• 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 Korea Genome Conference
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• 2002.08a
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• pp.46.1-46.1
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• 2002

• Bioindustry News
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• v.15 no.3
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• pp.16-19
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• 2002

• The Korea Genome Conference
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• 2005.09a
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• pp.45-45
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• 2005

• Journal of Integrative Natural Science
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• v.15 no.4
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• pp.153-161
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• 2022

c-Jun N-terminal kinases (JNKs) are members of MAPK family. Many genes can relay signals that promote inflammation, cell proliferation, or cell death which causes several diseases have been associated to mutations in the JNK gene family. The JNK2 gene is significantly more important in cancer development than the JNK1 and JNK3 genes. There are several different ways in which JNK2 contributes to breast cancer, and one of these is through its role in cell migration. As a result, this study's primary objective was to employ computational strategies to identify promising leads that potentially target the JNK2 protein in a strategy to alleviate breast cancer. We have derived these anticancer compounds from marine brown seaweed called Turbinaria conoides. We have identified compounds Ethane, 1, 1-diethoxy- and Butane, 2-ethoxy as promising anti-cancer drugs by molecular docking, DFT, and ADME study.

• YAKHAK HOEJI
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• v.57 no.6
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• pp.412-419
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• 2013

Drug discovery and development processes are time consuming and costly endeavors. It has been reported that on average it takes 10 to 15 years and costs more than $ 1billion to bring a molecule from discovery to market. Compounds fail for various reasons but one of the significant reasons that accounts for failures in clinical trials is poor prediction/understanding of pharmacokinetics and drug metabolism in human. In an effort to improve the number of compounds that exhibit optimal absorption, distribution, metabolism, elimination (ADME), and pharmacokinetic properties in human, drug metabolism, pharmacokinetic scientists have been continually developing new technologies and compound screening strategies. Over the last few years, accelerator mass spectrometry (AMS) and its applications to preclinical/clinical pharmacokinetics and ADME studies have significantly increased, particularly for new chemical/biological entities that are difficult to support with conventional radiolabel studies. In this review, the application of AMS for micro-dosing, micro-tracer absolute bioavailability, mass balance and metabolite profiling studies will be discussed.

• Journal of Radiation Industry
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• v.7 no.2_3
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• pp.209-219
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• 2013

RI-Biomics field comes into the limelight as a new fusion radiation technology. These rapid development of RI-Biomics cause the necessity of establishment of a new methodical education program model for consistent training of professional manpower in RI-ADME, Biomics field. But domestic current status is not satisfied to training human resource development in RI-Biomics. Actually domestic educational organization related to RI-Biomics just run educational programs oriented basic theory, so practical and fusion education are not existed nowadays for preliminary RI-Biomics expert. Therefore we established a new education program model for educate of the expert in RI-Biomics field to overcome current problem about the route of knowledge that has more monotonous and concentrated tendency and non-professional education. To improve universality and practicality, we conduct education-training model survey about domestic and foreign country. This new human resource development model will contribute to fostering new expert in RI-Biomics field.

• Journal of Pharmaceutical Investigation
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• v.38 no.3
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• pp.183-189
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• 2008

The present study compared the feasibility of Caco-2 and MDCK cells as an efficient in-vitro model for the drug classification based on Biopharmaceutics Classification System (BCS) as well as an in-vitro model for drug interactions mediated by P-gp inhibition or P-gp induction. Thirteen model drugs were selected to cover BCS Class I{\sim}IV$ and their membrane permeability values were evaluated in both Caco-2 and MDCK cells. P-gp inhibition studies were conducted by using vinblastine and verapamil in MDCK cells. P-gp induction studies were also performed in MDCK cells using rifampin and the P-gp expression level was determined by western blot analysis. Compared to Caco-2 cells, MDCK cells required shorter period of time to culture cells before running the transport study. Both Caco-2 and MDCK cells exhibited the same rank order relationship between in-vitro permeability values and human permeability values of all tested model compounds, implying that those in-vitro models may be useful in the prediction of human permeability (rank order) of new chemical entities at the early drug discovery stage. However, in the case of BCS drug classification, Caco-2 cells appeared to be more suitable than MDCK cells. P-gp induction by rifampin was negligible in MDCK-cells while MDCK cells appeared to be feasible for P-gp inhibition studies. Taken all together, the present study suggests that Caco-2 cells might be more applicable to the BCS drug classification than MDCK-cells, although MDCK cells may provide some advantage in terms of capacity and speed in early ADME screening process.

• Journal of Radiation Industry
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• v.7 no.2_3
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• pp.221-224
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• 2013

RI-Biomics is a new compound word of radiation technology and Biomics related to the study of life. RI-Biomics is high radiation fusion technology by combining evaluation of pharmacokinetics in vivo (RI-ADME) of new drugs and medical materials using radioisotope and molecular imaging technology using nuclear medicine equipments. RI-Biomics fields are emerging with the increasing usage of radioisotopes (RI). In this paper, we investigated the latest trends of radioisotope using in RI-Biomics fields. The representative radioisotopes are $^{14}C$, $^3H$ and $^{32}P$ for the optimization and the selection of candidates in the development process of new drugs among the RI-Biomics fields. As shown in the status of accumulated income of radioisotopes, using amounts of radioisotopes are showing a tendency to increase every year. $^{14}C$ is 61.6% increase of accumulated income growth rate and $^3H$ increased by 58.8% and $^{32}P$ increased by 33.9% in 2012 compared to 2007. These isotopes are used in a variety of fields as using of $^{14}C$ for microdosing test, development of [$^3H$]cholesterol absorption inhibitors, study of [$^{131}I$]pyronaridine tetraphosphate for malaria therapy. These are going on in vivo test sucessfully. So, clinical research step is expected to begin soon. Therefore, usages of radioisotopes are necessary and need for the evaluation of pharmacokinetics, optimization and the selection of new drug candidates in the development process of new drugs among the RI-Biomics fields. So, using of radioisotopes is predict to increase continuously except for primarily used $^{14}C$, $^3H$.

• Journal of Radiation Industry
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• v.11 no.1
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• pp.7-11
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• 2017

RI-Biomics is a fusion technology in radiation fields for evaluating in-vivo dynamics such as absorption, distribution, metabolism and excretion (RI-ADME) of new drugs and materials using radioisotopes and quantitative evaluation of their efficacy. RI-Biomics information is being provided by RIBio-Info developed as information system for distributing its information and three requirements for improving RIBio-Info system have been derived through reviewing recent big data trends in this study. Three requirements are defined as resource, technology and manpower, and some reviews for applying big data in RIBio-In system are suggested. Fist, applicable external big data have to be obtained, second, some infrastructures for realizing applying big data to be expanded, and finally, data scientists able to analyze large scale of information to be trained. Therefore, an original technology driven to analyze for atypical and large scale of data can be created and this stated technology can contribute to obtain a basis to create a new value in RI-Biomics field.