Front Immunol. 2021 Nov 2;12:757607. doi: 10.3389/fimmu.2021.757607. eCollection 2021.

ABSTRACT

BACKGROUND: Retinal neovascularization (RNV) membranes can lead to a tractional retinal detachment, the primary reason for severe vision loss in end-stage disease proliferative diabetic retinopathy (PDR). The aim of this study was to characterize the molecular, cellular and immunological features of RNV in order to unravel potential novel drug treatments for PDR.

METHODS: A total of 43 patients undergoing vitrectomy for PDR, macular pucker or macular hole (control patients) were included in this study. The surgically removed RNV and epiretinal membranes were analyzed by RNA sequencing, single-cell based Imaging Mass Cytometry and conventional immunohistochemistry. Immune cells of the vitreous body, also known as hyalocytes, were isolated from patients with PDR by flow cytometry, cultivated and characterized by immunohistochemistry. A bioinformatical drug repurposing approach was applied in order to identify novel potential drug options for end-stage diabetic retinopathy disease.

RESULTS: The in-depth transcriptional and single-cell protein analysis of diabetic RNV tissue samples revealed an accumulation of endothelial cells, macrophages and myofibroblasts as well as an abundance of secreted ECM proteins such as SPARC, FN1 and several types of collagen in RNV tissue. The immunohistochemical staining of cultivated vitreal hyalocytes from patients with PDR showed that hyalocytes express α-SMA (alpha-smooth muscle actin), a classic myofibroblast marker. According to our drug repurposing analysis, imatinib emerged as a potential immunomodulatory drug option for future treatment of PDR.

CONCLUSION: This study delivers the first in-depth transcriptional and single-cell proteomic characterization of RNV tissue samples. Our data suggest an important role of hyalocyte-to-myofibroblast transdifferentiation in the pathogenesis of diabetic vitreoretinal disease and their modulation as a novel possible clinical approach.

PMID:34795670 | PMC:PMC8593213 | DOI:10.3389/fimmu.2021.757607

Int J Pharm. 2021 Nov 15:121296. doi: 10.1016/j.ijpharm.2021.121296. Online ahead of print.

ABSTRACT

Treatment of acute myeloid leukaemia (AML) relies on decades-old drugs, and while recent years have seen some breakthroughs, AML is still characterised by poor prognosis and survival rate. Drug repurposing can expedite the preclinical development of new therapies, and by nanocarrier encapsulation, the number of potentially viable drug candidates can be further expanded. The anti-psychotic drug chlorpromazine (CPZ) has been identified as a candidate for repurposing for AML therapy. Nanoencapsulation may improve the suitability of CPZ for the treatment of AML by reducing its effect on the central nervous system. Using the emulsion-evaporation technique, we have developed PEGylated PLGA nanoparticles loaded with CPZ for AML therapy. The nanoparticles were characterised to be between 150 and 300 nm by DLS, of spherical morphology by TEM, with a drug loading of at least 6.0% (w/w). After an initial burst release of adsorbed drug, the remaining 80% of the drug was retained in the PLGA nanoparticles for at least 24 hours. The CPZ-loaded nanoparticles had equal cytotoxic potential towards AML cells to free CPZ, but acted more slowly, in line with the protracted drug release. Crucially, nanoparticles injected intravenously into zebrafish larvae did not accumulate in the brain, and nanoencapsulation also prevented CPZ from crossing an artificial membrane model. This demonstrates that the purpose for nanoencapsulation of CPZ is fulfilled, namely avoiding effects on the central nervous system while retaining the anti-AML activity of the drug.

PMID:34793932 | DOI:10.1016/j.ijpharm.2021.121296

J Biomol Struct Dyn. 2021 Nov 18:1-10. doi: 10.1080/07391102.2021.2002719. Online ahead of print.

ABSTRACT

Cancer care has become a challenge with the current COVID-19 pandemic scenario. Specially, cancers like small cell lung cancers (SCLC) are difficult to treat even in the normal situation due to their rapid growth and early metastasis. For such patients, treatment can't be compromised and care must be taken to ensure their minimum exposure to the ongoing spread of COVID-19 infection. For this reason, in-house treatments are being suggested for these patients. Another issue is that symptoms of SCLC match well with that of COVID-19 infection. Hence, the detection of COVID-19 may also get delayed leading to unnecessary complications. Thus, we have tried to investigate if the therapeutics that is currently used in lung cancer treatment can also act against SARS-CoV-2. If it is so, the same treatment protocols can be continued even if the SCLC patient had contracted COVID-19 without compromising the cancer care. For this, RNA dependent RNA polymerase (RdRP) from SARS-CoV-2 has been selected as drug target. Both docking and molecular dynamicssimulation analysis have indicated that Paclitaxel and Dacomitinib may be explored as multi-target drugs for both SCLC and COVID-19.Communicated by Ramaswamy H. Sarma.

PMID:34791969 | DOI:10.1080/07391102.2021.2002719

Brief Bioinform. 2021 Nov 12:bbab455. doi: 10.1093/bib/bbab455. Online ahead of print.

ABSTRACT

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for millions of deaths around the world. To help contribute to the understanding of crucial knowledge and to further generate new hypotheses relevant to SARS-CoV-2 and human protein interactions, we make use of the information abundant Biomine probabilistic database and extend the experimentally identified SARS-CoV-2-human protein-protein interaction (PPI) network in silico. We generate an extended network by integrating information from the Biomine database, the PPI network and other experimentally validated results. To generate novel hypotheses, we focus on the high-connectivity sub-communities that overlap most with the integrated experimentally validated results in the extended network. Therefore, we propose a new data analysis pipeline that can efficiently compute core decomposition on the extended network and identify dense subgraphs. We then evaluate the identified dense subgraph and the generated hypotheses in three contexts: literature validation for uncovered virus targeting genes and proteins, gene function enrichment analysis on subgraphs and literature support on drug repurposing for identified tissues and diseases related to COVID-19. The major types of the generated hypotheses are proteins with their encoding genes and we rank them by sorting their connections to the integrated experimentally validated nodes. In addition, we compile a comprehensive list of novel genes, and proteins potentially related to COVID-19, as well as novel diseases which might be comorbidities. Together with the generated hypotheses, our results provide novel knowledge relevant to COVID-19 for further validation.

PMID:34791019 | DOI:10.1093/bib/bbab455

Front Oral Health. 2021 Jun;2:689482. doi: 10.3389/froh.2021.689482. Epub 2021 Jun 8.

ABSTRACT

Head and neck squamous cell carcinomas (HNSCC) are loco-regionally aggressive tumors that often lead to debilitating changes in appearance, speech, swallowing and respiratory function in patients. It is therefore critical to develop novel targeted treatment strategies that can effectively target multiple components within the tumor microenvironment. In this regard, there has been an increased recognition of the role of neural signaling networks as mediators of disease progression in HNSCC. Here, we summarize the current knowledge on the mechanisms of adrenergic signaling in HNSCC specifically focusing on neurovascular crosstalk and the potential of targeting the adrenergic-angiogenic axis through repurposing of FDA-approved drugs against HNSCC.

PMID:34790909 | PMC:PMC8594278 | DOI:10.3389/froh.2021.689482

Front Oncol. 2021 Nov 1;11:757598. doi: 10.3389/fonc.2021.757598. eCollection 2021.

ABSTRACT

Inhibitors of tropomyosin-related kinases (TRKs) display remarkable outcomes in the regression of cancers harboring the Neurotrophin Receptors Tyrosine Kinase (NTRK) fusion gene. As a result, TRKs have become attractive targets in anti-cancer drug discovery programs. Here, we demonstrate that AZD4547, a highly potent and selective inhibitor of fibroblast growth factor receptor (FGFR), displays anti-tumor activity against KM12(Luc) harboring the TPM3-NTRK1 fusion gene associated with its direct inhibition of TRKs. The results of profiling, using a 64-member in-house cancer cell panel, show that AZD4547 displays anti-proliferation activity against KM12(Luc) with a GI50 of 100 nM. In vitro biochemical assays reveal that AZD4547 has IC50 values of 18.7, 22.6 and 2.9 nM against TRKA, B and C, respectively. In a cellular context, AZD4547 blocks auto-phosphorylation of TRKs and phosphorylation of its downstream molecules including PLC-gamma and AKT in a dose dependent manner. Also, AZD4547 at 0.1 μM concentration downregulates expression of MAPK target genes (DUSP6, CCND1 and ETV1) as well as the E2F pathway. Furthermore, AZD4547 induces G0/G1 arrest and apoptosis, and suppresses anchorage independent growth of KM12(Luc). Oral administration of 40 mpk AZD4547 dramatically delays tumor growth in a KM12(Luc) implemented xenograft model, without promoting body weight changes. The capability of AZD4547 to inhibit TRKA, TRKB and clinically relevant mutants (TRKA G595R, G667S, G667C and G667A) was also evaluated using Ba/F3 cells harboring the ETV6-NTRKs fusion gene. The combined observations demonstrate the potential application of AZD4547 for treatment of NTRK fusion driven cancers.

PMID:34790577 | PMC:PMC8591201 | DOI:10.3389/fonc.2021.757598

Front Pharmacol. 2021 Nov 1;12:752174. doi: 10.3389/fphar.2021.752174. eCollection 2021.

ABSTRACT

Drug repositioning is a drug discovery strategy in which an existing drug is utilized as a therapeutic agent for a different disease. As information regarding the safety, pharmacokinetics, and formulation of existing drugs is already available, the cost and time required for drug development is reduced. Conventional drug repositioning has been dominated by a method involving the search for candidate drugs that act on the target molecules of an organism in a diseased state through basic research. However, recently, information hosted on medical information and life science databases have been used in translational research to bridge the gap between basic research in drug repositioning and clinical application. Here, we review an example of drug repositioning wherein candidate drugs were found and their mechanisms of action against a novel therapeutic target were identified via a basic research method that combines the findings retrieved from various medical and life science databases.

PMID:34790124 | PMC:PMC8591243 | DOI:10.3389/fphar.2021.752174

BMC Med Inform Decis Mak. 2021 Nov 16;21(Suppl 9):304. doi: 10.1186/s12911-021-01664-x.

ABSTRACT

BACKGROUND: The historical data of rare disease is very scarce in reality, so how to perform drug repositioning for the rare disease is a great challenge. Most existing methods of drug repositioning for the rare disease usually neglect father-son information, so it is extremely difficult to predict drugs for the rare disease.

METHOD: In this paper, we focus on father-son information mining for the rare disease. We propose GRU-Cooperation-Attention-Network (GCAN) to predict drugs for the rare disease. We construct two heterogeneous networks for information enhancement, one network contains the father-nodes of the rare disease and the other network contains the son-nodes information. To bridge two heterogeneous networks, we set a mapping to connect them. What's more, we use the biased random walk mechanism to collect the information smoothly from two heterogeneous networks, and employ a cooperation attention mechanism to enhance repositioning ability of the network.

RESULT: Comparing with traditional methods, GCAN makes full use of father-son information. The experimental results on real drug data from hospitals show that GCAN outperforms state-of-the-art machine learning methods for drug repositioning.

CONCLUSION: The performance of GCAN for drug repositioning is mainly limited by the insufficient scale and poor quality of the data. In future research work, we will focus on how to utilize more data such as drug molecule information and protein molecule information for the drug repositioning of the rare disease.

PMID:34789254 | DOI:10.1186/s12911-021-01664-x

Cancer Cell Int. 2021 Nov 17;21(1):607. doi: 10.1186/s12935-021-02306-y.

ABSTRACT

BACKGROUND: Uveal melanoma (UM), the most prevalent intraocular tumor in adults, is a highly metastatic and drug resistant lesion. Recent studies have demonstrated cytotoxic and anti-metastatic effects of the antiprogestin and antiglucocorticoid mifepristone (MF) in vitro and in clinical trials involving meningioma, colon, breast, and ovarian cancers. Drug repurposing is a cost-effective approach to bring approved drugs with good safety profiles to the clinic. This current study assessed the cytotoxic effects of MF in human UM cell lines of different genetic backgrounds.

METHODS: The effects of incremental concentrations of MF (0, 5, 10, 20, or 40 μM) on a panel of human UM primary (MEL270, 92.1, MP41, and MP46) and metastatic (OMM2.5) cells were evaluated. Cells were incubated with MF for up to 72 h before subsequent assays were conducted. Cellular functionality and viability were assessed by Cell Counting Kit-8, trypan blue exclusion assay, and quantitative label-free IncuCyte live-cell analysis. Cell death was analyzed by binding of Annexin V-FITC and/or PI, caspase-3/7 activity, and DNA fragmentation. Additionally, the release of cell-free DNA was assessed by droplet digital PCR, while the expression of progesterone and glucocorticoid receptors was determined by quantitative real-time reverse transcriptase PCR.

RESULTS: MF treatment reduced cellular proliferation and viability of all UM cell lines studied in a concentration-dependent manner. A reduction in cell growth was observed at lower concentrations of MF, with evidence of cell death at higher concentrations. A significant increase in Annexin V-FITC and PI double positive cells, caspase-3/7 activity, DNA fragmentation, and cell-free DNA release suggests potent cytotoxicity of MF. None of the tested human UM cells expressed the classical progesterone receptor in the absence or presence of MF treatment, suggesting a mechanism independent of the modulation of the cognate nuclear progesterone receptor. In turn, all cells expressed non-classical progesterone receptors and the glucocorticoid receptor.

CONCLUSION: This study demonstrates that MF impedes the proliferation of UM cells in a concentration-dependent manner. We report that MF treatment at lower concentrations results in cell growth arrest, while increasing the concentration leads to lethality. MF, which has a good safety profile, could be a reliable adjuvant of a repurposing therapy against UM.

PMID:34789240 | DOI:10.1186/s12935-021-02306-y

BMC Med Inform Decis Mak. 2021 Nov 16;21(Suppl 9):251. doi: 10.1186/s12911-021-01614-7.

ABSTRACT

BACKGROUND: Drug repurposing is to find new indications of approved drugs, which is essential for investigating new uses for approved or investigational drug efficiency. The active gene annotation corpus (named AGAC) is annotated by human experts, which was developed to support knowledge discovery for drug repurposing. The AGAC track of the BioNLP Open Shared Tasks using this corpus is organized by EMNLP-BioNLP 2019, where the "Selective annotation" attribution makes AGAC track more challenging than other traditional sequence labeling tasks. In this work, we show our methods for trigger word detection (Task 1) and its thematic role identification (Task 2) in the AGAC track. As a step forward to drug repurposing research, our work can also be applied to large-scale automatic extraction of medical text knowledge.

METHODS: To meet the challenges of the two tasks, we consider Task 1 as the medical name entity recognition (NER), which cultivates molecular phenomena related to gene mutation. And we regard Task 2 as a relation extraction task, which captures the thematic roles between entities. In this work, we exploit pre-trained biomedical language representation models (e.g., BioBERT) in the information extraction pipeline for mutation-disease knowledge collection from PubMed. Moreover, we design the fine-tuning framework by using a multi-task learning technique and extra features. We further investigate different approaches to consolidate and transfer the knowledge from varying sources and illustrate the performance of our model on the AGAC corpus. Our approach is based on fine-tuned BERT, BioBERT, NCBI BERT, and ClinicalBERT using multi-task learning. Further experiments show the effectiveness of knowledge transformation and the ensemble integration of models of two tasks. We conduct a performance comparison of various algorithms. We also do an ablation study on the development set of Task 1 to examine the effectiveness of each component of our method.

RESULTS: Compared with competitor methods, our model obtained the highest Precision (0.63), Recall (0.56), and F-score value (0.60) in Task 1, which ranks first place. It outperformed the baseline method provided by the organizers by 0.10 in F-score. The model shared the same encoding layers for the named entity recognition and relation extraction parts. And we obtained a second high F-score (0.25) in Task 2 with a simple but effective framework.

CONCLUSIONS: Experimental results on the benchmark annotation of genes with active mutation-centric function changes corpus show that integrating pre-trained biomedical language representation models (i.e., BERT, NCBI BERT, ClinicalBERT, BioBERT) into a pipe of information extraction methods with multi-task learning can improve the ability to collect mutation-disease knowledge from PubMed.

PMID:34789238 | DOI:10.1186/s12911-021-01614-7

Bioinformatics. 2021 Nov 11:btab767. doi: 10.1093/bioinformatics/btab767. Online ahead of print.

ABSTRACT

SUMMARY: DeepKG is an end-to-end deep learning-based workflow that helps researchers automatically mine valuable knowledge in biomedical literature. Users can utilize it to establish customized knowledge graphs in specified domains, thus facilitating in-depth understanding on disease mechanisms and applications on drug repurposing and clinical research, etc. To improve the performance of DeepKG, a cascaded hybrid information extraction framework (CHIEF) is developed for training model of 3-tuple extraction, and a novel AutoML-based knowledge representation algorithm (AutoTransX) is proposed for knowledge representation and inference. The system has been deployed in dozens of hospitals and extensive experiments strongly evidence the effectiveness. In the context of 144,900 COVID-19 scholarly full-text literature, DeepKG generates a high-quality knowledge graph with 7,980 entities and 43,760 3-tuples, a candidate drug list, and relevant animal experimental studies are being carried out. To accelerate more studies, we make DeepKG publicly available and provide an online tool including the data of 3-tuples, potential drug list, question answering system, visualization platform.

AVAILABILITY: Free to all users: http://covidkg.ai/.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:34788369 | DOI:10.1093/bioinformatics/btab767

Transl Psychiatry. 2021 Nov 16;11(1):591. doi: 10.1038/s41398-021-01724-w.

ABSTRACT

Drug repurposing is an invaluable strategy to identify new uses for existing drug therapies that overcome many of the time and financial costs associated with novel drug development. The COVID-19 pandemic has driven an unprecedented surge in the development and use of bioinformatic tools to identify candidate repurposable drugs. Using COVID-19 as a case study, we discuss examples of machine-learning and signature-based approaches that have been adapted to rapidly identify candidate drugs. The Library of Integrated Network-based Signatures (LINCS) and Connectivity Map (CMap) are commonly used repositories and have the advantage of being amenable to use by scientists with limited bioinformatic training. Next, we discuss how these recent advances in bioinformatic drug repurposing approaches might be adapted to identify repurposable drugs for CNS disorders. As the development of novel therapies that successfully target the cause of neuropsychiatric and neurological disorders has stalled, there is a pressing need for innovative strategies to treat these complex brain disorders. Bioinformatic approaches to identify repurposable drugs provide an exciting avenue of research that offer promise for improved treatments for CNS disorders.

PMID:34785660 | DOI:10.1038/s41398-021-01724-w

J Biomol Struct Dyn. 2021 Nov 16:1-17. doi: 10.1080/07391102.2021.2000498. Online ahead of print.

ABSTRACT

COVID-19, for which no confirmed therapeutic agents are available, has claimed over 48,14,000 lives globally. A feasible and quicker method to resolve this problem may be 'drug repositioning'. We investigated selected FDA and WHO-EML approved drugs based on their previously promising potential as antivirals, antibacterials or antifungals. These drugs were docked onto the nsp12 protein, which reigns the RNA-dependent RNA polymerase activity of SARS-CoV-2, a key therapeutic target for coronaviruses. Docked complexes were reevaluated using MM-GBSA analysis and the top three inhibitor-protein complexes were subjected to 100 ns long molecular dynamics simulation followed by another round of MM-GBSA analysis. The RMSF plots, binding energies and the mode of physicochemical interaction of the active site of the protein with the drugs were evaluated. Suramin, Penciclovir, and Anidulafungin were found to bind to nsp12 with similar binding energies as that of Remdesivir, which has been used as a therapy for COVID-19. In addition, recent experimental evidences indicate that these drugs exhibit antiviral efficacy against SARS-CoV-2. Such evidence, along with the significant and varied physical interactions of these drugs with the key viral enzyme outlined in this investigation, indicates that they might have a prospective therapeutic potential in the treatment of COVID-19 as monotherapy or combination therapy with Remdesivir.

PMID:34784490 | DOI:10.1080/07391102.2021.2000498

J Chem Inf Model. 2021 Nov 16. doi: 10.1021/acs.jcim.1c01285. Online ahead of print.

ABSTRACT

The COVID-19 pandemic has catalyzed a widespread effort to identify drug candidates and biological targets of relevance to SARS-COV-2 infection, which resulted in large numbers of publications on this subject. We have built the COVID-19 Knowledge Extractor (COKE), a web application to extract, curate, and annotate essential drug-target relationships from the research literature on COVID-19. SciBiteAI ontological tagging of the COVID Open Research Data set (CORD-19), a repository of COVID-19 scientific publications, was employed to identify drug-target relationships. Entity identifiers were resolved through lookup routines using UniProt and DrugBank. A custom algorithm was used to identify co-occurrences of the target protein and drug terms, and confidence scores were calculated for each entity pair. COKE processing of the current CORD-19 database identified about 3000 drug-protein pairs, including 29 unique proteins and 500 investigational, experimental, and approved drugs. Some of these drugs are presently undergoing clinical trials for COVID-19. The COKE repository and web application can serve as a useful resource for drug repurposing against SARS-CoV-2. COKE is freely available at https://coke.mml.unc.edu/, and the code is available at https://github.com/DnlRKorn/CoKE.

PMID:34783553 | DOI:10.1021/acs.jcim.1c01285

J Control Release. 2021 Nov 12:S0168-3659(21)00610-6. doi: 10.1016/j.jconrel.2021.11.015. Online ahead of print.

ABSTRACT

Lung cancer (LC) is one of the leading causes of mortality accounting for almost 25% of cancer deaths throughout the world. The shortfall of affordable and effective first-line chemotherapeutics, the existence of resistant tumors, and the non-optimal route of administration contribute to poor prognosis and high mortality in LC. Administration of repurposed non-oncology drugs (RNODs) loaded in nanocarriers (NCs) via inhalation may prove as an effective alternative strategy to treat LC. Furthermore, their site-specific release through inhalation route using an appropriate inhalation device would offer improved therapeutic efficacy, thereby reducing mortality and improving patients' quality of life. The current manuscript offers a comprehensive overview on use of RNODs in LC treatment with an emphasis on their inhalation delivery and the associated challenges. The role of NCs to improve lung deposition and targeting of RNODs via inhalation are also elaborated. In addition, information about various RNODs in clinical trials for the treatment of LC, possibility for repurposing phytoceuticals against LC via inhalation and the bottlenecks associated with repurposing RNODs against cancer are also highlighted. Based on the reported studies covered in this manuscript, it was understood that delivery of RNODs via inhalation has emerged as a propitious approach. Hence, it is anticipated to provide effective first-line treatment at an affordable cost in debilitating LC from low and middle-income countries (LMIC).

PMID:34780880 | DOI:10.1016/j.jconrel.2021.11.015

OMICS. 2021 Nov 15. doi: 10.1089/omi.2021.0170. Online ahead of print.

ABSTRACT

Genomic medicine has made important strides over the past several decades, but as new insights and technologies emerge, the applications of genomics in medicine and planetary health continue to evolve and expand. An important grand challenge is harnessing and making sense of the genomic big data in ways that best serve public and planetary health. Because human health is inextricably intertwined with the health of planetary ecosystems and nonhuman animals, genomic medicine is in need of high throughput bioinformatics analyses to harness and integrate human and ecological multiomics big data. It is in this overarching context that artificial intelligence (AI), particularly machine learning and deep learning, offers enormous potentials to advance genomic medicine in a spirit of One Health. This expert review offers an analysis of the rapidly emerging role of AI in genomic medicine, including its current drivers, levers, opportunities, and challenges. The scope of AI applications in genomic medicine is broad, ranging from efficient and automated data analysis to drug repurposing and precision medicine, as with its challenges such as veracity of the big data that AI sorely depends on, social biases that the AI-driven algorithms can introduce, and how best to incorporate AI with human intelligence. The road ahead for AI in genomic medicine is complex and arduous and yet worthy of cautious optimism as we face future pandemics and ecological crises in the 21st century. Now is a good time to think about the role of AI in genomic medicine and planetary health.

PMID:34780300 | DOI:10.1089/omi.2021.0170

Patterns (N Y). 2021 Nov 9:100396. doi: 10.1016/j.patter.2021.100396. Online ahead of print.

ABSTRACT

We present two machine learning approaches for drug repurposing. While we have developed them for COVID-19, they are disease-agnostic. The two methodologies are complementary, targeting SARS-CoV-2 and host factors, respectively. Our first approach consists of a matrix factorisation algorithm to rank broad-spectrum antivirals. Our second approach, based on network medicine, uses graph kernels to rank drugs according to the perturbation they induce on a subnetwork of the human interactome that is crucial for SARS-CoV-2 infection/replication. Our experiments show that our top predicted broad-spectrum antivirals include drugs indicated for compassionate use in COVID-19 patients; and that the ranking obtained by our kernel-based approach aligns with experimental data. Finally, we present the COVID-19 Repositioning Explorer (CoREx), an interactive online tool to explore the interplay between drugs and SARS-CoV-2 host proteins in the context of biological networks, protein function, drug clinical use, and Connectivity Map. CoREx is freely available at: https://paccanarolab.org/corex/.

PMID:34778851 | PMC:PMC8576113 | DOI:10.1016/j.patter.2021.100396

Front Med (Lausanne). 2021 Oct 27;8:744697. doi: 10.3389/fmed.2021.744697. eCollection 2021.

ABSTRACT

SARS-CoV2 infection results in a range of symptoms from mild pneumonia to cardiac arrhythmias, hyperactivation of the immune response, systemic organ failure and death. However, the mechanism of action has been hard to establish. Analysis of symptoms associated with COVID-19, the activity of repurposed drugs associated with lower death rates or antiviral activity in vitro and a small number of studies describing interventions, point to the importance of electrolyte, and particularly potassium, homeostasis at both the cellular, and systemic level. Elevated urinary loss of potassium is associated with disease severity, and the response to electrolyte replenishment correlates with progression toward recovery. These findings suggest possible diagnostic opportunities and therapeutic interventions. They provide insights into comorbidities and mechanisms associated with infection by SARS-CoV2 and other RNA viruses that target the ACE2 receptor, and/or activate cytokine-mediated immune responses in a potassium-dependent manner.

PMID:34778307 | PMC:PMC8578622 | DOI:10.3389/fmed.2021.744697

Cogn Neurodyn. 2021 Nov 5:1-9. doi: 10.1007/s11571-021-09727-5. Online ahead of print.

ABSTRACT

The novel coronavirus disease, COVID-19, has rapidly spread worldwide. Developing methods to identify the therapeutic activity of drugs based on phenotypic data can improve the efficiency of drug development. Here, a state-of-the-art machine-learning method was used to identify drug mechanism of actions (MoAs) based on the cell image features of 1105 drugs in the LINCS database. As the multi-dimensional features of cell images are affected by non-experimental factors, the characteristics of similar drugs vary considerably, and it is difficult to effectively identify the MoA of drugs as there is substantial noise. By applying the supervised information theoretic metric-learning (ITML) algorithm, a linear transformation made drugs with the same MoA aggregate. By clustering drugs to communities and performing enrichment analysis, we found that transferred image features were more conducive to the recognition of drug MoAs. Image features analysis showed that different features play important roles in identifying different drug functions. Drugs that significantly affect cell survival or proliferation, such as cyclin-dependent kinase inhibitors, were more likely to be enriched in communities, whereas other drugs might be decentralized. Chloroquine and clomiphene, which block the entry of virus, were clustered into the same community, indicating that similar MoA could be reflected by the cell image. Overall, the findings of the present study laid the foundation for the discovery of MoAs of new drugs, based on image data. In addition, it provided a new method of drug repurposing for COVID-19.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-021-09727-5.

PMID:34777628 | PMC:PMC8570398 | DOI:10.1007/s11571-021-09727-5

Front Microbiol. 2021 Oct 28;12:739684. doi: 10.3389/fmicb.2021.739684. eCollection 2021.

ABSTRACT

Deep learning significantly accelerates the drug discovery process, and contributes to global efforts to stop the spread of infectious diseases. Besides enhancing the efficiency of screening of antimicrobial compounds against a broad spectrum of pathogens, deep learning has also the potential to efficiently and reliably identify drug candidates against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Consequently, deep learning has been successfully used for the identification of a number of potential drugs against SARS-CoV-2, including Atazanavir, Remdesivir, Kaletra, Enalaprilat, Venetoclax, Posaconazole, Daclatasvir, Ombitasvir, Toremifene, Niclosamide, Dexamethasone, Indomethacin, Pralatrexate, Azithromycin, Palmatine, and Sauchinone. This mini-review discusses recent advances and future perspectives of deep learning-based SARS-CoV-2 drug discovery.

PMID:34777286 | PMC:PMC8581544 | DOI:10.3389/fmicb.2021.739684