As the population ages, the life of women after menopause becomes much longer than the past, and the quality of life of old age becomes increasingly principal issue. There is a period that women experience the physical symptoms of menopause, although there are differences in degree, and the management of this period is a suitable time for women to improve their quality of life. According to the menopausal hormone therapy (MHT) and Timing Hypothesis, which has been proven in the Kronos Early Estrogen Prevention Study (KEEPS) and the Early vs Late Intervention Trial with Estradiol (ELITE) study, a relatively young woman before and after menopause can benefit from long-term beneficial effects such as prevention of osteoporosis and cardiovascular disease by early initiation of hormone therapy to alleviate menopausal symptoms. MHT should be considered for all women in healthy (without other important diseases) menopausal years, expecting to improve their quality of life through symptom relief in menopausal women and, in the long term, to prevent cardiovascular disease and osteoporosis. When applying hormone therapy to individuals, it is necessary to establish various treatment strategies according to the menopausal symptoms of individual patients (individualization of treatment) and judge the suitability of clinical application.

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.

OBJECTIVE: To develop a population pharmacokinetics (PK)/pharmacodynamics (PD) model for alcohol in healthy volunteers and to elucidate individual characteristics to affects alcohol's PK or PD including dissolved oxygen. METHODS: Following multiple intakes of total 540 mL alcohol (19.42 v/v%) to healthy volunteer, blood alcohol concentration was measured using a Breathe alcohol analyser (Lion SD-400 Alcolmeter®). A sequential population PK/PD modeling was performed using NONMEM (ver 7.3). RESULTS: Eighteen healthy volunteer were included in the study. PK model of alcohol was well explained by one-compartment model with first-order absorption and Michaelis-Menten elimination kinetics. K(a), V/F, V(max), K(m) is 8.1 hr⁻¹, 73.7 L, 9.65 g/hr, 0.041 g/L, respectively. Covariate analysis revealed that gender significantly influenced V(max) (Male vs Female, 9.65 g/hr vs 7.38 g/hr). PD model of temporary systolic blood pressure decreasing effect of alcohol was explained by biophase model with inhibitory E(max) model. K(e0), I(max), E(0), IC(50) were 0.23 hr⁻¹, 44.9 mmHg, 138 mmHg, 0.693 g/L, respectively. CONCLUSION: Model evaluation results suggested that this PK/PD model was robust and has good precision.

Background: Generally, pharmacokinetics (PK) models could be stratified into two models. The compartment PK model uses the concept of simple compartmentalization to describe complex bodies, and the physiologically based pharmacokinetic (PBPK) model describes the body using multi-compartment networking. Notwithstanding sharing a theoretical background in both models, there was still a lack of knowledge to enhance compatibility in both models. Objective: This study aimed to evaluate the compatibility among PBPK, lumping model and compartment PK model with voriconazole PK case study. Methods: The number of compartments and blood flow on each tissue in the PBPK model were modified using the lumping method, considering physiological similarities. The concentration-time profiles and area under the concentration-time curve (AUC) parameters were simulated at each model, assuming taken voriconazole oral 400 mg single dose. After that, those mentioned PK parameters were compared. Results: The PK profiles and parameters of voriconazole in the three models were similar that proves their compatibility. The AUC of central compartment in the PBPK and lumping model was within a 2-fold range compared to those in the 2-compartment model. The AUC of non-eliminating tissues compartment in the PBPK model was similar to those in the lumping model. Conclusion Regarding the compatibility of the three PK models, the utilization of the lumping method was confirmed by suggesting its reliable PK parameters with PBPK and compartment PK models. Further case studies are recommended to confirm our findings.

Background: Pembrolizumab, an anti-cancer drug, is known to increase the activity of the immune system, leading to side effects called immune-related adverse events (irAE), including type 1 diabetes. This study analyzed the correlation between blood glucose level and pembrolizumab administration and investigated the covariates that affect those changes in cancer treatment. Methods: The information of 133 adult cancer patients was obtained from the electronic medical record (EMR) to identify the changes in random blood glucose (RBG) levels during the pembrolizumab treatment. Subjects were classified into subgroups according to their baseline RBG level, history of diabetes, and the use of steroids, and linear regression analysis was conducted. In addition, a secondary analysis was performed within the group of subjects having a strong correlation to glycemic change, which was based on the Pearson correlation coefficient being less than -0.7 or greater than +0.7. Univariate and multivariate logistic regressions were conducted to identify the risk factors to glycemic increase. Results: The RBG level tended to descend without significant differences in total patients during the administration period of pembrolizumab. Despite the insignificance, the logistic regression analysis presents that the odds ratios of baseline RBG less than 130 mg/dL, prophylactic steroid use, and higher dose of pembrolizumab per cycle (mg/kg/ cycle) were greater than 1. Conclusions Prophylactic administration of steroids and a higher dose of pembrolizumab per cycle may increase the blood glucose level as irAE in cancer patients with a strong tendency to glycemic change.

Background: Generally, pharmacokinetics (PK) models could be stratified into two models. The compartment PK model uses the concept of simple compartmentalization to describe complex bodies, and the physiologically based pharmacokinetic (PBPK) model describes the body using multi-compartment networking. Notwithstanding sharing a theoretical background in both models, there was still a lack of knowledge to enhance compatibility in both models. Objective: This study aimed to evaluate the compatibility among PBPK, lumping model and compartment PK model with voriconazole PK case study. Methods: The number of compartments and blood flow on each tissue in the PBPK model were modified using the lumping method, considering physiological similarities. The concentration-time profiles and area under the concentration-time curve (AUC) parameters were simulated at each model, assuming taken voriconazole oral 400 mg single dose. After that, those mentioned PK parameters were compared. Results: The PK profiles and parameters of voriconazole in the three models were similar that proves their compatibility. The AUC of central compartment in the PBPK and lumping model was within a 2-fold range compared to those in the 2-compartment model. The AUC of non-eliminating tissues compartment in the PBPK model was similar to those in the lumping model. Conclusion Regarding the compatibility of the three PK models, the utilization of the lumping method was confirmed by suggesting its reliable PK parameters with PBPK and compartment PK models. Further case studies are recommended to confirm our findings.

Background: Pembrolizumab, an anti-cancer drug, is known to increase the activity of the immune system, leading to side effects called immune-related adverse events (irAE), including type 1 diabetes. This study analyzed the correlation between blood glucose level and pembrolizumab administration and investigated the covariates that affect those changes in cancer treatment. Methods: The information of 133 adult cancer patients was obtained from the electronic medical record (EMR) to identify the changes in random blood glucose (RBG) levels during the pembrolizumab treatment. Subjects were classified into subgroups according to their baseline RBG level, history of diabetes, and the use of steroids, and linear regression analysis was conducted. In addition, a secondary analysis was performed within the group of subjects having a strong correlation to glycemic change, which was based on the Pearson correlation coefficient being less than -0.7 or greater than +0.7. Univariate and multivariate logistic regressions were conducted to identify the risk factors to glycemic increase. Results: The RBG level tended to descend without significant differences in total patients during the administration period of pembrolizumab. Despite the insignificance, the logistic regression analysis presents that the odds ratios of baseline RBG less than 130 mg/dL, prophylactic steroid use, and higher dose of pembrolizumab per cycle (mg/kg/ cycle) were greater than 1. Conclusions Prophylactic administration of steroids and a higher dose of pembrolizumab per cycle may increase the blood glucose level as irAE in cancer patients with a strong tendency to glycemic change.

Background: Linezolid has been widely used in the treatment for multidrug-resistant tuberculosis. However, there are limitations to use it such as long treatment, because of related side effects, even adequate treatment period has been needed for remission of multidrug-resistant tuberculosis (MDR-TB). Method: The meta-analysis was performed based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. To choose literatures, systematic literature reviews were conducted with databases of PubMed, Web of Science, and EMBASE. Results: Efficacy and safety of Linezolid were determined by 85% (95% CI=79~89%, p<0.05) in the sputum culture conversion and 55% (95% CI=45~64%, p<0.01) in side effects related to linezolid, respectively. In addition, I2 was estimated by 72%. In the subgroup analysis, efficacy and safety by dose and region were analyzed. In the subgroup analysis, compared with the linezolid dose in groups greater than 600 mg/day and less than 600 mg/day, this study showed 85% (95% CI 79~90%, p>0.05) in 206 patients and 82% (95% CI 73~89%, p<0.05) in 297 patients, respectively. Also, in the subgroup analysis, adverse effects caused by linezolid occurred more than 50% of treated patients. Conclusion Therapeutic efficacy of linezolid for MDR-TB patients was confirmed regardless of the initial dose of linezolid, especially for sputum culture conversion and it was recommended that the dose of linezolid has been more effective below 600 mg/day. However, it should be necessary to closely monitored for safety issues since serious side effects possibly occurred by administration of long period treatment.

Alterations in sulfur amino acid metabolism are associated with an increased risk of a number of common late-life diseases, which raises the possibility that metabolism of sulfur amino acids may change with age. The present study was conducted to understand the age-related changes in hepatic metabolism of sulfur amino acids in 2-, 6-, 18- and 30-month-old male C57BL/6 mice. For this purpose, metabolite profiling of sulfur amino acids from methionine to taurine or glutathione (GSH) was performed. The levels of sulfur amino acids and their metabolites were not significantly different among 2-, 6- and 18-month-old mice, except for plasma GSH and hepatic homocysteine. Plasma total GSH and hepatic total homocysteine levels were significantly higher in 2-month-old mice than those in the other age groups. In contrast, 30-month-old mice exhibited increased hepatic methionine and cysteine, compared with all other groups, but decreased hepatic S-adenosylmethionine (SAM), S-adenosylhomocysteine and homocysteine, relative to 2-month-old mice. No differences in hepatic reduced GSH, GSH disulfide, or taurine were observed. The hepatic changes in homocysteine and cysteine may be attributed to upregulation of cystathionine β-synthase and down-regulation of γ-glutamylcysteine ligase in the aged mice. The elevation of hepatic cysteine levels may be involved in the maintenance of hepatic GSH levels. The opposite changes of methionine and SAM suggest that the regulatory role of SAM in hepatic sulfur amino acid metabolism may be impaired in 30-month-old mice.
Aging ; Amino Acids, Sulfur ; Animals ; Child, Preschool ; Cystathionine ; Cysteine ; Down-Regulation ; Glutathione ; Homocysteine ; Humans ; Infant ; Male* ; Metabolism* ; Metabolomics ; Methionine ; Mice* ; Plasma ; S-Adenosylhomocysteine ; S-Adenosylmethionine ; Sulfur* ; Taurine ; Up-Regulation

Background: Metabolically healthy obese (MHO) phenotype is metabolically heterogeneous in terms of type 2 diabetes (T2D). Previously, the triglyceride and glucose (TyG) index has been considered for identifying metabolic health and future risk of T2D. This study aimed to evaluate the risk of incident T2D according to obesity status and metabolic health, categorized by four different criteria and the TyG index. Methods: The study included 39,418 Koreans without T2D at baseline. The risk of T2D was evaluated based on four different definitions of metabolic health and obesity status and according to the baseline TyG index within each metabolic health and obesity group. Results: During the median follow-up at 38.1 months, 726 individuals developed T2D. Compared with the metabolically healthy non-obese (MHNO) group with low TyG index, the MHO group with high TyG index showed increased risk of T2D in all four definitions of metabolic health with multivariate-adjusted hazard ratios of 2.57 (95% confidence interval [CI], 1.76 to 3.75), 3.72 (95% CI, 2.15 to 6.43), 4.13 (95% CI, 2.67 to 6.38), and 3.05 (95% CI, 2.24 to 4.15), when defined by Adult Treatment Panel III, Wildman, Karelis, and homeostasis model assessment (HOMA) criteria, respectively. Conclusion MHO subjects with high TyG index were at an increased risk of developing T2D compared with MHNO subjects, regardless of the definition of metabolic health. TyG index may serve as an additional factor for predicting the individual risk of incident T2D in MHO subjects.