Blochduelund9448

Sulfonylurea (SU) and dipeptidyl peptidase-4 (DPP-4) inhibitors are most common secondary agents that are added to metformin monotherapy. Real-world studies have become increasingly important in providing evidence of treatment effectiveness in clinical practice and real-world data could help appropriate therapeutic information. Therefore, this study aims to compare the glycemic effectiveness of SU and DPP-4 inhibitors, which are added to metformin monotherapy in real clinical practice using electronic medical record (EMR) data. EMR data of type 2 diabetes patients treated at Seoul National University Hospital from December 2002 to December 2012 were retrieved and analyzed. The patients were divided into three groups patients who maintained metformin monotherapy (M), and patients who added SU (MS) or DPP-4 inhibitors (MD) to metformin monotherapy. The mean change in HbA1c level, the proportion of patients achieving the HbA1c target less then 7.0%, proportion of patients with treatment failure, and probability of treatment failure occurrence and changes in prescription were evaluated to compare glycemic control efficacy between SU and DPP-4 inhibitors. The MS showed significantly greater reduction in the Hb1Ac level than MD. The proportion of patients achieving HbA1c less then 7.0% is higher in MD, whereas the proportion of patients with treatment failure was greater in MS. The probability of the treatment failure and probability of changes in the prescription were lower in MD than MS with hazard ratio of 0.499 and 0.579, respectively. In conclusion, this real-world study suggested that DPP-4 inhibitors are expected to show more durable glycemic control efficacy than SU in long-term use.There are several hurdles to overcome before implementing pharmacogenomics (PGx) in precision medicine. One of the hurdles is unawareness of PGx by clinicians due to insufficient pharmacogenomic information on drug labels. Therefore, it might be important to implement PGx that reflects pharmacogenomic information on drug labels, standard of prescription for clinicians. This study aimed to evaluate the level at which PGx was being used in clinical practice by comparing the Clinical Pharmacogenetics Implementation Consortium and Dutch Pharmacogenetics Working Group guidelines and drug labels of the US Food and Drug Administration (FDA) and the Korea Ministry of Food and Drug Safety (MFDS). Two PGx guidelines and drugs labels were scrutinized, and the concordance of the pharmacogenomic information between guidelines and drug labels was confirmed. The concordance of the label between FDA and MFDS was analyzed. In FDA labels, the number of concordant drug with guidelines was 24, while 13 drugs were concordant with MFDS labels. The number of drugs categorized as contraindication, change dose, and biomarker testing required was 7, 12 and 12 for the FDA and 8, 5 and 4 for the MFDS, respectively. The pharmacogenomic information of 9 drugs approved by both FDA and MFDS was identical. In conclusion, pharmacogenomic information on clinical implementation guidelines was limited on both FDA and MFDS labels because of various reasons including the characteristics of the guidelines and the drug labels. Therefore, more effort from pharmaceutical companies, academia and regulatory affairs needs to be made to implement pharmacogenomic information on drug labels.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. GM6001 concentration The geometric mean ratio (GMR) and its 90% confidence intervals (CIs) of the plasma maximum concentration (Cmax) 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 Cmax 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.

ClinicalTrials.gov Identifier NCT02529800.

ClinicalTrials.gov Identifier NCT02529800.SAS® is commonly used for bioequivalence (BE) data analysis. R is a free and open software for general purpose data analysis, and is less frequently used than SAS® for BE data analysis. This tutorial explains how R can be used for BE data analysis to generate comparable results with SAS®. The main SAS® procedures for BE data analysis are PROC GLM and PROC MIXED, and the corresponding R main packages are "sasLM" and "nlme" respectively. For fixed effects only or balanced data, the SAS® PROC GLM and R "sasLM" provide good estimates; however, for a mixed-effects model with unbalanced data, the SAS® PROC MIXED and R "nlme" are better for providing estimates without bias. The SAS® and R scripts are provided for convenience.This tutorial introduces background and methods to predict the human volume of distribution (Vd) of drugs using in vitro and animal pharmacokinetic (PK) parameters. The physiologically based PK (PBPK) method is based on the familiar equation Vd = Vp + ∑ T (VT × ktp ). In this equation, Vp (plasma volume) and VT (tissue volume) are known physiological values, and ktp (tissue plasma partition coefficient) is experimentally measured. Here, the ktp may be predicted by PBPK models because it is known to be correlated with the physicochemical property of drugs and tissue composition (fraction of lipid and water). Thus, PBPK models' evolution to predict human Vd has been the efforts to find a better function giving a more accurate ktp. When animal PK parameters estimated using i.v. PK data in ≥ 3 species are available, allometric methods can also be used to predict human Vd. Unlike the PBPK method, many different models may be compared to find the best-fitting one in the allometry, a kind of empirical approach. Also, compartmental Vd parameters (e.