Abstract: BACKGROUND: The emergence of the novel coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to millions of infections and is exerting an unprecedented impact on society and economies worldwide. The evidence showed that heart failure (HF) is a clinical syndrome that could be encountered at different stages during the progression of COVID-19. Shenfu injection (SFI), a traditional Chinese medicine (TCM) formula has been widely used for heart failure therapy in China and was suggested to treat critical COVID-19 cases based on the guideline for diagnosis and treatment of COVID-19 (the 7th version) issued by National Health Commission of the People's Republic of China. However, the active components, potential targets, related pathways, and underlying pharmacology mechanism of SFI against COVID-19 combined with HF remain vague. OBJECTIVE: To investigate the effectiveness and possible pharmacological mechanism of SFI for the prevention and treatment of COVID-19 combined with HF. METHODS: In the current study, a network analysis approach integrating active compound screening (drug-likeness, lipophilicity, and aqueous solubility models), target fishing (Traditional Chinese Medicine Systems Pharmacology, fingerprint-based Similarity Ensemble Approach, and PharmMapper databases), compound-target-disease network construction (Cytoscape software), protein-protein interaction network construction (STRING and Cytoscape software), biological process analysis (STRING and Cytoscape plug-in Clue GO) and pathway analysis (Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis) was developed to decipher the active ingredients, potential targets, relevant pathways, and the therapeutic mechanisms of SFI for preventing and treating COVID-19 combined with HF. RESULTS: Finally, 20 active compounds (DL >/= 0.18, 1=Alog P = 5, and -5=LogS = -1) and 164 relevant targets of SFI were identified related to the development of COVID-19 combined with HF, which were mainly involved in three biological processes including metabolic, hemostasis, and cytokine signaling in immune system. The C-T-D network and reactome pathway analysis indicated that SFI probably regulated the pathological processes of heart failure, respiratory failure, lung injury, and inflammatory response in patients with COVID-19 combined with HF through acting on several targets and pathways. Moreover, the venn diagram was used to identify 54 overlapped targets of SFI, COVID-19, and HF. KEGG pathway enrichment analysis showed that 54 overlapped targets were highly enriched to several COVID-19 and HF related pathways, such as IL-17 signaling pathway, Th17 cell differentiation, and NF-kappa B signaling pathway. CONCLUSIONS: A comprehensive network analysis approach framework was developed to systematically elucidate the potential pharmacological mechanism of SFI for the prevention and treatment of SFI against COVID-19 combined with HF. The current study may not only provide in-depth understanding of the pharmacological mechanisms of SFI, but also a scientific basis for the application of SFI against COVID-19 combined with HF.

Abstract: Discover potential biomarkers of the response for anti-cancer therapies, including traditional Chinese medicine (TCM), is a critical but much different task in the field of cancer research. Based on accumulated data and sophisticated methods, multi-omics analysis provides a feasible strategy for the discovery of potential therapeutic biomarkers. Here, we screened the potential therapeutic biomarkers for anti-cancer compounds in TCM through multi-omics data analysis. Firstly, compounds in TCM were collected from the public databases. Then, the molecules that those compounds can intervene on cell lines were carefully filtered out from existing drug bioactivity datasets. Finally, multi-omics analysis including gene mutation analysis, differential expression gene analysis, copy number variation analysis and clinical survival analysis for pan-cancer were conducted to screen potential therapeutic biomarkers for compounds in TCM. 13 molecules of compounds in TCM namely ERBB2, MYC, FLT4, TEK, GLI1, TOP2A, PDE10A, SLC6A3, GPR55, TERT, EGFR, KCNA3 and HDAC4 are differentially expressed, high frequently mutated, obtain high copy number variation rate and also significant in survival, are considered as the potential therapeutic biomarkers.

Abstract: Drug repurposing in the context of neuroimmunological (NI) investigations is still in its primary stages. Drug repurposing is an important method that bypasses lengthy drug discovery procedures and focuses on discovering new usages for known medications. Neuroimmunological diseases, such as Alzheimer's, Parkinson's, multiple sclerosis, and depression, include various pathologies that result from the interaction between the central nervous system and the immune system. However, the repurposing of NI medications is hindered by the vast amount of information that needs mining. We previously presented Adera1.0, which was capable of text mining PubMed for answering query-based questions. However, Adera1.0 was not able to automatically identify chemical compounds within relevant sentences. To challenge the need for repurposing known medications for neuroimmunological diseases, we built a deep neural network named Adera2.0 to perform drug repurposing. The workflow uses three deep learning networks. The first network is an encoder and its main task is to embed text into matrices. The second network uses a mean squared error (MSE) loss function to predict answers in the form of embedded matrices. The third network, which constitutes the main novelty in our updated workflow, also uses a MSE loss function. Its main usage is to extract compound names from relevant sentences resulting from the previous network. To optimize the network function, we compared eight different designs. We found that a deep neural network consisting of an RNN neural network and a leaky ReLU could achieve 0.0001 loss and 67% sensitivity. Additionally, we validated Adera2.0's ability to predict NI drug usage against the DRUG Repurposing Hub database. These results establish the ability of Adera2.0 to repurpose drug candidates that can shorten the development of the drug cycle. The workflow could be download online.

Abstract: BACKGROUND: With the expansion of animal production, parasitic helminths are gaining increasing economic importance. However, application of several established deworming agents can harm treated hosts and environment due to their low specificity. Furthermore, the number of parasite strains showing resistance is growing, while hardly any new anthelminthics are being developed. Here, we present a bioinformatics workflow designed to reduce the time and cost in the development of new strategies against parasites. The workflow includes quantitative transcriptomics and proteomics, 3D structure modeling, binding site prediction, and virtual ligand screening. Its use is demonstrated for Acanthocephala (thorny-headed worms) which are an emerging pest in fish aquaculture. We included three acanthocephalans (Pomphorhynchus laevis, Neoechinorhynchus agilis, Neoechinorhynchus buttnerae) from four fish species (common barbel, European eel, thinlip mullet, tambaqui). RESULTS: The workflow led to eleven highly specific candidate targets in acanthocephalans. The candidate targets showed constant and elevated transcript abundances across definitive and accidental hosts, suggestive of constitutive expression and functional importance. Hence, the impairment of the corresponding proteins should enable specific and effective killing of acanthocephalans. Candidate targets were also highly abundant in the acanthocephalan body wall, through which these gutless parasites take up nutrients. Thus, the candidate targets are likely to be accessible to compounds that are orally administered to fish. Virtual ligand screening led to ten compounds, of which five appeared to be especially promising according to ADMET, GHS, and RO5 criteria: tadalafil, pranazepide, piketoprofen, heliomycin, and the nematicide derquantel. CONCLUSIONS: The combination of genomics, transcriptomics, and proteomics led to a broadly applicable procedure for the cost- and time-saving identification of candidate target proteins in parasites. The ligands predicted to bind can now be further evaluated for their suitability in the control of acanthocephalans. The workflow has been deposited at the Galaxy workflow server under the URL tinyurl.com/yx72rda7 .

Abstract: An individualized cancer therapy is ideally chosen to target the cancer's driving biological pathways, but identifying such pathways is challenging because of their underlying heterogeneity and there is no guarantee that they are druggable. We hypothesize that a cancer with an activated druggable cancer-specific pathway (DCSP) is more likely to respond to the relevant drug. Here we develop and validate a systematic method to search for such DCSPs, by (i) introducing a pathway activation score (PAS) that integrates cancer-specific driver mutations and gene expression profile and drug-specific gene targets, (ii) applying the method to identify DCSPs from pan-cancer datasets, and (iii) analyzing the correlation between PAS and the response to relevant drugs. In total, 4,794 DCSPs from 23 different cancers have been discovered in the Genomics of Drug Sensitivity in Cancer database and validated in The Cancer Genome Atlas database. Supporting the hypothesis, for the DCSPs in acute myeloid leukemia, cancers with higher PASs are shown to have stronger drug response, and this is validated in the BeatAML cohort. All DCSPs are publicly available at https://www.meb.ki.se/shiny/truvu/DCSP/.

Abstract: BACKGROUND: Microbes are associated with many human diseases and influence drug efficacy. Small-molecule drugs may revolutionize biomedicine by fine-tuning the microbiota on the basis of individual patient microbiome signatures. However, emerging endeavors in small-molecule microbiome drug discovery continue to follow a conventional "one-drug-one-target-one-disease" process. A systematic pharmacology approach that would suppress multiple interacting pathogenic species in the microbiome, could offer an attractive alternative solution. RESULTS: We construct a disease-centric signed microbe-microbe interaction network using curated microbe metabolite information and their effects on host. We develop a Signed Random Walk with Restart algorithm for the accurate prediction of effect of microbes on human health and diseases. With a survey on the druggable and evolutionary space of microbe proteins, we find that 8-10% of them can be targeted by existing drugs or drug-like chemicals and that 25% of them have homologs to human proteins. We demonstrate that drugs for diabetes can be the lead compounds for development of microbiota-targeted therapeutics. We further show that the potential drug targets that specifically exist in pathogenic microbes are periplasmic and cellular outer membrane proteins. CONCLUSION: The systematic studies of the polypharmacological landscape of the microbiome network may open a new avenue for the small-molecule drug discovery of the microbiome. We believe that the application of systematic method on the polypharmacological investigation could lead to the discovery of novel drug therapies.

Abstract: BACKGROUND: Biomedical translational science is increasingly using computational reasoning on repositories of structured knowledge (such as UMLS, SemMedDB, ChEMBL, Reactome, DrugBank, and SMPDB in order to facilitate discovery of new therapeutic targets and modalities. The NCATS Biomedical Data Translator project is working to federate autonomous reasoning agents and knowledge providers within a distributed system for answering translational questions. Within that project and the broader field, there is a need for a framework that can efficiently and reproducibly build an integrated, standards-compliant, and comprehensive biomedical knowledge graph that can be downloaded in standard serialized form or queried via a public application programming interface (API). RESULTS: To create a knowledge provider system within the Translator project, we have developed RTX-KG2, an open-source software system for building-and hosting a web API for querying-a biomedical knowledge graph that uses an Extract-Transform-Load approach to integrate 70 knowledge sources (including the aforementioned core six sources) into a knowledge graph with provenance information including (where available) citations. The semantic layer and schema for RTX-KG2 follow the standard Biolink model to maximize interoperability. RTX-KG2 is currently being used by multiple Translator reasoning agents, both in its downloadable form and via its SmartAPI-registered interface. Serializations of RTX-KG2 are available for download in both the pre-canonicalized form and in canonicalized form (in which synonyms are merged). The current canonicalized version (KG2.7.3) of RTX-KG2 contains 6.4M nodes and 39.3M edges with a hierarchy of 77 relationship types from Biolink. CONCLUSION: RTX-KG2 is the first knowledge graph that integrates UMLS, SemMedDB, ChEMBL, DrugBank, Reactome, SMPDB, and 64 additional knowledge sources within a knowledge graph that conforms to the Biolink standard for its semantic layer and schema. RTX-KG2 is publicly available for querying via its API at arax.rtx.ai/api/rtxkg2/v1.2/openapi.json . The code to build RTX-KG2 is publicly available at github:RTXteam/RTX-KG2 .

Abstract: (1) Background: Phenotypic and genotypic heterogeneity are characteristic features of cancer patients. To tackle patients' heterogeneity, immune checkpoint inhibitors (ICIs) represent some the most promising therapeutic approaches. However, approximately 50% of cancer patients that are eligible for treatment with ICIs do not respond well, especially patients with no targetable mutations. Over the years, multiple patient stratification techniques have been developed to identify homogenous patient subgroups, although matching a patient subgroup to a treatment option that can improve patients' health outcomes remains a challenging task. (2) Methods: We extended our Subgroup Discovery algorithm to identify patient subpopulations that could potentially benefit from immuno-targeted combination therapies in four cancer types: head and neck squamous carcinoma (HNSC), lung adenocarcinoma (LUAD), lung squamous carcinoma (LUSC), and skin cutaneous melanoma (SKCM). We employed the proportional odds model to identify significant drug targets and the corresponding compounds that increased the likelihood of stable disease versus progressive disease in cancer patients with the EGFR wild-type (WT) gene. (3) Results: Our pipeline identified six significant drug targets and thirteen specific compounds for cancer patients with the EGFR WT gene. Three out of six drug targets-FCGR2B, IGF1R, and KIT-substantially increased the odds of having stable disease versus progressive disease. Progression-free survival (PFS) of more than 6 months was a common feature among the investigated subgroups. (4) Conclusions: Our approach could help to better select responders for immuno-targeted combination therapies and improve health outcomes for cancer patients with no targetable mutations.

Abstract: Malignant central nervous system (CNS) cancers are among the most difficult to treat, with low rates of survival and a high likelihood of recurrence. This is primarily due to their location within the CNS, hindering adequate drug delivery and tumour access via surgery. Furthermore, CNS cancer cells are highly plastic, an adaptive property that enables them to bypass targeted treatment strategies and develop drug resistance. Potassium ion channels have long been implicated in the progression of many cancers due to their integral role in several hallmarks of the disease. Here, we will explore this relationship further, with a focus on malignant CNS cancers, including high-grade glioma (HGG). HGG is the most lethal form of primary brain tumour in adults, with the majority of patient mortality attributed to drug-resistant secondary tumours. Hence, targeting proteins that are integral to cellular plasticity could reduce tumour recurrence, improving survival. This review summarises the role of potassium ion channels in malignant CNS cancers, specifically how they contribute to proliferation, invasion, metastasis, angiogenesis, and plasticity. We will also explore how specific modulation of these proteins may provide a novel way to overcome drug resistance and improve patient outcomes.

Abstract: Transient Receptor Potential Melastatin 8 (TRPM8) from the melastatin TRP channel subfamily is a non-selective Ca(2+)-permeable ion channel with multimodal gating which can be activated by low temperatures and cooling compounds, such as menthol and icilin. Different conditions such as neuropathic pain, cancer, overactive bladder syndrome, migraine, and chronic cough have been linked to the TRPM8 mode of action. Despite the several potent natural and synthetic inhibitors of TRPM8 that have been identified, none of them have been approved for clinical use. The aim of this study was to discover novel blocking TRPM8 agents using automated patch clamp electrophysiology combined with a ligand-based virtual screening based on the SwissSimilarity platform. Among the compounds we have tested, nebivolol and carvedilol exhibited the greatest inhibitory effect, with an IC50 of 0.97 +/- 0.15 microM and 9.1 +/- 0.6 microM, respectively. This study therefore provides possible candidates for future drug repurposing and suggests promising lead compounds for further optimization as inhibitors of the TRPM8 ion channel.