Translational and Clinical Pharmacology最新文献

The general linear model (GLM) describes the dependent variable as a linear combination of independent variables and an error term. The GLM procedure of SAS^® and the "car" package in R calculate the type I, II, or III ANOVA (analysis of variance) tables. In this study, we validated the newly-developed R package, "sasLM," which is compatible with the GLM procedure of SAS^®. The "sasLM" package was validated by comparing the output with SAS^®, which is the current gold standard for statistical programming. Data from ten books and articles were used for validation. The results of the "sasLM" and "car" packages were compared with those in SAS^® using 194 models. All of the results in "sasLM" were identical to those of SAS^®, whereas more than 20 models in "car" showed different results from those of SAS^®. As the results of the "sasLM" package were similar to those in SAS^® PROC GLM, the "sasLM" package could be a viable alternative method for calculating the type II and III sum of squares. The newly-developed "sasLM" package is free and open-source, therefore it can be used to develop other useful packages as well. We hope that the "sasLM" package will enable researchers to conveniently analyze linear models.

This study compared the pharmacokinetics of a fixed-dose combination (FDC) of candesartan (16 mg) and amlodipine (10 mg) versus coadministration of individual formulations to clarify the bioequivalence of the FDC. In this randomized, open-label, single-dose, 2-treatment, 2-way crossover study, healthy Korean volunteers received a single dose of candesartan (16 mg) with amlodipine (10 mg) as either an FDC or single agents concomitantly administered, with a 2-week washout period. Serial blood samples were collected up to 72 hours after dosing for each treatment period, and plasma concentrations of candesartan and amlodipine were measured using a validated liquid chromatography-tandem mass spectrometry method. A total of 39 subjects completed the study. The geometric mean ratios (GMRs) and 90% confidence intervals (CIs) for the area under the plasma concentration-time curve from time 0 to the last measurement (AUC_0-t) and the peak plasma concentration (C_max) for candesartan were 1.0182 (0.9562-1.0841) and 0.9492 (0.8726-1.0324), respectively. The GMR and 90% CI for the AUC_0-t and C_max for amlodipine were 1.0552 (1.0255-1.0857) and 1.0668 (1.0259-1.1094), respectively. In conclusion, the new FDC formulation of candesartan (16 mg) and amlodipine (10 mg) was bioequivalent to the concomitant administration of single agents. A single dose of candesartan/amlodipine as the FDC or as single agents was well tolerated.

Trial registration: ClinicalTrials.gov Identifier: NCT02988362.

Despite quantitative increases and qualitative advances in pharmacogenomics (PGx) research, the clinical implementation of PGx-based personalized therapy has still been limited. The objective of this study was to assess physicians' self-reported knowledge of PGx-based personalized therapy, and to explore the most problematic and highest priority barriers preventing physicians from applying PGx into clinical practice under the Korean healthcare system. A 36-question survey was distributed to 53 physicians with various specialties in Korea. In the physicians' self-perceived knowledge, twenty-eight physicians (53%) reported a lack sufficient knowledge about PGx. The perceived largest barrier to clinical implementation of PGx was the high cost of PGx testing, followed by a lack of PGx education for healthcare providers or lack of clinical PGx experts. Physicians without clinical PGx experience or with indirect experience reported that the largest barrier to clinical implementation of PGx was the high cost of PGx testing, while physicians with clinical PGx experience pointed out that a lack of patients' education was the major concern, followed by a lack of PGx education for healthcare providers or lack of clinical PGx experts. The highest priority problem was reported to be a lack of actionable guidelines for drug selection and dosing using PGx. In conclusion, we should increase and expand extensive educational programs for healthcare providers and patients, and to develop and establish a clinical decision support systems for PGx-based personalized therapy in Korea.

The gut microbiome closely interacts with the host, and it has a major influence on drug response. Many studies have reported the possible microbial influences on drugs and the possible influences of drugs on the microbiome. This knowledge has led to a better understanding of intra- and inter-individual variabilities in clinical pharmacology. For a more precise understanding of the complex correlation between the microbiome and drugs, in this review, we summarized the current knowledge on the interactions between the gut microbiome and drug response. Moreover, we suggest gut microbiome-derived metabolites as possible modulators of drug response and recommend metabolomics as a powerful tool to achieve such understanding.

A fixed-dose combination (FDC) of gemigliptin/metformin can improve the medication adherence in patients with type 2 diabetes mellitus (T2DM). In this study, the pharmacokinetic (PK) and pharmacodynamic (PD) profiles of gemigliptin and metformin were compared between FDC and the corresponding loose combination under fasted and fed states. A two-part, randomized, open label, single-dose, two-way crossover study was conducted in healthy male subjects. Under fasted (part 1) or fed (part 2) state, 2 FDC tablets of gemigliptin/metformin sustained release (SR) 25/500 mg or loose combination with one tablet of gemigliptin 50 mg and two tablets of metformin extended release (XR) 500 mg were orally administered in each period with a 7-day washout. Serial blood samples were collected up to 48 hours to determine the drug concentration and the dipeptidyl peptidase 4 (DPP-4) activity. The concentration-time profiles of gemigliptin and metformin were similar between FDC and loose combination in both the fasted and fed states. Geometric mean ratios and 90% confidence intervals of FDC to loose combination for area under the concentration-time curve and maximum plasma concentration of gemigliptin and metformin were within the bioequivalence range (0.8-1.25) in both states. DPP-4 activity-time profiles of FDC were comparable to that of the loose combination, showing similar area under the DPP-4 inhibition-time curve and maximum DPP-4 inhibition between FDC and loose combination, regardless of the fasted or fed state. In conclusion, the PK/PD characteristics of gemigliptin and metformin were similar in FDC tablets and loose combination both in fasted and fed states.

Trial registration: ClinicalTrials.gov Identifier: NCT03355014.

Type 2 diabetes mellitus is a multifactorial condition characterized by high level of sugar in the blood. To control hyperglycemia, combination therapy is recommended if monotherapy fails to achieve glycemic control. The combination of a dipeptidyl peptidase-4 (DPP-4) inhibitor and a sodium-glucose cotransporter type 2 (SGLT2) inhibitor is a promising option of the combination therapies in terms of safety as well as efficacy. Despite of the value of combination therapy of these two agents, the pharmacokinetic drug interactions between these two classes of agents have been evaluated in a few drugs. Thus, we reviewed the potential pharmacokinetic drug interaction based on the in vitro metabolism- and transporter-mediated drug interaction information as well as drug interaction studies in human, between a DPP-4 inhibitor and a SGLT2 inhibitor which are marketed in South Korea.

YH4808 is a novel potassium-competitive acid blocker that was developed as a therapeutic agent for gastric acid-related diseases; it may replace proton pump inhibitors, which are widely used in combination with amoxicillin and clarithromycin for Helicobacter pylori eradication. We compared the pharmacokinetic (PK) profiles and safety of amoxicillin, clarithromycin, and YH4808 used as monotherapies or in combination for evaluating potential drug interactions. An open-label, randomized, single-dose, Latin-square (4 × 4) crossover study was conducted in 32 healthy Korean volunteers. Subjects were randomly assigned to one of the 4 treatment sequences that consisted of 4 periods separated by 21-day washout intervals. PK parameters of YH4808, amoxicillin and clarithromycin administered in combination were compared with those of the respective monotherapies. The geometric mean ratios of the maximum concentration (C_max) and the area under the time-concentration curve from time zero to time of the last quantifiable concentration (AUC_last) of YH4808 increased during the triple therapy by 48.6% and 29.1%, respectively. Similarly, the C_max and AUC_last of M3 (active metabolite of YH4808) increased by 23.3% and 16.0%, respectively. The C_max and AUC_last of clarithromycin increased by 27.4% and 30.5%, and those of 14-hydroxyclarithromycin were increased by 23.1% and 32.4%, respectively. The corresponding amoxicillin values decreased during the triple therapy by 21.5% and 15.6%, respectively. There was no clinically significant change in safety assessment related to either monotherapies or triple therapy. In conclusion, amoxicillin, clarithromycin and YH4808 administered as triple therapy did not exhibit significant PK interactions and were not associated with safety issues.

Trial registration: ClinicalTrials.gov Identifier: NCT01921647.

As the pharmaceutical industry in Korea is reaching the golden era of drug discovery due to increased investments in research and development and government funds, the need for a more efficient tool for the quantitative analysis has emerged. Therefore, the demand for pharmacometrics (PMx) consultancy services increased. Higher quality service suitable for regulatory submission and out-licensing deals were desired. In this analysis, we compiled and summarized 3 years of experiences of Q-fitter, the first PMx consultancy service company providing PMx analysis to the pharmaceutical industry in Korea. The projects were organized by companies, company types, indications, therapeutic areas, drug development stages, purposes, and scope of services. Within each category, we subcategorized the sections and assessed proportions and a year-over-year trend. As a result, we observed an increase in the number of projects in an average of ~170% per year, with the most frequent types of companies collaborated being the domestic pharmaceutical companies. Among the projects, ~72% involved modeling and simulation using population pharmacokinetic (PK) models, and the other included non-compartmental analysis (NCA), drug-drug interaction (DDI) prediction, and interpretation of the modeling results. The most sought-after purpose in PMx analysis was first-in-human (FIH) dose prediction followed by PK analysis, next clinical trial prediction, and scenario-based simulation. Oncology has been the top therapeutic area of interest every year consisting of ~38% of total projects, followed by Neurology (~13%). From this review, we were able to characterize the PMx service needs and spot the trend of current PMx practices in Korea.