METHOD AND APPARATUS FOR PERFORMING DRUG SCREENING

Provided are a method and apparatus for performing drug screening. The method includes: obtaining statistical data regarding prognostic indices that indicate a recurrence likelihood of a disease and expression levels of phenotype features from biological samples; obtaining drug data regarding expression levels of the determined phenotype features that are changed by administering different types of drugs to the biological samples; and screening efficacy of the administered drugs by using the obtained statistical data and the obtained drug data.

DETAILED DESCRIPTION

FIG. 1illustrates a drug screening system1according to an embodiment of the present disclosure. Referring toFIG. 1, the drug screening system1according to the current embodiment of the present disclosure includes an apparatus10for performing drug screening, biological samples20before drug administration is performed, and biological samples30after drug administration is performed.

Although not shown, one of ordinary skill in the art understands that the drug screening system1may further include a gene analyzing device, such as a polymerase chain reaction (PCR) device for detecting phenotypes or gene expression levels from the biological samples20or30, a microarray, a high content cell imaging device, a high content screening device, and a high throughput screening device.

That is, the drug screening system1illustrated inFIG. 1illustrates elements relating to the present embodiment to not make the features of the present embodiment vague. However, the drug screening system1may further include other general-use elements than elements illustrated inFIG. 1.

InFIG. 1, the biological samples20and30are separately illustrated so as to distinguish between the time before drug administration is performed and the time after drug administration is performed but they are the same. The number of biological samples20illustrated is three for convenience of explanation of the present embodiment and may be changed according to the environment of the drug screening system1.

A nucleic acid, such as deoxyribonucleic acid (DNA) of an object, corresponds to a gene substance including gene information about the object, i.e., a gene. A base sequence of the nucleic acid includes information about cells or tissue that constitutes the object. Thus, many studies for information about a thorough nucleic acid sequence of an individual have been performed in many fields, such as in understanding the phenomenon of life, in developing new drugs, in diagnosing and preventing a disease, and in studying genes of humans.

Compounds, such as drugs used as medicine for curing various diseases (for example, cancer, tumors, and the like) may affect part or the whole of a gene expression level. Thus, if the effect on the expression level of a gene can be precisely analyzed, the efficacy of the medicine, such as drugs, may be precisely monitored or predicted.

The apparatus10for performing drug screening of the drug screening system1ofFIG. 1is an apparatus for screening a drug having efficacy among drugs A, B, and C that are administered to the biological samples20. Here, the biological samples20and30are samples including an abnormal tissue, such as a cancer or tumor extracted from a patient, that are cell samples, tissue samples, or serum samples, for example. Hereinafter, the operation of the drug screening system1will be described in detail together with reference toFIG. 2which illustrates the apparatus10for performing drug screening.

FIG. 2is a detailed block diagram of a structure of the apparatus10for performing drug screening, according to an embodiment of the present disclosure. Referring toFIG. 2, the apparatus10for performing drug screening includes a data obtaining unit110, a phenotype feature analyzing unit120, and a drug screening unit130. The data obtaining unit110includes a prognostic index estimating unit111and an expression level analyzing unit112.

The apparatus10for performing drug screening may be implemented with one or more general-use processors. That is, the apparatus10for performing drug screening may be implemented with an array of logic gates or a combination of one or more general-use microprocessors and memory in which a program that may be executed by the microprocessor(s) is stored. Also, the apparatus10for performing drug screening may be implemented in the form of a module of an application program. Furthermore, one of ordinary skill in the art understands that the apparatus10for performing drug screening may be implemented with hardware having other forms that may perform operations as described herein.

The data obtaining unit110obtains statistical data regarding prognostic indices11and expression levels of phenotype features12from the biological samples20.

The prognostic index estimating unit111estimates prognostic indices11that indicate the recurrence likelihood of a disease, using gene expression data obtained from the biological samples20.

In certain aspects, the gene expression data is obtained from each of the biological samples20using reverse transcriptase polymerase chain reaction (RT-PCR) or a microarray, or any of several other well-known methods of obtaining gene expression data. A method of obtaining gene expression data is obvious to one of ordinary skill in the art, and thus detailed descriptions thereof will be omitted.

The prognostic indices11that are estimated by the prognostic index estimating unit111may include a recurrence score, a tumor grade, and a C-path score.

In certain aspects, the recurrence score is a value between 0 and 100 that is obtained from the result of performing a diagnostic test, such as an Oncotype DX® test that diagnoses breast cancer by analyzing 21 different genes from tissue of the breast cancer. The recurrence score is an index that indicates the recurrence likelihood of breast cancer and the effective period of chemotherapy. As the recurrence score increases, the recurrence likelihood of breast cancer increases.

The tumor grade and the C-path score are also indices that predict a recurrence rate of cancer using a biopsy of the tissue of the breast cancer.

Although various methods of predicting the recurrence likelihood of a disease, such as a cancer or tumor, are well known in the art, the recurrence score is generally known as the prognostic indices11having a relatively precise result. In the present embodiment, the recurrence score has been explained as an example, and aspects of the present disclosure are not limited thereto. That is, one of ordinary skill in the art understands that any prognostic indices that indicate the recurrence likelihood of the disease may be used.

Samples A, B, and C that are the biological samples20before drug administration is performed may be samples that are transfected by small interfering RNA (siRNA) and thus have different gene expression levels.

In particular, when the recurrence score is used, the biological samples20may correspond to cell lines that are classified into a high risk group according to the recurrence score. However, the cell lines of the high risk group are not necessarily used as the biological samples20.

FIG. 3is a table showing cell lines that may be used as the biological samples20, according to an embodiment of the present disclosure.

Referring toFIG. 3, data regarding 6 cell lines of breast cancer of a human are shown. As a result of obtaining gene expression levels of 21 genes of the breast cancer tissue regarding a recurrence score, cell lines, such as BT474 and MCF7, belong to a high risk group and are ER positive. Thus, it may be determined that BT474 and MCF7 may be used as appropriate biological samples20.

The biological samples20, such as samples A, B, and C, may correspond to samples of different cell lines. That is, the biological samples20may correspond to different cell lines that are extracted from breast cancer cells, as described inFIG. 3. However, a reason why different cell lines are used is that the biological samples20have different gene expression levels. Thus, a similar result to that where the above-described biological samples20are transfected by siRNA may be obtained.

Referring back toFIG. 2, the prognostic index estimating unit111estimates recurrence scores of the biological samples20. As described above, since the biological samples20are transfected by siRNA, they have different gene expression levels. Thus, the biological samples20may have recurrence scores that are not the same.

The expression level analyzing unit112analyzes the phenotype features12of the biological samples20using image data in which phenotypes expressed in the biological samples20are indicated.

In more detail, the expression level analyzing unit112obtains the image data regarding the phenotypes of the biological samples20from an imaging device, such as a high content cell imaging device, a high content screening device, or a high throughput screening device, and then analyzes the phenotype features12from the obtained image data. Here, before the image data is obtained, an operation of labeling the biological samples20with fluorescent dyes may be performed. The operation of obtaining the image data from the imaging device, such as the high content cell imaging device, the high content screening device, or the high throughput screening device, is well known to one of ordinary skill in the art and thus, detailed descriptions thereof will be omitted.

FIG. 4is a table showing phenotype features12analyzed by the expression level analyzing unit112, according to an embodiment of the present disclosure.

Referring toFIG. 4, at least one phenotype is expressed in each of the biological samples20, and relative values therefrom are analyzed as expression levels of the phenotype features12. In more detail, phenotypes of the biological samples20may be expressed in various types of image data, such as the intensity of brightness according to the result of hybridization of the fluorescent dyes, and the shape of a hybridized region. The expression level analyzing unit112analyzes expression levels of the phenotype features12by calculating a relative contribution of each of the phenotype features12.

As a result, the expression level analyzing unit112analyzes the expression levels of the phenotype features12from each of the biological samples20so as to obtain statistical data.

Referring back toFIG. 2, the phenotype feature analyzing unit120analyzes a correlation13between the prognostic indices11and the expression levels of the phenotype features12on each of the biological samples20. Then, the phenotype feature analyzing unit120determines phenotype features14in which the correlation13exists, among all phenotype features12that are analyzed by the expression level analyzing unit112, based on the analyzed correlation13.

In more detail, the phenotype feature analyzing unit120determines phenotype features14having a distribution of expression levels that indicate a positive correlation or negative correlation between the prognostic indices11, as the phenotype features14in which the correlation13exists, using the obtained statistical data.

FIG. 5is a graph for explaining an operation of analyzing the correlation13between the prognostic indices11and the phenotype features12using the phenotype feature analyzing unit120, according to an embodiment of the present disclosure. Referring toFIG. 5, the distribution of expression levels of the phenotype features12, such as phenotype feature1, phenotype feature2, and phenotype feature3in relation to the distribution of the prognostic indices11(recurrence scores) is shown.

In phenotype feature1, as the recurrence score increases, the expression level of phenotype feature1increases. Thus, the phenotype feature analyzing unit120analyzes that the recurrence score and phenotype feature1are in proportion to each other and a positive correlation between the recurrence score and phenotype feature1exists.

In phenotype feature2, as the recurrence score increases, the expression level of phenotype feature2decreases. Thus, the phenotype feature analyzing unit120analyzes that the recurrence score and phenotype feature2are in inverse proportion to each other and a negative correlation between the recurrence score and phenotype feature2exists.

In phenotype feature3, since the expression level of phenotype feature3is changed regardless of a change of the recurrence score, the phenotype feature analyzing unit120analyzes that no correlation13between the recurrence score and phenotype feature3exists.

As a result, the phenotype feature analyzing unit120determines the phenotype features14having the positive or negative correlation, such as phenotype features1and2, among various phenotype features12of the biological samples20.

Referring back toFIG. 2, next, various drugs for drug screening, such as drug A, drug B, and drug C, are administered to the biological samples20. As described above, each drug may cause a reaction for suppressing or increasing gene expression levels of the biological samples20or may not cause any reaction. In the present embodiment, unlike the biological samples20before drug administration is performed, after drug administration is performed, the biological samples will be referred to with reference numeral30(i.e., biological samples30).

The expression level analyzing unit112additionally obtains drug data regarding the expression levels of the phenotype features14that are changed and determined by administering different types of drugs to the biological samples30.

The imaging device, such as a high content cell imaging device, a high content screening device, or a high throughput screening device, is used to observe the expression levels of the phenotype features14that are changed by administering the drugs on the biological samples30.

Here, the phenotype features14to be observed correspond to features that are determined by the phenotype feature analyzing unit120that the correlation13exists in the phenotype features14. That is, since the determined phenotype features14are analyzed as relating to the prognostic indices11(a recurrence score), the phenotype features14are considered to relate to a malignant tumor.

Although, according to the related art, image data is obtained using a high content cell imaging device, a high content screening device, or a high throughput screening device, it is difficult to precisely analyze the meaning of a change of features or the efficacy of a drug by drug administration. In other words, it is difficult to precisely analyze features that clinically relate to a disease, among many features included in the image data.

However, in the drug screening system1ofFIG. 1, since the phenotype features14relating to the high recurrence likelihood of the disease are sorted from the biological samples20and are analyzed, the phenotype features14regarding the malignant tumor may be concentratively analyzed. Thus, experimental results may not be greatly affected by the ratio of the malignant tumor with respect to abnormal tissue samples extracted from a patient.

FIG. 6is a graph showing drug data regarding expression levels of the phenotype features14that are changed and determined by administering different types of drugs to the biological samples30, according to an embodiment of the present disclosure. The drug data ofFIG. 6is the result that is analyzed based on the image data obtained by the expression level analyzing unit112.

Referring toFIG. 6, a change trend of expression levels of the phenotype features14, such as phenotype features1and2, which have been determined by administration of the drugs A and B, is shown. The change trend is information that indicates whether the expression levels of the phenotype features14, such as phenotype features1and2, which have been determined by administration of the drugs A and B, increase or decrease based on a placebo.

Referring back toFIG. 2, the drug screening unit130screens the efficacy of the drugs A and B administered by using the correlation13that is analyzed by the phenotype feature analyzing unit120and the drug data that is obtained by the expression level analyzing unit112.

In more detail, the drug screening unit130screens the administered drugs A and B by predicting a change of the prognostic indices11(a recurrence score) that correspond to the change trend of the expression levels of the phenotype features14, such as phenotype features1and2, which are determined by the drugs A and B, using the analyzed correlation13.

The drug screening unit130screens a drug that is administered to the biological samples20wherein the predicted change is that the prognostic indices11(a recurrence score) will decrease, as a drug having efficacy.

That is, when the expression levels of the phenotype features14determined to have a positive correlation decrease or the expression levels of the phenotype features14determined to have a negative correlation increase, the drug screening unit130may predict that the prognostic indices11(recurrence score) will decrease. For understanding, the following description will be provided with reference toFIG. 6.

Referring to the drug data ofFIG. 6, as a result of administering drug A to sample A, it is analyzed that phenotype feature1increases compared to the placebo and phenotype feature2decreases compared to the placebo. According to the correlation13analyzed by the phenotype feature analyzing unit120, phenotype feature1is analyzed to have a positive correlation between phenotype feature1and the recurrence score, and phenotype feature2is analyzed to have a negative correlation between phenotype feature2and the recurrence score.

As a result, the drug screening unit130may analyze that drug A causes an increase in the recurrence score compared to the placebo, using the analyzed correlation13.

Referring back to the drug data ofFIG. 6, as a result of administering drug B to sample B, it is analyzed that phenotype feature1decreases compared to the placebo and phenotype feature2increases compared to the placebo. According to the correlation13analyzed by the phenotype feature analyzing unit120, phenotype feature1is analyzed to have a positive correlation between phenotype feature1and the recurrence score, and phenotype feature2is analyzed to have a negative correlation between phenotype feature2and the recurrence score.

As a result, the drug screening unit130may analyze that drug B causes a decrease in the recurrence score compared to the placebo, by using the analyzed correlation13.

It is well known that, as the recurrence score increases, the recurrence likelihood of the disease (e.g., breast cancer) increases. Thus, the drug screening unit130may predict that administration of drug A will cause an increase in the recurrence score, based on the above results. Thus, the drug screening unit130may screen drug A as a drug having no efficacy as an anticancer drug. However, the drug screening unit130may predict that administration of drug B will cause a decrease in the recurrence score, based on the above results. Thus, the drug screening unit130may screen drug B as a drug having efficacy as an anticancer drug.

The drug screening unit130screens the efficacy of the administered drugs, such as drug A, drug B, and drug C, using the above-described method so as to generate a drug profile15regarding the efficacy of the administered drugs, such as drug A, drug B, and drug C.

Therefore, according to the drug screening system1ofFIG. 1, since the efficacy of various types of drugs may be precisely screened using image data obtained from samples regarding abnormal tissues (e.g., a cancer, a tumor, and the like) extracted from a patient, the drug screening system1ofFIG. 1may be used in developing new drugs, preventing a disease, or providing an optimum treatment method to a patient at an early stage of a disease.

FIG. 7is a flowchart illustrating a method of performing drug screening, according to an embodiment of the present disclosure. Referring toFIG. 7, the method of performing drug screening, according to the current embodiment of the present disclosure, includes operations to be performed in a time sequence using the drug screening system1ofFIG. 1and the apparatus10for performing drug screening ofFIG. 2. Thus, although omitted below, the above descriptions regardingFIGS. 1 and 2also apply to the method of performing drug screening illustrated inFIG. 7.

In operation701, the data obtaining unit110obtains statistical data regarding the prognostic indices11(a recurrence score, a tumor grade, and the like) that indicate the recurrence likelihood of a disease and expression levels of the phenotype features12from the biological samples20.

In operation702, the phenotype feature analyzing unit120determines phenotype features14in which a correlation13exists, by analyzing the correlation13between the obtained prognostic indices11and the expression levels of the phenotype features12.

In operation703, the data obtaining unit110additionally obtains drug data regarding the expression levels of the phenotype features14that are changed and determined by administering different types of drugs to the biological samples30.

In operation704, the drug screening unit130screens the efficacy of the administered drugs using the analyzed correlation13and the obtained drug data.

FIG. 8is a flowchart illustrating a method of performing drug screening, according to another embodiment of the present disclosure. Referring toFIG. 8, the method of performing drug screening, according to the current embodiment of the present disclosure, includes operations to be performed in a time sequence using the drug screening system1ofFIG. 1and the apparatus10for performing drug screening ofFIG. 2. Thus, although omitted below, the above descriptions regardingFIGS. 1 and 2also apply to the method of performing drug screening illustrated inFIG. 8.

In operation801, the phenotype feature analyzing unit120analyzes the correlation13between the prognostic indices11(a recurrence score, a tumor grade, and the like) that indicate the recurrence likelihood of a disease and the expression levels of the phenotype features12from the biological samples20.

In operation802, the data obtaining unit110obtains data regarding the expression levels of the phenotype features14that are changed by administering different types of drugs to the biological samples30.

In operation803, the drug screening unit130predicts a change of the prognostic indices11that correspond to a change of the phenotype features14analyzed that the correlation13exists in the phenotype features14, from the obtained data, using the analyzed correlation13, and screens the administered drugs.

The embodiments of the present disclosure can be written as computer programs and can be implemented in general-use digital computers that execute the programs using a computer-readable recording medium. The structure of data used in the embodiments of the present disclosure can be recorded on the computer-readable recording medium by using several units. Examples of the computer-readable recording medium include magnetic storage media (e.g., ROM, floppy disks, hard disks, etc.), optical recording media (e.g., CD-ROMs, or DVDs), and storage media (e.g., transmission through the Internet).

As described above, according to the one or more of the above embodiments of the present disclosure, since the efficacy of various types of drugs can be precisely screened using image data obtained from samples regarding an abnormal tissue (a cancer, a tumor, and the like) extracted from a patient, the present disclosure can be used in developing new drugs, preventing a disease, or providing an optimum treatment method to a patient at an early stage of a disease.