Inflammation is a major contributing factor to the progression of atherosclerosis. Ginsenoside Rb1 (Rb1), an active saponin of Panax notoginseng, has been found to exert beneficial effects on inflammation and oxidative stress. This study investigated the ability of Rb1 to inhibit the formation of atherosclerotic plaques and the potential mechanisms. In this study, the effects of Rb1 on the development of atherosclerosis were investigated in ApoE-/- deficient mice fed with a western diet. Mice were intragastrically administrated with Rb1 (10 mg/kg) for 8 weeks. This study is that ginsenoside Rb1 exerted an inhibitory effect on early atherosclerosis in ApoE-/- mice via decreasing body weight and food intake daily, upregulating the lipid levels of serum plasma, including those of TC, TG and LDL-C and HDL-C and reducing the atherosclerotic plaque area, suppressing inflammatory cytokines (levels of IL-1β, IL-6 and TNF-α) in the serum of ApoE-/- mice, changing the expression levels of BCL-2, BAX, cleaved caspase-3 and cleaved caspase-9 and weakening apoptosis associated with anti-inflammatory activity. Hence, all these effects against atherosclerosis were tightly associated with regulation of necrosis or apoptosis associated with anti-inflammatory activity. Additionally, the results found that ginsenoside Rb1 increased autophagy flux to inhibit apoptosis via acceleration of autophagy by promoting transformation of LC3 from type I to type II in high-fat diet-induced atherosclerosis in ApoE-/- mice. This finding, along with those of the previous study, provides evidence that Rb1 promotes the process of autophagy to protect against atherosclerosis via regulating BCL-2 family-related apoptosis. These results indicate that Rb1 exhibits therapeutic effects in atherosclerosis by reversing the imbalance between apoptosis and autophagy.

In recent years, liver injury induced by Traditional Chinese Medicines (TCMs) has gained increasing attention worldwide. Assessing the hepatotoxicity of compounds in TCMs is essential and inevitable for both doctors and regulatory agencies. However, there has been no effective method to screen the hepatotoxic ingredients in TCMs available until now. In the present study, we initially built a large scale dataset of drug-induced liver injuries (DILIs). Then, 13 types of molecular fingerprints/descriptors and eight machine learning algorithms were utilized to develop single classifiers for DILI, which resulted in 5416 single classifiers. Next, the NaiveBayes algorithm was adopted to integrate the best single classifier of each machine learning algorithm, by which we attempted to build a combined classifier. The accuracy, sensitivity, specificity, and area under the curve of the combined classifier were 72.798, 0.732, 0.724, and 0.793, respectively. Compared to several prior studies, the combined classifier provided better performance both in cross validation and external validation. In our prior study, we developed a herb-hepatotoxic ingredient network and a herb-induced liver injury (HILI) dataset based on pre-clinical evidence published in the scientific literature. Herein, by combining that and the combined classifier developed in this work, we proposed the first instance of a computational toxicology to screen the hepatotoxic ingredients in TCMs. Then Polygonum multiflorum Thunb (PmT) was used as a case to investigate the reliability of the approach proposed. Consequently, a total of 25 ingredients in PmT were identified as hepatotoxicants. The results were highly consistent with records in the literature, indicating that our computational toxicology approach is reliable and effective for the screening of hepatotoxic ingredients in Pmt. The combined classifier developed in this work can be used to assess the hepatotoxic risk of both natural compounds and synthetic drugs. The computational toxicology approach presented in this work will assist with screening the hepatotoxic ingredients in TCMs, which will further lay the foundation for exploring the hepatotoxic mechanisms of TCMs. In addition, the method proposed in this work can be applied to research focused on other adverse effects of TCMs/synthetic drugs.