Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2.

Cell Discov. 2020;6:14

Authors: Zhou Y, Hou Y, Shen J, Huang Y, Martin W, Cheng F

Abstract
Human coronaviruses (HCoVs), including severe acute respiratory syndrome coronavirus (SARS-CoV) and 2019 novel coronavirus (2019-nCoV, also known as SARS-CoV-2), lead global epidemics with high morbidity and mortality. However, there are currently no effective drugs targeting 2019-nCoV/SARS-CoV-2. Drug repurposing, representing as an effective drug discovery strategy from existing drugs, could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we present an integrative, antiviral drug repurposing methodology implementing a systems pharmacology-based network medicine platform, quantifying the interplay between the HCoV-host interactome and drug targets in the human protein-protein interaction network. Phylogenetic analyses of 15 HCoV whole genomes reveal that 2019-nCoV/SARS-CoV-2 shares the highest nucleotide sequence identity with SARS-CoV (79.7%). Specifically, the envelope and nucleocapsid proteins of 2019-nCoV/SARS-CoV-2 are two evolutionarily conserved regions, having the sequence identities of 96% and 89.6%, respectively, compared to SARS-CoV. Using network proximity analyses of drug targets and HCoV-host interactions in the human interactome, we prioritize 16 potential anti-HCoV repurposable drugs (e.g., melatonin, mercaptopurine, and sirolimus) that are further validated by enrichment analyses of drug-gene signatures and HCoV-induced transcriptomics data in human cell lines. We further identify three potential drug combinations (e.g., sirolimus plus dactinomycin, mercaptopurine plus melatonin, and toremifene plus emodin) captured by the "Complementary Exposure" pattern: the targets of the drugs both hit the HCoV-host subnetwork, but target separate neighborhoods in the human interactome network. In summary, this study offers powerful network-based methodologies for rapid identification of candidate repurposable drugs and potential drug combinations targeting 2019-nCoV/SARS-CoV-2.

PMID: 32194980 [PubMed]

Prediction of the SARS-CoV-2 (2019-nCoV) 3C-like protease (3CL pro) structure: virtual screening reveals velpatasvir, ledipasvir, and other drug repurposing candidates.

F1000Res. 2020;9:129

Authors: Chen YW, Yiu CB, Wong KY

Abstract
We prepared the three-dimensional model of the SARS-CoV-2 (aka 2019-nCoV) 3C-like protease (3CL pro) using the crystal structure of the highly similar (96% identity) ortholog from the SARS-CoV. All residues involved in the catalysis, substrate binding and dimerisation are 100% conserved. Comparison of the polyprotein PP1AB sequences showed 86% identity. The 3C-like cleavage sites on the coronaviral polyproteins are highly conserved. Based on the near-identical substrate specificities and high sequence identities, we are of the opinion that some of the previous progress of specific inhibitors development for the SARS-CoV enzyme can be conferred on its SARS-CoV-2 counterpart.  With the 3CL pro molecular model, we performed virtual screening for purchasable drugs and proposed 16 candidates for consideration. Among these, the antivirals ledipasvir or velpatasvir are particularly attractive as therapeutics to combat the new coronavirus with minimal side effects, commonly fatigue and headache.  The drugs Epclusa (velpatasvir/sofosbuvir) and Harvoni (ledipasvir/sofosbuvir) could be very effective owing to their dual inhibitory actions on two viral enzymes.

PMID: 32194944 [PubMed - in process]

Repositioning of Hypoglycemic Drug Linagliptin for Cancer Treatment.

Front Pharmacol. 2020;11:187

Authors: Li Y, Li Y, Li D, Li K, Quan Z, Wang Z, Sun Z

Abstract
Background: Drug repositioning, development of new uses for marketed drugs, is an effective way to discover new antitumor compounds. In this study, we used a new method, filtering compounds via molecular docking to find key targets combination.
Methods: The data of gene expression in cancer and normal tissues of colorectal, breast, and liver cancer were obtained from The Cancer Genome Atlas Project (TCGA). The key targets combination was obtained from the protein-protein interaction network (PPI network) and the correlation analysis of the targets. Molecular docking was used to reposition the drugs which were obtained from DrugBank. MTT proliferation assay and animal experiments were used to verify the activity of candidate compounds. Flow cytometric analysis of proliferation, cell cycle and apoptosis, slice analysis, gene regulatory network, and Western blot were performed to elucidate the mechanism of drug action.
Results: CDK1 and AURKB were identified as a pair of key targets by the analysis of different expression gene from TCGA. Three compounds, linagliptin, mupirocin, and tobramycin, from 12 computationally predicted compounds, were verified to inhibit cell viability in HCT116 (colorectal), MCF7 (breast), and HepG2 (liver) cancer cells. Linagliptin, a hypoglycemic drug, was proved to inhibit cell proliferation by cell cycle arrest and induce apoptosis in HCT116 cells, and suppress tumor growth in nude mice bearing HCT116 cells. Linagliptin reduced the tumor size and decreased the expression of Ki67, a nuclear protein expressed in all proliferative cells. Gene regulatory network and Western blot analysis suggested that linagliptin inhibited tumor cell proliferation and promoted cell apoptosis through suppressing the expression and phosphorylation of Rb, plus down-regulating the expression of Pro-caspase3 and Bcl-2, respectively.
Conclusion: The combination of key targets based on the protein-protein interaction network that were built by the different gene expression of TCGA data to reposition the marketed drugs turned out to be a new approach to discover new antitumor drugs. Hypoglycemic drug linagliptin could potentially lead to novel therapeutics for the treatment of tumors, especially for colorectal cancer. Gene regulatory network is a valuable method for predicting and explaining the mechanism of drugs action.

PMID: 32194417 [PubMed]

Suramin Inhibits Chikungunya Virus Replication by Interacting with Virions and Blocking the Early Steps of Infection.

Viruses. 2020 Mar 17;12(3):

Authors: Albulescu IC, White-Scholten L, Tas A, Hoornweg TE, Ferla S, Kovacikova K, Smit JM, Brancale A, Snijder EJ, van Hemert MJ

Abstract
Chikungunya virus (CHIKV) is a mosquito-transmitted alphavirus that can cause a debilitating disease that is primarily characterized by persistent joint pain. CHIKV has been emerging globally, while neither a vaccine nor antiviral medication is available. The anti-parasitic drug suramin was previously shown to inhibit CHIKV replication. In this study we aimed to obtain more detailed insight into its mechanism of action. We found that suramin interacts with virions and can inhibit virus binding to cells. It also appeared to inhibit post-attachment steps of the infection process, likely by preventing conformational changes of the envelope glycoproteins required for fusion and the progression of infection. Suramin-resistant CHIKV strains were selected and genotyping and reverse genetics experiments indicated that mutations in E2 were responsible for resistance. The substitutions N5R and H18Q were reverse engineered in the E2 glycoprotein in order to understand their role in resistance. The binding of suramin-resistant viruses with these two E2 mutations was inhibited by suramin like that of wild-type virus, but they appeared to be able to overcome the post-attachment inhibitory effect of suramin. Conversely, a virus with a G82R mutation in E2 (implicated in attenuation of vaccine strain 181/25), which renders it dependent on the interaction with heparan sulfate for entry, was more sensitive to suramin than wild-type virus. Using molecular modelling studies, we predicted the potential suramin binding sites on the mature spikes of the chikungunya virion. We conclude that suramin interferes with CHIKV entry by interacting with the E2 envelope protein, which inhibits attachment and also interferes with conformational changes required for fusion.

PMID: 32191995 [PubMed - in process]

Prediction of Side Effects Using Comprehensive Similarity Measures.

Biomed Res Int. 2020;2020:1357630

Authors: Seo S, Lee T, Kim MH, Yoon Y

Abstract
Identifying the potential side effects of drugs is crucial in clinical trials in the pharmaceutical industry. The existing side effect prediction methods mainly focus on the chemical and biological properties of drugs. This study proposes a method that uses diverse information such as drug-drug interactions from DrugBank, drug-drug interactions from network, single nucleotide polymorphisms, and side effect anatomical hierarchy as well as chemical structures, indications, and targets. The proposed method is based on the assumption that properties used in drug repositioning studies could be utilized to predict side effects because the phenotypic expression of a side effect is similar to that of the disease. The prediction results using the proposed method showed a 3.5% improvement in the area under the curve (AUC) over that obtained when only chemical, indication, and target features were used. The random forest model delivered outstanding results for all combinations of feature types. Finally, after identifying candidate side effects of drugs using the proposed method, the following four popular drugs were discussed: (1) dasatinib, (2) sitagliptin, (3) vorinostat, and (4) clonidine.

PMID: 32190647 [PubMed - in process]

SAEROF: an ensemble approach for large-scale drug-disease association prediction by incorporating rotation forest and sparse autoencoder deep neural network.

Sci Rep. 2020 Mar 18;10(1):4972

Authors: Jiang HJ, Huang YA, You ZH

Abstract
Drug-disease association is an important piece of information which participates in all stages of drug repositioning. Although the number of drug-disease associations identified by high-throughput technologies is increasing, the experimental methods are time consuming and expensive. As supplement to them, many computational methods have been developed for an accurate in silico prediction for new drug-disease associations. In this work, we present a novel computational model combining sparse auto-encoder and rotation forest (SAEROF) to predict drug-disease association. Gaussian interaction profile kernel similarity, drug structure similarity and disease semantic similarity were extracted for exploring the association among drugs and diseases. On this basis, a rotation forest classifier based on sparse auto-encoder is proposed to predict the association between drugs and diseases. In order to evaluate the performance of the proposed model, we used it to implement 10-fold cross validation on two golden standard datasets, Fdataset and Cdataset. As a result, the proposed model achieved AUCs (Area Under the ROC Curve) of Fdataset and Cdataset are 0.9092 and 0.9323, respectively. For performance evaluation, we compared SAEROF with the state-of-the-art support vector machine (SVM) classifier and some existing computational models. Three human diseases (Obesity, Stomach Neoplasms and Lung Neoplasms) were explored in case studies. As a result, more than half of the top 20 drugs predicted were successfully confirmed by the Comparative Toxicogenomics Database(CTD database). This model is a feasible and effective method to predict drug-disease correlation, and its performance is significantly improved compared with existing methods.

PMID: 32188871 [PubMed - in process]

Exploratory Analysis of iPSCS-Derived Neuronal Cells as Predictors of Diagnosis and Treatment of Alzheimer Disease.

Brain Sci. 2020 Mar 13;10(3):

Authors: Cavalli E, Battaglia G, Basile MS, Bruno V, Petralia MC, Lombardo SD, Pennisi M, Kalfin R, Tancheva L, Fagone P, Nicoletti F, Mangano K

Abstract
Alzheimer's disease (AD) represents the most common neurodegenerative disorder, with 47 million affected people worldwide. Current treatment strategies are aimed at reducing the symptoms and do slow down the progression of the disease, but inevitably fail in the long-term. Induced pluripotent stem cells (iPSCs)-derived neuronal cells from AD patients have proven to be a reliable model for AD pathogenesis. Here, we have conducted an in silico analysis aimed at identifying pathogenic gene-expression profiles and novel drug candidates. The GSE117589 microarray dataset was used for the identification of Differentially Expressed Genes (DEGs) between iPSC-derived neuronal progenitor (NP) cells and neurons from AD patients and healthy donors. The Discriminant Analysis Module (DAM) algorithm was used for the identification of biomarkers of disease. Drugs with anti-signature gene perturbation profiles were identified using the L1000FWD software. DAM analysis was used to identify a list of potential biomarkers among the DEGs, able to discriminate AD patients from healthy people. Finally, anti-signature perturbation analysis identified potential anti-AD drugs. This study set the basis for the investigation of potential novel pharmacological strategies for AD. Furthermore, a subset of genes for the early diagnosis of AD is proposed.

PMID: 32183090 [PubMed]

Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace.

Brief Bioinform. 2020 Mar 18;:

Authors: Singh N, Chaput L, Villoutreix BO

Abstract
The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.

PMID: 32187356 [PubMed - as supplied by publisher]

Coupled matrix-matrix and coupled tensor-matrix completion methods for predicting drug-target interactions.

Brief Bioinform. 2020 Mar 18;:

Authors: Bagherian M, Kim RB, Jiang C, Sartor MA, Derksen H, Najarian K

Abstract
Predicting the interactions between drugs and targets plays an important role in the process of new drug discovery, drug repurposing (also known as drug repositioning). There is a need to develop novel and efficient prediction approaches in order to avoid the costly and laborious process of determining drug-target interactions (DTIs) based on experiments alone. These computational prediction approaches should be capable of identifying the potential DTIs in a timely manner. Matrix factorization methods have been proven to be the most reliable group of methods. Here, we first propose a matrix factorization-based method termed 'Coupled Matrix-Matrix Completion' (CMMC). Next, in order to utilize more comprehensive information provided in different databases and incorporate multiple types of scores for drug-drug similarities and target-target relationship, we then extend CMMC to 'Coupled Tensor-Matrix Completion' (CTMC) by considering drug-drug and target-target similarity/interaction tensors. Results: Evaluation on two benchmark datasets, DrugBank and TTD, shows that CTMC outperforms the matrix-factorization-based methods: GRMF, $L_{2,1}$-GRMF, NRLMF and NRLMF$\beta $. Based on the evaluation, CMMC and CTMC outperform the above three methods in term of area under the curve, F1 score, sensitivity and specificity in a considerably shorter run time.

PMID: 32186716 [PubMed - as supplied by publisher]

Breakthrough: Chloroquine phosphate has shown apparent efficacy in treatment of COVID-19 associated pneumonia in clinical studies.

Biosci Trends. 2020 Mar 16;14(1):72-73

Authors: Gao J, Tian Z, Yang X

Abstract
The coronavirus disease 2019 (COVID-19) virus is spreading rapidly, and scientists are endeavoring to discover drugs for its efficacious treatment in China. Chloroquine phosphate, an old drug for treatment of malaria, is shown to have apparent efficacy and acceptable safety against COVID-19 associated pneumonia in multicenter clinical trials conducted in China. The drug is recommended to be included in the next version of the Guidelines for the Prevention, Diagnosis, and Treatment of Pneumonia Caused by COVID-19 issued by the National Health Commission of the People's Republic of China for treatment of COVID-19 infection in larger populations in the future.

PMID: 32074550 [PubMed - indexed for MEDLINE]

[Therapeutic drug repositioning and Steinert's disease].

Rev Neurol. 2020 Apr 01;70(7):269

Authors: Gutiérrez-Gutiérrez G, Rosado-Bartolomé A

PMID: 32182375 [PubMed - as supplied by publisher]

Wikidata as a knowledge graph for the life sciences.

Elife. 2020 Mar 17;9:

Authors: Waagmeester A, Stupp G, Burgstaller-Muehlbacher S, Good BM, Griffith M, Griffith OL, Hanspers K, Hermjakob H, Hudson TS, Hybiske K, Keating SM, Manske M, Mayers M, Mietchen D, Mitraka E, Pico AR, Putman T, Riutta A, Queralt-Rosinach N, Schriml LM, Shafee T, Slenter D, Stephan R, Thornton K, Tsueng G, Tu R, Ul-Hasan S, Willighagen E, Wu C, Su AI

Abstract
Wikidata is a community-maintained knowledge base that has been assembled from repositories in the fields of genomics, proteomics, genetic variants, pathways, chemical compounds, and diseases, and that adheres to the FAIR principles of findability, accessibility, interoperability and reusability. Here we describe the breadth and depth of the biomedical knowledge contained within Wikidata, and discuss the open-source tools we have built to add information to Wikidata and to synchronize it with source databases. We also demonstrate several use cases for Wikidata, including the crowdsourced curation of biomedical ontologies, phenotype-based diagnosis of disease, and drug repurposing.

PMID: 32180547 [PubMed - in process]

"How can a drug to treat claudication in adults save preterm newborns?"

Eur J Pediatr. 2020 Mar 16;:

Authors: Kurul S, Allegaert K, Flint RB, Taal HR

PMID: 32179979 [PubMed - as supplied by publisher]

Teicoplanin: an alternative drug for the treatment of coronavirus COVID-19?

Int J Antimicrob Agents. 2020 Mar 13;:105944

Authors: Baron SA, Devaux C, Colson P, Raoult D, Rolain JM

Abstract
In December 2019, a new coronavirus, named SARS-CoV-2, has emerged from China causing pneumonia outbreaks first in the Wuhan region and have now spread worldwide because of its probable high transmission efficiency. Due to the lack of efficient and specific treatments and the need to contain the epidemic, drug repurposing appears to be the best tool to find therapeutic solution. Chloroquine, remdesivir, lopinavir, ribavirin or ritonavir have shown efficacy to inhibit coronavirus in vitro. Teicoplanin, an antibiotic used to treat staphylococci infection, previously showed efficacy to inhibit the first stage of MERS-coronarivus viral cycle in human cells. This activity is conserved on the SARS-Cov-2, thus placing teicoplanin as a potential treatment for patients with this virus.

PMID: 32179150 [PubMed - as supplied by publisher]

In silico screening of FDA approved drugs on AXL kinase and validation for breast cancer cell line.

J Biomol Struct Dyn. 2020 Mar 17;:1-21

Authors: Nagamalla L, Kumar JVS

Abstract
AXL kinase has been over expressed in many tumors and its involvement in cell proliferation, migration, survival, and resistance makes the kinase as attractive therapeutic target for many cancers. In this study, we performed a virtual screening of the food and drug administration (FDA) approved drug molecule database against AXL kinase for repurposing studies of breast cancer. We have identified three non-cancer drugs with good binding energies were subjected to in vitro breast cancer MCF-7 cell lines. Three drug molecules showing the activity with good IC50 values towards the cancer cell line. We also carried out a 2 dimensional (2D) quantitative structure activity relation (QSAR) studies on N-[4-(Quinolin-4-yloxy)phenyl]benzenesulfonamides derivatives to design potent inhibitors for AXL kinase. The final QSAR equation was robust with good predictivity and the statistical validation having R2 and Q2 values are 0.91 and 0.86, respectively. QSAR equation descriptors informs about the chemical properties of AXL inhibitors and helpful for designing novel inhibitors.

PMID: 32178589 [PubMed - as supplied by publisher]

Identification of amitriptyline HCl, flavin adenine dinucleotide, azacitidine and calcitriol as repurposing drugs for influenza A H5N1 virus-induced lung injury.

PLoS Pathog. 2020 Mar;16(3):e1008341

Authors: Huang F, Zhang C, Liu Q, Zhao Y, Zhang Y, Qin Y, Li X, Li C, Zhou C, Jin N, Jiang C

Abstract
Infection with avian influenza A H5N1 virus results in acute lung injury (ALI) and has a high mortality rate (52.79%) because there are limited therapies available for treatment. Drug repositioning is an economical approach to drug discovery. We developed a method for drug repositioning based on high-throughput RNA sequencing and identified several drugs as potential treatments for avian influenza A H5N1 virus. Using high-throughput RNA sequencing, we identified a total of 1,233 genes differentially expressed in A549 cells upon H5N1 virus infection. Among these candidate genes, 79 drug targets (corresponding to 59 approved drugs) overlapped with the DrugBank target database. Twenty-two of the 41 commercially available small-molecule drugs reduced H5N1-mediated cell death in cultured A549 cells, and fifteen drugs that protected A549 cells when administered both pre- and post-infection were tested in an H5N1-infection mouse model. The results showed significant alleviation of acute lung injury by amitriptyline HCl (an antidepressant drug), flavin adenine dinucleotide (FAD; an ophthalmic agent for vitamin B2 deficiency), azacitidine (an anti-neoplastic drug) and calcitriol (an active form of vitamin D). All four agents significantly reduced the infiltrating cell count and decreased the lung injury score in H5N1 virus-infected mice based on lung histopathology, significantly improved mouse lung edema by reducing the wet-to-dry weight ratio of lung tissue and significantly improved the survival of H5N1 virus-infected mice. This study not only identifies novel potential therapies for influenza H5N1 virus-induced lung injury but also provides a highly effective and economical screening method for repurposing drugs that may be generalizable for the prevention and therapy of other diseases.

PMID: 32176725 [PubMed - as supplied by publisher]

Comparative modelling and structure based drug repurposing of PAX2 transcription factor for targeting acquired chemoresistance in pancreatic ductal adenocarcinoma.

J Biomol Struct Dyn. 2020 Mar 15;:1-10

Authors: Aier I, Semwal R, Raj U, Varadwaj PK

Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a pancreatic malignancy suffering from poor prognosis; the worst among all types of cancer. Chemotherapy, which is the standard regime for treatment in most cases, is often rendered useless as drug resistance quickly sets in after prolonged exposure to the drug. The implication of PAX2 transcription factor in regulating several ATP-binding cassette (ABC) transporter proteins that are responsible for the acquisition of drug resistance in PDAC makes it a potential target for treatment purposes. In this study, the 3D structure of PAX2 protein was modelled, and the response of key amino acids to perturbation were identified. Subsequently, kappadione, a vitamin K derivative, was found to bind efficiently to PAX2 with a binding energy of -9.819 kcal/mol. The efficacy of mechanism and mode of binding was studied by docking the protein with DNA in the presence and absence of the drug. The presence of kappadione disrupted DNA binding with key effector resides, preventing the DNA from coming into contact with the binding region essential for protein translation. By occupying the DNA binding region and replacing it with a ligand, the mechanism by which DNA interacts with PAX2 could be manipulated. Inhibition of PAX2-DNA binding using kappadione and other small molecules can prove to be beneficial for combating chemoresistance in PDAC, as proposed through in silico approaches.

PMID: 32174259 [PubMed - as supplied by publisher]

Achievements and challenges in the use of metronomics for the treatment of breast cancer.

Biochem Pharmacol. 2020 Mar 12;:113909

Authors: Scharovsky OG, José Rico M, Mainetti LE, Perroud HA, Rozados VR

Abstract
Two interesting therapeutic proposals for cancer treatment emerged at the beginning of the 21st century. The first one was metronomic chemotherapy, which refers to the chronic administration of chemotherapeutic agents, in low doses, without extended drug-free periods. Then, the idea of drug repositioning in oncology, the use of well-known drugs that were created for other uses to be utilized in oncology, gained strength. Shortly after, the two strategies were merged in one, named metronomics. Both approaches share several features which make metronomics an appealing choice for cancer treatment: use of known and approved drugs, thus diminishing the time necessary to enter to the clinic, therapeutic effect, low toxicity, oral administration, better life quality, low costs because of the use of, generally, out of patent drugs, possibility of use, even in countries with very low economic resources. Many chemotherapy and repurposed drugs were tested with metronomics approaches for the treatment of mammary cancer, the most common malignancy in women worldwide, leading to high rates of mortality. The wide range of therapeutic models studied, paralleled the wide range of responses obtained, like tumor growth and metastasis inhibition, overall survival increase, lack of toxicity, better life quality, among others. The accomplishments reached, and the challenges faced by researchers, are discussed.

PMID: 32173366 [PubMed - as supplied by publisher]

Structure-activity relationship and cardiac safety of 2-aryl-2-(pyridin-2-yl)acetamides as a new class of broad-spectrum anticonvulsants derived from Disopyramide.

Bioorg Chem. 2020 Mar 05;98:103717

Authors: Dawidowski M, Król M, Szulczyk B, Chodkowski A, Podsadni P, Konopelski P, Ufnal M, Szuberski P, Wróbel MZ, Zhang Y, El Harchi A, Hancox JC, Jarkovska D, Mistrova E, Sviglerova J, Štengl M, Popowicz GM, Turło J

Abstract
A series of 2-aryl-2-(pyridin-2-yl)acetamides were synthesized and screened for their anticonvulsant activity in animal models of epilepsy. The compounds were broadly active in the 'classical' maximal electroshock seizure (MES) and subcutaneous Metrazol (scMET) tests as well as in the 6 Hz and kindling models of pharmacoresistant seizures. Furthermore, the compounds showed good therapeutic indices between anticonvulsant activity and motor impairment. Structure-activity relationship (SAR) trends clearly showed the highest activity resides in unsubstituted phenyl derivatives or compounds having ortho- and meta- substituents on the phenyl ring. The 2-aryl-2-(pyridin-2-yl)acetamides were derived by redesign of the cardiotoxic sodium channel blocker Disopyramide (DISO). Our results show that the compounds preserve the capability of the parent compound to inhibit voltage gated sodium currents in patch-clamp experiments; however, in contrast to DISO, a representative compound from the series 1 displays high levels of cardiac safety in a panel of in vitro and in vivo experiments.

PMID: 32171994 [PubMed - as supplied by publisher]

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