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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
Subject(s)
Drug MonitoringABSTRACT
There are some errors in the published article. The authors would like to make corrections in the original version of the article.
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The equations on page 162 should be corrected.
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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.
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BACKGROUND: In type 2 diabetes mellitus therapy, fixed-dose combination (FDC) can offer not only benefits in glucose control via the combined use of agents, but also increase patient compliance. The aim of this study was to assess the pharmacokinetic equivalence of the high dose of the FDC tablet (gemigliptin/metformin sustained release [SR] 50/1,000 mg) and a corresponding co-administered dose of individual tablets. METHODS: This study was randomized, open-label, single dose, two treatments, two-period, crossover study, which included 24 healthy subjects. Subjects received the FDC or individual tablets of gemigliptin (50 mg) and metformin XR (1,000 mg) in each period. Geometric mean ratios (GMRs) and 90% confidence intervals (CIs) of maximum plasma concentration (Cmax) and area under the plasma concentration-time curve from time zero to the time of the last quantifiable concentration (AUClast) of the FDC tablet and co-administration of individual tablet for both gemigliptin and metformin were calculated. RESULTS: The GMRs (FDC tablets/co-administration; 90% CIs) for Cmax and AUClast of gemigliptin were 1.079 (0.986–1.180) and 1.047 (1.014–1.080), respectively. For metformin, the GMRs for Cmax, and AUClast were 1.038 (0.995–1.083) and 1.041 (0.997–1.088), respectively. The 90% CIs for GMRs of Cmax and AUClast for gemigliptin and metformin fell entirely within bounds of 0.800–1.250. Both administration of FDC tablet and co-administration of individual tablets were well tolerated. CONCLUSION: FDC tablet exhibited pharmacokinetic equivalence and comparable safety and tolerability to co-administration of corresponding doses of gemigliptin and metformin XR as individual tablets. Trial registry at ClinicalTrials.gov, NCT02056600.
Subject(s)
Cross-Over Studies , Diabetes Mellitus, Type 2 , Glucose , Healthy Volunteers , Metformin , Patient Compliance , Pharmacokinetics , Plasma , TabletsABSTRACT
Drunk driving is a serious social problem. We estimated the blood alcohol concentration of a defendant on the request of local prosecutor's office in Korea. Based on the defendant's history, and a previously constructed pharmacokinetic model for alcohol, we estimated the possible alcohol concentration over time during his driving using a Bayesian method implemented in NONMEM®. To ensure generalizability and to take the parameter uncertainty of the alcohol pharmacokinetic models into account, a non-parametric bootstrap with 1,000 replicates was applied to the Bayesian estimations. The current analysis enabled the prediction of the defendant's possible blood alcohol concentrations over time with a 95% prediction interval. The results showed a high probability that the alcohol concentration was ≥ 0.05% during driving. The current estimation of the alcohol concentration during driving by the Bayesian method could be used as scientific evidence during court trials.
Subject(s)
Bayes Theorem , Blood Alcohol Content , Driving Under the Influence , Forensic Sciences , Korea , Pharmacology, Clinical , Social Problems , UncertaintyABSTRACT
A bioequivalence study is usually conducted with the same-day drug administration. However, hospitalization is occasionally separated for logistical, operational, or other reasons. Recently, there was a case of separate hospitalization because of difficulties in subject recruitment. This article suggests a better way of bioequivalence data analysis for the case of separate hospitalization. The key features are (1) considering the hospitalization date as a random effect than a fixed effect and 2) using “PROC MIXED” instead of “PROC GLM” to include incomplete subject data.
Subject(s)
Hospitalization , Statistics as Topic , Therapeutic EquivalencyABSTRACT
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.
Subject(s)
Adolescent , Child , Humans , Young Adult , Cacao , Caffeine , Central Nervous System , Coffee , Pharmacokinetics , Plants , Plasma , TeaABSTRACT
Simvastatin is a lipid-lowering drug that is metabolized to its active metabolite simvastatin acid (SA). We developed and validated a sensitive liquid chromatography-tandem mass spectrometry (LC/MS/MS) method to quantitate SA in human plasma using a liquid-liquid extraction method with methanol. The protonated analytes generated in negative ion mode were monitored by multiple reaction monitoring. Using 500-mL plasma aliquots, SA was quantified in the range of 0.1-100 ng/mL. Calibration was performed by internal standardization with lovastatin acid, and regression curves were generated using a weighting factor of 1/χ2. The linearity, precision, and accuracy of this assay for each compound were validated using quality control samples consisting of mixtures of SA (0.1, 0.5, 5, and 50 ng/mL) and plasma. The intra-batch accuracy was 95.3-107.8%, precision was -2.2% to -3.7%, and linearity (r2) was over 0.998 in the standard calibration range. The chromatographic running time was 3.0 min. This method sensitively and reliably measured SA concentrations in human plasma and was successfully used in clinical pharmacokinetic studies of simvastatin in healthy Korean adult male volunteers.
Subject(s)
Adult , Humans , Male , Calibration , Liquid-Liquid Extraction , Lovastatin , Mass Spectrometry , Methanol , Plasma , Protons , Quality Control , Running , Simvastatin , VolunteersABSTRACT
While phosphodiesterase type 5 inhibitors have been used for erectile dysfunction with acceptable safety profile, they can induce orthostatic hypotension in patients taking antihypertensive drugs with blood pressure lowering effect. This study evaluated the hemodynamic effects of 100 mg mirodenafil in hypertensive patients taking an amlodipine. Thirteen hypertensive patients who were taking 5 or 10 mg of amlodipine once daily participated in a randomized, double-blind, placebo-controlled, crossover study. A single oral dose of mirodenafil 100 mg or placebo was administered at 4.5 hour after administration of amlodipine. The maximal change in systolic and diastolic blood pressure (ΔmaxSBP and ΔmaxDBP) and pulse rate (ΔmaxPR) were compared between mirodenafil and placebo periods. Twelve patients completed this study and were included analysis. The values of ΔmaxPR in standing and supine position were significantly greater in the mirodenafil period (13.25±7.12 and 11.17±4.86 beats/minute) when compared to the placebo (8.50±4.72 and 6.58±3.90 beats/minute). The ΔmaxSBP and ΔmaxDBP in standing position appeared to be lower in the mirodenafil period, but they were not statistically different from those in the placebo period (ΔmaxSBP = -7.42±5.6 vs -4.42±5.37 mmHg and ΔmaxDBP = -7.17±5.72 vs -3.50±3.37 mmHg). Both ΔmaxSBP and ΔmaxDBP in standing and supine position were not significantly different between mirodenafil and placebo. This study demonstrated that mirodenafil exerted minimal hemodynamic effects in the patients taking amlodipine, that is unlikely associated with a clinically significant hypotensive event.
Subject(s)
Humans , Male , Amlodipine , Antihypertensive Agents , Blood Pressure , Cross-Over Studies , Erectile Dysfunction , Heart Rate , Hemodynamics , Hypotension, Orthostatic , Phosphodiesterase 5 Inhibitors , Posture , Supine PositionABSTRACT
The first-order conditional estimation (FOCE) method is more complex than the first-order (FO) approximation method because it estimates the empirical Bayes estimate (EBE) for each iteration. By contrast, it is a further approximation of the Laplacian (LAPL) method, which uses second-order expansion terms. FOCE without INTERACTION can only be used for an additive error model, while FOCE with INTERACTION (FOCEI) can be used for any error model. The formula for FOCE without INTERACTION can be derived directly from the extension of the FO method, while the FOCE with INTERACTION method is a slight simplification of the LAPL method. Detailed formulas and R scripts are presented here for the reproduction of objective function values by NONMEM.
Subject(s)
Bays , Methods , ReproductionABSTRACT
The objective of this study was to externally validate a new dosing scheme for busulfan. Thirty-seven adult patients who received busulfan as conditioning therapy for hematopoietic stem cell transplantation (HCT) participated in this prospective study. Patients were randomized to receive intravenous busulfan, either as the conventional dosage (3.2 mg/kg daily) or according to the new dosing scheme based on their actual body weight (ABW) (23×ABW(0.5) mg daily) targeting an area under the concentration-time curve (AUC) of 5924 µM·min. Pharmacokinetic profiles were collected using a limited sampling strategy by randomly selecting 2 time points at 3.5, 5, 6, 7 or 22 hours after starting busulfan administration. Using an established population pharmacokinetic model with NONMEM software, busulfan concentrations at the available blood sampling times were predicted from dosage history and demographic data. The predicted and measured concentrations were compared by a visual predictive check (VPC). Maximum a posteriori Bayesian estimators were estimated to calculate the predicted AUC (AUC(PRED)). The accuracy and precision of the AUC(PRED) values were assessed by calculating the mean prediction error (MPE) and root mean squared prediction error (RMSE), and compared with the target AUC of 5924 µM·min. VPC showed that most data fell within the 95% prediction interval. MPE and RMSE of AUCPRED were -5.8% and 20.6%, respectively, in the conventional dosing group and −2.1% and 14.0%, respectively, in the new dosing scheme group. These fi ndings demonstrated the validity of a new dosing scheme for daily intravenous busulfan used as conditioning therapy for HCT.
Subject(s)
Adult , Humans , Area Under Curve , Behavior Therapy , Body Weight , Busulfan , Drug Dosage Calculations , Hematopoietic Stem Cell Transplantation , Hematopoietic Stem Cells , Pharmacokinetics , Prospective StudiesABSTRACT
NONMEM(R) is the most-widely used nonlinear mixed effects modelling tool introduced into population PK/PD analysis. Even though thousands of pharmaceutical scientists utilize NONMEM(R) routinely for their data analysis, the various estimation methods implemented in NONMEM(R) remain a mystery for most users due to the complex statistical and mathematical derivations underlying the algorithm used in NONMEM(R). In this tutorial, we demonstrated how to directly obtain the objective function value and post hoc eta for the first order approximation method by the use of R. We hope that this tutorial helps pharmacometricians understand the underlying estimation process of nonlinear mixed effects modelling.