Pharmaceuticals (Basel). 2021 Jul 16;14(7):686. doi: 10.3390/ph14070686.

ABSTRACT

The intravascular parasitic worm Schistosoma mansoni is a causative agent of schistosomiasis, a disease of great global public health significance. Praziquantel is the only drug available to treat schistosomiasis and there is an urgent demand for new anthelmintic agents. Adopting a phenotypic drug screening strategy, here, we evaluated the antiparasitic properties of 46 commercially available cardiovascular drugs against S. mansoni. From these screenings, we found that amiodarone, telmisartan, propafenone, methyldopa, and doxazosin affected the viability of schistosomes in vitro, with effective concentrations of 50% (EC50) and 90% (EC90) values ranging from 8 to 50 µM. These results were further supported by scanning electron microscopy analysis. Subsequently, the most effective drug (amiodarone) was further tested in a murine model of schistosomiasis for both early and chronic S. mansoni infections using a single oral dose of 400 mg/kg or 100 mg/kg daily for five consecutive days. Amiodarone had a low efficacy in chronic infection, with the worm and egg burden reduction ranging from 10 to 30%. In contrast, amiodarone caused a significant reduction in worm and egg burden in early infection (>50%). Comparatively, treatment with amiodarone is more effective in early infection than praziquantel, demonstrating the potential role of this cardiovascular drug as an antischistosomal agent.

PMID:34358112 | DOI:10.3390/ph14070686

J Biomol Struct Dyn. 2021 Aug 6:1-16. doi: 10.1080/07391102.2021.1960608. Online ahead of print.

ABSTRACT

Drug repositioning has recently become one of the widely used drug design approaches in proposing alternative compounds with potentially fewer side effects. In this study, structure-based pharmacophore modelling and docking was used to screen existing drug molecules to bring forward potential modulators for ligand-binding domain of human glucocorticoid receptor (hGR). There exist several drug molecules targeting hGR, yet their apparent side effects still persist. Our goal was to disclose new compounds via screening existing drug compounds to bring forward fast and explicit solutions. The so-called shared pharmacophore model was created using the most persistent pharmacophore features shared by several crystal structures of the receptor. The shared model was first used to screen a small database of 75 agonists and 300 antagonists/decoys, and exhibited a successful outcome in its ability to distinguish agonists from antagonists/decoys. Then, it was used to screen a database of over 5000 molecules composed of FDA-approved, worldwide used and investigational drug compounds. A total of 110 compounds satisfying the pharmacophore requirements were subjected to different docking experiments for further assessment of their binding ability. In the final hit list of 54 compounds which fulfilled all scoring criteria, 19 of them were nonsteroidal and when further investigated, each presented a unique scaffold with little structural resemblance to any known nonsteroidal GR modulators. Independent 100 ns long MD simulations conducted on three selected drug candidates in complex with hGR displayed stable conformations incorporating several hydrogen bonds common to all three compounds and the reference molecule dexamethasone.Communicated by Ramaswamy H. Sarma.

PMID:34355665 | DOI:10.1080/07391102.2021.1960608

Front Microbiol. 2021 Jul 20;12:647693. doi: 10.3389/fmicb.2021.647693. eCollection 2021.

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected millions of people worldwide. Currently, many clinical trials in search of effective COVID-19 drugs are underway. Viral RNA-dependent RNA polymerase (RdRp) remains the target of choice for prophylactic or curative treatment of COVID-19. Nucleoside analogs are the most promising RdRp inhibitors and have shown effectiveness in vitro, as well as in clinical settings. One limitation of such RdRp inhibitors is the removal of incorporated nucleoside analogs by SARS-CoV-2 exonuclease (ExoN). Thus, ExoN proofreading activity accomplishes resistance to many of the RdRp inhibitors. We hypothesize that in the absence of highly efficient antivirals to treat COVID-19, combinatorial drug therapy with RdRp and ExoN inhibitors will be a promising strategy to combat the disease. To repurpose drugs for COVID-19 treatment, 10,397 conformers of 2,240 approved drugs were screened against the ExoN domain of nsp14 using AutoDock VINA. The molecular docking approach and detailed study of interactions helped us to identify dexamethasone metasulfobenzoate, conivaptan, hesperidin, and glycyrrhizic acid as potential inhibitors of ExoN activity. The results were further confirmed using molecular dynamics (MD) simulations and molecular mechanics combined with generalized Born model and solvent accessibility method (MM-GBSA) calculations. Furthermore, the binding free energy of conivaptan and hesperidin, estimated using MM-GBSA, was -85.86 ± 0.68 and 119.07 ± 0.69 kcal/mol, respectively. Based on docking, MD simulations and known antiviral activities, and conivaptan and hesperidin were identified as potential SARS-CoV-2 ExoN inhibitors. We recommend further investigation of this combinational therapy using RdRp inhibitors with a repurposed ExoN inhibitor as a potential COVID-19 treatment.

PMID:34354677 | PMC:PMC8329495 | DOI:10.3389/fmicb.2021.647693

Int J Psychiatry Clin Pract. 2021 Aug 4:1-13. doi: 10.1080/13651501.2021.1957117. Online ahead of print.

ABSTRACT

OBJECTIVE: The objective of this manuscript is to provide a comprehensive and critical overview of the current evidence on the association between Diabetes mellitus (DM) and mood disorders [i.e., Major depressive disorder (MDD) and bipolar disorder (BD)], and therapeutic opportunities.

METHODS: We searched in MEDLINE (via Ovid) for placebo-controlled clinical trials published in the last 20 years that assessed drug repurposing approaches for the treatment of DM or mood disorders.

RESULTS: We found seven studies that aimed to verify the effects of antidepressants in patients diagnosed with DM, and eight studies that tested the effect of antidiabetic drugs in patients diagnosed with MDD or BD. Most studies published in the last two decades did not report a positive effect of antidepressants on glycemic control in patients with DM. On the other hand, antidiabetic drugs seem to have a positive effect on the treatment of MDD and BD.

CONCLUSIONS: While effect of antidepressants on glycemic control in patients with DM is still controversial, the use of antidiabetic drugs may be a promising strategy for patients with MDD or BD. Prospective studies are still needed.Key pointsMood disorders in patients with DM affect glycemic control, potentially increasing mortality risk.The effect of antidepressants on glycemic control in patients with DM is still controversial. The coexistence of complicated DM and a mood disorders would require a careful, individualised, and comprehensive evaluation.Insulin resistance may increase the risk of depressive symptoms and is associated with worse outcomes in BD.The use antidiabetic drugs may be a promising strategy for patients with MDD or BD. However, prospective trials are needed to prove a potential antidepressant activity of antidiabetic drugs.

PMID:34348557 | DOI:10.1080/13651501.2021.1957117

J Biomol Struct Dyn. 2021 Aug 4:1-13. doi: 10.1080/07391102.2021.1958699. Online ahead of print.

ABSTRACT

Acinetobacter baumannii is an extremely dangerous multidrug-resistant (MDR) gram-negative pathogen which poses a serious life-threatening risk in immunocompromised patients. Phosphopantothenoyl cysteine synthetase (PPCS) catalyzes the formation of an amide bond between L-cysteine and phosphopantothenic acid (PPA) to form 4'- Phosphopantothenoylcysteine during Coenzyme A (CoA) biosynthesis. CoA is a crucial cofactor for cellular survival and inhibiting its synthesis will result in cell death. Bacterial PPCS differs from eukaryotic PPCS in a number of ways like it exists as a C-terminal domain of a PPCDC/PPCS fusion protein whereas eukaryotic PPCS exists as an independent protein. This difference makes it an attractive drug target. For which a conventional iterative approach of SBDD (structure-based drug design) was used, which began with three-dimensional structure prediction of AbPPCS using PHYRE 2.0. A database of FDA-approved compounds (Drug Bank) was then screened against the target of interest by means of docking score and glide energy, leading to the identification of 6 prominent drug candidates. The shortlisted 6 molecules were further subjected to all-atom MD simulation studies in explicit-solvent conditions (using AMBER force field). The MD simulation studies revealed that the ligands DB65103, DB449108 and DB443210, maintained several H-bonds with intense van der Waals contacts at the active site of the protein with high binding free energies: -11.42 kcal/mol, -10.49 kcal/mol and -10.98 kcal/mol, respectively, calculated via MM-PBSA method. Overall, binding of these compounds at the active site was found to be the most stable and robust highlighting the potential of these compounds to serve as antibacterials.Communicated by Ramaswamy H. Sarma.

PMID:34348086 | DOI:10.1080/07391102.2021.1958699

Aging Cell. 2021 Aug 4:e13441. doi: 10.1111/acel.13441. Online ahead of print.

ABSTRACT

The identification and validation of drugs that promote health during aging ("geroprotectors") are key to the retardation or prevention of chronic age-related diseases. Here, we found that most of the established pro-longevity compounds shown to extend lifespan in model organisms also alter extracellular matrix gene expression (i.e., matrisome) in human cell lines. To harness this observation, we used age-stratified human transcriptomes to define the age-related matreotype, which represents the matrisome gene expression pattern associated with age. Using a "youthful" matreotype, we screened in silico for geroprotective drug candidates. To validate drug candidates, we developed a novel tool using prolonged collagen expression as a non-invasive and in-vivo surrogate marker for Caenorhabditis elegans longevity. With this reporter, we were able to eliminate false-positive drug candidates and determine the appropriate dose for extending the lifespan of C. elegans. We improved drug uptake for one of our predicted compounds, genistein, and reconciled previous contradictory reports of its effects on longevity. We identified and validated new compounds, tretinoin, chondroitin sulfate, and hyaluronic acid, for their ability to restore age-related decline of collagen homeostasis and increase lifespan. Thus, our innovative drug screening approach-employing extracellular matrix homeostasis-facilitates the discovery of pharmacological interventions promoting healthy aging.

PMID:34346557 | DOI:10.1111/acel.13441

J Gen Virol. 2021 Aug;102(8). doi: 10.1099/jgv.0.001627.

ABSTRACT

Human respiratory syncytial virus (hRSV) is a major cause of respiratory illness in young children and can cause severe infections in the elderly or in immunocompromised adults. To date, there is no vaccine to prevent hRSV infections, and disease management is limited to preventive care by palivizumab in infants and supportive care for adults. Intervention with small-molecule antivirals specific for hRSV represents a good alternative, but no such compounds are currently approved. The investigation of existing drugs for new therapeutic purposes (drug repositioning) can be a faster approach to address this issue. In this study, we show that chloroquine and pyrimethamine inhibit the replication of human respiratory syncytial virus A (long strain) and synergistically increase the anti-replicative effect of ribavirin in cellulo. Moreover, chloroquine, but not pyrimethamine, inhibits hRSV replication in the mouse model. Our results show that chloroquine can potentially be an interesting compound for treatment of hRSV infection in monotherapy or in combination with other antivirals.

PMID:34342560 | DOI:10.1099/jgv.0.001627

Elife. 2021 Aug 3;10:e65543. doi: 10.7554/eLife.65543.

ABSTRACT

The discovery of a drug requires over a decade of intensive research and financial investments - and still has a high risk of failure. To reduce this burden, we developed the NICEdrug.ch resource, which incorporates 250,000 bioactive molecules, and studied their enzymatic metabolic targets, fate, and toxicity. NICEdrug.ch includes a unique fingerprint that identifies reactive similarities between drug-drug and drug-metabolite pairs. We validated the application, scope, and performance of NICEdrug.ch over similar methods in the field on golden standard datasets describing drugs and metabolites sharing reactivity, drug toxicities, and drug targets. We use NICEdrug.ch to evaluate inhibition and toxicity by the anticancer drug 5-fluorouracil, and suggest avenues to alleviate its side effects. We propose shikimate 3-phosphate for targeting liver-stage malaria with minimal impact on the human host cell. Finally, NICEdrug.ch suggests over 1300 candidate drugs and food molecules to target COVID-19 and explains their inhibitory mechanism for further experimental screening. The NICEdrug.ch database is accessible online to systematically identify the reactivity of small molecules and druggable enzymes with practical applications in lead discovery and drug repurposing.

PMID:34340747 | DOI:10.7554/eLife.65543

Front Biosci (Landmark Ed). 2021 Jul 30;26(7):222-234. doi: 10.52586/4936.

ABSTRACT

Introduction: The prediction of interacting drug-target pairs plays an essential role in the field of drug repurposing, and drug discovery. Although biotechnology and chemical technology have made extraordinary progress, the process of dose-response experiments and clinical trials is still extremely complex, laborious, and costly. As a result, a robust computer-aided model is of an urgent need to predict drug-target interactions (DTIs). Methods: In this paper, we report a novel computational approach combining fuzzy local ternary pattern (FLTP), Position-Specific Scoring Matrix (PSSM), and rotation forest (RF) to identify DTIs. More specially, the target primary sequence is first numerically characterized into PSSM which records the biological evolution information. Afterward, the FLTP method is applied in extracting the highly representative descriptors of PSSM, and the combinations of FLTP descriptors and drug molecular fingerprints are regarded as the complete features of drug-target pairs. Results: Finally, the entire features are fed into rotation forests for inferring potential DTIs. The experiments of 5-fold cross-validation (CV) achieve mean accuracies of 89.08%, 86.14%, 82.41%, and 78.40% on Enzyme, Ion Channel, GPCRs, and Nuclear Receptor datasets. Discussion: For further validating the model performance, we performed experiments with the state-of-art support vector machine (SVM) and light gradient boosting machine (LGBM). The experimental results indicate the superiorities of the proposed model in effectively and reliably detect potential DTIs. There is an anticipation that the proposed model can establish a feasible and convenient tool to identify high-throughput identification of DTIs.

PMID:34340269 | DOI:10.52586/4936

Drug Discov Today. 2021 Jul 30:S1359-6446(21)00335-4. doi: 10.1016/j.drudis.2021.07.026. Online ahead of print.

ABSTRACT

The COVID-19 pandemic has caused millions of deaths and massive societal distress worldwide. Therapeutic solutions are urgently needed but de novo drug development remains a lengthy process. One promising alternative is computational drug repurposing, which enables the prioritization of existing compounds through fast in silico analyses. Recent efforts based on molecular docking, machine learning, and network analysis have produced actionable predictions. Some predicted drugs, targeting viral proteins and pathological host pathways are undergoing clinical trials. Here, we review this work, highlight drugs with high predicted efficacy and classify their mechanisms of action. We discuss the strengths and limitations of the published methodologies and outline possible future directions. Finally, we curate a list of COVID-19 data portals and other repositories that could be used to accelerate future research.

PMID:34339864 | DOI:10.1016/j.drudis.2021.07.026

BMC Biol. 2021 Aug 2;19(1):156. doi: 10.1186/s12915-021-01086-1.

ABSTRACT

BACKGROUND: The emergence and continued global spread of the current COVID-19 pandemic has highlighted the need for methods to identify novel or repurposed therapeutic drugs in a fast and effective way. Despite the availability of methods for the discovery of antiviral drugs, the majority tend to focus on the effects of such drugs on a given virus, its constituent proteins, or enzymatic activity, often neglecting the consequences on host cells. This may lead to partial assessment of the efficacy of the tested anti-viral compounds, as potential toxicity impacting the overall physiology of host cells may mask the effects of both viral infection and drug candidates. Here we present a method able to assess the general health of host cells based on morphological profiling, for untargeted phenotypic drug screening against viral infections.

RESULTS: We combine Cell Painting with antibody-based detection of viral infection in a single assay. We designed an image analysis pipeline for segmentation and classification of virus-infected and non-infected cells, followed by extraction of morphological properties. We show that this methodology can successfully capture virus-induced phenotypic signatures of MRC-5 human lung fibroblasts infected with human coronavirus 229E (CoV-229E). Moreover, we demonstrate that our method can be used in phenotypic drug screening using a panel of nine host- and virus-targeting antivirals. Treatment with effective antiviral compounds reversed the morphological profile of the host cells towards a non-infected state.

CONCLUSIONS: The phenomics approach presented here, which makes use of a modified Cell Painting protocol by incorporating an anti-virus antibody stain, can be used for the unbiased morphological profiling of virus infection on host cells. The method can identify antiviral reference compounds, as well as novel antivirals, demonstrating its suitability to be implemented as a strategy for antiviral drug repurposing and drug discovery.

PMID:34334126 | DOI:10.1186/s12915-021-01086-1

Microb Pathog. 2021 Jul 29:105114. doi: 10.1016/j.micpath.2021.105114. Online ahead of print.

ABSTRACT

Understanding the pathogenesis of SARS-CoV-2 is essential for developing effective treatment strategies. Viruses hijack the host metabolism to redirect the resources for their replication and survival. The influence of SARS-CoV-2 on host metabolism is yet to be fully understood. In this study, we analyzed the transcriptomic data obtained from different human respiratory cell lines and patient samples (nasopharyngeal swab, peripheral blood mononuclear cells, lung biopsy, bronchoalveolar lavage fluid) to understand metabolic alterations in response to SARS-CoV-2 infection. We explored the expression pattern of metabolic genes in the comprehensive genome-scale network model of human metabolism, Recon3D, to extract key metabolic genes, pathways, and reporter metabolites under each SARS-CoV-2-infected condition. A SARS-CoV-2 core metabolic interactome was constructed for network-based drug repurposing. Our analysis revealed the host-dependent dysregulation of glycolysis, mitochondrial metabolism, amino acid metabolism, nucleotide metabolism, glutathione metabolism, polyamine synthesis, and lipid metabolism. We observed different pro- and antiviral metabolic changes and generated hypotheses on how the host metabolism can be targeted for reducing viral titers and immunomodulation. These findings warrant further exploration with more samples and in vitro studies to test predictions.

PMID:34333072 | DOI:10.1016/j.micpath.2021.105114

Comput Biol Med. 2021 Jul 23;136:104677. doi: 10.1016/j.compbiomed.2021.104677. Online ahead of print.

ABSTRACT

Viral epidemics and pandemics are considered public health emergencies. However, traditional and novel antiviral discovery approaches are unable to mitigate them in a timely manner. Notably, drug repurposing emerged as an alternative strategy to provide antiviral solutions in a timely and cost-effective manner. In the literature, many FDA-approved drugs have been repurposed to inhibit viruses, while a few among them have also entered clinical trials. Using experimental data, we identified repurposed drugs against 14 viruses responsible for causing epidemics and pandemics such as SARS-CoV-2, SARS, Middle East respiratory syndrome, influenza H1N1, Ebola, Zika, Nipah, chikungunya, and others. We developed a novel computational "drug-target-drug" approach that uses the drug-targets extracted for specific drugs, which are experimentally validated in vitro or in vivo for antiviral activity. Furthermore, these extracted drug-targets were used to fetch the novel FDA-approved drugs for each virus and prioritize them by calculating their confidence scores. Pathway analysis showed that the majority of the extracted targets are involved in cancer and signaling pathways. For SARS-CoV-2, our method identified 21 potential repurposed drugs, of which 7 (e.g., baricitinib, ramipril, chlorpromazine, enalaprilat, etc.) have already entered clinical trials. The prioritized drug candidates were further validated using a molecular docking approach. Therefore, we anticipate success during the experimental validation of our predicted FDA-approved repurposed drugs against 14 viruses. This study will assist the scientific community in hastening research aimed at the development of antiviral therapeutics.

PMID:34332351 | DOI:10.1016/j.compbiomed.2021.104677

Biomed Pharmacother. 2021 Jul 22;141:111916. doi: 10.1016/j.biopha.2021.111916. Online ahead of print.

ABSTRACT

Naodesheng (NDS) tablets have been widely used to treat ischemic stroke clinically. NDS relieves neurological function impairment and improve learning and memory in rats with focal cerebral ischemia, suggesting that NDS has potential for Alzheimer's disease (AD) treatment. However, there are no studies about its effective material basis and possible mechanisms. In this study, a systems pharmacology method was applied to reveal the potential molecular mechanism of NDS in the treatment of AD. First, we obtained 360 NDS candidate constituents through ADMET filter analysis. Then, 115 AD-related targets were uncovered by pharmacophore model prediction via mapping the predicted targets against AD-related proteins. In addition, compound-target and target-function networks were established to suggest potential synergistic effects among the candidate constituents. Furthermore, potential targets regulated by NDS were integrated into AD-related pathways to demonstrate the therapeutic mechanism of NDS in AD treatment. Subsequently, a validation experiment proved the therapeutic effect of NDS on cognitive dysfunction in rats with intracerebroventricular injection of Aβ. We found that administration of NDS tablets regulates β-amyloid metabolism, improves synaptic plasticity, inhibits neuroinflammation and improves learning and memory function. In conclusion, this is the first study to provide a comprehensive systems pharmacology approach to elucidate the potential therapeutic mechanism of NDS tablets for AD treatment. We suggest that the protective effects of NDS in neurodegenerative conditions could be partly attributed to its role in improving synaptic plasticity and inhibiting neuroinflammation via NF-κB signaling pathway inhibition and cAMP/PKA/CREB signaling pathway activation.

PMID:34328103 | DOI:10.1016/j.biopha.2021.111916

Science. 2021 Jul 30;373(6554):541-547. doi: 10.1126/science.abi4708. Epub 2021 Jun 22.

ABSTRACT

Repurposing drugs as treatments for COVID-19, the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has drawn much attention. Beginning with sigma receptor ligands and expanding to other drugs from screening in the field, we became concerned that phospholipidosis was a shared mechanism underlying the antiviral activity of many repurposed drugs. For all of the 23 cationic amphiphilic drugs we tested, including hydroxychloroquine, azithromycin, amiodarone, and four others already in clinical trials, phospholipidosis was monotonically correlated with antiviral efficacy. Conversely, drugs active against the same targets that did not induce phospholipidosis were not antiviral. Phospholipidosis depends on the physicochemical properties of drugs and does not reflect specific target-based activities-rather, it may be considered a toxic confound in early drug discovery. Early detection of phospholipidosis could eliminate these artifacts, enabling a focus on molecules with therapeutic potential.

PMID:34326236 | DOI:10.1126/science.abi4708

BMC Bioinformatics. 2021 Jul 29;22(Suppl 10):387. doi: 10.1186/s12859-021-04292-4.

ABSTRACT

BACKGROUND: Stroke has an acute onset and a high mortality rate, making it one of the most fatal diseases worldwide. Its underlying biology and treatments have been widely studied both in the "Western" biomedicine and the Traditional Chinese Medicine (TCM). However, these two approaches are often studied and reported in insolation, both in the literature and associated databases.

RESULTS: To aid research in finding effective prevention methods and treatments, we integrated knowledge from the literature and a number of databases (e.g. CID, TCMID, ETCM). We employed a suite of biomedical text mining (i.e. named-entity) approaches to identify mentions of genes, diseases, drugs, chemicals, symptoms, Chinese herbs and patent medicines, etc. in a large set of stroke papers from both biomedical and TCM domains. Then, using a combination of a rule-based approach with a pre-trained BioBERT model, we extracted and classified links and relationships among stroke-related entities as expressed in the literature. We construct StrokeKG, a knowledge graph includes almost 46 k nodes of nine types, and 157 k links of 30 types, connecting diseases, genes, symptoms, drugs, pathways, herbs, chemical, ingredients and patent medicine.

CONCLUSIONS: Our Stroke-KG can provide practical and reliable stroke-related knowledge to help with stroke-related research like exploring new directions for stroke research and ideas for drug repurposing and discovery. We make StrokeKG freely available at http://114.115.208.144:7474/browser/ (Please click "Connect" directly) and the source structured data for stroke at https://github.com/yangxi1016/Stroke.

PMID:34325669 | DOI:10.1186/s12859-021-04292-4

Curr Neuropharmacol. 2021 Jul 29. doi: 10.2174/1570159X19666210729123734. Online ahead of print.

ABSTRACT

BACKGROUND: Whereas the World faces an unprecedented pandemic caused by the SARS-CoV-2 virus, repositioning existing drugs to treat COVID-19 disease is urgently awaited, provided that high-quality scientific evidence supporting safety and efficacy in this new indication is gathered. Efforts concerning drug repositioning to COVID-19 were mostly focused on antiviral drugs or drugs targeting the late phase of the disease.

METHODS: Based on published research, the pharmacological activities of fluvoxamine and amantadine, two well-known drugs widely used in clinical practice for psychiatric and neurological diseases, respectively, have been reviewed, focusing on their potential therapeutic importance in the treatment of COVID-19.

RESULTS: Several preclinical and clinical reports were identified suggesting that these two drugs might exert protective effects in the early phases of COVID-19.

CONCLUSION: Preclinical and early clinical evidence are presented indicating that these drugs hold promise to prevent COVID-19 progression when administered early during infection.

PMID:34325642 | DOI:10.2174/1570159X19666210729123734

PLoS One. 2021 Jul 29;16(7):e0255270. doi: 10.1371/journal.pone.0255270. eCollection 2021.

ABSTRACT

The COVID-19 pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has become the current health concern and threat to the entire world. Thus, the world needs the fast recognition of appropriate drugs to restrict the spread of this disease. The global effort started to identify the best drug compounds to treat COVID-19, but going through a series of clinical trials and our lack of information about the details of the virus's performance has slowed down the time to reach this goal. In this work, we try to select the subset of human proteins as candidate sets that can bind to approved drugs. Our method is based on the information on human-virus protein interaction and their effect on the biological processes of the host cells. We also define some informative topological and statistical features for proteins in the protein-protein interaction network. We evaluate our selected sets with two groups of drugs. The first group contains the experimental unapproved treatments for COVID-19, and we show that from 17 drugs in this group, 15 drugs are approved by our selected sets. The second group contains the external clinical trials for COVID-19, and we show that 85% of drugs in this group, target at least one protein of our selected sets. We also study COVID-19 associated protein sets and identify proteins that are essential to disease pathology. For this analysis, we use DAVID tools to show and compare disease-associated genes that are contributed between the COVID-19 comorbidities. Our results for shared genes show significant enrichment for cardiovascular-related, hypertension, diabetes type 2, kidney-related and lung-related diseases. In the last part of this work, we recommend 56 potential effective drugs for further research and investigation for COVID-19 treatment. Materials and implementations are available at: https://github.com/MahnazHabibi/Drug-repurposing.

PMID:34324563 | DOI:10.1371/journal.pone.0255270

Mol Cell Biochem. 2021 Jul 29. doi: 10.1007/s11010-021-04230-1. Online ahead of print.

ABSTRACT

Despite the existing therapies and lack of receptors such as HER-2, estrogen receptor and progesterone receptor, triple-negative breast cancer is one of the most aggressive subtypes of breast cancer. TNBCs are known for their highly aggressive metastatic behavior and typically migrate to brain and bone for secondary site propagation. Many diseases share similar molecular pathology exposing new avenues in molecular signaling for engendering innovative therapies. Generation of newer therapies and novel drugs are time consuming associated with very high resources. In order to provide personalized or precision medicine, drug repositioning will contribute in a cost-effective manner. In our study, we have repurposed and used a neoteric combination of two drug molecules namely, fluvoxamine and tivozanib, to target triple-negative breast cancer growth and progression. Our combination regime significantly targets two diverse but significant pathways in TNBCs. Subsequent analysis on migratory, invasive, and angiogenic properties showed the significance of our repurposed drug combination. Molecular array data resulted in identifying the specific and key players participating in cancer progression when the drug combination was used. The innovative combination of fluvoxamine and tivozanib reiterates the use of drug repositioning for precision medicine and subsequent companion diagnostic development.

PMID:34324118 | DOI:10.1007/s11010-021-04230-1

Recent Pat Anticancer Drug Discov. 2021 Jul 28. doi: 10.2174/1574892816666210728123758. Online ahead of print.

ABSTRACT

BACKGROUND: The development of cancer drugs is among the most focused "bench to bedside activities" to improve human health. Because of the amount of data publicly available to cancer research, drug development for cancers has significantly benefited from big data and AI. In the meantime, challenges, like curating the data of low quality, remain to be resolved.

OBJECTIVE: This review focused on the recent advancements in and challenges of AI in developing cancer drugs.

METHOD: We discussed target validation, drug repositioning, de novo design, and compounds' synthetic strategies.

RESULTS AND CONCLUSION: AI can be applied to all stages during drug development, and some excellent reviews detailing the applications of AI in specific stages are available.

PMID:34323201 | DOI:10.2174/1574892816666210728123758