Translational and Clinical Pharmacology最新文献

Insoles are used to treat various foot diseases, including plantar foot, diabetic foot ulcers, and refractory plantar fasciitis. In this study, we investigated the effects of 3-dimensional image-based (3-D) insole in healthy volunteers with no foot diseases. Additionally, the comfort of the 3-D insole was compared with that of a custom-molded insole. A single-center, randomized, open clinical trial was conducted to address the effectiveness of insole use in a healthy population with no foot or knee disease. Two types of arch support insoles were evaluated for their effectiveness: a 3-D insole and a custom-molded insole. Fifty Korean volunteers participated in the study and were randomly allocated into the "3-D insole" (n = 40) or "custom-molding insole" (n = 10) groups. All subjects wore 3-D insoles or custom-molded insoles for 2 weeks. The sense of wearing shoes (Visual Analog Scale [VAS] and score) and fatigue of the foot were used to assess the insole effects at the end of the 2-week study period. The 3-D insole groups showed significantly improved sense of wearing shoes (VAS, p = 0.0001; score, p = 0.0002) and foot fatigue (p = 0.0005) throughout the study period. Although the number of subjects was different, the custom-molding insole group showed no significant changes in the sense of wearing shoes (VAS, 0.1188; score, p = 0.1483). Foot fatigue in the 3-D insole group improved significantly (p = 0.0005), which shows that a 3-D insole might have favorable effects on foot health in a healthy population.

Trial registration: Clinical Research Information Service Identifier: KCT0008100.

P-glycoprotein (P-gp) is a transporter that plays an excretory role in epithelial cells. It is encoded by ABCB1, and single nucleotide polymorphisms (SNPs) in this gene can affect systemic drug exposure. Dapagliflozin and sitagliptin, used in type 2 diabetes treatment, are P-gp substrates. Here, we aimed to investigate whether ABCB1 polymorphisms affect dapagliflozin and sitagliptin pharmacokinetics (PK) in healthy Korean subjects. The study population consisted of 100 healthy Korean subjects (94 men and 6 women) who participated in four different clinical trials and received dapagliflozin and sitagliptin doses of 10 and 100 mg, respectively. We determined ABCB1 genotypes for the C3435T, C1236T, and G2677T/A SNPs. The relationship between the genotypes and dapagliflozin PKs was examined. Dapagliflozin and sitagliptin PK parameters were not statistically significantly affected by ABCB1 SNP genotypes. However, homozygous 3435TT subjects showed higher dapagliflozin PK parameters than CT and CC subjects. In subjects with the 3435TT and those with 3435CC and 3435CT genotypes, mean C_max, AUC_inf, and AUC_0-1 values of dapagliflozin were 223.06 ng/mL and 194.81 ng /mL (p = 0.2767), 673.58 ng*h/mL and 573.96 ng*h/mL (p = 0.0492), and 128.53 ng*h/mL and 104.61 ng*h/mL (p = 0.2678), respectively. In summary, dapagliflozin and sitagliptin PK parameters were not significantly different between individuals with C1236T and C2677T/A ABCB1 genetic polymorphisms. Dapagliflozin exhibited higher systemic exposure in 3435TT subjects than in CC/CT subjects.

Nafamostat has been actively studied for its neuroprotective activity and effect on various indications, such as coronavirus disease 2019 (COVID-19). Nafamostat has low water solubility at a specific pH and is rapidly metabolized in the blood. Therefore, it is administered only intravenously, and its distribution is not well known. The main purposes of this study are to predict and evaluate the pharmacokinetic (PK) profiles of nafamostat in a virtual healthy population under various dosing regimens. The most important parameters were assessed using a physiologically based pharmacokinetic (PBPK) approach and global sensitivity analysis with the Sobol sensitivity analysis. A PBPK model was constructed using the SimCYP^® simulator. Data regarding the in vitro metabolism and clinical studies were extracted from the literature to assess the predicted results. The model was verified using the arithmetic mean maximum concentration (C_max), the area under the curve from 0 to the last time point (AUC_0-t), and AUC from 0 to infinity (AUC_0-∞) ratio (predicted/observed), which were included in the 2-fold range. The simulation results suggested that the 2 dosing regimens for the treatment of COVID-19 used in the case reports could maintain the proposed effective concentration for inhibiting severe acute respiratory syndrome coronavirus 2 entry into the plasma and lung tissue. Global sensitivity analysis indicated that hematocrit, plasma half-life, and microsomal protein levels significantly influenced the systematic exposure prediction of nafamostat. Therefore, the PBPK modeling approach is valuable in predicting the PK profile and designing an appropriate dosage regimen.

The treatment of carbapenem-resistant Enterobacteriaceae (CRE) is diverse in each region due to the difference in local resistant patterns of CRE. We aimed to explore how physicians in Thailand decide on selection options for treating CRE infections. In this study, 25 physicians who were not infectious disease (ID) specialists participated in this semi-structured in-depth interview. We found that they, in general, did not provide empiric antibiotics for the treatment of CRE. However, some patients, e.g., those with prior carbapenems exposure may have brought CRE to physicians' attention. ID specialists played critical roles in both empiric and specific CRE treatment. There were multiple scenarios when CRE management deviated from recommendations, especially when physicians perceived that the evidence that supported the recommendations was weak. Several supportive factors, challenges, and improvements were also suggested. In conclusion, ID specialists, adequate information, and consistent implementation of infectious control policy are crucial to the treatment and prevention of CRE infection.

For personalized drug dosing, prediction models may be utilized to overcome the inter-individual variability. Multiple linear regression has been used as a conventional method to model the relationship between patient features and optimal drug dose. However, linear regression cannot capture non-linear relationships and may be adversely affected by non-normal distribution and collinearity of data. To overcome this hurdle, machine learning models have been extensively adapted in drug dose prediction. In this tutorial, random forest and neural network models will be trained in tandem with a multiple linear regression model on the International Warfarin Pharmacogenetics Consortium dataset using the scikit-learn python library. Subsequent model analyses including performance comparison, permutation feature importance computation and partial dependence plotting will be demonstrated. The basic methods of model training and analysis discussed in this article may be implemented in drug dose-related studies.

A 76-year-old female visited the emergency department with complaining of dizziness and syncope. She had a history of paroxysmal atrial fibrillation (AF) and had been prescribed flecainide 50 mg and apixaban 5 mg 12-hourly in another hospital 1 day before the presentation. Upon admission, her electrocardiogram showed profound bradycardia and extremely long sinus arrest, which required temporary cardiac pacing. Within 24 hours, her intrinsic rhythm was restored, and the temporary pacemaker was removed. Transthoracic and transesophageal echocardiography revealed no structural heart disease or thrombus in the left atrial appendage. Cardiac computed tomography showed no coronary artery stenosis, but a pulmonary thrombus in the right pulmonary artery. She underwent an electrophysiology study, and four pulmonary vein (PV) isolations were attempted to treat the paroxysmal AF. A bidirectional PV conduction block was acquired in all PVs despite spontaneous dissociation of PV potential in the right PV. Programmed stimulation following ablation resulted in sinus node dysfunction. After the procedure, the patient did not complain of dizziness and syncope for 72 hours of telemetry monitoring. She was discharged with anticoagulant and did not show any further symptoms for 6 months. Flecainide acetate is a class Ic antiarrhythmics, and its clinical efficacy has been confirmed in several clinical trials. However, it can unmask sinus node dysfunction in asymptomatic patients with paroxysmal AF. Clinicians should screen candidates for sinus nodal diseases when prescribing flecainide.

Melatonin is an endogenous chronobiological regulator secreted mainly from the pineal gland, which has been used as a dietary supplement in the treatment of sleep problems, including insomnia, parasomnia, and circadian rhythm sleep disorders. However, the short half-life and rapid metabolism of melatonin limit its suitability as a drug. There are many melatonergic drugs used in the treatment of sleep disorders and several drugs are under investigation for approval. Ramelteon was the first melatonergic agonist approved as hypnotic agent by U.S. Food and Drug Administration for the treatment of insomnia. It exhibits higher selective affinity for melatonin 1a (MT₁) receptor than melatonin 1b (MT₂) receptor. This selectivity suggests that it targets sleep onset with no significant adverse effect or dependency. Agomelatin, naphtahalenic compound, act as a potent MT₁/MT₂ melatonergic receptor agonist and serotonergic receptor antagonist was approved for treatment of depression in 2009. This dual action drug is the first melatonergic agent used in depression. Another melatonergic agonist, tasimelteon has high affinity for the MT₁/MT₂ receptors in humans. It was approved for the treatment of non-24 hours sleep-wake rhythm disorder. The newly developed melatonin and melatonergic drugs have the potential to be used extensively in various clinical situations and substitute the old benzodiazepine and its derivatives in the treatment of insomnia. However, the efficacy and safety of newly developed melatonergic drugs should be elucidated through long-term clinical trials.

A new fixed-dose combination (FDC) formulation of raloxifene 60 mg and cholecalciferol 800 IU was developed to improve the medication compliance and overall efficacy of raloxifene treatment in postmenopausal osteoporosis patients. The aim of this study was to compare the pharmacokinetics between two tablets of FDC formulation of raloxifene/cholecalciferol and the two products administered concomitantly at respective doses. This randomized, open-label, single-dose, two-treatment, two-way crossover study included 46 volunteers. During each treatment period, subjects received the test formulation (FDC formulation containing raloxifene and cholecalciferol) or the reference formulation (co-administration of raloxifene and cholecalciferol), with a 14-d washout period. Serial blood samples were collected periodically over 96 hours after drug intake. In total, 46 subjects completed the study. The geometric mean ratios and its 90% confidence intervals of the FDC to the single agents for the area under the concentration-time curve from zero to the last quantifiable time point and the maximum plasma concentration met the regulatory criteria for bioequivalence: 1.1364 (1.0584-1.2201) and 1.1010 (0.9945-1.2188) for raloxifene and 1.0266 (0.9591-1.0989) and 1.0354 (0.9816-1.0921) for baseline-corrected cholecalciferol, respectively. Both formulations were well tolerated. No significant differences was observed in the incidence of adverse events between the two treatments. It was concluded that two tablets of the newly developed FDC formulation of raloxifene and cholecalciferol and the corresponding two agents administered concomitantly at respective doses were bioequivalent.

Trial registration: ClinicalTrials.gov Identifier: NCT03010267.

As real-world data (RWD) becomes more available and the methodology for handling RWD evolves, the use of RWD in drug development and drug approval is drawing interest. One of the ways RWD can be applied to a clinical trial is using an external control, a cohort of patients established separately serving as a control group for the clinical trial's treatment group. Although external controls have the possibility of bias as a result of differences in baseline characteristics between the external control and experimental groups, selecting an appropriate data source and ensuring comparability through proper handling of the data can increase the utility of external controls, raising the efficiency of drug development. This article discusses several topics relevant to using external controls in clinical trials, including the definition of external control, the selection of data sources, the strategy ensuring comparability, current regulatory circumstances, and future directions.

Obesity has been a growing worldwide concern, and surgical intervention including bariatric surgery is considered as one of the options for treatment. However, there still is controversy over the change in pharmacokinetics (PKs) of drugs after the surgery. To investigate the potential covariates that can influence the area under the curve (AUC) and maximum plasma concentration (C_max), the design of previous studies was reviewed based on pre-determined eligibility criteria. Each study calculated the ratios of the AUC and C_max before and after bariatric surgery. These studies investigated whether the PK parameters were affected by the time after the surgery or by the type of control group. The ratio of the AUC calculated in the early and late follow-up period was similar across Roux-en Y gastric bypass patients. No significant difference in the PK parameters was found between the pre-surgical patients and matched healthy subjects. However, certain control groups could be preferable depending on the purpose of the clinical trial. Although C_max was inconsistent compared to the AUC, insufficient sampling of the time points may have caused such an inconsistency. This is the first article exploring the appropriate methodology in designing clinical studies for changes in the PK characteristics of orally administered drugs in patients with bariatric surgery.