Inhalation and ingestion of Synthetic musks in pregnant women: In silico spontaneous abortion risk evaluation and control.

Synthetic musks (SMs) are odor additives commonly used in the personal care products. Their wide existence in the environment and the recently reported adverse impact on the production and activity of progesterone and estrogen have raised pregnancy red flags and even lead to a pregnancy loss. Apart from the suggestion of limiting SM contact and exposure, effective abortion risk control measures for SMs remain to be blank. Facing the above challenges, this study tried to establish a new theoretical circumvention strategy to reduce the abortion risk of SMs to pregnant women by designing the supplementary diet plan and environmentally friendly SMs derivatives using molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) models. According to the supplementary diet plan, the diet combination of vitamin E, vitamin B, niacin, vitamin A, and vitamin B were confirmed to not only provide essential nutrients for human health, but also reduce the abortion risk in pregnant women in daily life. The multi-activity (binding ability of SMs with progesterone-estrogen) 3D-QSAR model was constructed to screen SMs derivatives. The LibDock score, a parameter reflecting the binding ability between SMs' Derivative-24 with progesterone-estrogen, decreased as much as 137.67% compared with its precursor galaxolide (HHCB). The 3D-QSAR models assisted screening indicated that Derivative-24 had lower environmental impacts (i.e., bioconcentration and mobility) and improved functional properties (odor stability, musky scent, and odor intensity). The integration of the optimum candidate, Derivative-24, with optimum three supplementary diet plans exhibited a much lower abortion risk than HHCB, demonstrating the effectiveness of the proposed theoretical circumvention strategy as a comprehensive abortion risk control measure. It also shed light on the design of new pharmaceutical and personal care products using advanced computing tools.