RIFM fragrance ingredient safety assessment, cinnamyl alcohol, CAS Registry Number 104-54-1

Cinnamyl Alcohol Attenuates Adipogenesis in 3T3-L1 Cells by Arresting the Cell Cycle

Cinnamyl alcohol (CA) is an aromatic compound found in several plant-based resources and has been shown to exert anti-inflammatory and anti-microbial activities. However, the anti-adipogenic mechanism of CA has not been sufficiently studied. The present study aimed to investigate the effect and mechanism of CA on the regulation of adipogenesis. As evidenced by Oil Red O staining, Western blotting, and real-time PCR (RT-PCR) analyses, CA treatment (6.25-25 μM) for 8 d significantly inhibited lipid accumulation in a concentration-dependent manner and downregulated adipogenesis-related markers (peroxisome proliferator-activated receptor γ (PPARγ), CCAAT/enhancer-binding protein α (C/EBPα), fatty acid binding protein 4 (FABP4), adiponectin, fatty acid synthase (FAS)) in 3-isobutyl-1-methylxanthine, dexamethasone, and insulin(MDI)-treated 3T3-L1 adipocytes. In particular, among the various differentiation stages, the early stage of adipogenesis was critical for the inhibitory effect of CA. Cell cycle analysis using flow cytometry and Western blotting showed that CA effectively inhibited MDI-induced initiation of mitotic clonal expansion (MCE) by arresting the cell cycle in the G0/G1 phase and downregulating the expression of C/EBPβ, C/EBPδ, and cell cycle markers (cyclin D1, cyclin-dependent kinase 6 (CDK6), cyclin E1, CDK2, and cyclin B1). Moreover, AMP-activated protein kinase α (AMPKα), acetyl-CoA carboxylase (ACC), and extracellular signal-regulated kinase 1/2 (ERK1/2), markers of upstream signaling pathways, were phosphorylated during MCE by CA. In conclusion, CA can act as an anti-adipogenic agent by inhibiting the AMPKα and ERK1/2 signaling pathways and the cell cycle and may also act as a potential therapeutic agent for obesity.

Combinative ex vivo studies and in silico models ProTox-II for investigating the toxicity of chemicals used mainly in cosmetic products

Human data on remains sparse and of varying quality and reproducibility. Ex vivo experiments and animal experiments currently is the most preferred way to predict the skin sensitization approved by the regulatory agencies across the world. However, there is a constant need and demand to reduce animal experiments and provide the scope of alternative methods to animal testing. In this study, we have compared the predictive performance of the published computational tools such as ProTox-II, SuperCYPsPred with the data obtained from ex-vivo experiments. From the results of the retrospective analysis, it can be observed that the computational predictions are in agreement with the experimental results. The computational models used here are generative models based on molecular structures and machine learning algorithms and can be applied also for the prediction of skin sensitization. Besides prediction of the toxicity endpoints, the models can also provide deeper insights into the molecular mechanisms and adverse outcome pathways (AOPs) associated with the chemicals used in cosmetic products.