KR20160149620A - Method and apparatus inferring new drug indication using the complementarity between disease signatures and drug effects - Google Patents
Method and apparatus inferring new drug indication using the complementarity between disease signatures and drug effects Download PDFInfo
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Abstract
Description
The present invention relates to a technique and apparatus for regenerating a new drug utilizing complementarity of disease and drug-specific clinical variables, and more particularly, to a disease-specific clinical characteristic vector (Clinical Disease Signature Vector) and a drug- (Clinical Drug Effect Vector), and based on the direction information (Relation Directionality) they contain.
Recently, multinational pharmaceutical companies are facing a crisis due to the deterioration of profitability due to the expiration of the patent term and the increase of new drug development costs. Despite the accumulation of large volumes of data in a variety of new drug development technologies and biotech fields, the number of new drug licenses is declining.
As a result of the increased costs required for the discovery of new drugs and the increased safety regulations, it is necessary to develop low-cost / high-efficiency new drugs in order to overcome these crises. Drug Reposition It is attracting attention as a new method.
Drug Reposition identifies the new therapeutic effects of drugs on existing drugs that have passed the toxicity and safety tests and has the advantage of reducing the cost and time required for the development of new drugs.
In this study, we investigated the relationship between the chemical structure of drug and target, the relationship between drug and target, and the gene expression pattern between drug and disease (Gene Expression Pattern of Chemical and Disease ) Information. This Molecular-Centered approach analyzes the relationship between drugs, targets and diseases in a systematic and comprehensive way, but due to the complexity of the physiological system of the human body, Is not reproducible in actual clinical practice (Translational Problem). In addition, since the three - dimensional structural information of drug and target is mostly provided by prediction, it is difficult to accurately identify the relationship between drug and target.
Recently, Electronic Health Records (EHR) system has become popular and a large scale clinical information database is being built. This clinical information reflects the phenotypic reaction of drug and disease states, and thus it is becoming popular as new information for regenerating new drugs that can overcome limitations of previous studies.
The present invention proposes a new drug regeneration framework that utilizes the complementarity between the clinical features of the disease (clinical disease signature) and the clinical drug effect (clinical drug effect) based on large-scale clinical information.
The present invention has been made in order to solve the problems of the existing studies for creating new medicines as described above, and the object of the present invention is to provide a medical treatment method and a medicament, Clinical Drug Effect) and measuring the complementarity between the disease and the drug from the directional information contained in each vector to provide a new drug regeneration technique and apparatus.
The solution to the problem of the present invention is not limited to those mentioned above, and other solutions not mentioned can be clearly understood by those skilled in the art from the following description.
According to an aspect of the present invention, there is provided a method for generating a disease-specific clinical feature vector, the method comprising: generating a disease-specific clinical feature vector representing a state of a clinical parameter in a disease state; A second storage unit for generating and storing a drug-specific clinical effect vector indicating a state of clinical variables appearing when the drug is administered; A comparison unit comparing the directionality of the clinical variables of the disease-specific clinical feature vector and the drug-specific clinical effect vector; And a judging unit for judging whether the drug has a drug effect on the disease based on the result of the comparing unit. The present invention provides an apparatus for regenerating a new drug using complementarity of disease and drug-specific clinical variables. In addition, the disease-specific clinical feature vector can be identified by a t-test in which the clinical variables are significantly increased or decreased in a specific disease state, and the error detection rate (FDR) is less than 5% May be selected and stored.
In addition, the drug-specific clinical effect vector may find a clinical parameter associated with the drug, and may determine and store the direction between the drug and the clinical parameter.
Also, the determination unit may calculate a new drug regeneration index using complementarity between the disease-specific clinical feature vector and the drug-specific clinical effect vector, and the new drug regeneration index may be defined by the following equation.
(Where: Score Disease- Drug : New drug re-creation index, α: Weighting factor for the complement score set by the user, C: Number of clinical variables with complementarity between disease and drug, A: Disease- CV: Clinical vector, C (CV Disease , CV Drug ): Disease-specific clinical characteristic vector (UP) or Down (DOWN), drug-specific clinical effect vector (ASSOCIATION) (CV Disease , CV Drug ): The ASSOCIATION score between the disease-specific clinical effect vector and the drug-specific clinical effect vector.
Storing a disease-specific clinical feature vector indicative of a status of clinical variables in a first storage; Storing a drug-specific clinical effect vector indicating a state of clinical variables appearing in administration of the drug to the second storage unit; Comparing the disease-specific clinical feature vector with a directionality of a clinical variable of the drug-specific clinical effect vector; And determining whether the drug is effective for the disease based on the results of the comparison unit.
In addition, the disease-specific clinical feature vector can be identified by a t-test in which the clinical variables are significantly increased or decreased in a specific disease state, and the error detection rate (FDR) is less than 5% May be selected and stored.
In addition, the drug-specific clinical effect vector may be identified by finding a clinical parameter associated with the drug and determining the directionality between the drug and the clinical parameter.
In addition, the determining step may utilize the complementarity between the disease-specific clinical feature vector and the drug-specific clinical effect vector, and the new drug regeneration index may be defined by the following equation.
(Where: Score Disease- Drug : New drug re-creation index, α: Weighting factor for the complement score set by the user, C: Number of clinical variables with complementarity between disease and drug, A: Disease- CV: Clinical vector, C (CV Disease , CV Drug ): Disease-specific clinical characteristic vector (UP) or Down (DOWN), drug-specific clinical effect vector (ASSOCIATION) (CV Disease , CV Drug ): The ASSOCIATION score between the disease-specific clinical effect vector and the drug-specific clinical effect vector.
In the present invention, a method and apparatus for reestablishing a new medicine taking into account the complexity of the physiological system of the human body by utilizing electronic medical records is proposed. This is due to the structural similarity of the drug, the physical interactions between the drug and the target, and the translational problem that can be seen in existing molecular-based approaches using gene expression pattern information between drugs and diseases Problems that can not be reproduced) can be solved.
In addition, electronic medical records (EHRs) continue to increase over time, so that more accurate drug candidates can be predicted in the future based on vastly accumulated data.
The effects of the present invention are not limited to those mentioned above, and other effects not mentioned may be clearly understood by those skilled in the art from the following description.
Figure 1 shows the relationship between disease and the clinical parameters of the drug.
FIG. 2 is a diagram illustrating a framework for calculating drug re-creation scores by generating disease-specific clinical effect vectors and drug-specific clinical effect vectors according to an embodiment of the present invention.
FIG. 3 is a diagram illustrating a method of calculating a new drug re-creation index according to an embodiment of the present invention.
FIG. 4 is a view showing the significance (P-Value) of the relationship between a disease and a drug according to an embodiment of the present invention.
5 is a configuration diagram of an apparatus for calculating a new drug re-creation index according to an embodiment of the present invention.
FIG. 6 is a flowchart illustrating a process of calculating a new drug re-creation index according to an embodiment of the present invention.
Best Mode for Carrying Out the Invention Hereinafter, with reference to the accompanying drawings, preferred embodiments of a drug re-creation technique utilizing complementarity of disease and drug-specific clinical variables according to the present invention will be described in detail. In the drawings, the same reference numerals are used to designate the same or similar components throughout the drawings. In the following description of the present invention, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear.
Referring to FIGS. 1 to 6, the present invention discloses a novel drug (drug) that utilizes complementarity between a clinical clinical signature of a disease and a clinical drug effect based on large-scale clinical information We propose a re-creation framework. The proposed method is based on the data preprocessing, generation of disease-specific clinical signature vector, creation of drug-specific clinical effect vector, complementation of the vector, And a process of calculating a score index (Score Disease- Drug ).
1 and 2 show a part of a data preprocessing process of the present invention, and a data preprocessing process of the present invention is performed as follows.
First, EHRs-based clinical information was obtained through a sample of the epidemiological database called NHANES as a data source. NHANES is a major program of the Centers for Disease Control and Prevention (CDC) whose purpose is to assess the health and nutritional status of adults and children in the United States. NHANES includes demographics, eating habits, health questions, and the results of laboratory tests. Drug information was retrieved from the DrugBank database, and the literature information was obtained from PubMed biomedical literature data.
Based on the NHANES data, each sample was stratified into disease states and analyzed. Next, in order to analyze the clinical variables related to the disease, the clinical parameters were filtered through laboratory tests through the following criteria.
First, each variable should be expressed as a numerical value of a statistical analysis (t-test).
Second, each variable should have a clinical meaning (eg, glucose or triglyceride)
Third, each variable should be represented as a consistent unit of measurement.
A total of 717 clinical variables were considered in the study by filtering the clinical variables according to the criteria described above. In addition, the sample size of the disease state is an important factor indicating statistical significance. Therefore, the number of patients sufficient to perform the statistical analysis was secured and its disease state was considered.
Two preprocessing methodological steps (logarithmic transformation and Z-transformation) are performed to obtain reliable results. Since most clinical parameter measurements initially had asymmetric distributions in a small range, log transformations were applied to each clinical variable. Then, each variable was Z-transformed to adjust the average and standard deviation. These two steps were implemented in SAS.
Hereinafter, the framework proposed in the present invention will use the clinical and literature information as the main data, and from these two data, a disease-specific clinical feature vector (Clinical Disease Signature Vector) and a drug- (Clinical Drug Effect Vector). Each vector contains 717 Clinical Variables, changes in clinical variables according to disease, and Relation Directionality, which indicates the effect of the drug on clinical variables. Based on this information, we measure the complementarity between the disease and the drug, and at the same time, they are expressed as the Repositioning Score of the drug.
Figures 1 and 2 illustrate the step of generating a disease-specific clinical feature vector (Clinical Disease Signature Vector).
Using the disease information from each individual recorded in the Electronic Health Record (EHR), the EHR samples are classified into disease states and normal states. At this time, each sample is represented by 717 clinical variables.
For each clinical variable, t-test was used to find out the significant increase or decrease in the specific disease status. In this process, the error detection rate (FDR) was adjusted to 5% Of the clinical variables are selected as disease-specific clinical variables.
For the clinical variables judged to be significant, the direction of the variable is determined by using the average value of the variables in the normal group and the disease group. That is, if the disease state has a higher value than the normal state, the direction is UP and the opposite direction is DOWN.
Finally, the vector value for each disease consists of 717 clinical variables and their directionality (UP or DOWN).
Figures 1 and 2 also illustrate the step of generating a drug-specific clinical effect vector (Clinical Drug Effect Vector).
This vector provides information on how drugs affect 717 clinical variables. A list of 1,578 FDA-approved drugs was obtained from DrugBank, and PubMed abstract was used for literature information.
This process is divided into two main categories: first, to find out the clinical variables related to the drug; second, to determine the direction between the drug and the clinical variables. UP if the value increases, DOWN if the opposite.)
First, clinical variables that are significantly associated with drugs use Co-Occurrence-based Literature Mining technology. The statistical significance between drug and clinical variables was obtained through Fisher's Exact Test. Clinical parameters were selected to reduce error detection rate (FDR) to less than 5% in order to correct errors for Multiple Statistical Test.
To obtain directional information between drugs and variables, we apply the Literature Mining (Shallow Linguistic Kernel) method based on Supervised Learning.
In this case, even though the clinical variables found to be significant in Co-Occurrence-based Literature Mining can not be unconditionally defined in Literature Mining based on Supervised Learning, the directionality of this case is defined as ASSOCIATION .
Finally, the vector value for each drug is composed of 717 clinical variables and their directionality (UP, DOWN or ASSOCIATION).
FIG. 3 is a graph showing the relationship between the new drug regeneration index and the drug resistance index using the complementarity of directionality in the disease-specific clinical feature vector (Clinical Disease Signature Vector) and the drug-specific clinical effect vector Fig.
The denominator of the equation in FIG. 3 is the total number of clinical variables, and Complementarity is the complementarity score, which is the sum of the disease-specific Clinical Disease Signature Vector and the direction of the Clinical Drug Effect Vector And ASSOCIATION has UP or DOWN direction in the disease-specific clinical feature vector (Clinical Disease Signature Vector), and the drug-specific clinical effect vector ASSOCIATION Number of clinical variables with directionality. The new drug re-creation index is then calculated.
As an embodiment of the present invention, medicines were sorted in the order of high possibility of re-creating new drugs (new drug re-creation index) for five diseases through the proposed framework. At this time, the higher the new drug re-creation index, the more significant the possibility of drug re-creation, and the lower the significance (p-value).
FIG. 4 is a diagram showing a comparison with a Disease-Drug Pair provided in a CTD (Comparative Toxicogenomics Database) and a Clinical Trials database (correct answer set). Referring to FIG. 4, it can be seen that each disease-drug relationship pair proven in the two independent correct answers databases has a lower significance probability, that is, a higher drug rebuild index, than the unproven disease-drug relationship pairs in the correct answer database .
Specifically, the X-axis of the graph of FIG. 4 represents the significance (p-value) of the Disease-Drug Score (Score Disease - Drug ), and is calculated as follows. Calculate the new drug regeneration index for all combinable disease-drug relationship pairs and place it in the Background Distribution. The significance (p-value) of the relevant index is calculated by taking into account where the new drug re-creation index of a particular disease-drug relationship pair is located in this distribution. If the new drug re-creation index of the disease-drug relationship pair for which the significance (p-value) is to be calculated has a value higher than the average value in the background distribution, the probability of such a value is lowered. Since this probability is a p-value, the higher the value of the New drug re-creation index, the lower the significance probability.
The Y axis of the graph in Fig. 4 represents the density (density). The number of disease-drug pairs having the corresponding significance (p-value) is counted to show to what extent the total number is occupied. At this time, if the number of disease-drug pairs is large, the density is increased.
Referring to FIG. 4, the blue color of the graph is a pair of density plots in which the actual disease-drug relationship exists in the CTD and the Clinical Trials database (correct answer set), and the red color represents the density of the pair having no actual disease- Plot. In order for the proposed method to be reliable, the disease-drug relationship pairs provided by the CTD and the Clinical Trials database should have a high value (low significance (p-value)).
In the graph, the disease-drug relationship pairs (Blue Density Plots) provided by the CTD and Clinical Trials databases are distributed biased toward low p-values, and the disease-drug relationship pairs (Red Density Plot) is biased toward high p-value (low value of new drug regeneration index).
Thus, the proposed methodology gives a high index to the correct answer for the disease-drug relationship pair, and the disease-drug relationship pairs that are given a high index (low p-value) . FIG. 5 shows a
In addition, the disease-specific clinical feature vector (10) finds clinical variables that are significantly increased or decreased in a specific disease state through t-test, and the error detection rate (FDR) is less than 5% Of the total number of clinical variables.
In addition, the drug-specific clinical effect vector (20) finds the clinical variables associated with the drug and stores the orientation between the drug and the clinical variables.
Clinical parameters related to drug use are based on Co-Occurrence-based Literature Mining technology. To obtain statistical significance between drug and clinical variables, Fisher's Exact Test's error detection rate (FDR) is less than 5% Lt; / RTI >
In addition, the directional information between the drug and the clinical variables is obtained by applying a Literature Mining technique based on Supervised Learning (Shallow Linguistic Kernel).
In addition, the
(Where: Score Disease- Drug : New drug re-creation index, α: Weighting factor for the complement score set by the user, C: Number of clinical variables with complementarity between disease and drug, A: Disease- CV: Clinical vector, C (CV Disease , CV Drug ): Disease-specific clinical characteristics (CV), Disease-specific clinical characteristics A (CV Disease , CV Drug ): ASSOCIATION score between the disease-specific clinical effect vector and the drug-specific clinical effect vector.
FIG. 6 is a flowchart illustrating a procedure of storing (S100) a disease-specific
In addition, the disease-specific clinical feature vector (10) finds clinical variables that are significantly increased or decreased in a specific disease state through t-test, and the error detection rate (FDR) is less than 5% Of the total number of clinical variables.
In addition, the drug-specific clinical effect vector (20) finds the clinical variables associated with the drug and stores the orientation between the drug and the clinical variables.
Clinical parameters related to drug use are based on Co-Occurrence-based Literature Mining technology. To obtain statistical significance between drug and clinical variables, Fisher's Exact Test's error detection rate (FDR) is less than 5% Lt; / RTI >
In addition, directional information between drug and clinical variables is obtained by applying a Literature Mining technique (Shallow Linguistic Kernel) based on Supervised Learning.
Further, the judgment step utilizes the complementarity between the disease-specific
(Where: Score Disease- Drug : New drug re-creation index, α: Weighting factor for the complement score set by the user, C: Number of clinical variables with complementarity between disease and drug, A: Disease- CV: Clinical vector, C (CV Disease , CV Drug ): Disease-specific clinical characteristic vector (UP) or Down (DOWN), drug-specific clinical effect vector (ASSOCIATION) (CV Disease , CV Drug ): The ASSOCIATION score between the disease-specific clinical effect vector and the drug-specific clinical effect vector.
The foregoing description is merely illustrative of the technical idea of the present invention, and various changes and modifications may be made by those skilled in the art without departing from the essential characteristics of the present invention. Therefore, the embodiments disclosed in the present invention are intended to illustrate rather than limit the scope of the present invention, and the scope of the technical idea of the present invention is not limited by these embodiments. The scope of protection of the present invention should be construed according to the following claims, and all technical ideas within the scope of equivalents should be construed as falling within the scope of the present invention.
10: disease-specific clinical characteristics vector
20: Drug-specific clinical effect vector
100: first storage unit 200: second storage unit
300: comparison unit 400:
Claims (8)
A second storage unit (200) for generating and storing a drug-specific clinical effect vector (20) indicative of a state of clinical variables appearing upon drug administration;
A comparison unit 300 for comparing the directionality of the clinical variables of the disease-specific clinical feature vector 10 and the drug-specific clinical effect vector 20; And
And a judging unit (400) for judging whether the drug has a drug effect on the disease based on a result of the comparing unit (300). The drug regeneration technique utilizing complementarity of disease and drug- Device.
The disease-specific clinical feature vector 10 is t-tested to identify clinically relevant variables that are significantly increased or decreased in a specific disease state, and the error detection rate (FDR) is less than 5% The method comprising the step of selecting and storing only clinical variables having a value of at least one of the following:
The drug-specific clinical effect vector (20) finds clinical variables associated with the drug and identifies the direction between the drug and the clinical variables to determine the direction of the new drug, which utilizes the complementary nature of the stored disease- and drug- Apparatus for re - creation techniques.
The determination unit
The new drug regeneration index is calculated using complementarity between the disease-specific clinical feature vector (10) and the drug-specific clinical effect vector (20) Apparatus for reintroduction of new drugs using complementarity of drug - specific clinical variables.
(Where: Score Disease - Drug : New drug regeneration index, α: Weighting factor for complementarity score set by the user, C: Number of clinical variables with complementarity between disease and drug, A: Disease- CV: Clinical vector, C (CV Disease , CV Drug ): Disease-specific clinical characteristic vector (UP) or Down (DOWN), drug-specific clinical effect vector (ASSOCIATION) (CV Disease , CV Drug ): The ASSOCIATION score between the disease-specific clinical effect vector and the drug-specific clinical effect vector.
A step S200 of storing a drug-specific clinical effect vector indicating a state of clinical variables appearing in drug administration in the second storage unit 200;
Comparing (S300) the directionality of the disease-specific clinical feature vector and the clinical variables of the drug-specific clinical effect vector; And
Determining whether the drug has a drug effect on the disease based on a result of the comparison unit (300); and calculating (S400) whether the drug has a drug effect on the disease based on a result of the comparison unit (300).
The disease-specific clinical feature vector 10 is t-tested to identify clinically significant variables that are significantly increased or decreased in a specific disease state, and the error detection rate (FDR) is less than 5% The method of regenerating new drugs using the complementarity of disease and drug-specific clinical variables, including the step of selecting and storing only clinical variables having a value.
The drug-specific clinical effect vector (20) finds clinical variables associated with the drug and identifies the direction between the drug and the clinical variables to determine the direction of the new drug, which utilizes the complementary nature of the stored disease- and drug- Re - creation technique.
In the determining step S400,
Utilizing the complementarity between the disease-specific clinical feature vector (10) and the drug-specific clinical effect vector (20), the new drug re-creation index is a complement of the disease and drug- New drug re - creation techniques utilized.
(Where: Score Disease- Drug : New drug re-creation index, α: Weighting factor for the complement score set by the user, C: Number of clinical variables with complementarity between disease and drug, A: Disease- CV: Clinical vector, C (CV Disease , CV Drug ): Disease-specific clinical characteristic vector (UP) or Down (DOWN), drug-specific clinical effect vector (ASSOCIATION) (CV Disease , CV Drug ): The ASSOCIATION score between the disease-specific clinical effect vector and the drug-specific clinical effect vector.
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| KR20190028417A (en) * | 2019-03-12 | 2019-03-18 | 한국과학기술원 | Method for predicting drug candidate for diseases by using human metabolite specific for the disease target metabolizing enzyme |
| KR20210055314A (en) * | 2019-11-07 | 2021-05-17 | 울산대학교 산학협력단 | Method and system for selecting new drug repositioning candidate |
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| KR20190028417A (en) * | 2019-03-12 | 2019-03-18 | 한국과학기술원 | Method for predicting drug candidate for diseases by using human metabolite specific for the disease target metabolizing enzyme |
| KR20210055314A (en) * | 2019-11-07 | 2021-05-17 | 울산대학교 산학협력단 | Method and system for selecting new drug repositioning candidate |
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