Abstract: The study of screening active constituents from traditional Chinese medicine (TCM) is important for explicating the mechanism of action of TCM and further evaluating the safety and efficacy effectively. However, detecting and identifying the active constituents from complicated biological samples still remain a challenge. Here, a practical, quick, and novel integrated strategy from in vitro, in situ, in vivo to in silico for rapidly screening the active constituents was developed. Firstly, the chemical profile of TCM in vitro was identified using UPLC-Q Exactive-Orbitrap HRMS. Secondly, the in situ intestinal perfusion with venous sampling (IPVS) method was used to investigate the intestinal absorption components. Thirdly, after intragastric administration of the TCM extract, the in vivo absorbed prototype components were detected and identified. Finally, the target network pharmacology approach was applied to explore the potential targets and possible mechanisms of the absorbed components from TCM. The reliability and availability of this approach was demonstrated using Tongfengding capsule (TFDC) as an example of herbal medicine. A total of 141 compounds were detected and identified in TFDC, and among them, 64 components were absorbed into the plasma. Then, a total of 35 absorbed bioactive components and 50 related targets shared commonly by compounds and gout were integrated via target network pharmacology analysis. Ultimately, the effects of the absorbed components on metabolism pathways were verified by experiments. These results demonstrated that this original method may provide a practical tool for screening bioactive compounds from TCM treating particular diseases. Furthermore, it also can clarify the potential mechanism of action of TCM and rationalize the application of TFDC as an effective herbal therapy for gout.

Abstract: Zhuling Jisheng decoction is employed for the treatment of bladder urothelial cancer in clinical practice of traditional Chinese medicine. However, there are few studies on its precise mechanism. For the antibladder cancer action of Zhuling Jisheng decoction, a network pharmacological technique was used to design a component/target/pathway molecular regulatory network. The TCMSP dataset was used to identify the chemical makeup of Zhuling Jisheng decoction, which was then analyzed and assessed for oral bioavailability and pharmacological similarity. The chemical composition of Zhuling Jisheng decoction was identified through the TCMSP database, and it was evaluated and screened based on oral bioavailability and drug similarity. The GEO database was searched for genes associated with urothelial bladder carcinoma, and gene targets associated with bladder urothelial cancer resistance were chosen by comparison. The function and linked pathways of the target genes were examined and screened using annotation, visualization, and a comprehensive discovery database. The impact of Zhuling Jisheng decoction on urothelial bladder cancer was studied using Cytoscape software to create a component/target/pathway network. Finally, 69 and 55 target genes were discovered for noninvasive bladder urothelial cancer and invasive bladder urothelial cancer, respectively. In noninvasive urothelial cancer, 118 pathways were highly enriched, including the TNF signaling pathway and the IL-17 signaling route. 103 pathways were highly enriched in invasive urothelial cancer, including the p53 signaling route, bladder cancer route, and calcium signaling route. There were 18 and 15 drug targets associated with noninvasive and invasive bladder urothelial carcinoma prognoses. Many signaling pathways directly act on tumours, and indirect pathways inhibit the development of bladder urothelial carcinoma. This research establishes a scientific foundation for further research into the framework of action of Zhuling Jisheng decoction in the therapy of bladder urothelial cancer.

Abstract: A classical drug repurposing approach was applied to find new putative GPR40 allosteric binders. A two-step computational protocol was set up, based on an initial pharmacophoric-based virtual screening of the DrugBank database of known drugs, followed by docking simulations to confirm the interactions between the prioritised compounds and GPR40. The best-ranked entries showed binding poses comparable to that of TAK-875, a known allosteric agonist of GPR40. Three of them (tazarotenic acid, bezafibrate, and efaproxiral) affect insulin secretion in pancreatic INS-1 832/13 beta-cells with EC50 in the nanomolar concentration (5.73, 14.2, and 13.5 nM, respectively). Given the involvement of GPR40 in type 2 diabetes, the new GPR40 modulators represent a promising tool for therapeutic intervention towards this disease. The ability to affect GPR40 was further assessed in human breast cancer MCF-7 cells in which this receptor positively regulates growth activities (EC50 values were 5.6, 21, and 14 nM, respectively).

Abstract: Severe mortality due to the COVID-19 pandemic resulted from the lack of effective treatment. Although COVID-19 vaccines are available, their side effects have become a challenge for clinical use in patients with chronic diseases, especially cancer patients. In the current report, we applied network pharmacology and systematic bioinformatics to explore the use of biochanin A in patients with colorectal cancer (CRC) and COVID-19 infection. Using the network pharmacology approach, we identified two clusters of genes involved in immune response (IL1A, IL2, and IL6R) and cell proliferation (CCND1, PPARG, and EGFR) mediated by biochanin A in CRC/COVID-19 condition. The functional analysis of these two gene clusters further illustrated the effects of biochanin A on interleukin-6 production and cytokine-cytokine receptor interaction in CRC/COVID-19 pathology. In addition, pathway analysis demonstrated the control of PI3K-Akt and JAK-STAT signaling pathways by biochanin A in the treatment of CRC/COVID-19. The findings of this study provide a therapeutic option for combination therapy against COVID-19 infection in CRC patients.

Abstract: Long non-coding RNAs (lncRNAs) are related to a variety of human diseases. However, little is known about the role of lncRNA in intervertebral disc degeneration (IDD). LncRNA expression profile of human IDD were downloaded from Gene Expression Omnibus (GEO) database. Potential biomarkers and therapeutic drugs for IDD were analyzed by weighted gene co-expression network analysis (WGCNA), R software package Limma, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We identified 1455 differentially expressed genes and 423 differentially expressed lncRNAs. Twenty-six co-expression modules were obtained, among them, the tan, brown, and turquoise modules were most closely related to IDD. The turquoise module contained a large number of differential expressed lncRNAs and genes, these genes were mainly enriched in the MAPK signaling pathway, TGF-beta signaling pathway. Furthermore, we obtained 11,857 LmiRM-Degenerated, these lncRNAs and genes showed higher differential expression multiples and higher expression correlation. After constructing a disease-gene interaction network, 25 disease-specific genes and 9 disease-specific lncRNAs were identified. Combined with the drug-target gene interaction network, three drugs, namely, Calcium citrate, Calcium Phosphate, and Calcium phosphate dihydrate, which may have curative effects on IDD, were determined. Finally, a genetic diagnosis model and lncRNA diagnosis model with 100% diagnostic performance in both the training data set and the validation data set were established based on these genes and lncRNA. This study provided new diagnostic features for IDD and could help design personalized treatment of IDD.

Abstract: Background: Drug repurposing provides an effective method for high-speed, low-risk drug development. Clinical phenotype-based screening exceeded target-based approaches in discovering first-in-class small-molecule drugs. However, most of these approaches predict only binary phenotypic associations between drugs and diseases; the types of drug and diseases have not been well exploited. Principally, the clinical phenotypes of a known drug can be divided into indications (Is), side effects (SEs), and contraindications (CIs). Incorporating these different clinical phenotypes of drug-disease associations (DDAs) can improve the prediction accuracy of the DDAs. Methods: We develop Drug Disease Interaction Type (DDIT), a user-friendly online predictor that supports drug repositioning by submitting known Is, SEs, and CIs for a target drug of interest. The dataset for Is, SEs, and CIs was extracted from PREDICT, SIDER, and MED-RT, respectively. To unify the names of the drugs and diseases, we mapped their names to the Unified Medical Language System (UMLS) ontology using Rest API. We then integrated multiple clinical phenotypes into a conditional restricted Boltzmann machine (RBM) enabling the identification of different phenotypes of drug-disease associations, including the prediction of as yet unknown DDAs in the input. Results: By 10-fold cross-validation, we demonstrate that DDIT can effectively capture the latent features of the drug-disease association network and represents over 0.217 and over 0.072 improvement in AUC and AUPR, respectively, for predicting the clinical phenotypes of DDAs compared with the classic K-nearest neighbors method (KNN, including drug-based KNN and disease-based KNN), Random Forest, and XGBoost. By conducting leave-one-drug-class-out cross-validation, the AUC and AUPR of DDIT demonstrated an improvement of 0.135 in AUC and 0.075 in AUPR compared to any of the other four methods. Within the top 10 predicted indications, side effects, and contraindications, 7/10, 9/10, and 9/10 hit known drug-disease associations. Overall, DDIT is a useful tool for predicting multiple clinical phenotypic types of drug-disease associations.

Abstract: Salvianolic acid C (SAC) is a major bioactive component of Salvia miltiorrhiza Bunge (Danshen), a Chinese herb for treating ischemic stroke (IS). However, the mechanism by which SAC affects the IS has not yet been evaluated, thus a network pharmacology integrated molecular docking strategy was performed to systematically evaluate its pharmacological mechanisms, which were further validated in rats with cerebral ischemia. A total of 361 potential SAC-related targets were predicted by SwissTargetPrediction and PharmMapper, and a total of 443 IS-related targets were obtained from DisGeNET, DrugBank, OMIM, and Therapeutic Target database (TTD) databases. SAC-related targets were hit by the 60 targets associated with IS. By Gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment combined with the protein-protein interaction (PPI) network and cytoHubba plug-ins, nine related signaling pathways (proteoglycans in cancer, pathways in cancer, PI3K-Akt signaling pathway, Focal adhesion, etc.), and 20 hub genes were identified. Consequently, molecular docking indicated that SAC may interact with the nine targets (F2, MMP7, KDR, IGF1, REN, PPARG, PLG, ACE and MMP1). Four of the target proteins (VEGFR2, MMP1, PPARgamma and IGF1) were verified using western blot. This study comprehensively analyzed pathways and targets related to the treatment of IS by SAC. The results of western blot also confirmed that the SAC against IS is mainly related to anti-inflammatory and angiogenesis, which provides a reference for us to find and explore the effective anti-IS drugs.

Abstract: Drug-drug interactions play a vital role in drug research. However, they may also cause adverse reactions in patients, with serious consequences. Manual detection of drug-drug interactions is time-consuming and expensive, so it is urgent to use computer methods to solve the problem. There are two ways for computers to identify drug interactions: one is to identify known drug interactions, and the other is to predict unknown drug interactions. In this paper, we review the research progress of machine learning in predicting unknown drug interactions. Among these methods, the literature-based method is special because it combines the extraction method of DDI and the prediction method of DDI. We first introduce the common databases, then briefly describe each method, and summarize the advantages and disadvantages of some prediction models. Finally, we discuss the challenges and prospects of machine learning methods in predicting drug interactions. This review aims to provide useful guidance for interested researchers to further promote bioinformatics algorithms to predict DDI.

Abstract: Persistence is a transient state that poses an important health concern in cancer therapy. The mechanisms associated with persister phenotypes are highly diverse and complex, and many aspects of persister cell physiology remain to be explored. We applied a melanoma cell line and panel of chemotherapeutic agents to show that melanoma persister cells are not necessarily preexisting dormant cells; in fact, they may be induced by cancer chemotherapeutics. Our metabolomics analysis and phenotype microarray assays further demonstrated a transient upregulation in Krebs cycle metabolism in persister cells. We also verified that targeting electron transport chain activity can significantly reduce melanoma persister levels. The reported metabolic remodeling feature seems to be a conserved characteristic of melanoma persistence, as it has been observed in various melanoma persister subpopulations derived from a diverse range of chemotherapeutics. Elucidating a global metabolic mechanism that contributes to persister survival and reversible switching will ultimately foster the development of novel cancer therapeutic strategies.

Abstract: Streptococcus pneumoniae is a notorious pathogen that affects approximately 450 million people worldwide and causes up to four million deaths per annum. Despite availability of antibiotics (i.e., penicillin, doxycycline, or clarithromycin) and conjugate vaccines (e.g., PCVs), it is still challenging to treat because of its drug resistance ability. The rise of antibiotic resistance in S. pneumoniae is a major source of concern across the world. Computational subtractive genomics is one of the most applied techniques in which the whole proteome of the bacterial pathogen is gradually reduced to a limited number of potential therapeutic targets. Whole-genome sequencing has greatly reduced the time required and provides more opportunities for drug target identification. The goal of this work is to evaluate and analyze metabolic pathways in serotype 14 of S. pneumonia to identify potential drug targets. In the present study, 47 potent drug targets were identified against S. pneumonia by employing the computational subtractive genomics approach. Among these, two proteins are prioritized (i.e., 4-oxalocrotonate tautomerase and Sensor histidine kinase uniquely present in S. pneumonia) as novel drug targets and selected for further structure-based studies. The identified proteins may provide a platform for the discovery of a lead drug candidate that may be capable of inhibiting these proteins and, therefore, could be helpful in minimizing the associated risk related to the drug-resistant S. pneumoniae. Finally, these enzymatic proteins could be of prime interest against S. pneumoniae to design rational targeted therapy.