Abstract: Cancer therapy is a strategic measure in inhibiting breast cancer stem cell (BCSC) pathways. Naringenin, a citrus flavonoid, was found to increase breast cancer cells' sensitivity to chemotherapeutic agents. Bioinformatics study and 3D tumorsphere in vitro modeling in breast cancer (mammosphere) were used in this study, which aims to explore the potential therapeutic targets of naringenin (PTTNs) in inhibiting BCSCs. Bioinformatic analyses identified direct target proteins (DTPs), indirect target proteins (ITPs), naringenin-mediated proteins (NMPs), BCSC regulatory genes, and PTTNs. The PTTNs were further analyzed for gene ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, protein-protein interaction (PPI) networks, and hub protein selection. Mammospheres were cultured in serum-free media. The effects of naringenin were measured by MTT-based cytotoxicity, mammosphere forming potential (MFP), colony formation, scratch wound-healing assay, and flow cytometry-based cell cycle analyses and apoptosis assays. Gene expression analysis was performed using real-time quantitative polymerase chain reaction (q-RT PCR). Bioinformatics analysis revealed p53 and estrogen receptor alpha (ERalpha) as PTTNs, and KEGG pathway enrichment analysis revealed that TGF-ss and Wnt/ss-catenin pathways are regulated by PTTNs. Naringenin demonstrated cytotoxicity and inhibited mammosphere and colony formation, migration, and epithelial to mesenchymal transition in the mammosphere. The mRNA of tumor suppressors P53 and ERalpha were downregulated in the mammosphere, but were significantly upregulated upon naringenin treatment. By modulating the P53 and ERalpha mRNA, naringenin has the potential of inhibiting BCSCs. Further studies on the molecular mechanism and formulation of naringenin in BCSCs would be beneficial for its development as a BCSC-targeting drug.

Abstract: Radix Angelicae biseratae is a widely used Chinese traditional herbal medicine for osteoarthritis (OA). Its therapeutic efficacy has been confirmed in clinical practice. However, its mechanisms of action in treating OA have remained elusive. The purpose of the present study was to identify active components with good oral bioavailability and drug-like properties from Radix Angelicae biseratae through systematic pharmacology and in vitro experiments to determine targets of Radix Angelicae biseratae in the treatment of OA. The functional components of Radix Angelicae biseratae were screened from the Traditional Chinese Medicine Systems Pharmacology database based on oral bioavailability and drug-like properties. Subsequently, the databases STITCH, Open Targets Platform and DrugBank were searched and microarray analysis was performed to screen the active components of Radix Angelicae biseratae to treat OA and predict its potential target proteins. The interaction network and protein interaction network were then generated and examined, molecular docking between active components and targets was performed and the enrichment of potential target proteins was analyzed. Finally, reverse transcription-quantitative (RT-q)PCR and western blot analyses were used to verify the therapeutic effect of Radix Angelicae biseratae extract on the expression of OA-associated target proteins. The results provided eight active components in Radix Angelicae biseratae, which were firmly linked to 20 targets of OA. In combination with molecular docking and the analysis of the interaction network between components and targets, it was suggested that sitosterol was a major active component of Radix Angelicae biseratae in the treatment of OA. Protein interaction network analysis suggested that prostaglandin-endoperoxide synthase 2 (PTGS2), nitric oxide synthase 3 and cytochrome P450 2B6 may be critical targets for Radix Angelicae biseratae in the treatment of OA. In addition, RT-qPCR and western blot analyses suggested that Radix Angelicae biseratae extract inhibited the mRNA and protein expression of PTGS2 in degenerative articular cartilage cells in vitro, whilst other targets remain to be verified. Functional enrichment analysis indicated that Radix Angelicae biseratae confers pharmacological efficacy towards OA through exerting anti-inflammatory effects and immune regulation.

Abstract: Viruses rely on their host for reproduction. Here, we made use of genomic and structural information to create a biomass function capturing the amino and nucleic acid requirements of SARS-CoV-2. Incorporating this biomass function into a stoichiometric metabolic model of the human lung cell and applying metabolic flux balance analysis, we identified host-based metabolic perturbations inhibiting SARS-CoV-2 reproduction. Our results highlight reactions in the central metabolism, as well as amino acid and nucleotide biosynthesis pathways. By incorporating host cellular maintenance into the model based on available protein expression data from human lung cells, we find that only few of these metabolic perturbations are able to selectively inhibit virus reproduction. Some of the catalysing enzymes of such reactions have demonstrated interactions with existing drugs, which can be used for experimental testing of the presented predictions using gene knockouts and RNA interference techniques. In summary, the developed computational approach offers a platform for rapid, experimentally testable generation of drug predictions against existing and emerging viruses based on their biomass requirements.

Abstract: A pre-eminent subtype of lung carcinoma, Non-small cell lung cancer accounts for paramount causes of cancer-associated mortality worldwide. Undeterred by the endeavour in the treatment strategies, the overall cure and survival rates for NSCLC remain substandard, particularly in metastatic diseases. Moreover, the emergence of resistance to classic anticancer drugs further deteriorates the situation. These demanding circumstances culminate the need of extended and revamped research for the establishment of upcoming generation cancer therapeutics. Drug repositioning introduces an affordable and efficient strategy to discover novel drug action, especially when integrated with recent systems biology driven stratagem. This review illustrates the trendsetting approaches in repurposing along with their numerous success stories with an emphasize on the NSCLC therapeutics. Indeed, these novel hits, in combination with conventional anticancer agents, will ideally make their way the clinics and strengthen the therapeutic arsenal to combat drug resistance in the near future.

Abstract: Spike glycoprotein is essential for the reproduction of the SARS-CoV-2 virus, and its inhibition using already approved antiviral drugs may open new avenues for treatment of patients with the COVID-19 disease. Because of that we analyzed the inhibition of SARS-CoV-2 spike glycoprotein with FDA-approved antiviral drugs and their double and triple combinations. We used the Vini in silico model of cancer to perform this virtual drug screening, showing HIV drugs to be the most effective. Besides, the combination of cobicistat-abacavir-rilpivirine HIV drugs demonstrated the highest in silico efficacy of inhibiting SARS-CoV-2 spike glycoprotein. Therefore, a clinical trial of cobicistat-abacavir-rilpivirine on a limited number of COVID-19 patients in moderately severe and severe condition is warranted.

Abstract: OBJECTIVE: To explore the strategy of pregnancy-preserving and maternal- fetal management in patients with primary gynecologic neuroendocrine tumors (gNETs) during pregnancy. METHODS: We performed whole genome sequencing (WGS) for analyzing maternal and fetal somatic and germline single nucleotide variations (SNVs) and small insertions and deletions (InDels) for a 29-year-old pregnant woman diagnosed with stage IB2 large cell neuroendocrine carcinoma (LCNEC) and adenocarcinoma in the cervix. A systematic literature review was performed to explore the strategies for treatment of such rare histological type while maintaining pregnancy. RESULTS: Global case analysis of cervical NETs during pregnancy suggested that negative lymph node metastasis and an early FIGO stage were potentially associated with a good prognosis of the patients. In the case presented herein, a pregnancy-preserving strategy was adopted and favorable maternal-fetal outcomes were achieved after neoadjuvant chemotherapy, radical surgery and postoperative systemic chemotherapy. At 35(+5) weeks, the fetus was delivered by caesarian section, and the patient has by now had a disease-free survival of 19 months postoperatively. WGS analysis revealed 6 missense somatic pathogenic mutations in two cancer tissues of the patient, and among them KARS and VEGFA were related with targeted therapy. Five pathogenic germline variants were detected in the patient and her son, suggesting the necessity of a long-term follow-up schedule including precise genetic counselling for both the mother and the child. CONCLUSIONS: Although gNETs in pregnancy are rare and highly risky, pregnancy-preserving managements of gNETs can still be considered and favorable maternalfetal outcomes are possible with proper assessment of the clinical indications and implementation of multimodal treatments. Precise treatment and follow-up strategies based on the results of WGS for risk-reducing intervention of cancer recurrence or occurrence can potentially benefit the patient and the neonate.

Abstract: Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have recently shown excellent performance in PLI prediction. However, the performance is highly dependent on protein and ligand features utilized for the DNN model. Moreover, in current models, the deciphering of how protein features determine the underlying principles that govern PLI is not trivial. In this work, we developed a DNN framework named SSnet that utilizes secondary structure information of proteins extracted as the curvature and torsion of the protein backbone to predict PLI. We demonstrate the performance of SSnet by comparing against a variety of currently popular machine and non-Machine Learning (ML) models using various metrics. We visualize the intermediate layers of SSnet to show a potential latent space for proteins, in particular to extract structural elements in a protein that the model finds influential for ligand binding, which is one of the key features of SSnet. We observed in our study that SSnet learns information about locations in a protein where a ligand can bind, including binding sites, allosteric sites and cryptic sites, regardless of the conformation used. We further observed that SSnet is not biased to any specific molecular interaction and extracts the protein fold information critical for PLI prediction. Our work forms an important gateway to the general exploration of secondary structure-based Deep Learning (DL), which is not just confined to protein-ligand interactions, and as such will have a large impact on protein research, while being readily accessible for de novo drug designers as a standalone package.

Abstract: BACKGROUND This study explored the mechanism of action of Ephedrae Herba-Cinnamomi Ramulus couplet medicine (MGCM) at the pharmacological level in the treatment of psoriasis. MATERIAL AND METHODS The active ingredients in MGCM were mined through literature retrieval and the BATMAN-TCM database, and potential targets were predicted. In addition, targets associated with psoriasis were acquired using multiple disease-related databases. Thereafter, an interaction network between candidate MGCM targets and the known psoriasis-associated targets was constructed based on the protein-protein interaction (PPI) data, using the STRING database. Then, the topological parameter degree was determined for mining the core targets for MGCM in the treatment of psoriasis, which also represented the major hubs within the PPI network. In addition, the core networks of targets and ingredients were constructed using Cytoscape software to apply MGCM in the treatment for psoriasis. These core targets were then analyzed for Gene Ontology biological processes and Kyoto Encyclopedia of Genes and Genomes pathway enrichment using OmicShare. RESULTS The ingredient-target core network of MGCM for treating psoriasis was constructed; it contained 52 active ingredients and corresponded to 19 core targets. In addition, based on enrichment analysis, these core targets were majorly enriched for several biological processes (immuno-inflammatory responses, leukocyte differentiation, energy metabolism, angiogenesis, and programmed cell death) together with the relevant pathways (Janus kinase-signal transducer and activator of transcription, toll-like receptors, nuclear factor kappaB, vascular endothelial growth factor, and peroxisome proliferator-activated receptor), thus identifying the possible mechanism of action of MGCM in treating psoriasis. CONCLUSIONS The present network pharmacology study indicated that MGCM alleviates various pathological factors of psoriasis through multiple compounds, multiple targets, and multiple pathways.

Abstract: Huntington disease (HD) is a devastating and presently untreatable neurodegenerative disease characterized by progressively disabling motor and mental manifestations. The sigma-1 receptor (sigma1R) is a protein expressed in the central nervous system, whose 3D structure has been recently determined by X-ray crystallography and whose agonists have been shown to have neuroprotective activity in neurodegenerative diseases. To identify therapeutic agents against HD, we have implemented a drug repositioning strategy consisting of: (i) Prediction of the ability of the FDA-approved drugs publicly available through the ZINC database to interact with sigma1R by virtual screening, followed by computational docking and visual examination of the 20 highest scoring drugs; and (ii) Assessment of the ability of the six drugs selected by computational analyses to directly bind purified sigma1R in vitro by Surface Plasmon Resonance and improve the growth of fibroblasts obtained from HD patients, which is significantly impaired with respect to control cells. All six of the selected drugs proved able to directly bind purified sigma1R in vitro and improve the growth of HD cells from both or one HD patient. These results support the validity of the drug repositioning procedure implemented herein for the identification of new therapeutic tools against HD.

Abstract: Lead generation for difficult-to-drug targets that have large, featureless, and highly lipophilic or highly polar and/or flexible binding sites is highly challenging. Here, we describe how cores of macrocyclic natural products can serve as a high-quality in silico screening library that provides leads for difficult-to-drug targets. Two iterative rounds of docking of a carefully selected set of natural-product-derived cores led to the discovery of an uncharged macrocyclic inhibitor of the Keap1-Nrf2 protein-protein interaction, a particularly challenging target due to its highly polar binding site. The inhibitor displays cellular efficacy and is well-positioned for further optimization based on the structure of its complex with Keap1 and synthetic access. We believe that our work will spur interest in using macrocyclic cores for in silico-based lead generation and also inspire the design of future macrocycle screening collections.