KR20160146353A - Markers for predicting pharmacodynamic response to imatinib and predicting method using thereof - Google Patents

Abstract

The present invention relates to a marker for predicting a drug reaction to an imatinib and a method for predicting a drug reaction using the same. According to the present invention, twelve types of uria metabolomes provide information required for predicting a drug reaction of an object before imatinib delivery to provide an optimum therapy to the object, thereby providing an efficient therapy environment in many different areas such as a time, a cost, etc.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a drug for predicting drug response to imatinib, and a method for predicting drug response using the same,

The present invention relates to a marker for predicting a drug response to imatinib and a method for predicting a drug response using the marker. More particularly, the present invention relates to a marker for predicting drug response to imatinib, A marker composition for predicting drug response to imatinib comprising at least one urinary metabolite selected from the group consisting of acetic acid, beta-sitosterol, epinephrine, sphingosine, 7-dihydrocholesterol, betaine and adenine and Lt; RTI ID = 0.0 > imatinib < / RTI >

It is well known that the effects of drugs vary depending on the individual. Factors inducing individual differences in these drug reactions include biological factors such as age, body weight, sex, and constitution of the patient, individual environmental factors such as smoking, drinking, eating habits, concomitant medication, or the pharmacokinetics And genetic factors that affect their physical or pharmacodynamic properties. Taking into consideration the individual differences of the drug reactions, the selection of the optimal treatment for individual patients is a central theme of personalized medicine. In other words, conventionally, the treatment method has been applied uniformly according to the type and degree of the disease, such as "dyslipidemia in hypertension ". However, as medical technology has developed and various treatment methods have been applied to a single disease, Development of customized medicine that can select a suitable treatment method for the patient is required depending on the patient's sex, age, constitution, occupation, local environment, and the like. For example, if a patient with a specific illness arrives at the hospital, consult a doctor and refer to the symptoms and the doctor will prescribe the medication or provide appropriate treatment. Thus, when a doctor prescribes a medication to a patient, the physician's subjective judgment based on the result of the diagnosis prescribes the appropriate medication. In some cases, the prescription drug may not show appropriate therapeutic effect. In such a case, Because they may experience trial and error, there is a need to develop customized medicine to minimize these unnecessary trial and error.

The term personalized medicine was first mentioned in 1998, and it is the term that has been said to be the most important key word in modern medicine, even though its history is very short, first introduced to Medline after 1999. The term 'customized medicine' now includes a wide range of content and meaning, and many terms are used today, which are referred to by the same or similar terms, such as Evidence Based Medicine, Customized medicine, omics-based medicine, intermediary medicine, system medicine, etc. are referred to as related terms. Although its definition is not clearly established, it is defined as 'medicine that selects the most appropriate treatment that predicts the greatest therapeutic effect and minimum drug adverse reaction based on an individual's genetic, environmental, and biological characteristics' You can do it.

This customized medicine can ultimately be applied to all diseases, but in order to reduce the time and cost burden in the treatment of diseases requiring long term illnesses, it is necessary to predict the response of the patient to the drug, It plays an important role.

In recent years, metabolomics, one of the "-omics" types, has been used as a tool for studying low-molecular metabolites produced through specific biological processes, including genomics (genomics) and proteomics (proteomics) Is a systematic and comprehensive study of the profiles of students. Specifically, the term "metabolism" refers to a collective study that re-interprets metabolism networks by systematically identifying and quantifying the behavior and changes of metabolites in cells or tissues, and associating metabolic groups with various physiological and pathological conditions from the results . Metabolism is the only field of study in which the relationship between the gene expression phenotype and the intracellular changes that can not be interpreted solely by the analysis of the protein body is examined through the whole metabolic network and the cause of the change of end product is analyzed through the obtained results. In addition to genomics, transcriptional biology, and proteomics, studies are underway to investigate the structure of complex living organisms by synthesizing information on cellular functions obtained through metabolic profiling.

Metabolic studies are based on the understanding of metabolic changes as a network model, the ultraprecision analytical technique for detecting and identifying changes in the metabolome, and the statistical analysis for correlating the results with the physiological state of the living body. Metabolite profiling researches systematically identify and quantify the behavior and changes of metabolites in cells or tissues. By associating metabolism groups with physiological states, they understand and re-interpret metabolic networks, It is a new paradigm to understand as a network model. Profiling research, which is an ultraprecision analytical technique, can be divided into global (non-targeted) profiling, which analyzes the metabolites as a whole, and targeted profiling, which analyzes specific metabolites such as lipids and hormones.

There are various metabolites (or metabolites) associated with various metabolic pathways in the body, but they do not provide meaningful information in predicting disease or drug response, and various metabolites are organically linked It is difficult to obtain meaningful information by deriving a specific relationship between metabolizers because it provides comprehensive information.

Therefore, the present inventors studied the method of realizing the customized drug prescription based on the metabolomics, and analyzed the profile of the specific 12 metabolites contained in the urine, so that the drug response of the patient before the imatinib administration And the present invention has been completed.

It is therefore an object of the present invention to provide a pharmaceutical composition which is useful for the treatment and / or prophylaxis of diseases such as tyramine, orotidine, L-histidine, 16 beta -hydroxyestradiol, thiamine, (3-indoleacetic acid), beta-sitosterol, epinephrine, sphingosine, 7-dehydrocholesterol, betaine and adenine The present invention provides a marker composition for predicting drug response to imatinib comprising at least one urinary metabolite selected from the group consisting of

Another object of the present invention is to provide a method for the preparation of imatinib from the urine samples of individuals prior to administration of tymine, orotidine, L-histidine, 16? -Hydroxyestradiol, Thiamine, 3-indoleacetic acid, beta-sitosterol, epinephrine, sphingosine, 7-dehydrocholesterol, beta- Wherein the method comprises analyzing at least one metabolite profile selected from the group consisting of betaine and adenine. The present invention also provides a method for providing information necessary for predicting drug response to imatinib, comprising analyzing at least one metabolite profile selected from the group consisting of betaine and adenine.

In order to accomplish the object of the present invention, the present invention provides a pharmaceutical composition comprising tyramine, orotidine, L-histidine, 16β-hydroxyestradiol, thiamine ), 3-indoleacetic acid, beta-sitosterol, epinephrine, sphingosine, 7-dehydrocholesterol, betaine ) And adenine. ≪ Desc / Clms Page number 2 > < RTI ID = 0.0 >

In order to achieve another object of the present invention, the present invention provides a pharmaceutical composition comprising tyramine, orotidine, L-histidine, 16? -Hydroxyestrase Dihydrocholesterol, 16-hydroxyestradiol, thiamine, 3-indoleacetic acid, beta-sitosterol, epinephrine, sphingosine, 7-dihydrocholesterol Comprising analyzing at least one metabolite profile selected from the group consisting of 7-dehydrocholesterol, betaine, and adenine, to provide information necessary for predicting drug response to imatinib do.

Hereinafter, the present invention will be described in detail.

The present inventors have found that in urine metabolites, tyramine, orotidine, L-histidine, 16β-hydroxyestradiol, thiamine, 3-indole acetic acid 3-indoleacetic acid, beta-sitosterol, epinephrine, sphingosine, 7-dehydrocholesterol, betaine, adenine, Of urine metabolism can be used as a marker for predicting the drug response to imatinib of an individual, which is disclosed for the first time in the present invention. This is well illustrated in the embodiments of the present invention.

Accordingly, the present invention provides a pharmaceutical composition comprising tyramine, orotidine, L-histidine, 16? -Hydroxyestradiol, thiamine, 3-indoleacetic acid 3-indoleacetic acid, beta-sitosterol, epinephrine, sphingosine, 7-dehydrocholesterol, betaine and adenine. Wherein the at least one urinary metabolite is selected from the group consisting of: < RTI ID = 0.0 > (I) < / RTI >

As used herein, the term "marker" generally refers to an analyte that is detectable differentially from a sample to be analyzed and that displays the specific state of the cell or individual, including the disease state. The analyte is detectable differentially if it can be quantitatively or qualitatively distinguishable from a cell or an individual.

The term "imatinib " of the present invention means a compound that chemically reacts with 4- (4-methylpiperazin-1-ylmethyl) -N- [4- 4-yl) pyrimidin-2-yl (4-methylpiperazin- 1 -ylmethyl) -N- [4-methyl- -amino) phenyl] -benzamide). The above imatinib is known as a therapeutic agent for treating chronic myelogenous leukemia by specifically inhibiting the activity of tyrosine kinase by binding to the ATP binding site of tyrosine kinase whose expression is induced by the Bcr-Abl gene, and its pharmacologically acceptable One type of salt, imatinib mesylate, is marketed under the trade name Gleevec (TM), Novartis Pharmaceuticals, USA. In addition, other types of tyrosine such as platelet-derived growth factor receptor beta (PDGF-beta), Akt (protein kinase B), extracellular regulated kinase 1 and 2 (ERK 1 and ERK2) It is known that it exhibits an inhibitory effect against kinase. Concretely, with respect to the efficacy of imatinib (Gleevec), the Food and Drug Administration-approved disease (indications) include susceptibility to chronic myelogenous leukemia, gastrointestinal stromal tumors (GIST) The following diseases associated with tyrosine kinase include the following diseases in which no existing therapeutic agent or therapeutic regimen fails or there is no clinical treatment method with clear benefit: (PDGFR) gene rearrangement in adult patients with myelodysplastic syndrome / myeloproliferative disorder, FIP1L1-PDGFRα rearrangement confirmed in adult patients, hyperammonemic syndrome / chronic eosinophilic leukemia, resection in adult patients Recurrent or metastatic elevated dermal fibrosarcoma that is not possible.

The term " drug response (sex) " means a state change in the symptom of the affected part is improved by the drug in the individual suffering from the specific disease. Specifically, in the present invention, the drug is imatinib, and the specific disease is not particularly limited as long as it is a disease known to have therapeutic efficacy for imatinib, for example, chronic myelogenous leukemia, gastrointestinal stromal tumor (GIST) , And the following diseases associated with imatinib-susceptible tyrosine kinases: the following diseases in which no existing therapeutic agent or therapeutic regimen fails or there is no clinical treatment method with definite benefit; (PDGFR) gene rearrangement in adult patients with myelodysplastic syndrome and myeloproliferative disorders, hyperacidity syndrome and chronic eosinophilic leukemia with FIP1L1-PDGFRα rearrangement confirmed in adult patients, resection in adult patients Recurrent or metastatic elevated dermal fibrosarcoma that is not possible.

 Therefore, the drug response of the present invention specifically includes chronic myelogenous leukemia, gastrointestinal stromal tumors (GIST), myelodysplastic syndrome and myeloproliferative diseases in which specific gene rearrangement has been confirmed, hyperacidity syndrome in which specific gene rearrangement has been confirmed, Eosinophilic leukemia, < RTI ID = 0.0 > eosinophilic < / RTI > leukemia, and some raised skin fibrosarcoma.

The 'reaction' may be understood as susceptibility or sensitivity, and the terms are used interchangeably herein. Thus, the presence of a drug response (or susceptibility) to a particular drug (imatinib) means that the individual is more likely to exhibit therapeutic efficacy against the drug than an individual determined to have no drug response (susceptibility).

The present invention also relates to the use of a compound of formula (I) or a pharmaceutically acceptable salt thereof in the manufacture of a medicament for the treatment of tymine, orotidine, L-histidine, 16? -Hydroxyestradiol, thiamine thiamine, 3-indoleacetic acid, beta-sitosterol, epinephrine, sphingosine, 7-dehydrocholesterol, betaine and analyzing at least one metabolite profile selected from the group consisting of betaine, adenine, and the like.

As used herein, The term "subject" refers to a human ( Homo sapiens ) that is used interchangeably with a "subject" or "subject" and may require the administration of imatinib in the present specification. Preferably, And most preferably a Korean, especially a Korean male.

The term 'urine' refers to a liquid that is wasted in the blood and water is filtered from the kidney to be entrapped in the bladder and discharged out of the body through the urethra. In the present invention, the subject (subject) may need to control dietary and lifestyle prior to and during the collection of the urine sample, for example, the restriction (prohibition) of caffeine-containing foods before and during urine sample collection, , High-fat dietary restrictions, high-salt dietary restrictions, drug intake restrictions, smoking cessation, abstinence, fasting or fasting. The control of the dietary and living conditions may be performed 10 to 168 hours before the time of urine sample collection, and a person skilled in the art will be able to determine the appropriate time according to each restriction requirement.

The urine sample can be used for analysis as it is when it is first collected from an individual, and an optional pretreatment process such as filtration, concentration, purification, protein preparation is additionally performed for ease of analysis, processing, . Preferably, the urine sample of the present invention may be one that has undergone deproteinization treatment of the originally collected urine. The proteolytic treatment may be performed by a known protein removal method, for example, acetonitrile treatment, heating (boiling) method, salting-out, trichloroacetic acid method, picric acid picric acid method, perchloric acid method, Folin-Wu method, Somogyi method, metaphosphate method, collodion membrane or cellophane acetyl cellulose ) Membrane or the like, but the present invention is not limited thereto. Most preferably, the proteolytic treatment of the present invention may be by protein precipitation by addition of acetonitrile.

The term " metabolite " is a generic term for a substance produced as a result of a change in a substance in a living body. The metabolite is a low molecular substance and is an intermediate or a final product of metabolic processes. Metabolites are generally quantifiable small molecules that best represent the phenotype of a living body. In the present invention, thiamine, orotidine, L-histidine, 16? -Hydroxyestradiol, thiamine, 3-indoleacetic It is characterized by complex analysis of one or more of the 12 metabolites: acid, beta-sitosterol, epinephrine, sphingosine, 7-dihydrocholesterol, betaine and adenine.

The term "profile" in the present invention can broadly refer to any information about a marker. The information may be qualitative (e.g., presence or absence) or quantitative (e.g., level, relative level, signal intensity, or quantity).

Likewise, the term " metabolite profile " in the present invention is meant to include qualitative or quantitative information on the above-mentioned 12 metabolites. Here, the metabolite profile indicates that each metabolite can show meaningful information separately, and that there is a significant difference between two or more metabolic interrelationships (eg, although they are not significant in different data, Information can be displayed. The metabolite profile may be understood as data obtained through metabolite profiling techniques known in the art and the data may represent the eigenvalues of the subject material depending on the measuring or analytical instrument.

The term " metabolite profiling " refers to the act of collecting and processing property information (or data) with respect to a desired metabolite, wherein in some embodiments of the invention, Mass spectrometry, mass spectrometry, matrix-assisted laser desorption / ionization-time-of-flight (MALDI-TOF) mass spectrometry, mass spectrometry, high performance liquid chromatography Spectroscopy, electrospray ionization (ESI) mass spectrometry, surface-enhanced laser desorption / ionization-time (SELDI-TOF) mass spectrometry, quadrupole-time (Q-TOF) mass spectrometry, Mass spectrometry (APPI-MS), Fourier transform mass spectrometry (FTMS), matrix-assisted laser desorption / ionization-Fourier transformation-ion cyclotron resonance MALDI-FT-ICR) mass spectrometry, secondary ion mass spectrometry (SIMS), radiation immunoassay, microfluidic chip-based assays, detection of fluorescence, detection of chemiluminescence, or a combination thereof .

For metabolic studies, it is necessary to understand the changes of metabolites as a network model, and to analyze and identify the changes of the metabolome, and statistical analysis to correlate the results with the physiological state of the living body It becomes the basis. Metabolite profiling researches systematically identify and quantify the behavior and changes of metabolites in cells or tissues. By associating metabolism groups with physiological states, they understand and re-interpret metabolic networks, It is a new paradigm to understand as a network model. Profiling research, which is an ultraprecision analytical technique, can be roughly divided into global (non-targeted) profiling, which analyzes the metabolites as a whole, and targeted profiling, which analyzes specific metabolites such as lipids and hormones.

In the present invention, the metabolite profile is characterized in that targeted profiling is performed on the 12 metabolite materials. The targeting profiling analyzes the kinds and quantitative changes of a desired specific metabolite among cells or total metabolites in vivo. In the present invention, the analysis of the detection and the quantitative change of the above 12 metabolites is performed It is a feature. Metabolism profiling performed in the present invention is not particularly limited as long as it is by the targeted profiling method known in the art.

Preferably, in the present invention, the 12 metabolite profiles may be determined by mass spectra for the metabolites. Therefore, the present invention relates to a method for separating (collecting) the metabolites from a urine sample of an individual (subject), analyzing the metabolites by a mass spectrometer, and using each metabolite profile information obtained from spectroscopic peaks, Predicting the drug response of an individual prior to administration. For example, urine metabolism profiling using mass spectrometry techniques such as LC-MS can provide a comprehensive and specific metabolite phenotype. That is, the metabolic profile of the present invention may be obtained by analyzing a urine sample of an individual prior to administration of imatinib by a mass spectrometric technique on the metabolite. Therefore, in the present invention, the profiling of the above 12 metabolites may be a series of information processing processes using peak intensity data for each metabolite derived by mass spectrometry.

Such mass spectrometry is well known in the art (Burlingame et al., Anal. Chem. 70: 647R-716R (1998); Kinter and Sherman, Protein Sequencing and Identification Using Tandem Mass Spectrometry, Wiley-Interscience, New York (2000)). The basic process associated with mass spectrometry is the generation of gaseous ions derived from the sample and their mass measurement.

The movement of gas-phase ions can be precisely controlled using the electromagnetic field generated within the mass spectrometer. The movement of ions in these electromagnetic fields is proportional to the m / z of the ions, and m / z forms the basis for measuring the mass of the sample. The transfer of ions in these electromagnetic fields makes it possible for ions to be contained and focused, which is explained by the high sensitivity of mass spectrometry. During the course of the m / z measurement, the ions are delivered to the particle detector with high efficiency, and the detector records the arrival of such ions. The amount of ions at each m / z is shown by the peaks in the graph, where the x axis is m / z and the y axis is the relative abundance ratio. Depending on the mass spectrometer, it has the ability to separate peaks between closely related ions in different levels of resolution (resolution), ie mass. Resolution (resolution) is defined as R = m / delta m (R = m / m), where m is the ion mass and delta m is the mass difference between the two peaks of the mass spectrum. For example, a mass spectrometer with a resolution of 1000 can separate ions with m / z of 100.0 from ions with an m / z of 100.1.

In general, a mass spectrometer (or mass spectrometer) has the following main components; A sample inlet, an ion source, a mass analyzer, a detector, a vacuum system, a device-conditioning system, and a data system. Differences in the sample injector, ion source, and mass analyzer generally define the type of equipment and its performance. For example, the injection port may be a capillary-column liquid chromatography source or it may be a direct probe or stage such as that used in a matrix-assisted laser deviation. For example, a common ion source is an electrospray that includes nano spray and micro spray or matrix-assisted laser escape. Exemplary mass spectrometers include a quadrupole mass filter, an ion trap mass analyzer, and a time-of-flight mass analyzer. The ion source and mass analyzer can be used in various combinations, which allows flexibility in the design of a detection protocol that is user-friendly. In addition, the mass spectrometer can be programmed to send all ions from the ion source either continuously or simultaneously to the mass spectrometer. The mass spectrometer can also be programmed to select a specific mass of ions to be sent to the mass spectrometer while blocking other ions. The ability to precisely control the movement of ions in mass spectrometers allows for greater options in detection protocols. That is, the ability to select the mass range as described above can reduce the background noise in the analysis and increase the signal to noise ratio.

Specifically, the ion forming process is the starting point for mass spectrometry analysis. Several ionization methods are available and the choice of ionization method depends on the sample being analyzed. A relatively mild ionization process, such as, for example, electron spray ionization (ESI), may be desirable. In the ESI method, a solution containing the sample is passed through a precise needle at a high potential, which creates a strong electric field due to a precise spray of highly charged drop flats directed into the mass spectrometer. Since ESI can produce directly charged molecules from solution, samples can coexist from the liquid chromatography system. For example, a mass spectrometer has an inlet for liquid chromatography (LC), such as HPLC, and the fraction flows from the chromatography to the mass spectrometer. This in-line arrangement of liquid chromatography and mass spectrometry is sometimes referred to as LC-MS.

 Other ionization processes include, for example, fast-atom bombardment (FAB), where neutral atoms of the high-energy beam collide with solid samples to cause desorption and ionization. Matrix-assisted laser desorption ionization (MALDI) is a method in which a laser pulse collides with a sample to crystallize in a UV-absorbing compound matrix. Other ionization processes known in the art include, for example, plasma and glow discharge, plasma desorption ionization, resonance ionization, and secondary ionization.

Also, some forms of mass spectrometry and mass spectrometry are described as follows, but the types of mass spectrometry and mass spectrometry applicable to the present invention are not limited to the following; Quadrupole mass spectrometry uses a quadrupole mass filter or analyzer. This type of mass spectrometer consists of four rods arranged as two sets of two electrically connected rods. The combination of the rf and dc voltages is applied to each pair of rods forming a field that causes oscillating motion of the ions as they move from the beginning to the end of the mass filter. The result of this chapter is the creation of a high-pass in a pair of rods and a low-pass filter in the other pair of rods. The overlap between the high-pass and low-pass filters leaves a limited m / z through both filters and can cross the length of the quadrupole. This m / z remains selected and stable in the quadrupole mass filter while all other m / z are in an unstable orbit and remain in the mass filter. The mass spectrum is obtained by ramping the applied field, and the increasing m / z is selected to pass through the mass filter and reaches the detector. In addition, the quadrupole can also be prepared to contain and transfer all m / z ions by applying an rf-single field. This enables the quadrupole to act as a lens or focusing system in the area of the mass spectrometer, where ion transfer is required without a mass filter.

Ion trap mass spectrometry uses an ion trap mass spectrometer. In this mass spectrometer, a field is applied and all m / z ions are initially trapped and oscillated in the mass spectrometer. The ions enter the ion trap from the ion source through a focusing device, such as an octapole lens system. The ion trap occurs in the trapping region before excitation and discharge with the detector through the electrode. Mass spectrometry is accomplished by sequentially applying a voltage that increases the amplification of the oscillation by discharging ions of m / z that rise out of the trap or into the detector. In contrast to quadrupole mass spectrometry, all ions except those with the chosen m / z are retained in the mass spectrometer. One advantage to ion traps is that they are very sensitive as long as they are careful to limit the number of ions trapped in an hour. Control of the number of ions is achieved by varying the time over which the ions are injected into the trap. The mass resolution of the ion trap is similar to that of a quadrupole mass filter, although the ion trap has a low m / z limit.

Time-of-flight mass spectrometry uses a time-of-flight mass spectrometer. For this method of m / z analysis, the ions are first given a fixed amount of kinetic energy by acceleration in an electric field (generated by a high voltage). After acceleration, the ions enter the field-free or "drift" region, where it travels at a rate that is inversely proportional to its m / z. Therefore, ions with low m / z move faster than ions with high m / z. The time required for the ions to move the length of the long-absence region is measured and used to calculate the m / z of the ions. One consideration in this type of mass spectrometry is a set of ions that are introduced and studied into the analyzer at the same time. For example, this type of mass spectrometry is well suited for ionization techniques such as MALDI to generate ions in short well-defined pulses. Another consideration is the regulation of ion-generated rate diffusion with diversity in their kinetic energy. Longer flight tube, ion reflector, or higher acceleration The use of voltage can help minimize the effects of speed spreading. The flight time mass spectrometer has a high level of sensitivity and a wider m / z range than a quadrupole or ion trap mass spectrometer. In addition, since mass spectrometer scanning is not required, fast data can be obtained with this type of mass spectrometer.

As another example, the mass spectrometers may be provided in the form of a mass spectrometer connected to another mass spectrometer, which is referred to as tandem mass spectrometry (MS / MS).

A chromatography step may be preceded prior to use of the mass spectrometer to separate the metabolite material from the urine sample. Chromatographic mass spectrometry techniques are available for separating target metabolites contained in a complex mixture. The chromatographic procedure may further be used to remove salts, enzymes, or other buffering components other than the subject metabolites of the present invention, for example, by in-line or off-line reversed-phase HPLC Desalting of the sample using the column can be used to increase the efficiency of the ionization process and thus increase the sensitivity of the detection by mass spectrometry. The chromatography may be performed using gas chromatograph (GC), liquid chromatography (LC), or high performance liquid chromatography (LC), depending on the type of mobile phase, Liquid Chromatography (hereinafter referred to as HPLC), and Supercritical Fluid Chromatography. A more detailed chromatographic method according to the separation principle is known in the art, and it is obvious to those skilled in the art that the present invention is not limited to the chromatographic types exemplified in this specification, but can be selectively applied as needed.

In the present invention, various mass spectrometry methods known in the art can be performed by appropriately selecting and performing various mass spectrometry methods known in the art as described above, but the present invention is not limited thereto. However, the 'chromatography-mass spectrometry technique' . ≪ / RTI > For example, using a liquid chromatography-mass spectrometer (LC-MS) or a gas chromatography-mass spectrometer (GC-MS). Preferably, the mass spectrometry of the present invention may be LC-MS (or HPLC-MS) or LC-MS / MS and most preferably the MS is an ion source of electrospray ionization ion-trap mass spectrometry (LC-ESI-MS) or liquid chromatography-electrospray ionization-ion trap mass spectrometry (LC-ESI-MS) . Commercial products for the mass spectrometer of the present invention include, but are not limited to, for example, Finnigan (TM) LXQ (TM).

The metabolic profiling of the present invention using the mass spectrometer and its information processing process can be illustrated as follows; E.g The peak intensities (peak values may be the abundance or ratio of the molecules of interest) are measured using LC-MS analysis for some or all of the 12 urine metabolites from the patient's urine samples , The peak intensity is normalized by performing log ₂ normalization using a quantile normalization algorithm and then the value obtained by substituting the normalized metabolite intensity into a specific formula And the like can be used. At this time, it can be determined that there is or is no drug reaction to imatinib when the value derived from substituting into the specific calculation formula is larger or smaller than the reference value based on a certain reference value.

The 12 kinds of urinary metabolites described in the present invention provide information necessary for predicting drug response of an individual (subject) prior to the administration of imatinib, so that it is possible to provide an optimal treatment method for an individual, The effect of providing an efficient treatment environment is excellent.

Fig. 1 shows a pretreatment process of a urine sample obtained from a subject.
Figure 2 shows chromatograms of various metabolites analyzed from urine samples, wherein the 12 metabolite markers of the present invention were marked in red.
Figure 3 shows a principle component analysis (PCA) scatter plot obtained from a subject's urine and quality control (QC) samples. The green dot ( ● ) represents the sample before imatinib administration, the blue point ( ◆ ) represents the sample after imatinib administration, and the red dot ( ■ ) represents the QC sample. A well-packed QC sample means that the assay is valid and that there is no major instrument bias in LC / MS analysis.
Figure 4 shows PLS-DA modeling of urine LC / MS metabolite data for selection of metabolites with high correlation with drug response of imatinib. Here, each point represents a urine sample of the collected subject, a black dot (I) represents a sample before imatinib administration, and a red point (X) represents a sample after imatinib administration, clearly separating before and after administration.
FIG. 5 shows R ² (fitness) and Q ² (predictability of model) values for internal validation of the PLS-DA model of the present invention, and checking for accidental correlation and data over-fitting It is for this reason. The R ² value was 0.746, which was relatively high, and the Y-axis was valid at -0.0625.

Hereinafter, the present invention will be described in detail.

However, the following examples are illustrative of the present invention, and the present invention is not limited to the following examples.

<Experimental Method>

1) Research methods and eligibility

A prospective exploratory study was conducted and 20 healthy male volunteers who received informed consent were included. Patients who were younger than 20 years of age or had a clinically significant disease or history of cardiovascular disease, liver, kidney, hematologic tumor, respiratory system, endocrine system, central nervous system, mental disease or musculoskeletal system were excluded. All subjects received oral imatinib 400 mg once daily during the study period.

2) Profiling LC / MS-based random metabolites

LC / MS analysis was performed according to the following procedure: urine sample preparation, LC / MS analysis, peak detection and alignment, peak intensity normalization ), And multivariate statistical analysis. An LC / MS analysis system consisting of Alliance HPLC (Waters Corporation, Milford, Mass.) Connected to a linear ion-trap mass spectrometer Finnigan ™ LXQ ™ (Thermo Electron, Waltham, The Finnigan ™ LXQ ™ ion trap mass spectrometer includes a syringe pump, a divert / inject valve, an electrospray ionization (ESI) source, an MS detector, and an Xcalibur data system.

3) Preparation of Urine Sample

For metabolism analysis, urine samples were collected for 12 hours prior to imatinib and 12 hours after imatinib, based on the time of imatinib administration (0 h). For the urine sample pretreatment process, an internal standard (IS) was used to prepare L- Phenyl-d ₅ -alanine, methylprednisolone and progesterone-d9 (progesterone-2,2,4, 100 μl of each urine sample was mixed (vortex, 2 min) in 200 μl of acetonitrile (AN) containing 6,6,17α, 21,21,21-d ₉ and incubated at -20 ° C. for 30 minutes And the pretreatment was carried out by an altar bag method. Pretreatment The samples were centrifuged at 4 占 폚 at 13200 rpm for 10 minutes, and 5 占 퐇 of the supernatant was used for LC / MS analysis (Fig. 1).

4) LC / MS analysis

5 [mu] l of the prepared sample was injected into the LC / MS system. Phenomenex Kinetex C18 (150 X 2.1 mm i.d., 2.6 μm particle size) column was used for the separation of urine metabolites at 40 ° C. Distilled Water (DW) (mobile phase A) containing 0.1% formic acid and acetonitrile (mobile phase B) containing 0.1% formic acid were used as a mobile phase. The gradient of the mobile phase A and the mobile phase B was used in a mixture of 90% of mobile phase A up to 0-3 min, 20% of mobile phase A up to 12 min, 10% of mobile phase A up to 29 min and 90% / min, an injection amount of 5 mu L, and an analysis time of 35 minutes.

The mass spectrometer was 150-2000 m / z with an electro-spray ionization voltage of 5 kV, capillary temperature of 280 ° C, sheath gas flow of 12 units, tube lens ) 120 V and a full-scan positive-ion mode at a capillary voltage of 38 V. The full- To avoid systematic variations, all samples were randomly used prior to LC / MS analysis and quality control (QC) samples were used with internal standards in all samples.

5) Peak detection and alignment

To convert the chromatogram information of metabolites into a dataset suitable for multivariate statistical analysis, data preprocessing uses XCMS software version 1.16.1 to perform background subtractions, peak filtering and detection detection, and peak alignment. The program converts a two-dimensional data matrix (a pair of m / z and T) that can easily process 3D LC / MS data (m / z, retention time (T), ion intensity). This processing step is done using default XCMS parameters as follows (http://www.bioconductor.org/packages/release/bioc/html/xcms.html): 0.1 m / z for peak picking, peak-width at half-maximum of 30 s, and peak group bandwidth of 10; 10. The peak of each metabolite detected after peak-detection and de-convolution by XCMS was identified by its m / z and T, respectively. Blank chromatograms were used as background subtraction in the sample chromatogram using the Xcalibur software utility (Thermo Electron, 2.0.SR2). The files were then converted to NetCDF format using the Xcalibur file-converting utility (version 2.0).

6) Data normalization

The measured peak intensities are quantized using a quantile normalization algorithm of the preprocessCore package (R) (version 2.9.0) (Bolstad BM et al., Bioinformatics 2002; 19 (2): 185-193) log2 intensity to remove the systematic variation. The normalized peak intensities were then converted to CSV files to produce annotated peak data tables according to unique peak identifiers and normalized peak intensities with m / z and T were used for statistical analysis . The variation of total T and peak area (coefficient of variation, CV) was not more than 20%. Peaks with unacceptable variations in QC samples (CV > 20%) and peaks corresponding to m / z of drug or drug metabolites were excluded from the converted peak data.

7) Multivariate data analysis

PCA (principal component analysis) and PLS-DA (partial analysis) were performed using SIMCA P + software (version 11; Umetrics, Uppsala, Sweden) in order to find the major metabolites associated with drug response prediction of imatinib. multivariate analysis of least squares discriminant analysis. The PLS-DA model was internally validated by applying 20 random permutation tests (20 random permutation tests), and the fitness (&lt; RTI ID = 0.0 &gt; R ² ) and prediction ability (Q ² ).

6) PCA and PLS-DA models

PCA and PLS-DA modeling to find major peaks that show differences before and after imatinib administration. And major metabolites were identified by comparing major peaks with standard compounds of the metabolites. The variable influence on projection (VIP) was also essentially tested to determine and analyze the specific metabolites or patterns observed in the data that most contributed to the drug response before and after the administration of imatinib. We used VIP values to select metabolic profiles (VIP ≥ 1.0) that are most relevant to predicting the drug response of imatinib in the PLS-DA model.

7) Identification of metabolites

MS / MS spectra of m / z and T were obtained by applying LC / MS / MS based method (tandem mass spectrometry) to select major metabolites (VIP ≥ 1.0). Then, Hmp [http: //www.hmpdacc-resources, Otr / CRi-bin / hmp cataloR / main CRi] and Metlin [Metabolite and Tandem MS Database (http://metlin.scripps.edu/index.php); Candidate metabolites were identified by matching MS / MS spectra of metabolite ions to metabolic databases and related literature such as Smith CAM et al., Ther Drug Monit 2005; 27 (6): 747-751. Due to some limitations of the MS technique and limited availability of metabolic resources, not all selected metabolites could be named.

Example 1. Characteristics and study methods of subjects

20 healthy adult men with or without clinically significant disease corresponding to cardiovascular, liver, kidney, blood tumor, respiratory system, endocrine system, central nervous system, mental disease and musculoskeletal system Respectively. The mean age (years) of the subjects was 24.1 ± 1.7, height (cm) was 175.6 ± 4.8, and body weight (kg) was 71.9 ± 8.3. All subjects were admitted to a designated room the night before the imatinib medication to ensure strict control of diet and activities that could have a significant impact on the metabolism, and during the hospitalization period, any food other than meals and drinking water provided at the clinical trial center I did not allow it. In particular, caffeinated beverages were prohibited during the hospital stay, and drinking and smoking were restricted from one day before admission until discharge. They were also instructed to fast for at least 10 hours before imatinib. The pre-dose was performed for 12 hours before 8:00 am before dosing and 8:00 am before dosing on the next day. All subjects received oral imatinib 400 mg once a day at 8 am on the day of dosing. After dosing, urine was collected for 12 hours from 8 am to 8 pm (post-dose).

Example 2. Profiling of urine metabolites before and after administration of imatinib

Urinary samples collected before and after administration of imatinib in 20 healthy subjects were used to obtain a randomized metabolite profile. After excluding peaks corresponding to drugs and drug metabolites, 862 common metabolite ions were obtained as metabolite datasets from endogenous metabolites with respective intensities (peak regions). The PCA plots show that the quality control values are well gathered in one place, and the reproducibility and stability of all the analyzed samples were confirmed (see FIG. 3). PCA analysis was performed to confirm that the samples before and after the imatinib dose were well classified by group and were again classified by PLS-DA modeling.

Example 3. Screening for Metabolites with High Correlation with Therapeutic Response of Imatinib

PCA and PLS-DA models were used to separate before and after the imatinib dose, which clearly showed good separation. The PLS-DA score shows two groups before and after administration of imatinib between the subject samples (see FIG. 4). This affected the formation of metabolites due to the imatinib dose, which means that the metabolites before and after the administration differ markedly. Using the PLS-DA model, candidate metabolites with the highest correlation with the therapeutic response of imatinib were selected. The PLS-DA model showed high predictability (Q ² = 0.974). To analyze the most significant metabolites, VIP values were used to reflect the relative importance of each metabolite in predicting the therapeutic response of imatinib.

The PLS-DA model was confirmed by internal validation with a random permutation test (Fredrik Lindgren BH, et al., Journal of Chemometrics 1998; 10 (5-6): 521-532). As shown in FIG. 5, the fit (R ² ) and predictability (Q ² ) of the permuted model (left end) are much smaller than the original model (right end). The negative intercept (-0.0625) of the Q ² regression line also indicates that there is no over-fitting of the data in this model. These results are the result of verifying the validity of the PLS-DA model and the selected metabolite markers to predict the therapeutic response of imatinib.

Example 4. Identification of Major Metabolites Associated with Therapeutic Response of Imatinib

In order to identify metabolites with a variable importance plot (VIP) value of 354 selected metabolites, 30 metabolites were identified by matching the MS / MS spectra of the metabolite ions to the metabolic database and related literature. MS / MS data and retention time data (see FIG. 2) of the last 12 metabolites were confirmed by LC / MS / MS based method. The functions associated with the 12 major metabolites identified are summarized in Table 1 in descending order of VIP.

Metabolite Retention time (min) VIP Pathway / Function 7-dehydrocholesterol 16.75 1.8 Steroid Biosynthesis Thiamine 2.48 1.5 Thiamine metabolism Tyramine 2.67 1.3 Tyrosine metabolism Betaine 2.63 1.3 Betaine metabolism Adenine 2.55 1.3 Purine metabolism 3-indoleacetic acid 14.37 1.2 Tryptophan metabolism Epinephrine 2.9 1.2 Catecholamine biosynthesis Sphingosine 16.12 1.2 Sphingolipid metabolism Orotidine 17.6 1.1 Pyrimidine metabolism L-histidine 2.35 One Histidine metabolism 16β-hydroxyestradiol 19.57 One Glycerolipid metabolism Beta-Sitosterol 19.83 One Steroid biosynthesis

As described above, the present invention relates to a marker for predicting a drug response to imatinib and a method for predicting a drug response using the same, wherein the 12 kinds of urine metabolites described in the present invention are administered to an individual ), It is possible to provide an optimum treatment method for an individual, thereby providing an efficient treatment environment in various aspects such as time and cost, and thus, there is a high possibility of industrial application.

Claims (4)

Tyramine, orotidine, L-histidine, 16? -Hydroxyestradiol, thiamine, 3-indoleacetic acid ), Beta-sitosterol, epinephrine, sphingosine, 7-dehydrocholesterol, betaine, and adenine. A marker composition for predicting drug response to imatinib, comprising a urinary metabolite.
Prior to the administration of imatinib, tyramine, orotidine, L-histidine, 16 beta -hydroxyestradiol, thiamine, 3 -Indoleacetic acid, beta-sitosterol, epinephrine, sphingosine, 7-dehydrocholesterol, betaine, and adenine and analyzing at least one metabolite profile selected from the group consisting of adenine. &lt; RTI ID = 0.0 &gt; 18. &lt; / RTI &gt;
3. The method of claim 2, wherein the metabolite profile is determined by a mass spectrum for the metabolite.
3. The method of claim 2, wherein the metabolite profile is obtained by analyzing a urine sample of a subject prior to imatinib administration by a chromatographic-mass spectrometric technique.

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