Suk-Jae Chung

Prof. Suk-Jae Chung

Seoul National University, Seoul, Republic of Korea

Research Focus & Metrics

Academic Career Length

25Years (2002-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 March 2010 in Journal of Pharmaceutical Sciences
Verified
Journal Article
Closed access
PDF file
MKMi‐Hwa Kim
Mi‐Hwa Kim
HMHan‐Joo Maeng
Han‐Joo Maeng
KYKyung‐Ha Yu
Kyung‐Ha Yu
KLKyeong‐Ryoon Lee
Kyeong‐Ryoon Lee
TTTakashi Tsuruo
Takashi Tsuruo
SCSuk-Jae Chung
Suk-Jae Chung

1 September 2015 in Journal of Pharmaceutical Sciences
Verified
Journal Article
Closed access
PDF file
JLJong-Hwa Lee
Jong-Hwa Lee
CNChi-Kyoung Noh
Chi-Kyoung Noh
CYChang-Soon Yim
Chang-Soon Yim
YJYoo-Seong Jeong
Yoo-Seong Jeong
SASung Hoon Ahn
Sung Hoon Ahn
SCSuk-Jae Chung
Suk-Jae Chung

26 April 2020 in Pharmaceutics
Verified
Journal Article
Open access
PDF file
YSYoo-Kyung Song
Yoo-Kyung Song
JYJin-Ha Yoon
Jin-Ha Yoon
JWJong Kyu Woo
Jong Kyu Woo
JKJu-Hee Kang
Ju-Hee Kang
KLKyeong-Ryoon Lee
Kyeong-Ryoon Lee
Han-Joo MaengHan-Joo Maeng
Han-Joo Maeng
The potential inhibitory effect of quercetin, a major plant flavonol, on breast cancer resistance protein (BCRP) activity was investigated in this study. The presence of quercetin significantly increased the cellular accumulation and associated cytotoxicity of the BCRP substrate mitoxantrone in human cervical cancer cells (HeLa cells) in a concentration-dependent manner. The transcellular efflux of prazosin, a stereotypical BCRP substrate, was also significantly reduced in the presence of quercetin in a bidirectional transport assay using human BCRP-overexpressing cells; further kinetic analysis revealed IC50 and Ki values of 4.22 and 3.91 μM, respectively. Moreover, pretreatment with 10 mg/kg quercetin in rats led to a 1.8-fold and 1.5-fold increase in the AUC8h (i.e., 44.5 ± 11.8 min∙μg/mL vs. 25.7 ± 9.98 min∙μg/mL, p < 0.05) and Cmax (i.e., 179 ± 23.0 ng/mL vs. 122 ± 23.2 ng/mL, p < 0.05) of orally administered sulfasalazine, respectively. Collectively, these results provide evidence that quercetin acts as an in vivo as well as in vitro inhibitor of BCRP. Considering the high dietary intake of quercetin as well as its consumption as a dietary supplement, issuing a caution regarding its food–drug interactions should be considered.

1 June 2017 in European Journal of Pharmaceutics and Biopharmaceutics
Verified
Journal Article
Closed access
YJYoo-Seong Jeong
Yoo-Seong Jeong
CYChang-Soon Yim
Chang-Soon Yim
HRHeon-Min Ryu
Heon-Min Ryu
CNChi-Kyoung Noh
Chi-Kyoung Noh
YSYoo-Kyung Song
Yoo-Kyung Song
SCSuk-Jae Chung
Suk-Jae Chung

29 May 2021 in Pharmaceutics
Verified
Journal Article
Open access
PDF file
YJYoo-Seong Jeong
Yoo-Seong Jeong
MKMin-Soo Kim
Min-Soo Kim
NLNora Lee
Nora Lee
ALAreum Lee
Areum Lee
YCYoon-Jee Chae
Yoon-Jee Chae
KLKyeong-Ryoon Lee
Kyeong-Ryoon Lee
Fexuprazan is a new drug candidate in the potassium-competitive acid blocker (P-CAB) family. As proton pump inhibitors (PPIs), P-CABs inhibit gastric acid secretion and can be used to treat gastric acid-related disorders such as gastroesophageal reflux disease (GERD). Physiologically based pharmacokinetic (PBPK) models predict drug interactions as pharmacokinetic profiles in biological matrices can be mechanistically simulated. Here, we propose an optimized and validated PBPK model for fexuprazan by integrating in vitro, in vivo, and in silico data. The extent of fexuprazan tissue distribution in humans was predicted using tissue-to-plasma partition coefficients in rats and the allometric relationships of fexuprazan distribution volumes (VSS) among preclinical species. Urinary fexuprazan excretion was minimal (0.29–2.02%), and this drug was eliminated primarily by the liver and metabolite formation. The fraction absorbed (Fa) of 0.761, estimated from the PBPK modeling, was consistent with the physicochemical properties of fexuprazan, including its in vitro solubility and permeability. The predicted oral bioavailability of fexuprazan (38.4–38.6%) was within the range of the preclinical datasets. The Cmax, AUClast, and time-concentration profiles predicted by the PBPK model established by the learning set were accurately predicted for the validation sets.