ABSTRACT
Acetaminophen is known to be generally safe, and the occurrence of anaphylaxis due to acetaminophen has been rarely reported. We report a case of acetaminopheninduced anaphylaxis in a healthy male subject who participated in a clinical trial on the pharmacokinetics of ibandronate. The subject had not experienced an allergic reaction to acetaminophen prior to this incident. The patient received 1300 mg oral acetaminophen at about 12 hours after receiving 150 mg ibandronate. After about 10 minutes, the subject developed whole-body urticaria and hypotension. The temporal association suggested that the anaphylaxis was due to acetaminophen and not ibandronate. Anaphylaxis could occur due to acetaminophen even in the absence of allergic reactions in the first dosing.
ABSTRACT
Public disclosure of approved clinical trials in a reliable registry can provide the transparency of the study. Although the registration of clinical trials has increased remarkably, the integrity of the data is not always satisfactory. In this study, we analyzed public clinical trial databases updated by the Ministry of Food and Drug Safety (MFDS) and Clinical Research Information Service (CRIS) registry to provide an overview of the trends of clinical trials approved between 2017 and 2019 in Korea. Information on clinical trials approved between January 1, 2017 and December 31, 2019 was collected from two databases. Trial information was categorized and summarized by study phase, therapeutic area, and location of the participating centers. A total of 655 to 715 clinical trials were newly approved annually by MFDS during the period from 2017 to 2019. Phase 1 clinical trials accounted for the largest proportion (31.0%), followed by phase 3 (29.5%), investigator-initiated trials (24.1%), phase 2 (14.6%), and phase 4 (0.5%). The number of clinical trials classified as an Antineoplastic and immunomodulating agent was the greatest (40.1%) regardless of the study phase. The similar result was obtained from CRIS registry where therapeutic area Neoplasms (15.9%) accounted for the largest number. The number of clinical trials performed in Seoul and Gyeonggi-do was approximately 70% of the total trials. In conclusion, our study provided a comprehensive overview of clinical trials in Korea from 2017 to 2019. The discrepancy between clinical trial registries could be resolved by introducing standardized database and guidelines.
ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
This tutorial explains the basic principles of mechanistic ligand-receptor interaction model, which is an operational model of agonism. A growing number of agonist drugs, especially immune oncology drugs, is currently being developed. In this tutorial, time-dependent ordinary differential equation for simple E(max) operational model of agonism was derived step by step. The differential equation could be applied in a pharmacodynamic modeling software, such as NONMEM, for use in non-steady state experiments, in which experimental data are generated while the interaction between ligand and receptor changes over time. Making the most of the non-steady state experimental data would simplify the experimental processes, and furthermore allow us to identify more detailed kinetics of a potential drug. The operational model of agonism could be useful to predict the optimal dose for agonistic drugs from in vitro and in vivo animal pharmacology experiments at the very early phase of drug development.
Subject(s)
Animals , Felodipine , In Vitro Techniques , Kinetics , PharmacologyABSTRACT
There are some errors in the published article. The authors would like to make corrections in the original version of the article.
ABSTRACT
Noncompartmental analysis (NCA) is a primary analytical approach for pharmacokinetic studies, and its parameters act as decision criteria in bioequivalent studies. Currently, NCA is usually carried out by commercial softwares such as WinNonlin®. In this article, we introduce our newly-developed two R packages, NonCompart (NonCompartmental analysis for pharmacokinetic data) and ncar (NonCompartmental Analysis for pharmacokinetic Report), which can perform NCA and produce complete NCA reports in both pdf and rtf formats. These packages are compatible with CDISC (Clinical Data Interchange Standards Consortium) standard as well. We demonstrate how the results of WinNonlin® are reproduced and how NCA reports can be obtained. With these R packages, we aimed to help researchers carry out NCA and utilize the output for early stages of drug development process. These R packages are freely available for download from the CRAN repository.
ABSTRACT
Caffeine is a naturally-occurring central nervous system stimulant found in plant constituents including coffee, cocoa beans, and tea leaves. Consumption of caffeine through imbibing caffeinated drinks is rapidly growing among children, adolescents, and young adults, who tend to be more caffeine-sensitive than the rest of the general public; consequently, caffeine-related toxicities among these groups are also growing in number. However, a quantitative and interactive tool for predicting the plasma caffeine concentration that may lead to caffeine intoxication has yet to be developed. Using the previously established population-pharmacokinetic model, we developed “caffsim” R package and its web-based applications using Shiny and EDISON (EDucation-research Integration through Simulation On the Net). The primary aim of the software is to easily predict and calculate plasma caffeine concentration and pharmacokinetic parameters and visualize their changes after single or multiple ingestions of caffeine. The caffsim R package helps understand how plasma caffeine concentration changes over time and how long toxic concentration of caffeine can last in caffeine-sensitive groups. It may also help clinical evaluation of relationship between caffeine intake and toxicities when suspicious acute symptoms occur.