• 대한화학회지
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• 제53권6호
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• pp.653-662
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• 2009

흡수, 분포, 대사, 배설 특성 및 독성을 예측하기 위한 효과적인 툴을 개발하는 것은 신약개발의 초기단계에서 NCE(new chemical entity)에 대한 가장 중요한 업무 중의 하나이다. 최근에 이런 시도중의 하나로서 ADME/T(absorption, distribution, metabolism, excretion, toxicity)관련 성질들의 예측에 support vector machine(SVM)을 이용하고 있다. 그리고 SVM은 ADME/T 성질들을 정확하게 예측하는데 많이 사용 되고 있다. 그러나 SVM 모델링에 두 가지 문제가 있다. 특성 선택(feature selection) 과 매개변수 설정(parameter setting)은 여전히 해결해야 할 과제이다. 이 두 가지 문제들은 SVM 분류의 효율성과 정확도에 결정적인 영향을 끼친다. 특히 특성 선택과 최적화된 SVM 변수의 설정은 서로 영향을 주기 때문에 동시에 다루어져야 한다. 여기서 우리는 genetic algorithm(GA) – 특성 선택에 사용 – 과 grid search(GS) method– 변수최적화에 사용 – 두 가지를 통합하는 효과적인 해결책을 제시하였다. ADME/T관련 성질 중 하나인 심장부정맥을 야기시키는 hERG 이온채널 저해제 분류 모델이 여기서 제안된 GA-GS-SVM을 위해 할당되고 테스트 되었다. 1891개의 화합물을 가지는 트레이닝 셋으로 단일 모델 3개, 앙상블 모델 3개, 총 6개의 모델을 만들었고 175개의 외부 데이터를 테스트 셋으로 사용하여 검증하였다. 데이터의 불균형 문제를 해결하기 위하여 GA-GS-SVM 단일 모델에 의한 예측 정확도와 GA-GS-SVM 앙상블 모델 예측 정확도를 비교하였으며, 앙상블모델을 사용하여 예측의 정확도를 높일 수 있었다.

• 한국유전체학회:학술대회논문집
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• 한국유전체학회 2002년도 The 11th Korea Genome Conference
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• pp.46.1-46.1
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• 2002

• 한국유전체학회:학술대회논문집
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• 한국유전체학회 2005년도 The 14th Korea Genome Conference
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• pp.45-45
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• 2005

• 약학회지
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• 제57권6호
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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 Pharmaceutical Investigation
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• 제38권3호
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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.

• Molecular & Cellular Toxicology
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• 제1권1호
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• pp.13-16
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• 2005

The current vision of toxicogenomics is the development of methods or platforms to predict toxicity of un characterized chemicals by using '-omics' information in pre-clinical stage. Because each chemical has different ADME (absorption, distribution, mechanism, excretion) and experimental animals have lots of variation, precise prediction of chemical's toxicity based on '-omics' information and toxicity data of known chemicals is very difficult problem. So, the importance of bioinformatics is more emphasized on toxicogenomics than other functional genomics studies because these problems can not be solved only with experiments. Thus, toxicoinformatics covers all information-based analytical methods from gene expression (bioinformatics) to chemical structures (cheminformatics) and it also deals with the integration of wide range of experimental data for further extensive analyses. In this review, the overall strategy to toxicoinformatics is discussed.

• Bulletin of the Korean Chemical Society
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• 제33권4호
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• pp.1123-1127
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• 2012

P-gp (P-glycoprotein) is a member of the ATP binding cassette (ABC) family of transporters. It transports many kinds of anticancer drugs out of the cell. It plays a major role as a cause of multidrug resistance (MDR). MDR function may be a cause of the failure of chemotherapy in cancer and influence pharmacokinetic properties of many drugs. Hence classification of candidate drugs as substrates or nonsubstrate of the P-gp is important in drug development. Therefore to identify whether a compound is a P-gp substrate or not, in silico method is promising. Recursive Partitioning (RP) method was explored for prediction of P-gp substrate. A set of 261 compounds, including 146 substrates and 115 nonsubstrates of P-gp, was used to training and validation. Using molecular descriptors that we can interpret their own meaning, we have established two models for prediction of P-gp substrates. In the first model, we chose only 6 descriptors which have simple physical meaning. In the training set, the overall predictability of our model is 78.95%. In case of test set, overall predictability is 69.23%. Second model with 2D and 3D descriptors shows a little better predictability (overall predictability of training set is 79.29%, test set is 79.37%), the second model with 2D and 3D descriptors shows better discriminating power than first model with only 2D descriptors. This approach will be used to reduce the number of compounds required to be run in the P-gp efflux assay.

• 약학회지
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• 제51권6호
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• pp.463-468
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• 2007

Chong Myung Tang is consisted of three medicinal herbs (Acori Graminei Rhizoma, Polygalae Radix and Hoelen cum Radix). It has been used as a medicine for the purpose of learning and memory improvement. In this paper, Chong Myung Tang was screened the biological activities for Alzheimer's disease. The extract (70% ethanol) of Acari Graminei Rhizoma (1 mg/ml) showed that acetylcholinesterase (AChE) and amyloid beta ($A{\beta}$) peptide aggregation inhibitory potency are 43.1% and 76.5%, respectively. The extract of Polygalae Radix showed inhibitory activity against $A{\beta}_{1-42}$ peptide aggregation (51.5%). To predict the drug-likeness, oral absorption ability; blood-brain barrier (BBB) penetraion rate, mutagenecity and carcinogenicity; in silico screening was performed against 16 compounds in the three medicinal herbs. According to the results, all compounds have appropriate chemical structures as medicines. The six compounds in Acori Graminei Rhizoma and the five compounds in Hoelen cum Radix showed excellent oral absorption rate and BBB penetration rate. The four compounds in Polygalae Radix showed excellent oral absorption rate, but their BBB penetration was presented low rate. And, the extract of Hoelen cum Radix didn't show AChE and $A{\beta}_{1-42}$ peptide aggregation inhibitory activities in vitro. Therefore, their activity in brain may be other mechanism. According to all of the results, in silico prediction technology is convenient and effective to determine biological active compounds in medicinal herbs.