Arbutin (Arb) and deoxyArbutin (dA) are both effective hypopigmentation agents. However, they are glucoside derivatives of hydroquinone (HQ), which may be decayed into HQ under higher energy environments. Therefore, safety and toxicity are very important issues when considering the usage of these compounds. However, no study has verified the properties of Ultra-Violet B (UVB)-irradiated Arb and dA. In this work, we investigated the cytotoxicity and hypopigmentation effects of UVB-irradiated Arb and dA in Detroit 551 human fibroblast cells and B16-F10 mouse melanoma cells. The results showed that UVB-irradiated Arb and dA have strong cytotoxicity for the fibroblast cells, especially for dA, the caspase-3 is also activated by the treatment of UVB-irradiated dA in Detroit 551 cells. The results correlated with the produced HQ. In addition, UVB-irradiated Arb and dA suppressed the production of melanin in melanoma cells; this is due to the release of HQ that compensates for the UVB triggered Arb and dA decomposition.

The value of fine and specialty chemicals is often determined by the specific requirements in their physical and chemical properties. Therefore, it is most desirable to design the structure of chemicals to meet some targeted material properties. In the past, the design of specialty chemicals has been based largely on experience and trial-and-error. However, recent advances in computational chemistry and machine learning can offer a new path to this problem. In this presentation, we demonstrate a successful example where the structure of a chemical of specified value of octanol−water partition coefficient (K ow ) can be predicted by computers. This method consists of two parts, the first being a robust method, the COSMO-SAC activity coefficient model, that predicts the activity coefficient with input of only the molecular structure. The second component of this method is a derivative-free optimization algorithm that searches in the multidimensional structure space for the desired value of K ow . In particular, the genetic algorithm (GA), based on the Darwinian theory of evolution and natural selection, combined with simulated annealing (SA) is adopted for this purpose. Compared to other optimization algorithms, GA can overcome the problem of being trapped in local minima and SA can help improve the convergence. Therefore, the GA−SA combination has been found to be very suitable for molecular design. We show that the value of K ow can be achieved within 1% of the target in 30 generations with a proper set of evolution parameters (including the size of the population, the probability of selection, the rate of temperature annealing, etc.). The same method can be applied to the search for chemicals with other desired properties, such as vapor pressure and solubility.

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