Expert Opin Ther Targets. 2022 Jan 27. doi: 10.1080/14728222.2022.2035720. Online ahead of print.

ABSTRACT

INTRODUCTION: Endogenous inflammatory signaling molecules resulting from deregulated immune responses, can impair airway epithelial barrier function and predispose individuals with airway inflammatory diseases to exacerbations and lung infections. Targeting the specific endogenous factors disrupting the airway barrier therefore has the potential to prevent disease exacerbations without affecting the protective immune responses.

AREAS COVERED: Here, we review the endogenous factors and specific mechanisms disrupting airway epithelial barrier during inflammation and reflect on whether these factors can be specifically targeted by repurposed existing drugs. Literature search was conducted using PubMed, drug database of US FDA and European Medicines Agency until and including September 2021.

EXPERT OPINION: IL-4 and IL-13 signaling are the major pathways disrupting the airway epithelial barrier during airway inflammation. However, blocking IL-4/IL-13 signaling may adversely affect protective immune responses and increase susceptibility of host to infections. An alternate approach to modulate airway epithelial barrier function involves targeting specific downstream component of IL-4/IL-13 signaling or different inflammatory mediators responsible for regulation of airway epithelial barrier. Airway epithelium-targeted therapy using inhibitors of HDAC, HSP90, MIF, mTOR, IL-17A and VEGF may be a potential strategy to prevent airway epithelial barrier dysfunction in airway inflammatory diseases.

PMID:35085478 | DOI:10.1080/14728222.2022.2035720

PLoS One. 2022 Jan 27;17(1):e0262751. doi: 10.1371/journal.pone.0262751. eCollection 2022.

ABSTRACT

Drug repurposing can be an interesting strategy for an emergency response to the severe acute respiratory syndrome-coronavirus-2, (SARS-COV-2), the causing agent of the coronavirus disease-19 (COVID-19) pandemic. For this, we applied the Connectivity Map (CMap) bioinformatic resource to identify drugs that generate, in the CMap database, gene expression profiles (GEP) that negatively correlate with a SARS-COV-2 GEP, anticipating that these drugs could antagonize the deleterious effects of the virus at cell, tissue or organism levels. We identified several anti-cancer compounds that target MDM2 in the p53 pathway or signaling proteins: Ras, PKBβ, Nitric Oxide synthase, Rho kinase, all involved in the transmission of proliferative and growth signals. We hypothesized that these drugs could interfere with the high rate of biomass synthesis in infected cells, a feature shared with cancer cells. Other compounds including etomoxir, triacsin-c, PTB1-IN-3, are known to modulate lipid metabolism or to favor catabolic reactions by activating AMPK. Four different anti-inflammatory molecules, including dexamethasone, fluorometholone and cytosporone-b, targeting the glucocorticoid receptor, cyclooxygenase, or NUR77 also came out of the analysis. These results represent a first step in the characterization of potential repositioning strategies to treat SARS-COV-2.

PMID:35085325 | DOI:10.1371/journal.pone.0262751

Front Med (Lausanne). 2022 Jan 10;8:817663. doi: 10.3389/fmed.2021.817663. eCollection 2021.

ABSTRACT

Repurposing of authorised medicines has been under discussion for a long time. Drug repurposing is the process of identifying a new use for an existing medicine in an indication outside the scope of the original approved indication. Indeed, the COVID-19 health crisis has brought the concept to the frontline by proving the usefulness of this practise in favour of patients for an early access to treatment. Under the umbrella of the Pharmaceutical Committee and as a result of the discussions at the European Commission Expert Group on Safe and Timely Access to Medicines for Patients (STAMP) a virtual Repurposing Observatory Group (RepOG) was set up in 2019 to define and test the practical aspects of a pilot project thought to provide support to "not-for-profit" stakeholders generating or gathering data for a new therapeutic use for an authorised medicine. The group's initial plan was impacted by the outbreak of the SARS-CoV-2 pandemic and the launch of the pilot needed to be postponed. This article describes the progress and the activities conducted by the group during this past and yet extraordinary 2020-2021 to keep the project alive and explores on the background of this topic together with the obvious opportunities this health crisis has brought up in terms of repurposing of medicines.

PMID:35083258 | PMC:PMC8784735 | DOI:10.3389/fmed.2021.817663

Front Pharmacol. 2022 Jan 10;12:838902. doi: 10.3389/fphar.2021.838902. eCollection 2021.

ABSTRACT

[This corrects the article DOI: 10.3389/fphar.2021.714643.].

PMID:35082684 | PMC:PMC8784882 | DOI:10.3389/fphar.2021.838902

Sci Rep. 2022 Jan 26;12(1):1437. doi: 10.1038/s41598-022-05335-3.

ABSTRACT

Control theory has seen recently impactful applications in network science, especially in connections with applications in network medicine. A key topic of research is that of finding minimal external interventions that offer control over the dynamics of a given network, a problem known as network controllability. We propose in this article a new solution for this problem based on genetic algorithms. We tailor our solution for applications in computational drug repurposing, seeking to maximize its use of FDA-approved drug targets in a given disease-specific protein-protein interaction network. We demonstrate our algorithm on several cancer networks and on several random networks with their edges distributed according to the Erdős-Rényi, the Scale-Free, and the Small World properties. Overall, we show that our new algorithm is more efficient in identifying relevant drug targets in a disease network, advancing the computational solutions needed for new therapeutic and drug repurposing approaches.

PMID:35082323 | DOI:10.1038/s41598-022-05335-3

Diabetologia. 2022 Jan 26. doi: 10.1007/s00125-021-05645-7. Online ahead of print.

ABSTRACT

AIMS/HYPOTHESIS: Diabetes and hyperlipidaemia are common comorbidities in people with hypertension. Despite similar protective effects on CVD, different classes of antihypertensive drugs have different effects on CVD risk factors, including diabetes, glucose metabolism and lipids. However, these pleiotropic effects have not been assessed in long-term, large randomised controlled trials, especially for East Asians.

METHODS: We used Mendelian randomisation to obtain unconfounded associations of ACE inhibitors, β-blockers (BBs) and calcium channel blockers (CCBs). Specifically, we used genetic variants in drug target genes and related to systolic BP in Europeans and East Asians, and applied them to the largest available genome-wide association studies of diabetes (74,124 cases and 824,006 controls in Europeans, 77,418 cases and 356,122 controls in East Asians), blood glucose levels, HbA1c, and lipids (LDL-cholesterol, HDL-cholesterol and triacylglycerols) (approximately 0.5 million Europeans and 0.1 million East Asians). We used coronary artery disease (CAD) as a control outcome and used different genetic instruments and analysis methods as sensitivity analyses.

RESULTS: As expected, genetically proxied ACE inhibition, BBs and CCBs were related to lower risk of CAD in both ancestries. Genetically proxied ACE inhibition was associated with a lower risk of diabetes (OR 0.85, 95% CI 0.78-0.93), and genetic proxies for BBs were associated with a higher risk of diabetes (OR 1.05, 95% CI 1.02-1.09). The estimates were similar in East Asians, and were corroborated by systematic review and meta-analyses of randomised controlled trials. In both ancestries, genetic proxies for BBs were associated with lower HDL-cholesterol and higher triacylglycerols, and genetic proxies for CCBs were associated with higher LDL-cholesterol. The estimates were robust to the use of different genetic instruments and analytical methods.

CONCLUSIONS/INTERPRETATION: Our findings suggest protective association of genetically proxied ACE inhibition with diabetes, while genetic proxies for BBs and CCBs possibly relate to an unfavourable metabolic profile. Developing a deeper understanding of the pathways underlying these diverse associations would be worthwhile, with implications for drug repositioning as well as optimal CVD prevention and treatment strategies in people with hypertension, diabetes and/or hyperlipidaemia.

PMID:35080656 | DOI:10.1007/s00125-021-05645-7

J Infect Public Health. 2022 Jan 19:S1876-0341(22)00014-4. doi: 10.1016/j.jiph.2022.01.011. Online ahead of print.

ABSTRACT

OBJECTIVES: To clarify the work done by using AI for identifying the genomic sequences, development of drugs and vaccines for COVID-19 and to recognize the advantages and challenges of using such technology.

METHODS: A non-systematic review was done. All articles published on Pub-Med, Medline, Google, and Google Scholar on AI or digital health regarding genomic sequencing, drug development, and vaccines of COVID-19 were scrutinized and summarized.

RESULTS: The sequence of SARS- CoV-2 was identified with the help of AI. It can help also in the prompt identification of variants of concern (VOC) as delta strains and Omicron. Furthermore, there are many drugs applied with the help of AI. These drugs included Atazanavir, Remdesivir, Efavirenz, Ritonavir, and Dolutegravir, PARP1 inhibitors (Olaparib and CVL218 which is Mefuparib hydrochloride), Abacavir, Roflumilast, Almitrine, and Mesylate. Many vaccines were developed utilizing the new technology of bioinformatics, databases, immune-informatics, machine learning, and reverse vaccinology to the whole SARS-CoV-2 proteomes or the structural proteins. Examples of these vaccines are the messenger RNA and viral vector vaccines. AI provides cost-saving and agility. However, the challenges of its usage are the difficulty of collecting data, the internal and external validation, ethical consideration, therapeutic effect, and the time needed for clinical trials after drug approval. Moreover, there is a common problem in the deep learning (DL) model which is the shortage of interpretability.

CONCLUSION: The growth of AI techniques in health care opened a broad gate for discovering the genomic sequences of the COVID-19 virus and the VOC. AI helps also in the development of vaccines and drugs (including drug repurposing) to obtain potential preventive and therapeutic agents for controlling the COVID-19 pandemic.

PMID:35078755 | DOI:10.1016/j.jiph.2022.01.011

J Biomed Semantics. 2022 Jan 24;13(1):3. doi: 10.1186/s13326-021-00258-w.

ABSTRACT

BACKGROUND: Drug repurposing can improve the return of investment as it finds new uses for existing drugs. Literature-based analyses exploit factual knowledge on drugs and diseases, e.g. from databases, and combine it with information from scholarly publications. Here we report the use of the Open Discovery Process on scientific literature to identify non-explicit ties between a disease, namely epilepsy, and known drugs, making full use of available epilepsy-specific ontologies.

RESULTS: We identified characteristics of epilepsy-specific ontologies to create subsets of documents from the literature; from these subsets we generated ranked lists of co-occurring neurological drug names with varying specificity. From these ranked lists, we observed a high intersection regarding reference lists of pharmaceutical compounds recommended for the treatment of epilepsy. Furthermore, we performed a drug set enrichment analysis, i.e. a novel scoring function using an adaptive tuning parameter and comparing top-k ranked lists taking into account the varying length and the current position in the list. We also provide an overview of the pharmaceutical space in the context of epilepsy, including a final combined ranked list of more than 70 drug names.

CONCLUSIONS: Biomedical ontologies are a rich resource that can be combined with text mining for the identification of drug names for drug repurposing in the domain of epilepsy. The ranking of the drug names related to epilepsy provides benefits to patients and to researchers as it enables a quick evaluation of statistical evidence hidden in the scientific literature, useful to validate approaches in the drug discovery process.

PMID:35073996 | DOI:10.1186/s13326-021-00258-w

BMC Cancer. 2022 Jan 24;22(1):100. doi: 10.1186/s12885-022-09197-w.

ABSTRACT

BACKGROUND: Platinum chemoresistance results in high-grade serous ovarian cancer (HGSOC) disease recurrence. Recent treatment advances using checkpoint inhibitor immunotherapy has not benefited platinum-resistant HGSOC. In ovarian cancer, DNA methyltransferase inhibitors (DNMTi) block methylation and allow expression of silenced genes, primarily affecting immune reactivation pathways. We aimed to determine the epigenome and transcriptome response to sequential treatment with DNMTi and carboplatin in HGSOC.

METHODS: In vitro studies with azacitidine or carboplatin alone and in sequential combination. Response was determined by cell growth, death and apoptosis. Genome-wide DNA methylation levels and transcript expression were compared between untreated and azacitidine and carboplatin sequential treatment.

RESULTS: Sequential azacitidine and carboplatin significantly slowed cell growth in 50% of cell lines compared to carboplatin alone. The combination resulted in significantly higher cell death in 25% of cell lines, and significantly higher cell apoptosis in 37.5% of cell lines, than carboplatin alone. Pathway analysis of upregulated transcripts showed that the majority of changes were in immune-related pathways, including those regulating response to checkpoint inhibitors.

CONCLUSIONS: Sequential azacitidine and carboplatin treatment slows cell growth, and demethylate and upregulate pathways involved in immune response, suggesting that this combination may be used to increase HGSOC response to immune checkpoint inhibitors in platinum-resistant patients who have exhausted all currently-approved avenues of treatment.

PMID:35073851 | DOI:10.1186/s12885-022-09197-w

Neurotherapeutics. 2022 Jan 24. doi: 10.1007/s13311-022-01182-2. Online ahead of print.

ABSTRACT

Marketed drugs for Parkinson's disease (PD) treat disease motor symptoms but are ineffective in stopping or slowing disease progression. In the quest of novel pharmacological approaches that may target disease progression, drug-repurposing provides a strategy to accelerate the preclinical and clinical testing of drugs already approved for other medical indications. Here, we targeted the inflammatory component of PD pathology, by testing for the first time the disease-modifying properties of the immunomodulatory imide drug (IMiD) pomalidomide in a translational rat model of PD neuropathology based on the intranigral bilateral infusion of toxic preformed oligomers of human α-synuclein (H-αSynOs). The neuroprotective effect of pomalidomide (20 mg/kg; i.p. three times/week 48 h apart) was tested in the first stage of disease progression by means of a chronic two-month administration, starting 1 month after H-αSynOs infusion, when an already ongoing neuroinflammation is observed. The intracerebral infusion of H-αSynOs induced an impairment in motor and coordination performance that was fully rescued by pomalidomide, as assessed via a battery of motor tests three months after infusion. Moreover, H-αSynOs-infused rats displayed a 40-45% cell loss within the bilateral substantia nigra, as measured by stereological counting of TH + and Nissl-stained neurons, that was largely abolished by pomalidomide. The inflammatory response to H-αSynOs infusion and the pomalidomide treatment was evaluated both in CNS affected areas and peripherally in the serum. A reactive microgliosis, measured as the volume occupied by the microglial marker Iba-1, was present in the substantia nigra three months after H-αSynOs infusion as well as after H-αSynOs plus pomalidomide treatment. However, microglia differed for their phenotype among experimental groups. After H-αSynOs infusion, microglia displayed a proinflammatory profile, producing a large amount of the proinflammatory cytokine TNF-α. In contrast, pomalidomide inhibited the TNF-α overproduction and elevated the anti-inflammatory cytokine IL-10. Moreover, the H-αSynOs infusion induced a systemic inflammation with overproduction of serum proinflammatory cytokines and chemokines, that was largely mitigated by pomalidomide. Results provide evidence of the disease modifying potential of pomalidomide in a neuropathological rodent model of PD and support the repurposing of this drug for clinical testing in PD patients.

PMID:35072912 | DOI:10.1007/s13311-022-01182-2

Mol Divers. 2022 Jan 24. doi: 10.1007/s11030-022-10382-z. Online ahead of print.

ABSTRACT

The consumption of drug combinations, named polypharmacy, is commonly used for treating patients with several diseases or those with complex conditions. However, the main drawback of polypharmacy is the increased probability of harmful side effects. The polypharmacy side effects are caused by an interaction between two medications. It means that the drug-drug interaction causes changes in their activities due to interfering in each other's performance. Therefore, discovering these side effects is one of the most challenging and important aspects of drug production and consumption as it is associated with human health. In this paper, a method has been introduced for predicting the polypharmacy side effects, called PSECNN. It is a multi-label multi-class deep learning method that combines various basic features of drugs to predict the polypharmacy side effects. Firstly, PSECNN collects five basic features of drugs, such as individual drug's side effects, drug-protein interactions, chemical substructures, targets, and enzymes in order to create a novel combination of drug features. A feature extraction module creates five feature vectors with the same dimension for each drug based on the Jaccard similarity index. Based on the feature vectors, a unique representative is then created for each drug. These representative vectors are given in pairs as input to the deep neural network to predict the occurrence probability of side effects. According to the experimental evaluations, PSECNN could outperform the state-of-the-art polypharmacy side effects prediction methods up to 74%. It has been found that PSECNN has better performance with polypharmacy side effects with a cause of molecular basis due to the novel combination of basic drug features.

PMID:35072838 | DOI:10.1007/s11030-022-10382-z

Proc Natl Acad Sci India Sect B Biol Sci. 2022 Jan 15:1-9. doi: 10.1007/s40011-021-01319-x. Online ahead of print.

ABSTRACT

The sudden outbreak of the novel coronavirus infection (COVID-19, SARS-CoV-2 virus) is posing a significant threat by affecting millions of people across the globe showing mild to severe symptoms of pneumonia and acute respiratory distress. The absence of precise information on primary transmission, diagnosis, prognosis, and therapeutics for patients with COVID-19 makes prevention and control tough. In the current scenario, only supportive treatment is available, which in turn possess a biggest challenge for scientists to develop specific drugs and vaccines for COVID-19. Further, India, with the second largest populated country and fluctuating climatic conditions quarterly, has high vulnerability towards COVID-19 infection. Thus, this highlights the importance of a better understanding of the COVID-19 infection, pathology, diagnosis and its treatment. The present review article has been intended to discuss the COVID-19 biology, mechanism of infection in humans with primary effects on pregnancy, the nervous system, diabetes, and cardiovascular disease. The article will also discuss the drug repurposing strategy as an alternative line of treatment and clinical practices recommended by the World Health Organization and other government agencies and represent the COVID-19 scenario with the Indian context.

PMID:35068664 | PMC:PMC8761055 | DOI:10.1007/s40011-021-01319-x

J Biomol Struct Dyn. 2022 Jan 22:1-14. doi: 10.1080/07391102.2022.2028677. Online ahead of print.

ABSTRACT

The Human Immunodeficiency Virus (HIV) infection is a global pandemic that has claimed 33 million lives to-date. One of the most efficacious treatments for naïve or pretreated HIV patients is the HIV integrase strand transfer inhibitors (INSTIs). However, given that HIV treatment is life-long, the emergence of HIV strains resistant to INSTIs is an imminent challenge. In this work, we showed two best regression QSAR models that were constructed using a boosted Random Forest algorithm (r2 = 0.998, q210CV = 0.721, q2external_test = 0.754) and a boosted K* algorithm (r2 = 0.987, q210CV = 0.721, q2external_test = 0.758) to predict the pIC50 values of INSTIs. Subsequently, the regression QSAR models were deployed against the Drugbank database for drug repositioning. The top-ranked compounds were further evaluated for their target engagement activity using molecular docking studies and accelerated Molecular Dynamics simulation. Lastly, their potential as INSTIs were also evaluated from our literature search. Our study offers the first example of a large-scale regression QSAR modelling effort for discovering highly active INSTIs to combat HIV infection.Communicated by Ramaswamy H. Sarma.

PMID:35067186 | DOI:10.1080/07391102.2022.2028677

Interdiscip Sci. 2022 Jan 23. doi: 10.1007/s12539-021-00487-8. Online ahead of print.

ABSTRACT

The coronavirus disease (COVID-19) has led to an rush to repurpose existing drugs, although the underlying evidence base is of variable quality. Drug repurposing is a technique by taking advantage of existing known drugs or drug combinations to be explored in an unexpected medical scenario. Drug repurposing, hence, plays a vital role in accelerating the pre-clinical process of designing novel drugs by saving time and cost compared to the traditional de novo drug discovery processes. Since drug repurposing depends on massive observed data from existing drugs and diseases, the tremendous growth of publicly available large-scale machine learning methods supplies the state-of-the-art application of data science to signaling disease, medicine, therapeutics, and identifying targets with the least error. In this article, we introduce guidelines on strategies and options of utilizing machine learning approaches for accelerating drug repurposing. We discuss how to employ machine learning methods in studying precision medicine, and as an instance, how machine learning approaches can accelerate COVID-19 drug repurposing by developing Chinese traditional medicine therapy. This article provides a strong reasonableness for employing machine learning methods for drug repurposing, including during fighting for COVID-19 pandemic.

PMID:35066811 | DOI:10.1007/s12539-021-00487-8

Vaccines (Basel). 2021 Dec 26;10(1):24. doi: 10.3390/vaccines10010024.

ABSTRACT

The COVID-19 pandemic caused by SARS-CoV-2 is unprecedented in recent memory owing to the non-stop escalation in number of infections and deaths in almost every country of the world. The lack of treatment options further worsens the scenario, thereby necessitating the exploration of already existing US FDA-approved drugs for their effectiveness against COVID-19. In the present study, we have performed virtual screening of nutraceuticals available from DrugBank against 14 SARS-CoV-2 proteins. Molecular docking identified several inhibitors, two of which, rutin and NADH, displayed strong binding affinities and inhibitory potential against SARS-CoV-2 proteins. Further normal model-based simulations were performed to gain insights into the conformational transitions in proteins induced by the drugs. The computational analysis in the present study paves the way for experimental validation and development of multi-target guided inhibitors to fight COVID-19.

PMID:35062685 | DOI:10.3390/vaccines10010024

Viruses. 2022 Jan 8;14(1):109. doi: 10.3390/v14010109.

ABSTRACT

Enterovirus 71 (EV71) is an etiological agent of hand foot and mouth disease and can also cause neurological complications in young children. However, there are no approved drugs as of yet to treat EV71 infections. In this study, we conducted antiviral drug screening by using a Food and Drug Administration (FDA)-approved drug library. We identified five drugs that showed dose-dependent inhibition of viral replication. Sertraline was further characterized because it exhibited the most potent antiviral activity with the highest selectivity index among the five hits. The antiviral activity of sertraline was noted for other EV serotypes. The drug's antiviral effect is not likely associated with its approved indications as an antidepressant and its mode-of-action as a selective serotonin reuptake inhibitor. The time-of-addition assay revealed that sertraline inhibited an EV71 infection at the entry stage. We also showed that sertraline partitioned into acidic compartments, such as endolysosomes, to neutralize the low pH levels. In agreement with the findings, the antiviral effect of sertraline could be greatly relieved by exposing virus-infected cells to extracellular low-pH culture media. Ultimately, we have identified a use for an FDA-approved antidepressant in broad-spectrum EV inhibition by blocking viral entry through the alkalization of the endolysosomal route.

PMID:35062313 | DOI:10.3390/v14010109

IEEE/ACM Trans Comput Biol Bioinform. 2022 Jan 21;PP. doi: 10.1109/TCBB.2022.3144429. Online ahead of print.

ABSTRACT

The matrix factorization model has become the cornerstone technique for computational drug repositioning due to its ease of implementation and excellent scalability. However, the matrix factorization model uses the inner product operation to represent the association between drugs and diseases, which is lacking in expressive ability. Moreover, the degree of similarity of drugs or diseases could not be implied on their respective latent factor vectors, which is not satisfy the common sense of drug discovery. Therefore, a neural metric factorization model for computational drug repositioning (NMFDR) is proposed in this work. We novelly consider the latent factor vector of drugs and diseases as a point in the high-dimensional coordinate system and propose a generalized Euclidean distance to represent the association between drugs and diseases to compensate for the shortcomings of the inner product operation. Furthermore, by embedding multiple drug (disease) metrics information into the encoding space of the latent factor vector, the information about the similarity between drugs (diseases) can be reflected in the distance between latent factor vectors. Finally, we conduct wide analysis experiments on three real datasets to demonstrate the effectiveness of the above improvement points and the superiority of the NMFDR model.

PMID:35061591 | DOI:10.1109/TCBB.2022.3144429

Parasitology. 2021 Nov;148(13):1680-1690. doi: 10.1017/S0031182021001402. Epub 2021 Aug 12.

ABSTRACT

Chagas disease is a potentially life-threatening protozoan infection affecting around 8 million people, for which only chemotherapies with limited efficacy and severe adverse secondary effects are available. The aetiological agent, Trypanosoma cruzi, displays varied cell invading tactics and triggers different host cell signals, including the Wnt/β-catenin pathway. Poly(ADP-ribose) (PAR) can be synthetized by certain members of the poly(ADP-ribose) polymerase (PARP) family: PARP-1/-2 and Tankyrases-1/2 (TNKS). PAR homoeostasis participates in the host cell response to T. cruzi infection and TNKS are involved in Wnt signalling, among other pathways. Therefore, we hypothesized that TNKS inhibitors (TNKSi) could hamper T. cruzi infection. We showed that five TNKSi (FLALL9, MN64, XAV939, G007LK and OULL9) diminished T. cruzi infection of Vero cells. As most TNKSi did not affect the viability of axenically cultivated parasites, our results suggested that TNKSi were interfering with parasite–host cell signalling. Infection by T. cruzi induced nuclear translocation of β-catenin, as well as upregulation of TNF-α expression and secretion. These changes were hampered by TNKSi. Further signals should be monitored in this model and in vivo. As a TNKSi has entered cancer clinical trials with promising results, our findings encourage further studies aiming at drug repurposing strategies.

PMID:35060470 | DOI:10.1017/S0031182021001402

J Chem Inf Model. 2022 Jan 20. doi: 10.1021/acs.jcim.1c00431. Online ahead of print.

ABSTRACT

In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, host-pathogen, and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. Here, we describe a workflow we designed for a semiautomated integration of rapidly emerging data sets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 63 278 host-host protein, and 1221 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is made publicly accessible via a web interface and via API calls based on the Bolt protocol. Details for accessing the database are provided on a landing page (https://neo4covid19.ncats.io/). We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19.

PMID:35057621 | DOI:10.1021/acs.jcim.1c00431

Pharmaceutics. 2022 Jan 12;14(1):176. doi: 10.3390/pharmaceutics14010176.

ABSTRACT

Since 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been rapidly spreading worldwide, causing hundreds of millions of infections. Despite the development of vaccines, insufficient protection remains a concern. Therefore, the screening of drugs for the treatment of coronavirus disease 2019 (COVID-19) is reasonable and necessary. This study utilized bioinformatics for the selection of compounds approved by the U.S. Food and Drug Administration with therapeutic potential in this setting. In addition, the inhibitory effect of these compounds on the enzyme activity of transmembrane protease serine 2 (TMPRSS2), papain-like protease (PLpro), and 3C-like protease (3CLpro) was evaluated. Furthermore, the capability of compounds to attach to the spike-receptor-binding domain (RBD) was considered an important factor in the present assessment. Finally, the antiviral potency of compounds was validated using a plaque reduction assay. Our funnel strategy revealed that tamoxifen possesses an anti-SARS-CoV-2 property owing to its inhibitory performance in multiple assays. The proposed time-saving and feasible strategy may accelerate drug screening for COVID-19 and other diseases.

PMID:35057070 | DOI:10.3390/pharmaceutics14010176