the time of measurement. When the severity of dyskinesia is high, the eyelids may droop and vibrate irregularly. Therefore, for example, the severity of dyskinesia can be estimated from a decrease in eyelid opening or an increase in its variance.

[0105] In one aspect of the present disclosure, there are provided a method for predicting the presence or absence, severity, and score of dyskinesia based on ocular information, a computer program for implementing the method or a recording medium for storing the program, a system, or a user device constituting a part of the system, etc. The method includes the steps of: A) acquiring ocular information of the patient; B) calculating the estimated presence or absence, amount, or level of L-DOPA or an L-DOPA-related compound in the patient's body based on the ocular information; and C) estimating or predicting the presence or absence, severity, or score of dyskinesia of the patient based on the ocular information.

[0106] (System for predicting the presence, absence, amount or level, or fluctuations thereof, of dopamine or a substance biologically equivalent to dopamine in a subject's living body) The above-mentioned method for predicting the presence, absence, amount or level, or fluctuations thereof of dopamine or a substance biologically equivalent to dopamine in a subject's living body is carried out, for example, using the system 10 of the present disclosure.

[0107] The system 10 is configured to calculate the presence or absence, amount or level, or fluctuations thereof, of dopamine or a substance bioequivalent to dopamine in the subject's living body based on the subject's ocular information.

[0108] 1 shows an example of the configuration of a system 10. The system 10 includes at least an information acquisition unit 11 and a calculation unit 12.

[0109] The information acquisition unit 11 is configured to acquire ocular information of the subject. The information acquisition unit 11 can acquire the ocular information by any means. For example, the information acquisition unit 11 may acquire ocular information stored in a storage unit external or internal to the system 10, or may acquire the ocular information by extracting the ocular information from an ocular information source. The ocular information source includes optical information (images and reflected light), physical information (vibrations, etc.), electrical information (electric potential, current, etc.), chemical information, or a combination thereof, of the subject's eye. The ocular information source may be, for example, at least one image of the subject's eye, electrooculography measurements, movement information of a body part of the subject (e.g., acceleration measurements), reflected light information of illumination light irradiated on the eye (cornea and / or sclera), current information acquired by a search coil method, or electromotive force information acquired from a piezoelectric element probe in direct contact with the eye. The reflected light information may be, for example, information acquired by a simple sensor such as a photodiode, or information acquired by image analysis. The movement information may be, for example, information obtained by image analysis or information obtained by distance measurement.

[0110] The eyeball information includes, for example, information about blinks, information about pupil coordinates or the relative position of the eyeballs, information about eyeball movement, and information about pupil diameter. Blinks include, for example, spontaneous blinks, voluntary blinks, and reflex blinks. Information about blinks includes, for example, but is not limited to, blink frequency or number of blinks, blink duration, inter-blink time, eye-closing time, eye-opening speed, eye-closing speed, eyelid movement width, and eyelid opening degree. For example, information about pupil coordinates or the relative position of the eyeballs includes, but is not limited to, the presence or absence, amount, or level of movement distance, movement direction, speed, acceleration, angular velocity, saccades, smooth eye movement, vestibulo-ocular reflex, convergence / divergence, and fixational eye movements (ocular tremor, drift, microsaccades).

[0111] Information about blinks can be obtained from eyeball images based on the pupil or eyelid in the image. For example, if an eyeball is irradiated with infrared light, photographed, and binarized using an appropriate threshold, the pupil is extracted as an ellipse with relatively low brightness information in the eyeball image. Ellipse fitting is performed on the binarized eyeball image. If the ellipse fitting is performed with a certain level of accuracy (an ellipse is recognized in the image), it can be inferred that the eye is open. If the accuracy is less than this level (an ellipse is not recognized in the image), it can be inferred that the eye is closed. A blink can be represented as an event in which the accuracy of the ellipse fitting decreases below a certain level within a time window of, for example, 0.25 seconds, and then increases again above this level. Information about blinks can also be obtained by capturing and analyzing the potential difference between the retina and cornea (electrooculography) during eyeball movement or eyelid movement using electrodes attached above and below and / or to the left and right of the eye. Information about blinks can also be obtained by extracting electrooculography waveforms characteristic of blinks. Specifically, the differential waveform of the electrooculogram during a blink first shows a sharp negative peak, then crosses the baseline and produces a positive peak, which corresponds to the reciprocating movement of the eyelids. This type of positive-negative reversal of the differential waveform is not observed in typical eye movements.

[0112] In this specification, "blink duration" can be defined as the time required for a blink event indicated by a change in pupil fitting level or a change in the electrooculography system. The number of blinks or blink frequency can be obtained as the number of blink events within a time window of, for example, three minutes, or as the number of blink events divided by the window time. The interblink time can be expressed as the time separating one blink event from the next.

[0113] The eye opening speed and the eye closing speed can be obtained by dividing the number of frames at that time or the eyelid movement width described below by the time required for the ellipse fitting accuracy to decrease below a certain level (eye closing event) and the time required for the ellipse fitting accuracy to increase from the certain level to above the certain level (eye opening event) in the case of an eyeball image during the above-mentioned blink event. In the case of an electro-oculography, the eye opening speed and the eye closing speed can be obtained by dividing the time required for the electro-oculography waveform to rise to a peak and the time required for it to decay from the peak, respectively.

[0114] When the boundary between the eyelid and the eyeball in an eyeball image is obtained by image processing such as edge detection based on brightness information, the eye-closing time is defined as the time during which the eyeball is covered by the eyelid by, for example, 80% or more in area, and can be extracted as the time per one occurrence or as a percentage within a certain time window. In electro-oculography, eye-closing and eye-opening correspond to the rise to a peak and decay from the peak, respectively, in the electro-oculography waveform, so the eye-closing time can be obtained as the time width exceeding any appropriate threshold (for example, 75% of the peak of the electro-oculography waveform).

[0115] In an eyeball image, the eyelid movement width can be expressed as the number of pixel units by which the eyelid moves during a measurement time of interest, such as the aforementioned eye-opening or eye-closing event, or as an actual distance corrected from the size of the captured image. In electro-oculography measurement, the eyelid movement width can be estimated from the peak size of the electro-oculography during a blink. In an eyeball image, the eyelid opening degree can be expressed as the distance or number of pixel units between two appropriately selected points (e.g., points where the curvature of each edge is maximum) on the upper and lower eyelids.

[0116] Information about eye movement can be obtained from eye images based on, for example, changes in pupil position information. In eye images, the distance of eye movement can be expressed as the number of pixel units by which the pupil position moves in an image over a certain time window, or as an angle (amplitude) corrected to account for the fact that the eye is spherical. Similarly, in eye images, the direction of eye movement can be expressed as the direction of displacement of the pupil position from a unit time before, with the pupil position in normal position taken as the origin. In electrooculography (EOG) measurement, the direction and distance (amplitude) of eye movement and the electrooculography output are measured in advance to clarify the correspondence between them. This allows the electrooculography information to be converted into eye movement information, and the movement distance and direction to be detected.

[0117] Eye velocity is expressed as the distance traveled by the eyeball, corrected to a value per unit time, typically one second. Eye acceleration is expressed as the value obtained by differentiating the eyeball velocity with respect to any unit of time. Eye angular velocity is expressed as the value obtained by differentiating the eyeball movement direction with respect to any unit of time. Saccades are fast, intermittent eye movements, reaching a maximum speed of 30 to 700 degrees per second. In time-series data around a data point where such high speeds exceed a certain threshold, a range in which the eyeball velocity continuously exceeds the stopping threshold can be defined as a single saccade. Smooth eye movement is a smooth, gentle eye movement of approximately 30 degrees per second, in which a moving object is captured and tracked by the fovea. For example, smooth eye movement can be considered to occur when the eyeball velocity and direction are approximately aligned with a visual stimulus (such as a view from a train window in the real world or a smoothly moving dot on a display in an experimental environment). The vestibulo-ocular reflex (VES) is an eye movement that automatically compensates for changes in gaze direction when the head is rotated without tilting. Experimentally, it can be defined as eye movements in which the velocity and acceleration of eye movements roughly match those of the head, but in the opposite direction. Convergence and divergence are the inward and outward movements of the eyes when looking at a nearby object, respectively, and the outward movements of the eyes when shifting the gaze point from near to far. These can be detected by the inward or outward movements of the eyes in response to approaching or receding visual stimuli. Fixational eye movements refer to minute eye movements observed during tasks requiring fixation on a single point and are classified as ocular tremors, drift, and microsaccades. While fixational eye movements can be difficult to detect using electrooculography, they can be detected using eye image measurements with high temporal resolution (100 Hz or higher, preferably 400 Hz or higher). Ocular tremor is shown in frequency analysis of eye movements as minute, periodic movements at 50 to 200 Hz, with a movement distance of less than 0.02 degrees. Drift is a slow movement with a movement speed of about 0.015 to 0.5 degrees per second. Microsaccades have a movement distance of about 0.015 to 0.5 degrees, but can reach a maximum speed of 20 to 100 degrees per second. Pupil diameter is obtained from the pupil image.Note that the pupil image becomes elliptical depending on the imaging direction, but the pupil diameter can be estimated by correcting the ellipticity.

[0118] The information regarding blinks and the information regarding pupil coordinates or the relative position of the eyeball may also include calculated values ​​of the information. The calculated values ​​may include, for example, function outputs (such as powers, logarithms, distributions, and frequency analysis values) using the information as input variables. The calculated values ​​may include, for example, calculated values ​​of values ​​within each section (intra-section calculated values) when data is divided by a predetermined threshold (for example, a time-related threshold (time window), an absolute value threshold, or a relative value threshold). The intra-section calculated values ​​include, but are not limited to, various statistical quantities such as maximum, minimum, average, median, variance, 25% percentile, 75% percentile, and frequency analysis spectrum. The calculated values ​​may include, for example, calculated values ​​of intra-section calculated values ​​of multiple sections (inter-section calculated values). The inter-section calculated values ​​include, but are not limited to, weighted sums, differences, time derivatives, time integrals, ratios, correlation coefficients, and covariances.

[0119] The eye information may include, for example, information about blinking, information about pupil coordinates or the relative position of the eye, information about eye movement (i.e., a first parameter), a calculated value (a second parameter) obtained by processing the first parameter, or both the first parameter and the second parameter.

[0120] In a preferred embodiment, the first parameter includes a blink duration, and the second parameter includes a ratio between a long blink duration and a short blink duration. Here, the criterion for distinguishing between a long blink duration and a short blink duration may be a fixed criterion or a criterion that varies from patient to patient. The criterion that varies from patient to patient can be obtained, for example, from ocular information sources obtained from patients. The criterion that varies from patient to patient can be obtained, for example, by a method including: (1) deriving time-series data of blink frequency and the blink duration of each blink from ocular information sources obtained from one or more patients; (2) classifying the time-series data of blink frequency into multiple data according to the length of the blink duration; (3) calculating a similarity between each of the classified multiple data and other data among the classified multiple data adjacent in terms of blink duration; and (4) setting a threshold for distinguishing between the blink durations based on the similarity. In (2), it is preferable to classify into a larger number of data, because this improves the resolution for searching for the threshold. In (3) and (4), any criterion can be used as the similarity. As an example, the similarity can be a correlation coefficient. For example, if the correlation coefficient of two pieces of time-series data with different blink durations is relatively high or the time-change patterns are similar, the two pieces of time-series data can be clustered into one cluster. On the other hand, if the correlation coefficient of two pieces of time-series data with different blink durations is relatively low or the time-change patterns are dissimilar, the two pieces of time-series data will be clustered into different clusters, and the blink duration that distinguishes the two pieces of time-series data will be set as the threshold for distinguishing the blink durations. The above-mentioned method also has the advantage of being able to compress the dimension of the data while maintaining the order of the feature (here, the magnitude relationship between the durations).

[0121] The eyeball information acquired by the information acquisition means 11 is passed to the calculation means 12 .

[0122] The calculation means 12 is configured to calculate the estimated presence or absence, amount or level, or fluctuation value of dopamine or a substance biologically equivalent to dopamine in the subject's living body based on the ocular information.

[0123] The calculation means 12 can calculate the estimated presence or absence, amount or level, or fluctuation value of dopamine or a substance biologically equivalent to dopamine in the subject's living body, for example, by using a trained model. The trained model has learned the relationship between, for example, ocular information and the estimated presence or absence, amount or level, or fluctuation value of dopamine or a substance biologically equivalent to dopamine in the living body.

[0124] FIG. 2 shows an example of the structure of a neural network 20 for constructing a trained model that can be used by the calculation means 12.

[0125] The neural network 20 has an input layer, at least one hidden layer, and an output layer. The number of nodes in the input layer of the neural network 20 corresponds to the dimensionality of the input data. The number of nodes in the output layer of the neural network 20 corresponds to the dimensionality of the output data. For example, if the output is to indicate the estimated presence or absence (i.e., whether or not there is) of dopamine or a substance bioequivalent to dopamine in the subject's living body, the number of nodes in the output layer is one. The hidden layer of the neural network 20 can include any number of nodes.

[0126] The weighting coefficients of each node in the hidden layer of the neural network 20 can be calculated based on previously acquired data. The process of calculating these weighting coefficients is a learning process. The learning process may be supervised learning or unsupervised learning.

[0127] In the case of supervised learning, for example, weight coefficients for each node can be calculated so that when a patient's eye information is input to the input layer, the output layer value indicates the estimated presence, amount, or level of dopamine or a substance bioequivalent to dopamine in the patient's body, or its fluctuation. This can be done, for example, by backpropagation. While a large training data set is preferable, using too large a data set can easily lead to overfitting.

[0128] For example, a set of (input training data, output training data) for supervised learning may be (ocular information of a first patient, a value indicating the estimated presence or absence, amount or level, or fluctuation value of dopamine or a substance bioequivalent to dopamine in the first patient's living body), (ocular information of a second patient, a value indicating the estimated presence or absence, amount or level, or fluctuation value of dopamine or a substance bioequivalent to dopamine in the second patient's living body), ... (ocular information of an i-th patient, a value indicating the estimated presence or absence, amount or level, or fluctuation value of dopamine or a substance bioequivalent to dopamine in the i-th patient's living body), ... etc. When ocular information obtained from a new patient is input to the input layer of such a trained neural network model, a value indicating the estimated presence or absence, amount or level, or fluctuation value of dopamine or a substance bioequivalent to dopamine in the patient's living body is output to the output layer.

[0129] The calculation means 12 may utilize, for example, a plurality of trained models. For example, a first trained model of the plurality of trained models can output a value indicating the estimated presence or absence of dopamine or a substance bioequivalent to dopamine in the patient's body, a second trained model of the plurality of trained models can output a value indicating the amount or level of dopamine or a substance bioequivalent to dopamine in the patient's body, and a third trained model of the plurality of trained models can output a value indicating the fluctuation value of the amount or level of dopamine or a substance bioequivalent to dopamine in the patient's body.

[0130] The trained model described above is not limited to the neural network model described above, and any other machine learning model can be used. For example, a random forest can be used. For example, a simple model constructed by simply setting weight coefficients obtained by prior training can also be used.

[0131] For example, the calculation means 12 may calculate the estimated presence or absence, amount or level, or fluctuation value of dopamine or a substance biologically equivalent to dopamine in the subject's living body on a rule-based basis, without relying on machine learning.

[0132] In one example, the technology of the present disclosure can be implemented without using a machine learning process by using a system that predicts the amount of dopamine or a substance bioequivalent to dopamine based on the amount and time course of drug administration based on a pharmacokinetic model, or a system that estimates the appropriate level (insufficient / excessive) of dopamine or a substance bioequivalent to dopamine based solely on motor symptoms. Alternatively, the present disclosure can be implemented by collecting data from a system implemented without using a machine learning process and training a model as virtual ground truth values, or by using these systems in combination and training a trained model using the data as model variables or for correcting model output values.

[0133] The estimated presence, amount, or level of dopamine or a substance bioequivalent to dopamine in a subject's living body, or fluctuations thereof, calculated by system 10 can be used for any purpose. For example, a doctor can use the value calculated by system 10 as an index for diagnosis. For example, a patient can use the value calculated by system 10 as an index for understanding their own condition.

[0134] In one embodiment, the system 10' of the present disclosure can utilize the estimated presence, absence, amount, or level of dopamine or a substance bioequivalent to dopamine, or variations thereof, in the subject's living body to estimate the patient's condition. Estimating the patient's condition includes, for example, estimating the patient's dyskinesia score. In another embodiment, the system 10' of the present disclosure can utilize the estimated presence, absence, amount, or level of dopamine or a substance bioequivalent to dopamine, or variations thereof, in the subject's living body to estimate the estimated effective amount or level of a therapeutic or prophylactic drug or other medical technique for a Parkinson's disease patient being treated with L-DOPA, an L-DOPA-related compound, or a dopamine agonist.

[0135] FIG. 3 shows an example of the configuration of the system 10'.

[0136] The system 10′ includes at least an information acquisition unit 11, a calculation unit 12, and an estimation unit 13. The configuration of the system 10′ is the same as the configuration of the system 10, except that the system 10′ includes the estimation unit 13. In Fig. 3, the same components as those described above with reference to Fig. 1 are denoted by the same reference numerals, and descriptions thereof will be omitted here.

[0137] The estimated presence or absence, amount or level, or fluctuation value thereof of dopamine or a substance biologically equivalent to dopamine in the subject's living body calculated by the calculation means 12 is passed to the estimation means 13.

[0138] In one embodiment, the estimation means 13 is configured to estimate the state of the patient based on the estimated presence or absence, amount or level, or fluctuations thereof, of dopamine or a substance bioequivalent to dopamine in a living body.

[0139] The estimation means 13 can estimate the patient's condition by, for example, comparing one or more thresholds set for the patient with the estimated presence, amount, or level of dopamine or a substance bioequivalent to dopamine in the body, or fluctuations thereof. The one or more thresholds set for the patient can be calculated, for example, based on ocular information acquired in advance or in real time by the information acquisition means 11 (e.g., the patient's condition can be manually calibrated from a doctor's or a patient diary). The thresholds can be set by a specialist and / or the patient themselves based on ocular information acquired in advance or in real time, or the system itself can set the thresholds based on trends learned from past data of the patient or other patient groups. The one or more thresholds set for the patient can be calculated, for example, based on other information in addition to the ocular information (e.g., the patient's condition can be manually calibrated from a doctor's or a patient diary). The other information can include, but is not limited to, information acquired by a wearable device, a medication record kept by the patient, etc. For example, when information acquired by a wearable device, a medication record kept by a patient, or other information indicates a certain condition of a patient, the eye information at that time can be set as a threshold for that condition. The threshold may be, for example, a fixed value for each patient, or may be a variable value. If the threshold is a variable value, it may vary depending on, for example, time, location, situation, etc.

[0140] The estimation means 13 can also estimate the state of a Parkinson's disease patient undergoing treatment with L-DOPA, an L-DOPA related compound, or a dopamine agonist, for example, by the above-mentioned method.

[0141] In another embodiment, the estimation means 13 is configured to estimate an estimated effective amount or effective level of a therapeutic or prophylactic drug or other medical technology for a Parkinson's disease patient being treated with L-DOPA or an L-DOPA-related compound or a dopamine agonist, based on the estimated presence / absence, amount or level of L-DOPA or an L-DOPA-related compound or a dopamine agonist in a living body, or fluctuations thereof.

[0142] (System for Estimating the State of a Parkinson's Disease Patient Being Treated with L-DOPA or an L-DOPA-Related Compound or a Dopamine Agonist) The above-described method for estimating the state of a Parkinson's disease patient being treated with L-DOPA or an L-DOPA-related compound or a dopamine agonist or the method for predicting a dyskinesia score can be carried out, for example, using the above-described system 10′.

[0143] As described above, the system 10′ is configured to calculate the estimated presence or absence, amount or level, or fluctuation value of dopamine or a substance biologically equivalent to dopamine in a living body based on ocular information, and to estimate the patient's condition based on the calculated value.

[0144] (System for Evaluating Therapeutic or Preventive Drugs or Other Medical Techniques for Parkinson's Disease Patients Being Treated with L-DOPA or L-DOPA-Related Compounds or Dopaminergic Drugs) The above-described method for evaluating therapeutic or preventive drugs or other medical techniques for Parkinson's disease patients being treated with L-DOPA or L-DOPA-related compounds or dopaminergic drugs is carried out using, for example, the above-described system 10'.

[0145] As described above, the system 10′ is configured to calculate the estimated presence or absence, amount or level of dopamine or a substance biologically equivalent to dopamine in a living body, or a fluctuation value thereof, based on ocular information, and to calculate the estimated effective amount or effective level of the therapeutic or prophylactic drug or other medical technology based on the calculated value.

[0146] (System Implementation Example) The above-described systems 10 and 10' can be implemented, for example, in a system 1000 including a user device and a server device connected via a network.

[0147] FIG. 4 is a diagram illustrating an example of the configuration of a system 1000 according to the present disclosure.

[0148] The system 1000 includes at least one user device 100 , a server device 200 connected to the at least one user device 100 via a network 400 , and a database unit 300 connected to the server device 200 .

[0149] The user device 100 may be any terminal device, such as a smartphone, a tablet computer, smart glasses, a smart watch, a laptop computer, a desktop computer, or an eye tracker. The user device 100 can communicate with the server device 200 via a network 400. The type of network 400 is not important. For example, the user device 100 may communicate with the server device 200 via the Internet or via a LAN. Although three user devices 100 are depicted in FIG. 4 , the number of user devices 100 is not limited to this. The number of user devices 100 may be any number equal to or greater than one.

[0150] The server device 200 can communicate with at least one user device 100 via the network 400. The server device 200 can also communicate with a database unit 300 connected to the server device 200.

[0151] For example, the database unit 300 connected to the server device 200 may store eye information of a plurality of subjects that has been acquired in advance. The stored eye information may be used, for example, to construct a trained model. For example, the database unit 300 may store the constructed trained model.

[0152] FIG. 5A shows an example of the configuration of the user device 100.

[0153] The user device 100 includes a communication interface unit 110 , an input unit 120 , a display unit 130 , a memory unit 140 , and a processor unit 150 .

[0154] The communication interface unit 110 controls communication via the network 400. The processor unit 150 of the user device 100 can receive information from outside the user device 100 and can transmit information to outside the user device 100 via the communication interface unit 110. For example, the processor unit 150 of the user device 100 can receive information from the server device 200 and can transmit information to the server device 200 via the communication interface unit 110. The communication interface unit 110 can control communication in any manner.

[0155] The input unit 120 enables a user to input information into the user device 100. It does not matter in what manner the input unit 120 enables a user to input information into the user device 100. For example, if the input unit 120 is a touch panel, the user may input information by touching the touch panel. Alternatively, if the input unit 120 is a mouse, the user may input information by operating the mouse. Alternatively, if the input unit 120 is a keyboard, the user may input information by pressing keys on the keyboard. Alternatively, if the input unit 120 is a microphone, the user may input information by voice.

[0156] Display 130 can be any display for displaying information.

[0157] The memory unit 140 stores programs for executing processes in the user device 100, data required for executing the programs, and the like. The memory unit 140 stores, for example, a program for predicting the presence, amount, or level of dopamine or a substance biologically equivalent to dopamine, or fluctuations thereof, a program for estimating a patient's condition, and / or a program for evaluating therapeutic or prophylactic drugs or other medical technologies for Parkinson's disease patients being treated with L-DOPA, an L-DOPA-related compound, or a dopamine agonist (e.g., a program for implementing the processes shown in Figures 6 to 8 , described below). The memory unit 140 may also store an application that implements any function. For example, the memory unit 140 may store an application for acquiring eye information by tracking pupils in eye images (eye tracking). This allows the user device 100 to have a portion that realizes the eye tracking function. The memory unit 140 may also store, for example, an application for acquiring eye information from electro-oculography measurements. This allows the user device 100 to have a portion that realizes the electro-oculography function. In addition, the memory unit 140 may store an application for acquiring eyeball information by other means. Here, it does not matter how the program is stored in the memory unit 140. For example, the program may be pre-installed in the memory unit 140. Alternatively, the program may be installed in the memory unit 140 by being downloaded via the network 400. The memory unit 140 may be implemented by any storage means.

[0158] The processor unit 150 controls the overall operation of the user device 100. The processor unit 150 reads and executes programs stored in the memory unit 140. This allows the user device 100 to function as a device that executes desired steps. The processor unit 150 may be implemented by a single processor or by multiple processors.

[0159] In addition to the above-described configuration, the user device 100 may include an imaging unit 160. The imaging unit 160 is any unit for acquiring an image. The imaging unit 160 may be, for example, a camera. Here, "image" includes still images and video images. The imaging unit 160 may be, for example, a camera built into the user device 100, or an external camera attached to the user device 100. For example, if the user device 100 is a smartphone, the camera built into the smartphone can be used as the imaging unit 160.

[0160] The user device 100 may include an electro-oculography acquisition unit 170 in addition to the above-described configuration or instead of the above-described imaging unit 160. The electro-oculography acquisition unit 170 is any unit for acquiring an electro-oculography. The electro-oculography acquisition unit 170 is, for example, a plurality of electrodes that can be placed on the skin around the eye. For example, the electro-oculography acquisition unit 170 may be electrodes built into the user device 100 or external electrodes attached to the user device 100. For example, if the user device 100 is a glasses-type eye tracker, electrodes built into the glasses-type eye tracker can be used as the electro-oculography acquisition unit 170.

[0161] In the example shown in FIG. 5A , each component of the user device 100 is provided within the user device 100, but the present disclosure is not limited to this. Any of the components of the user device 100 may also be provided externally to the user device 100. For example, if the input unit 120, display unit 130, memory unit 140, processor unit 150, and imaging unit 160 are each configured as separate hardware components, the hardware components may be connected via any network. In this case, the type of network does not matter. The hardware components may be connected, for example, via a LAN, wirelessly, or via a wired connection. The user device 100 is not limited to a specific hardware configuration. For example, it is within the scope of the present disclosure to configure the processor unit 150 using analog circuits rather than digital circuits. The configuration of the user device 100 is not limited to the above-described configuration as long as it can realize its functions.

[0162] FIG. 5B shows an example of the configuration of the server device 200.

[0163] The server device 200 includes a communication interface unit 210 , a memory unit 220 , and a processor unit 230 .

[0164] The communication interface unit 210 controls communication via the network 400. The communication interface unit 210 also controls communication with the database unit 300. The processor unit 230 of the server device 200 is capable of receiving information from outside the server device 200 and transmitting information to outside the server device 200 via the communication interface unit 210. The processor unit 230 of the server device 200 is capable of receiving information from the user device 100 and transmitting information to the user device 100 via the communication interface unit 210. The communication interface unit 210 can control communication in any manner.

[0165] The memory unit 220 stores programs required for the execution of the processes of the server device 200, data required for the execution of those programs, and the like. For example, the memory unit 220 stores part or all of a program for predicting the presence, amount, or level of dopamine or a substance biologically equivalent to dopamine in a subject's living body, or fluctuations therein; a program for estimating a patient's condition; and / or a program for evaluating therapeutic or prophylactic drugs or other medical technologies for Parkinson's disease patients being treated with L-DOPA, an L-DOPA-related compound, or a dopamine agonist (e.g., a program implementing the processes shown in Figures 6 to 8 described below). The memory unit 220 may also store, for example, an application for acquiring eye information by tracking pupils in eye images (eye tracking) or an application for acquiring eye information from electro-oculography measurements. The memory unit 220 may also store, for example, an application for acquiring eye information by other means. The memory unit 220 may be implemented by any storage means.

[0166] The processor unit 230 controls the overall operation of the server device 200. The processor unit 230 reads out a program stored in the memory unit 220 and executes the program. This allows the server device 200 to function as a device that executes desired steps. The processor unit 230 may be implemented by a single processor or by multiple processors.

[0167] In the example shown in FIG. 4B , each component of the server device 200 is provided within the server device 200, but the present disclosure is not limited to this. Any of the components of the server device 200 may also be provided externally to the server device 200. For example, if the memory unit 220 and the processor unit 230 are each configured as separate hardware components, the hardware components may be connected via any network. In this case, the type of network does not matter. The hardware components may be connected, for example, via a LAN, wirelessly, or via a wired connection. The server device 200 is not limited to a specific hardware configuration. For example, it is within the scope of the present disclosure for the processor unit 230 to be configured using analog circuits rather than digital circuits. The configuration of the server device 200 is not limited to the one described above, as long as it can realize its functions.

[0168] In the examples shown in FIGS. 4 and 5B , the database unit 300 is provided outside the server device 200, but the present disclosure is not limited to this. The database unit 300 can also be provided inside the server device 200. In this case, the database unit 300 may be implemented by the same storage means as the storage means that implements the memory unit 220, or by a storage means separate from the storage means that implements the memory unit 220. In either case, the database unit 300 is configured as a storage unit for the server device 200. The configuration of the database unit 300 is not limited to a specific hardware configuration. For example, the database unit 300 may be configured as a single hardware component or multiple hardware components. For example, the database unit 300 may be configured as an external hard disk drive for the server device 200, or as cloud storage connected via a network.

[0169] The components of the system 10 may be provided, for example, in the user device 100, in the server device 200, or distributed across both the user device 100 and the server device 200. When the components of the system 10 are distributed across both the user device 100 and the server device 200, the information acquisition means 11 may be provided in the user device 100, and the calculation means 12 may be provided in the server device 200.

[0170] Similarly, the components of system 10′ may be provided, for example, in user device 100, in server device 200, or distributed across both user device 100 and server device 200. When the components of system 10′ are distributed across both user device 100 and server device 200, information acquisition means 11 may be provided in user device 100 and calculation means 12 and estimation means 13 may be provided in server device 200, or information acquisition means 11 and calculation means 12 may be provided in user device 100 and estimation means 13 may be provided in server device 200.

[0171] In one embodiment, in the system 10 ′, the information acquisition unit 11 is provided in the user device 100 , and the calculation unit 12 and the estimation unit 13 are provided in the server device 200 .

[0172] FIG. 6 is a data flow diagram showing data exchange between the user device 100 and the server device 200 in this embodiment.

[0173] In step S601, the user device 100 acquires an eyeball information source. The eyeball information source may be, for example, at least one image of a subject's eyeball or electro-oculography measurement values. Preferably, the eyeball information source may be multiple images of the subject's eyeball or a time series of electro-oculography measurement values. This is because the multiple images or the time series of electro-oculography measurement values ​​may include a time component of the eyeball information.

[0174] For example, the imaging unit 160 of the user device 100 can capture at least one image by capturing an image of the eyeball. For example, the imaging unit 160 of the user device 100 may capture an image of the eyeball of the subject when the subject is aware of the subject, such as when the subject is staring at the imaging unit 160, or may capture an image of the eyeball of the subject when the subject is not aware of the subject, such as when the subject is within the field of view of the imaging unit 160. By capturing an image of the eyeball of the subject when the subject is not aware of the subject, an eyeball information source can be obtained without imposing a burden on the subject.

[0175] For example, the electro-oculography acquisition means 170 of the user device 100 can acquire electro-oculography measurement values ​​by measuring the potential around the eyes. For example, the electro-oculography acquisition means 170 of the user device 100 may acquire electro-oculography measurement values ​​when the subject is aware, such as by having the subject attach electrodes around the eyes, or may acquire electro-oculography measurement values ​​when the subject is not aware, such as by using electrodes built into a glasses-type device worn by the subject. By capturing an image of the subject's eyeball when the subject is not aware, an eyeball information source can be acquired without imposing a burden on the subject.

[0176] In step S602, the user device 100 acquires eyeball information. For example, the information acquisition unit 11, which may be included in the processor unit 150 of the user device 100, acquires the eyeball information from the eyeball information source acquired in step S601. The eyeball information includes, for example, information about blinking, information about pupil coordinates or the relative position of the eyeball, and information about eyeball movement. The information acquisition unit 11 can acquire the eyeball information from the eyeball information source by, for example, any known method.

[0177] In step S603, the user device 100 transmits the eyeball information to the server device 200. For example, the processor unit 150 of the user device 100 transmits the eyeball information to the server device 200 via the communication interface unit 110. The server device 200 receives the transmitted eyeball information. For example, the processor unit 230 of the server device 200 receives the eyeball information from the user device 100 via the communication interface unit 210.

[0178] In step S604, the server device 200 calculates the estimated presence or absence, amount, or level of dopamine or a substance biologically equivalent to dopamine in the subject's living body based on the eyeball information. For example, the calculation means 12 that may be included in the processor unit 230 of the server device 200 calculates the estimated presence or absence, amount, or level of dopamine or a substance biologically equivalent to dopamine in the subject's living body based on the eyeball information received in step S603.

[0179] The calculation means 12 can calculate the estimated presence or absence, amount, or level of dopamine or a substance biologically equivalent to dopamine in the subject's living body, for example, by using a trained model. The trained model has learned the relationship between the ocular information and the estimated presence or absence, amount, or level of dopamine or a substance biologically equivalent to dopamine in the subject's living body, and when the ocular information received in step S603 is input to the trained model, the estimated presence or absence, amount, or level of the corresponding in-vivo dopamine or a substance biologically equivalent to dopamine is output.

[0180] In step S605, the server device 200 estimates the condition of the subject based on the estimated presence, amount, or level of dopamine or a substance bioequivalent to dopamine in the subject's living body. Alternatively, if the subject is a Parkinson's disease patient being treated with L-DOPA, an L-DOPA-related compound, or a dopamine agonist, the server device 200 estimates an estimated effective amount or effective level of a therapeutic or prophylactic drug or other medical technology for the patient. For example, the estimation means 13 that may be included in the processor unit 230 of the server device 200 estimates the condition of the patient or estimates an estimated effective amount or effective level of a therapeutic or prophylactic drug or other medical technology for the patient based on the estimated presence, amount, or level of dopamine or a substance bioequivalent to dopamine in the subject's living body calculated in step S604.

[0181] The estimation means 13 can estimate the patient's condition by, for example, comparing one or more thresholds set for the patient with the estimated presence, amount, or level of dopamine or a substance bioequivalent to dopamine in the body, or variations thereof. The one or more thresholds set for the patient can be calculated, for example, based on ocular information acquired in advance or in real time by the information acquisition means 11. The thresholds may be set by a specialist and / or the patient themselves based on ocular information acquired in advance or in real time, or may be set by the system itself based on previously learned trends. The one or more thresholds set for the patient can be calculated, for example, based on other information in addition to ocular information. Examples of other information include, but are not limited to, information acquired by a wearable device, medication records kept by the patient, etc. For example, when information acquired by a wearable device, medication records kept by the patient, or other information indicates a certain condition of the patient, the ocular information at that time can be set as the threshold for that condition. The threshold may be, for example, a fixed value for each patient or a variable value. If the threshold is a variable value, it may vary depending on, for example, time, place, situation, etc.

[0182] The estimation means 13 can also estimate the state of a Parkinson's disease patient undergoing treatment with L-DOPA, an L-DOPA related compound, or a dopamine agonist, for example, by the above-mentioned method.

[0183] In another embodiment, the estimation means 13 is configured to estimate an estimated effective amount or effective level of a therapeutic or prophylactic drug or other medical technology for a Parkinson's disease patient being treated with L-DOPA, an L-DOPA-related compound, or a dopamine agonist, based on the estimated presence or absence, amount, or level of dopamine or a substance bioequivalent to dopamine in a living body, or fluctuations thereof.

[0184] In step S606, the server device 200 transmits the estimation result of step S605 to the user device 100. For example, the processor unit 230 of the server device 200 transmits the estimation result of step S605 to the user device 100 via the communication interface unit 210. The user device 100 receives the transmitted estimation result. For example, the processor unit 150 of the user device 100 receives the estimation result from the server device 200 via the communication interface unit 110.

[0185] In step S607, the user device 100 presents the inference result received in step S606 to the subject. The presentation may take any form. For example, the display unit 130 of the user device 100 may display the inference result. Alternatively, a microphone that may be included in or connected to the user device 100 may present the inference result audibly. Alternatively, a printer that may be included in or connected to the user device 100 may print the inference result.

[0186] This allows the subject (or the doctor, nurse, or other medical professional treating the subject) to know the subject's condition or the likely effective amount or level of a therapeutic or prophylactic drug or other medical technique for the subject.

[0187] In this embodiment, the user device 100 can obtain an estimation result estimated from the eyeball information simply by acquiring the eyeball information and transmitting it to the server device 200. The user device 100 does not need to bear the calculation load imposed on the calculation means 12 and the estimation means 13.

[0188] In another embodiment, the information acquisition means 11 and the calculation means 12 of the system 10 ′ may be included in the user device 100 , and the estimation means 13 may be included in the server device 200 .

[0189] FIG. 7 is a data flow diagram showing data exchange between the user device 100 and the server device 200 in this embodiment.

[0190] In step S701, the user device 100 acquires an eyeball information source. Step S701 is the same as step S601.

[0191] In step S702, the user device 100 acquires eyeball information. Step S702 is the same as step S602.

[0192] In step S703, the user device 100 calculates the estimated presence or absence, amount, or level of dopamine or a substance bioequivalent to dopamine in the subject's living body based on the eyeball information. For example, the calculation means 12 that may be included in the processor unit 150 of the user device 100 calculates the estimated presence or absence, amount, or level of dopamine or a substance bioequivalent to dopamine in the subject's living body based on the eyeball information received in step S603.

[0193] The calculation means 12 can calculate the estimated presence or absence, amount, or level of dopamine or a substance biologically equivalent to dopamine in the subject's living body, for example, by using a trained model. The trained model has learned the relationship between ocular information and the estimated presence or absence, amount, or level of dopamine or a substance biologically equivalent to dopamine in the subject's living body, and when the ocular information acquired in step S702 is input to the trained model, the estimated presence or absence, amount, or level of the corresponding in-vivo dopamine or a substance biologically equivalent to dopamine is output.

[0194] In step S704, the user device 100 transmits information indicating the estimated presence, amount, or level of dopamine or a substance bioequivalent to dopamine in a living body calculated in step S703 to the server device 200. For example, the processor unit 150 of the user device 100 transmits the information indicating the estimated presence, amount, or level of dopamine or a substance bioequivalent to dopamine in a living body to the server device 200 via the communication interface unit 110. The server device 200 receives the transmitted information indicating the estimated presence, amount, or level of dopamine or a substance bioequivalent to dopamine in a living body. For example, the processor unit 230 of the server device 200 receives the information indicating the estimated presence, amount, or level of dopamine or a substance bioequivalent to dopamine in a living body from the user device 100 via the communication interface unit 210.

[0195] In step S705, the server device 200 estimates the condition of the subject based on the estimated presence, amount, or level of dopamine or a substance bioequivalent to dopamine in the subject's living body. Alternatively, if the subject is a Parkinson's disease patient being treated with L-DOPA, an L-DOPA-related compound, or a dopamine agonist, the server device 200 estimates an estimated effective amount or effective level of a therapeutic or prophylactic drug or other medical technology for the patient. For example, the estimation means 13 that may be included in the processor unit 230 of the server device 200 estimates the condition of the patient or estimates an estimated effective amount or effective level of a therapeutic or prophylactic drug or other medical technology for the patient based on the estimated presence, amount, or level of dopamine or a substance bioequivalent to dopamine in the subject's living body received in step S704. Step S705 is a step similar to step S605.

[0196] In step S706, the server device 200 transmits the estimation result in step S605 to the user device 100. Step S706 is the same as step S606.

[0197] In step S707, the user device 100 presents the estimation result received in step S606 to the subject. Step S707 is the same as step S607.

[0198] This allows the subject (or the doctor, nurse, or other medical professional treating the subject) to know the subject's condition or the likely effective amount or level of a therapeutic or prophylactic drug or other medical technique for the subject.

[0199] In this embodiment, the user device 100 acquires eyeball information, calculates the estimated presence or absence, amount or level of dopamine or a substance biologically equivalent to dopamine in the body, and transmits this to the server device 200, thereby allowing the user device 100 to obtain an estimation result estimated from the estimated presence or absence, amount or level of dopamine or a substance biologically equivalent to dopamine in the body.

[0200] In another embodiment, the information acquiring means 11, the calculating means 12, and the estimating means 13 of the system 10′ may be included in the server device 200.

[0201] FIG. 8 is a data flow diagram showing data exchange between the user device 100 and the server device 200 in this embodiment.

[0202] In step S801, the user device 100 acquires an eyeball information source. Step S801 is the same as step S601.

[0203] In step S802, the user device 100 transmits the eyeball information source acquired in step S801 to the server device 200. For example, the processor unit 150 of the user device 100 transmits the eyeball information source to the server device 200 via the communication interface unit 110. The server device 200 receives the transmitted eyeball information source. For example, the processor unit 230 of the server device 200 receives the transmitted eyeball information source from the user device 100 via the communication interface unit 210.

[0204] In step S803, the server device 200 acquires eyeball information. For example, the information acquisition unit 11 that may be included in the processor unit 230 of the server device 200 acquires the eyeball information from the eyeball information source received in step S802. The eyeball information includes, for example, information about blinking, information about pupil coordinates or the relative position of the eyeball, and information about eyeball movement. The information acquisition unit 11 can acquire the eyeball information from the eyeball information source by, for example, any known method.

[0205] In step S804, the server device 200 calculates the estimated presence or absence, amount, or level of dopamine or a substance biologically equivalent to dopamine in the subject's living body based on the eyeball information. Step S804 is a step similar to step S604.

[0206] In step S805, the server device 200 estimates the condition of the subject based on the estimated presence, amount, or level of dopamine or a substance bioequivalent to dopamine in the subject's living body. Alternatively, if the subject is a Parkinson's disease patient being treated with L-DOPA, an L-DOPA-related compound, or a dopamine agonist, the server device 200 estimates an estimated effective amount or effective level of a therapeutic or prophylactic drug or other medical technology for the patient. For example, the estimation means 13 that may be included in the processor unit 230 of the server device 200 estimates the patient's condition or estimates an estimated effective amount or effective level of a therapeutic or prophylactic drug or other medical technology for the patient based on the estimated presence, amount, or level of dopamine or a substance bioequivalent to dopamine in the subject's living body calculated in step S804. Step S805 is a step similar to step S805.

[0207] In step S806, the server device 200 transmits the estimation result in step S805 to the user device 100. Step S806 is the same as step S606.

[0208] In step S807, the user device 100 presents the estimation result received in step S806 to the subject. Step S807 is the same as step S607.

[0209] This allows the subject (or the doctor, nurse, or other medical professional treating the subject) to know the subject's condition or the likely effective amount or level of a therapeutic or prophylactic drug or other medical technique for the subject.

[0210] In this embodiment, the user device 100 can obtain an estimation result estimated from the eyeball information source simply by acquiring the eyeball information source and transmitting it to the server device 200. The user device 100 does not need to bear the calculation load for acquiring the eyeball information in addition to the calculation load on the calculation means 12 and the estimation means 13.

[0211] In another embodiment, the information acquisition means 11, calculation means 12, and estimation means 13 of the system 10′ may be provided in the user device 100. In this case, the user device 100 does not need to communicate with the server device 200 and can operate standalone. The user device 100 performs, for example, the processes of steps S601 to S607 other than steps S603 and S606.

[0212] In the examples described above with reference to Figures 6 to 8, the processes are described as being performed in a specific order, but the order of each process is not limited to that described and may be performed in any order that is logically possible.

[0213] 6 to 8, at least one of the processes of each step shown in Figures 6 to 8 can be implemented by the processor unit 150 and the program stored in the memory unit 140 of the user device 100, and / or the processor unit 230 and the memory unit 220 of the server device 200, but the present disclosure is not limited to this. At least one of the processes of each step shown in Figures 6 to 8 may be implemented by a hardware configuration such as a control circuit.

[0214] The system of the present disclosure can be used in combination with a means capable of outputting indicators related to Parkinson's disease, for example.

[0215] In one example, the means capable of outputting indicators related to Parkinson's disease is a wearable device configured to be worn on the patient's body.

[0216] The wearable device may be attached to at least one limb of a patient and configured to measure limb movement (e.g., tremor) caused by Parkinson's disease. The wearable device may measure limb movement using, for example, an inertial sensor mounted therein and output velocity data, acceleration data, angular velocity data, and / or angular acceleration data. The output data may be analyzed and used as an index related to Parkinson's disease. Such a wearable device is, for example, described in Rob Powers1 et al., "Smartwatch inertial sensors continuously monitor real-world motor fluctuations in Parkinson's disease," Sci. Transl. Med. 13, eabd7865 (2021) 3 February 2021.

[0217] By using such a wearable device to obtain indices related to a patient's Parkinson's disease in advance, these indices can be used to calculate thresholds for estimating the patient's condition.

[0218] For example, movement measurement data and eye information are continuously acquired using a wearable device in combination with the information acquisition means 11. The eye information when the movement measurement data indicates that symptoms of Parkinson's disease are present can be set as a threshold value for the symptoms of Parkinson's disease.

[0219] The systems, apparatuses, and devices of the present disclosure may optionally be configured with one or more sensors that collect health data of the user other than indicators related to Parkinson's disease. The health data may include any suitable data related to the user's health. In some examples, the device may be configured to capture health data from the user. Such health data may indicate the user's pulse rate, heart rate, heart rate variability measurements, temperature data, number of steps taken, standing and sitting time, number of calories burned, number of minutes exercised, and / or any other suitable data. The device may be configured with one or more input devices by which the user can interact with the device. The device may also be configured with one or more output devices for outputting any suitable output information. For example, the device may be configured to output visual, audio, and / or tactile information. In some examples, the output information may be presented to the user in a manner that guides the user to perform one or more actions related to breathing. For example, the output information may include a changing progress indicator (e.g., some type of visual information). The progress indicator can be presented on a graphical user interface of the device, as further described herein, and can be configured to guide the user through a series of breathing movements included in the breathing sequence. The output information can be presented by an application running on the device.

[0220] The device may be associated with a second device (e.g., a paired device (e.g., a wearable device such as a watch or eyeglasses) or a host device). In some examples, this may include a device paired with the second device in any suitable manner. Pairing two devices optionally allows the second device to function in place of the device. The device, the second device, or any suitable combination of the device and the second device may generate output information based at least in part on the health data. Optional electrodes may be placed to obtain further parameters. Health indicators that may be calculated using electrodes include, but are not limited to, cardiac function (ECG, EKG), water content, body fat percentage, galvanic skin resistance, and combinations thereof.

[0221] The present disclosure may apply systems, apparatuses, and methods for the operation of a wearable device that depends on whether the wearable device is being worn. Such a wearable device may be attached to a portion of a user's body via an attachment member and may operate in at least a connected state and a disconnected state. One or more sensors disposed within the wearable device and / or the attachment member may detect when the device is attached to an object or in close proximity to an object. One or more sensors disposed within the wearable device and / or the attachment member may detect that the attached object, if present, is part of the user's body. Such technology is known, and may be, for example, the technology described in WO 2015 / 116163.

[0222] It can also be linked to or used in conjunction with electronic medical record applications such as Welby. For example, eye movement images can be captured periodically at home for approximately five minutes using a smartphone camera or a stationary eye tracker, and the parameters can be recorded. This can then be combined with the medication record and patient diary (symptom) data already included in the app to optimize the relationship between symptoms and eye movement parameters. The data can be transmitted remotely, allowing doctors to optimize medication prescriptions. Contactless eye trackers or eye trackers available from Tobii Technology (Sweden) can be used.

[0223] In one embodiment, the present disclosure provides a system for realizing as an application, the system being used for treating a treatable disease. The system includes a server and a user terminal. Medical characteristics indicating a patient's medical characteristics related to the disease are clustered into various medical characteristics (e.g., behavioral medical characteristics, knowledge medical characteristics, and cognitive medical characteristics). The server stores the plurality of medical characteristics, each associated with one or more therapies, and each medical characteristic is further associated with another type of medical characteristic (e.g., one of behavioral medical characteristics, knowledge medical characteristics, and cognitive medical characteristics). The server further selects a treatment to be performed from the plurality of medical characteristics, from among the plurality of medical characteristics, from among the treatments associated with a specific medical characteristic selected as a treatment target and from the treatments associated with each of the various medical characteristic information associated with the selected medical characteristic. The server transmits treatment information for the selected treatment. The user terminal can be configured to present information for the treatment based on the received treatment information. In some embodiments, the selection of a treatment by a medical professional serves as training information, and changes the probability of selecting a treatment, thereby increasing the probability of selecting a treatment that is considered to be highly effective, enabling more effective treatment. Alternatively, the cluster factor for an individual patient can be modified based on the efficacy information. For example, the cluster factor of a cluster to which medical characteristics associated with a treatment that has been proven effective belongs is increased, and if the treatment is ineffective, it is decreased. Which cluster a treatment for may be effective for may vary depending on the patient. A cluster to which medical characteristics associated with an effective treatment belongs is likely to be an effective cluster for that patient, so by increasing the cluster factor, the probability of selecting a treatment for that cluster is increased, enabling more effective treatment.

[0224] (Note) In this specification, "or" is used when "at least one or more" of the items listed in the sentence can be employed. The same applies to "alternative." In this specification, when it is specified that "within a range of two values," the range includes the two values ​​themselves.

[0225] All references cited herein, including scientific literature, patents, patent applications, and the like, are incorporated by reference in their entirety to the same extent as if each were specifically set forth.

[0226] The present disclosure has been described above by showing preferred embodiments for ease of understanding. The present disclosure will be described below based on examples. However, the above description and the following examples are provided for illustrative purposes only and are not intended to limit the present disclosure. The present invention will be further described in detail below by reference examples, examples, and test examples. However, these are provided for illustrative purposes only and do not limit the present disclosure. The compound names shown in the following reference examples and examples do not necessarily conform to the IUPAC nomenclature. Abbreviations may be used to simplify the description, but these abbreviations have the same meaning as those described above. The scope of the present disclosure is not limited to the embodiments or examples specifically described herein, but is limited only by the claims.

[0227] Examples are described below. Animals used in the following examples were in compliance with Japanese and other laws and regulations, and animal welfare standards were observed, and in the case of humans, the Declaration of Helsinki was observed. The specific reagents used were those listed in the examples, but equivalent products from other manufacturers (Sigma-Aldrich, Wako Pure Chemical Industries, Nakarai, R&D Systems, etc.) can also be used.

[0228] (Reference Examples) (Reference Example 1: Evaluation of striatal dopamine release in PD-LID model rats) (Test Method) A representative experimental model for Parkinson's disease is a rat striatal dopamine neuron destruction model involving local administration of 6-hydroxydopamine (hereinafter sometimes referred to as "6-OHDA") to one side of the brain (6-OHDA unilaterally treated rats (6-OHDA lesioned rats)). Male Wistar rats (12 weeks old, Japan SLC) were used to prepare the model animals. Desipramine hydrochloride (25 mg / kg; Wako Pure Chemical Industries) was administered intraperitoneally, and 30 minutes after administration, the rats were anesthetized with isoflurane by inhalation using a general anesthesia machine for laboratory animals. Under isoflurane anesthesia, rats were fixed in a stereotaxic apparatus, and the skull was exposed by incising the skin of the head with a scalpel. The coordinates of the bregma (AP: 0, ML: 0, DV: 0) were confirmed, and the coordinates of the right medial forebrain bundle (AP: -4.4 mm, ML: 1.5 mm, DV: 7.8 mm from bregma) were measured. After inserting an injection tube into the measurement coordinates, 6-OHDA (9 μg / 4 μL; Sigma-Aldrich), which has dopaminergic neurodegenerative properties, was locally injected. Two weeks after surgery, apomorphine hydrochloride 0.5 hydrate (0.5 mg / kg; Wako Pure Chemical Industries) was administered subcutaneously, and rotational movement contralateral to the 6-OHDA injection site was observed. Rats that rotated 7 or more times per minute were used as unilaterally 6-OHDA-treated rats. To prepare PD-LID model rats, a mixture of levodopa methyl ester hydrochloride (6 mg / kg; Sigma-Aldrich) and benserazide hydrochloride (15 mg / kg; Sigma-Aldrich) dissolved in saline (hereinafter referred to as "levodopa combination solution") was intraperitoneally administered once daily to 6-OHDA-lesioned rats. Repeated administration of the levodopa combination solution was continued for at least 3 weeks.

[0229] To measure striatal dopamine release, a guide cannula was implanted into the striatum of PD-LID model rats and chromatography was performed using the method described in the reference (Pharmacol Res Perspect. 2015 Jun; 3(3): e00142.). On the day of the test, a dialysis probe was inserted into the striatum along the guide cannula. The tape was applied 60 cm ２ The tape was applied to the abdomen of rats so that the concentration was 1 / kg (containing 6.5% W / V tandospirone free form). Four hours after application, a levodopa-combined solution was administered intraperitoneally, and the dialysate was collected every 10 minutes into a sample vial. The amount of dopamine in the collected dialysate was measured using an HPLC-ECD system (Eicom Co., Ltd.). The results in the figure are shown as mean ± standard error. Animals in which more than 50% of the tape peeled off during the test were excluded from the analysis. Statistical analysis of the test results was performed by comparing with the placebo tape group using a t-test. * indicates p<0.05, indicating a significant difference from the placebo tape group.

[0230] (Results) Dopamine change (pg) was measured at each sampling point (every 10 minutes) and calculated as the change from baseline (the average of four samples taken before administration of the levodopa combination solution). Measurements were taken up to 420 minutes after administration of the levodopa combination solution. Tandospirone tape reduced dopamine release in the striatum between 30 and 150 minutes after levodopa administration and increased dopamine release from 150 minutes onward ( Figure 9A ). The time during which a change in dopamine release of 0.2 pg or more was observed was calculated, and a significant prolongation was observed with tandospirone tape compared with the placebo tape group ( Figure 9B ). However, no significant difference was observed in total dopamine release after levodopa administration ( Figure 9C ).

[0231] As described above, it was found that administration of tandospirone suppressed the rapid fluctuations and the time-dependent decrease in dopamine levels in the synaptic cleft of the striatum.

[0232] (Creation of a Rhesus Monkey Model of MPTP-Induced Parkinson's Disease with Levodopa-Induced Dyskinesia (PD-LID) and Measurement of Eye Movements) 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) is a neurotoxin that specifically destroys dopaminergic neurons. Dopaminergic neurons projecting from the substantia nigra to the striatum are destroyed by intravenous, intramuscular, or surgical administration of MPTP into the internal carotid artery. This animal model is widely used to evaluate the efficacy of Parkinson's disease therapeutics. Repeated administration of levodopa to this model is known to induce dyskinesia, similar to the dyskinesias induced by long-term administration of levodopa in clinical settings in Parkinson's disease patients. This model was used to evaluate the effects of dopamine-related substances on eye movements. Male rhesus monkeys (Hamley, Inc.) were used in this study. MPTP (0.4 or 0.6 mg / kg) was administered once or twice a week until stable Parkinsonian symptoms were observed, followed by levodopa (20 or 30 mg / kg) once or twice a week until stable dyskinesias were observed.

[0233] Eye movements were measured using this model monkey. To measure eye movements, we used the Pupil Labs eye tracking headset, Pupil Core. This device is an eye tracker worn on the face like glasses. The device irradiates the eyeball with infrared light from LEDs attached to the eye, and the illuminated eyeball is captured on video using a high-speed camera at 120 to 200 Hz, and the video is recorded on a PC. The LED light and camera unit of this device were transferred to an eyeglass-type frame made of ABS, which was made to fit the head size of a rhesus monkey.

[0234] This model monkey was seated in a monkey chair and equipped with the monkey eye-tracking headset described above, and eye images were recorded. The acquired eye images were converted into time-series data of pupil coordinates and ellipse fitting accuracy, which serves as an index of pupil detection, using Pupil Labs' analysis software, Pupil Player (Version 2.4.0), and then analyzed using a homemade program written in Python 3.5. The ellipse fitting accuracy, which serves as the threshold for eye closure or eye opening, was set to 0.5. A transition from 0.5 or greater to less than 0.5 was considered an eye-closure event, and a transition from less than 0.5 to 0.5 or greater was considered an eye-open event. The segment in which the elapsed time from an eye-closure event to an eye-open event was between 0.05 and 0.80 seconds was identified as a blink.

[0235] (Reference Example 2: Effect of L-DOPA on increasing eyeblinks in rhesus monkeys with MPTP-induced PD-LID) (Test method) The above-mentioned model monkeys were seated in monkey chairs and equipped with an eye-tracking headset. 30 minutes later, a solvent (0.5% aqueous methylcellulose 400 solution, Nacalai Tesque) or L-DOPA / benserazide suspended in a solvent (L-DOPA at 22 mg / kg, benserazide at ¼ the weight of L-DOPA) was orally administered, and eye movements were measured for 120 to 180 minutes after administration.

[0236] (Results) When the number of blinks was counted every 3 minutes, there was no significant change in the number of blinks throughout the measurement period for vehicle administration (Fig. 10A), whereas L-DOPA administration increased the number of blinks from approximately 30 minutes after administration, peaking between 30 and 100 minutes after administration (Fig. 10B). For a given individual, when the number of blinks was counted from 60 to 150 minutes after the start of measurement for one vehicle administration and three L-DOPA administrations, a clear tendency for the number of blinks to increase in the L-DOPA group compared to the vehicle administration was observed (Fig. 10C).

[0237] (Reference Example 3: Variation in the number of blinks for each duration during L-DOPA administration) (Test method) The model monkey described above in the above Reference Example was seated in a monkey chair and equipped with an eye-tracking headset. 30 minutes later, L-DOPA / BenseraZide (22 mg / kg L-DOPA, ¼ weight of BenseraZide) suspended in a solvent (0.5% aqueous methylcellulose 400, Nacalai Tesque) was orally administered, and eye movement was measured for 180 minutes after administration.

[0238] Blinks were classified according to their duration T (s) into the following categories: 0.05≦T<0.14, 0.14≦T<0.16, 0.16≦T<0.18, 0.18≦T<0.20, 0.20≦T<0.25, 0.25≦T<0.80, and tabulated every 3 min (Fig. 11A-F).

[0239] (Results) In one example, the number of blinks with a relatively long duration (0.18≦T<0.25) peaked around 30 minutes after administration and then rapidly decreased (Fig. 11D, E). The number of blinks with a relatively short duration (T<0.16) gradually increased from 30 minutes after administration until the end of measurement (Fig. 11A, B). The number of blinks with intermediate durations (0.16≦T<0.18) peaked around 30 minutes after administration and rapidly decreased, but then gradually increased until the end of measurement, showing an intermediate trend (Fig. 11C).

[0240] (Reference Example 4: Relationship between dyskinesia and blink frequency upon administration of L-DOPA) (Test method) The model monkey described above in the above Reference Example was seated in a monkey chair and equipped with an eye-tracking headset. 30 minutes later, L-DOPA / BenseraZide (22 mg / kg L-DOPA, ¼ weight of BenseraZide) suspended in a solvent (0.5% aqueous methylcellulose 400, Nacalai Tesque) was orally administered, and eye movement was measured for 180 minutes after administration.

[0241] Dyskinesia was evaluated by an evaluator skilled in behavioral evaluation based on the recorded video. Dyskinesia scores were based on the Revised Non-Human Primate Dyskinesia Rating Scale (J Neurosci 2001; 21: 6853-6861). If no dyskinesia was observed, the score was 0. If dyskinesia was observed for less than 30% of the evaluation time, it was considered mild dyskinesia and given a score of 1. If dyskinesia was observed for 30% or more of the evaluation time but did not interfere with normal behavior, it was considered moderate dyskinesia and given a score of 2. If dyskinesia was observed for 30% or more but less than 70% of the evaluation time and interfered with normal behavior, it was considered severe dyskinesia and given a score of 3. If dyskinesia was observed for 70% or more of the evaluation time and interfered with normal behavior, it was considered severe dyskinesia and given a score of 4. Systemic dyskinesia was also evaluated as a particularly severe form of dyskinesia. Based on the UDysRS, a clinical assessment scale that incorporates site-specific dyskinesia into its assessment, systemic dyskinesia was defined as dyskinesia occurring in four or more of the six areas of the face, right arm, left arm, trunk, right leg, and left leg. If systemic dyskinesia occurred for 30% or more of the assessment time, the score was assigned as 1, and if systemic dyskinesia occurred for 70% or more of the assessment time, the score was assigned as 2.

[0242] (Results) Blink frequency peaked approximately 30 to 60 minutes after L-DOPA administration (Figure 12A). However, the dyskinesia score began to increase at the peak of blinks and reached its peak after the peak of blinks (Figure 12B). This trend was also observed in the score of generalized dyskinesia, with an even more pronounced delay relative to the blink score (Figure 12C). Given that dyskinesia involves excessive activation of dopamine function in the brain, these findings cannot be explained by previously reported findings that blink frequency simply shows a positive correlation with dopamine function in the brain, suggesting that dyskinesia cannot be detected simply by the magnitude of blink frequency alone.

[0243] Example 1: Prediction of dyskinesia score based on ocular information (1) Test method The model monkeys described above were seated in monkey chairs and equipped with eye-tracking headsets. Thirty minutes later, L-DOPA / BenseraZide (22 mg / kg L-DOPA, ¼ weight of BenseraZide) suspended in a solvent (0.5% aqueous methylcellulose 400, Nacalai Tesque) was orally administered, and eye movements were measured for 180 minutes after administration.

[0244] The model monkeys were given a transdermal administration of either an ointment containing tandospirone or a placebo ointment containing no tandospirone. Tandospirone has been reported to exhibit antidyskinetic effects by suppressing the release of dopamine in the brain. The dorsum of each monkey was shaved, and the ointment was applied to a 4 cm x 10 cm area 19 hours before the test. The area was then covered with tape and a clean cloth, and the monkey was then dressed in a jacket.

[0245] Dyskinesia was assessed by analyzing videos of the model monkeys and scoring them by an evaluator skilled in behavioral evaluation. Dyskinesia scores were assessed based on the Revised Non-Human Primate Dyskinesia Rating Scale (J. Neurosci. 2001; 21: 6853-6861). If no dyskinesia was observed, the score was 0; if dyskinesia was observed for less than 30% of the evaluation time, it was considered mild dyskinesia and given a score of 1; if dyskinesia was observed for 30% or more of the evaluation time but did not interfere with normal behavior, it was considered moderate dyskinesia and given a score of 2; if dyskinesia was observed for 30% or more but less than 70% of the evaluation time and interfered with normal behavior, it was considered severe dyskinesia and given a score of 3; and if dyskinesia was observed for 70% or more of the evaluation time and interfered with normal behavior, it was considered severe dyskinesia and given a score of 4. Systemic dyskinesia was also evaluated as a particularly severe type of dyskinesia. Based on the UDysRS, a clinical assessment scale that incorporates site-specific dyskinesia into its assessment, generalized dyskinesia was defined as dyskinesia occurring in four or more of the six areas of the face, right arm, left arm, trunk, right leg, and left leg. If generalized dyskinesia occurred for more than 30% of the assessment time, the score was assigned as 1, and if generalized dyskinesia occurred for more than 70% of the assessment time, the score was assigned as 2.

[0246] A linear regression model was used to predict the scores of dyskinesia and generalized dyskinesia. The predicted value h by the linear regression model is calculated by multiplying n feature values ​​(x １ , x ２ ,...x ｎ ) and n parameters (θ １ , θ ２ , ...θ ｎ ) with the bias parameter θ ０ This is generally expressed as follows:

[0247] The loss function J of the linear regression model can be expressed as follows, where y is the correct value: A combination of parameters θ that minimizes the loss function J was searched for.

[0248] Each independent 3-minute time window in the measurement was used as one data point. The correct answer value y was set to the dyskinesia score for that 3-minute period. The feature value x was calculated as follows according to the blink duration T (s): １ ~x ６ The data were classified into the following categories and aggregated for each time window to create a feature value. １ :0.05≦T<0.14, x ２ :0.14≦T<0.16, x ３ :0.16≦T<0.18, x ４ :0.18≦T<0.20, x ５ :0.20≦T<0.25, x ６ : 0.25≦T<0.80

[0249] Furthermore, in estimating the generalized dyskinesia score, in addition to the above feature, the total number of blinks in the corresponding time window x ｔｏｔａｌ , a short blink x with a blink duration T<0.16 ｓ and long blink x where T≧0.16 ｌ was added as a new feature. Furthermore, the number of short blinks in the same time window x ｓ Number of long blinks x ｌ Ratio divided by x Ｒａｔｉｏ was used as an additional feature.

[0250] For model training, 80% (56 points) of the data points (n = 70) obtained from individuals administered L-DOPA but not administered tandospirone were used. The remaining 20% ​​(14 points) of the data points were used as test data to evaluate the performance of the model. The correlation coefficient r between the predicted value and the correct value for the test data was calculated and used as a guide for the predictive performance of the trained model.

[0251] To predict dyskinesia scores when tandospirone was administered, a model was trained using all data from when tandospirone was not administered, and the features from when tandospirone was administered were input into the trained model to output a predicted value.

[0252] (Results) In an individual who was administered L-DOPA but not administered tandospirone, the dyskinesia score h(x) every 3 minutes and the blink frequency (feature x) divided by blink duration were compared. １~x ６ ) and the corresponding trained parameters θ １ ~θ ６ are shown in Figures 13A to 13G. That is, the dyskinesia score h(x) for this individual during any 3-minute period is estimated by the following formula:

[0253] From this, the feature x is the parameter that best explains the dyskinesia score. ２ The second is the relatively short-lasting blink, followed by the ５ , x ６ This indicates that blinks with long durations are a parameter that relatively strongly explains low dyskinesia scores. Furthermore, when the correlation coefficient between the predicted value and the correct value for 20 test data points was calculated using this model, it was r = 0.96, indicating a very strong correlation (Figure 13H). Therefore, it is suggested that these blink-related features are very useful for predicting dyskinesia scores.

[0254] Furthermore, the generalized dyskinesia score h(x) and the number of preblinks (feature x) for each individual were calculated. ｔｏｔａｌ ), short blink frequency (feature x ｓ ), long blink frequency (x ｌ ), and the ratio of short blinks to long blinks (x Ｒａｔｉｏ ) and the corresponding trained parameters θ ｓ , θ ｌ , θ Ｒａｔｉｏ 14A to 14D. That is, the generalized dyskinesia score h(x) for this individual during any 3-minute period is estimated by the following formula:

[0255] From this, the ratio of short blinks to long blinks, x, was determined to be the parameter that best explained the generalized dyskinesia score. Ｒａｔｉｏ In addition, a parameter that suggests a low generalized dyskinesia score is x, which is a blink with a relatively long duration. ６Furthermore, when the correlation coefficient between the predicted value and the correct value for 20 test data points was calculated using this model, it was r = 0.77, indicating a positive correlation (Figure 14E). These parameters were suggested to be useful for estimating severe dyskinesia.

[0256] Furthermore, when the trained model was trained using all the blink data and dyskinesia scores from the same L-DOPA-treated but tandospirone-treated individuals, and the trained model was input with blink data from L-DOPA-treated and tandospirone-treated individuals to predict dyskinesia scores, the correlation coefficient r was 0.84, indicating a strong correlation (Figures 15A and 15B). These results suggest that this trained model based on blink parameters was able to detect a reduction in dyskinesia scores due to tandospirone administration, suggesting its usefulness in evaluating the efficacy of therapeutic drugs for Parkinson's disease.

[0257] (Example 1': Prediction of dyskinesia score based on ocular information by another method (1))

[0258] (Test Method) Dyskinesia and generalized dyskinesia scores were predicted by a different method using the same measurement data obtained in Example 1. The prediction was performed using random forest.

[0259] Regression using a random forest model is performed by majority voting of multiple decision trees. For each decision tree, the mean square error (MSE) for each node t is defined as follows, and a decision tree that minimizes it is created. Here, N ｔ represents the number of training data, represents the correct answer for the training data, represents the mean of the set Dt included in the splitting condition t of the decision tree.

[0260] Each independent 3-minute time window in the measurement was used as one data point. The input values ​​were the blink duration for that 3-minute period and parameters derived from the blink duration, and the output value was the score for that 3-minute period. Parameters derived from blink duration included the ratio of short to long blinks, the median blink duration, the squared mean blink duration, the proportion of blinks whose duration was shorter than the first quartile, the number of blinks whose duration was shorter than the first quartile, the proportion of blinks whose duration was between the first and third quartiles, the number of blinks whose duration was between the first and third quartiles, the standard deviation of blink duration, the square root of the number of blinks, the logarithm of the mean blink duration, the squared number of blinks, and the mean blink duration. Here, the ratio of short blinks to long blinks refers to the ratio obtained by dividing the number of short blinks with a blink duration T<0.16 by the number of long blinks with T≧0.16.

[0261] To construct the random forest model, 80% (56 points) of the data points (n = 70) obtained from individuals administered L-DOPA but not administered tandospirone were used as training data. The remaining 20% ​​(14 points) of the data points were used as test data to evaluate the performance of the random forest model. The correlation coefficient r between the predicted value and the correct value for the test data was calculated and used as a measure of the predictive performance of the random forest model.

[0262] FIG. 18A shows an example of a decision tree constructing a random forest model constructed to predict dyskinesia scores.

[0263] Here, Duration ratio is the ratio of short blinks to long blinks, Duration median is the median blink duration, Duration mean_power is the square of the average blink duration, 25-75%_r is the proportion of blinks whose blink duration is between the first and third quartiles, Duration_SD is the standard deviation of the blink duration, all_sqrt is the square root of the number of blinks, Duration_mean_log is the logarithm of the average blink duration, all_power is the square of the number of blinks, Duration_mean is the average blink duration, and all is the number of blinks.

[0264] 18B shows another example of a decision tree used to construct a random forest model for predicting dyskinesia scores, using the total number of blinks, the number of blinks whose duration falls between the first and third quartiles, the proportion of blinks whose duration falls between the first and third quartiles, and the mean blink duration.

[0265] Here, all is the number of blinks, 25-75% is the number of blinks whose blink duration is between the first and third quadrants, 25-75%_r is the proportion of blinks whose blink duration is between the first and third quadrants, and Duration_mean is the mean blink duration.

[0266] Test data was input into the constructed random forest model to predict dyskinesia scores.

[0267] FIG. 18C shows the results of predictions by a random forest model constructed to predict dyskinesia scores.

[0268] FIG. 18C(a) shows the results from a random forest model with the decision tree shown in FIG. 18A, and FIG. 18C(b) shows the results from a random forest model with the decision tree shown in FIG. 18B.

[0269] In Figures 18C(a) and 18C(b), ● indicates the results when training data is input into the random forest model, and □ indicates the results when test data is input into the random forest model.

[0270] In FIG. 18C(a), when training data was input into the random forest model, the correlation coefficient r between the predicted value and the correct value was 0.978. Furthermore, when test data was input into the random forest model, the correlation coefficient r between the predicted value and the correct value was 0.932. A high correlation coefficient was also obtained for the test data. On the other hand, in FIG. 18C(b), when training data was input into the random forest model, the correlation coefficient r between the predicted value and the correct value was 0.980. However, when test data was input into the random forest model, the correlation coefficient r between the predicted value and the correct value was 0.597, indicating insufficient prediction accuracy. These findings suggest that the parameters used in the random forest model of FIG. 18A (i.e., Duration ratio, Duration median, Duration mean_power, 25-75%_r, Duration_SD, all_sqrt, Duration_mean_log, all_power, Duration_mean, and all) are more useful for predicting dyskinesia scores than other parameters (e.g., the combination of parameters used in the random forest model of FIG. 18B).

[0271] FIG. 19A shows an example of a decision tree constructing a random forest model constructed to predict generalized dyskinesia scores.

[0272] Here, <25%_r is the ratio of blinks whose blink duration is shorter than their first quartile, Duration ratio is the ratio of short blinks to long blinks, 25-75% is the number of blinks whose blink duration is between their first and third quartiles, 25-75%_r is the ratio of blinks whose blink duration is between their first and third quartiles, <25% is the number of blinks whose blink duration is shorter than their first quartile, all_sqrt is the square root of the number of blinks, Duration median is the median of the blink duration, and Duration_mean_log is the logarithm of the mean blink duration.

[0273] 19B shows another example of a decision tree for constructing a random forest model for predicting generalized dyskinesia scores, which utilized the number of blinks with a blink duration equal to or greater than the third quartile and less than the mean + SD, the proportion of blinks with a blink duration equal to or greater than the third quartile and less than the mean + SD, the number of blinks with a blink duration equal to or greater than the mean + SD, the proportion of blinks with a blink duration equal to or greater than the mean + SD, and the mean blink duration.

[0274] Here, 75%-mean+1SD is the number of blinks whose blink duration is equal to or greater than the third quartile and less than the mean+SD, 75%-mean+1SD_r is the proportion of blinks whose blink duration is equal to or greater than the third quartile and less than the mean+SD, outlier is the number of blinks whose blink duration is equal to or greater than the mean+SD, outlier_r is the proportion of blinks whose blink duration is equal to or greater than the mean+SD, and Duration_mean is the mean blink duration.

[0275] Test data was input into the constructed random forest model to predict generalized dyskinesia scores.

[0276] FIG. 19C shows the results of predictions from a random forest model constructed to predict generalized dyskinesia scores.

[0277] FIG. 19C(a) shows the results from a random forest model with the decision tree shown in FIG. 19A, and FIG. 19C(b) shows the results from a random forest model with the decision tree shown in FIG. 19B.

[0278] In Figures 19C(a) and 19C(b), ● indicates the results when training data is input into the random forest model, and □ indicates the results when test data is input into the random forest model.

[0279] In FIG. 19C(a), when training data was input into the random forest model, the correlation coefficient r between the predicted value and the correct value was 0.834. Furthermore, when test data was input into the random forest model, the correlation coefficient r between the predicted value and the correct value was 0.873. A high correlation coefficient was also obtained for the test data. On the other hand, in FIG. 19C(b), when training data was input into the random forest model, the correlation coefficient r between the predicted value and the correct value was 0.969. However, when test data was input into the random forest model, the correlation coefficient r between the predicted value and the correct value was 0.416, indicating low prediction accuracy. These findings suggest that the parameters used in the random forest model of FIG. 19A (i.e., <25%_r, Duration ratio, 25-75%, 25-75%_r, <25%, all_sqrt, Duration median, Duration_mean_log) are more useful for predicting generalized dyskinesia scores than other parameters (e.g., the combination of parameters used in the random forest model of FIG. 19B).

[0280] Example 2: Prediction of dyskinesia score based on eyeball information (2) In this example, prediction of dyskinesia score was performed based on eyeball information other than blinking.

[0281] (Method) We estimate the severity of dyskinesia at the time of measurement using information on eye movement other than blinking. Utilizing the fact that eye tremor increases when the severity of dyskinesia is high, we perform frequency analysis of pupil coordinates, eye movement distance, and eye movement speed to obtain characteristic spectral bands, and predict the dyskinesia score based on the correlation with the severity of dyskinesia.

[0282] In addition to blinking, information on eyelid opening is also used to estimate the severity of dyskinesia at the time of measurement. When the severity of dyskinesia is high, the eyelids droop and vibrate irregularly, and the severity of dyskinesia is estimated from a decrease in eyelid opening and an increase in its variance.

[0283] (Example 3: Predicting L-DOPA concentration in peripheral blood based on ocular information) (Test method) The model monkey described above was seated in a monkey chair and equipped with an eye-tracking headset. Thirty minutes later, L-DOPA / BenseraZide (22 mg / kg L-DOPA, ¼ weight of BenseraZide) suspended in a solvent (0.5% aqueous methylcellulose 400, Nacalai Tesque) was orally administered, and eye movement was measured for 180 minutes after administration.

[0284] Blood samples were taken from the upper arm of the model monkey every 30 minutes, and the L-DOPA concentration in the plasma was determined using an HPLC system.

[0285] A linear regression model was used to predict the plasma L-DOPA concentration. Each independent 3-minute time window in the measurement was used as one data point. The correct answer y was set to the plasma L-DOPA concentration (ng / mL) for that 3-minute period predicted by linearly interpolating the L-DOPA measurement results every 30 minutes. The feature value x was calculated as follows according to the blink duration T (s): １ ~x ６ The data were classified into the following categories and aggregated for each time window to create a feature value. １ :0.05≦T<0.14, x ２ :0.14≦T<0.16, x ３ :0.16≦T<0.18, x ４ :0.18≦T<0.20, x ５:0.20≦T<0.25, x ６ : 0.25≦T<0.80

[0286] For model training, 80% (56 points) of the data points (n = 70) obtained from the L-DOPA-administered model monkey were used. The remaining 20% ​​(14 points) of the data points were used as test data to evaluate the performance of the model. The correlation coefficient r between the predicted value and the correct value for the test data was calculated and used as a guide for the predictive performance of the trained model.

[0287] (Results) In a model monkey administered L-DOPA, the plasma L-DOPA concentration h(x) every 3 minutes and the blink frequency (feature x) divided by blink duration were measured. １ ~x ６ ) and the corresponding trained parameters θ １ ~θ ６ 16A to 16G. That is, the plasma L-DOPA concentration h(x) (ng / mL) for any 3 minutes in this individual is estimated by the following formula:

[0288] From this, it can be seen that the parameter that best explains plasma L-DOPA concentration is the blink of medium duration, feature x4, followed by the blink of long duration, x6, which is a parameter that relatively strongly explains low plasma L-DOPA concentration. Furthermore, when this model was used, the correlation coefficient between the predicted value and the correct value for 20 test data points was calculated to be r = 0.83, indicating a strong correlation (Figure 16H). Therefore, it is suggested that these blink-related features are useful for predicting plasma L-DOPA concentration.

[0289] Example 3': Predicting L-DOPA Concentration in Peripheral Blood Based on Ocular Information Using Another Method

[0290] (Test Method) The L-DOPA concentration in peripheral blood was predicted by a different method using the same measurement data obtained in Example 3. The prediction was performed using random forest.

[0291] Regression using a random forest model is performed by majority voting of multiple decision trees. For each decision tree, the mean square error (MSE) for each node t is defined as follows, and a decision tree that minimizes it is created. Here, N ｔ represents the number of training data, represents the correct answer for the training data, represents the mean of the set Dt included in the splitting condition t of the decision tree.

[0292] Each independent 3-minute time window in the measurement was used as one data point. The input values ​​were the blink duration for that 3-minute period and parameters derived from the blink duration. The output value was the plasma L-DOPA concentration (ng / mL) predicted for that 3-minute period by linear interpolation of the L-DOPA measurement results every 30 minutes. Parameters derived from blink duration included the ratio of short blinks to long blinks, the median blink duration, the squared mean blink duration, the proportion of blinks whose blink duration was shorter than the first quartile, the number of blinks whose blink duration was shorter than the first quartile, the proportion of blinks whose blink duration was between the first and third quartiles, the number of blinks whose blink duration was between the first and third quartiles, the standard deviation of blink duration, the square root of the number of blinks, the logarithm of the mean blink duration, the squared number of blinks, and the mean blink duration. Here, the ratio of short blinks to long blinks refers to the ratio obtained by dividing the number of short blinks with a blink duration T<0.16 by the number of long blinks with T≧0.16.

[0293] To construct the random forest model, 80% (56 points) of the data points (n = 70) obtained from the L-DOPA-administered model monkey were used. The remaining 20% ​​(14 points) of the data points were used as test data to evaluate the performance of the random forest decision tree. The correlation coefficient r between the predicted value and the correct value for the test data was calculated and used as a measure of the predictive performance of the random forest model.

[0294] FIG. 20A shows an example of a decision tree constructing a random forest model constructed to predict L-DOPA concentrations in peripheral blood.

[0295] Here, Duration ratio is the ratio of short blinks to long blinks, <25%_r is the proportion of blinks whose blink duration is shorter than its first quartile, Duration median is the median blink duration, 25-75%_r is the proportion of blinks whose blink duration is between its first and third quartiles, 25-75% is the number of blinks whose blink duration is between its first and third quartiles, Duration max is the maximum blink duration, Duration_SD is the standard deviation of blink duration, <25% is the number of blinks whose blink duration is shorter than its first quartile, all_sqrt is the square root of the number of blinks, Duration_mean is the mean blink duration, Duration_mean_log is the logarithm of the mean blink duration, and Duration mean_power is the square of the mean blink duration.

[0296] 20B shows another example of a decision tree for constructing a random forest model for predicting peripheral blood L-DOPA concentrations, which uses the total number of blinks, the number of blinks whose duration is equal to or greater than the third quartile and less than the mean + SD, the proportion of blinks whose duration is equal to or greater than the third quartile and less than the mean + SD, the number of blinks whose duration is equal to or greater than the mean + SD, the proportion of blinks whose duration is equal to or greater than the mean + SD, and the mean blink duration.

[0297] Here, all is the number of blinks, 75%-mean+1SD is the number of blinks whose blink duration is equal to or greater than the third quartile and less than the mean+SD, 75%-mean+1SD_r is the proportion of blinks whose blink duration is equal to or greater than the third quartile and less than the mean+SD, outlier is the number of blinks whose blink duration is equal to or greater than the mean+SD, outlier_r is the proportion of blinks whose blink duration is equal to or greater than the mean+SD, and Duration_mean is the mean blink duration.

[0298] Test data was input into the constructed random forest model to predict L-DOPA concentrations in peripheral blood.

[0299] FIG. 20C shows the results of prediction using a random forest model constructed to predict L-DOPA concentrations in peripheral blood.

[0300] FIG. 20C(a) shows the results from a random forest model with the decision tree shown in FIG. 20A, and FIG. 20C(b) shows the results from a random forest model with the decision tree shown in FIG. 20B.

[0301] In Figures 20C(a) and 20C(b), ● indicates the results when training data is input into the random forest model, and □ indicates the results when test data is input into the random forest model.

[0302] In FIG. 20C(a), when training data was input into the random forest model, the correlation coefficient r between the predicted value and the correct value was 0.974. Furthermore, when test data was input into the random forest model, the correlation coefficient r between the predicted value and the correct value was 0.770. A high correlation coefficient was also obtained for the test data. On the other hand, in FIG. 20C(c), when training data was input into the random forest model, the correlation coefficient r between the predicted value and the correct value was 0.947. However, when test data was input into the random forest model, the correlation coefficient r between the predicted value and the correct value was 0.152, indicating extremely low prediction accuracy. These findings suggest that the parameters used in the random forest model of Figure 20A (i.e., Duration ratio, <25%_r, Duration median, 25-75%_r, 25-75%, Duration max, Duration_SD, <25%, all_sqrt, Duration_mean, Duration_mean_log, and Duration mean_power) are more useful for predicting dyskinesia scores than other parameters (e.g., the combination of parameters used in the random forest model of Figure 20B).

[0303] (Example 4: Predicting the presence / absence, amount or level of L-DOPA, or fluctuations thereof, based on eyeball information) In this example, the presence / absence, amount or level of L-DOPA, or fluctuations thereof, is predicted based on eyeball information, which is typically blinking.

[0304] When L-DOPA is administered to a Parkinson's disease patient, the number of blinks is measured for each blink time (e.g., >0.25 s, 0.25 s, 0.20 s, 0.16 s, 0.14 s, <0.12 s, etc.). The correlation between these measurements and the occurrence of Parkinson's symptoms, motor complications, and psychiatric symptoms, or increases or decreases in dopamine levels, the presence or absence of L-DOPA, and fluctuations in dopamine levels, etc., is obtained in advance.

[0305] The number of blinks per blink time is measured from a subject patient, and the presence or absence, amount or level of L-DOPA, or fluctuations thereof is predicted from this measurement value. As a method for predicting the presence or absence, amount or level of L-DOPA in Parkinson's disease, or fluctuations thereof, the concentrations of L-DOPA, DA (dopamine), DOPAC (3,4-dihydroxyphenylacetic acid), HVA (homovanillic acid), 3-OMD (3-O-methyldopa), noradrenaline, etc. in plasma or cerebrospinal fluid after administration of L-DOPA are measured, or as a method for measuring L-DOPA in the brain, imaging diagnosis using positron emission tomography (PET) or single photon emission computed tomography (SPECT) can be mentioned. As an example of brain imaging diagnosis, [ １１ C] Lacropride, [ １１ C] SCH23390, [ １１ C] 2-carbomethoxy-3-(4-fluorophenyl)tropane ([ １１ C]CFT), [ １１ C] dihydrotetrabenazine ([ １１ C]DTBZ), [ １２３ I]N-(3-fluoropropyl)-2β-carbomethoxy-3β-(4-iodophenyl)nortropane ([ １２３ I]-FP-CIT), [ １２３ I] indobenzamide ([１２３ I] IBZM), 6-[ １８ F]fluoro-L-DOPA ([ １８ F] FDOPA), [ １８ F]fluorodeoxyglucose ([ １８ F]FDG) and the like can be used.

[0306] (Example 5: Predicting the presence / absence, amount or level, or fluctuations thereof of L-DOPA based on eyeball information) In this example, the presence / absence, amount or level, or fluctuations thereof of L-DOPA is predicted based on information on eyeball movement as eyeball information other than blinking. Utilizing the fact that eye tremor increases when the amount of L-DOPA in the body is excessive and the severity of dyskinesia is high, frequency analysis of pupil coordinates, eyeball movement distance, and eyeball movement speed is performed to obtain characteristic spectral bands, and the presence / absence, amount or level, or fluctuations thereof of L-DOPA in the body can be predicted. Furthermore, information on the degree of eyelid opening can be used as eyeball information other than blinking. When the amount of L-DOPA is low, the eyelids droop and move poorly. Furthermore, when the amount of L-DOPA in the body is excessive and the severity of dyskinesia is high, the eyelids droop and vibrate irregularly. By utilizing these, the presence or absence, amount or level of L-DOPA, or fluctuations thereof, can be predicted from the decrease in eyelid opening degree and the increase or decrease in its dispersion.

[0307] When L-DOPA is administered to a Parkinson's disease patient, eye information other than blinking, such as pupil coordinates, eye movement distance, frequency analysis spectrum of eye movement speed, eyelid opening degree and its variance, is measured. Correlations between these measurements and the occurrence of wearing-off or dyskinesia, increases or decreases in dopamine levels, the presence or absence of L-DOPA, and fluctuations thereof, are obtained in advance.

[0308] Eye information other than blinking, such as pupil coordinates, eye movement distance, frequency analysis spectrum of eye movement speed, eyelid opening degree and its variance, is measured from the patient, and the presence or absence, amount or level of L-DOPA, or its fluctuations, is predicted from these measurements. As a method for predicting the presence or absence, amount or level of L-DOPA in Parkinson's disease, or fluctuations thereof, the concentrations of L-DOPA, DA (dopamine), DOPAC (3,4-dihydroxyphenylacetic acid), HVA (homovanillic acid), 3-OMD (3-O-methyldopa), noradrenaline, etc. in plasma or cerebrospinal fluid after administration of L-DOPA are measured, or as a method for measuring L-DOPA in the brain, imaging diagnosis using positron emission tomography (PET) or single photon emission computed tomography (SPECT) can be mentioned. As an example of brain imaging diagnosis, [ １１ C] Lacropride, [ １１ C] SCH23390, [ １１ C] 2-carbomethoxy-3-(4-fluorophenyl)tropane ([ １１ C]CFT), [ １１ C] dihydrotetrabenazine ([ １１ C]DTBZ), [ １２３ I]N-(3-fluoropropyl)-2β-carbomethoxy-3β-(4-iodophenyl)nortropane ([ １２３ I]-FP-CIT), [ １２３ I] indobenzamide ([ １２３ I] IBZM), 6-[ １８ F]fluoro-L-DOPA ([ １８ F] FDOPA), [ １８ F]fluorodeoxyglucose ([ １８ F]FDG) may also be used.

[0309] Example 6: Measurement of striatal dopamine concentration in MPTP-induced Parkinson's disease model monkeys (Preparation of model animals) In this example, to measure striatal dopamine release, an MPTP-induced unilateral Parkinson's disease model was prepared with reference to the method described in the reference literature (Yamagata Medical Journal 2009; 27(2): 119-133). Male cynomolgus monkeys aged 3 to 5 years (Shin Nippon Scientific Research Co., Ltd.) were used to prepare the model animals. Atropine sulfate hydrate (0.01 mg / kg; Tanabe Mitsubishi Pharma Corporation) was administered intramuscularly as a preanesthetic medication. Next, ketamine hydrochloride (10 mg / kg; Arevipharma GmbH or Daiichi Sankyo Propharma Co., Ltd.) was administered intramuscularly, and buprenorphine hydrochloride (0.01 mg / kg; Otsuka Pharmaceutical Co., Ltd.) was administered intramuscularly immediately before surgery. A catheter (Nippon Becton Co., Ltd.) was inserted and placed into the left internal carotid artery under inhalation anesthesia with isoflurane (0.5-2%; DS Pharma Animal Health Co., Ltd. or Mylan Pharmaceuticals Co., Ltd.). The catheter was inserted into the left internal carotid artery, and the other end was routed subcutaneously to the back and placed in place. Two to three days after the placement surgery, MPTP-HCl (0.6 mg / kg; Sigma-Aldrich) was administered intra-arterially through the placed catheter under anesthesia with intramuscular ketamine hydrochloride. A second MPTP administration was performed in the same manner after a 1-2 week washout period. Improvement of the condition of model animals following administration of anti-Parkinson's drugs was confirmed by oral administration of L-DOPA (Sigma-Aldrich) and benzerazide hydrochloride (Sigma-Aldrich) followed by measurement of the parkinsonism score.

[0310] (Guide cannula placement surgery in the striatum) For guide cannula placement in the striatum, after induction anesthesia with ketamine hydrochloride, general anesthesia is administered by inhalation of a mixture of nitrogen and oxygen (2:1) in combination with isoflurane. After the animal's head is secured in a stereotaxic apparatus, the scalp is incised, the muscles and periosteum are peeled off, the skull is exposed, a contrast agent (Eisai Co., Ltd.) is added to the reference points, MRI images are taken, and the coordinates of the placement site are obtained. The guide cannula is fixed to a stereo guide (Eicom Co., Ltd.) and guided to the coordinates of the placement site. The guide cannula lumen is protected, and the sutures are removed when healing of the surgical site is confirmed or two weeks after surgery.

[0311] (Microdialysis) Using the microdialysis method, samples were taken from before L-DOPA administration until 200 minutes after administration to measure the amount of dopamine released in the striatum. After induction anesthesia with ketamine hydrochloride, a microdialysis probe (Eicom Co., Ltd.) was inserted into the guide cannula under isoflurane inhalation anesthesia, and the tip of the probe was allowed to reach the target brain coordinate. L-DOPA or a control substance was then administered while perfusing with artificial cerebrospinal fluid (Otsuka Pharmaceutical Factory, Inc.) using a syringe pump, and samples were collected. The amount of dopamine in the collected dialysate was measured using a fluorescence detector or electrochemical detector.

[0312] Example 7: Searching for Objective Parameters of Ocular Information Correlated with Parkinson's Symptoms. Objective parameters of ocular information correlated with Parkinson's disease patients' symptoms are obtained by measuring ocular information, observing the patient's condition, and measuring blood levodopa concentrations in Parkinson's disease patients taking levodopa before and after levodopa administration. As an example of a clinical testing method, the Parkinson's disease patient's background information (age, sex, medical history, treatment history, etc.), general condition, Parkinson's symptoms, blood tests, etc. are screened, and then the patient is allowed to acclimate to the ocular information measurement for about one week to confirm the effect on the patient's condition. After confirming the off state before the start of the test, levodopa is administered, and ocular information, observation of the patient's condition, and measurement of blood levodopa concentrations are performed over a period of up to 8 hours. The patient's condition is observed the day after the test.

[0313] The flow of the above test is illustrated in FIG.

[0314] (Implementation Example 8: Example in Application) As shown in these examples, it is possible to estimate the amount of dopamine in the body from eye movement measurements such as blink parameters, and it is also possible to estimate objective measurements such as the severity of dyskinesia that coincide with clinical assessment in Parkinson's disease patients. This could be a means of resolving various problems currently facing Parkinson's disease treatment. An example is shown below.

[0315] (Patient) This implementation example shows an application method related to Parkinson's disease. Potential users of this implementation example include all Parkinson's disease patients, from those immediately after diagnosis to those in the advanced and late stages. Furthermore, potential users include those who have not been diagnosed with Parkinson's disease but are suspected of having it, or those considered to be at high risk of developing Parkinson's disease. Examples of people considered to be at high risk of developing Parkinson's disease include those with mutations in genes that cause hereditary Parkinson's disease (e.g., SNCA, PARK2, PINK1, etc.), those with olfactory disorders, those with REM sleep behavior disorder, and relatively elderly individuals aged 60 to 70 years or older.

[0316] (Wearing) In one embodiment, a user can measure eye movements on a daily basis by wearing smart glasses with an eye movement measurement function, an electrooculography measurement device, or eyeglasses with an external eye movement measurement device. In another embodiment, a non-wearable device can also be used, such as a fixed eye tracking device, a smartphone with a camera function, a tablet device, or a personal computer, allowing eye movement measurements to be performed for a relatively short period of time.

[0317] (Daily Life / Measurement) While it is expected that the most extensive data will be obtained by wearing the eye movement measurement device throughout the day, excluding sleep and bathing, for example, the user may wear it only at specific times of the day, such as 1-3 hours after waking up, to obtain relatively stable and continuous data. For non-wearable devices, eye movement measurements can be performed for 3-5 minutes each time, for example, at the patient's home or a medical institution, using a stationary eye tracking device or a smartphone, tablet, or personal computer with a camera function. Measurements can be performed approximately 1-5 times a day to observe changes over time. Eye movement data can be measured not only daily, but also periodically. While daily measurement is most desirable for regular measurements, periodic measurements such as 1-3 times a week, once every 1-3 months, or once every 6 months to 12 months can also capture changes over time. The measurement environment is not particularly important as long as it does not affect the performance of the measurement device, but is expected to be the user's home, a medical institution, or a regular health checkup.

[0318] (Diagnosis and administration by a doctor) The eye movement data obtained in this way is stored in the user device and / or server device, and various estimation algorithms are used to output indicators of the amount of dopamine in the brain, indicators of the severity of motor symptoms or dyskinesia, etc.

[0319] Such embodiments may provide a solution to the problems facing today's treatment of Parkinson's disease.

[0320] For example, symptom assessment in Parkinson's disease patients is performed using subjective assessment scales, such as the UdysRS and patient diaries, by clinicians and patients. However, the accuracy of such subjective assessment indices is influenced by many factors, including the clinician's experience and training, the patient's temperament, and the personal relationship between the medical professional and the patient, leading to variable results between assessors. Furthermore, subjective assessment scales require active assessment by clinicians and patients, resulting in various costs and limitations. Clinician assessments are typically conducted during clinic visits approximately once every one to three months, providing only a single point in time within a day. However, because Parkinson's disease exhibits significant diurnal and interday fluctuations, it is difficult to obtain measurements that truly reflect the patient's condition through such infrequent and short-term assessments.

[0321] In one example of this embodiment, eye movement monitoring provides a new index for objectively assessing Parkinson's disease motor symptoms, motor complications, and in vivo dopamine levels. While the frequency and duration of assessments may vary depending on the embodiment, all measurements are convenient for patients. Even in the absence of active symptom monitoring by clinicians or patients, measurements are easily performed. This provides a means for easily and objectively measuring, analyzing, and reporting a patient's condition remotely and continuously, much more frequently than in a clinical setting. These processes are performed almost automatically. Output results are automatically converted into a report format that can be used as a clinical tool that clinicians can refer to at their discretion. Objective and continuous data collection enables more accurate capture of patient symptoms, including circadian and interday fluctuations. Using this information, clinicians can more effectively adjust the dosage and type of medication to reduce the frequency and severity of motor symptoms and complications, such as dyskinesias and ON / OFF periods. This enables precision medicine, which provides treatments optimized for individual symptoms without additional burden on clinicians or patients, thereby improving patients' quality of life. A broader benefit of this embodiment is that it reduces the frequency with which patients need to visit medical institutions in person, thereby reducing the financial burden on patients, medical institutions, and the nation for healthcare, and is expected to bring healthcare economics closer to optimization.

[0322] In one example of this embodiment, in addition to the above information, by simultaneously referencing, analyzing, and reporting peripheral information such as measurement records based on the past eye movements of the user and other users, as well as patient diaries, evaluation records by clinicians, and medication status, it is possible to provide even more useful information to encourage clinicians to make more appropriate decisions.

[0323] In addition, in one example of this embodiment, when a patient is receiving device-assisted treatment such as deep brain stimulation therapy or administration of an L-DOPA preparation via a transdermal or enteral device, the output motor symptoms, motor complications, or estimated values ​​of in vivo dopamine levels can be used as input values ​​for a device-assisted treatment algorithm, making it possible to construct a closed-loop system that controls the degree of device treatment in real time in response to fluctuations in symptoms, such as the dosage and administration rate of the L-DOPA preparation, and the stimulation frequency and intensity of deep brain stimulation therapy, within a safe range.

[0324] In one embodiment, some items in the report reporting the patient's condition can be viewed not only by medical professionals but also by the patient, and by visualizing the patient's symptoms and the degree of improvement, it is possible to encourage active participation in treatment. Furthermore, it is possible to provide the patient with alerts and reminders to take medication in response to worsening symptoms, including those at the present time or in the future, several minutes to several tens of minutes in the future, and in accordance with the time that has passed since taking the medication.

[0325] In recent years, objective evaluation systems for Parkinson's disease symptoms using wristwatch-type accelerometers and angular velocity measurement devices (IMUs) have been investigated. However, the symptoms that can be evaluated are limited to dyskinetic chorea and resting tremor, and they can only capture one aspect of the diverse manifestations of Parkinson's disease motor symptoms and motor complications. Furthermore, since they are ineffective for symptoms that do not occur on the arm where the device is worn, they are not applicable to all patients and do not necessarily lead to a solution to the problem. This disclosure focuses on eye movement, which directly reflects dopamine function in the brain, and is distinct from IMU-based systems that directly quantify the motor symptoms and motor complications of Parkinson's disease. As a result, embodiments of the present disclosure may be able to sensitively capture changes in brain dopamine function, which is the essence of the pathology.

[0326] In one example of this embodiment, the progression of Parkinson's disease can be estimated by measuring the amount of dopamine in the body and / or the responsiveness to therapeutic drugs such as L-DOPA over time using eye movement. Therefore, by periodically performing measurements according to this embodiment on patients taking or being treated with disease-modifying or preventive drugs for Parkinson's disease, candidate drugs or treatments, regenerative cell medicines, etc., the inhibitory effect of these treatments on the progression of Parkinson's disease can be evaluated. Currently, the main methods for evaluating the effects of disease-modifying drugs for Parkinson's disease are invasive and expensive imaging diagnostic methods such as PET and SPECT, which measure intracerebral dopamine receptors and dopamine transporters, or subjective and inaccurate motor symptom assessments by clinicians, as mentioned above. Alpha-synuclein-related substances, which are believed to be causative agents of Parkinson's disease, have also been investigated as biomarkers in the blood, but no established indicators exist. The lack of such biomarkers for assessing the progression of the disease makes it difficult to evaluate disease-modifying drugs, and is one of the reasons why a radical treatment for Parkinson's disease has not yet been established. In this situation, eye movement indicators directly evaluate dopamine function in the brain and can be easily measured frequently, continuously, objectively, and therefore can serve as a means for quantitatively assessing the progression of Parkinson's disease. Therefore, such embodiments are expected to be useful for evaluating the effects of disease-modifying drugs.

[0327] Furthermore, in this example embodiment, the user whose eye movement is being measured does not need to be a patient with a confirmed diagnosis of Parkinson's disease. For example, the subject may be a patient with suspected Parkinson's disease or a person considered to be at high risk of developing Parkinson's disease. Examples of suspected Parkinson's disease cases include, in addition to Parkinson's disease, patients with actual parkinsonism syndromes such as multiple system atrophy, corticobasal degeneration, and supranuclear progressive palsy, as well as patients with essential tremor. It is known that responsiveness to L-DOPA is limited for diseases other than Parkinson's disease. In such users, measuring eye movement can be used to estimate in vivo dopamine levels and motor symptoms, and further measuring responsiveness to Parkinson's disease medications such as L-DOPA, which can be used as a biomarker to assist in disease differentiation and promote appropriate treatment. Furthermore, individuals considered to be at high risk for developing Parkinson's disease include, for example, individuals with mutations in genes that cause hereditary Parkinson's disease (e.g., SNCA, PARK2, PINK1, etc.), individuals with olfactory impairment, individuals with REM sleep behavior disorder, and relatively elderly individuals aged 60 to 70 years or older. In such individuals, measuring eye movement to estimate the amount of dopamine in the body and further measuring responsiveness to Parkinson's disease treatment drugs such as L-DOPA can detect declines in dopamine function before the onset of Parkinson's disease, making it possible to use this as a biomarker for early diagnosis. It is said that motor symptoms in Parkinson's disease patients appear only after approximately 60% of dopaminergic neurons have already been lost. Even if there were disease-modifying drugs that are effective in neuroprotection, their effectiveness would be limited once neuronal loss has occurred to the extent that motor symptoms occur. In other words, in order to realize a radical treatment for Parkinson's disease, we face the difficult task of identifying and treating patients before the onset of Parkinson's disease. It is not yet clear to what extent dopaminergic nerve loss causes abnormalities in eye movement, but because it is thought to more directly reflect dopamine function, there is a high possibility that abnormalities can be detected earlier than with general motor symptoms, and the possibility of detection is thought to be further enhanced by conducting thorough analysis as shown in this disclosure.In other words, users with abnormal eye movements may themselves be at high risk for Parkinson's disease.

[0328] Therefore, in such an example, it becomes possible to screen patients from an early stage when Parkinson's disease has not yet manifested as motor symptoms, and it may be possible to prescribe disease-modifying drugs at a stage when their effectiveness can be expected. Furthermore, a broader advantage of such an embodiment is that, for example, if a treatment method that can serve as a preventive measure against Parkinson's disease, such as vaccine therapy, antibody drugs, or nucleic acid drugs against alpha-synuclein, is developed, administering it to all elderly people is unrealistic from a medical economics perspective, but if high-risk groups or very early-stage patients with Parkinson's disease can be screened based on eye movements, medical economically optimal prescriptions may be realized.

[0329] (Note) As described above, the present disclosure has been illustrated using preferred embodiments thereof, but it is understood that the scope of the present disclosure should be interpreted solely by the claims. It is understood that the patents, patent applications, and other documents cited herein are to be incorporated by reference into this specification in their entirety as if the contents themselves were specifically set forth herein. This application claims priority to Japanese Patent Application No. 2021-40515, filed on March 12, 2021, with the Japan Patent Office, the contents of which are incorporated by reference in their entirety as if they constitute the contents of this application.

[0330] The present disclosure provides techniques for estimating or predicting the status of Parkinson's disease patients and providing treatments that improve their quality of life.

[0331] 10, 10', 1000 System 11 Information acquisition means 12 Calculation means 13 Estimation means 100 User device 200 Server device 300 Database unit 400 Network

Claims

1. 1. A method for estimating or predicting the status of a Parkinson's disease patient being treated with L-DOPA or an L-DOPA related compound or a dopamine agonist, comprising: A) acquiring ocular information of the patient; B) estimating or predicting a condition of the patient based on the ocular information; The method includes:

2. The step B) is inputting the eye information into a trained model, the trained model having learned a relationship between the eye information and a patient's condition; Obtaining an output from the trained model; and The method of claim 1 , comprising:

3. The step B) is a) calculating an estimated presence, amount or level of dopamine or a substance bioequivalent to dopamine in the patient's body based on the ocular information; b) estimating or predicting the condition of the patient from the presence, absence, amount or level, or a variation thereof; The method of claim 1 , comprising:

4. The step a) comprises: inputting the eye information into a trained model, the trained model having learned a relationship between the eye information and the presence or absence, amount or level of dopamine or a substance biologically equivalent to dopamine in the living body; Obtaining an output from the trained model; and The method of claim 3 , comprising:

5. The method according to claim 1, wherein the condition includes the presence or absence, degree or score of at least one selected from the group consisting of dyskinesia, wearing off, ON-OFF, no-on phenomenon, delayed on phenomenon, akinesia, resting tremor, muscular rigidity, postural instability, forward leaning posture, freezing phenomenon, sleep disorder, mental / cognitive / behavioral disorder, autonomic nerve disorder and sensory disorder.

6. The step B) is Predicting or predicting the presence or absence, degree or score of symptoms of Parkinson's disease or the presence or absence, degree or score of dyskinesia of the patient based on the eyeball information. The method of claim 5 , comprising:

7. The step b) comprises:

4. The method of claim 3, comprising estimating or predicting a state of the patient by comparing an output from the model to one or more thresholds tailored to the patient.

8. The method of claim 7 , further comprising calculating one or more patient-specific thresholds.

9. The step A) comprises: Obtaining at least one ocular information source; extracting the eye information from the eye information source; The method of claim 1 , comprising:

10. The at least one ocular information source comprises optical, physical or electrical information of the patient's eye or of the muscles or surrounding skin involved in eye movement, or a combination of two or more thereof; and / or 10. The method of claim 9, wherein acquiring the at least one ocular information source comprises acquiring the at least one ocular information source using at least one selected from the group consisting of an image analysis method, a reflected light method, a distance measurement method, an electrooculography method, a search coil method, and a probe method.

11. Extracting the eye information from the eye information source comprises: deriving a first parameter related to the eye of the patient from the ocular information source; processing the first parameter to derive a second parameter related to the eye of the patient; and 11. The method of claim 10, comprising:

12. The first parameter includes at least one selected from the group consisting of blink frequency or number of blinks for spontaneous, voluntary or reflex blinks, blink duration, inter-blink time, eye closure time, eye opening speed, eye closing speed, eyelid movement width, eyelid opening degree, movement distance, movement direction, speed, acceleration, angular velocity, saccade, smooth eye movement, vestibulo-ocular reflex, convergence / divergence, and fixational eye movements (ocular tremor, drift, microsaccades), and / or The method according to claim 11, wherein the second parameter includes at least one selected from the group consisting of a function output having the first parameter as an input variable, an intra-partition calculation value which is a calculation value within each partition when data of the first parameter is divided into a plurality of partitions by a predetermined threshold value, and an inter-partition calculation value which is a calculation value of the intra-partition calculation value of the plurality of partitions.

13. The intra-division calculation values ​​include various statistics such as maximum, minimum, average, median, variance, 25% percentile, 75% percentile, and frequency analysis spectrum, and / or The method of claim 12 , wherein the inter-category calculations include weighted sums, differences, time derivatives, time integrals, ratios, correlation coefficients, and covariances.

14. The first parameter includes a blink duration; The second parameter includes a blink frequency classified based on blink duration, or a long blink frequency and a short blink frequency, or a ratio of a long blink frequency to a short blink frequency. The method of claim 11.

15. The method of claim 14 , further comprising obtaining criteria for segmenting the blink duration from the ocular information source.

16. Obtaining a criterion for dividing the blink duration from the eye information source includes: deriving time series data of blink frequency and blink duration for each blink from an ocular information source obtained from the patient or other patient; Dividing the time series data of the blink frequency into a plurality of data according to the length of the blink duration; Calculating a similarity between each of the classified plurality of data and other data among the classified plurality of data adjacent in the blink duration; setting a threshold value for dividing the blink duration based on the similarity; and 16. The method of claim 15, comprising:

17. 1. A system for estimating or predicting the status of a Parkinson's disease patient being treated with L-DOPA or an L-DOPA related compound or a dopamine agonist or dopaminergic agent, comprising: means for acquiring ocular information of the patient; means for estimating or predicting a condition of the patient based on the eye information; A system comprising:

18. The system of claim 17 , wherein the system is a user device.

19. The user device is an information processing device selected from the group consisting of a smartphone, a tablet computer, a smart glass, a smart watch, a laptop computer, and a desktop computer; and / or The system according to claim 18 , wherein the user device includes a portion that realizes a function of measuring optical information and / or electrooculography of the eye or muscles or surrounding skin involved in eye movement.

20. The system includes a user device and a server device, the user device comprises the information acquisition means, a transmission means for transmitting the eyeball information to the server device, and a reception means for receiving a result estimated by the estimation means from the server device; The server device includes a receiving means for receiving the eyeball information from the user device, the calculation means, the estimation means, and a transmission means for transmitting the result estimated by the estimation means to the user device, and / or The system includes a user device and a server device, the user device comprises the information acquisition means, the calculation means, a transmission means for transmitting the calculated presence / absence, amount or level to the server device, and a reception means for receiving a result estimated by the estimation means from the server device; the server device comprises a receiving means for receiving the calculated presence / absence, amount or level from the user device, the estimation means, and a transmitting means for transmitting a result estimated by the estimation means to the user device; 20. The system of claim 17.

21. A program for estimating or predicting the status of a Parkinson's disease patient undergoing treatment with L-DOPA or an L-DOPA related compound or a dopamine agonist, the program being executed on a computer system having a processor, the program comprising: A) acquiring ocular information of the patient; B) estimating or predicting a condition of the patient based on the ocular information; A program that causes the processor to perform a process including the steps of:

22. 22. The program of claim 21, wherein the computer system is a user device.

23. A system for managing the health of a patient with Parkinson's disease, comprising: An estimation or prediction system configured to carry out the method of claim 1; and means for administering additional treatment to the Parkinson's disease patient when the estimation or prediction system determines that the Parkinson's disease patient should be administered the treatment. A system comprising:

24. A system for managing the health of a patient with Parkinson's disease, comprising: An estimation or prediction system configured to carry out the method of claim 1; When the estimation or prediction system judges that the patient with Parkinson's disease should be administered L-DOPA or an L-DOPA-related compound or a dopamine agonist, an adjunct to L-DOPA, a dopamine nerve function restoring agent, a dopamine-producing cell pharmaceutical, or a dopamine-producing gene therapy, or a surgical therapy, the system provides a means for administering the L-DOPA or an L-DOPA-related compound or a dopamine agonist, an adjunct to L-DOPA, a dopamine nerve function restoring agent, a dopamine-producing cell pharmaceutical, or a dopamine-producing gene therapy, or a surgical therapy to the patient with Parkinson's disease. A system comprising:

25. 25. The system of claim 24, wherein the surgical therapy comprises deep brain stimulation or stereotactic ablation.

26. A system for managing the health of a patient with Parkinson's disease, comprising: An estimation or prediction system configured to carry out the method of claim 1; If the estimation or prediction system determines that additional treatment should be performed on the Parkinson's disease patient, the treatment is a means for issuing an alert regarding the treatment. A system comprising:

27. 1. A program for health management of a patient with Parkinson's disease, the program being executed on a computer system having a processor, the program comprising: Carrying out the method according to claim 1; and if the method determines that the Parkinson's disease patient should be administered additional treatment, providing instructions for administering the treatment to the Parkinson's disease patient. A program that causes the processor to perform a process including the steps of:

28. 1. A program for health management of a patient with Parkinson's disease, the program being executed on a computer system having a processor, the program comprising: Carrying out the method according to claim 1; when it is determined by the method that the patient with Parkinson's disease should be administered L-DOPA or an L-DOPA-related compound or a dopamine agonist, an adjunct to L-DOPA, a dopamine nerve function restoring agent, a dopamine-producing cell pharmaceutical product, or a dopamine-producing gene therapy, or a surgical therapy including deep brain stimulation therapy or stereotactic ablation, an instruction is given to administer the L-DOPA or an L-DOPA-related compound or a dopamine agonist, an adjunct to L-DOPA, a dopamine nerve function restoring agent, a dopamine-producing cell pharmaceutical product, or a dopamine-producing gene therapy, or a surgical therapy including deep brain stimulation therapy or stereotactic ablation to the patient with Parkinson's disease. A program that causes the processor to perform a process including the steps of:

29. 1. A program for health management of a patient with Parkinson's disease, the program being executed on a computer system having a processor, the program comprising: Carrying out the method according to claim 1; If the method determines that additional treatment should be administered to the Parkinson's disease patient, issuing an alert regarding the treatment. A program that causes the processor to perform a process including the steps of: