Abstract: BACKGROUND: The coronavirus disease (COVID-19) is a pandemic disease that threatens worldwide public health, and rheumatoid arthritis (RA) is the most common autoimmune disease. COVID-19 and RA are each strong risk factors for the other, but their molecular mechanisms are unclear. This study aims to investigate the biomarkers between COVID-19 and RA from the mechanism of pyroptosis and find effective disease-targeting drugs. METHODS: We obtained the common gene shared by COVID-19, RA (GSE55235), and pyroptosis using bioinformatics analysis and then did the principal component analysis(PCA). The Co-genes were evaluated by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and ClueGO for functional enrichment, the protein-protein interaction (PPI) network was built by STRING, and the k-means machine learning algorithm was employed for cluster analysis. Modular analysis utilizing Cytoscape to identify hub genes, functional enrichment analysis with Metascape and GeneMANIA, and NetworkAnalyst for gene-drug prediction. Network pharmacology analysis was performed to identify target drug-related genes intersecting with COVID-19, RA, and pyroptosis to acquire Co-hub genes and construct transcription factor (TF)-hub genes and miRNA-hub genes networks by NetworkAnalyst. The Co-hub genes were validated using GSE55457 and GSE93272 to acquire the Key gene, and their efficacy was assessed using receiver operating curves (ROC); SPEED2 was then used to determine the upstream pathway. Immune cell infiltration was analyzed using CIBERSORT and validated by the HPA database. Molecular docking, molecular dynamics simulation, and molecular mechanics-generalized born surface area (MM-GBSA) were used to explore and validate drug-gene relationships through computer-aided drug design. RESULTS: COVID-19, RA, and pyroptosis-related genes were enriched in pyroptosis and pro-inflammatory pathways(the NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome complex, death-inducing signaling complex, regulation of interleukin production), natural immune pathways (Network map of SARS-CoV-2 signaling pathway, activation of NLRP3 inflammasome by SARS-CoV-2) and COVID-19-and RA-related cytokine storm pathways (IL, nuclear factor-kappa B (NF-kappaB), TNF signaling pathway and regulation of cytokine-mediated signaling). Of these, CASP1 is the most involved pathway and is closely related to minocycline. YY1, hsa-mir-429, and hsa-mir-34a-5p play an important role in the expression of CASP1. Monocytes are high-caspase-1-expressing sentinel cells. Minocycline can generate a highly stable state for biochemical activity by docking closely with the active region of caspase-1. CONCLUSIONS: Caspase-1 is a common biomarker for COVID-19, RA, and pyroptosis, and it may be an important mediator of the excessive inflammatory response induced by SARS-CoV-2 in RA patients through pyroptosis. Minocycline may counteract cytokine storm inflammation in patients with COVID-19 combined with RA by inhibiting caspase-1 expression.

Abstract: BACKGROUND: Diabetes has become a serious global public health problem. With the increasing prevalence of type 2 diabetes mellitus (T2DM), the incidence of complications of T2DM is also on the rise. Sitagliptin, as a targeted drug of DPP4, has good therapeutic effect for T2DM. It is well known that sitagliptin can specifically inhibit the activity of DPP4 to promote insulin secretion, inhibit islet beta cell apoptosis and reduce blood glucose levels, while other pharmacological mechanisms are still unclear, such as improving insulin resistance, anti-inflammatory, anti-oxidative stress, and anti-fibrosis. The aim of this study was to explore novel targets and potential signaling pathways of sitagliptin for T2DM. METHODS: Firstly, network pharmacology was applied to find the novel target most closely related to DPP4. Semi-flexible molecular docking was performed to confirm the binding ability between sitagliptin and the novel target, and molecular dynamics simulation (MD) was carried to verify the stability of the complex formed by sitagliptin and the novel target. Furthermore, surface-plasmon resonance (SPR) was used to explored the affinity and kinetic characteristics of sitagliptin with the novel target. Finally, the molecular mechanism of sitagliptin for T2DM was predicted by the enrichment analysis of GO function and KEGG pathway. RESULTS: In this study, we found the cell surface receptor-angiotensin-converting enzyme 2 (ACE2) most closely related to DPP4. Then, we confirmed that sitagliptin had strong binding ability with ACE2 from a static perspective, and the stability of sitagliptin-ACE2 complex had better stability and longer binding time than BAR708-ACE2 in simulated aqueous solution within 50 ns. Significantly, we have demonstrated a strong affinity between sitagliptin and ACE2 on SPR biosensor, and their kinetic characteristics were "fast binding/fast dissociation". The guiding significance of clinical administration: low dose can reach saturation, but repeated administration was needed. Finally, there was certain relationship between COVID-19 and T2DM, and ACE2/Ang-(1-7)/Mas receptor (MasR) axis may be the important pathway of sitagliptin targeting ACE2 for T2DM. CONCLUSION: This study used different methods to prove that ACE2 may be another novel target of sitagliptin for T2DM, which extended the application of ACE2 in improving diabetes mellitus.

Abstract: Application of artificial intelligence (AI) in precision oncology typically involves predicting whether the cancer cells of a patient (previously unseen by AI models) will respond to any of a set of existing anticancer drugs, based on responses of previous training cell samples to those drugs. To expand the repertoire of anticancer drugs, AI has also been used to repurpose drugs that have not been tested in an anticancer setting, i.e., predicting the anticancer effects of a new drug on previously unseen cancer cells de novo. Here, we report a computational model that addresses both of the above tasks in a unified AI framework. Our model, referred to as deep learning-based graph regularized matrix factorization (DeepGRMF), integrates neural networks, graph models, and matrix-factorization techniques to utilize diverse information from drug chemical structures, their impact on cellular signaling systems, and cancer cell cellular states to predict cell response to drugs. DeepGRMF learns embeddings of drugs so that drugs sharing similar structures and mechanisms of action (MOAs) are closely related in the embedding space. Similarly, DeepGRMF also learns representation embeddings of cells such that cells sharing similar cellular states and drug responses are closely related. Evaluation of DeepGRMF and competing models on Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets show its superiority in prediction performance. Finally, we show that the model is capable of predicting effectiveness of a chemotherapy regimen on patient outcomes for the lung cancer patients in The Cancer Genome Atlas (TCGA) dataset*.

Abstract: Toutongning capsule (TTNC) is an effective and safe traditional Chinese medicine used in the treatment of migraine. In this present study, a multiscale strategy was used to systematically investigate the mechanism of TTNC in treating migraine, which contained UPLC-UESI-Q Exactive Focus network pharmacology and experimental verification. First, 88 compounds were identified by the UPLC-UESI-Q Exactive Focus method for TTNC. Then, the target fishing for these compounds was performed by means of an efficient drug similarity search tool. Third, a series of network pharmacology experiments were performed to predict the key compounds, targets, and pathways. They were protein-protein interaction (PPI), KEGG pathway enrichment analysis, and herbs-compounds-targets-pathways (H-C-T-P) network construction. As a result, 18 potential key compounds, 20 potential key targets, and 6 potential signaling pathways were obtained for TTNC in treatment with migraine. Finally, molecular docking and experimental were carried out to verify the key targets. In short, the results showed that TTNC is able to treat migraine through multiple components, multiple targets, and multiple pathways. This work may provide a theoretical basis for further research on the molecular mechanism of TTNC in the treatment of migraine.

Abstract: Background: To examine the potential therapeutic targets of Chinese medicine formula San-Miao-San (SMS) in the treatment of osteoarthritis (OA), we analyzed the active compounds of SMS and key targets of OA and investigated the interacting pathways using network pharmacological approaches and molecular docking analysis. Methods: The active compounds of SMS and OA-related targets were searched and screened by TCMSP, DrugBank, Genecards, OMIM, DisGeNet, TTD, and PharmGKB databases. Venn analysis and PPI were performed for evaluating the interaction of the targets. The topological analysis and molecular docking were used to confirm the subnetworks and binding affinity between active compounds and key targets, respectively. The GO and KEGG functional enrichment analysis for all targets of each subnetwork were conducted. Results: A total of 57 active compounds and 203 targets of SMS were identified by the TCMSP and DrugBank database, while 1791 OA-related targets were collected from the Genecards, OMIM, DisGeNet, TTD, and PharmGKB databases. By Venn analysis, 108 intersection targets between SMS targets and OA targets were obtained. Most of these intersecting targets involve quercetin, kaempferol, and wogonin. Moreover, intersecting targets identified by PPI analysis were introduced into Cytoscape plug-in CytoNCA for topological analysis. Hence, nine key targets of SMS for OA treatment were obtained. Furthermore, the potential binding conformations between active compounds and key targets were found through molecular docking analysis. According to the DAVID enrichment analysis, the main biological processes of SMS in the treatment of OA include oxidative stress, response to reactive oxygen species, and apoptotic signaling pathways. Finally, we found wogonin, the key compound in SMS, might play a pivotal role on Toll-like receptor, IL-17, TNF, osteoclast differentiation, and apoptosis signaling pathways through interacting with four key targets. Conclusions: Therefore, this study elucidated the potential active compounds and key targets of SMS in the treatment of OA based on network pharmacology.

Abstract: Several studies suggested the role of heme iron, but not non-heme iron in colorectal cancer. A network and system biology-based approach was used to understand the role of heme and non-heme iron on colorectal cancer etiology. Heme and non-heme iron targets were screened in addition to CRC targets. The protein-protein interaction map of both iron targets was prepared with CRC targets. Moreover, functional enrichment analysis was performed in order to understand their role in cancer etiology. The heme iron is predicted to modulate several cancer-associated pathways. Our results indicate several targets and pathways, including IL-4/IL-13, ACE, and HIF-1 signaling, that may have an important role in heme iron-mediated CRC and must be given consideration for understanding their role in colorectal cancer.

Abstract: BACKGROUND: Interstitial lung disease (ILD) is associated with substantial morbidity and mortality, which is one of the key systematic manifestations of connective tissue disease (CTD). Tripterygium wilfordii, known as Leigongteng in Chinese, has been applied to treat connective tissue disease-related interstitial lung disease (CTD-ILD) for many years. Triptolide is a key effective component from Tripterygium wilfordii. But the molecular mechanism of Triptolide for treating CTD-ILD is not yet clear. METHODS: Gaining insight into the molecular mechanism of Triptolide intervention CTD-ILD, we used the method of network pharmacology. And then we conducted drug-target networks to analyse the potential protein targets between Triptolide and CTD-ILD. Finally, AutoDock Vina was selected for molecular docking. RESULTS: By analysing the interaction genes between Triptolide and CTD-ILD, 242 genes were obtained. The top 10 targets of the highest enrichment scores were STAT3, AKT1, MAPK1, IL6, TP53, MAPK3, RELA, TNF, JUN, JAK2. GO and KEGG enrichment analysis exhibited that multiple signalling pathways were involved. PI3K-Akt, multiple virus infections, cancer signalling, chemokine, and apoptosis signalling pathway are the main pathways for Triptolide intervention CTD-ILD. And it is related to various biological processes such as inflammation, infection, cell apoptosis, and cancer. Molecular docking shows Triptolide can bind with its target protein in a good bond by intermolecular force. CONCLUSIONS: This study preliminarily reveals the internal molecular mechanism of Triptolide interfere with CTD-ILD through multiple targets, multiple access, validated through molecular docking.KEY MESSAGESTriptolide intervention CTD-ILD, which are related to various biological processes such as inflammation, infection, cell apoptosis, and cancer.PI3K-Akt, multiple virus infections, and apoptosis signalling pathway are the main pathways for Triptolide intervention CTD-ILD.Triptolide can bind with related target protein in a good bond by Intermolecular force, exhibiting a good docking activity.

Abstract: Liver toxicity is a major adverse drug reaction that accounts for drug failure in clinical trials and withdrawal from the market. Therefore, predicting potential liver toxicity at an early stage in drug discovery is crucial to reduce costs and the potential for drug failure. However, current in vivo animal toxicity testing is very expensive and time consuming. As an alternative approach, various machine learning models have been developed to predict potential liver toxicity in humans. This chapter reviews current advances in the development and application of machine learning models for prediction of potential liver toxicity in humans and discusses possible improvements to liver toxicity prediction.

Abstract: INTRODUCTION: Cancer patients with Covid-19 are exposed to treatment combinations that can potentially result in interactions that adversely affect patient outcomes. This study aimed to identify potential drug-drug interactions between antineoplastic agents and medicines used to treat Covid-19. METHODS: We conducted a search for potential interactions between 201 antineoplastic agents and 26 medicines used to treat Covid-19 on the Lexicomp((R)) and Micromedex((R)) databases. The following data were extracted: interaction severity ("major" and "contraindicated") and interaction effects (pharmacokinetic and pharmacodynamic). We also sought to identify the therapeutic indication of the antineoplastic drugs involved in the potential drug-drug interactions. RESULTS: A total of 388 "major" or "contraindicated" drug-drug interactions were detected. Eight drugs or combinations (baricitinib, lopinavir/ritonavir, atazanavir, darunavir, azithromycin, chloroquine, hydroxychloroquine, and sirolimus) accounted for 91.5% of these interactions. The class of antineoplastic agents with the greatest potential for interaction was tyrosine kinase inhibitors (accounting for 46.4% of all interactions). The findings show that atazanavir, baricitinib, and lopinavir/ritonavir can affect the treatment of all common types of cancer. The most common pharmacokinetic effect of the potential drug-drug interactions was increased plasma concentration of the antineoplastic medicine (39.4%). CONCLUSIONS: Covid-19 is a recent disease and pharmacological interventions are undergoing constant modification. This study identified a considerable number of potential drug-drug interactions. In view of the vulnerability of patients with cancer, it is vital that health professionals carefully assess the risks and benefits of drug combinations.

Abstract: BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a long-term respiratory disorder marked by restricted airflow and persistent respiratory symptoms. According to previous studies, icariin combined with nobiletin (I&N) significantly ameliorates COPD, but the therapeutic mechanisms remain unclear. PURPOSE: The aim of the study is to investigate the therapeutic mechanisms of I&N against COPD using network pharmacology and experimental validation. METHODS: The targets of I&N and related genes of COPD were screened and their intersection was selected. Next, the protein-protein interaction (PPI) networks, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Further, a COPD rat model was established to validate the effect and mechanisms of I&N. RESULTS: 445 potential targets I&N were obtained from SwissTargetPrediction, STITCH 5.0, and PharmMapper databases. 1831 related genes of COPD were obtained from GeneCards, DrugBank, and DisGeNet databases. 189 related genes were screened via matching COPD targets with I&N. 16 highest score targets among 189 targets were obtained according to PPI networks. GO and KEGG pathway enrichment analyses of 16 highest score targets suggested that these key genes of I&N were mostly enriched in the tumor necrosis factor (TNF) pathway, mitogen-activated protein kinase (MAPK) pathway, and phosphatidyl inositol 3-kinase (PI3K)-protein kinase B (AKT) pathway. Therefore, the treatments of I&N for COPD were connected with inflammation-related pathways. In in vivo experiments, the studies indicated that I&N improved the lung function and alleviated the damage of pulmonary histopathology. Moreover, I&N reduced levels of interleukin (IL)-6, IL-1beta, and TNF-alpha in lung tissues of COPD rats and inhibited the activation of the MAPK pathway and PI3K-Akt pathway. CONCLUSIONS: Icariin combined with nobiletin has therapeutic effects on COPD by inhibiting inflammation. The potential mechanisms of I&N may relate to the MAPK pathway and PI3K-Akt pathway.