Exposure to ultraviolet (UV) radiation damages the skin and increases the risk of skin cancer. Sunscreen is used to protect the skin from the harmful effects of UV radiation. However, the chemical UV filters used in sunscreen can show toxicity and cause allergic reactions. A safe sunscreen that includes a lower content of chemical UV filters and exerts an excellent effect on UV protection needs to be developed. The objective of this study was to investigate whether the addition of afzelin to sunscreen could improve the sun protection factor (SPF). A synergistic effect between afzelin and organic sunscreen agents including padimate O and oxybenzone was confirmed. Interestingly, 100% in vitro SPF-boosting was observed when afzelin (0.05%) was applied with a standard SPF formulation containing organic sunscreens while afzelin alone had no contribution to the SPF. In vivo SPF analysis of the standard SPF formulation showed an SPF value of 13.3 that increased to 20.1 when supplemented with afzelin (0.05%). Additionally, afzelin showed no skin irritation in s human trial. These results suggest that afzelin is useful as a natural additive in sunscreen formulations and provides an SPF-boosting effect. Afzelin supplementation to the formulation showed the potential to reduce the use of synthetic photoprotectors, which could minimize the risk of synthetic agent toxicity.
Bitter tastes are innately aversive and are thought to help protect animals from consuming poisons. Children are extremely sensitive to drug tastes, and their compliance is especially poor with bitter medicine. Therefore, judging whether a drug is bitter and adopting flavor correction and taste-masking strategies are key to solving the problem of drug compliance in children. Although various machine learning models for bitterness and sweetness prediction have been reported in the literature, no learning model or bitterness database for children’s medication has yet been reported. In this study, we trained four different machine learning models to predict bitterness. The goal of this study was to develop and validate a machine learning model called the “Children’s Bitter Drug Prediction System’’ (CBDPS) based on Tkinter, which predicts the bitterness of a medicine based on its chemical structure. Users can enter the Simplified Molecular-Input Line-Entry System (SMILES) formula for a single compound or multiple compounds, and CBDPS will predict the bitterness of children's medicines made from those XGBoost-Molecular ACCess System (XgBoost-MACCS) model yielded an accuracy of 88% under cross-validation.
α,β-Unsaturated oximes underwent electrophilic epoxidation with in-situ-generated dimethyldioxirane to give the corresponding epoxides in good yields. This reaction is an example of “carbonyl umpolung” by transformation of α,β-unsaturated ketones to their oximes. Nucleophilic ring-opening reactions of the epoxides afforded α-substituted products. Shi asymmetric epoxidation of the oximes proceeded with moderate asymmetric selectivity.