Physiologically-based pharmacokinetic modeling of nafamostat to support dose selection for treatment of pediatric patients with COVID-19

Pediatric patients with coronavirus disease 2019 (COVID-19) are increasing, and severe cases such as multisystem inflammatory syndrome are being reported. Nafamostat, a repurposing drug, is currently being explored for the treatment of COVID-19 in adults. However, the data supporting its exposure in pediatrics remains scarce. Physiologically-based pharmacokinetic (PBPK) modeling enables the prediction of drug exposure in pediatrics based on ontogeny of metabolic enzymes and age dependent anatomical and physiological changes. The study aimed to establish a PBPK model of nafamostat in adults, then scale the adult PBPK model to children for predicting pediatric exposures of nafamostat and an optimal weight-based nafamostat dose in pediatric population. The developed model adequately described adult exposure data in healthy volunteers following i.v. administration with three doses (10, 20, and 40 mg). Scaling adult PBPK models to five pediatric groups predicted that as age advances from neonate to adult, the exposure of nafamostat slightly increased from neonate to infant, steadily decreased from infant to child, and then increased from child to adult after the administration of 0.2 mg/kg/h for 14 days, a dosing regimen being conducted in a clinical trial for COVID-19. Based on the fold change of predicted area under the curve for the respective pediatric group over those of adults, weight-based dosages for each pediatric group may be suggested. The novel PBPK model described in this study may be useful to investigate nafamostat pharmacokinetics in a pediatric subgroup further.

Erratum: Development of R packages: ‘NonCompart’ and ‘ncar’ for noncompartmental analysis (NCA)

There are some errors in the published article. The authors would like to make corrections in the original version of the article.

Erratum: Comparison of various estimation methods for the parameters of Michaelis-Menten equation based on in vitro elimination kinetic simulation data

There are some errors in the published article. The authors would like to make corrections in the original version of the article.

Development of R packages: ‘NonCompart’ and ‘ncar’ for noncompartmental analysis (NCA)

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.

Comparison of various estimation methods for the parameters of Michaelis-Menten equation based on in vitro elimination kinetic simulation data

The Michaelis-Menten equation is one of the best-known models describing the enzyme kinetics of in vitro drug elimination experiments, and takes a form of equation relating reaction rate (V) to the substrate concentration ([S]) via the maximum reaction rate (Vmax) and the Michaelis constant (Km). The current study was conducted to compare the accuracy and precision of the parameter estimates in the Michaelis-Menten equation from various estimation methods using simulated data. One thousand replicates of simulated [S] over serial time data were generated using the results of a previous study, incorporating additive or combined error models as a source of random variables in the Monte-Carlo simulation using R. From each replicate of simulated data, Vmax and Km were estimated by five different methods, including traditional linearization methods and nonlinear ones without linearization using NONMEM. The relative accuracy and precision of the estimated parameters were compared by the median values and their 90% confidence intervals. Overall, Vmax and Km estimation by nonlinear methods (NM) provided the most accurate and precise results from the tested 5 estimation methods. The superiority of parameter estimation by NM was even more evident in the simulated data incorporating the combined error model. The current simulation study suggests that NMs using a program such as NONMEM provide more reliable and accurate parameter estimates of the Michaelis-Menten equation than traditional linearization methods in in vitro drug elimination kinetic experiments.

Pharmacokinetic Equivalence of the High Dose Strength Fixed-Dose Combination Tablet of Gemigliptin/Metformin Sustained Release (SR) and Individual Component Gemigliptin and Metformin XR Tablets in Healthy Subjects

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.

Caffsim: simulation of plasma caffeine concentrations implemented as an R package and Web-applications

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.