Pharmacokinetic Interactions Between Bazedoxifene and Cholecalciferol: An Open-Label, Randomized, Crossover Study in Healthy Male Volunteers.

Purpose: The combined administration of bazedoxifene, a tissue-selective estrogen receptor modulator, and cholecalciferol can be a promising therapeutic option for postmenopausal osteoporosis patients. This study aimed to examine the pharmacokinetic interactions between these two drugs and the tolerability of their combined administration in healthy male subjects. Patients and Methods: Thirty male volunteers were randomly assigned to one of the six sequences comprised of three treatments: bazedoxifene 20 mg monotherapy, cholecalciferol 1600 IU monotherapy, and combined bazedoxifene and cholecalciferol therapy. For each treatment, a single dose of the investigational drug(s) was administered orally, and serial blood samples were collected to measure the plasma concentrations of bazedoxifene and cholecalciferol. Pharmacokinetic parameters were calculated using the non-compartmental method. The point estimate and 90% confidence interval (CI) of the geometric mean ratio (GMR) were obtained to compare the exposures of combined therapy and monotherapy. The pharmacokinetic parameters compared were the maximum plasma concentration (Cmax) and the area under the plasma concentration-time curve from time zero to the last quantifiable concentration (AUClast). The safety and tolerability of the combined therapy were assessed in terms of the frequency and severity of adverse events (AEs). Results: For bazedoxifene, the GMR (90% CI) of the combined therapy to monotherapy was 1.044 (0.9263-1.1765) for Cmax and 1.1329 (1.0232-1.2544) for AUClast. For baseline-adjusted cholecalciferol, the GMR (90% CI) of the combined therapy to monotherapy was 0.8543 (0.8005-0.9117) for Cmax and 0.8056 (0.7445-0.8717) for AUClast. The frequency of AEs observed was not significantly different between the combined therapy and monotherapy, and their severity was mild in all cases. Conclusion: A mild degree of pharmacokinetic interaction was observed when bazedoxifene and cholecalciferol were administered concomitantly to healthy male volunteers. This combined therapy was well tolerated at the dose levels used in the present study.

First-in-Human, Double-Blind, Randomized Controlled Trial of an Oral Dose of GnRH Antagonist TU2670 in Healthy Women.

OBJECTIVE: To evaluate the safety, tolerability, pharmacokinetics, and pharmacodynamics of TU2670, a novel orally active, nonpeptide gonadotropin-releasing hormone (GnRH) antagonist administered to healthy female participants. METHODS: This was a first-in-human, multicenter, phase 1, randomized, double-blind, placebo-controlled, single-dose ascending trial that took place in multiple medical centers. A total of 16 healthy premenopausal women (23 to 45 years of age) were randomized and received 20, 40, 80, and 160 mg TU2670 (GnRH antagonist) or placebo 7 days (±1 day) after the onset of menstrual bleeding. We performed a noncompartmental analysis for pharmacokinetic parameters and calculated relative minimum concentration values (Cmin, % Baseline) of serum pharmacodynamic (PD) markers (luteinizing hormone [LH], follicle-stimulating hormone [FSH], and estradiol). RESULTS: There were no significant differences among treatments with respect to vital signs, electrocardiography, adverse events, ovulation test results, and ultrasonography. The median Tmax of TU2670 occurred 0.75 to 1.00 hours after dosing, and concentrations then declined, with a mean apparent half-life (t1/2) of 3.0 to 5.9 hours. AUClast (17.7-417.9 ng·h/mL) and Cmax (8.1-95.4 ng/mL) increased in a dose-dependent manner. The PD analysis after a single administration of TU2670 revealed dose-dependent suppression of LH, FSH, and estradiol. Maximal suppression of the pre-dose baseline (%) was 58% to 82% at 6 to 8 hours for LH, 28% to 39% at 6 to 12 hours for FSH, and 34% to 82% at 12 to 24 hours for estradiol. CONCLUSION: The single administration of TU2670 in healthy premenopausal women was well tolerated and resulted in the dose-dependent suppression of LH, FSH, and estradiol, suggesting rapid and significant inhibition of pituitary and ovarian hormones.

Disease Progression Modeling Analysis of the Change of Bone Mineral Density by Postoperative Hormone Therapies in Postmenopausal Patients With Early Breast Cancer.

The osteoporosis incidence in postmenopausal patients on aromatase inhibitors (AI) is much higher than in those on tamoxifen, and adverse effects other than musculoskeletal disorders are less on AI than on tamoxifen. In this study we performed disease-progression modeling in postmenopausal patients with early breast cancer who had received 5 years of postoperative hormone therapy. Clinical data from postmenopausal patients who had received postoperative hormonal therapy and met the predefined selection criteria were retrospectively collected in an anonymized way. Disease-progression modeling and simulations were performed using NONMEM version 7.42. A first-order deterioration model with a combination of a symptomatic model (when a drug effect provides a transient bad effect by offsetting the severity of the disease) and a disease-modifying model (when a drug affects the disease progression rate) was used. Vitamin D supplementation was found to have a disease-modifying effect in osteoporosis, whereas AI decreased the bone mineral density by a t score of -0.21. However, after stopping the AI, the estrogen level reverted to normal, thereby reexercising protective effects against bone loss. In the simulation the probability of osteoporosis increased by 10% in the AI group compared with the other groups (tamoxifen, no-treatment group) during the medication period. Tamoxifen showed no significant effects in the final model.

Whole-Body Physiologically Based Pharmacokinetic Modeling of Trastuzumab and Prediction of Human Pharmacokinetics.

In the present study, we evaluated the pharmacokinetics (PK) of trastuzumab and sought to predict human PK based on animal studies, through the use of optical imaging and a whole-body physiologically based pharmacokinetic (WB-PBPK) modeling approach. The PK study was conducted in 24 mice, where serial blood samples were withdrawn and major organs were isolated after the last blood withdrawal. The drug concentrations in blood and major organs were measured via optical imaging. The WB-PBPK model was constructed using known physiological values including the volumes of major organs and blood/lymphatic flow. The NONMEM software (version 7.3) was used to determine tissue partition coefficients. Using the WB-PBPK model, a clinical trial simulation was performed with reference to human physiological values acquired from the literature. The simulated human PK was then compared with the actual PK observed in the previous study in which healthy male subjects received 6 mg/kg trastuzumab (Herceptin®) via intravenous route. The ratio of the simulated versus observed area under the concentration-time curve was 1.02 and that of maximal concentration was 0.72. The current study describes the potential synergistic applications of WB-PBPK and optical imaging in human PK prediction based on preclinical data obtained in early-stage drug development.

Performance of a mixture model by the degree of a missing categorical covariate when estimating clearance in NONMEM.

The accuracy and predictability of mixture models in NONMEM® may change depending on the relative size of inter-individual differences and the size of the differences in the parameters between subpopulations. This study explored the accuracy of mixture models when dealing with missing a categorical covariate under various situations that may occur in reality. We generated simulation data under various scenarios where genotypes representing extensive metabolizers (EM) and poor metabolizers (PM) of drug-metabolizing enzymes affect the clearance of a drug by different degrees, and the inter-individual variations in clearance are different for each scenario. From each simulated datum, a specific proportion of the covariate (genotype information) was randomly removed. Based on these simulation data, the proportion of each individual subpopulation and the clearance were estimated using a mixture model. Overall, the clearance estimate was more accurate when the difference in clearance between subpopulations was large, and the inter-individual variations were small. In some scenarios that showed higher ETA or epsilon shrinkage, the clearance estimates were significantly biased. The mixture model made better predictions for individuals in the EM subpopulation than for individuals in the PM subpopulation. However, the estimated values were not significantly affected by the tested ratio, if the sample size was secured to some extent. The current simulation study suggests that when the coefficient of variation of inter-individual variations of clearance exceeds 40%, the mixture model should be used carefully, and it should be taken into account that shrinkage can bias the results.

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.

Erratum: Development of R packages: 'NonCompart' and 'ncar' for noncompartmental analysis (NCA).

[This corrects the article on p. 10 in vol. 26.].

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.