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Pohang, South Korea

Nam K.-Y.,YOUAI Co. | Choi N.S.,YOUAI Co. | Han C.K.,YOUAI Co. | Ahn S.K.,Incheon National University
Bioorganic and Medicinal Chemistry Letters | Year: 2012

Chalcones have an affinity for many receptors, enzymes, and transcription factors as flavonoid analogues. Their most studied pharmacological action is that of vasodilatation due to inhibition of phosphodiesterase 5A1 (PDE5A1). To this end, we have established a recursive partitioning model with 3 chemical descriptors for the prediction of compounds that can inhibit PDE5A1. This model was able to predict active compounds with an accuracy of 82.8%. Compound 4 was found to be a potent and selective inhibitor, with a relatively low IC 50 value. The binding mechanism of this compound was also investigated through molecular docking studies. © 2012 Elsevier Ltd. All rights reserved.

Screening V.,YOUAI Co. | Kim N.D.,YOUAI Co. | Lee Y.,YOUAI Co. | Ahn S.K.,Incheon National University
Bulletin of the Korean Chemical Society | Year: 2012

The 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) enzyme is involved in modulation of glucocorticoid activity within target tissues. This enzyme may contribute to obesity and/or metabolic disease through its action in adipose or liver tissue. Inhibition of 11β-HSD1 has major therapeutic potential for glucocorticoidassociated diseases, including obesity, diabetes (wound healing), and muscle atrophy. To develop such therapeutics, we performed a pharmacophore-based virtual screening (VS) for identification of novel 11β-HSD1 inhibitors and found that the VS hit compounds show potent inhibition of 11β-HSD1 enzyme activity. Further, we present a binding model for active compounds. The proposed pharmacophore may serve as a useful guideline for future design of new chemical entities as 11β-HSD1-targeted antidiabetic agents.

Joung J.Y.,Bioinformatics and Molecular Design Research Center | Joung J.Y.,Yonsei University | Kim H.,Yonsei University | Kim H.M.,Gachon University | And 3 more authors.
Bulletin of the Korean Chemical Society | Year: 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.

Ahn J.-H.,Incheon National University | Ahn S.K.,Incheon National University | Ahn S.K.,YOUAI Co. | Lee M.,Incheon National University
Biochemical and Biophysical Research Communications | Year: 2012

In human cancers, B-Raf is the most frequently mutated protein kinase in the MAPK signaling cascade, making it an important therapeutic target. We recently discovered a potent and selective B-Raf inhibitor, UI-152, by using a structure-based drug design strategy. In this study, we examined whether B-Raf inhibition by UI-152 may be an effective therapeutic strategy for eliminating cancer cells transformed with v-Ha- ras (Ras-NIH 3T3). UI-152 displayed selective cytotoxicity toward Ras-NIH 3T3 cells while having little to no effect on non-transformed NIH 3T3 cells. We found that treatment with UI-152 markedly increased autophagy and, to a lesser extent, apoptosis. However, inhibition of autophagy by addition of 3-MA failed to reverse the cytotoxic effects of UI-152 on Ras-NIH 3T3 cells, demonstrating that apoptosis and autophagy can act as cooperative partners to induce growth inhibition in Ras-NIH 3T3 cells treated with UI-152. Most interestingly, cell responses to UI-152 appear to be paradoxical. Here, we showed that although UI-152 inhibited ERK, it induced B-Raf binding to Raf-1 as well as Raf-1 activation. This paradoxical activation of Raf-1 by UI-152 is likely to be coupled with the inhibition of the mTOR pathway, an intracellular signaling pathway involved in autophagy. We also showed for the first time that, in multi-drug resistant cells, the combination of UI-152 with verapamil significantly decreased cell proliferation and increased autophagy. Thus, our findings suggest that the inhibition of autophagy, in combination with UI-152, offers a more effective therapeutic strategy for v-Ha- ras-transformed cells harboring wild-type B-Raf. © 2011 Elsevier Inc.

Nam K.-Y.,YOUAI Co. | Oh W.S.,Bioinformatics and Molecular Design Research Center | Kim C.,Korea Institute of Oriental Medicine | Song M.,Korea Institute of Oriental Medicine | And 6 more authors.
Bulletin of the Korean Chemical Society | Year: 2011

The NF-κB system of transcription factors plays a crucial role in inflammatory diseases, making it an important drug target. We combined quantitative structure activity relationships for predicting the activity of new compounds and quantitative dynamic models for the NF-κB network with intracellular concentration models. GFA-MLR QSAR analysis was employed to determine the optimal QSAR equation. To validate the predictability of the IKKb QSAR model for an external set of inhibitors, a set of ordinary differential equations and mass action kinetics were used for modeling the NF-κB dynamic system. The reaction parameters were obtained from previously reported research. In the IKKb QSAR model, good cross-validated q 2 (0.782) and conventional r 2 (0.808) values demonstrated the correlation between the descriptors and each of their activities and reliably predicted the IKKβ activities. Using a developed simulation model of the NF-κB signaling pathway, we demonstrated differences in IκB mRNA expression between normal and different inhibitory states. When the inhibition efficiency increased, inhibitor 1 (PS-1145) led to long-term oscillations. The combined computational modeling and NF-κB dynamic simulations can be used to understand the inhibition mechanisms and thereby result in the design of mechanism-based inhibitors.

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