ACS Chem Neurosci. 2021 Nov 22. doi: 10.1021/acschemneuro.1c00521. Online ahead of print.

ABSTRACT

Tyrosine kinase inhibitors (TKIs) are antitumor compounds that prevent the phosphorylation of proteins in a biological environment. However, the multitarget performance of TKIs promotes them as possible candidates for drug repositioning. In this work, interaction and inhibition studies through spectroscopic and computational techniques to evaluate the binding effectiveness of lapatinib and pazopanib TKIs to acetylcholinesterase (AChE) are reported. The results indicated potent inhibition at the μM level. The types of inhibition were identified, with pazopanib acting through non-competitive inhibition and lapatinib through acompetitive inhibition. The fluorescence suppression studies indicate a static mechanism for lapatinib-AChE and pazopanib-AChE systems, with a binding constant in the order of 105 M-1. The obtained thermodynamic parameters reveal interactions driven by van der Waals forces and hydrogen bonds in the lapatinib-AChE system (ΔH° and ΔS° < 0). In contrast, the pazopanib-AChE system shows positive ΔH° and ΔS°, characteristic of hydrophobic interactions. The Foster resonance energy transfer study supports the fluorescence studies performed. The 3D fluorescence studies suggest changes in the microenvironment of the tryptophan and tyrosine residues of the protein in contact with lapatinib and pazopanib. The results suggest effective inhibition and moderate interaction of the drugs with AChE, making them interesting for conducting more in-depth repositioning studies as AChE inhibitors.

PMID:34808043 | DOI:10.1021/acschemneuro.1c00521

Infect Dis Ther. 2021 Nov 22. doi: 10.1007/s40121-021-00559-8. Online ahead of print.

ABSTRACT

Antimicrobials are essential for combating infectious diseases. However, an increase in resistance to them is a major cause of concern. The empirical use of drugs in managing COVID-19 and the associated secondary infections have further exacerbated the problem of antimicrobial resistance. Hence, the situation mandates exploring and developing efficient alternatives for the treatment of bacterial and fungal infections in patients suffering from COVID-19 or other viral infections. In this review, we have described the alternatives to conventional antimicrobials that have shown promising results and are at various stages of development. An acceleration of efforts to investigate their potential as therapeutics can provide more treatment options for clinical management of drug-resistant secondary bacterial and fungal infections in the current pandemic and similar potential outbreaks in the future. The alternatives include bacteriophages and their lytic enzymes, anti-fungal enzymes, antimicrobial peptides, nanoparticles and small molecule inhibitors among others. What is required at this stage is to critically examine the challenges in developing the listed compounds and biomolecules as therapeutics and to establish guidelines for their safe and effective application within a suitable time frame. In this review, we have attempted to highlight the importance of rational use of antimicrobials in patients suffering from COVID-19 and boost the deployment of alternative therapeutics.

PMID:34807451 | DOI:10.1007/s40121-021-00559-8

Inform Med Unlocked. 2021;27:100787. doi: 10.1016/j.imu.2021.100787. Epub 2021 Nov 15.

ABSTRACT

Search for new antiviral medications has surged in the past two years due to the COVID-19 crisis. Toll-like receptor 7 (TLR7) is among one of the most important TLR proteins of innate immunity that is responsible for broad antiviral response and immune system control. TLR7 agonists, as both vaccine adjuvants and immune response modulators, are among the top drug candidates for not only our contemporary viral pandemic but also other diseases. The agonists of TLR7 have been utilized as vaccine adjuvants and antiviral agents. In this study, we hybridized a statistical learning-based QSAR model with molecular docking and molecular dynamics simulation to extract new antiviral drugs by drug repurposing of the DrugBank database. First, we manually curated a dataset consisting of TLR7 agonists. The molecular descriptors of these compounds were extracted, and feature engineering was done to restrict the number of features to 45. We applied a statistically inspired modification of the partial least squares (SIMPLS) method to build our QSAR model. In the next stage, the DrugBank database was virtually screened structurally using molecular docking, and the top compounds for the guanosine binding site of TLR were identified. The result of molecular docking was again screened by the ligand-based approach of QSAR to eliminate compounds that do not display strong EC50 values by the previously trained model. We then subjected the final results to molecular dynamics simulation and compared our compounds with imiquimod (an FDA-approved TLR7 agonist) and compound 1 (the most active compound against TLR7 in vitro, EC50 = 0.2 nM). Our results evidently demonstrate that cephalosporins and nucleotide analogues (especially acyclic nucleotide analogues such as adefovir and cidofovir) are computationally potent agonists of TLR7. We finally reviewed some publications about cephalosporins that, just like pieces of a puzzle, completed our conclusion.

PMID:34805481 | PMC:PMC8591993 | DOI:10.1016/j.imu.2021.100787

Turk J Biol. 2021 Aug 30;45(4):459-468. doi: 10.3906/biy-2106-61. eCollection 2021.

ABSTRACT

With the emergence of the new SARS-CoV-2 virus, drug repurposing studies have gained substantial importance. Combined with the efficacy of recent improvements in ligand- and target-based virtual screening approaches, virtual screening has become faster and more productive than ever. In the current study, an FDA library of approved drugs and compounds under clinical investigation were screened for their antiviral activity using the antiviral therapeutic activity binary QSAR model of the MetaCore/MetaDrug platform. Among 6733-compound collection, we found 370 compounds with a normalized therapeutic activity value greater than a cutoff of 0.75. Only these selected compounds were used for molecular docking studies against the SARS-CoV-2 main protease (Mpro). After initial short (10 ns) molecular dynamics (MD) simulations with the top-50 docking scored compounds and following molecular mechanics generalized born surface area (MM/GBSA) calculations, top-10 compounds were subjected to longer (100 ns) MD simulations and end-point MM/GBSA estimations. Our virtual screening protocol yielded Cefuroxime pivoxetil, an ester prodrug of second-generation cephalosporin antibiotic Cefuroxime, as being a considerable molecule for drug repurposing against the SARS-CoV-2 Mpro.

PMID:34803447 | PMC:PMC8573836 | DOI:10.3906/biy-2106-61

Turk J Biol. 2021 Aug 30;45(4):442-458. doi: 10.3906/biy-2012-52. eCollection 2021.

ABSTRACT

Despite COVID-19 turned into a pandemic, no approved drug for the treatment or globally available vaccine is out yet. In such a global emergency, drug repurposing approach that bypasses a costly and long-time demanding drug discovery process is an effective way in search of finding drugs for the COVID-19 treatment. Recent studies showed that SARS-CoV-2 uses neuropilin-1 (NRP1) for host entry. Here we took advantage of structural information of the NRP1 in complex with C-terminal of spike (S) protein of SARS-CoV-2 to identify drugs that may inhibit NRP1 and S protein interaction. U.S. Food and Drug Administration (FDA) approved drugs were screened using docking simulations. Among top drugs, well-tolerated drugs were selected for further analysis. Molecular dynamics (MD) simulations of drugs-NRP1 complexes were run for 100 ns to assess the persistency of binding. MM/GBSA calculations from MD simulations showed that eltrombopag, glimepiride, sitagliptin, dutasteride, and ergotamine stably and strongly bind to NRP1. In silico Alanine scanning analysis revealed that Tyr297, Trp301, and Tyr353 amino acids of NRP1 are critical for drug binding. Validating the effect of drugs analyzed in this paper by experimental studies and clinical trials will expedite the drug discovery process for COVID-19.

PMID:34803446 | PMC:PMC8573850 | DOI:10.3906/biy-2012-52

J Ginseng Res. 2021 Sep;45(5):610-616. doi: 10.1016/j.jgr.2021.06.008. Epub 2021 Jun 24.

ABSTRACT

BACKGROUND: Thymic stromal lymphopoietin (TSLP) acts as a master switch for inflammatory responses. Ginsenoside Rg3 (Rg3) which is an active ingredient of Panax ginseng Meyer (Araliaceae) is known to possess various therapeutic effects. However, a modulatory effect of Rg3 on TSLP expression in the inflammatory responses remains poorly understood.

METHODS: We investigated antiinflammatory effects of Rg3 on an in vitro model using HMC-1 cells stimulated by PMA plus calcium ionophore (PMACI), as well as an in vivo model using PMA-induced mouse ear edema. TSLP and vascular endothelial growth factor (VEGF) levels were detected using enzyme-linked immunosorbent assay or real-time PCR analysis. Murine double minute 2 (MDM2) and hypoxia-inducible factor 1α (HIF1α) expression levels were detected using Western blot analysis.

RESULTS: Rg3 treatment restrained the production and mRNA expression levels of TSLP and VEGF in activated HMC-1 cells. Rg3 down-regulated the MDM2 expression level increased by PMACI stimulation. The HIF1α expression level was also reduced by Rg3 in activated HMC-1 cells. In addition, Rg3-administered mice showed the decreased redness and ear thickness in PMA-irritated ear edema. Rg3 inhibited the TSLP and VEGF levels in the serum and ear tissue homogenate. Moreover, the MDM2 and HIF1α expression levels in the ear tissue homogenate were suppressed by Rg3.

CONCLUSION: Taken together, the current study identifies new mechanistic evidence about MDM2/HIF1α pathway in the antiinflammatory effect of Rg3, providing a new effective therapeutic strategy for the treatment of skin inflammatory diseases.

PMID:34803431 | PMC:PMC8587510 | DOI:10.1016/j.jgr.2021.06.008

Philos Trans A Math Phys Eng Sci. 2022 Jan 10;380(2214):20210127. doi: 10.1098/rsta.2021.0127. Epub 2021 Nov 22.

ABSTRACT

During the COVID-19 pandemic, more than ever, data science has become a powerful weapon in combating an infectious disease epidemic and arguably any future infectious disease epidemic. Computer scientists, data scientists, physicists and mathematicians have joined public health professionals and virologists to confront the largest pandemic in the century by capitalizing on the large-scale 'big data' generated and harnessed for combating the COVID-19 pandemic. In this paper, we review the newly born data science approaches to confronting COVID-19, including the estimation of epidemiological parameters, digital contact tracing, diagnosis, policy-making, resource allocation, risk assessment, mental health surveillance, social media analytics, drug repurposing and drug development. We compare the new approaches with conventional epidemiological studies, discuss lessons we learned from the COVID-19 pandemic, and highlight opportunities and challenges of data science approaches to confronting future infectious disease epidemics. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.

PMID:34802267 | DOI:10.1098/rsta.2021.0127

Carbohydr Res. 2021 Nov 13;511:108478. doi: 10.1016/j.carres.2021.108478. Online ahead of print.

ABSTRACT

α-Glucosidase is a promising target for the treatment of diabetes. Drug repurposing can increase the chances of discovering an active inhibitor. Therefore, this study aimed to identify potential α-glucosidase inhibitor using drug repurposing and in silico strategies. We identified critical amino acid residues of the three α-glucosidase proteins. Based on cross molecular docking studies of three α-glucosidase proteins and drugs in the FDA database, we screened hits with the favorable binding affinities and modes targeting the three proteins. Subsequently, an in vitro activity assay showed that raloxifene was an excellent inhibitor of α-glucosidase. Moreover, molecular dynamics simulations of raloxifene and three proteins were performed to assess the stability of the protein-hit systems in physiological conditions and clarify protein-hit interactions. We also performed the binding free energy calculation, Hirshfeld surface and alanine scanning mutagenesis analyses. These results demonstrated that binding between raloxifene and the three proteins was stable, and the critical amino acid residues of the three proteins formed stable contacts with raloxifene. The molecular mechanisms agree well with its activity, reinforcing that raloxifene is a candidate α-glucosidase inhibitor. Our study smoothes the path for the development of novel a-glucosidase inhibitors with high efficacy and low toxicity for the treatment of diabetes.

PMID:34801925 | DOI:10.1016/j.carres.2021.108478

Arch Biochem Biophys. 2021 Nov 17:109085. doi: 10.1016/j.abb.2021.109085. Online ahead of print.

ABSTRACT

The identification of biomolecules associated with papillary thyroid cancer (PTC) has upmost importance for the elucidation of the disease mechanism and the development of effective diagnostic and treatment strategies. Despite particular findings in this regard, a holistic analysis encompassing molecular data from different biological levels has been lacking. In the present study, a meta-analysis of four transcriptome datasets was performed to identify gene expression signatures in PTC, and reporter molecules were determined by mapping gene expression data onto three major cellular networks, i.e., transcriptional regulatory, protein-protein interaction, and metabolic networks. We identified 282 common genes that were differentially expressed in all PTC datasets. In addition, six proteins (FYN, JUN, LYN, PML, SIN3A, and RARA), two Erb-B2 receptors (ERBB2 and ERBB4), two cyclin-dependent receptors (CDK1 and CDK2), and three histone deacetylase receptors (HDAC1, HDAC2, and HDAC3) came into prominence as proteomic signatures in addition to several metabolites including lactaldehyde and proline at the metabolome level. Significant associations with calcium and MAPK signaling pathways and transcriptional and post-transcriptional activities of 12 TFs and 110 miRNAs were also observed at the regulatory level. Among them, six miRNAs (miR-30b-3p, miR-15b-5p, let-7a-5p, miR-130b-3p, miR-424-5p, and miR-193b-3p) were associated with PTC for the first time in the literature, and the expression levels of miR-30b-3p, miR-15b-5p, and let-7a-5p were found to be predictive of disease prognosis. Drug repositioning and molecular docking simulations revealed that 5 drugs (prochlorperazine, meclizine, rottlerin, cephaeline, and tretinoin) may be useful in the treatment of PTC. Consequently, we report here biomolecule candidates that may be considered as prognostic biomarkers or potential therapeutic targets for further experimental and clinical trials for PTC.

PMID:34800440 | DOI:10.1016/j.abb.2021.109085

J Appl Microbiol. 2021 Nov 20. doi: 10.1111/jam.15377. Online ahead of print.

ABSTRACT

AIM: Antimicrobial resistance (AMR) has become a global concern. Developing novel antimicrobials is one of the most effective approaches in tackling AMR. Considering its relatively low cost and risk, drug repurposing has been proposed as a valuable approach for novel antimicrobial discovery. The aim of this study was to screen for antimicrobial compounds against Streptococcus suis, an important zoonotic bacterial pathogen, from an FDA-approved drug library.

METHODS AND RESULTS: In this study, we tested the antimicrobial activity of 1815 FDA-approved drugs against S. suis. 67 hits were obtained that showed a growth inhibition of more than 98%. After excluding already known antibiotics and antiseptics, twelve compounds were subjected to minimal inhibition concentration (MIC) assessment against S. suis. This showed that pralatrexate, daunorubicin (hydrochloride), teniposide, aclacinomycin A hydrochloride, and floxuridine gave a relatively low MIC, ranging from 0.85 to 5.25 μg/mL. Apart from pralatrexate, the remaining four drugs could also inhibit the growth of antimicrobial-resistant S. suis. It was also demonstrated that these four drugs had better efficacy against Gram-positive bacteria than Gram-negative bacteria. Cytotoxicity assays showed that floxuridine and teniposide had a relatively high 50% cytotoxic concentration (CC50 ). The in vivo efficacy of floxuridine was analyzed using a Galleria mellonella larvae infection model, and the results showed that floxuridine was effective in treating S. suis infection in vivo.

CONCLUSIONS: Five compounds from the FDA-approved drug library showed high antimicrobial activity against S. suis, among which floxuridine displayed potent in vivo efficacy that is worth further development.

SIGNIFICANCE AND IMPACT OF STUDY: Our study identified several antimicrobial compounds that are effective against S. suis, which provides a valuable starting point for further antimicrobial development.

PMID:34800069 | DOI:10.1111/jam.15377

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

ABSTRACT

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

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

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

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

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

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

ABSTRACT

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

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

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

ABSTRACT

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

PMID:34791969 | DOI:10.1080/07391102.2021.2002719

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

ABSTRACT

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

PMID:34791019 | DOI:10.1093/bib/bbab455

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

ABSTRACT

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

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

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

ABSTRACT

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

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

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

ABSTRACT

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

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

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

ABSTRACT

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

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

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

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

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

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

ABSTRACT

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

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

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

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

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

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

ABSTRACT

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

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

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

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

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