Previously, we reported that the hot water extract of Hydrangea serrata leaves (WHS) and its active component, hydrangenol, possess in vitro and in vivo effects on skin wrinkles and moisturization. We conducted a randomized, double-blind, placebo-controlled trial to clinically evaluate the effect of WHS on human skin. Participants (n = 151) were randomly assigned to receive either WHS 300 mg, WHS 600 mg, or placebo, once daily for 12 weeks. Skin wrinkle, hydration, elasticity, texture, and roughness parameters were assessed at baseline and after 4, 8, and 12 weeks. Compared to the placebo, skin wrinkles were significantly reduced in both WHS groups after 8 and 12 weeks. In both WHS groups, five parameters (R1–R5) of skin wrinkles significantly improved and skin hydration was significantly enhanced when compared to the placebo group after 12 weeks. Compared with the placebo, three parameters of skin elasticity, including overall elasticity (R2), net elasticity (R5), and ratio of elastic recovery to total deformation (R7), improved after 12 weeks of oral WHS (600 mg) administration. Changes in skin texture and roughness were significantly reduced in both WHS groups. No WHS-related adverse reactions were reported. Hence, WHS could be used as a health supplement for skin anti-aging.

The statistical procedures as outlined by the European Medicines Agency (EMA) and United States Food and Drug Administration (FDA) guidelines for bioequivalence testing of highly variable drugs (HVDs) are complex. Additionally, the sample size is affected by clinical study designs or practical real-world problems, such as dropout rate or study budget. To overcome these difficulties, we propose a model-based approach for the selection of a study design with a sample size that satisfies the bioequivalence criteria using simulation studies based on a pharmacokinetic (PK) model. The designed approach was implemented using a simulation procedure considering some conventionally measured factors, such as geometric mean ratio and within-subject coefficient of variation, with various PK information important in determining bioequivalence. All simulation results were assessed according to the EMA and FDA guidelines. Furthermore, power calculations from simulation results were interpreted with regard to PK characteristics and compared among 2 × 2, 3 × 3, and 2 × 4 crossover designs to determine the efficient design considering appropriate sample size and duration of the clinical study. The proposed approach can be applied to bioequivalence studies of all drugs. However, the current study was targeted at HVDs, which are highly likely to require detailed decision making for sample size and study design.

Most therapeutic drug monitoring (TDM) packages are based on the maximum a posteriori (MAP) estimation. In this study, HMCtdm, a new TDM package, was developed using a Hamiltonian Monte Carlo (HMC) simulation. The estimation process of HMCtdm for the drugs amikacin, vancomycin, theophylline, and phenytoin was based on the R package Torsten. The prior pharmacokinetic (PK) models of the drugs were derived from the Abbottbase^® pharmacokinetics systems (PKS) program. The performance of HMCtdm for each drug was assessed through internal and external validations. The internal validation results of the HMCtdm were compared with those of a MAP-based estimation. The developed open-source HMCtdm package is user friendly. The validation results were reviewed and interpreted using the mean percentage error and root mean squared error. The successful transplantation of the prior PK structures (used in PKS) was confirmed by comparing the validation results with a MAP estimation. An open-source HMC-based TDM package was also successfully developed in this study, and its performance was evaluated. This package can be operated by users unfamiliar with C++ and can be further developed for various applications.