Method for predicting prognosis of diseases caused by accumulation of abnormal proteins

Abstract

To provide a method for predicting the effect of oral medication on a disease caused by the accumulation of abnormal proteins in a subject, and a method for evaluating BPSD. [Solution] A method for predicting the effectiveness of oral medication in a subject against a disease caused by the accumulation of abnormal proteins, which includes evaluating the amount of abnormal protein secreted into a culture medium of neurons derived from subject-derived iPS cells and the maximum blood concentration of the oral medication in the subject as indicators, and a method for assessing BPSD in a subject, which includes a step of measuring at least one of the subject's amount of conversation when not at rest and the average number of steps taken during the day.

Description

[Technical Field]

[0001] The present invention relates to a method for predicting the effect of an oral medication on a disease caused by the accumulation of abnormal proteins in a subject. More specifically, the prediction method of the present invention involves evaluating the amount of abnormal protein secreted into a culture medium of neurons derived from subject-derived induced pluripotent stem cells (iPS cells) and the maximum blood concentration of the oral medication in the subject as indicators. The present invention also relates to a method for assessing behavioral and psychological symptoms of dementia (BPSD). More specifically, the assessment method of the present invention involves measuring at least one of the amount of conversation while not resting and the average number of steps taken during the day in a subject. [Background technology]

[0002] In recent years, the incidence of various diseases has increased due to factors such as aging, and the development of drugs intended to treat and prevent these diseases is progressing. The effects of oral medications vary from person to person. This can be broadly divided into two categories: (1) differences in pharmacokinetics and (2) differences in cellular responsiveness. Regarding (1), differences in pharmacokinetics, such as absorption and metabolism, result in differences in drug concentrations exposed to cells in the target organ. Generally, measuring drug blood concentrations and evaluating pharmacokinetics can reveal individual differences in the effects of oral medications. Regarding (2), differences in the effects of drugs on target organ cells vary depending on individual genetic background, aging, and the cellular environment. In this regard, induced pluripotent stem cells (iPS cells) have made it possible to analyze cells with individual genetic backgrounds, thereby enabling the evaluation of individual differences in drug responsiveness at the cellular level.

[0003] Currently, attempts are being made to predict the (long-term) effects of oral medications on subjects using disease-specific iPS cells, etc., but to the inventors' knowledge, only methods using biomarkers specific to individual diseases have been investigated (Non-Patent Document 1).

[0004] Dementia, one of the diseases on the rise, is a major cause of people requiring nursing care. Behavioral and psychological symptoms associated with dementia (BPSD) refer to various symptoms associated with dementia, such as delusions, agitation, irritability, anxiety, and apathy, and place a mental and physical burden on caregivers at home or in nursing facilities. Early detection of BPSD and the development of appropriate prevention and treatment methods are urgent issues in a world where the population is aging.

[0005] Because BPSD often occurs in real life and cannot be observed in a doctor's office, it has traditionally been assessed and scored using a questionnaire administered to caregivers. However, questionnaires have limitations as an indicator of BPSD because they rely on caregivers' memory recall, the same symptoms may be perceived differently depending on the family member's personality, and the scale cannot capture subtle changes. Furthermore, describing abnormal behaviors, such as verbal abuse, can be a painful experience for family members. Therefore, although alternative BPSD assessment methods to questionnaires have been explored, to the inventors' knowledge, no objective, quantitative, highly sensitive, and minimally invasive BPSD assessment method has yet been established, based on items that can be measured using existing wearable activity monitors. [Prior art documents] [Non-patent literature]

[0006] [Non-Patent Document 1] Keiko Imamura et al., EClinicalMedicine. 2022 Oct 25:53:101707 [Non-patent document 2] Ta-Wei Guu et al., Alzheimer's Dement. 2024;20:3211-3218 Summary of the Invention [Problem to be solved by the invention]

[0007] Therefore, an object of the present invention is to provide a method for predicting the effectiveness of an oral medication in a subject against a disease caused by the accumulation of abnormal proteins. More specifically, the prediction method of the present invention involves evaluation using as indicators the amount of abnormal protein secreted into a culture medium of neurons derived from subject-derived induced pluripotent stem cells (iPS cells) and the maximum blood concentration of the oral medication in the subject. Another object of the present invention is to provide a method for assessing BPSD. More specifically, the evaluation method of the present invention involves measuring at least one of the amount of conversation while not resting and the average number of steps taken during the day in a subject. Another object of the present invention is to provide a program or the like for such prediction and evaluation. [Means for solving the problem]

[0008] The present inventors have conducted investigator-initiated clinical trials to investigate the safety and efficacy of bromocriptine for familial Alzheimer's disease and have been analyzing the results. The inventors conceived the idea that changes in clinical indicators could be predicted using the blood concentration of bromocriptine in subjects and the responsiveness of neurons derived from subject-derived iPS cells to bromocriptine (specifically, differences in the amount of abnormal protein secreted into the culture medium). As a result of extensive research into this idea, they found a correlation between the change in NPI-12 after the start of oral bromocriptine administration (particularly after 32 weeks of oral administration) and the bromocriptine responsiveness of neurons derived from iPS cells generated from subjects adjusted by the maximum blood concentration (Cmax) of bromocriptine (e.g., Cmax after the subject's first oral administration of bromocriptine).

[0009] In addition, in the investigator-initiated clinical trial, the inventors had subjects wear a wristwatch-type wearable activity monitor equipped with sensors such as an acceleration sensor, a conversation sensor, a pulse sensor, a temperature sensor, and a UV sensor. The inventors calculated and recorded data on the number of steps taken, whether or not they were talking, their pulse rate, skin temperature, UV index, whether or not they were awake or asleep, their activity status (resting or activity level), and their energy expenditure. While analyzing the recorded data, the inventors came up with the idea of ​​classifying the number of steps taken and the amount of conversation time as indicators of BPSD (digital features). After extensive research into this idea, they found that the amount of conversation during non-resting periods and the average number of steps taken during the day can be used to predict the prognosis of BSPD in subjects. Based on these findings, the present inventors have conducted further research and have completed the present invention.

[0010] That is, the present invention is as follows. [1] A method for predicting the effectiveness of an oral medication against a disease caused by the accumulation of abnormal proteins in a subject, comprising evaluating the amount of abnormal protein secreted into a culture medium by neurons derived from subject-derived iPS cells and the maximum blood concentration of the oral medication in the subject as indicators. [2] The method according to [1], wherein the amount of abnormal protein secreted into the culture medium is the amount of abnormal protein secreted into the culture medium before and after the addition of the oral medication. [3] The method according to [1] or [2], wherein the maximum blood concentration of the oral medication in the subject is the maximum blood concentration after the initial administration of the oral medication to the subject. [4] The method according to [3], wherein the maximum blood concentration is measured after the first administration and before the second administration. [5] The method according to any one of [1] to [4], wherein the effect of the oral medication on a disease caused by accumulation of abnormal proteins in the subject is the effect at least 8 months after administration of the oral medication. [6] The method according to any one of [1] to [5], wherein the abnormal protein is a protein associated with the onset of a neurodegenerative disease. [7] The method according to [6], wherein the secretion amount of the protein associated with the onset of the neurodegenerative disease is an Aβ42 / 40 ratio, and the protein associated with the onset of the neurodegenerative disease is Aβ. [8] The method according to any one of [1] to [7], wherein the oral medication is a drug for preventing or treating a neurodegenerative disease. [9] The method according to any one of [1] to [8], wherein the evaluation is performed based on the calculation formula (I): (amount of abnormal protein secreted into the culture medium by neurons derived from subject-derived iPS cells after addition of the oral medication - amount of abnormal protein secreted into the culture medium by neurons derived from subject-derived iPS cells before addition of the oral medication) / amount of abnormal protein secreted into the culture medium by neurons derived from subject-derived iPS cells before addition of the oral medication × 100 × maximum blood concentration of the oral medication in the subject / maximum blood concentration of the specified oral medication.

[10] The method according to [9], wherein the evaluation is performed by comparing the value of the formula (I) with a preset threshold value.

[11] The oral medication is a compound represented by the following formula (I):

[0011] [ka] or formula (II):

[0012] [ka]

[0013] [In formulas (I) and (II), R 1 represents a hydrogen atom or a halogen atom; R 2 and R 3each independently represents a linear or branched alkyl group having 1 to 5 carbon atoms or an aryl group having 6 to 10 carbon atoms. The method according to any one of [1] to

[10] , comprising a compound represented by the following formula:

[12] R 1 is a bromine atom, and R 2 is a methyl group or an isopropyl group, and R 3

[11] The method according to

[11] , wherein

[13]

[12] The method according to

[12] , wherein the compound is bromocriptine or a salt thereof.

[14] A computer program for predicting the effect of an oral medication on a disease caused by accumulation of abnormal proteins in a subject, Computer, (P1) a reception unit that receives data on the amount of abnormal protein secreted into a culture medium of neurons derived from subject-derived iPS cells obtained from the subject and data on the maximum blood concentration of oral medication in the subject; (P2) a calculation unit that calculates, from the input data, a score for predicting efficacy against diseases caused by accumulation of abnormal proteins; and (P3) An output section that outputs the calculation results obtained in the calculation section A program to function as a

[15] (P4) The program described in

[14] for functioning as a judgment unit that compares the calculation result output in (P3) with a preset threshold value to judge the effect on diseases caused by the accumulation of abnormal proteins.

[16] The program according to

[14] or

[15] , wherein the compound is bromocriptine or a salt thereof.

[17] A method for assessing BPSD in a subject, comprising measuring at least one of the amount of non-resting conversation and the average number of steps taken during the day in the subject.

[18] The method according to

[17] , in which BPSD is an impulse control disorder.

[19] The method described in

[17] , where BPSD is apathy.

[20] The method according to

[18] , wherein the impulse control difficulties are one or more symptoms selected from agitation, irritability, and abnormal behavior. [twenty one] A screening method for preventive or therapeutic drugs for BPSD, comprising a step of determining whether a subject's BPSD has improved based on at least one of the amount of conversation while not resting and the average number of steps during the day obtained from the subject before administration of a candidate substance for the preventive or therapeutic drug for BPSD, and the amount of conversation while not resting and the average number of steps during the day obtained from the subject after administration of the candidate substance. [twenty two] A computer program for assessing BPSD in a subject, comprising: Computer, (P1) a receiving unit that receives at least one piece of information obtained from the subject, such as the number of steps and the amount of conversation; (P2) a calculation unit that calculates a score for evaluating the subject's BPSD from the input data; and (P3) An output section that outputs the calculation results obtained in the calculation section A program to function as a [twenty three] The program described in

[22] , wherein the score for assessing the subject's BPSD is at least one of the amount of conversation while not resting and the average number of steps taken during the day. [twenty four] A recording medium on which the program described in

[22] or

[23] is recorded. [twenty five] The program according to

[24] , wherein the recording medium is a non-transitory computer-readable recording medium. [Effects of the Invention]

[0014] By using data based on differentiated cells (e.g., neurons) derived from subject-derived iPS cells and the subject's blood drug concentration (Cmax), it becomes possible to predict clinical trial results with high accuracy, and clinical trials can be conducted by focusing on subjects who are expected to respond well (enrichment design). In particular, the prediction method of the present invention uses the Cmax at the subject's first oral administration of a drug to predict the subject's symptoms from 32 weeks onward, which is expected to lead to reductions in the cost, duration, and number of subjects of clinical trials. This invention enables objective, quantitative, highly sensitive, and minimally invasive assessment of BPSD, and is expected to reduce the burden on families of BPSD patients, who must report abnormal behavior, such as verbal abuse, of their loved ones. [Brief explanation of the drawings]

[0015] [Figure 1] Figure 1 shows the blood bromocriptine concentrations measured after the initial oral administration of 2.5 mg of bromocriptine on Day 1. The maximum bromocriptine concentration (Cmax) varied between subjects, ranging from 32.5 pg / mL to 314 pg / mL. [Figure 2] Figure 2 shows the percentage difference (ΔAβ42 / 40 ratio) between the Aβ42 / 40 ratio after bromocriptine administration and the Aβ42 / 40 ratio after no drug administration in neurons derived from iPS cells from participants (subjects) in the bromocriptine-treated clinical trial. Individual differences in response to bromocriptine treatment (0.2 μM) were observed between subjects. [Figure 3] Figure 3 shows an overview of the evaluation of the therapeutic effect prediction score (disease effect prediction score) that predicts clinical symptoms. The evaluation was performed by analyzing the correlation between the therapeutic response of clinical symptoms in the bromocriptine-treated group and the therapeutic effect prediction score calculated from the drug response of iPS cell-derived neurons adjusted by the drug blood concentration Cmax. [Figure 4]Figure 4 shows the correlation between the treatment effect prediction score (disease effect prediction score) and clinical score. The drug responsiveness (ΔAβ42 / 40 ratio) of neurons derived from subject-derived iPS cells, adjusted by the drug's blood concentration Cmax, was strongly correlated with the change in NPI-12 from the start of treatment with the investigational drug at 32 weeks (Figure 4, left) and 36 weeks (Figure 4, right). Figure 4 shows Pearson's r value and P value, and the linear regression line. [Figure 5] Figure 5 shows the results of examining the correlation between the index using AUC and the change in NPI-12. As a result, no significant correlation was observed between AUC and the change in NPI-12 from the start of administration of the study drug at weeks 32 and 36. [Figure 6] Figure 6 shows the results of examining the drug responsiveness (ΔAβ42 / 40 ratio) of neurons derived from subject-derived iPS cells, adjusted by AUC, as a candidate predictive score. As a result, no significant correlation was observed with the change in NPI-12 from the start of administration of the investigational drug at weeks 32 and 36. [Figure 7] Figure 7 shows the change in daytime step count and apathy sub-item score for a representative case (subject 08) (Figure 7, left). Furthermore, a linear mixed model showed that the change in daytime step count was negatively associated with the change in apathy NPI-12 sub-item score (n = 8, p = 0.009) (Figure 7, right). [Figure 8] Figure 8 shows the change in non-resting conversation volume and irritability sub-item score for a representative case (subject 04) (Figure 8, left). Furthermore, a linear mixed model showed that the change in non-resting conversation volume was positively correlated with the change in irritability NPI-12 sub-item score (n = 8, p < 0.001) (Figure 8, right). [Figure 9] Figure 9 shows that for the NPI subscales of agitation and abnormal behavior, the change in non-resting conversation volume was positively associated with the change in the NPI-12 irritability subscale score using a linear mixed model (p < 0.02, p = 0.001). [Figure 10]Figure 10 shows that the sum of the NPI subscales of irritability, agitation, and abnormal behavior was positively associated with the amount of non-resting conversation (p < 0.001). Non-resting conversation is considered to be a digital index that captures not only irritability but also impulse-related behavioral symptoms (impulse dyscontrol). DETAILED DESCRIPTION OF THE INVENTION

[0016] 1. Prediction method of the present invention The present invention provides a method for predicting the effect of an oral medication on a disease caused by the accumulation of abnormal proteins in a subject (hereinafter also referred to as the "prediction method of the present invention"), which comprises evaluating the amount of abnormal protein secreted into a culture medium by neurons derived from subject-derived iPS cells and the maximum blood concentration of the oral medication in the subject as indicators. Herein, the "amount of abnormal protein secreted into a culture medium by neurons derived from subject-derived iPS cells" and the "maximum blood concentration of the oral medication in the subject" may be referred to as "Indicator 1" and "Indicator 2", respectively.

[0017] In the present invention, "prediction of the effect of an oral medication on a disease caused by the accumulation of abnormal proteins in a subject" may be a prediction of the effect at any time point after administration of the oral medication, but in one embodiment, it is the effect at least 8 months after administration of the oral medication. In a further embodiment, it is the effect at least 8 months after the initial administration of the oral medication. In other words, in one embodiment, the prediction method of the present invention is a method for predicting the state of a given disease in a subject at least 8 months later based on the results of the initial administration of the oral medication to the subject (prognosis prediction method). The prediction method of the present invention is capable of predicting the prognosis of a given disease in a subject based on at least the initial administration of the oral medication to the subject, and is therefore also useful as a method for assisting companion diagnostics to evaluate whether the therapeutic effect of the oral medication in the subject can be expected.

[0018] (1) Indicator 1 As used herein, the term "abnormal protein" refers to a protein whose original function has been lost or reduced, and typically refers to a protein that has been misfolded or aggregated due to abnormal mRNA production, abnormal translation elongation or termination, chemical modification, or the like, and whose accumulation contributes to the onset of disease. In one embodiment of the present invention, the abnormal protein is a protein associated with the onset of neurodegenerative disease, and specific examples include amyloid beta protein (hereinafter also referred to as "Aβ"), tau protein, α-synuclein, polyglutamine, TDP-43, superoxide dismutase 1, and the like. In one embodiment of the present invention, the abnormal protein is Aβ.

[0019] As used herein, "diseases caused by the accumulation of abnormal proteins" are not particularly limited as long as the accumulation of abnormal proteins (or abnormal protein aggregates) is one of the causes of the onset of the disease. Examples include abnormal protein accumulation neurodegenerative diseases, which are progressive diseases in which the accumulation of abnormal proteins (or abnormal protein aggregates) gradually degenerates neurons in the central nervous system, leading to dysfunction and cell death. More specifically, examples of the condition include, but are not limited to, Alzheimer's disease (whether sporadic or familial), mild cognitive impairment (MCI), amyotrophic lateral sclerosis (ALS), Huntington's disease, spinocerebellar degeneration, multiple sclerosis, Creutzfeldt-Jakob disease, progressive multifocal leukoencephalopathy, dementia with Lewy bodies, corticobasal degeneration, Parkinson's disease, Parkinson's syndrome, frontotemporal dementia (FTD), spastic paraplegia, non-herpetic acute limbic encephalitis, and age-related macular degeneration (including precursor lesions, atrophic type, and exudative type).

[0020] The amount of abnormal protein secretion used as an indicator in the prediction method of the present invention is the amount of abnormal protein secreted into the culture medium after the addition of at least an oral medication as described below, typically within 96 hours after the addition of the oral medication. In one embodiment, it is the amount of abnormal protein secreted 48 hours after the addition of the oral medication. In one embodiment, the amount of abnormal protein secreted used as an indicator in the prediction method of the present invention is the amount of abnormal protein secreted into the culture medium without the addition of the oral medication (more specifically, before the addition of the oral medication) and after the addition of the oral medication. Furthermore, the amount of abnormal protein secreted may be the total weight or mass per unit volume, or may be the ratio of a specific protein to another specific protein other than the specific protein. In one embodiment, the amount of abnormal protein secreted is the Aβ42 / 40 ratio. The abnormal protein used as an indicator in the prediction method of the present invention may be measured by a method known per se. For example, the abnormal protein of interest can be quantified using an antibody that specifically recognizes the abnormal protein of interest, by Western blotting, immunostaining, enzyme-linked immunosorbent assay (e.g., EIA, ELISA, etc.), LC / MS, LC-MS / MS, RT-QuIC, immunoprecipitation (IP), etc.

[0021] In the present invention, the "subject" is not particularly limited, but includes humans and non-human mammals (e.g., mice, rats, hamsters, guinea pigs, rabbits, cats, dogs, cows, horses, sheep, monkeys, etc.), and preferably primates such as humans and monkeys.

[0022] In the present invention, the "iPS cell-derived neurons" are not particularly limited as long as they secrete the abnormal protein of interest into a culture medium, and can be differentiated or induced from iPS cells by methods known per se (e.g., WO2019 / 004321, WO2014 / 148646, Hester ME et al., Mol. Therapy, 19:1905-1912 (2011), Pang ZP et al., Nature, 476:220-223 (2012), etc.).

[0023] Furthermore, iPS cells or neurons may be those into which one or more mutations have been introduced into a specific gene by a method known per se, or those into which a mutant of a specific gene has been exogenously introduced. The specific gene may be any known causative gene for a disease caused by the accumulation of abnormal proteins. For example, if the disease caused by the accumulation of abnormal proteins is a disease caused by the accumulation of Aβ (e.g., Alzheimer's disease), the iPS cells or neurons may be those into which a mutation has been introduced into one or more genes selected from the group consisting of amyloid precursor protein (APP), presenilin 1 (PSEN1), and presenilin 2 (PSEN2), or those into which a mutant of these genes has been exogenously introduced.

[0024] For example, APP mutations include dup APP mutation, APP KM670 / 671NL mutation, APP D678N mutation, APP E682K mutation, APP A692G mutation, APP E693K mutation, APP E693Q mutation, APP E693G mutation, APP E693del (APP E693Δ) mutation, APP D694N mutation, APP L705V mutation, APP A713T mutation, APP T714A mutation, APP T714I mutation, APP V715M mutation, APP V715A mutation, APP I716V mutation, APP I716F mutation, APP I716T mutation, and APP V717I mutation.

[0025] Furthermore, for example, presenilin 1 mutations include PSEN1 A79V mutation, PSEN1 V82L mutation, PSEN1 ΔI83 / M84 mutation, PSEN1 L85P mutation, PSEN1 V89L mutation, PSEN1 C92S mutation, PSEN1 V94F mutation, PSEN1 V96F mutation, PSEN1 V97L mutation, PSEN1 F105I mutation, PSEN1 F105L mutation, PSEN1 L113Q mutation, PSEN1 L113P mutation, PSEN1 Intron4; InsTAC mutation, PSEN1 Y115H mutation, PSEN1 Y115D mutation, PSEN1 Y115C mutation, PSEN1 T116N mutation, PSEN1 T116I mutation, PSEN1 P117A mutation, PSEN1 P117S mutation, PSEN1 P117R mutation, PSEN1 P117L mutation, PSEN1 E120K mutation, PSEN1 E120D mutation, PSEN1 E123K mutation, PSEN1 N135D mutation, PSEN1 N135S mutation, PSEN1 A136G mutation, PSEN1 F139V mutation, PSEN1 F139K mutation, PSEN1 F139T mutation, PSEN1 F139I mutation, PSEN1 I143F mutation, PSEN1 I143N mutation, PSEN1 I143T mutation, PSEN1 F146L mutation, PSEN1 F146V mutation, PSEN1 F146I mutation, PSEN1 T147I mutation, PSEN1 L153V mutation, PSEN1 Y154N mutation, PSEN1 Y154C mutation, PSEN1 InsFI Mutation, PSEN1 H163Y mutation, PSEN1 H163R mutation, PSEN1 W165G mutation, PSEN1 W165C mutation, PSEN1 L166del mutation, PSEN1 L166H mutation, PSEN1 L166P mutation, PSEN1 L166R mutation, PSEN1 ΔI167 mutation, PSEN1 ΔI168 mutation, PSEN1 S169P mutation, PSEN1 S169L mutation, PSEN1 S170F mutation, PSEN1 L171P mutation, PSEN1 L173W mutation, PSEN1 L173F mutation, PSEN1 L174F mutation, PSEN1 L174R mutation, PSEN1 F177L mutation, PSEN1 F177S mutation, PSEN1 S178Pmutation, PSEN1 G183V mutation, PSEN1 E184D mutation, PSEN1 G206S mutation, PSEN1 G206D mutation, PSEN1 G206A mutation, PSEN1 G206V mutation, PSEN1 G209R mutation, PSEN1 G209E mutation, PSEN1 G209V mutation, PSEN1 I213L mutation, PSEN1 I213F mutation, PSEN1 I213T mutation, PSEN1 H214D mutation, PSEN1 H214Y mutation, PSEN1 G217D mutation, PSEN1 L219F mutation, PSEN1 L219P mutation, PSEN1 Q222R mutation, PSEN1 Q222H mutation, PSEN1 Q223R mutation, PSEN1 L226F mutation, PSEN1 L226R mutation, PSEN1 I229F mutation, PSEN1 A231T mutation, PSEN1 A231V mutation, PSEN1 F233L mutation, PSEN1 F233V mutation, PSEN1 F233T mutation, PSEN1 F233I mutation, PSEN1 L235V mutation, PSEN1 L235P mutation, PSEN1 F237I mutation, PSEN1 F237L mutation, PSEN1 T245P mutation, PSEN1 A246E mutation, PSEN1 L248R mutation, PSEN1 L250V mutation, PSEN1 L250S mutation, PSEN1 Y256S mutation, PSEN1 A260V mutation, PSEN1 V261L mutation, PSEN1 V261F mutation, PSEN1 L262F mutation, PSEN1 C263R mutation, PSEN1 C263F mutation, PSEN1 P264L mutation, PSEN1 G266S mutation, PSEN1 P267S mutation, PSEN1 P267L mutation, PSEN1 R269G mutation, PSEN1 R269H mutation, PSEN1 L271V mutation, PSEN1 V272A mutation, PSEN1 E273A mutation, PSEN1 T274R mutation, PSEN1 R278K mutation, PSEN1 R278T mutation, PSEN1 R278I mutation, PSEN1 R278S mutation, PSEN1 E280A mutation, PSEN1 E280G mutation, PSEN1 L282V mutation, PSEN1 L282F mutation, PSEN1 L282R mutation, PSEN1 P284S mutation, PSEN1 P284L Mutation, PSEN1A285V mutation, PSEN1 L286V mutation, PSEN1 L286P mutation, PSEN1 Δ9 mutation, PSEN1 Δ9Finn mutation, PSEN1 869-22_869-23ins18 mutation, PSEN1 T291P mutation, PSEN1 R358Q mutation, PSEN1 S365A mutation, PSEN1 S365Y mutation, PSEN1 R377F mutation, PSEN1 G378E mutation, PSEN1 G378V mutation, PSEN1 L381V mutation, PSEN1 G384A mutation, PSEN1 F386S mutation, PSEN1 S390I mutation, PSEN1 V391F mutation, PSEN1 L392V mutation, PSEN1 L392P mutation, PSEN1 G394V mutation, PSEN1 N405S These include PSEN1 A409T mutation, PSEN1 C410Y mutation, PSEN1 V412I mutation, PSEN1 L418F mutation, PSEN1 L420R mutation, PSEN1 L424V mutation, PSEN1 L424F mutation, PSEN1 L424H mutation, PSEN1 L424R mutation, PSEN1 A426P mutation, PSEN1 A431E mutation, PSEN1 A431V mutation, PSEN1 A434C mutation, PSEN1 L435F mutation, PSEN1 P436S mutation, PSEN1 P436Q mutation, PSEN1 I439V mutation, and PSEN1 ΔT440 mutation.

[0026] For example, presenilin 2 mutations include PSEN2 R71W mutation, PSEN2 A85V mutation, PSEN2 T122P mutation, PSEN2T122R mutation, PSEN2 N141I mutation, PSEN2 V148I mutation, PSEN2 F174V mutation, PSEN2 S175C mutation, PSEN2 Y231C mutation, PSEN2 Q228L mutation, PSEN2 F239V mutation, PSEN2 F239I mutation, PSEN2 T430F mutation, and PSEN2 D439A mutation.

[0027] The culture medium used in the present invention is not particularly limited as long as it can at least culture the desired neurons, and may be a basal medium, but is preferably a medium suitable for inducing differentiation into neurons (hereinafter, sometimes referred to as a "neuronal differentiation-inducing medium"). The neuronal differentiation-inducing medium can be prepared by adding neurotrophic factors to a basal medium to which neurotrophic factors have been added. Neurotrophic factors are ligands for membrane receptors that play an important role in the survival and maintenance of neuronal function. Examples of neurotrophic factors include nerve growth factor (NGF), brain-derived neurotrophic factor (BDNF), neurotrophin 3 (NT-3), neurotrophin 4 / 5 (NT-4 / 5), neurotrophin 6 (NT-6), basic FGF, acidic FGF, FGF-5, epidermal growth factor (EGF), hepatocyte growth factor (HGF), insulin, insulin-like growth factor 1 (IGF-1), insulin-like growth factor 2 (IGF-2), glia cell line-derived neurotrophic factor (GDNF), transforming growth factor (TGF-b2), transforming growth factor (TGF-b3), interleukin 6 (IL-6), ciliary neurotrophic factor (CNTF), and LIF.

[0028] Examples of the basal medium include Glasgow's Minimal Essential Medium (GMEM), IMDM, Medium 199, Eagle's Minimum Essential Medium (EMEM), αMEM, Dulbecco's modified Eagle's Medium (DMEM), Ham's F12 (F12), Dulbecco's Modified Eagle Medium:Nutrient Mixture F-12 (DMEM / F-12), RPMI 1640, Fischer's medium, Neurobasal Medium (Lifetechnologies), and mixed media thereof. The basal medium may contain serum or may be serum-free.

[0029] If necessary, the medium may contain one or more serum substitutes such as Knockout Serum Replacement (KSR) (a serum substitute for FBS used in ES cell culture), N2 supplement (Invitrogen), B27 supplement (Invitrogen), albumin, transferrin, apotransferrin, fatty acids, insulin, collagen precursors, trace elements, 2-mercaptoethanol, and 3'-thiolglycerol, and may also contain one or more substances such as lipids, amino acids, L-glutamine, Glutamax (Invitrogen), non-essential amino acids, vitamins, growth factors, small molecules, antibiotics, antioxidants, pyruvate, buffers, inorganic salts, selenate, progesterone, and putrescine.

[0030] The culture temperature for the neurons derived from subject-derived iPS cells before and after the addition of the oral medication is not particularly limited, but is approximately 30 to 40°C, preferably 37°C, and the culture is performed in an atmosphere of CO2-containing air, with the CO2 concentration preferably being approximately 2 to 5%.

[0031] (2) Indicator 2 The maximum blood concentration (Cmax) of an oral drug in a subject used as an indicator in the prediction method of the present invention is not particularly limited as long as it is the maximum blood concentration after administration of the oral drug to the subject, but is typically the maximum blood concentration after the first administration of the oral drug to the subject. In one embodiment, it is the maximum blood concentration after the first administration of the oral drug to the subject and before the second administration. In another embodiment, it is within 24 hours, preferably within 6 hours, of the first administration of the oral drug to the subject (e.g., 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours after the first administration, etc.).

[0032] The oral medication is not particularly limited as long as it can be expected to have a therapeutic or preventive effect against diseases caused by the accumulation of abnormal proteins. For example, it may be an existing drug, a drug currently under development, a drug candidate compound, or a drug to be developed in the future. Furthermore, the oral medication may be, for example, the active ingredient itself (i.e., a compound) or a (pharmaceutical) composition. In the present invention, "treatment" also encompasses alleviation or improvement of symptoms, and prevention, delay, or cessation of the progression of a disease or symptom, or the manifestation of symptoms. In one embodiment, the oral medication is a prophylactic or therapeutic drug for a neurodegenerative disease. Examples of prophylactic or therapeutic drugs for neurodegenerative diseases include those described in U.S. Patent Application No. 11,554,116.

[0033] Specifically, peptide-type ergot alkaloids (also called ergopeptines) are derivatives in which three peptides or the like are added to the ergoline ring at the same positions as the amide group of a lysergic acid derivative. This structure contains proline and two other α-amino acids, which are linked by a cyclol. More specifically, the structural formula of peptide-type ergot alkaloids is shown as the following formula (I) or (II).

[0034] [ka]

[0035] [ka]

[0036] R 1 represents a hydrogen atom or a halogen atom; R 2 and R 3 are each independently a linear or branched alkyl group having 1 to 5 carbon atoms or an aryl group having 6 to 10 carbon atoms.

[0037] Examples of the halogen atom include a fluorine atom, a chlorine atom, a bromine atom, and an iodine atom, and a bromine atom is preferred. Examples of the alkyl group include methyl, ethyl, n-propyl, isopropyl, n-butyl, isobutyl, sec-butyl, tert-butyl, n-pentyl, isopentyl, tert-pentyl, neopentyl, 2-pentyl, and 3-pentyl. 2 As R, a methyl group and an isopropyl group are preferred. 3 Preferred examples of the aryl group include an isopropyl group, an isobutyl group, and a sec-butyl group. Examples of the aryl group include a phenyl group, a benzyl group, a tolyl group, an o-xylyl group, and a naphthyl group, and a benzyl group is preferred.

[0038] Specific examples of the compound represented by formula (I) or formula (II) include, for example, R 2 Ergotoxines in which the amino acid at position R is valine, 2 Examples of ergotoxines include ergocristine, dihydroergocristine, ergocornine, dihydroergocornine, α-ergocryptine (and the R of these compounds) as shown in Table 1. 1Examples of ergotamines include ergotamine, dihydroergotamine, ergovaline, dihydroergovaline, α-ergosine, dihydroα-ergosine, β-ergosine, and dihydroβ-ergosine. Of these, α-ergocryptine, bromocriptine, ergocristine, dihydroergocristine, ergotamine, and dihydroergotamine are preferred.

[0039] [Table 1]

[0040] The above-mentioned compounds may be commercially available or may be prepared by a method known per se for each compound. For example, the distributor of each compound in the United States can be found at Drugs@FDA (http: / / www.accessdata.fda.gov / scripts / cder / drugsatfda / index.cfm). Bromocriptine can be prepared by the method described in U.S. Pat. No. 3,752,814, U.S. Pat. No. 3,752,888, or a method equivalent thereto. Other compounds can be prepared in a similar manner.

[0041] The compounds serving as active ingredients of oral medications used in the prediction method of the present invention include not only free forms but also pharmacologically acceptable salts thereof. Pharmacologically acceptable salts vary depending on the type of compound, but include, for example, inorganic base salts such as alkali metal salts (sodium salt, potassium salt, etc.), alkaline earth metal salts (calcium salt, magnesium salt, etc.), aluminum salts, and ammonium salts, as well as base addition salts such as organic base salts such as trimethylamine, triethylamine, pyridine, picoline, ethanolamine, diethanolamine, triethanolamine, dicyclohexylamine, and N,N'-dibenzylethylenediamine, as well as inorganic acid salts such as mesylate, hydrochloride, hydrobromide, sulfate, hydroiodide, nitrate, and phosphate, and acid addition salts such as organic acid salts such as citrate, oxalate, acetate, formate, propionate, benzoate, trifluoroacetate, maleate, tartrate, methanesulfonate, benzenesulfonate, and paratoluenesulfonate.

[0042] When a compound serving as an active ingredient of an oral medication used in the prediction method of the present invention has isomers such as optical isomers, stereoisomers, positional isomers, and rotational isomers, either one of the isomers or a mixture thereof is also included in the compound. For example, when any of the compounds listed in Table 1 has optical isomers, optical isomers resolved from the racemate are also included in the compound. These isomers can be obtained individually by known synthesis methods, separation methods (e.g., concentration, solvent extraction, column chromatography, recrystallization, etc.), optical resolution methods (e.g., fractional recrystallization, chiral column method, diastereomer method, etc.), etc.

[0043] The compound serving as the active ingredient of the oral medication used in the prediction method of the present invention may be a crystal, and the compound may be in either a single crystalline form or a mixture of crystalline forms. The crystal can be produced by crystallization using a crystallization method known per se.

[0044] The compound serving as an active ingredient of an oral drug used in the prediction method of the present invention may be a solvate (e.g., a hydrate, etc.) or a non-solvate (e.g., a non-hydrate, etc.). In addition, the compound serving as an active ingredient of an oral drug used in the prediction method of the present invention may be an isotope (e.g., 3 H, 14 C. 35 S, 125 The compound may be a compound labeled with, for example, I.

[0045] (3) Evaluation based on indicators 1 and 2 The specific evaluation in the prediction method of the present invention is based on the above-mentioned indicators 1 and 2 and is calculated by the following formula (I): (Amount of abnormal protein secreted into the culture medium by neurons derived from iPS cells from the subject after the addition of oral medication - Amount of abnormal protein secreted into the culture medium by neurons derived from iPS cells from the subject before the addition of oral medication) / Amount of abnormal protein secreted into the culture medium by neurons derived from iPS cells from the subject before the addition of oral medication x 100 x Maximum blood concentration of oral medication in the subject / Maximum blood concentration of the specified oral medication This can be done based on:

[0046] In formula (I), the "maximum blood concentration of a given oral medication" (hereinafter sometimes referred to as the reference value) refers to the value (concentration) of the subject (one individual) with the highest maximum blood concentration of the administered oral medication among all subjects (n≧1 individuals) subjected to the prediction method of the present invention. When multiple oral medications are administered, a reference value can be set for each oral medication.

[0047] Specific evaluation in the prediction method of the present invention may be performed by comparing the value (calculated value) obtained from the above formula (I) with a predetermined threshold (cutoff value). For example, the threshold may be appropriately set based on the relationship between the value of formula (I) and the BSPD evaluation scale. Assessment scales for BSPD include the Neuropsychiatric Inventory (NPI) (12-item interview, assessing the severity of BPSD on a scale of 0 to 120), the NPI-12 (assessing on a scale of 0 to 144), the NPI-NH (NPI-NursingHome version) (12-item interview, assessing the severity of BPSD on a scale of 0 to 120), the NPI-Q (NPI-Brief Questionnaire Form) (12-item self-administered, assessing the severity on a scale of 0 to 30), the Cohen-Mansfield Agitation Inventory (CMAI) (22-item self-administered, assessing the severity on a scale of 22 to 154), the Behavioral Pathology in Alzheimer's Disease Rating Scale (BEHAVE-AD) (25-item interview, assessing the severity on a scale of 0 to 75), and the Dementia Behavior Disturbance Scale (DBD). Examples of such BPSD assessment scales include the Abe BPSD score (BPSD+Q: Behavioral and Psychological Symptom Questionnaire for Dementia) (28 self-report items, severity assessed on a scale of 0 to 112), DBD13 (13 self-report items, severity assessed on a scale of 0 to 52), Abe BPSD score (BPSD+Q: Behavioral and Psychological Symptom Questionnaire for Dementia) (10 self-report items, severity assessed on a scale of 0 to 44), BPSD+Q (27 self-report items, severity assessed on a scale of 0 to 125), and BPSD13Q (13 self-report items, severity assessed on a scale of 0 to 65). In one embodiment, the BPSD assessment scale is the NPI-12. Alternatively, for example, a receiver operating characteristic (ROC) curve may be created using commercially available analytical software. The values ​​at which the diagnostic sensitivity and specificity are as close to 100% as possible may then be determined and set as the threshold. Furthermore, for example, the "mean value + 2 standard deviations" of the values ​​obtained by the above formula (I) for a large number of healthy individuals may be set as the threshold.

[0048] Specific evaluations in the prediction method of the present invention include, for example, evaluation of whether the BPSD rating scale does not improve or improves by a certain number of points (or by more than a certain number of points) at any time point after administration of an oral medication to a subject. In one embodiment, the state of a given disease of a subject is evaluated at least 8 months after administration of the oral medication to the subject. In a further embodiment, the state of a given disease of a subject is evaluated at least 8 months after the first administration of the oral medication to the subject.

[0049] In one embodiment, when the oral medication is bromocriptine and the amount of secreted abnormal protein is the Aβ42 / 40 ratio, the preset threshold is as follows: The threshold for an improvement of 0 points or more in the NPI-12 is -1.90% in formula (I). The threshold for a 5-point improvement in NPI-12 is -3.14% for formula (I). The threshold for a 10-point improvement in NPI-12 is -4.38% for formula (I). The threshold for a 15-point improvement in NPI-12 is -5.61% for the value of formula (I), and The threshold for a 20-point improvement in NPI-12 is -6.85% in formula (I). is.

[0050] The prediction method of the present invention may be implemented using a computer and a program executed by the computer. Thus, in another aspect, there is provided a computer program for predicting the effect of an oral medication on a disease caused by the accumulation of abnormal proteins in a subject, the computer program comprising: Computer, (P1) a reception unit that receives data on the amount of abnormal protein secreted into a culture medium of neurons derived from subject-derived iPS cells obtained from the subject and data on the maximum blood concentration of oral medication in the subject; (P2) a calculation unit that calculates, from the input data, a score for predicting efficacy against diseases caused by accumulation of abnormal proteins; and (P3) An output section that outputs the calculation results obtained in the calculation section A program to function as is also provided.

[0051] In one aspect, the computer program comprises: (P4) A program for functioning as a judgment unit that compares the calculation result output in (P3) with a preset threshold value to judge the effect on diseases caused by the accumulation of abnormal proteins. is.

[0052] The reception unit P1 is a program part for receiving prediction target data as an input data group from an external input device, a computer on which the program of the present invention is executed, or an external computer. The input data group may be configured to directly receive an input data group sent by an operator. Alternatively, the input data group may be configured to access a known storage device (SSD, HDD, BD (Blu-ray (registered trademark) Disc), DVD, CD, etc.) or an external database and retrieve data based on the data name input by the operator.

[0053] The external device for inputting the input data group to the reception unit P1 is typically an external computer connected via the Internet so as to be accessible to the computer on which the program of the present invention is executed, but may also be a memory (USB memory) connected via an interface such as USB to the computer on which the program of the present invention is executed, or various data reading devices. The reception unit P1 is configured to accept the input data group from the computer or various external devices and to transfer the input data group to the calculation unit P2.

[0054] The calculation unit P2 is a program part that processes the input data group delivered from the reception unit P1 and executes the prediction method of the present invention described above to calculate the efficacy prediction score for diseases caused by the accumulation of abnormal proteins. Specifically, the input data group is input into the above-mentioned calculation formula (I) and the efficacy prediction score for diseases caused by the accumulation of abnormal proteins is accepted. The efficacy prediction score for diseases caused by the accumulation of abnormal proteins obtained from calculation formula (I) is delivered to the output unit P3.

[0055] The output unit P3 is a program portion that outputs the effect prediction score for a disease caused by abnormal protein accumulation calculated by the calculation unit P2. The calculation results of the calculation unit P2 may be output, for example, by outputting whether or not an oral medication administered to a subject will improve a disease caused by abnormal protein accumulation, outputting the probability that an oral medication administered to a subject will improve a disease caused by abnormal protein accumulation, or outputting the score (or score or more) by which an oral medication administered to a subject will improve a disease caused by abnormal protein accumulation. The calculation results may be predictions of effect at any time point after administration of the oral medication. In one embodiment, the calculation results are predictions of effect at least 8 months after administration of the oral medication. In another embodiment, the calculation results are predictions of effect at least 8 months after the initial administration of the oral medication. The predetermined output destination to which the prediction results are output is not particularly limited and may be a predetermined output destination such as various image display devices, various print output devices, or a computer capable of communicating with a computer running the program of the present invention.

[0056] In one embodiment, the program of the present invention as described above is provided in the form of a recording medium on which the program is recorded. The recording medium may be a non-transitory computer-readable recording medium. Such a medium is not particularly limited as long as it can be read by a machine such as a computer, and examples thereof include an SSD, HDD, BD, DVD, CD, and USB memory.

[0057] 2. Evaluation method of the present invention The present invention provides a method for assessing BPSD in a subject (hereinafter also referred to as the "assessment method of the present invention"), which comprises measuring at least one of the subject's conversation volume while not at rest and the subject's average number of steps during the day. In this specification, the "subject's conversation volume while not at rest" and the "average number of steps" may be referred to as "feature 1" and "feature 2," respectively.

[0058] (1) Feature 1 In this specification, "non-resting" refers to a state in which a subject is walking, running, or performing other physical activities (physical activities). In one aspect, this refers to a state in which physical activities are recorded by an activity monitor equipped with an accelerometer (acceleration sensor). In addition, for example, classification of whether or not a subject is non-resting can be performed using a commercially available wearable device (for example, a commercially available product such as Silmee) as described below. TM W20 (TDK Corporation), Silmee TM W22 (TDK Corporation), etc.). In this specification, "amount of conversation" refers to the amount of sound components detected during a predetermined period. The amount of conversation measured in the present invention may be, for example, the total amount of conversation during a predetermined period, or the average amount of conversation per day during the predetermined period. The predetermined period is not particularly limited, but examples include one week, two weeks, three weeks, four weeks, one month, two months, and three months. The amount of conversation measured in the present invention may exclude short utterances or noise that are not conversation. Specifically, for example, the amount of conversation measured in the present invention may be measured using a conversation detector or method known per se (for example, JP 2017-10166, JP 2021-192754, JP 2023-116604, etc.) or a wearable device equipped with such a detector (for example, commercially available products such as Silmee TM W20 (TDK Corporation), Silmee TM Values ​​(e.g., conversation time) measured by a measurement system such as W22 (TDK Corporation) may be processed so that they can be used in the evaluation method of the present invention. Specific examples of such processing include processing to calculate the amount of conversation as shown in Table 3 below. In one embodiment, the amount of conversation is conversation time.

[0059] In this specification, the "average number of steps during the day" refers to the average number of steps (number of steps) taken per day from sunrise to sunset for a predetermined period. The predetermined period is not particularly limited, but examples include one week, two weeks, three weeks, four weeks, one month, two months, and three months. In one embodiment, the period from sunrise to sunset is from 6:00 AM to 6:00 PM (6:00 PM). The average number of steps taken during the day measured in the present invention can be specifically measured using, for example, a pedometer known per se (for example, JP 2015-106389 A, JP 2021-106320 A, JP 2021-192754 A, etc.) or a wearable device equipped with such a pedometer (for example, commercially available products such as Silmee TM W20 (TDK Corporation), Silmee TM Values ​​measured by a pedometer (e.g., W22 (TDK Corporation) or the like) (e.g., the total number of steps in a given period) may be processed so that they can be used in the evaluation method of the present invention. As an example of processing, the total number of steps taken during the day for one week may be accumulated as data, and then the average number of steps taken during the day per day may be calculated. More specifically, for example, processing may be performed using the method shown in Table 3 below.

[0060] In the evaluation method of the present invention, features other than feature 1 and feature 2 (for example, average number of steps (nighttime), amount of conversation (at rest), average pulse rate (asleep, wakefulness), sleep duration (nighttime, nap), average skin temperature (at rest, not at rest), average energy expenditure (daytime, nighttime)) may be measured and used in combination with feature 1 and / or feature 2. Among the above features, those referred to as "average" may be average values ​​per day for a predetermined period.

[0061] In one embodiment, the features including features 1 and 2 are set through the following processing. Specifically, a wearable device equipped with a conversation detector and a pedometer acquires minute-by-minute data on the number of steps (quantitative value), whether or not a conversation occurred (0 or 1), pulse rate (quantitative value), skin temperature (quantitative value), wakefulness state (wakefulness or sleep), activity state (resting to activity, 6 levels), and energy expenditure (quantitative value). The total number of steps, total conversation time, average pulse rate, average skin temperature, sleep / nap time, and total energy expenditure are obtained as measurement items per day (Table 2). Next, the data are classified by time period and state as shown in Table 3 below, and then aggregated (calculated) to obtain processed features. The feature combination of "measurement item" and "classification" shown in Table 3 below may be processed as a daily feature (average value) for a predetermined period (e.g., 1 week, 2 weeks, 3 weeks, 4 weeks, 1 month, 2 months, or 3 months).

[0062] [Table 2]

[0063] [Table 3]

[0064] As used herein, BPSD includes abnormalities in perception and thinking (e.g., hallucinations, delusions, etc.), difficulty regulating emotions (e.g., depression, anxiety, euphoria, etc.), social inadequacy (e.g., disinhibition, etc.), difficulty controlling impulses (e.g., excitement, irritability (irritability), abnormal behavior, etc.), decreased motivation (e.g., apathy, etc.), abnormal sleep, abnormal eating behavior, etc.

[0065] In one embodiment, the method for assessing an impulse control disorder in a subject includes measuring at least the amount of non-resting conversation. In another embodiment, the impulse control disorder is one or more symptoms selected from agitation, irritability, and abnormal behavior.

[0066] In one aspect is a method of assessing apathy in a subject, comprising measuring at least the average number of steps taken during the day.

[0067] Evaluating a subject's BPSD includes determining (predicting) whether the subject has BPSD and determining (predicting) the severity of the subject's BPSD, and the determination includes assisting a physician in making a diagnosis. Evaluating a subject's BPSD also includes predicting changes in BPSD (prognosis prediction) following administration of oral medications such as those described in "1. Prediction Method of the Present Invention" for subjects who have or may have developed BPSD. Changes in BPSD include worsening, improvement, or maintenance (no change) of BPSD. Furthermore, evaluation of a subject's BPSD may be performed using an evaluation scale such as that described in "(3) Evaluation Based on Indicators 1 and 2" in "1. Prediction Method of the Present Invention" above. Furthermore, BPSD may be assessed for, for example, abnormalities in perception and thinking (e.g., hallucinations, delusions, etc.), difficulty regulating emotions (e.g., depression, anxiety, euphoria, etc.), social inadequacy (e.g., disinhibition, etc.), difficulty controlling impulses (e.g., excitement, irritability (irritability), abnormal behavior, etc.), decreased motivation (e.g., apathy, etc.), sleep abnormalities, and eating behavior abnormalities.

[0068] The subject's BPSD may be evaluated by comparing the feature amount 1 or 2 per day during a predetermined period (e.g., week 0 to week 1) with the feature amount 1 or 2 per day during another predetermined period (e.g., week 1 to week 2). Alternatively, the feature amounts 1 or 2 may be compared for three or more predetermined periods. Alternatively, the feature amount 1 or 2 per day during a predetermined period may be compared with a reference value. Examples of the reference value used in the present invention include the feature amount 1 or 2 of a healthy subject (e.g., a subject who has not developed BPSD) and the feature amount 1 or 2 of a subject who has or may have developed BPSD before administering oral medication.

[0069] Regarding the assessment of a subject's BPSD, an increase in the subject's non-resting conversation volume (Feature 1) predicts worsening of BPSD (particularly impulse control disorders (e.g., agitation, irritability, abnormal behavior, etc.)). Furthermore, an increase in the subject's average number of steps taken during the day (Feature 2) predicts improvement of BPSD (particularly apathy).

[0070] Alternatively, thresholds may be set in advance for feature quantities 1 and 2, or for changes in feature quantities 1 and 2 over a specific period, and the subject's BPSD may be evaluated based on whether the value is above or below the threshold. Methods for calculating the threshold are well known in the art, and the threshold may be set after creating a receiver operating characteristic (ROC) curve using analysis software (e.g., statistical analysis software JMP, statistical analysis software R, and the scikit-learn package in Python) based on the values ​​of feature quantities 1 and 2. An ROC curve may also be created by combining feature quantity 1 or 2 with one or more of the other features described above.

[0071] The evaluation method of the present invention may be performed using a computer and a program executed on the computer. Thus, in another aspect, there is provided a computer program for evaluating BPSD in a subject, comprising: Computer, (P1) a receiving unit that receives at least one piece of information obtained from the subject, such as the number of steps and the amount of conversation; (P2) a calculation unit that calculates a score for evaluating the subject's BPSD from the input data; and (P3) An output section that outputs the calculation results obtained in the calculation section A program to function as is also provided.

[0072] In one aspect, the computer program comprises: (P4) A program for functioning as an evaluation unit (determination unit) that compares the calculation result output in (P3) with a preset threshold value to evaluate (determine) the subject's BPSD. As described above, evaluation includes determining (predicting) whether a subject has BPSD and determining (predicting) the degree of BPSD in a subject, and determination includes assisting a physician in making a diagnosis. Furthermore, evaluation of a subject's BPSD also includes predicting (prognostic prediction) changes in BPSD due to the administration of oral medications such as those described in "1. Prediction method of the present invention" for subjects who have or may have developed BPSD.

[0073] The reception unit P1 is a program part for receiving prediction target data as an input data group from an external input device, a computer on which the program of the present invention is executed, or an external computer. The input data group may be configured to directly receive an input data group sent by an operator. Alternatively, the input data group may be configured to access a known storage device (SSD, HDD, BD (Blu-ray (registered trademark) Disc), DVD, CD, etc.) or an external database and retrieve data based on the data name input by the operator.

[0074] The external device for inputting the input data group to the reception unit P1 is typically an external computer connected via the Internet so as to be accessible to the computer on which the program of the present invention is executed, but may also be a memory (USB memory) connected via an interface such as USB to the computer on which the program of the present invention is executed, or various data reading devices. The reception unit P1 is configured to accept the input data group from the computer or various external devices and to transfer the input data group to the calculation unit P2.

[0075] The calculation unit P2 is a program portion that processes the input data group delivered from the reception unit P1 and executes the evaluation method of the present invention to calculate a score for evaluating the subject's BPSD. Specifically, the calculation unit P2 receives the input data group shown in Table 2 above, classifies the data by time period and state as shown in Table 3 above, and accepts the score for evaluating the subject's BPSD, which is a processed feature value obtained by aggregating (calculating). The obtained score for evaluating the subject's BPSD is delivered to the output unit P3. The score calculated by the method shown in Table 3 above may also be calculated as a change amount by comparing scores over two or more predetermined periods. In one embodiment, the score for evaluating BPSD is at least one of the amount of conversation while not resting and the average number of steps taken during the day.

[0076] The output unit P3 is a program portion that outputs the score for evaluating the subject's BPSD calculated by the calculation unit P2. The calculation results of the calculation unit P2 may be output, for example, by outputting whether the subject has BPSD, by outputting the degree of the subject's BPSD, or by outputting the probability of a change (improvement or worsening) in BPSD (due to oral medication administered to the subject). The predetermined output destination to which the prediction results are output is not particularly limited and may be a predetermined output destination such as various image display devices, various print output devices, or a computer capable of communicating with a computer on which the program of the present invention is executed.

[0077] In one embodiment, the program of the present invention as described above is provided in the form of a recording medium on which the program is recorded. The recording medium may be a non-transitory computer-readable recording medium. Such a medium is not particularly limited as long as it can be read by a machine such as a computer, and examples thereof include an SSD, HDD, BD, DVD, CD, and USB memory.

[0078] For matters other than those mentioned above in the evaluation method of the present invention, the contents described above in "1. Prediction method of the present invention" are all applicable.

[0079] 3. Screening Method of the Present Invention The present invention provides a screening method for preventive or therapeutic drugs for BPSD, which includes a step of determining whether a subject's BPSD has improved based on at least one of the amount of conversation while not resting and the average number of steps during the day obtained from the subject before administration of a candidate substance for the preventive or therapeutic drug for BPSD, and the amount of conversation while not resting and the average number of steps during the day obtained from the subject after administration of the candidate substance.

[0080] The "amount of conversation while not at rest" and "average number of steps taken during the day" of the subject used in the screening method of the present invention can be obtained by the method described above in "2. Evaluation method of the present invention."

[0081] In the screening method of the present invention, the timing of "before administration of a candidate substance" is not particularly limited as long as it is before administration of the candidate substance. Similarly, the timing of "after administration of a candidate substance" is not particularly limited as long as it is after administration of the candidate substance. In the screening methods of the present invention, the "candidate substance" is not particularly limited as long as it can be expected to have a therapeutic or preventive effect on BPSD. For example, it may be an existing drug, a drug or drug candidate compound currently under development, or a drug to be developed in the future. Furthermore, the candidate substance may be, for example, the active ingredient itself (i.e., a compound) or a (pharmaceutical) composition. In the screening methods of the present invention, "treatment" also includes the alleviation or improvement of symptoms, and the prevention, delay, or cessation of the progression of a disease or symptom, or the manifestation of symptoms.

[0082] In the screening method of the present invention, whether or not a subject's BPSD has improved can be determined (predicted) as having worsened BPSD (particularly, impulse control disorders (e.g., agitation, irritability, abnormal behavior, etc.)) if the subject's amount of conversation while not at rest increases before and after administration of a candidate substance. Furthermore, whether or not a subject's BPSD has improved can be determined (predicted) as having improved BPSD (particularly, apathy) if the subject's average number of steps during the day (feature quantity 2) increases before and after administration of a candidate substance.

[0083] For matters other than those mentioned above in the screening method of the present invention, the contents described above in "1. Prediction method of the present invention" and "2. Evaluation method of the present invention" are all applicable.

[0084] The present invention will be explained in more detail below with reference to examples, but the present invention is not limited to these examples in any way. [Example]

[0085] Example 1: Design and evaluation of disease response prediction score (1) Investigator-initiated clinical trials A phase 1 / 2 investigator-initiated clinical trial was conducted to explore the safety and efficacy of oral bromocriptine hydrochloride in eight patients (subjects) with familial Alzheimer's disease (ClinicalTrials.gov Identifier: NCT04413344, Clinical Research Submission and Publication System Registration Number: jRCT2041200008). The clinical trial was a multicenter, double-blind, controlled trial with an open-label extension period, and the double-blind efficacy phase lasted 36 weeks. Five subjects received the active drug, bromocriptine.

[0086] The primary endpoints for evaluating efficacy were the Japanese version of the Severe Impairment Battery (SIB-J) and the 12-item Neuropsychiatric Inventory (NPI-12). The NPI-12 is a questionnaire-based assessment index that quantitatively evaluates behavioral and psychological symptoms of dementia (BPSD), with a minimum score of 0 and a maximum score of 120, with higher scores indicating more severe symptoms. The values ​​related to the NPI-12 in this example (e.g., the change in NPI-12 from the start of administration of the investigational drug) are based on values ​​obtained in previous investigator-initiated clinical trials.

[0087] (2) Bromocriptine blood concentration analysis On the first day of administration, blood samples were taken immediately before administration of 2.5 mg of bromocriptine, and 1, 2, 3, 4, and 6 hours after administration, and blood bromocriptine concentrations were analyzed using liquid chromatography-tandem mass spectrometry. As a result, individual differences in drug blood concentrations were observed, reflecting individual absorption and metabolic kinetics.

[0088] The blood bromocriptine concentrations measured after the initial oral administration of 2.5 mg of bromocriptine on Day 1 are shown in Figure 2. The maximum blood concentration (Cmax) of bromocriptine varied between subjects, ranging from 32.5 pg / mL to 314 pg / mL.

[0089] (3) Analysis of Aβ production from iPS cells derived from clinical trial participants (subjects) Before bromocriptine administration, peripheral blood mononuclear cells (PBMCs) were collected from all subjects in the clinical trial. iPS cells derived from the subjects were generated by introducing human cDNAs into PBMCs using episomal reprogramming vectors (Oct3 / 4, Sox2, KLF4, L-MYC, LIN28, mp53DD, and EBNA1).

[0090] iPS cells were cultured under feeder-free conditions on plates coated with iMatrix-511 (Nippi, Tokyo, Japan) using StemFit (AK02N; Ajinomoto, Tokyo, Japan). To rapidly and potently differentiate iPS cells into cortical neurons, we used the direct conversion method. Transfection of iPS cells with human neurogenin 2 cDNA under a tetracycline-inducible promoter was performed using the piggyBac transposon system with Lipofectamine LTX (Thermo Fisher Scientific, Inc., Waltham, MA). After selection with G418 disulfate (Nacalai Tesque, Kyoto, Japan), G418-resistant colonies were picked and subclones were selected. Transient expression of the NGN2 gene efficiently differentiated iPS cells into neurons with a MAP2 / DAPI purity of 96% or higher. After differentiating neurons were seeded and allowed to settle in 96-well plates (Corning, Tewksbury, MA), the culture medium was replaced with 120 μl of fresh medium containing 0.2 μM, 1 μM, or 5 μM bromocriptine (Fujifilm Wako Pure Chemical Industries, Osaka, Japan) as a vehicle with 0.1% DMSO (Nacalai Tesque), or 0.1% DMSO alone as a negative control. After 48 hours, Aβ40 and Aβ42 levels in the culture medium were assessed using the Human (6E10) Aβ 3-Plex Kit (Meso Scale Discovery, Rockville, MD). Quantification and analysis of Aβ levels were performed using Discovery Workbench version 4 (Meso Scale Discovery).

[0091] The results above indicated that when a low concentration (0.2 μM) of bromocriptine was added to the culture medium, the effect of bromocriptine on the Aβ42 / 40 ratio in the culture medium, an indicator of Aβ production in the culture medium, varied between subjects, suggesting that this reflected differences in the responsiveness of subjects to the drug at the cellular level (Figure 1).

[0092] Figure 2 shows the percentage difference between the Aβ42 / 40 ratio after bromocriptine administration and the Aβ42 / 40 ratio after no drug administration (ΔAβ42 / 40 ratio) in iPSC-derived neurons from participants in the bromocriptine-treated group. Individual differences in response to bromocriptine treatment (0.2 μM) were observed among subjects.

[0093] (4) Design of a treatment effect prediction score A therapeutic effect prediction score (disease effect prediction score) calculated from the drug responsiveness of neurons derived from subject-derived iPS cells adjusted by the drug blood concentration Cmax was calculated as follows. Specifically, the bromocriptine responsiveness of neurons derived from subject-derived iPS cells was adjusted by setting the blood bromocriptine concentration of subject 1 (Figure 1), who had the highest Cmax value among the subjects, as the reference value, and converting each subject's Cmax to a relative value against the reference value and multiplying it. Bromocriptine responsiveness of neurons derived from subject-derived iPS cells was evaluated by the ratio of the Aβ42 / 40 ratio in the culture medium to the value before administration. As a result of the calculation, the formula (I) for calculating the designed disease effect prediction score was as follows: (Amount of abnormal protein secreted into the culture medium by neurons derived from iPS cells from the subject after the addition of oral medication - Amount of abnormal protein secreted into the culture medium by neurons derived from iPS cells from the subject before the addition of oral medication) / Amount of abnormal protein secreted into the culture medium by neurons derived from iPS cells from the subject before the addition of oral medication x 100 x Maximum blood concentration of oral medication in the subject / Maximum blood concentration of the specified oral medication As stated above.

[0094] (5) Evaluation of treatment effect prediction score The treatment effect prediction score (disease response prediction score) was evaluated using the change in NPI-12 from the start of treatment at 36 weeks, the primary analysis time point for evaluating the efficacy of the investigational drug, and the change in NPI-12 from the start of treatment at 32 weeks if no subjects dropped out. Treatment effects 8 months or more after the start of treatment were predicted, and the relationship between the change in NPI-12 at 32 and 36 weeks and the treatment effect prediction score was examined using a correlation coefficient. The correlation between the treatment response of clinical symptoms in the bromocriptine treatment group and the treatment effect prediction score, calculated from the drug response of neurons derived from subject-derived iPS cells adjusted for drug blood concentration Cmax, was analyzed (Figure 3).

[0095] The results above showed a strong correlation between the change in NPI-12 at 32 and 36 weeks after the start of oral administration and the therapeutic effect prediction score (disease response prediction score) calculated from the drug responsiveness of neurons derived from subject-derived iPS cells adjusted by the drug's blood concentration Cmax (Figure 4). As can be seen from Figure 4, the drug responsiveness (ΔAβ42 / 40 ratio) of neurons derived from subject-derived iPS cells adjusted by the drug's blood concentration Cmax strongly correlated with the change in NPI-12 from the start of investigational drug administration at 32 and 36 weeks. Figure 4 also shows Pearson's r and P values, along with a linear regression line. These results demonstrate that changes in clinical parameters at 32 and 36 weeks can be predicted based on the neuronal cell counts derived from subject-derived iPS cells obtained by the first day of administration and the drug's blood concentration Cmax on the first day of administration.

[0096] (6) Examination of the correlation between the index using AUC and the change in NPI-12 In order to prove that the treatment effect prediction score (disease effect prediction score) used in the above-mentioned prediction method of the present invention is significantly superior to other indices, the following research was also carried out. The area under the curve (AUC) of blood drug concentrations over time is generally thought to be related to the efficacy of oral medication. However, no significant correlation was observed between AUC and the change in NPI-12 levels from the start of administration of the study drug at weeks 32 and 36 (Figure 5).

[0097] Furthermore, the following research was also conducted to prove that the concept of adjusting the drug responsiveness of neurons derived from subject-derived iPS cells used in the prediction method of the present invention by adjusting the drug blood concentration Cmax is extremely superior. Specifically, we considered the drug responsiveness (ΔAβ42 / 40 ratio) of neurons derived from subject-derived iPS cells, adjusted by AUC, as a candidate predictive score, but no significant correlation was found with the change in NPI-12 from the start of administration of the investigational drug at weeks 32 and 36 (Figure 6).

[0098] (7) Examination of the correlation between subject background information, blood test indices, and changes in NPI-12 Furthermore, we investigated whether clinical information and blood test results obtained up to the start of treatment, which can be obtained through conventional interviews and examinations, could be used as predictors of changes in NPI-12 at 32 and 36 weeks. Specifically, we evaluated the correlation between age at onset, sex, age at enrollment, disease duration, cognitive function at the start of treatment, NPI-12 score at the start of treatment, blood Aβ42 / 40, Aβ42, Aβ40, phosphorylated tau, total tau, neurofilament light chain, and GFAP levels at the start of treatment, and bromocriptine Cmax and Tmax on the first day of treatment and changes in NPI-12 from the start of treatment at 32 and 36 weeks. However, no significant correlation was found.

[0099] From the studies in (1) to (7) of Example 1 above, it was understood that the concept of the present invention, which uses as an evaluation index a therapeutic effect prediction score (disease effect prediction score) calculated from the drug responsiveness of neurons derived from subject-derived iPS cells adjusted by the drug blood concentration Cmax shown in the above-mentioned calculation formula (I), makes it possible to accurately predict future therapeutic effects, and is far superior to other indices.

[0100] Example 2: Design and evaluation of digital indices (digital features) that can predict BPSD As described above, the present inventors have conducted investigator-initiated clinical trials for familial Alzheimer's disease and collected data using wristwatch-type wearable activity monitors with built-in conversation sensors (Kondo T, et al. BMJ Open 2021;11:e051343. doi:10.1136 / bmjopen-2021-051343). The present inventors discovered the following method for designing (processing) digital features based on information on the subject's condition and time of day, which are useful for predicting BPSD from wearable activity monitor data obtained in investigator-initiated clinical trials.

[0101] (1) Data collection method using a wearable activity monitor The wristwatch-type wearable activity monitor used was equipped with an acceleration sensor, a conversation sensor, a pulse sensor, a temperature sensor, and a UV sensor. In the investigator-initiated clinical trial, subjects wore the monitor almost constantly (more than 80% of the trial period), including while sleeping, to record data. These sensors calculated and recorded the number of steps taken, whether or not the subject was talking, pulse rate, skin temperature, UV index, whether or not the subject was awake or asleep, activity status (resting or activity level), and energy expenditure every minute. Data from the wearable activity monitor was stored in the device for up to one month and was periodically output to a PC by medical professionals. The data obtained every minute by the wearable activity monitor and the daily measurement items are shown in Table 4 below.

[0102] [Table 4]

[0103] (2) Designing digital indicators (digital features) that can predict BPSD Based on the above recorded measurement items, a digital index (feature) capable of predicting BPSD was developed through the following process. First, we predicted that the correlation between the measurement item indexes and BPSD would differ depending on the subject's state (time of day, activity level, sleep / wake state, etc.). Specifically, for example, if motivation improves, daytime activity is expected to increase through participation in various social activities. On the other hand, nighttime activity may not change if the subject maintains an appropriate lifestyle rhythm. Furthermore, the amount of conversation during rest reflects listening to audio and casual everyday conversation. On the other hand, conversation during activity may be associated with anger and excitement. Based on the above predictions, the data of the measurement items obtained by the wearable activity monitor was processed as shown in Table 5 below to obtain digital indicators (digital features).

[0104] [Table 5]

[0105] (3) Evaluation of the correlation between processed digital features and BPSD The digital features processed as described above were averaged over a one-month period, and the change in the average value from the start of study drug administration was evaluated for correlation with changes (worsening or improvement) in BPSD, such as irritability, agitation, and apathy. Predicted BPSD was assessed monthly using the NPI-12 (Neuropsychiatric Inventory 12 items). The NPI-12 is a standard BPSD assessment method used worldwide. Caregivers are interviewed regarding the severity and frequency of 12 BPSD sub-items, resulting in a score of 12 points per sub-item, for a total of 144 points.

[0106] We evaluated the potential for predicting BPSD by analyzing correlations between BPSD change data from the NPI-12 (up to 10 months and a total of 70 time points) obtained in our investigator-initiated clinical trials, which were of GCP (good clinical practice standards for pharmaceuticals), and the digital features described above, developed by processing data from a wearable activity tracker over a total of 40,000 hours. In this evaluation, we defined the change in the NPI-12 subscales from the start of the investigator-initiated clinical trial as the change in BPSD, and identified indicators correlated with the digital features as predictive indicators. These results are shown in Table 6 below. Cells without results in Table 6 indicate errors in the linear mixed model calculations used in this evaluation.

[0107] [Table 6]

[0108] (4) Evaluation result 1 Table 6 shows that an increase in daytime step counts (6:00 AM to 6:00 PM) predicts improvement in BPSD (apathy). An increase in daytime step counts is associated with a decrease (improvement) in the apathy NPI-12 subitem score. Figure 7 shows the progression of daytime step counts and apathy subitem score for a representative case (subject 08). A linear mixed model showed a negative correlation between the change in daytime step counts and the change in the apathy NPI-12 subitem score (n = 8, p = 0.009) (Table 6, Figure 7).

[0109] (5) Evaluation result 2 Table 6 shows that an increase in non-resting conversation volume predicts worsening of BPSD (impulse control disorder (agitation, irritability)). As non-resting conversation volume increases, the NPI-12 subitem score for BPSD (impulse control disorder (agitation, irritability)) increases (worsens). The change in non-resting conversation volume and the irritability subitem score for a representative case (subject 04) are shown (Figure 8). A linear mixed model showed a positive correlation between the change in non-resting conversation volume and the change in the irritability NPI-12 subitem score (n = 8, p < 0.001) (Table 6, Figure 8).

[0110] (6) Evaluation result 3 Table 6 shows that non-resting conversation volume is also associated with BPSD (impulse control disorder (agitation, abnormal behavior)). For the NPI subitems of agitation and abnormal behavior, a linear mixed model showed a positive correlation between the change in non-resting conversation volume and the change in the NPI-12 irritability subitem score (p < 0.02, p = 0.001) (Table 6, Figure 9).

[0111] (7) Evaluation result 4 Table 6 shows that non-resting conversation volume predicts the sum of the subitems of BPSD (impulse control disorder (irritability, agitation, and abnormal behavior)). Irritability, agitation, and abnormal behavior have recently been considered to reflect a behavioral symptom called "impulse dyscontrol" (Mortby et al., International psychogeriatrics 30:221-232, 2018). The sum of the NPI subitems of irritability, agitation, and abnormal behavior showed a positive correlation with non-resting conversation volume (p < 0.001) (Table 6, Figure 10). This indicates that non-resting conversation volume is a digital indicator (feature) that captures not only irritability but also impulse-related behavioral symptoms (impulse dyscontrol).

[0112] From the above, we have discovered a method for processing digital indicators that can quantitatively and non-invasively evaluate BPSD, which was previously assessed by an expert interview, using digital devices. In particular, we have found the following: 1) Change in daytime step count predicts change in NPI-12 apathy subscale score. 2) Change in non-resting conversation volume predicts change in NPI-12 irritability subscale scores. 3) The amount of conversation during non-resting periods is also related to excitement and abnormal behavior. 4) The amount of conversation during non-resting periods is related to impulse dyscontrol, a behavioral symptom that includes irritability, excitement, and abnormal behavior. It was understood that. [Industrial Applicability]

[0113] According to the present invention, by using data based on differentiated cells (e.g., neurons) derived from subject-derived iPS cells and the subject's blood drug concentration (Cmax), it is possible to predict clinical trial results with high accuracy, and clinical trials can be conducted by focusing on subjects who are expected to respond to the drug (enrichment design), which is useful. In particular, the prediction method of the present invention uses the Cmax at the first oral administration of a drug to a subject (target) to predict the subject's symptoms from 32 weeks after administration, which is expected to lead to reductions in the cost, duration, and number of subjects of clinical trials, and is therefore useful. The present invention is useful because it enables objective, quantitative, highly sensitive, and minimally invasive evaluation of BPSD. The evaluation method of the present invention is also useful because it is expected to reduce the burden on families of BPSD patients, who have to report abnormal behavior such as verbal abuse by their loved ones.

Claims

1. A method for predicting the effectiveness of an oral medication in a subject against a disease caused by the accumulation of abnormal proteins, comprising evaluating the amount of abnormal protein secreted into a culture medium of neurons derived from subject-derived iPS cells and the maximum blood concentration of the oral medication in the subject as indicators.

2. The method according to claim 1, wherein the amount of the abnormal protein secreted into the culture medium is the amount of the abnormal protein secreted into the culture medium before and after the addition of the oral medication.

3. The method of claim 1 or 2, wherein the maximum blood concentration of the oral medication in the subject is the maximum blood concentration after the first administration of the oral medication to the subject.

4. The method of claim 3, wherein the maximum blood concentration is after the first administration and before the second administration.

5. The method according to any one of claims 1 to 4, wherein the effect of the oral medication on a disease caused by accumulation of abnormal proteins in the subject is an effect at least 8 months after administration of the oral medication.

6. The method according to any one of claims 1 to 5, wherein the abnormal protein is a protein associated with the onset of a neurodegenerative disease.

7. The method according to claim 6, wherein the secretion level of the protein associated with the onset of the neurodegenerative disease is an Aβ42 / 40 ratio, and the protein associated with the onset of the neurodegenerative disease is Aβ.

8. The method according to any one of claims 1 to 7, wherein the oral medication is a preventive or therapeutic drug for a neurodegenerative disease.

9. The method according to any one of claims 1 to 8, wherein the evaluation is performed based on formula (I): (amount of abnormal protein secreted into culture medium by neurons derived from subject-derived iPS cells after addition of oral medication - amount of abnormal protein secreted into culture medium by neurons derived from subject-derived iPS cells before addition of oral medication) / amount of abnormal protein secreted into culture medium by neurons derived from subject-derived iPS cells before addition of oral medication x 100 x maximum blood concentration of oral medication in the subject / maximum blood concentration of a predetermined oral medication.

10. The method according to claim 9, wherein the evaluation is performed by comparing the value of the formula (I) with a preset threshold value.

11. The oral medication is a compound represented by the following formula (I): 【Chemistry 1】 Or formula (II): 【Chemistry 2】 [In formulas (I) and (II), R １ represents a hydrogen atom or a halogen atom; R ２ and R ３ each independently represents a linear or branched alkyl group having 1 to 5 carbon atoms or an aryl group having 6 to 10 carbon atoms. The method of any one of claims 1 to 10, comprising a compound represented by the formula:

12. R １ is a bromine atom, and R ２ is a methyl group or an isopropyl group, and R ３ The method of claim 11, wherein is an isopropyl group, an isobutyl group, a sec-butyl group, or a benzyl group.

13. 13. The method of claim 12, wherein the compound is bromocriptine or a salt thereof.

14. A computer program for predicting the effect of an oral medication on a disease caused by accumulation of abnormal proteins in a subject, Computer, (P1) a reception unit that receives data on the amount of abnormal protein secreted into a culture medium of neurons derived from subject-derived iPS cells obtained from the subject and data on the maximum blood concentration of oral medication in the subject; (P2) a calculation unit that calculates an effect prediction score for a disease caused by accumulation of abnormal proteins from the input data; and (P3) An output section that outputs the calculation results obtained by the calculation section A program to function as a

15. (P4) The program described in claim 14, which functions as a judgment unit that compares the calculation result output in (P3) with a predetermined threshold value to judge the effect on diseases caused by the accumulation of abnormal proteins.

16. The program according to claim 14 or 15, wherein the compound is bromocriptine or a salt thereof.

17. A method for assessing BPSD in a subject, comprising measuring at least one of the subject's non-resting speech volume and average daytime steps.

18. 18. The method of claim 17, wherein the BPSD is an impulse control disorder.

19. 18. The method of claim 17, wherein the BPSD is apathy.

20. 19. The method of claim 18, wherein the impulse control difficulties are one or more symptoms selected from agitation, irritability, and abnormal behavior.

21. A screening method for preventive or therapeutic drugs for BPSD, comprising a step of determining whether a subject's BPSD has improved based on at least one of the amount of conversation while not resting and the average number of steps taken during the day obtained from the subject before administration of a candidate substance for the preventive or therapeutic drug for BPSD, and the amount of conversation while not resting and the average number of steps taken during the day obtained from the subject after administration of the candidate substance.

22. 1. A computer program for assessing a subject's BPSD, comprising: Computer, (P1) a receiving unit that receives at least one piece of information obtained from the subject, the number of steps and the amount of conversation; (P2) a calculation unit that calculates a score for evaluating the subject's BPSD from the input data; and (P3) An output section that outputs the calculation results obtained by the calculation section A program to function as a

23. 23. The program of claim 22, wherein the score for assessing the subject's BPSD is at least one of the amount of non-resting conversation and the average number of steps taken during the day.

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