• 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

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

• 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 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.

• Molecular & Cellular Toxicology
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• v.1 no.1
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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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• v.33 no.4
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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.

• YAKHAK HOEJI
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• v.51 no.6
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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.