KR101896637B1 - Method for prediction of QT prolongation syndrome by drug - Google Patents

Abstract

The present invention relates to a new long QT interval prediction model capable of evaluating a long QT syndrome by a drug, and a method for predicting a long QT syndrome using the same. More specifically, a long QT interval prediction model utilizing an hERG IC50 value of a drug, the maximum blood concentration (Cmax) of the drug, and under-curve area (AUCinf) of the drug has been developed, and the accuracy in prediction according to a result of predicting a long QT syndrome by a drug using the long QT interval prediction model is high, thereby performing a new reliable long QT test in a new drug development process by using a method for predicting a long QT syndrome.

Description

Methods for predicting QT prolongation syndrome by drug

The present invention relates to a new QT interval extension prediction model and a QT extension syndrome prediction method using the same, which can more accurately evaluate drug-induced QT extension syndrome in the course of drug development.

In the early 1990s, terfenadine, an antihistamine, caused heart attacks and caused 350 deaths. Astemizole, another antihistaminic agent, was also withdrawn from the market due to QT prolongation syndrome and torsades de pointes, and other drugs except for cardiovascular drugs, And sudden death (Crumb W. et al., 1999).

Regulatory authorities in the US, Europe, and Japan have focused on the cardiovascular side effects of drugs that can lead to sudden death, especially the QT prolongation syndrome. They feel the need to evaluate and predict the cardiovascular side effects of existing drugs and new drugs under development. . The ICH Guidelines recommend in vitro and in vivo studies to assess the risk of cardiovascular side effects of drugs. .

The in vivo study predicts the QT prolongation syndrome by measuring the QT interval (QT interval) of the animal after drug injection and comparing and evaluating the results with the positive control group. The QT interval is the interval between the QRS complex, which signifies depolarization of the ventricle in the ECG, and the end of the T wave, which is the repolarization of the ventricle. The prolongation of the QT interval is the depolarization of the heart, And the time at which the heart is repolarized after the contraction becomes longer than usual. This process can lead to early-after depolarization (EAD), which in turn increases the likelihood of ventricular fibrillation. Thus, QT interval is one of the important factors involved in the pathogenesis of polymorphic ventricular tachycardia, syncope, and sudden death. The QT interval changes according to the heart rate, which becomes shorter when the heart rate is faster, and becomes longer when the heart rate is slower. Therefore, when assessing the QT interval for cardiac events, use the corrected QT interval (QT interval).

In vitro studies have shown that the inhibitory activity of the human ether-a-go-related gene (hERG), which indicates the activity of the potassium channel of the heart, that is, the IC obtained from the hERG assay ₅₀ values (Sanguinetti MC et al., 1995). Inhibition of the hERG channel by drugs inhibits cardiac repolarization by the transport of potassium ions, leading to an initial depolarization state due to the introduction of calcium ions (January CT et al., 1991).

The classification of whether QT interval extension according to hERG IC ₅₀ value is not accurately presented in the current guidelines, but there are standards used by many papers and pharmaceutical companies. According to this classification criteria, development will be discontinued if the hERG IC ₅₀ value is less than 0.1 μM and development beyond 10 μM will be allowed except for the possibility of QT interval extension.

However, there is a limitation in evaluating whether the QT interval is prolonged according to the IC ₅₀ value obtained through the hERG assay, and it is low in relation to the prediction of QT prolongation by the drug in vivo . This low correlation has a major impact on drug development progress in new drugs being developed (Fermini B., et al., 2003). For example, taking into account the pharmacokinetics parameters of the new substance does not affect QT interval prolongation, it is possible that the hERG IC ₅₀ value may cause development to be discontinued, while the QT prolongation syndrome The development of the drug may be problematic because it is judged that the QT interval extension does not occur only by the hERG IC ₅₀ value of the drug. Thus, it can be seen that the hERG assay alone is not sufficient for judging drug safety.

Therefore, in order to solve this problem, a safety margin value has been proposed in which the hERG assay value is corrected using the maximum blood concentration (C _max ) of the drug, which is a pharmacokinetic parameter of the drug. The safety inverse value is an evaluation method that takes into account the pharmacokinetic parameters of the drug by correcting the IC ₅₀ value obtained through the hERG assay by dividing by the C _max value (free C _max ) of the unconjugated blood drug. Drugs with a safety negative value of less than 30 on a 30 basis are drugs that are expected to exhibit a QT interval prolongation, values greater than 30 are classified as drugs that are not expected to show a QT interval prolongation, (Yao X., et al., 2008) are classified as safe drugs for QT interval prolongation. However, despite the fact that pharmacokinetic parameters of the drug were introduced and corrected, there is a limit to predicting the extent of QT interval prolongation in an in vivo test or clinical trial.

Therefore, the present inventors have found that the hERG IC ₅₀ value obtained in vitro and the maximum blood concentration (C _max ) and curve of the drug, which are the pharmacokinetic parameters affecting the distribution and excretion of the drug, in order to increase the prediction accuracy of the drug- The area under the curve (AUC _inf ) and the change rate of the QTc value obtained in vivo were used to develop a prediction model of QT interval extension, and the present invention was completed by confirming whether or not the QT extension syndrome was caused by the drug Could.

In the meantime, US Patent Application Publication No. 2009-0326401 discloses a method for diagnosing cardiac function disorder risk by using electrocardiogram data showing electrocardiographic changes due to treatment of a compound. In Korean Patent Laid-Open Publication No. 2013-0091574, The FT prolongation using the hERG IC ₅₀ value of the present invention and the pharmacokinetic parameter of the drug, the maximum blood concentration (C _max ) and area under the curve (AUC _inf ) of the drug, The method of predicting the syndrome has not been described.

US Patent Application Publication No. 2009-0326401, Method and system for dynamical systems modeling of electrocardiogram data, December 31, 2009. Korean Patent Laid-Open Publication No. 2013-0091574, an apparatus and method for generating atrial fibrillation prediction model, and an apparatus and method for predicting atrial fibrillation.

Crumb W. et al., QT interval prolongation by non-cardiovascular drugs: issues and solutions for novel drug development, Pharm. Sci. Technol. Today, 2, 270-280, 1999. Fermini B. et al., The impact of drug-induced QT interval prolongation on drug discovery and development, Nat. Rev. Drug Discov., 2, 439-447, 2003. January C.T. et al., Triggered activity in the heart: cellular mechanisms of early after-depolarizations, Eur Heart J., 12, 4-9, 1991. Sanguinetti M.C., et al. A mechanistic link between an inherited and an acquired cardiac arrhythmia: HERG encodes the IKr potassium channel, Cell, 81, 299-307, 1995. Yao X., et al., Predicting QT prolongation in humans during early drug development using hERG inhibition and an anesthetized guinea-pig model, British Journal of Pharmacology, 154 (7), 1446-1456, 2008.

It is an object of the present invention to provide a new QT interval extension prediction model and a QT extension syndrome prediction method which can more accurately evaluate drug-induced QT extension syndrome in a drug development process.

The invention hERG (human ether-a-go -go-related gene) of the drug and inputting the measured IC ₅₀ values; Measuring and inputting the blood drug concentration of the animal to which the drug is administered; Calculating a maximum blood concentration (C _max ) and area under the curve (AUC _inf ) of the drug through the inputted blood drug concentration; And applying the hERG IC ₅₀ value, the maximum blood concentration of the drug, and the area under the curve to the QT interval extension prediction model.

The QT interval extension prediction model is expressed by the following equation (2)

&Quot; (2) "

Where Base is the change in QTc interval (ΔQTc interval) when the hERG IC ₅₀ value converges to zero and α is the slope at which the maximum variation of the QTc interval decreases from the Base value as the hERG IC ₅₀ value increases. TVα is the α value considering the C _max and AUC _inf of the drug, θ _i is the representative value of α, θ ₁ is the degree of influence on the α value according to the C _max of the drug, ₂ is the degree of influence on the α value according to the AUC _inf of the drug, and η _i is the difference of α value of each drug.

The hERG IC ₅₀ value can be obtained by in vitro hERG assay.

The maximum blood concentration (C _max ) and the under-curve area (AUC _inf ) of the drug can be obtained by performing a noncomapartment analysis on the blood concentration value of the drug with time obtained in vivo.

The QT interval extension prediction model can combine the hERG IC ₅₀ value, the maximum blood concentration and the area under the curve of the drug to improve the accuracy of the QT extension syndrome prediction.

The QT prolongation syndrome prediction method can be used to evaluate QT prolongation by a drug in a cardiovascular stability test of a candidate drug.

Hereinafter, the present invention will be described in detail.

The present invention relates to a method for predicting a QT extension syndrome, and a QT interval extension prediction model expressed by the following equation (2) can be used.

&Quot; (2) "

The QT interval extension prediction model is a combination of the hERG IC ₅₀ value of the drug, the maximum blood concentration of the drug, and the area under the curve to improve the accuracy of the QT prolongation syndrome prediction.

The hERG IC ₅₀ value of the drug can be obtained via the hERG assay in vitro.

The hERG assay is a test for determining the degree of inhibition by the drug of hERG that expresses the cardiac channel of the heart (I _kr channel), and a patch clamp method and a binding assay method can be used .

In the patch clamp method, a cell line having the hEGR gene can be tested by a whole-cell voltage clamp method to obtain an IC ₅₀ value which is a concentration causing 50% of the maximum inhibitory amount of the hERG current.

In the binding assay method, the IC ₅₀ value can be obtained by observing the change in fluorescence with respect to potassium ion (K ⁺ ) using a fluorescent indicator after administering the drug to the cell membrane.

The maximum blood concentration and the area under the curve of the drug can be obtained through in vivo experiments, that is, a result of checking the blood concentration of the drug with time using an animal model.

The animal model should have the potassium channel of the heart, and representative animal models may be Beagle dog, Cynomolgus monkey and Guinea pig.

The maximum blood concentration and curved area of the drug can be obtained by performing non-compartmental analysis using the blood concentration value of the drug according to the time obtained from the animal model.

The non-compartmental analysis is a method of analyzing the blood concentration change of the drug over time without setting the compartment, and it is possible to calculate the maximum drug concentration in the blood concentration graph of the drug over time, The area under the curve can be calculated by calculating the area of the graph using the trapezoidal rule.

The maximum blood concentration of the drug means the maximum drug concentration in the blood that can be exposed to the body after drug administration.

The area under the curve means the total amount of drug that is exposed to the body after drug administration.

The QT interval extension prediction model improves the accuracy of prediction of the QT extension syndrome by combining the hERG IC ₅₀ value of the drug with the maximum blood concentration of the drug and the area under the curve.

 The QT prolongation syndrome prediction method using the QT interval extension prediction model which improves the accuracy of the prediction of the QT extension syndrome improves the reliability and prediction power of the drug-induced QT extension evaluation in the cardiovascular stability test of the candidate drug substance, During development, the number of drugs dropped from development can be reduced, the success rate of new drug development and cardiovascular stability can be increased, and the cost of new drug development can be reduced accordingly.

The present invention relates to a new QT interval prolongation prediction model capable of evaluating drug-induced QT prolongation syndrome and to a method of predicting QT prolongation syndrome using the same, wherein the hERG IC ₅₀ value of the drug and the maximum blood concentration (C _max ) We have developed a predictive model of QT interval extension using area under the curve (AUC _inf ) and confirmed the accuracy of the QT extension syndrome prediction result using this model.

Thus, it is expected that a reliable new QT extension evaluation can be performed in the development of a new drug using the QT interval extension prediction model of the present invention and the QT extension syndrome prediction method using the same.

FIG. 1 shows the results of the QT interval change analysis of guinea pig according to administration of a drug.
FIG. 2 is a diagram showing the maximum variation of the QTc interval obtained by taking the natural log according to the hERG IC ₅₀ value for constructing the QT interval extension prediction model of the present invention, and shows a straight line for deriving the linear regression equation.
FIG. 3 shows the result of checking the accuracy of the QT interval extension prediction model of the present invention.
Figure 4 shows the algorithm of the QT extension syndrome prediction method of the present invention.

Hereinafter, preferred embodiments of the present invention will be described in detail. However, the present invention is not limited to the embodiments described herein but may be embodied in other forms. Rather, the intention is to provide an exhaustive, complete, and complete disclosure of the principles of the invention to those skilled in the art.

≪ Example 1: Selection of test drug &

The test drug to be used to confirm the occurrence of the QT syndrome was selected using the QT syndrome prediction method of the present invention.

In order to select drugs with various hERG IC ₅₀ values and blood concentrations after finding the hERG IC ₅₀ value and the reference value of the safety in the literature, the hERG IC ₅₀ value The drugs were divided into 4 groups based on 1 μM and 10 μM which are widely used and the reference values of 30 and 300 as safety standards. A total of 13 drugs were selected as shown in Table 2 below.

group hERG IC ₅₀ ([mu] M) Safe station reference value QT syndrome occurrence rate Ⅰ <1 <30 height Ⅱ <1 > 30 and <300 usually Ⅲ <1 > 300 lowness IV > 1 and <10 > 300 none

number Drug name hERG IC ₅₀
(μM) Test concentration
(Mg / kg) group One Dofetilide 0.06 0.05 Ⅰ 2 Droperidol 0.091 2.5 3 Astemizole 0.1 0.5 4 Halofantrine 0.197 25 5 Ketanserin 0.38 3 6 Clomipramine 0.083 5 Ⅱ 7 Domperidone 0.16 1.5 8 Solifenacin 0.27 3 9 Sunitinib 0.27 15 10 Ziprasidone 0.165 10 Ⅲ 11 Loratadine 0.173 3 12 Perphenazine &lt; / RTI &gt; 1.003 1.5 IV 13 Olanzapine (olanzapine) 6 One

&Lt; Example 2: Control of experimental animals &

An animal experiment, guinea pig (SPF Hartley male guinea pig, 6 weeks old), was purchased from Orient Bios Inc. (Korea). Guinea pigs were housed and adapted for 2 weeks in an animal laboratory maintained at a temperature of 20 ± 3 ° C and a humidity of 50 ± 10%, and were allowed free access to water and diet.

Example 3 Pharmacodynamic Experiment of Drug &gt;

The guinea pigs adapted for 2 weeks in Example 2 were anesthetized by intraperitoneal injection with sodium pentobarbital (60 mg / kg) and then placed on a homeothermic blanket (Havard, USA) And the body temperature of the guinea pig was maintained at 37 DEG C by inserting a probe in the rectum. The hair removal area was sterilized with 70% ethanol, and respiration was maintained using a guinea pig respirator (SAR 830 / P ventilator, CWE, Ardmore, Pa. USA). A polyethylene tube (PE10, PE50, Intramedic Clay Adams of Becton Dickinson) was inserted into the jugular vein of the guinea pig for administration of the test drug, and the electrocardiogram was recorded by connecting the lead II electrode. The guinea pigs were allowed to stabilize for about 20 minutes to keep the heart rate constant and then the test drug of Table 2 was administered via the jugular vein for 1 minute to each test concentration and the electrocardiogram was recorded. At this time, the test drug was dissolved in PEG 400 (polyethylene glycol 400).

)을 이용하였다. 시험 결과는 시험 약물을 투여하기 전의 수치에 대한 변화율(%)로 표기하였고, 그 결과를 도 1에 나타내었다. The QT interval and the heart rate (RR interval) for correction were analyzed using the PO-NEH-MA system (Gould, USA) and the correction of the QT interval was performed using the Fridericia's formular, QTcF, Quot;

) Were used. The test results were expressed as a percentage change in the value before administration of the test drug, and the results are shown in FIG.

As shown in FIG. 1, it was confirmed that the QT interval was significantly increased in each of the drug groups after the administration of the six drugs, and the same results were obtained in the other six drugs except the six drugs in FIG.

Example 4 Pharmacokinetic Experiment of Drug &gt;

Example 4-1. Establish LC-MS / MS assay for analyzing plasma concentrations of test drugs

The analytical method using liquid chromatography coupled with tandem mass spectrometry (LC-MS / MS) was established for analyzing plasma concentrations of test drugs.

A stock sample was prepared by dissolving acetonitrile (ACN) to 1 mg / ml by purchasing the selected standard substance (Sigma-Aldrich, O.N., CAN). A storage sample of each substance was diluted with acetonitrile to 100 μg / ml, 50 μg / ml, 10 μg / ml, 1 μg / ml, 0.5 μg / ml and 0.1 μg / ml. Samples of 0.1 μg / ml in the diluted samples were injected into the mass spectrometer and analyzed using MRM (multiple reaction monitoring) mode.

In order to establish the optimum mass analyzer condition, the mass value (Q1) after ionization of the analyte is compared with the mass value (Q3) and the respective energy value of the decomposed ion material produced by decomposing the ionized analyte into electric energy, Conditions. Typical electrical energy used in the mass spectrometer is DP (declustering potential, energy to prevent clusters of ions when they are ionized), EP (enhance potential, mass spectrometry for mass spectrometry Energy required to enter the interior) and CE (collision energy, the energy to make the precursor ions into the final ions of Q3).

To make a quality control sample, 5 μl of the diluted storage sample was injected into 45 μl of co-plasma, 200 μl of internal standard (IS) dissolved in acetonitrile was added, and shaking voltexing) and centrifuged at 13,200 rpm for 10 minutes at 4 占 폚. After centrifugation, the supernatant was sampled for quality control samples and used to establish the mass spectrometry analysis conditions using this sample. The LC-MS / MS method for analyzing the blood concentration of the drug was established using the calibration curve and the validation performed using the mass spectrometry analysis conditions. The results are shown in Table 3 . At this time, the validation was performed in accordance with the FDA Guideline (Bioanalytical Method Validation).

Drug name Q1? Q3 DP EP CE column Mobile phase A Mobile phase B Mobile phase
ratio Flow rate
(Ml / min) Dofettilide 441.4
→ 198 23 10 38.3 Gemini Distilled water Methanol 10:90 0.35 Draw ferry stone 380.2
→ 165 86 10 41 X-terra Distilled water Acetonitrile 10:90 0.4 Astemizole 459.3
→ 135 121 10 51 Gemini Distilled water Acetonitrile 10:90 0.5 Halofantrine 501
→ 142.2 41 10 34.98 X-terra Distilled water Aceto
Nitrile 2:98 0.45 Ketanserine 395.7
→ 189 117 10 36.5 Gemini Distilled water Aceto
Nitrile 10:90 0.4 Clomipramine 315.19
→ 58.1 101 10 59 Gemini Distilled water Acetonitrile 2:98 0.6 Domperidone 426.3
→ 175 121 10 43 Gemini Distilled water Acetonitrile 10:90 0.4 Solifenacin 362.8
→ 193 54 7.8 43.7 X-terra Distilled water Aceto
Nitrile 5:95 0.4 Sunitinib 398.56
→ 282.7 42 8 32 Gemini 0.1%
Formic acid Aceto
Nitrile 5:95 0.45 Ziprasidone 413.26
→ 194 116 10 39 X-terra Distilled water Acetonitrile 5:95 0.4 Laura Tadin 382.5
→ 336.6 71 11 28.8 Gemini Distilled water Aceto
Nitrile 5:95 0.4 Perphenazine 403.5
→ 143 28 10 37.2 X-terra Distilled water Aceto
Nitrile 10:90 0.4 Olanzapine 313.2
→ 256.2 91 10 39 Gemini Distilled water Acetonitrile 10:90 0.4 DP: declustering potential
EP: enhance potential
CE: collision energy

Example 4-2. Blood drug concentration measurement

The guinea pigs of Example 2 were anesthetized by administering 50 mg / kg of zoletil and fixed on the operating table. The surgical site was disinfected with 70% ethanol and then epilated. After epilation, the position of the jugular vein was confirmed, the skin was thinned with tweezers, and the jugular vein was sutured using surgical forceps and forceps and tweezers. A catheter (MRE tube + PE50 tube) was inserted into the jugular vein, and the animal was removed from the back so that it could not be eaten, then sutured well and stabilized for a day.

10 minutes, 15 minutes, 20 minutes, 25 minutes, 30 minutes, 1 hour, 2 hours, 4 hours after administration of the test drug based on the drug and concentration shown in Table 2 after the guinea pigs were stabilized , And 250 [mu] l of blood was collected at 6 hours. Blood was drawn through a catheter inserted into the jugular vein.

Blood was centrifuged at 13,200 rpm for 10 minutes at 4 ° C to collect plasma and stored at -80 ° C until analysis. In the analysis, 50 혈 of plasma was added with 200 내부 of the internal standard prepared in Example 4-1, followed by shaking for 5 minutes, centrifugation at 13,200 rpm for 10 minutes at 4 캜, and then the supernatant was collected, -1. &Lt; / RTI &gt;

Example 4-3. Calculating Pharmacokinetic Parameters of Drugs

The pharmacokinetic parameters of the drug were calculated using the noncompartmental analysis technique using the blood drug concentration results determined by the LC-MS / MS method.

The non-compartmental analysis was performed on the blood concentration of the drug according to the time taken in Example 4-2 to determine the maximum blood concentration (C _max ) and area under the curve (AUC) of the drug as basic pharmacokinetic parameters _inf ) was calculated. The results are shown in Table 4.

Drug name C _max ([mu] g / ml) AUC _inf (占 퐂 / h / ml) Dofettilide 0.12 0.11 0.07 ± 0.06 Draw ferry stone 3.50 ± 1.00 0.79 + 0.38 Astemizole 0.55 + 0.21 0.33 + 0.11 Halofantrine 380.17 + - 184.44 72.38 ± 26.79 Ketanserine 3.22 ± 0.59 1.04 + - 0.24 Clomipramine 45.41 ± 13.30 16.51 + - 4.97 Domperidone 0.45 ± 0.20 0.28 + 0.03 Solifenacin 0.88 ± 0.18 1.53 + - 0.34 Sunitinib 1.39 + - 0.46 2.37 ± 0.75 Ziprasidone 2.97 + - 0.67 1.63 + - 0.60 Laura Tadin 1.52 ± 1.20 0.59 + - 0.50 Perphenazine 2.60 ± 1.32 1.17 + - 0.67 Olanzapine 2.62 ± 0.39 1.43 + - 0.39

As shown in Table 4, pharmacokinetic information such as the maximum blood concentration (C _max ) and the under-curve area (AUC _inf ) of each test drug was confirmed.

Example 5 Construction of a prediction model of QT extension syndrome [

For the QT extension syndrome, we used regression analysis such as linear regression model and multiple regression model through the r program which is free public statistical software, The nonlinear mixed effect model was used to compare the r ² value and the predictive power of the QT prolongation syndrome and the pharmacokinetic parameters. The QT interval extension prediction model of the present invention was constructed.

In order to construct the QT interval extension prediction model of the present invention, the value obtained by observing the maximum change amount of the QTc interval varying after the administration of the drug was used according to the hERG IC ₅₀ value of the drug. 2 shows the hERG IC ₅₀ value of the drug as x axis and the value obtained by taking the natural logarithm as the maximum change amount of the QTc interval as the y axis.

As shown in FIG. 2, as the hERG IC ₅₀ value of the drug increases, the maximum variation of the QTc interval decreases as a whole. Regression analysis was performed to explain this aspect, and the regression analysis line of FIG. 2 was obtained. The linear regression equation of Equation (1), which can explain the straight line, was obtained.

[Equation 1]

In Equation (1), ln (ΔQTc interval) is a value obtained by taking a natural logarithm of the maximum change in the QTc interval depending on the hERG IC ₅₀ value in drugs, and ln (Base) is a value obtained when the hERG IC ₅₀ value converges to zero QTc is the value obtained by taking the natural logarithm of the interval change (QTc). α means the degree of decrease, that is, the slope, of the drug according to the hERG IC ₅₀ value.

The inverse function of the linear regression equation of Equation (1) is used to calculate the QTc interval of the drug according to the equation (2) of the equation (2) to explain the QTc interval decreases at the first rate according to the hERG IC ₅₀ value of the drug, 1.

Also, the maximum variation of the QTc interval according to the hERG IC ₅₀ value of the present invention is influenced by the degree of exposure of the drug. Therefore, in order to enhance the predictability of the QT prolongation syndrome, the maximum blood concentration (C _max ) and under-curve area (AUC _inf ) of the drug were used to represent exposure of the drug. C _max is the blood concentration at which the drug reaches its maximum concentration after administration of the drug, and AUC _inf is the area under the curve of the blood concentration profile of the drug over time, which indicates the extent of exposure of the drug to the body to be. That is, when calculating α, which is a parameter for explaining the maximum change in the QTc interval which varies according to the hERG IC ₅₀ value in the present invention, _Cmax and AUC _inf , which are representative parameters representing exposure of the drug, To derive Equation (2-2) of Equation (2). When the C _max and the AUC _inf are added to the equation (2-2), the objective function value is measured to determine whether the predictive power of the QT extension syndrome prediction model of the present invention increases or decreases. The following equation (2) is derived.

&Quot; (2) &quot;

In Equation (2), Base is the change in QTc interval (ΔQTc) when the hERG IC ₅₀ value converges to 0, and α denotes a slope in which the maximum variation of the QTc interval decreases from the Base value as the hERG IC ₅₀ value increases This is a parameter that affects the maximum variation of the QTc interval. TVα is the α value considering the C _max and AUC _inf of the drug, θ _i is the representative value of α, θ ₁ is the degree of influence on the α value according to the C _max of the drug, ₂ is the degree of influence on the α value according to the AUC _inf of the drug, and η _i is the difference of α value of each drug.

The objective function value used to determine the increase or decrease in the predictive power of the QT prolongation syndrome prediction model of the present invention was measured by the following equation (3).

&Quot; (3) &quot;

In Equation (3), n denotes the total number of data, obs _j denotes an observation value, pred _j denotes a predicted value by the model, and var _j denotes variance of residuals of the model. Therefore, the lower the objective function value, the less the residuals of the predicted values due to the observed values and the model, and the statistically good model. If the objective function value is 3.84 lower when one variable is added in the model, (p < 0.05).

In the QT prolongation syndrome prediction model of the present invention, the objective function value when C _max and AUC _inf were not added as a covariance to α was 6.461, and the objective function value in the final model with C _max and AUC _inf as covariance was - 19.185, and the objective function value decreased by 25.646, indicating that the statistical significance was improved. Therefore, the maximum variation of the QTc interval can be predicted according to the hERG IC ₅₀ value and slope α of the drug as shown in Equation 2, and a final model considering the C _max and the AUC _inf , which means exposure of the drug, was developed for the slope α .

Example 6. Confirmation of accuracy of prediction model of QT interval extension>

In order to confirm the accuracy of the QT interval extension prediction model constructed in the fifth embodiment, a visual predictive check was used. The results are shown in FIG.

FIG. 3 shows ln DELTA QTc values according to the hERG IC ₅₀ value and the AUC _inf and C _max of the drug. The values of the fifth and median 95th percentile of the simulation obtained using the predictive model constructed in Example 5 above, And the median value of 95 minutes.

It was thus found that the predictive power of the model for estimating the QTc interval change according to the hERG IC ₅₀ value of the QT interval extension prediction model of the present invention and the AUC _inf and C _max of the drug was sufficient.

Example 7. Construction of algorithm for predicting QT extension syndrome &

Using the QT interval extension prediction model constructed in the fifth embodiment, the algorithm of the QT extension syndrome prediction method is illustrated in FIG.

By applying the algorithm of Fig. 4 to the drug-induced QT prolongation syndrome prediction in the cardiovascular stability test of the candidate drug substance, it is possible to improve the reliability and predictive power of QT prolongation evaluation by the drug, It is expected that the number of drugs to be excluded from the drug will be reduced and the success rate of new drug development and cardiovascular safety will be increased.

Claims (5)

Drugs of hERG (human ether-a-go -go-related gene) inputting to measure the IC ₅₀ values;
Measuring and inputting the blood drug concentration of the animal to which the drug is administered;
Calculating a maximum blood concentration (C _max ) and area under the curve (AUC _inf ) of the drug through the inputted blood drug concentration; And
Applying the hERG IC ₅₀ value, the maximum blood concentration of the drug (C _max ) and the area under the curve (AUC _inf ) to the QT interval extension prediction model;
Wherein the QT prolongation syndrome prediction method comprises:
The QT interval extension prediction model is expressed by the following equation (2)
&Quot; (2) &quot;

Where Base is the QTc interval change (ΔQTc) when the hERG IC ₅₀ value converges to zero and α is the slope at which the maximum variation of the QTc interval decreases from the Base value as the hERG IC ₅₀ value increases. TVα is the α value considering the C _max and AUC _inf of the drug, θ _i is the representative value of α, θ ₁ is the degree of influence on the α value according to the C _max of the drug, ₂ is the degree of influence on the α value according to the AUC _inf of the drug, and η _i is the difference of α value of each drug. The method according to claim 1,
Wherein said hERG IC ₅₀ value is obtained in vitro by an hERG assay. The method according to claim 1,
The maximum blood concentration (C _max ) and under-curve area (AUC _inf ) of the drug are obtained by performing noncompartmental analysis through the blood concentration value of the drug according to the time obtained in vivo QT prolongation syndrome. The method according to claim 1,
The QT interval extension prediction model is based on a combination of the hERG IC ₅₀ value, the maximum blood concentration (C _max ) and the area under the curve (AUC _inf ) of the drug to improve the accuracy of the prediction of the QT extension syndrome . 5. The method according to any one of claims 1 to 4,
Wherein the method for predicting the QT prolongation syndrome is used for evaluating the prolongation of the QT by the drug during the cardiovascular stability test of the candidate drug.

Images