Deciphering the mechanism of Indirubin and its derivatives in the inhibition of Imatinib resistance using a "drug target prediction-gene microarray analysis-protein network construction" strategy

A Thia-Analogous Indirubin N-Glycoside Disrupts Mitochondrial Function and Causes the Death of Human Melanoma and Cutaneous Squamous Cell Carcinoma Cells

Skin cancer is the most common malignant disease worldwide and, therefore, also poses a challenge from a pharmacotherapeutic perspective. Derivatives of indirubin are an interesting option in this context. In the present study, the effects of 3-[3'-oxo-benzo[b]thiophen-2'-(Z)-ylidene]-1-(β-d-glucopyranosyl)-oxindole (KD87), a thia-analogous indirubin N-glycoside, on the viability and mitochondrial properties of melanoma (A375) and squamous cell carcinoma cells (A431) of the skin were investigated. In both cell lines, KD87 caused decreased viability, the activation of caspases-3 and -7, and the inhibition of colony formation. At the mitochondrial level, a concentration-dependent decrease in both the basal and ATP-linked oxygen consumption rate and in the reserve capacity of oxidative respiration were registered in the presence of KD87. These changes were accompanied by morphological alterations in the mitochondria, a release of mitochondrial cytochrome c into the cytosol and significant reductions in succinate dehydrogenase complex subunit B (SDHB, subunit of complex II) in A375 and A431 cells and NADH:ubiquinone oxidoreductase subunit B8 (NDUFB8, subunit of complex I) in A375 cells. The effect of KD87 was accompanied by a significant upregulation of the enzyme heme oxygenase-1, whose inhibition led to a partial but significant reduction in the metabolic-activity-reducing effect of KD87. In summary, our data show a mitochondria-targeting effect of KD87 as part of the cytotoxic effect of this compound on skin cancer cells, which should be considered in future studies with this class of compounds.

Assessing the Anti-inflammatory Effects of Bacopa-Derived Bioactive Compounds Using Network Pharmacology and In Vitro Studies

Bacopa monnieri is reported as a potent Indian medicinal plant that possesses numerous pharmacological activities due to the presence of various bioactive compounds. These pharmacological activities were used in the ancient medicine system to cure inflammatory conditions. Bacopa has the ability to reduce acute pain and inflammation by inhibiting the enzyme cyclo-oxygenase-2 (COX-2) and reducing COX-2-arbitrated prostanoid mediators. Moreover, the anti-inflammatory property may also be associated with the neuroprotective activity of Bacopa. Considering this importance, the current pilot study focused on the anti-inflammatory potential of various phytocompounds of bacopa and their interaction with inflammation responsible genes such as COX2, iNOS, LOX, STAT3, CCR1, and MMP9 through pharmacology analysis of its systems. Docking results revealed that, quercetin (QR) showed significant binding energies with inflammatory genes. Hence, we selected QR as a potential phytocompound for further in vitro experiments. This existing study aimed to evaluate the efficacy of QR as a potent anti-inflammatory compound against lipopolysaccharide (LPS)-stimulated RAW264.7 macrophages. The in vitro analysis concludes that QR effectively reduces the production of nitric oxide (NO) in LPS-induced RAW264.7 cells and downregulates the expression of COX-2 and iNOS genes due to the inhibitory potential of QR on LPS-stimulated NO production.

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

Cryptotanshinone Is a Intervention for ER-Positive Breast Cancer: An Integrated Approach to the Study of Natural Product Intervention Mechanisms

Background: Resistance to endocrine therapy has hampered clinical treatment in patients with ER-positive breast cancer (BRCA). Studies have confirmed that cryptotanshinone (CPT) has cytotoxic effects on BRCA cells and can significantly inhibit the proliferation and metastasis of ER-positive cancer cells.

Methods: We analyzed the gene high-throughput data of ER-positive and negative BRCA to screen out key gene targets for ER-positive BRCA. Finally, the effects of CPT on BRCA cells (MCF-7 and MDA-MB-231) were examined, and quantitative RT-PCR was used to evaluate the expression of the key targets during CPT intervention.

Results: A total of 169 differentially expressed genes were identified, and revealed that CPT affects the ER-positive BRCA cells by regulating CDK1, CCNA2, and ESR1. The overall experimental results initially show that MCF-7 cells were more sensitive to CPT than MDA-MB-231 cells, and the expression of ESR1 was not affected in the BRCA cells during CPT intervention, while the expression of CDK1 and CCNA2 were significantly down-regulated.

Conclusion: CPT can inhibit the proliferation and migration of BRCA cells by regulating CDK1, CCNA2, and ESR1, especially in ER-positive BRCA samples. On the one hand, our research has discovered the possible mechanism that CPT can better interfere with ER+ BRCA; on the other hand, the combination of high-throughput data analysis and network pharmacology provides valuable information for identifying the mechanism of drug intervention in the disease.