A wave of new technologies has created opportunities for the cost-effective generation of high-throughput profiles of biological systems, foreshadowing a "data-driven science" era. The large variety of data available from biological research is also a rich resource that can be used for innovative endeavors. However, we are facing considerable challenges in big data deposition, integration, and translation due to the complexity of biological data and its production at unprecedented exponential rates. To address these problems, in 2020, the Korean government officially announced a national strategy to collect and manage the biological data produced through national R&D fund allocations and provide the collected data to researchers. To this end, the Korea Bioinformation Center (KOBIC) developed a new biological data repository, the Korea BioData Station (K-BDS), for sharing data from individual researchers and research programs to create a data-driven biological study environment. The K-BDS is dedicated to providing free open access to a suite of featured data resources in support of worldwide activities in both academia and industry.

The current guidelines for therapeutic drug monitoring (TDM) of vancomycin suggest a target 24-hour area under the curve (AUC 0-24 ) of 400 to 600 mg*h/L for serious methicillinresistant Staphylococcus aureus infections. In this study, the predictabilities of acute kidney injury (AKI) of various TDM target parameters, target levels, and sampling methods were evaluated in patients who underwent TDM from January 2020 to December 2020. The AUC 0-24 and trough values were calculated by both one- and two-point sampling methods, and were evaluated for the predictability of AKI. Among the AUC 0-24 cutoff comparisons, the threshold value of 500 mg*h/L in the two sampling methods was statistically significant (P = 0.042) when evaluated for the predictability of AKI. Analysis by an receiver operating characteristic curve estimated an AUC 0-24 cutoff value of 563.45 mg*h/L as a predictor of AKI, and was proposed as the upper limit of TDM target.

Lespedeza cuneata (LC) is a perennial plant used in herbal medicine to treat numerous diseases, including prostatic hyperplasia, diabetes, early atherosclerosis, and hematuria. Reference collections of bioactive compounds of LC are crucial for the determination of their pharmacological properties. However, little is known regarding its anti-oxidative and anti-inflammatory effects in alveolar macrophage (MH-S) cells. This study examined whether LC can inhibit reactive oxygen species and Coal fly ash (CFA) induced inflammation in MH-S cells. The anti-oxidative effects of LC were evaluated using 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) assays, anti-inflammatory effects were examined using nitric oxide (NO) assay, and cytotoxicity was analyzed using methyl thiazolyl tetrazolium assay. The expression of inflammatory cytokine genes was assessed through a reverse-transcription polymerase chain reaction. Our results revealed that LC exhibited high radical scavenging activity and a dose-dependent (7.8–1,000 μg/mL) inhibition of oxidation as compared to ascorbic acid and Trolox. It also inhibited CFA-induced NO production in MH-S cells. Moreover, it suppressed the CFA exposure-mediated expression of pro-inflammatory mediators and cytokines, including inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), interleukin (IL)-1β, IL-6, and tumor necrosis factor-α. These results suggest that LC is a potent antioxidant and anti-inflammatory agent that can be useful as a nutraceutical product.

Digoxin, a cardiac glycoside, is commonly prescribed to treat heart failure and atrial fibrillation. Because digoxin acts as a substrate of P-glycoprotein (P-gp), its blood concentration may be reduced by P-gp inducers such as rifampin. To assess the real-world implications of this drug-drug interaction, a retrospective analysis was carried out on the Clinical Data Warehouse at Seoul National University Hospital between 2012 and 2017. Eleven patients who received both digoxin and rifampin with satisfying the inclusion/exclusion criteria were identified. The C trough values of digoxin monotherapy were compared to those of the combination therapy with rifampin. Results demonstrated that the systemic exposure of orally administered digoxin decreased by 40% with the concurrent use of rifampin. Clinicians should be aware of potential drug interactions between digoxin and rifampin, as adjustments to digoxin dosage might be necessary for patients receiving rifampin or other P-gp inducer drugs.

Genetic polymorphisms of enzymes and transporters associated with the absorption, distribution, metabolism, and elimination (ADME) of drugs are one of the major factors that contribute to interindividual variations in drug response. In the present study, we aimed to elucidate the pharmacogenetic profiles of the Korean population using the Affymetrix Drug Metabolizing Enzyme and Transporters (DMET™) platform. A total of 1,012 whole blood samples collected from Korean subjects were genotyped using the DMET™ plus microarray. In total, 1,785 single nucleotide polymorphism (SNP) markers for 231 ADME genes were identified. The genotype and phenotype of 13 clinically important ADME genes implemented in the Clinical Pharmacogenetics Implementation Consortium guidelines were compared among different ethnic groups. Overall, the genotype frequencies of the Korean population were similar to those of the East Asian population. Several genes, notably CYP2C19 and VKORC1, showed marked differences in Koreans compared to Europeans (EURs) or Africans (AFRs). The percentage of CYP2C19 poor metabolizers was 15% in Koreans and less than 3% in EURs or AFRs. The frequencies of causative SNPs of the VKORC1 gene for the low warfarin dose phenotype were 90%, 60%, and 10% in Koreans, EURs and AFRs, respectively. Our findings can be utilized for optimal pharmacotherapy in Korean patients.

The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and ensuing computational problems. In Korea, the amount of genomic data has been increasing rapidly in the recent years. Leveraging these big data requires researchers to use large-scale computational resources and analysis pipelines. A promising solution for addressing this computational challenge is cloud computing, where CPUs, memory, storage, and programs are accessible in the form of virtual machines. Here, we present a cloud computing-based system, Bio-Express, that provides user-friendly, cost-effective analysis of massive genomic datasets. Bio-Express is loaded with predefined multi-omics data analysis pipelines, which are divided into genome, transcriptome, epigenome, and metagenome pipelines. Users can employ predefined pipelines or create a new pipeline for analyzing their own omics data. We also developed several web-based services for facilitating downstream analysis of genome data. Bio-Express web service is freely available at https://www.bioexpress.re.kr/.

Tamsulosin, an alpha-1 adrenoreceptor antagonist, has been used as a primary option for medical treatment of benign prostate hyperplasia. An open-label, single-dose, randomized, three-treatment, three-period, three sequence crossover study was conducted to evaluate the pharmacokinetics (PKs) of 0.2 and 0.4 mg tamsulosin hydrochloride (HCl) in the fed versus the fasted state. Subjects were randomly assigned to three sequences and received one of the following treatments at each period: tamsulosin HCl 0.2 or 0.4 mg in the fed state with a high-fat meal, or tamsulosin HCl 0.4 mg in the fasted state. Blood samples for the PK analysis were collected at pre-dose and up to 48 h post-dose. The PK parameters were calculated by a non-compartmental method. The geometric mean ratio (GMR) and its 90% confidence intervals (CIs) of the plasma maximum concentration (C max ) and area under concentration curve from time zero to last measurable concentration (AUClast) were calculated. Twenty-two subjects completed the study. The systemic exposure of tamsulosin 0.4 mg decreased approximately 9% in the fed state compared to the fasted state, and the time to reach peak concentration was slightly delayed in the fed state. The dose normalized GMR and its 90% CIs of C max and AUClast for 0.2 and 0.4 mg tamsulosin in the fed state were within 0.8 and 1.25 range. Systemic exposure of tamsulosin was decreased in the fed condition compared to the fasted condition. Linear PK profiles were observed between 0.2 and 0.4 mg tamsulosin in the fed state.

Tamsulosin, an alpha-1 adrenoreceptor antagonist, has been used as a primary option for medical treatment of benign prostate hyperplasia. An open-label, single-dose, randomized, three-treatment, three-period, three sequence crossover study was conducted to evaluate the pharmacokinetics (PKs) of 0.2 and 0.4 mg tamsulosin hydrochloride (HCl) in the fed versus the fasted state. Subjects were randomly assigned to three sequences and received one of the following treatments at each period: tamsulosin HCl 0.2 or 0.4 mg in the fed state with a high-fat meal, or tamsulosin HCl 0.4 mg in the fasted state. Blood samples for the PK analysis were collected at pre-dose and up to 48 h post-dose. The PK parameters were calculated by a non-compartmental method. The geometric mean ratio (GMR) and its 90% confidence intervals (CIs) of the plasma maximum concentration (C max ) and area under concentration curve from time zero to last measurable concentration (AUClast) were calculated. Twenty-two subjects completed the study. The systemic exposure of tamsulosin 0.4 mg decreased approximately 9% in the fed state compared to the fasted state, and the time to reach peak concentration was slightly delayed in the fed state. The dose normalized GMR and its 90% CIs of C max and AUClast for 0.2 and 0.4 mg tamsulosin in the fed state were within 0.8 and 1.25 range. Systemic exposure of tamsulosin was decreased in the fed condition compared to the fasted condition. Linear PK profiles were observed between 0.2 and 0.4 mg tamsulosin in the fed state.

The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and ensuing computational problems. In Korea, the amount of genomic data has been increasing rapidly in the recent years. Leveraging these big data requires researchers to use large-scale computational resources and analysis pipelines. A promising solution for addressing this computational challenge is cloud computing, where CPUs, memory, storage, and programs are accessible in the form of virtual machines. Here, we present a cloud computing-based system, Bio-Express, that provides user-friendly, cost-effective analysis of massive genomic datasets. Bio-Express is loaded with predefined multi-omics data analysis pipelines, which are divided into genome, transcriptome, epigenome, and metagenome pipelines. Users can employ predefined pipelines or create a new pipeline for analyzing their own omics data. We also developed several web-based services for facilitating downstream analysis of genome data. Bio-Express web service is freely available at https://www.bioexpress.re.kr/.

Asian populations contain a variety of ethnic groups that have ethnically specific genetic differences. Ethnic variants may be highly relevant in disease and human differentiation studies. Here, we identified ethnically specific variants and then investigated their distribution across Asian ethnic groups. We obtained 58,960 Pan-Asian single nucleotide polymorphisms of 1,953 individuals from 72 ethnic groups of 11 Asian countries. We selected 9,306 ethnic variant single nucleotide polymorphisms (ESNPs) and 5,167 ethnic variant copy number polymorphisms (ECNPs) using the nearest shrunken centroid method. We analyzed ESNPs and ECNPs in 3 hierarchical levels: superpopulation, subpopulation, and ethnic population. We also identified ESNP- and ECNP-related genes and their features. This study represents the first attempt to identify Asian ESNP and ECNP markers, which can be used to identify genetic differences and predict disease susceptibility and drug effectiveness in Asian ethnic populations.
Asian Continental Ancestry Group ; Classification ; Disease Susceptibility ; DNA Copy Number Variations ; Ethnic Groups ; Genetic Variation* ; Genotype ; Humans ; Polymorphism, Single Nucleotide