Res Sq. 2023 Oct 19:rs.3.rs-3443080. doi: 10.21203/rs.3.rs-3443080/v1. Preprint.

ABSTRACT

We developed a computational framework that integrates Genome-Wide Association Studies (GWAS) and post-GWAS analyses, designed to facilitate drug repurposing for COVID-19 treatment. The comprehensive approach combines transcriptomic-wide associations, polygenic priority scoring, 3D genomics, viral-host protein-protein interactions, and small-molecule docking. Through GWAS, we identified nine druggable host genes associated with COVID-19 severity and SARS-CoV-2 infection, all of which show differential expression in COVID-19 patients. These genes include IFNAR1, IFNAR2, TYK2, IL10RB, CXCR6, CCR9, and OAS1. We performed an extensive molecular docking analysis of these targets using 553 small molecules derived from five therapeutically enriched categories, namely antibacterials, antivirals, antineoplastics, immunosuppressants, and anti-inflammatories. This analysis, which comprised over 20,000 individual docking analyses, enabled the identification of several promising drug candidates. All results are available via the DockCoV2 database (https://dockcov2.org/drugs/). The computational framework ultimately identified nine potential drug candidates: Peginterferon alfa-2b, Interferon alfa-2b, Interferon beta-1b, Ruxolitinib, Dactinomycin, Rolitetracycline, Irinotecan, Vinblastine, and Oritavancin. While its current focus is on COVID-19, our proposed computational framework can be applied more broadly to assist in drug repurposing efforts for a variety of diseases. Overall, this study underscores the potential of human genetic studies and the utility of a computational framework for drug repurposing in the context of COVID-19 treatment, providing a valuable resource for researchers in this field.

PMID:37886583 | PMC:PMC10602133 | DOI:10.21203/rs.3.rs-3443080/v1

Recent Adv Inflamm Allergy Drug Discov. 2023 Oct 25. doi: 10.2174/0127722708268538231010041307. Online ahead of print.

ABSTRACT

BACKGROUND: Antibacterial and antimalarial drugs play a critical role in combating infectious diseases. It is a continuous work to develop new types of antibacterial and antimalarial drugs.

OBJECTIVES: To better understand current landscape and association of antibacterial and antimalarial agents, the European patent analysis was performed.

METHODS: Antibacterial and antimalarial agents were analyzed by patent analysis. Patent documents from January 2003 to May 2022 were retrieved and analyzed.

RESULTS: The present study indicated there were virtually three therapeutic approaches for antibacterial agents, including chemical drugs, biological products and siRNA technology. Chemical drugs were a mainstream therapeutic approach for development of both antibacterial and antimalarial agents. However, the present study found that in contrast to antimalarials, siRNA technology had been initially explored as therapeutic strategy for antibacterial agents. Also, our study is the first to show that there is a low correlation between antibacterial and antimalarial agents.

CONCLUSION: Globally, our study is the first one to show that it may be not a fast approach to discover antimalarial drugs from antibacterial agents based on drug repurposing. siRNA technology as therapeutic strategy had been explored and used in antibacterial field.

PMID:37885108 | DOI:10.2174/0127722708268538231010041307

STAR Protoc. 2023 Oct 25;4(4):102666. doi: 10.1016/j.xpro.2023.102666. Online ahead of print.

ABSTRACT

Biomedical knowledge graphs (BKGs) provide a new paradigm for managing abundant biomedical knowledge efficiently. Today's artificial intelligence techniques enable mining BKGs to discover new knowledge. Here, we present a protocol for implementing a computational pipeline for biomedical knowledge discovery (BKD) based on a BKG. We describe steps of the pipeline including data processing, implementing BKD based on knowledge graph embeddings, and prediction result interpretation. We detail how our pipeline can be used for drug repurposing hypothesis generation for Parkinson's disease. For complete details on the use and execution of this protocol, please refer to Su et al.1.

PMID:37883224 | DOI:10.1016/j.xpro.2023.102666

Cancer Sci. 2023 Oct 26. doi: 10.1111/cas.15994. Online ahead of print.

ABSTRACT

Genetic mutations in the isocitrate dehydrogenase (IDH) gene that result in a pathological enzymatic activity to produce oncometabolite have been detected in acute myeloid leukemia (AML) patients. While specific inhibitors that target mutant IDH enzymes and normalize intracellular oncometabolite level have been developed, refractoriness and resistance has been reported. Since acquisition of pathological enzymatic activity is accompanied by the abrogation of the crucial WT IDH enzymatic activity in IDH mutant cells, aberrant metabolism in IDH mutant cells can potentially persist even after the normalization of intracellular oncometabolite level. Comparisons of isogenic AML cell lines with and without IDH2 gene mutations revealed two mutually exclusive signalings for growth advantage of IDH2 mutant cells, STAT phosphorylation associated with intracellular oncometabolite level and phospholipid metabolic adaptation. The latter came to light after the oncometabolite normalization and increased the resistance of IDH2 mutant cells to arachidonic acid-mediated apoptosis. The release of this metabolic adaptation by FDA-approved anti-inflammatory drugs targeting the metabolism of arachidonic acid could sensitize IDH2 mutant cells to apoptosis, resulting in their eradication in vitro and in vivo. Our findings will contribute to the development of alternative therapeutic options for IDH2 mutant AML patients who do not tolerate currently available therapies.

PMID:37882467 | DOI:10.1111/cas.15994

J Biomol Struct Dyn. 2023 Oct 26:1-10. doi: 10.1080/07391102.2023.2256864. Online ahead of print.

ABSTRACT

A number of multidisciplinary methods have piqued the interest of researchers as means to accelerate and lower the cost of medication creation. The goal of this research was to find target proteins and then select a lead drug against SARS-CoV-2. The three-dimensional structure is taken from the RCSB PDB using its specific PDB ID 6lu7. Virtual screening based on pharmacophores is performed using Molecular Operating Environment software. We looked for a potent inhibitor in the FDA-approved database. For docking, AutoDock Vina uses Pyrx. The compound-target protein binding interactions were tested using BIOVIA Discovery Studio. The stability of protein and inhibitor complexes in a physiological setting was investigated using Desmond's Molecular Dynamics Simulation (MD simulation). According to our findings, we repurpose the FDA-approved drugs ZINC000169677008 and ZINC000169289767, which inhibit the activity of the virus's main protease (6lu7). The scientific community will gain from this finding, which might create new medicine. The novel repurposed chemicals were promising inhibitors with increased efficacy and fewer side effects.Communicated by Ramaswamy H. Sarma.

PMID:37882340 | DOI:10.1080/07391102.2023.2256864

Arch Pharm (Weinheim). 2023 Oct 25:e2300340. doi: 10.1002/ardp.202300340. Online ahead of print.

ABSTRACT

Drug repurposing is an emerging field in drug development that has provided many successful drugs. In the current study, paracetamol, a known antipyretic and analgesic agent, was chemically modified to generate paracetamol derivatives as anticancer and anticyclooxygenase-2 (COX-2) agents. Compound 11 bearing a fluoro group was the best cytotoxic candidate with half-maximal inhibitory concentration (IC50 ) values ranging from 1.51 to 6.31 μM and anti-COX-2 activity with IC50 = 0.29 μM, compared to the standard drugs, doxorubicin and celecoxib. The cell cycle and apoptosis studies revealed that compound 11 possesses the ability to induce cell cycle arrest in the S phase and apoptosis in colon Huh-7 cells. These results were strongly supported by docking studies, which showed strong interactions with the amino acids of the COX-2 protein, and in silico pharmacokinetic predictions were found to be favorable for these newly synthesized paracetamol derivatives. It can be concluded that compound 11 could block cell growth and proliferation by inhibiting the COX-2 enzyme in cancer therapy.

PMID:37880869 | DOI:10.1002/ardp.202300340

J Pharm Biomed Anal. 2023 Oct 5;237:115770. doi: 10.1016/j.jpba.2023.115770. Online ahead of print.

ABSTRACT

Soon after the outset of the Coronavirus Disease 2019 (COVID-19) pandemic (March-April 2020), formulations of the old antimalarial racemic drug hydroxychloroquine (HCQ) sulfate were authorized by the U.S. Food and Drug Administration (FDA) for emergency treatment of hospitalized patients with COVID-19. A call for the chiral switch of HCQ to the single enantiomer (S)-(+)-HCQ for treating the disease followed. The above authorizations were later withdrawn. Angiotensin-converting enzyme 2 (ACE2) has been recognized to be the forward receptor of SARS-CoV-2, the virus responsible for COVID-19. The objective of the present study was to evaluate the chiral distinction in the potential preferential binding of the HCQ enantiomers to ACE2, as a basis for its future drug repurposing, using high-field solution Nuclear Magnetic Resonance (NMR) spectroscopy. Proton selective spin-lattice relaxation rates were measured for selected diagnostic nuclei; in particular, protons belonging to the quinoline ring proved to be the most affected by the presence of the protein, for both (S)-(+)-HCQ and (R)-(-)-HCQ enantiomers. An increase in mono-selective relaxation rates was observed for both enantiomers. A significant difference in the magnitude of the increase was detected for all protons investigated, up to a 5-fold and an 8-fold increase in the case of (R)-(-)-HCQ and (S)-(+)-HCQ, respectively. Furthermore, comparison between the normalized mono-selective relaxation rates of the two HCQ enantiomers in their binary mixtures with ACE2 pointed out a certain preference for the (S)-(+)-HCQ enantiomer over (R)-(-)-HCQ in the interaction with ACE2. The findings form the basis for a future application of the drug repurposing/chiral-switch combination strategy to racemic HCQ in previously reported indications for hydroxychloroquine treatment and in the search for new indications in which ACE2 receptors are involved.

PMID:37879140 | DOI:10.1016/j.jpba.2023.115770

J Biomol Struct Dyn. 2023 Oct 25:1-17. doi: 10.1080/07391102.2023.2272753. Online ahead of print.

ABSTRACT

In silico docking studies serve as a swift and efficient means to sift through a vast array of natural and synthetic small molecules, aiding in the identification of potential inhibitors for cancer biomarkers. One such biomarker, ceruloplasmin (CP), has been implicated in various tumor types due to its overexpression, earning it recognition as a marker of aggressive tumors. This study focused on pinpointing inhibitors for the CP -Myeloperoxidase (MPO) interaction site, a complex formation known to impede HOCl production, a crucial process for inducing apoptotic cell death in tumor cells. The initial phase of our investigation involved in silico docking studies, which screened a diverse library of phytochemicals and marine compounds. Through this process, we identified several promising drug candidates based on their binding affinities. Subsequently, these candidates underwent rigorous filtration based on Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties. Finally, we subjected the selected compounds to molecular dynamics (MDs) simulation to further assess their viability. Lycoperoside F, a steroidal alkaloid glycoside derived from tomatoes (Lycopersicon esculentum), stood out with notable interactions at the binding site. Another noteworthy compound was Xyloglucan (XG) oligosaccharides, predominantly found in the primary cell walls of higher plants. During the subsequent MDs simulations, these interactions were accompanied by highly stable root mean square deviation (RMSD) plots, signifying the consistency and robustness of the observed MDs behavior. XG oligosaccharides demonstrated the highest binding affinity with CP, reaffirming their potential as strong candidates. Additionally, Ardimerin digallate, known as a retroviral ribonuclease H inhibitor for HIV-1 and HIV-2, displayed favorable interactions at the MPO interaction site. Given that promising drug candidates must meet stringent criteria, including non-toxicity, effectiveness, specificity, stability and potency, these phytochemicals have the potential to progress to in vitro studies as CP inhibitors. Ultimately, this could contribute to the suppression of tumor growth, marking a significant step in cancer treatment research.Communicated by Ramaswamy H. Sarma.

PMID:37878121 | DOI:10.1080/07391102.2023.2272753

Nat Rev Drug Discov. 2023 Oct 23. doi: 10.1038/d41573-023-00168-9. Online ahead of print.

NO ABSTRACT

PMID:37872324 | DOI:10.1038/d41573-023-00168-9

ChemMedChem. 2023 Oct 23:e202300282. doi: 10.1002/cmdc.202300282. Online ahead of print.

ABSTRACT

Drug repurposing has emerged as an attractive strategy for accelerating drug discovery for cancer treatment. In this study, we investigated combining Tranylcypromine (TCP) with a number of well-characterized drugs. Among these combinations, ML385 exhibited synergistic effects in combination with TCP. Specifically, our results showed that the combination of TCP and ML385 resulted in a significant reduction in tumor proliferation while neither drug affected cancer cell growth meaningfully on its own. While further studies are needed to understand fully the extent of the synergistic efficacy, the underlying respective mechanisms and the potential side effects of this approach, our study has yielded a promising start for the development of an effective combination cancer therapy.

PMID:37871186 | DOI:10.1002/cmdc.202300282

Naunyn Schmiedebergs Arch Pharmacol. 2023 Oct 23. doi: 10.1007/s00210-023-02796-9. Online ahead of print.

ABSTRACT

To gain a comprehensive overview of the landscape of clinical trials for the H1-receptor antagonists (H1R antagonists) cetirizine, levocetirizine, loratadine, desloratadine, and fexofenadine and their potential use cases in drug repurposing (the use of well-known drugs outside the scope of the original medical indication), we analyzed trials from clincialtrials.gov using novel custom-coded software, which itself is also a key emphasis of this paper. To automate data acquisition from clincialtrials.gov via its API, data processing, and storage, we created custom software by leveraging a variety of open-source tools. Data were stored in a relational database and annotated facilitating a specially adapted web application. Through the data analysis, we identified use cases for repurposing and reviewed backgrounds and results in the scientific literature. Even though we found very few trials with published results for repurpose indications, extended literature research revealed some prominent use cases: Cetirizine seems promising in mitigating infusion-associated reactions and is also more effective than placebo in the treatment of androgenetic alopecia. Loratadine may be beneficial in the prophylaxis of G-CSF-related bone pain. In COVID-19, H1R antagonists may be helpful, but placebo-controlled scientific evidence is needed. For asthma, the effect of H1R antagonists only seems to be secondary by alleviating allergy symptoms. Our novel method to find potential use cases for repurposing of H1R antagonists allows for high automation, reduces human error, and was successful in revealing potential areas of interest. The software could be used for similar research questions and analyses in the future.

PMID:37870580 | DOI:10.1007/s00210-023-02796-9

Heliyon. 2023 Oct 11;9(10):e20889. doi: 10.1016/j.heliyon.2023.e20889. eCollection 2023 Oct.

ABSTRACT

Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder with a worldwide prevalence of 6-10 % of women of reproductive age. PCOS is a risk factor for cardiometabolic disorders such as type 2 diabetes, myocardial infarction, and stroke in addition to exhibiting signs of hyperandrogenism and anovulation. However, there is no known cure for PCOS, and medications have only ever been used symptomatically, with a variety of adverse effects. Drugs made from natural plant products may help treat PCOS because several plant extracts have been widely recognized to lessen the symptoms of PCOS. In light of this, 72 current studies on natural products with the potential to control PCOS were examined. By controlling the PI3K/AKT signaling pathway and decreasing NF-κB and cytokines such as tumor necrosis factor (TNF), interleukin-1 (IL-1), and interleukin-6 (IL-6), certain plant-derived chemicals might reduce inflammation. Other substances altered the HPO axis, which normalized hormones. Additionally, other plant components increased glutathione (GSH), superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) levels to reduce radiation-induced oxidative stress. The other substances prevented autophagy by impairing beclin 1, autophagy-related 5 (ATG5), and microtubule-associated protein 1A/1B-light chain 3 - II (LC3- II). The main focus of this comprehensive review is the possibility of plant extracts as natural bio-resources of PCOS treatment by regulating inflammation, hormones, reactive oxygen species (ROS), or autophagy.

PMID:37867816 | PMC:PMC10589870 | DOI:10.1016/j.heliyon.2023.e20889

Curr Diabetes Rev. 2023 Oct 20. doi: 10.2174/0115733998249061231009093006. Online ahead of print.

ABSTRACT

BACKGROUND: Patients with diabetes suffer from major complications like Diabetic Retinopathy, Diabetic Coronary Artery Disease, and Diabetic Foot ulcers (DFUs). Diabetes complications are a group of ailments whose recovery time is especially delayed, irrespective of the underlying reason. The longer duration of wound healing enhances the probability of problems like sepsis and amputation. The delayed healing makes it more critical for research focus. By understanding the molecular pathogenesis of diabetic wounds, it is quite easy to target the molecules involved in the healing of wounds. Recent research on beta-adrenergic blocking drugs has revealed that these classes of drugs possess therapeutic potential in the healing of DFUs. However, because the order of events in defective healing is adequately defined, it is possible to recognize moieties that are currently in the market that are recognized to aim at one or several identified molecular processes.

OBJECTIVE: The aim of this study was to explore some molecules with different therapeutic categories that have demonstrated favorable effects in improving diabetic wound healing, also called the repurposing of drugs.

METHOD: Various databases like PubMed/Medline, Google Scholar and Web of Science (WoS) of all English language articles were searched, and relevant information was collected regarding the role of beta-adrenergic blockers in diabetic wounds or diabetic foot ulcers (DFUs) using the relevant keywords for the literature review.

RESULT: The potential beta-blocking agents and their mechanism of action in diabetic foot ulcers were studied, and it was found that these drugs have a profound effect on diabetic foot ulcer healing as per reported literatures.

CONCLUSION: There is a need to move forward from preclinical studies to clinical studies to analyze clinical findings to determine the effectiveness and safety of some beta-antagonists in diabetic foot ulcer treatment.

PMID:37867269 | DOI:10.2174/0115733998249061231009093006

Comput Biol Chem. 2023 Oct 11;107:107969. doi: 10.1016/j.compbiolchem.2023.107969. Online ahead of print.

ABSTRACT

Protein-ligand interaction plays a crucial role in drug discovery, facilitating efficient drug development and enabling drug repurposing. Several computational algorithms, such as Graph Neural Networks and Convolutional Neural Networks, have been proposed to predict the binding affinity using the three-dimensional structure of ligands and proteins. However, there are limitations due to the need for experimental characterization of the three-dimensional structure of protein sequences, which is still lacking for some proteins. Moreover, these models often suffer from unnecessary complexity, resulting in extraneous computations. This study presents ResBiGAAT, a novel deep learning model that combines a deep Residual Bidirectional Gated Recurrent Unit with two-sided self-attention mechanisms. ResBiGAAT leverages protein and ligand sequence-level features and their physicochemical properties to efficiently predict protein-ligand binding affinity. Through rigorous evaluation using 5-fold cross-validation, we demonstrate the performance of our proposed approach. The model exhibits competitive performance on an external dataset, highlighting its generalizability. Our publicly available web interface, located at resbigaat.streamlit.app, allows users to conveniently input protein and ligand sequences to estimate binding affinity.

PMID:37866117 | DOI:10.1016/j.compbiolchem.2023.107969

Sci Rep. 2023 Oct 21;13(1):18022. doi: 10.1038/s41598-023-45347-1.

ABSTRACT

Drug designing is high-priced and time taking process with low success rate. To overcome this obligation, computational drug repositioning technique is being promptly used to predict the possible therapeutic effects of FDA approved drugs against multiple diseases. In this computational study, protein modeling, shape-based screening, molecular docking, pharmacogenomics, and molecular dynamic simulation approaches have been utilized to retrieve the FDA approved drugs against AD. The predicted MADD protein structure was designed by homology modeling and characterized through different computational resources. Donepezil and galantamine were implanted as standard drugs and drugs were screened out based on structural similarities. Furthermore, these drugs were evaluated and based on binding energy (Kcal/mol) profiles against MADD through PyRx tool. Moreover, pharmacogenomics analysis showed good possible associations with AD mediated genes and confirmed through detail literature survey. The best 6 drug (darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar) further docked and analyzed their interaction behavior through hydrogen binding. Finally, MD simulation study were carried out on these drugs and evaluated their stability behavior by generating root mean square deviation and fluctuations (RMSD/F), radius of gyration (Rg) and soluble accessible surface area (SASA) graphs. Taken together, darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar displayed good lead like profile as compared with standard and can be used as possible therapeutic agent in the treatment of AD after in-vitro and in-vivo assessment.

PMID:37865690 | DOI:10.1038/s41598-023-45347-1

Drug Resist Updat. 2023 Oct 10;71:101011. doi: 10.1016/j.drup.2023.101011. Online ahead of print.

ABSTRACT

The overexpression of the human ATP-binding cassette (ABC) transporters in cancer cells is a common mechanism involved in developing multidrug resistance (MDR). Unfortunately, there are currently no approved drugs specifically designed to treat multidrug-resistant cancers, making MDR a significant obstacle to successful chemotherapy. Despite over two decades of research, developing transporter-specific inhibitors for clinical use has proven to be a challenging endeavor. As an alternative approach, drug repurposing has gained traction as a more practical method to discover clinically effective modulators of drug transporters. This involves exploring new indications for already-approved drugs, bypassing the lengthy process of developing novel synthetic inhibitors. In this context, we will discuss the mechanisms of ABC drug transporters ABCB1 and ABCG2, their roles in cancer MDR, and the inhibitors that have been evaluated for their potential to reverse MDR mediated by these drug transporters. Our focus will be on providing an up-to-date report on approved drugs tested for their inhibitory activities against these drug efflux pumps. Lastly, we will explore the challenges and prospects of repurposing already approved medications for clinical use to overcome chemoresistance in patients with high tumor expression of ABCB1 and/or ABCG2.

PMID:37865067 | DOI:10.1016/j.drup.2023.101011

Brief Bioinform. 2023 Sep 22;24(6):bbad371. doi: 10.1093/bib/bbad371.

ABSTRACT

Drug-gene interaction prediction occupies a crucial position in various areas of drug discovery, such as drug repurposing, lead discovery and off-target detection. Previous studies show good performance, but they are limited to exploring the binding interactions and ignoring the other interaction relationships. Graph neural networks have emerged as promising approaches owing to their powerful capability of modeling correlations under drug-gene bipartite graphs. Despite the widespread adoption of graph neural network-based methods, many of them experience performance degradation in situations where high-quality and sufficient training data are unavailable. Unfortunately, in practical drug discovery scenarios, interaction data are often sparse and noisy, which may lead to unsatisfactory results. To undertake the above challenges, we propose a novel Dynamic hyperGraph Contrastive Learning (DGCL) framework that exploits local and global relationships between drugs and genes. Specifically, graph convolutions are adopted to extract explicit local relations among drugs and genes. Meanwhile, the cooperation of dynamic hypergraph structure learning and hypergraph message passing enables the model to aggregate information in a global region. With flexible global-level messages, a self-augmented contrastive learning component is designed to constrain hypergraph structure learning and enhance the discrimination of drug/gene representations. Experiments conducted on three datasets show that DGCL is superior to eight state-of-the-art methods and notably gains a 7.6% performance improvement on the DGIdb dataset. Further analyses verify the robustness of DGCL for alleviating data sparsity and over-smoothing issues.

PMID:37864294 | DOI:10.1093/bib/bbad371

Eur J Pediatr. 2023 Oct 21. doi: 10.1007/s00431-023-05263-y. Online ahead of print.

ABSTRACT

Noonan syndrome belongs to the family of RASopathies, a group of multiple congenital anomaly disorders caused by pathogenic variants in genes encoding components or regulators of the RAS/mitogen-activated protein kinase (MAPK) signalling pathway. Collectively, all these pathogenic variants lead to increased RAS/MAPK activation. The better understanding of the molecular mechanisms underlying the different manifestations of NS and RASopathies has led to the identification of molecular targets for specific pharmacological interventions. Many specific agents (e.g. SHP2 and MEK inhibitors) have already been developed for the treatment of RAS/MAPK-driven malignancies. In addition, other molecules with the property of modulating RAS/MAPK activation are indicated in non-malignant diseases (e.g. C-type natriuretic peptide analogues in achondroplasia or statins in hypercholesterolemia). Conclusion: Drug repositioning of these molecules represents a challenging approach to treat or prevent medical complications associated with RASopathies. What is Known: • Noonan syndrome and related disorders are caused by pathogenic variants in genes encoding components or regulators of the RAS/mitogen-activated protein kinase (MAPK) signalling pathway, resulting in increased activation of this pathway. • This group of disorders is now known as RASopathies and represents one of the largest groups of multiple congenital anomaly diseases known. What is New: • The identification of pathophysiological mechanisms provides new insights into the development of specific therapeutic strategies, in particular treatment aimed at reducing RAS/MAPK hyperactivation. • Drug repositioning of specific agents already developed for the treatment of malignant (e.g. SHP2 and MEK inhibitors) or non-malignant diseases (e.g. C-type natriuretic peptide analogues in achondroplasia or statins in hypercholesterolaemia) represents a challenging approach to the treatment of RASopathies.

PMID:37863846 | DOI:10.1007/s00431-023-05263-y

Bioinformatics. 2023 Oct 20:btad644. doi: 10.1093/bioinformatics/btad644. Online ahead of print.

ABSTRACT

MOTIVATION: The reconstruction of small key regulatory networks that explain the differences in the development of cell (sub)types from single-cell RNA sequencing is a yet unresolved computational problem.

RESULTS: To this end, we have developed SCANet, an all-in-one package for single-cell profiling that covers the whole differential mechanotyping workflow, from inference of trait/cell-type-specific gene co-expression modules, driver gene detection, and transcriptional gene regulatory network reconstruction to mechanistic drug repurposing candidate prediction. To illustrate the power of SCANet, we examined data from two studies. First, we identify the drivers of the mechanotype of a cytokine storm associated with increased mortality in patients with acute respiratory illness. Secondly, we find 20 drugs for 8 potential pharmacological targets in cellular driver mechanisms in the intestinal stem cells of obese mice.

AVAILABILITY: SCANet is a free, open-source, and user-friendly Python package that can be seamlessly integrated into single-cell-based systems medicine research and mechanistic drug discovery.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:37862243 | DOI:10.1093/bioinformatics/btad644

J Med Internet Res. 2023 Oct 20;25:e45225. doi: 10.2196/45225.

ABSTRACT

BACKGROUND: The global pandemics of severe acute respiratory syndrome, Middle East respiratory syndrome, and COVID-19 have caused unprecedented crises for public health. Coronaviruses are constantly evolving, and it is unknown which new coronavirus will emerge and when the next coronavirus will sweep across the world. Knowledge graphs are expected to help discover the pathogenicity and transmission mechanism of viruses.

OBJECTIVE: The aim of this study was to discover potential targets and candidate drugs to repurpose for coronaviruses through a knowledge graph-based approach.

METHODS: We propose a computational and evidence-based knowledge discovery approach to identify potential targets and candidate drugs for coronaviruses from biomedical literature and well-known knowledge bases. To organize the semantic triples extracted automatically from biomedical literature, a semantic conversion model was designed. The literature knowledge was associated and integrated with existing drug and gene knowledge through semantic mapping, and the coronavirus knowledge graph (CovKG) was constructed. We adopted both the knowledge graph embedding model and the semantic reasoning mechanism to discover unrecorded mechanisms of drug action as well as potential targets and drug candidates. Furthermore, we have provided evidence-based support with a scoring and backtracking mechanism.

RESULTS: The constructed CovKG contains 17,369,620 triples, of which 641,195 were extracted from biomedical literature, covering 13,065 concept unique identifiers, 209 semantic types, and 97 semantic relations of the Unified Medical Language System. Through multi-source knowledge integration, 475 drugs and 262 targets were mapped to existing knowledge, and 41 new drug mechanisms of action were found by semantic reasoning, which were not recorded in the existing knowledge base. Among the knowledge graph embedding models, TransR outperformed others (mean reciprocal rank=0.2510, Hits@10=0.3505). A total of 33 potential targets and 18 drug candidates were identified for coronaviruses. Among them, 7 novel drugs (ie, quinine, nelfinavir, ivermectin, asunaprevir, tylophorine, Artemisia annua extract, and resveratrol) and 3 highly ranked targets (ie, angiotensin converting enzyme 2, transmembrane serine protease 2, and M protein) were further discussed.

CONCLUSIONS: We showed the effectiveness of a knowledge graph-based approach in potential target discovery and drug repurposing for coronaviruses. Our approach can be extended to other viruses or diseases for biomedical knowledge discovery and relevant applications.

PMID:37862061 | DOI:10.2196/45225