Dong-Kyu Lee’s research while affiliated with Chung-Ang University and other places

Lysine-specific histone demethylase (KDM) 5 inhibition by KDM5-C70 induces astrocytogenesis and highlights the importance of modulation of histone methylation in cell fate specification. This study investigated the role of the histone demethylase inhibitor KDM5-C70 in modulating the metabolic and lipidomic landscape during astrocyte differentiation of rat neural stem cells (NSCs). Using chemical derivatisation combined with gas chromatography-mass spectrometry, 42 metabolites were detected, indicating potential regulation of phospholipid metabolism. Subsequent lipidomic analysis, employing reverse-phase liquid chromatography with high-resolution quadrupole time-of-flight mass spectrometry, identified 180 lipid species and 9 lipid subclasses. Integrative analysis revealed that KDM5-C70 promoted astrocytogenesis through epigenetic changes linked to the attenuation of phosphatidylethanolamine (PE) biosynthesis pathways. The reduced expression of transcripts related to PE highlighted the significance of the PE pathway in influencing cell fate decisions. These quantitative metabolomic and lipidomic analyses not only advance our understanding of NSC differentiation but also lay the groundwork for potential therapeutic strategies targeting metabolic pathways in neurodegenerative diseases and neural injuries. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-025-88636-7.

This chapter presents a comprehensive approach to profiling plant-derived primary metabolites using metabolomics, highlighting its critical role in decoding the biosynthesis of bioactive plant compounds. It details the utilization of gas chromatography-mass spectrometry (GC-MS) for the effective analysis and profiling of these metabolites. The process, encompassing extraction methods, chemical derivatization, and data processing, is thoroughly outlined. This methodology outlines comprehensive procedures for each stage of the workflow, encompassing metabolite extraction, GC-MS analysis, and data alignment, to produce a metabolomics dataset.

This study aimed to develop a model incorporating natural language processing analysis for the simplified molecular-input line-entry system (SMILES) to predict clearance (CL) and volume of distribution at steady state (Vd,ss) in humans. The construction of CL and Vd,ss prediction models involved data from 435 to 439 compounds, respectively. In machine learning, features such as animal pharmacokinetic data, in vitro experimental data, molecular descriptors, and SMILES were utilized, with XGBoost employed as the algorithm. The ChemBERTa model was used to analyze substance SMILES, and the last hidden layer embedding of ChemBERTa was examined as a feature. The model was evaluated using geometric mean fold error (GMFE), r2, root mean squared error (RMSE), and accuracy within 2- and 3-fold error. The model demonstrated optimal performance for CL prediction when incorporating animal pharmacokinetic data, in vitro experimental data, and SMILES as features, yielding a GMFE of 1.768, an r2 of 0.528, an RMSE of 0.788, with accuracies within 2-fold and 3-fold error reaching 75.8% and 81.8%, respectively. The model's performance in Vd,ss prediction was optimized by leveraging animal pharmacokinetic data and in vitro experimental data as features, yielding a GMFE of 1.401, an r2 of 0.902, an RMSE of 0.413, with accuracies within 2-fold and 3-fold error reaching 93.8% and 100%, respectively. This study has developed a highly predictive model for CL and Vd,ss. Specifically, incorporating SMILES information into the model has predictive power for CL.

Pralidoxime chloride, a highly hydrophilic antidote, cannot be effectively separated by reverse-phase high-performance liquid chromatography (RP-HPLC), unless the mobile-phase composition is varied. However, the use of ion-pairing reagents for pralidoxime separation is hindered by the persistent contamination of the stationary phase or chromatography system inside the HPLC system. Thus, this study aimed to develop a simple, rapid, and robust method based on RP-HPLC to determine pralidoxime chloride in antidote autoinjectors using a chaotropic salt as the mobile-phase additive. The use of UV detection at 270 nm allowed for the simultaneous detection of pralidoxime chloride and the internal standard, pyridine-2-aldoxime. The addition of chaotropic salts (NaPF6, NaBF4, and NaClO4) and an ionic liquid ([EMIM]PF6) increased the retention time of pralidoxime chloride. Among them, NaPF6 exhibited the highest capacity factor in the reverse-phase C18 column. Increasing the salt concentration increased the capacity factor and the number of theoretical plates. Analytical method validation was performed to assess the linearity, accuracy, precision, recovery, and repeatability, according to the Ministry of Food and Drug Safety guidelines. Additionally, this newly developed method exhibits an adequate separation capability, making it a potential substitute for the current method employed in the United States/Korean Pharmacopoeia, and it ensures the necessary durability to maintain the robustness and reliability of the analytical system.

The HFF (Health Functional Foods) Code provides protocols for the quantitative analysis of 68 types of HFF ingredients. Astaxanthin is one of the HFF with antioxidant properties, preventing cell damage and reducing eye fatigue, becoming popular in the HFF market. To improve the astaxanthin assay protocol, we changed the LC condition from a tertiary pump to a binary pump and optimized the mobile phase and gradient accordingly. Based on the differences in the enzymes, we also observed the efficiency of the conversion of astaxanthin ester to free astaxanthin. We validated our modified method based on AOAC guidelines, including linearity (R20.99), selectivity, accuracy (recovery 92~105%), and precision (%RSD 2). An applicability was evaluated on 15 health functional food products, and the recovery values were satisfied within the range of 80-120%. In this study, we modernized the analytical method of astaxanthin by modifying the LC conditions, and standardized the cholesterol esterase with the highest efficiency.