Background/Aims: Tegoprazan, a novel potassium-competitive acid blocker, is expected to overcome the limitations of proton pump inhibitors and effectively control nocturnal acid breakthrough. To evaluate the pharmacodynamics of tegoprazan versus dexlansoprazole regarding nocturnal acid breakthrough in healthy subjects. Methods: In a randomized, open-label, single-dose, balanced incomplete block crossover study, 24 healthy male volunteers were enrolled and randomized to receive oral tegoprazan (50, 100, or 200 mg) or dexlansoprazole (60 mg) during each of two administration periods, separated by a 7- to 10-day washout period. Blood samples were collected for pharmacokinetic parameter analysis; gastric monitoring was performed for pharmacodynamic parameter evaluation. Results: All 24 subjects completed the study. Average maximum plasma concentration, area under the plasma concentration–time curve, and mean time with gastric pH >4 and pH >6 for tegoprazan demonstrated dose-dependent incremental increases. All the tegoprazan groups reached mean pH ≥4 within 2 hours, whereas the dexlansoprazole group required 7 hours after drug administration. Based on pharmacodynamic parameters up to 12 hours after evening dosing, 50, 100, and 200 mg of tegoprazan presented a stronger acid-suppressive effect than 60 mg of dexlansoprazole. Moreover, the dexlansoprazole group presented a comparable acid-suppressive effect with the tegoprazan groups 12 hours after dosing. Conclusions All the tegoprazan groups demonstrated a significantly faster onset of gastric pH increase and longer holding times above pH >4 and pH >6 up to 12 hours after evening dosing than the dexlansoprazole group.

Wald confidence interval has been used as the conventional method of interval estimation for the parameters in nonlinear models. Because Wald confidence interval is symmetric around the point estimate, it does not reflect the asymmetry of the likelihood profile in nonlinear regression. In contrast, a likelihood interval is estimated directly from the likelihood profile and does reflect the shape of the likelihood profile. However, the lack of software for the estimation of likelihood intervals and visualization of likelihood profiles posed an obstacle to the use of likelihood intervals in nonlinear models. There was a need for software implementation to tackle these tasks. Likelihood interval estimation and likelihood profile plotting for nonlinear models had not been previously implemented in R software. This article describes the implementation of likelihood interval estimation and likelihood profile plotting in the wnl R software package. To demonstrate the usage of implemented functions, an example of fitting a nonlinear pharmacokinetic model to concentration-time data is presented.

In healthcare situations, time-to-event (TTE) data are common outcomes. A parametric approach is often employed to handle TTE data because it is possible to easily visualize different scenarios via simulation. Not all pharmacometricians are familiar with the use of non-linear mixed effects models (NONMEMs) to deal with TTE data. Therefore, this tutorial simply explains how to analyze TTE data using NONMEM. We show how to write the code and evaluate the model. We also provide an example of a hands-on model for training.

Analysis of a 2 × 2 table for clinical data involves computing the point estimate and confidence interval for risk difference, relative risk, or odds ratio. While point estimates of these comparative parameters are uniquely defined, several statistical methods have been proposed to estimate the confidence interval for each parameter. The Miettinen-Nurminen (MN) score method is expected to be used increasingly over traditional interval estimation methods. The MN score method has not been previously implemented in R software for data with stratification. There is a need for a comprehensive software implementation of the MN score method. This article describes the implementation of the MN score method in the sasLM R software package. To demonstrate the usage of the sasLM functions introduced, recently published clinical data are provided as examples.

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.

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.

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.

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.

The analytical solution for multi-compartment models with a non-zero initial condition is complex because of the inter-compartmental transfer. An elegant solution and its implementation in the ‘wnl' R package can be useful in solving examples of textbooks and developing software of therapeutic drug monitoring, pharmacokinetic simulation, and parameter estimation. This solution uses Laplace transformation, convolution, matrix inversion, and the fact that the general solution of an inhomogeneous ordinary differential equation is the sum of a homogenous and a particular solution, together.
Drug Monitoring

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.