JP2022026407A - Method for evaluating placebo effect - Google Patents

Abstract

To assess a placebo effect on an individual sleep improvement.SOLUTION: Provided are a marker for evaluating a placebo effect on a sleep improvement and a method for evaluating a placebo effect on a sleep improvement using the marker.SELECTED DRAWING: None

Description

The present invention relates to a method for evaluating a placebo effect on sleep improvement in an individual, and a marker for that purpose.

In recent years, many people have sleep problems. Insomnia leads to a state of perceived deterioration in sleep quality, such as poor sleep, light sleep, waking up early, and not feeling well asleep. Poor sleep quality not only leads to serious illnesses such as myocardial infarction and cerebral infarction, but can also reduce work efficiency due to daytime sleepiness and induce serious accidents. Improving sleep quality is an important health challenge. Various sleep-improving agents have been developed for the purpose of improving sleep quality.

Generally, the effect of a drug is evaluated by comparing the effect of the test drug with a placebo containing no active ingredient. However, when taking a placebo, the psychological effect of taking the drug may improve the symptoms, which is called the placebo effect. If the placebo effect is large, the effect of the study drug will be diluted, which will hinder the accurate evaluation of the drug effect. The placebo effect is known to be particularly pronounced in cases of psychiatric or nervous system disorders. Evaluation of sleep-improving agents is also usually done by comparative studies with placebo. However, since sleep is strongly influenced by mental or neurological conditions, the placebo effect on sleep improvement tends to be relatively large, which hinders accurate evaluation of sleep-improving agents.

In recent years, in order to reduce the effect of the placebo effect on the effect of the study drug, a placebo read-in study has been conducted in which subjects are given only the placebo before the comparative study of the study drug and the fake drug, and the subjects who have a strong placebo effect are excluded from the study. It has been implemented. However, the placebo lead-in test as described above cannot sufficiently reduce the placebo effect, and the placebo lead-in test has drawbacks such as an increase in the cost of the test, an increase in the burden on the subject, and a prolongation of the test period. (See Non-Patent Document 1). Non-Patent Document 2 reports that the score of the Life Orientation Test is associated with the placebo effect on sleep improvement, and it is proposed to consider this in the evaluation of the placebo effect.

Techniques for investigating the current and future physiological states of human organisms by analyzing nucleic acids such as DNA and RNA in biological samples have been developed. Nucleic acid derived from a living body can be extracted from tissues such as blood, body fluids, secretions and the like. Patent Documents 1 and 2 describe that nucleic acids such as RNA derived from the skin cells of a subject are separated from lipids (SSL) on the surface of the skin and used as a sample for analysis of a living body.

International Publication No. 2018/008319 International Publication No. 2020/091044

Clin Invest, 2013, 3 (9): 823-833 J Psychosom Res, 2007, 62 (5): 563-570

The present invention provides a method for assessing a placebo effect on sleep improvement in an individual, and a marker for that purpose.

In one aspect, the present invention has the following genes: TNK2, PPP1R3B, WDR33, HIF1AN, HSP90B1, AP2M1, GPR108, FCGR3B, DNTTIP1, C6orf1, FAM192A, TEN1, TMEM123, KAT5, DYNC1H1, CLITCRA Markers for assessing the effect of placebo on sleep improvement in subjects, including at least one selected from the group consisting of nucleic acids encoding TRIM28, LAMTOR4, PTGES3, FBP1, and SDF4, and proteins encoded by the gene. offer.
In another aspect, the invention provides a method of assessing a placebo effect on a subject's sleep improvement, comprising measuring the expression of the marker in the subject.
In another aspect, the invention provides a method of selecting a subject who does not have a high placebo effect on sleep improvement, comprising measuring the expression of the marker in the subject.
In another aspect, the present invention is a method for evaluating the effectiveness of a sleep improving agent, and sleep improvement by administration of a sleep improving agent in a subject who does not have a high placebo effect on the sleep improvement selected by the selection method. Provide methods, including assessing efficacy.

According to the present invention, the placebo effect on sleep improvement of an individual subject can be evaluated without actually having the subject take the placebo. Further, according to the present invention, those who show a high placebo effect can be excluded from the subjects of the test for evaluating the sleep improving agent in advance, so that a test for reducing the influence of the placebo effect such as the placebo lead-in test can be performed. Even without this, the effect of excessive placebo effects on the evaluation of sleep-improving agents can be suppressed. Therefore, the present invention realizes an accurate evaluation of the effect of the sleep improving agent.

All patented, non-patented, and other publications cited herein are incorporated herein by reference in their entirety.

The names of the genes disclosed herein are in accordance with the Official Symbol described in NCBI ([www.ncbi.nlm.nih.gov/]).

As used herein, the term "skin surface lipids (SSL)" refers to a fat-soluble fraction present on the surface of the skin and is sometimes referred to as sebum. In general, SSL mainly contains secretions secreted from exocrine glands such as sebaceous glands in the skin, and is present on the surface of the skin in the form of a thin layer covering the skin surface.

As used herein, "skin" is a general term for regions including the epidermis, dermis, hair follicles, and tissues such as sweat glands, sebaceous glands, and other glands on the body surface, unless otherwise specified.

As used herein, "improvement in sleep" preferably refers to an improvement in sleep quality, i.e., overall satisfaction with sleep, not limited to sleep time. "Sleep improvement" can be evaluated, for example, by the OSA sleep questionnaire (Yuka Yamamoto et al .: Brain and Psychiatric Medicine 10: 401-409, 1999). The OSA sleep questionnaire evaluates the quality of sleep based on the individual's subjective symptoms, that is, the general lifestyle, the physical and mental state before bedtime, and the quality of subjective sleep when waking up. More specifically, the OSA sleep questionnaire comprehensively evaluates sleep quality from five evaluation scales of factors 1 to 5. The first factor is the evaluation of drowsiness when waking up, the second factor is the evaluation of sleep onset and sleep maintenance, the third factor is the evaluation of dreams, the fourth factor is the evaluation of fatigue recovery, and the fifth factor is the evaluation. It is an evaluation of sleep time. Therefore, "sleep improvement" as used herein means improving one or more of the five factors evaluated in the OSA sleep questionnaire: drowsiness when waking up, sleep onset and sleep maintenance, dreams, recovery from fatigue, and sleep time. It can be. Alternatively, "sleep improvement" herein is reflected in an increase in the mean score of the five factors in the OSA sleep questionnaire. For example, the magnitude of the overall "sleep improvement" effect caused by the placebo effect can be expressed as an increase in the average score of the five factors in the OSA sleep questionnaire.

(1. Marker for evaluating the placebo effect on sleep improvement)
If the placebo effect on sleep improvement in an individual can be predicted, those who show a high placebo effect can be excluded from the subjects of the test for evaluation of sleep improving agents in advance. Further, as a result, the influence of the excessive placebo effect on the evaluation of the sleep improving agent can be suppressed, so that an accurate evaluation of the effect of the sleep improving agent can be realized.

The inventor has found that a subject's susceptibility to placebo is reflected in the expression of a particular gene in the subject prior to application of placebo.
As shown in the examples described later, after collecting a sample for gene expression analysis of the subject in advance, the subject was given a placebo and its sleep improving effect was examined. As a result, the gene expression pattern in the pre-collected sample was different between the group of subjects showing a high placebo effect and the group of subjects showing a low placebo effect. Nucleic acids encoding genes that show different expression between these two groups, and proteins encoded by those genes, are the subject's response to the sleep-improving agent placebo, in other words, placebo's sleep improvement for the subject. It can be used as a marker to evaluate the effect. For example, to evaluate the placebo effect on sleep improvement of a subject by using the expression of the nucleic acid encoding the gene or the protein encoded by those genes as an index, or to select a subject whose placebo effect on sleep improvement is not high. Will be possible.

Therefore, in one aspect, the invention provides a marker for assessing the placebo effect on a subject's sleep improvement. The marker provided in the present invention for evaluating the placebo effect on sleep improvement of a subject (hereinafter, also referred to as the marker of the present invention) actually exhibits the placebo effect on sleep improvement of an individual subject from which the marker is derived. Allows the subject to be evaluated without applying a placebo.

The marker of the present invention may include at least one selected from the group consisting of the nucleic acid encoding the 156 gene shown in Table 1 below and the protein encoded by the gene (hereinafter, also referred to as the marker in Table 1). The marker of the present invention may be a nucleic acid (nucleic acid marker) encoding a gene shown in Table 1 below, a protein encoded by the gene (protein marker), or a combination thereof. , Preferably a nucleic acid marker.

In one embodiment, the markers of the present invention are nucleic acids encoding the genes shown in Tables 2 and 3 (hereinafter, also referred to as nucleic acid markers in Tables 2 and 3, respectively), and proteins encoded by the genes (hereinafter, also referred to as “nucleic acid markers”). It contains at least one selected from the group consisting of (also referred to as protein markers in Tables 2 and 3), respectively. The nucleic acid markers and protein markers in Table 2 may be collectively referred to as the markers in Table 2, and the nucleic acid markers and protein markers in Table 3 may be collectively referred to as the markers in Table 3. Preferably, the markers in Tables 2 and 3 are nucleic acid markers.
As shown in Examples described later, the 47 genes shown in Table 2 are highly expressed in the group having a high placebo effect, and the 33 genes shown in Table 3 are expressed low in the group having a high placebo effect. Therefore, the markers in Table 2 are positive markers with high expression levels in subjects with a high placebo effect on sleep improvement. On the other hand, the markers in Table 3 are negative markers whose expression levels are low in subjects having a high placebo effect on sleep improvement. In the present invention, either one of the former positive marker and the latter negative marker may be used, or both may be used in combination.

In another embodiment, the markers of the present invention are nucleic acids encoding 100 genes shown in Table 4 (hereinafter, also referred to as nucleic acid markers in Table 4), and proteins encoded by the genes (hereinafter, proteins in Table 4). It contains at least one selected from the group consisting of (also referred to as a marker). The nucleic acid markers and protein markers in Table 4 may be collectively referred to as the markers in Table 4. Preferably, the marker in Table 4 is a nucleic acid marker.
As shown in the examples described later, for the genes shown in Table 4, a random forest is used as a machine learning algorithm with the data of the expression level in the subject as an explanatory variable and whether the placebo effect on sleep improvement is high or low as an objective variable. It is a gene corresponding to the top 100 variable importance based on the Gini coefficient when trying to extract a feature amount gene and construct a prediction model using it.

In a preferred embodiment, the markers of the present invention are nucleic acids encoding the 24 genes shown in Table 5 (hereinafter, also referred to as nucleic acid markers in Table 5), and proteins encoded by the genes (hereinafter, protein markers in Table 5). Also includes at least one selected from the group consisting of (also referred to as). The nucleic acid markers and protein markers in Table 5 may be collectively referred to as the markers in Table 5. Preferably, the marker in Table 5 is a nucleic acid marker.
The genes shown in Table 5 are genes commonly contained in Table 2 or 3 and Table 4.

In a preferred embodiment, the markers of the invention are at least one selected from the group consisting of the markers in Table 5 and at least one selected from the group consisting of the markers in Table 1 (but in Table 5). It may contain something other than a marker). For example, the marker of the present invention is at least selected from the group consisting of the markers in Table 2, the marker in Table 3, or the marker in Table 4, in addition to at least one selected from the group consisting of the markers in Table 5. It may contain one kind (but other than the markers in Table 5).
In this case, it is preferable that at least one selected from the group consisting of the markers in Table 5 is all 24 markers shown in Table 5.

In another preferred embodiment, all of the nucleic acid markers in Tables 2 and 3 or all of the protein markers in Tables 2 and 3 are used in combination as the markers of the invention.
In another preferred embodiment, all of the nucleic acid or protein markers in Table 4 are combined and used as markers of the invention.
In another preferred embodiment, all of the nucleic acid or protein markers in Table 5 are combined and used as markers of the invention.
In another preferred embodiment, all of the nucleic acid or protein markers in Table 1 are combined and used as markers of the invention.

The marker of the present invention can be prepared according to a conventional method from a biological sample collected from a subject, for example, cells, body fluid (blood, etc.), urine, secretions (saliva, SSL, etc.). For example, commercially available kits can be used to prepare nucleic acids or proteins from biological samples. Preferably, the marker of the present invention is a nucleic acid marker, and preferred examples of nucleic acids prepared from biological samples include genomic DNA and mRNA.

Examples of subjects for which a biological sample containing the marker of the present invention is collected include those who can be the subjects of the evaluation test of the sleep improving agent, for example, healthy subjects who do not feel sleep problems, and symptoms of insomnia. Those who have, those who desire to improve the quality of sleep, and the like.

More preferably, the marker of the invention is a nucleic acid or protein prepared from the subject's SSL, more preferably mRNA. Examples of the skin portion from which the SSL is collected include skin at any portion of the body such as the head, face, neck, trunk, and limbs, and a portion with a large amount of sebum secretion, for example, facial skin is preferable.

For the collection of SSL from the subject's skin, any means used to recover or remove SSL from the skin can be employed. Preferably, an SSL absorbent material, an SSL adhesive material, or an instrument that scrapes SSL from the skin, which will be described later, can be used. The SSL absorbent material or the SSL adhesive material is not particularly limited as long as it is a material having an affinity for SSL, and examples thereof include polypropylene and pulp. As a more detailed example of the procedure for collecting SSL from the skin, a method of absorbing SSL into a sheet-like material such as an oil removing paper or an oil removing film, a method of adhering SSL to a glass plate, a tape, etc., a spatula, a scraper, etc. There is a method of scraping off the SSL and collecting it. In order to improve the adsorptivity of SSL, an SSL-absorbing material pre-impregnated with a highly lipophilic solvent may be used. On the other hand, if the SSL-absorbent material contains a highly water-soluble solvent or water, the adsorption of SSL is inhibited, so that it is preferable that the content of the highly water-soluble solvent or water is small. The SSL absorbent material is preferably used in a dry state.

The collected SSL may be immediately used in the nucleic acid or protein extraction step described later, or may be stored until it is used in the nucleic acid or protein extraction step. When stored, SSL is preferably stored under low temperature conditions. The storage temperature condition of SSL may be 0 ° C. or lower, preferably −20 ± 20 ° C. to −80 ± 20 ° C., more preferably −20 ± 10 ° C. to −80 ± 10 ° C., and further preferably −20. The temperature is ± 20 ° C to −40 ± 20 ° C, more preferably −20 ± 10 ° C to −40 ± 10 ° C, still more preferably −20 ± 10 ° C, still more preferably −20 ± 5 ° C. The storage period of SSL is not particularly limited, but is preferably 12 months or less, for example, 6 hours or more and 12 months or less, more preferably 6 months or less, for example, 1 day or more and 6 months or less, still more preferably 3 months or less, for example. 3 days or more and 3 months or less.

For the extraction of nucleic acid or protein from the collected SSL, the method usually used for extraction or purification of nucleic acid or protein from a biological sample can be used. Examples of nucleic acid extraction or purification methods include phenol / chloroform methods, AGPC (acid guanidinium thiocyanate-phenol-chloroform extraction) methods, or columns such as TRIzol®, RNAy®, and QIAzol®. , A method using special magnetic particles coated with silica, a method using Solid Phase Reversible Immobilization magnetic particles, extraction with a commercially available RNA extraction reagent such as ISOGEN, and the like. Commercially available protein extraction reagents such as QIAzol Lysis Reagent (Qiagen) can be used for protein extraction or purification.

(2. Method for evaluating the placebo effect on sleep improvement)
In another aspect, the present invention relates to the above 1. A method for evaluating the placebo effect on sleep improvement of a subject using the marker of the present invention described in the above is provided. In this method, the placebo effect on sleep improvement of an individual subject is evaluated based on the expression of the marker of the present invention in the individual subject. In still another aspect, the present invention relates to the above 1. Provided is a method for selecting a subject who does not have a high placebo effect on sleep improvement using the marker of the present invention described in the above. In this method, subjects who do not have a high placebo effect on sleep improvement are selected based on the expression of the markers of the invention in individual subjects. Hereinafter, the above methods may be collectively referred to as the method of the present invention.

(2.1 Analysis of marker expression)
The subject subjected to the method of the present invention is the same as the subject from which the biological sample containing the marker of the present invention described above is collected. The subject is a person who has not been treated with a placebo. Therefore, the method of the present invention makes it possible to evaluate the placebo effect on sleep improvement of the subject or select a subject who does not have a high placebo effect on sleep improvement without actually applying the placebo to the subject.

In a preferred embodiment, the method of the invention comprises measuring the expression of the marker of the invention in a biological sample taken from a subject. The type of the biological sample is as described above, preferably SSL. In one embodiment, the method of the invention may further comprise collecting the SSL of the subject. The procedure for collecting SSL and the procedure for extracting markers from SSL are as described above.

The expression of the marker of the present invention can be measured according to the nucleic acid or protein quantification method commonly used in the art. For example, the expression level of a nucleic acid marker may be measured according to a procedure for gene expression analysis commonly used in the art. Examples of methods for gene expression analysis include methods for quantifying DNA or its amplification products by real-time PCR, multiplex PCR, microarray, sequencing, chromatography and the like. When the nucleic acid is RNA, it may be quantified by the above method after converting RNA into cDNA by reverse transcription. The expression level of a protein marker can be measured using protein quantification methods commonly used in the art, such as ELISA, immunostaining, fluorescence, electrophoresis, chromatography, mass analysis and the like. The calculated marker expression level may be an expression level based on the absolute amount of the target marker in the biological sample, or may be a relative expression level to the expression level of other standard substances or all nucleic acids or proteins. ..

Preferably, the marker of the invention used in the method of the invention is an SSL-derived mRNA. The RNA is converted to cDNA by reverse transcription, then the cDNA is subjected to PCR to purify the resulting reaction product. Primers targeting the specific RNA to be analyzed may be used for the reverse transcription, but random primers are preferably used for more comprehensive nucleic acid storage and analysis. A general reverse transcriptase or reverse transcriptase kit can be used for the reverse transcription. Preferably, a highly accurate and efficient reverse transcriptase or reverse transcriptase kit is used, for example, M-MLV Reverse Transcriptase and its variants, or commercially available reverse transcriptase or reverse transcriptase kits. For example, PrimeScript (registered trademark) Reverse Transcriptase series (Takara Bio Co., Ltd.), SuperScript (registered trademark) Reverse Transcriptase series (Thermo Scientific) and the like can be mentioned. SuperScript (registered trademark) III Reverse Transcriptase, SuperScript (registered trademark) VILO cDNA Synthesis kit (both from Thermo Scientific) and the like are preferably used. In the PCR of the obtained cDNA, only the specific DNA may be amplified using a primer pair targeting the specific DNA to be analyzed, or a plurality of DNAs may be amplified using a plurality of primer pairs. good. Preferably, the PCR is multiplex PCR. Multiplex PCR is a method of simultaneously amplifying a plurality of gene regions by simultaneously using a plurality of primer pairs in a PCR reaction system. Multiplex PCR can be carried out using a commercially available kit (for example, Ion AmpliSeqTranscriptome Human Gene Expression Kit; Life Technologies Japan Co., Ltd., etc.).

By adjusting the reaction conditions of the reverse transcription and PCR, the yield of the PCR reaction product is further improved, and the accuracy of the analysis using the PCR reaction product is further improved. Preferably, in the extension reaction in the reverse transcription, the temperature is preferably adjusted to 42 ° C. ± 1 ° C., more preferably 42 ° C. ± 0.5 ° C., still more preferably 42 ° C. ± 0.25 ° C. The reaction time is preferably adjusted to 60 minutes or longer, more preferably 80 to 120 minutes. Further, preferably, the temperature of the annealing and extension reaction in the PCR is preferably 62 ° C. ± 1 ° C., more preferably 62 ° C. ± 0.5 ° C., still more preferably 62 ° C. ± 0.25 ° C. Therefore, in the PCR, the annealing and extension reactions are preferably carried out in one step. The time of the annealing and extension reaction steps can be adjusted depending on the size of the DNA to be amplified and the like, but is preferably 14 to 18 minutes. The conditions of the denaturation reaction in the PCR can be adjusted depending on the DNA to be amplified, preferably 95 to 99 ° C. for 10 to 60 seconds. Reverse transcription and PCR at temperature and time as described above can be performed using a thermal cycler commonly used for PCR.

Purification of the reaction product obtained by the PCR is preferably performed by size separation of the reaction product. By size separation, the desired PCR reaction product can be separated from the primers and other impurities contained in the PCR reaction solution. The size separation of DNA can be performed by, for example, a size separation column, a size separation chip, magnetic beads that can be used for size separation, and the like. Preferred examples of magnetic beads that can be used for size separation include Solid Phase Reversible Immobilization (SPRI) magnetic beads such as Aple XP.

The purified PCR reaction product may be subjected to further treatment necessary for subsequent quantitative analysis. For example, for sequencing DNA, the purified PCR reaction product can be prepared into a suitable buffer solution, the PCR primer region contained in the PCR amplified DNA can be cleaved, or the adapter sequence can be added to the amplified DNA. May be further added. For example, the purified PCR reaction product is prepared into a buffer solution, the PCR primer sequence is removed and adapter ligation is performed on the amplified DNA, and the obtained reaction product is amplified as necessary for quantitative analysis. Library can be prepared. These operations are performed, for example, by 5 × VILO RT Reaction Mix attached to SuperScript (registered trademark) VILO cDNA Synthesis kit (Life Technologies Japan Co., Ltd.), and Ion AmpliSeq Transcriptome Human Gene Expression Co., Ltd. (Life Technologies Japan Co., Ltd.). ) Can be used according to the protocol included in each kit using the 5 × Ion Ampliseq HiFi Mix and the Ion Ampliseq Transcriptome Human Gene Expression Core Panel. A next-generation sequencer (for example, Ion S5 / XL system, Life Technologies Japan Co., Ltd.) is preferably used for sequencing. RNA expression can be quantified based on the number of reads created by sequencing (read count).

(2.2 Evaluation of placebo effect based on marker expression level)
In one embodiment of the method of the invention, the expression level of the marker of the invention (marker of the invention contained in the biological sample collected from the subject) derived from each subject is measured by the placebo effect on the sleep improvement of the subject. It is used as an index for evaluating (for example, whether the placebo effect is high or low) or for selecting a subject whose placebo effect is not high.

As mentioned above, the markers in Tables 2 and 3 are markers whose expression levels vary depending on the placebo effect on sleep improvement in the subject. More specifically, the markers in Table 2 are positive markers whose expression levels are high in the group with a high placebo effect on sleep improvement, while the markers in Table 3 are their expression levels in the group with a high placebo effect on sleep improvement. Is a low negative marker. Therefore, the high placebo effect (eg, whether the placebo effect is high or low) on the sleep improvement of the subject can be evaluated by using the positive marker or the expression level of the negative marker as an index, or the subject can evaluate the subject. It is possible to determine whether or not the person has a high placebo effect on sleep improvement.

In the present embodiment, the positive marker and one or more markers selected from the negative marker in the biological sample collected from the subject can be used as the target marker used as the above-mentioned evaluation or selection index. In the method of the present invention, any one of the positive marker and the negative marker may be used as the target marker, or two or more kinds selected from the positive marker and the negative marker may be combined as the target marker. You may use it. Alternatively, a nucleic acid marker or a protein marker consisting of the positive marker and all of the negative markers may be used as a target marker in combination.

For example, by measuring the expression level of the target marker and comparing the measured expression level of the target marker with the reference value, whether or not the subject has a high placebo effect on sleep improvement (in other words, the subject). Whether or not the placebo effect on sleep improvement is high) can be determined.
The reference value is a predetermined value, for example, the expression level of the target marker in a person having a high placebo effect on sleep improvement (high placebo effect group) or a person having a low placebo effect on sleep improvement (low placebo effect group). Statistical values (eg, average values) can be used. When a plurality of types of nucleic acids or proteins are used as target markers, it is preferable to obtain a reference value for each nucleic acid or protein.

In one example, if the expression level of the positive marker derived from the subject is equal to or lower than the low placebo effect group, the subject is not highly effective for sleep improvement (in other words, for the subject's sleep improvement). The placebo effect is not high). Alternatively, if the expression level of the positive marker derived from the subject is lower than that of the high placebo effect group, the subject does not have a high placebo effect on sleep improvement (in other words, the placebo effect on the subject's sleep improvement is not high). ).
On the other hand, if the expression level of the positive marker is higher than that of the low placebo effect group, or equal to or higher than that of the high placebo effect group, the subject is a person who does not have a high placebo effect on sleep improvement (in other words). The placebo effect on the sleep improvement of the subject is not high).

In another example, if the expression level of the negative marker derived from the subject is equal to or higher than the low placebo effect group, the subject is not highly effective for sleep improvement (in other words, the subject's sleep). The placebo effect on improvement is not high). Alternatively, if the expression level of the negative marker derived from the subject is higher than that of the high placebo effect group, the subject is not highly effective for sleep improvement (in other words, the placebo effect for sleep improvement of the subject is not high). ).
On the other hand, if the expression level of the negative marker is lower than that of the low placebo effect group, or equal to or lower than that of the high placebo effect group, the subject is a person who does not have a high placebo effect on sleep improvement (in other words, the subject). The placebo effect on sleep improvement is not high).

The difference between the expression level of the marker derived from the subject and the reference value can be evaluated, for example, by whether or not the two are statistically significantly different. When a plurality of markers are used as the target markers, the expression level of each target marker is compared with the reference value, and a fixed ratio, for example, 50% or more, preferably 70% or more, more preferably 90% or more, still more preferable. By examining whether the expression level of the 100% marker is different from the reference value, the placebo effect on the sleep improvement of the subject can be evaluated.

(2.3 Evaluation of placebo effect based on predictive model)
In another embodiment of the method of the present invention, data on the expression of the marker of the present invention (marker of the present invention contained in a biological sample collected from the subject) (hereinafter referred to as an expression profile) is used. Based on the constructed predictive model, the placebo effect on sleep improvement of the subject (for example, whether the placebo effect is high or low) is evaluated, or the subject whose placebo effect is not high is selected. Examples of expression profiles include data on expression levels such as the number of sequencing reads.

For example, the expression profile for each of one or more markers (nucleic acid or protein) obtained from each person in a teacher sample population (eg, a population containing a high placebo effect group and a low placebo effect group) is used as an explanatory variable. Prediction to evaluate the placebo effect on sleep improvement in any subject by machine learning with data on the high or low placebo effect on sleep improvement in each individual (eg, high or low placebo effect) as the objective variable A model (eg, a discriminant formula) can be constructed.
The predictive model constructed can be used to assess the placebo effect (eg, high or low placebo effect) on sleep improvement in individual subjects, or to select subjects who do not have a high placebo effect.

In the present specification, "feature amount" is synonymous with "explanatory variable" in machine learning. In the present specification, a marker whose expression profile is used as an explanatory variable (feature amount) for machine learning may be referred to as a "feature amount marker (group)". When the feature amount marker is a nucleic acid marker, it may be referred to as a feature amount gene.

The feature amount marker (group) used in this embodiment may be at least one selected from the group consisting of the nucleic acid encoding the gene shown in Table 1 and the protein encoded by the gene. The expression profile of the feature amount marker may be an absolute value, a relative value, or may be normalized. When a plurality of markers are used as the feature amount marker group, for example, a plurality of markers having a high correlation with the high placebo effect on sleep improvement are selected from the markers of the present invention, and each of their expression profiles is used as an explanatory variable. Can be used.

In a preferred embodiment, all of the nucleic acid markers in Tables 2 and 3 or all of the protein markers in Tables 2 and 3 are combined and used as a feature amount marker group. In another preferred embodiment, all of the nucleic acid or protein markers in Table 4 are combined and used as a feature amount marker group. In another preferred embodiment, all of the nucleic acid or protein markers in Table 5 are combined and used as a feature amount marker group. In another preferred embodiment, all of the nucleic acid or protein markers in Table 1 are combined and used as a feature amount marker group.

In a preferred embodiment, as a teacher sample for machine learning, a feature amount marker (group) in a person having a high placebo effect on sleep improvement (high placebo effect group) and a person having a low placebo effect on sleep improvement (low placebo effect group). ) Expression profile is used. Using the teacher sample, a discriminant (prediction model) for dividing the subject into a high placebo effect group and a low placebo effect group is constructed. As the explanatory variable used for constructing the discriminant, the expression profile of the feature amount marker (group) can be used. As the objective variable, for example, a variable indicating whether the person from which the feature amount marker (group) is derived is a high placebo effect group or a low placebo effect group can be used. Based on the constructed discriminant, the cutoff value for discriminating between the high placebo effect group and the low placebo effect group can be obtained. Next, the expression profile of the characteristic amount marker (group) derived from the subject whose placebo effect is to be investigated is measured, the obtained measured value is substituted into the discriminant, and the result obtained from the discriminant is the cutoff value. By comparing with, it is determined whether the subject is a high placebo effect group or a low placebo effect group.

For example, a statistically significant difference between the two groups to be discriminated (high placebo effect group and low placebo effect group), for example, a marker whose expression profile significantly fluctuates between the two groups is defined as a feature amount marker (group). , The expression profile can be used as an explanatory variable. Alternatively, the feature amount marker (group) can be extracted by using a known algorithm such as a machine learning algorithm. For example, feature markers (groups) can be extracted using the high degree of variable importance in the random forest shown below, the "Boruta" package in R language, and the like.

Alternatively, when the marker expression profile is used to build the predictive model, the predictive model may be built after compressing the data by dimension reduction, if necessary. For example, a plurality of markers are extracted from the nucleic acid markers encoding the gene clusters shown in Table 1. Next, principal component analysis is performed on the expression profile of the extracted marker. Subject by machine learning with one or more principal components calculated by the principal component analysis as explanatory variables and the variable representing whether the explanatory variable is derived from the high placebo effect group or the low placebo effect group as the objective variable. A predictive model can be constructed to determine whether is a high placebo effect group or a low placebo effect group.

As the algorithm for constructing the prediction model, a known algorithm such as an algorithm used for machine learning can be used. Examples of machine learning algorithms are, but not limited to, linear regression models, lassos, random forests, neural nets, and linear kernel support vector machines (SVMs). Algorithms such as linear)), support vector machine of rbf kernel (SVM (rbf)) can be mentioned. Random forest is preferable as the algorithm used in the present invention.

A model in which verification data is input to the constructed prediction model to calculate a prediction value, and the prediction value best matches the measured value, for example, a model having the largest accuracy with respect to the measured value of the predicted value. Alternatively, the model having the smallest root-mean-squared error (RMSE) of the difference between the predicted value and the measured value can be selected as the optimum model. By inputting the expression profile of the feature marker (group) actually measured from the subject to the constructed predictive model, it is possible to evaluate the placebo effect (for example, whether the placebo effect is high or low) on sleep improvement in the subject. can. Subjects who are determined to have a low placebo effect (or subjects who are not determined to have a high placebo effect) can be selected as subjects who do not have a high placebo effect on sleep improvement.

(3. Evaluation kit for placebo effect on sleep improvement)
In a further aspect, the present invention relates to the above 2. Provided is a kit for evaluating the placebo effect on sleep improvement of a subject according to the method of the present invention described in. In one embodiment, the kit of the invention comprises a reagent or instrument for measuring the expression of the markers of the invention described above. For example, the kit of the present invention may include reagents for amplifying or quantifying the nucleic acid markers of the present invention (eg, reverse transcriptase, PCR reagents, sequencing adapter sequences, etc.). Preferably, the kit of the invention comprises indicators or guidance for assessing the expression of the markers of the invention. For example, the kit of the present invention provides guidance explaining the relationship between the increase / decrease in the expression level of each marker and the height of the placebo effect, or guidance explaining the reference value of the expression level of each marker for evaluating the placebo effect, or It may be equipped with guidance on discriminants based on predictive models and feature markers to be input to them. Further, the kit of the present invention includes a biological sampling device (for example, the above-mentioned SSL-absorbing material or SL adhesive material), a reagent for extracting the marker of the present invention from a biological sample (for example, a reagent for nucleic acid purification), and the like. Further may be provided.

(4. Method for evaluating the effectiveness of sleep improving agents)
In a further aspect, the invention provides a method of assessing the effectiveness of a sleep improving agent. The method comprises assessing the sleep-improving effect of administration of a sleep-improving agent in a subject. As the subject, the above 2. A person who does not have a high placebo effect on sleep improvement selected by the method of the present invention described in the above is used.

The subject is administered a sleep-improving agent according to the method of a general sleep-improving agent evaluation test. Administration of the sleep-improving agent to the subject can be done blindly. When conducting a comparative study with a placebo, the subjects can be divided into two groups, one of which is given a sleep-improving agent and the other of which is given a placebo. Alternatively, the subject may be given the sleep-improving agent and placebo alternately according to common cross-matching techniques. The subject is also investigated for sleep status before and after administration of the sleep-improving agent (or placebo) according to common sleep-improving agent evaluation test techniques.

The sleep-improving agent administered to the subject may be a drug, a quasi-drug, or a food form. The foods include foods, supplements, etc. that have the concept of improving the quality of sleep and that indicate that fact as necessary. Examples of the form of the drug (including non-medicinal products) include an oral administration form using tablets, capsules, granules, powders, syrups and the like, or a parenteral administration form using injections, suppositories, inhalants and the like. Although it may be mentioned, it is preferably an oral administration form. The food forms include soft drinks, tea-based drinks, coffee drinks, fruit juice drinks, carbonated drinks, jelly, wafers, biscuits, bread, noodles, sausages and other foods and drinks, and various foods such as nutritional foods. , Examples of the nutritional supplement composition in the same form (tablets, capsules, syrup, etc.) as the above-mentioned orally administered preparation. The sleep-improving agent administered to the subject is preferably a food, more preferably a beverage or a supplement.

The placebo administered to the subject does not contain the active ingredient in the sleep improving agent to be evaluated, but can be appropriately produced so that the appearance, flavor, and administration form are the same.

The evaluation of the sleep improving effect in the subject to whom the sleep improving agent was administered can be carried out according to the method of the evaluation test of a general sleep improving agent. For example, the effectiveness of the sleep-improving agent can be evaluated based on the results of a survey of the sleep state of the subject before and after administration of the sleep-improving agent to be evaluated. Alternatively, the effectiveness of the sleep-improving agent can be evaluated based on the expression information of sleep-related factors in the body of the subject obtained by gene analysis, biochemical test, or the like.

When a comparison or crossmatch with placebo is performed, the sleep-improving effect of placebo in subjects receiving placebo is evaluated by the same procedure as above. When the sleep improving effect by the administration of the sleep improving agent is higher than the sleep improving effect by the administration of the placebo, it can be evaluated that the sleep improving agent is effective.

Hereinafter, the present invention will be described in more detail based on examples, but the present invention is not limited thereto.

Example 1 Construction of a placebo effect prediction model for sleep improvement using expression-variable genes 1) Test participants Test participants were recruited under the name of an evaluation test for sleep-improving agents. Among the applicants, "Very good", "Somewhat good", "Somewhat bad" to the question "How do you evaluate your sleep quality as a whole in the past month?" Of the four stages of "very bad", 103 healthy men and women in their 30s and 60s who were dissatisfied with sleep who answered "slightly bad" or "very bad" were selected as test participants.

2) Collection of sebum RNA Before the placebo ingestion test, sebum containing RNA was recovered from all faces of the test participants using an oil removal film (5 x 8 cm, made of polypropylene, 3M company). The degreasing film was transferred to a glass vial and stored at −80 ° C. until used for RNA extraction.

3) Placebo intake test As the placebo of the sleep improving agent, a sugar-coated tablet (350 mg / tablet) containing maltitol and cellulose as main components, which does not contain a material showing a sleep improving effect, was used. Participants were given a placebo without giving any information as to whether or not it contained a sleep-improving active ingredient, and 4 tablets of the placebo were taken once a day for 1 week, about 30 to 60 minutes before bedtime. I let you. For test participants, the OSA Sleep Questionnaire (Yuka Yamamoto et al .: Brain and Psychiatric Medicine 10:), which is a psychological scale to evaluate sleep introspection at the time of waking up, on the morning of the day of ingestion and the next morning after ingestion for one week. 401-409, 1999) was asked to answer. Based on the changes before and after ingestion of the OSA sleep questionnaire score (mean value of the scores of factors 1 to 5), the effect of placebo intake on the sleep quality of the study participants was evaluated. As a result, it was confirmed that the degree of improvement in sleep before and after taking placebo for one week varied among the study participants.

4) Selection of subjects Among the test participants, the top 25% (26 people) with the highest degree of improvement in the OSA sleep questionnaire score were designated as those with a high placebo effect on sleep improvement (high placebo effect group). The bottom 25% (26 people) who had a low degree of improvement in the OSA sleep questionnaire score were selected as those with a low placebo effect on sleep improvement (low placebo effect group). A total of 52 test participants in both selected groups were used as subjects, and the oil-removing film containing the sebum collected in 2) was subjected to the following analysis.

5) RNA preparation and sequencing The oil removal film was cut to an appropriate size, and RNA was extracted using QIAzol Lysis Reagent (Qiagen) according to the attached protocol. Based on the extracted RNA, cDNA was synthesized by reverse transcription at 42 ° C. for 90 minutes using a SuperScript VILO cDNA Synthesis kit (Life Technologies Japan Co., Ltd.). The random primer included in the kit was used as the primer for the reverse transcription reaction. From the obtained cDNA, a library containing DNA derived from the 20802 gene was prepared by multiplex PCR. Multiplex PCR was performed using IonAmpliSeqTranstriptome Human Gene Expression Kit (Life Technologies Japan Co., Ltd.) at [99 ° C, 2 minutes → (99 ° C, 15 seconds → 62 ° C, 16 minutes) × 20 cycles → 4 ° C, Hold. ] Condition. The obtained PCR product was purified by Aple XP (Beckman Coulter, Inc.), and then buffer reconstruction, primer sequence digestion, adapter ligation and purification, and amplification were performed to prepare a library. The prepared library was loaded into the Ion 540 Chip and sequenced using the Ion S5 / XL system (Life Technologies Japan KK). The gene from which each read sequence was derived was determined by gene mapping each read sequence obtained by sequencing using hg19 AmpliSeq Transcriptome ERCC v1, which is a reference sequence of the human genome.

6) Data analysis and selection of feature gene The read count of each read obtained by sequencing the SSL-derived RNA of the subject acquired in 5) above was used as the data of the expression level of each RNA. In order to correct the difference in the total read count between the samples, each read count was converted into an RPM (Reads per million mapped reads) value. However, reads with a read count of less than 10 were treated as missing values. Student's t-test was performed between groups (high placebo effect group and low placebo effect group) based on the expression information of 3072 genes for which expression level data that was not a defect value was obtained in 90% or more of the subjects. , P <0.05 was extracted. The 80 genes shown in Table 6 whose expression varies statistically significantly between the groups were obtained. Since the expression level of 47 genes in Table 6A was higher in the high placebo effect group, the expression level was high in the subjects having a high placebo effect on the sleep improving effect (positive marker). Since the expression level of 33 genes in Table 6B was lower in the high placebo effect group, the expression level was low in the subjects having a high placebo effect on the sleep improving effect (negative marker). These genes were selected as the feature genes used to construct the following predictive model.

7) Model construction In order to approximate the expression level data of the feature amount gene selected in 6) from the RPM value according to the negative binomial distribution to the normal distribution, the logarithm of the base 2 (Log ₂ (RPM + 1)) to which the integer 1 is added. Value). The Log ₂ (RPM + 1) value of the obtained 80 feature gene expression level data is used as an explanatory variable, and whether the placebo effect on sleep improvement is high or low (high placebo effect group or low placebo effect group) is used as the objective variable. Using this, a predictive model was constructed to discriminate between the high placebo effect group and the low placebo effect group. The random forest algorithm was specified as a method in the "caret" package of R language, and the optimum value of the number of variables (mtry value) used for constructing one decision tree was tuned. Using the mtry value determined by tuning, the random forest algorithm was executed and the estimated error rate (OOB error rate) was calculated. As a result, in the model when the expression level data of all 80 genes were used as variables, the OOB errar rate was 26.9%, and the genes shown in Table 6 were used as markers for evaluating the placebo effect on sleep improvement of individuals. It was shown that it can be used as.

Example 2 Construction of a placebo effect prediction model for sleep improvement using genes with high importance of random forest variables 1) Usage data 90% or more of the subjects acquired in 6) of Example 1 have expression level data that is not a missing value. The obtained 3072 genes were used in the following analysis. The gene expression level data was converted from the RPM value following the negative binomial distribution to the logarithm value of the base 2 (Log ₂ (RPM + 1) value) to which the integer 1 was added in order to approximate the normal distribution.

2) Selection of feature gene The feature gene used for building a predictive model was selected using a random forest algorithm. The Log ₂ (RPM + 1) value of the 3072 gene expression level data obtained in 1) is used as an explanatory variable, and whether the placebo effect on sleep improvement is high or low (high placebo effect group or low placebo effect group) is used as the objective variable. Using this, a predictive model was constructed to discriminate between the high placebo effect group and the low placebo effect group. The random forest algorithm was specified as a method in the "caret" package of R language, and the optimum value of the number of variables (mtry value) used for constructing one decision tree was tuned. Using the mtry value determined by tuning, a random forest algorithm was executed to calculate the top 100 genes of variable importance based on the Gini coefficient (Table 7). These 100 genes were selected as feature genes to be used for constructing a predictive model.

3) Model construction Using the Log ₂ (RPM + 1) value of the expression level data of the 100 feature gene selected in 2) as an explanatory variable, whether the placebo effect on sleep improvement is high or low (high placebo effect group or low placebo effect group) A) was used as the objective variable to construct a predictive model that discriminates between the high placebo effect group and the low placebo effect group. The random forest algorithm was specified as a method in the "caret" package of R language, and the optimum value of the number of variables (mtry value) used for constructing one decision tree was tuned. Using the mtry value determined by tuning, the random forest algorithm was executed and the estimated error rate (OOB error rate) was calculated. As a result, in the model when the expression level data of 100 genes was used as a variable, the OOB errator rate was 17.3%, and the genes shown in Table 7 were used as markers for evaluating the placebo effect in improving sleep for individuals. It was shown that it can be used.

Example 3 Construction of a placebo effect prediction model based on the feature amount genes duplicated in Examples 1 and 2 1) Selection of feature amount genes Table 8 of the feature amount genes used in Examples 1 and 2 The 24 genes shown in (1) were commonly used in both examples. These 24 genes were selected as feature genes to be used for constructing a predictive model.

2) Model construction Using the Log ₂ (RPM + 1) value of the expression level data of the 24 feature gene selected in 1) as an explanatory variable, whether the placebo effect on sleep improvement is high or low (high placebo effect group or low placebo effect group) A) was used as the objective variable to construct a predictive model that discriminates between the high placebo effect group and the low placebo effect group. The random forest algorithm was specified as a method in the "caret" package of R language, and the optimum value of the number of variables (mtry value) used for constructing one decision tree was tuned. Using the mtry value determined by tuning, the random forest algorithm was executed and the estimated error rate (OOB error rate) was calculated. As a result, in the model when the expression level data of 24 genes was used as a variable, the OOB eraser rate was 28.9%, and the genes shown in Table 8 were used as markers for evaluating the placebo effect in improving sleep for individuals. It was shown that it can be used.

Example 4 Construction of a placebo effect prediction model for sleep improvement based on all the feature gene used in any of Examples 1 and 2 1) Selection of feature gene Used in any of Examples 1 and 2. Feature genes (a total of 156 genes shown in Table 1) were selected as the feature genes used in the construction of the predictive model.

2) Model construction Using the Log ₂ (RPM + 1) value of the expression level data of the 156 feature gene selected in 1) as an explanatory variable, whether the placebo effect on sleep improvement is high or low (high placebo effect group or low placebo effect group) A) was used as the objective variable to construct a predictive model that discriminates between the high placebo effect group and the low placebo effect group. The random forest algorithm was specified as a method in the "caret" package of R language, and the optimum value of the number of variables (mtry value) used for constructing one decision tree was tuned. Using the mtry value determined by tuning, the random forest algorithm was executed and the estimated error rate (OOB error rate) was calculated. As a result, in the model when the expression level data of 156 genes was used as a variable, the OOB eraser rate was 28.9%, and the above 156 genes could be used as a marker for evaluating the placebo effect on sleep improvement in individuals. It has been shown.

Claims (16)

The following genes: TNK2, PPP1R3B, WDR33, HIF1AN, HSP90B1, AP2M1, GPR108, FCGR3B, DNTTIP1, C6orf1, FAM192A, TEN1, TMEM123, KAT5, DYNC1H1, CLTC, RAB11FIP4, CLTC, RAB11FIP4, SWAP A marker for evaluating the placebo effect on sleep improvement of a subject, which comprises at least one selected from the group consisting of a nucleic acid encoding SDF4 and a protein encoded by the gene. The marker according to claim 1, further comprising at least one selected from the group consisting of the nucleic acid encoding the gene shown in Table 1 below and the protein encoded by the gene.

The marker according to claim 1, further comprising at least one selected from the group consisting of a nucleic acid encoding a gene shown in Table 2 below and a protein encoded by the gene.

The marker according to claim 1, further comprising at least one selected from the group consisting of a nucleic acid encoding a gene shown in Table 3 below and a protein encoded by the gene.

The marker according to any one of claims 1 to 4, which is a nucleic acid marker. The marker according to claim 5, wherein the nucleic acid is mRNA collected from a lipid on the surface of the skin. The marker according to any one of claims 1 to 6, wherein the subject is a person to whom the placebo has not been applied. A method for assessing a placebo effect on a subject's sleep improvement, comprising measuring the expression of the marker according to any one of claims 1-7 in the subject. Further including assessing the placebo effect on the subject's sleep improvement using predictive models, including
The predictive model was constructed by machine learning with the expression level of the marker in each person of the teacher sample population as an explanatory variable and whether the placebo effect was high or low in each person of the group as an objective variable.
The teacher sample population includes those with a high and low placebo effect on sleep improvement.
The method according to claim 8. The method of claim 8, further comprising assessing the placebo effect on sleep improvement of the subject based on the expression level of the marker. A method of selecting a subject who does not have a high placebo effect on sleep improvement, comprising measuring the expression of the marker according to any one of claims 1 to 7 in the subject. Predictive models also include selecting subjects who do not have a high placebo effect on sleep improvement.
The predictive model was constructed by machine learning with the expression level of the marker in each person of the teacher sample population as an explanatory variable and whether the placebo effect was high or low in each person of the group as an objective variable.
The teacher sample population includes those with a high and low placebo effect on sleep improvement.
11. The method of claim 11. 11. The method of claim 11, further comprising selecting subjects who do not have a high placebo effect on sleep improvement based on the expression level of the marker. The method according to any one of claims 8 to 13, wherein the subject is a person who has not been applied with a placebo. A method for evaluating the effectiveness of a sleep-improving agent, which is a method for evaluating sleep improvement by administration of a sleep-improving agent in a subject who does not have a high placebo effect on sleep improvement selected by the method according to any one of claims 11 to 14. A method that involves assessing the effect. To evaluate the sleep-improving effect of administration of placebo in subjects who do not have a high placebo effect on sleep improvement selected by the method according to any one of claims 11 to 14, and to evaluate the sleep-improving effect by administration of placebo.
When the sleep improving effect by the administration of the sleep improving agent is higher than the sleep improving effect by the administration of the placebo, it is evaluated that the sleep improving agent is effective.
15. The method of claim 15, further comprising.