• Analytical Science and Technology
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• v.24 no.6
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• pp.533-543
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• 2011

The hERG (human ether-a-go-go related gene) ion channel is a main factor for cardiac repolarization, and the blockade of this channel could induce arrhythmia and sudden death. Therefore, potential hERG ion channel inhibitors are now a primary concern in the drug discovery process, and lots of efforts are focused on the minimizing the cardiotoxic side effect. In this study, $IC_{50}$ data of 202 organic compounds in HEK (human embryonic kidney) cell from literatures were used to develop predictive 2D-QSAR model. Multiple linear regression (MLR), Support Vector Machine (SVM), and artificial neural network (ANN) were utilized to predict inhibition concentration of hERG ion channel as machine learning methods. Population based-forward selection method with cross-validation procedure was combined with each learning method and used to select best subset descriptors for each learning algorithm. The best model was ANN model based on 14 descriptors ($R^2_{CV}$=0.617, RMSECV=0.762, MAECV=0.583) and the MLR model could describe the structural characteristics of inhibitors and interaction with hERG receptors. The validation of QSAR models was evaluated through the 5-fold cross-validation and Y-scrambling test.

• Journal of the Korean Chemical Society
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• v.53 no.6
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• pp.653-662
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• 2009

Developing effective tools for predicting absorption, distribution, metabolism, excretion properties and toxicity (ADME/T) of new chemical entities in the early stage of drug design is one of the most important tasks in drug discovery and development today. As one of these attempts, support vector machines (SVM) has recently been exploited for the prediction of ADME/T related properties. However, two problems in SVM modeling, i.e. feature selection and parameters setting, are still far from solved. The two problems have been shown to be crucial to the efficiency and accuracy of SVM classification. In particular, the feature selection and optimal SVM parameters setting influence each other, which indicates that they should be dealt with simultaneously. In this account, we present an integrated practical solution, in which genetic-based algorithm (GA) is used for feature selection and grid search (GS) method for parameters optimization. hERG ion-channel inhibitor classification models of ADME/T related properties has been built for assessing and testing the proposed GA-GS-SVM. We generated 6 different models that are 3 different single models and 3 different ensemble models using training set - 1891 compounds and validated with external test set - 175 compounds. We compared single model with ensemble model to solve data imbalance problems. It was able to improve accuracy of prediction to use ensemble model.

• The Korean Journal of Physiology and Pharmacology
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• v.16 no.5
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• pp.327-332
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• 2012

Sertraline is a commonly used antidepressant of the selective serotonin reuptake inhibitors (SSRIs) class. In these experiments, we have used the whole cell patch clamp technique to examine the effects of sertraline on the major cardiac ion channels expressed in HEK293 cells and the native voltage-gated $Ca^{2+}$ channels in rat ventricular myocytes. According to the results, sertraline is a potent blocker of cardiac $K^+$ channels, such as hERG, $I_{Ks}$ and $I_{K1}$. The rank order of inhibitory potency was hERG > $I_{K1}$ > $I_{Ks}$ with $IC_{50}$ values of 0.7, 10.5, and 15.2 ${\mu}M$, respectively. In addition to $K^+$ channels, sertraline also inhibited $I_{Na}$ and $I_{Ca}$, and the $IC_{50}$ values are 6.1 and 2.6 ${\mu}M$, respectively. Modification of these ion channels by sertraline could induce changes of the cardiac action potential duration and QT interval, and might result in cardiac arrhythmia.

• The Korean Journal of Physiology and Pharmacology
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• v.23 no.5
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• pp.393-402
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• 2019

Aurora kinases inhibitors, including ZM447439 (ZM), which suppress cell division, have attracted a great deal of attention as potential novel anti-cancer drugs. Several recent studies have confirmed the anti-cancer effects of ZM in various cancer cell lines. However, there have been no studies regarding the cardiac safety of this agent. We performed several cytotoxicity, invasion and migration assays to examine the anti-cancer effects of ZM. To evaluate the potential effects of ZM on cardiac repolarisation, whole-cell patch-clamp experiments were performed with human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) and cells with heterogeneous cardiac ion channel expression. We also conducted a contractility assay with rat ventricular myocytes to determine the effects of ZM on myocardial contraction and/or relaxation. In tests to determine in vitro efficacy, ZM inhibited the proliferation of A549, H1299 (lung cancer), MCF-7 (breast cancer) and HepG2 (hepatoma) cell lines with $IC_{50}$ in the submicromolar range, and attenuated the invasive and metastatic capacity of A549 cells. In cardiac toxicity testing, ZM did not significantly affect $I_{Na}$, $I_{Ks}$ or $I_{K1}$, but decreased $I_{hERG}$ in a dose-dependent manner ($IC_{50}$: $6.53{\mu}M$). In action potential (AP) assay using hiPSC-CMs, ZM did not induce any changes in AP parameters up to $3{\mu}M$, but it at $10{\mu}M$ induced prolongation of AP duration. In summary, ZM showed potent broad-spectrum anti-tumor activity, but relatively low levels of cardiac side effects compared to the effective doses to tumor. Therefore, ZM has a potential to be a candidate as an anti-cancer with low cardiac toxicity.