A highly viscous substance was prepared by evaporating an ethanol solution containing two hydrophilic vitamins; vitamin C, and vitamin B6. The viscous substance and physical mixture of the two vitamins were tested using a differential scanning calorimeter and an X-ray diffractometer. The highly viscous substance was found to be a liquid crystal (LC) made of these two hydrophilic vitamins. Determination by proton nuclear magnetic resonance measurement suggested that intramolecular hydrogen bonding in vitamin B6 was eliminated by the LC formation. This LC compound showed high solubility in 1,3-butanediol (almost 87%). Much higher skin permeation of both vitamin C and B6 was also observed from the LC compound than that from the physical mixture. The present LC compound containing vitamin C and vitamin B6 may be useful for pharmaceutical and cosmeceutical applications.

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Direct compression is a popular choice as it provides the simplest way to prepare the tablet. It can be easily adopted when the active pharmaceutical ingredient (API) is unstable in water or to thermal drying. An optimal formulation of preliminary mixed powders (premix powders) is beneficial if prepared in advance for tableting use. The aim of this study was to find the optimal formulation of the premix powders composed of lactose (LAC), cornstarch (CS), and microcrystalline cellulose (MCC) by using statistical techniques. Based on the “Quality by Design” concept, a (3,3)-simplex lattice design consisting of three components, LAC, CS, and MCC was employed to prepare the model premix powders. Response surface method incorporating a thin-plate spline interpolation (RSM-S) was applied for estimation of the optimum premix powders for tableting use. The effect of tablet shape identified by the surface curvature on the optimization was investigated. The optimum premix powder was effective when the premix was applied to a small quantity of API, although the function of premix was limited in the case of the formulation of large amount of API. Statistical techniques are valuable to exploit new functions of well-known materials such as LAC, CS, and MCC.

Various stresses and strains are generated on the surface and inside of pharmaceutical tablets when an external force is applied. In addition, stresses in various directions can remain on the surface and inside the tablets because they are generally prepared by compaction of pharmaceutical powders using dies and punches. As it is difficult to measure the stress and strain generation in the tablets experimentally, a numerical simulation was applied by employing a finite element method (FEM). An elastic model is often used to represent stress and strain generation after loading an external force to tablets, and the Drucker–Prager cap (DPC) model has been widely recognized for representing the remaining stress distributions during the compaction of powder to tablet form. Firstly, this article describes an FEM simulation of the stress generation on the surface of the scored tablets after loading the bending force from the back side of the tablets. Next, the FEM simulation was introduced to determine the effect of diametrical compression on the stress and strain generation in the tablets by comparing the results measured experimentally. Furthermore, the residual stresses remaining inside the tablets were simulated using FEM, in which powder compaction was represented as the DPC model. A clear difference was observed in the residual stress distributions between the flat and convex tablets. This indicates that FEM simulation is useful for achieving a science-based understanding of critical quality attributes in various types of tablets.

Coronavirus disease (COVID-19) cases are continuing to rise around the world, with more than 607 million confirmed cases and more than 6.51 million deaths as of September 2022. The World Health Organization (WHO) has emphasized the importance of hygiene in the ongoing COVID-19 pandemic, especially in elderly and bedridden patients. The use of no-rinse shampoo represents a simple change in hair-cleansing products. Daily hospital hair washing for the elderly, bedridden, and ICU patients would be more readily adopted. The objective of this study was to design and optimize a new no-rinse shampoo based on electrochemically reduced water (ERW) using response surface methodology. The relationship between coconut-based surfactant mixtures in a no-rinse shampoo and the resulting physicochemical properties, effectiveness (antibacterial and antifungal activity), and stability of the shampoo was investigated. The vesicle size, size distribution, zeta potential, conductivity, pH, foamability, wetting time, turbidity, and stability of the model formulation were optimized. The optimal formulation with the appropriate physicochemical properties and stability was selected. The effectiveness of the optimal formulation was compared to that of commercially available products. The dry shampoo (DS4) containing 3.5% of the mixed detergent with ERW may prevent infection by Escherichia coli, Staphylococcus epidermidis, Staphylococcus aureus, and Candida albicans. The no-rinse shampoo based on ERW successfully demonstrated good stability in addition to efficacy in terms of antibacterial and antifungal activity. The treated hair fiber was not significantly different from that of the intact hair fiber. Under the scanning electron microscopy (SEM) and the atomic force microscopy (AFM), the cuticle layer of the treated hair fiber was not damaged. Thus, no-rinse shampoos may reduce the time of hair rinsing and improve the quality of life of caregivers.

Quality by design (QbD) is an essential concept for modern manufacturing processes of pharmaceutical products. Understanding the science behind manufacturing processes is crucial; however, the complexity of the manufacturing processes makes implementing QbD challenging. In this study, structural equation modeling (SEM) was applied to understand the causal relationships between variables such as process parameters, material attributes, and quality attributes. Based on SEM analysis, we identified a model composed of the above-mentioned variables and their latent factors without including observational data. Difficulties in fitting the observed data to the proposed model are often encountered in SEM analysis. To address this issue, we adopted Bayesian estimation with Markov chain Monte Carlo simulation. The tableting process involving the wet-granulation process for acetaminophen was employed as a model case for the manufacturing process. The results indicate that SEM analysis could be useful for implementing QbD for the manufacturing processes of pharmaceutical products.