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Bulletin of the Korean Chemical Society (BKCS)

ISSN 0253-2964(Print)
ISSN 1229-5949(Online)
Volume 32, Number 12
BKCSDE 32(12)
December 20, 2011 

Computational Drug Discovery Approach Based on Nuclear Factor-kB Pathway Dynamics
Ky-Youb Nam,*, Won Seok Oh, Chul Kim, Miyoung Song, Jong Young Joung, Sunyoung Kim, Jaeseong Park, Sin Moon Gang, YoungUk Cho, Kyoung Tai No*,
Drug design, NF-κB pathway, Inflammatory model, Quantitative structure-activity relationship, Dynamic simulation
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 IKKβ 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 q2 (0.782) and conventional r2 (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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