Jae-Hee Kwon’s research while affiliated with Ewha Womans University and other places

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.

Aims Glucagon‐like peptide‐1 receptor agonist (GLP‐1RA) may promote bone formation, but conversely, they could also weaken bones due to the reduction in mechanical load associated with weight loss. However, the clinical effects in humans have not been clearly demonstrated. This meta‐analysis aimed to evaluate whether GLP‐1RAs affect BMD and bone turnover markers. Material and Methods PubMed, Embase, and Scopus were searched on June 13, 2024. The eligibility criteria were: (1) human studies, (2) receiving a GLP‐1RA for more than 4 weeks, (3) an untreated control group or a placebo group, (4) reporting of at least one BMD or bone turnover marker, and (5) an RCT design. The risk of bias was assessed using the Cochrane risk of bias 2 tool. Fixed‐ or random‐effects meta‐analysis was performed according to heterogeneity. Results Seven studies were included in the meta‐analysis. GLP‐1RAs did not significantly change BMD in the femoral neck (mean difference [MD], 0.01 g/cm ² ; 95% CI, −0.01–0.04 g/cm ² ), in the total hip (MD, −0.01 g/cm ² ; 95% CI, −0.02–0.01 g/cm ² ), and in the lumbar spine (MD, 0 g/cm ² ; 95% CI, −0.02–0.02 g/cm ² ). C‐terminal telopeptide of type 1 collagen (CTX), a bone resorption marker, significantly increased after GLP‐1RA treatment (MD, 0.04 μg/L; 95% CI, 0.01–0.07 μg/L). GLP‐1RAs did not significantly change bone formation markers such as procollagen type 1 N‐terminal propeptide, bone‐specific alkaline phosphatase, osteocalcin. Conclusions GLP‐1RA did not affect BMD and bone formation markers. However, GLP‐1RAs led to a significant increase in CTX.

Objective: This study aimed to assess the potential of PARP inhibitors to prevent cardiotoxicity. Methods: First, a re-analysis and update of a previously published study was conducted. Additional searches were conducted of the PubMed and Cochrane Central Register of Controlled Trials databases on 2 June 2023. After the selection process, the pooled odds ratio (OR) for cardiac adverse events (AEs) was calculated. Second, the FAERS database was examined for 10 frequently co-administered anticancer agents. The reporting odds ratio (ROR) was calculated based on the occurrence of cardiac AEs depending on the co-administration of PARP inhibitors. Results: Seven studies were selected for the meta-analysis. Although not statistically significant, co-administration of PARP inhibitors with chemotherapy/bevacizumab decreased the risk of cardiac AEs (Peto OR = 0.61; p = 0.36), while co-administration with antiandrogens increased the risk of cardiac AEs (Peto OR = 1.83; p = 0.18). A total of 19 cases of cardiac AEs were reported with co-administration of PARP inhibitors in the FAERS database. Co-administration of PARP inhibitors with chemotherapy/bevacizumab significantly decreased the risk of cardiac AEs (ROR = 0.352; 95% confidence interval (CI), 0.194–0.637). On the other hand, for antiandrogens co-administered with PARP inhibitors, the ROR was 3.496 (95% CI, 1.539–7.942). The ROR for immune checkpoint inhibitors co-administered with PARP inhibitors was 0.606 (95% CI, 0.151–2.432), indicating a non-significant effect on cardiac AEs. Conclusion: This study reports that PARP inhibitors show cardioprotective effects when used with conventional anticancer agents.

Natural language processing (NLP) technology has recently used to predict substance properties based on their Simplified Molecular-Input Line-Entry System (SMILES). We aimed to develop a model predicting human skin sensitizers by integrating text features derived from SMILES with in vitro test outcomes. The dataset on SMILES, physicochemical properties, in vitro tests (DPRA, KeratinoSensTM, h-CLAT, and SENS-IS assays), and human potency categories for 122 substances sourced from the Cosmetics Europe database. The ChemBERTa model was employed to analyze the SMILES of substances. The last hidden layer embedding of ChemBERTa was tested with other features. Given the modest dataset size, we trained five XGBoost models using subsets of the training data, and subsequently employed bagging to create the final model. Notably, the features computed from SMILES played a pivotal role in the model for distinguishing sensitizers and non-sensitizers. The final model demonstrated a classification accuracy of 80% and an AUC-ROC of 0.82, effectively discriminating sensitizers from non-sensitizers. Furthermore, the model exhibited an accuracy of 82% and an AUC-ROC of 0.82 in classifying strong and weak sensitizers. In summary, we demonstrated that the integration of NLP of SMILES with in vitro test results can enhance the prediction of health hazard associated with chemicals.

This study aimed to identify the development of hepatitis B or C infection in diabetes patients compared to those without and to elucidate factors associated with the prevalence of hepatitis B or C infection in diabetes. We conducted a cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) 2013–2018. As evaluation factors, we included variables such as age, race, illicit drug use, and poverty. The diabetic group had a significantly higher prevalence of hepatitis B or C infection than the non-diabetic group (odds ratio (OR) = 1.73; 95% confidence interval (CI), 1.36–2.21, p < 0.01). In multivariate Cox regression, non-poverty and non-illicit drug use were lower risk factors contributing to hepatitis development in diabetes (hazard ratio (HR) = 0.50; 95% CI, 0.32–0.79, p < 0.01, and HR = 0.05; 95% CI, 0.03–0.08, p < 0.01, respectively). Logistic regression also showed that these factors were significant contributors to hepatitis development in the diabetic group (p < 0.01). In patients with diabetes, the development of hepatitis was higher than that in those without, and hepatitis development was influenced by poverty and illicit drug use. This may provide supporting evidence of response strategies for diabetes to care for hepatitis development in advance.

Purpose: Coronavirus disease 2019 (COVID-19) has emerged as a serious threat to public health; anticancer-repositioning treatment strategy has been formulated to treat the disease. However, evidence supporting the efficacy and safety of repositioned anticancer treatment in treating COVID-19-infected non-cancer patients (CINPs) is limited. Therefore, this study analyzed published randomized controlled trials (RCTs) evaluating the impact of anticancer drugs compared to current standards of care (SOCs) on CINP treatment. Materials and methods: The PubMed and Embase databases were searched to identify eligible RCTs. Outcome measures included mortality, the use of mechanical ventilation (MV), and serious adverse events (SAEs). Results: 25 RCTs were reviewed in our study. Compared to SOCs, repositioned anticancer therapy for treating CINPs was associated with mortality reduction (odds ratio (OR) = 0.78, 95% confidence interval (CI) = 0.65 - 0.94, p = 0.01). Using the repositioned anticancer treatment exhibited statistically significant reduction, in both the number of CINPs using MV (OR = 0.67, 95% CI = 0.51 - 0.88, p = 0.004) and experiencing SAEs (OR = 0.79, 95% CI = 0.69 - 0.91, p = 0.0009). Conclusion: Conclusively, repositioned anticancer treatment was shown significant differences from SOCs in treating CINPs, which appears to be more associated with mortality, MV use, and SAE development reduction in CINPs.

It is important to find effective and safe pharmacological options for managing cluster headache (CH) because there is limited evidence from studies supporting the general efficacy and safety of pharmacological therapies. This systematic review and network meta-analysis (NMA) analyzed published randomized controlled trials (RCTs) to evaluate the efficacy and safety of pharmacological treatments in patients with CH. The PubMed and Embase databases were searched to identify RCTs that evaluated the efficacy and safety of pharmacological treatments for CH. Efficacy outcomes included frequency and duration of attacks, pain-free rate, and the use of rescue agents. Safety outcomes were evaluated based on the number of patients who experienced adverse events. A total of 23 studies were included in the analysis. The frequency of attacks was reduced (mean difference (MD) = −1.05, 95% confidence interval (CI) = −1.62 to −0.47; p = 0.0004), and the pain-free rate was increased (odds ratio (OR) = 3.89, 95% CI = 2.76–5.48; p < 0.00001) in the pharmacological treatment group, with a lower frequency of rescue agent use than the placebo group. Preventive, acute, and triptan or non-triptan therapies did not show significant differences in efficacy (p > 0.05). In the NMA, different results were shown among the interventions; for example, zolmitriptan 5 mg was more effective than zolmitriptan 10 mg in the pain-free outcome (OR = 0.40, 95% CI = 0.19–0.82; p < 0.05). Pharmacological treatment was shown to be more effective than placebo to manage CH with differences among types of therapies and individual interventions, and it was consistently shown to be associated with the development of adverse events. Thus, individualized therapy approaches should be applied to treat CH in real-world practice.