Abstract: Background: Polypharmacy in abortive medications is often inevitable for patients with refractory headaches. Objective: We seek to enumerate an exhaustive list of headaches abortive medications that are without drug-drug interactions. Methods: We updated a list of acute medications based on the widely used Jefferson Headache Manual with novel abortive medications including ubrogepant, lasmiditan, and rimegepant. Opioids and barbiturate-containing products are excluded. From this resultant list of medications, we then conducted an exhaustive search of all pair-wise interactions via DrugBank's API. Using this interaction list, we filtered all possible two, three, and four drug combinations of abortive medications. The list of medications was then reapplied to DrugBank to verify the lack of known drug-drug interactions. Results: There are 192 medication combinations that do not contain any drug-drug interactions. Most common elements in these combinations are ubrogepant, prochlorperazine, followed by tizanidine. There are 67 three-drug combinations that do not contain interactions. Only two of the four-drug combinations do not yield some form of drug-drug interactions. Conclusion: This list of headaches abortive medications without drug-drug interactions is a useful tool for clinicians seeking to more effectively manage refractory headaches by implementing a rational polypharmacy.

Abstract: A global, comprehensive and open access listing of approved anticancer drugs does not currently exist. Partial information is available from multiple sources, including regulatory authorities, national formularies and scientific agencies. Many such data sources include drugs used in oncology for supportive care, diagnostic or other non-antineoplastic uses. We describe a methodology to combine and cleanse relevant data from multiple sources to produce an open access database of drugs licensed specifically for therapeutic antineoplastic purposes. The resulting list is provided as an open access database, (http://www.redo-project.org/cancer-drugs-db/), so that it may be used by researchers as input for further research projects, for example literature-based text mining for drug repurposing.

Abstract: The application of bioinformatics to vaccine research and drug discovery has never been so essential in the fight against infectious diseases. The greatest combat of the 21st century against a debilitating disease agent SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus discovered in Wuhan, China, December 2019, has piqued an unprecedented usage of bioinformatics tools in deciphering the molecular characterizations of infectious pathogens. With the viral genome data of SARS-COV-2 been made available barely weeks after the reported outbreak, bioinformatics platforms have become an all-time critical tool to gain time in the fight against the disease pandemic. Before the outbreak, different platforms have been developed to explore antigenic epitopes, predict peptide-protein docking and antibody structures, and simulate antigen-antibody reactions and lots more. However, the advent of the pandemic witnessed an upsurge in the application of these pipelines with the development of newer ones such as the Coronavirus Explorer in the development of efficacious vaccines, drug repurposing, and/or discovery. In this review, we have explored the various pipelines available for use, their relevance, and limitations in the timely development of useful therapeutic candidates from genomic data knowledge to clinical therapy.

Abstract: Coronavirus spread is an emergency reported globally, and a specific treatment strategy for this significant health issue is not yet identified. COVID-19 is a highly contagious disease and needs to be controlled promptly as millions of deaths have been reported. Due to the absence of proficient restorative alternatives and preliminary clinical restrictions, FDA-approved medications can be a decent alternative to deal with the coronavirus malady (COVID-19). The present study aims to meet the imperative necessity of effective COVID-19 drug treatment with a computational multi-target drug repurposing approach. This study focused on screening the FDA-approved drugs derived from the fungal source and its derivatives against the SARS-CoV-2 targets. All the selected drugs showed good binding affinity towards these targets, and out of them, bromocriptine was found to be the best candidate after the screening on the COVID-19 targets. Further, bromocriptine is analyzed by molecular simulation and MM-PBSA study. These studies suggested that bromocriptine can be the best candidate for TMPRSS2, Main protease, and RdRp protein. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-021-00089-8.

Abstract: Drug-target interactions provide useful information for biomedical drug discovery as well as drug development. However, it is costly and time consuming to find drug-target interactions by experimental methods. As a result, developing computational approaches for this task is necessary and has practical significance. In this study, we establish a novel dual Laplacian graph regularized logistic matrix factorization model for drug-target interaction prediction, referred to as DLGrLMF briefly. Specifically, DLGrLMF regards the task of drug-target interaction prediction as a weighted logistic matrix factorization problem, in which the experimentally validated interactions are allocated with larger weights. Meanwhile, by considering that drugs with similar chemical structure should have interactions with similar targets and targets with similar genomic sequence similarity should in turn have interactions with similar drugs, the drug pairwise chemical structure similarities as well as the target pairwise genomic sequence similarities are fully exploited to serve the matrix factorization problem by using a dual Laplacian graph regularization term. In addition, we design a gradient descent algorithm to solve the resultant optimization problem. Finally, the efficacy of DLGrLMF is validated on various benchmark datasets and the experimental results demonstrate that DLGrLMF performs better than other state-of-the-art methods. Case studies are also conducted to validate that DLGrLMF can successfully predict most of the experimental validated drug-target interactions.

Abstract: Introduction: There is an urgent need to identify therapies that prevent SARS-CoV-2 infection and improve the outcome of COVID-19 patients. Objective: Based upon clinical observations, we proposed that some psychotropic and antihistaminic drugs could protect psychiatric patients from SARS-CoV-2 infection. This observation is investigated in the light of experimental in vitro data on SARS-CoV-2. Methods: SARS-CoV-2 high-throughput screening results are available at the NCATS COVID-19 portal. We investigated the in vitro anti-viral activity of many psychotropic and antihistaminic drugs using chemoinformatics approaches. Results and Discussion: We analyze our clinical observations in the light of SARS-CoV-2 experimental screening results and propose that several cationic amphiphilic psychotropic and antihistaminic drugs could protect people from SARS-CoV-2 infection; some of these molecules have very limited adverse effects and could be used as prophylactic drugs. Other cationic amphiphilic drugs used in other disease areas are also highlighted. Recent analyses of patient electronic health records reported by several research groups indicate that some of these molecules could be of interest at different stages of the disease progression. In addition, recently reported drug combination studies further suggest that it might be valuable to associate several cationic amphiphilic drugs. Taken together, these observations underline the need for clinical trials to fully evaluate the potentials of these molecules, some fitting in the so-called category of broad-spectrum antiviral agents. Repositioning orally available drugs that have moderate side effects and should act on molecular mechanisms less prone to drug resistance would indeed be of utmost importance to deal with COVID-19.

Abstract: Semi-supervised deep learning for the biomedical graph and advanced manufacturing graph is rapidly becoming an important topic in both academia and industry. Many existing types of research focus on semi-supervised link prediction and node classification, as well as the application of these methods in sustainable development and advanced manufacturing. To date, most manufacturing graph neural networks are mainly evaluated on social and information networks, which improve the quality of network representation y integrating neighbor node descriptions. However, previous methods have not yet been comprehensively studied on biomedical networks. Traditional techniques fail to achieve satisfying results, especially when labeled nodes are deficient in number. In this paper, a new semi-supervised deep learning method for the biomedical graph via sustainable knowledge transfer called SeBioGraph is proposed. In SeBioGraph, both node embedding and graph-specific prototype embedding are utilized as transferable metric space characterized. By incorporating prior knowledge learned from auxiliary graphs, SeBioGraph further promotes the performance of the target graph. Experimental results on the two-class node classification tasks and three-class link prediction tasks demonstrate that the SeBioGraph realizes state-of-the-art results. Finally, the method is thoroughly evaluated.

Abstract: G-protein-coupled receptors (GPCRs) are membrane proteins which play an important role in many cellular processes and are excellent drug targets. Despite the existence of several US Food and Drug Administration (FDA)-approved GPCR-targeting drugs, there is a continuing challenge of side effects owing to the nonspecific nature of drug binding. We have investigated the diversity of the ligand binding site for this class of proteins against their cognate ligands using computational docking, even if their structures are known already in the ligand-complexed form. The cognate ligand of some of these receptors dock at allosteric binding site with better score than the binding at the conservative site. Interestingly, amino acid residues at such allosteric binding site are not conserved across GPCR subfamilies. Such a computational approach can assist in the prediction of specific allosteric binders for GPCRs.

Abstract: Background: The anti-tumor properties of curcumin have been demonstrated for many types of cancer. However, a systematic functional and biological analysis of its target proteins has yet to be fully documented. The aim of this study was to explore the underlying mechanisms of curcumin and broaden the perspective of targeted therapies. Methods: Direct protein targets (DPTs) of curcumin were searched in the DrugBank database. Using the STRING database, the interactions between curcumin and DPTs and indirect protein targets (IPTs) weres documented. The protein-protein interaction (PPI) network of curcumin-mediated proteins was visualized using Cytoscape. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed for all curcumin-mediated proteins. Furthermore, the cancer targets were searched in the Comparative Toxicogenomics Database (CTD). The overlapping targets were studied using Kaplan-Meier analysis to evaluate cancer survival. Further genomic analysis of overlapping genes was conducted using the cBioPortal database. Lastly, MTT, quantitative polymerase chain reaction (qPCR), and western blot (WB) analysis were used to validate the predicted results on hepatocellular carcinoma (HCC) cells. Results: A total of five DPTs and 199 IPTs were found. These protein targets were found in 121 molecular pathways analyzed via KEGG enrichment. Based on the anti-tumor properties of curcumin, two pathways were selected, including pathways in cancer (36 genes) and HCC (22 genes). Overlapping with 505 HCC-related gene sets identified in CTD, five genes (TP53, RB1, TGFB1, GSTP1, and GSTM1) were finally identified. High mRNA levels of TP53, RB1, and GSTM1 indicated a prolonged overall survival (OS) in HCC, whereas elevated mRNA levels of TGFB1 were correlated with poor prognosis. The viability of both HepG2 cells and Hep3B cells was significantly reduced by curcumin at concentrations of 20 or 30 muM after 48 or 72 h of culture. At a concentration of 20 muM curcumin cultured for 48 h, the expression of TGFB1 and GSTP1 in Hep3B cells was reduced significantly in qPCR analysis, and reduced TGFB1 protein expression was also found in Hep3B cells.

Abstract: Background: Intervertebral disc degeneration (IVDD) is the most significant cause of low back pain, the sixth-largest disease burden globally, and the leading cause of disability. This study is aimed at investigating the molecular biological mechanism of Danggui-Sini formula (DSF) mediated IVDD treatment. Methods: A potential gene set for DSF treatment of IVDD was identified through TCMSP, UniProt, and five disease gene databases. A protein interaction network of common targets between DSF and IVDD was established by using the STRING database. GO and KEGG enrichment analyses were performed using the R platform to discover the potential mechanism. Moreover, AutoDock Vina was used to verify molecular docking and calculate the binding energy. Results: A total of 119 active ingredients and 136 common genes were identified, including 10 core genes (AKT1, IL6, ALB, TNF, VEGFA, TP53, MAPK3, CASP3, JUN, and EGF). Enrichment analysis results showed that the therapeutic targets of DSF for diseases mainly focused on the AGE-RAGE signaling pathway involved in diabetic complications, IL-17 signaling pathway, TNF signaling pathway, Toll-like receptor signaling pathway, apoptosis, cellular senescence, PI3K-Akt signaling pathway, and FoxO signaling pathway. These biological processes are induced mainly in response to oxidative stress and reactive oxygen species and the regulation of apoptotic signaling pathways. Molecular docking showed that there was a stable affinity between the core genes and the key components. Conclusions: The combination of network pharmacology and molecular docking provides a practical way to analyze the molecular biological mechanism of DSF-mediated IVDD treatment, which confirms the "multicomponent, multitarget and multipathway" characteristics of DSF and provides an essential theoretical basis for clinical practice.