Perphenazine Attenuates the Pro-Inflammatory Responses in Mouse Models of Th2-Type Allergic Dermatitis.

Int J Mol Sci. 2020 May 03;21(9):

Authors: Heo MJ, Choi SY, Lee C, Choi YM, An IS, Bae S, An S, Jung JH

Abstract
Developing dermatitis therapeutics has been faced with challenges including adverse effects of topical steroid and high cost of new developing drugs. Here, we found the expression levels of dopamine receptor D2 is higher in skin biopsies of dermatitis patients and an oxazolone-induced animal model of dermatitis. We used perphenazine, an FDA-approved dopamine receptor antagonist to determine the therapeutic effect. Two different animal models including 12-o-tetradecanoylphorbol-13-acetate (TPA) and oxazolone (OXA)-induced dermatitis were employed. TPA and OXA-mediated ear swelling was attenuated by perphenazine. Moreover, perphenazine inhibited infiltrated mast cells into lesion area. We found levels of serum IgE, histamine and cytokines are decreased in mice cotreated with perphenazine and OXA compared to OXA-treated mice. Overall, this is a first study showing that the FDA-approved, anti-psychotic drug, perphenazine, alleviates animal models of dermatitis.

PMID: 32375285 [PubMed - in process]

6 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"drug repositioning" OR "drug repurposing"

These pubmed results were generated on 2020/05/07

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7 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"drug repositioning" OR "drug repurposing"

These pubmed results were generated on 2020/05/06

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Matrix Factorization-based Technique for Drug Repurposing Predictions.

IEEE J Biomed Health Inform. 2020 May 01;:

Authors: Ceddia G, Pinoli P, Ceri S, Masseroli M

Abstract
Classical drug design methodologies are hugely costly and time-consuming, with approximately 85% of the new proposed molecules failing in the first three phases of the FDA drug approval process. Thus, strategies to find alternative indications for already approved drugs that leverage computational methods are of crucial relevance. We previously demonstrated the efficacy of the Non-negative Matrix Tri-Factorization, a method that allows exploiting both data integration and machine learning, to infer novel indications for approved drugs. In this work, we present an innovative enhancement of the NMTF method that consists of a shortest-path evaluation of drug-protein pairs using the protein-to-protein interaction network. This approach allows inferring novel protein targets that were never considered as drug targets before, increasing the information fed to the NMTF method. Indeed, this novel advance enables the investigation of drug-centric predictions, simultaneously identifying therapeutic classes, protein targets and diseases associated with a particular drug. To test our methodology, we applied the NMTF and shortest-path enhancement methods to an outdated collection of data and compared the predictions against the most updated version, obtaining very good performance, with an Average Precision Score of 0.82. The data enhancement strategy allowed increasing the number of putative protein targets from 3,691 to 15,295, while the predictive performance of the method is slightly increased. Finally, we also validated our top-scored predictions according to the literature, finding relevant confirmation of predicted interactions between drugs and protein targets, as well as of predicted annotations between drugs and both therapeutic classes and diseases.

PMID: 32365039 [PubMed - as supplied by publisher]

Identification of novel antiplasmodial compound by hierarquical virtual screening and in vitro assays.

J Biomol Struct Dyn. 2020 May 04;:1-14

Authors: Costa Júnior DB, Araújo JSC, Oliveira LM, Neri FSM, Moreira POL, Taranto AG, Fonseca AL, Varotti FP, Leite FHA

Abstract
Malaria is an infectious disease caused by protozoa of the genus Plasmodium spp. with approximately 219 million cases in 2017. P. falciparum is main responsible for the most severe form of the disease, cerebral malaria. Despite of public health impacts, chemotherapy against malaria is still limited due to the emergence of drug resistance cases used in monotherapy and combination therapies. Thus, the development of new antimalarial drugs becomes emergency. One way of achieve this goal is to explore essential and/or unique therapeutic targets of the parasite, or at least sufficiently different to ensure selective inhibition. Enoil-ACP reductase (ENR) is a NADH-dependent enzyme responsible for the limiting step of the type II fatty acid biosynthetic pathway (FAS II). Thus, pharmacophore and docking based virtual screening were applied to prioritize molecules for in vitro assays against P. falciparum W2 strain. The application of successive filters at OOCC database (n = 618) resulted in the identification of one molecule (13) (EC50 = 0.098 ± 0.021 μM) with similar biological activity to artemether. The molecule 13 is a typical drug repurposing case due to previous other approved therapeutic uses on Chinese medicine as a non-specific cholinergic antagonist, thus it could be accelerated the drug development process. Additionally, molecular dynamics studies were used to confirm stability of the molecular interactions identified by molecular docking. Thus, representative structures of P. falciparum ENR can be used in a study to propose new derivatives for evaluation of biological activity in vitro and in vivo.

PMID: 32364060 [PubMed - as supplied by publisher]

Using Integrated Computational Approaches to Identify Safe and Rapid Treatment for SARS -CoV- 2.

J Biomol Struct Dyn. 2020 May 04;:1-11

Authors: Al-Khafaji K, Al-DuhaidahawiL D, Taskin Tok T

Abstract
SARS-CoV-2 is a new generation of coronavirus, which was firstly determined in Wuhan, China, in December 2019. So far, however, there no effective treatment has been found to stop this new generation of coronavirus but discovering of the crystal structure of SARS-CoV-2 main protease (SARS-CoV-2 M pro) may facilitate searching for new therapies for SARS-COV-2 . The aim was to assess the effectiveness of available FDA approved drugs which can construct a covalent bond with Cys145 inside binding site SARS-CoV-2 main protease by using covalent docking screening.
METHOD: We conducted the covdock module MMGBSA module in the Schrodinger suite 2020-1, to examine the covalent bonding utilizing. Besides, we submitted the top three drugs to molecular dynamics simulations via Gromacs 2018.1.
RESULTS: The covalent docking showed that saquinavir, ritonavir, remdesivir, delavirdine, cefuroxime axetil, oseltamivir and prevacid have the highest binding energies MMGBSA of -72.17, -72.02, -65.19, -57.65, -54.25, -51.8, -51.14 kCal/mol, respectively. 50 ns molecular dynamics simulation was conducted for saquinavir, ritonavir and remdesivir to evaluate the stability of these drugs inside the binding pocket of SARS-CoV-2 main protease.
CONCLUSION: The current study provides a powerful in silico results, means for rapid screening of drugs as anti-protease medications and recommend that the above-mentioned drugs can be used in the treatment of SARS-CoV-2 in combined or sole therapy.

PMID: 32364041 [PubMed - as supplied by publisher]

Structure-activity relationship analyses of fusidic acid derivatives highlight crucial role of the C-21 carboxylic acid moiety to its anti-mycobacterial activity.

Bioorg Med Chem. 2020 Apr 25;:115530

Authors: Singh K, Kaur G, Shanika PS, Dziwornu GA, Okombo J, Chibale K

Abstract
Fusidic acid (FA) is a potent congener of the fusidane triterpenoid class of antibiotics. Structure-activity relationship (SAR) studies suggest the chemical structure of FA is optimal for its antibacterial activity. SAR studies from our group within the context of a drug repositioning approach in tuberculosis (TB) suggest that, as with its antibacterial activity, the C-21 carboxylic acid group is indispensable for its anti-mycobacterial activity. Further studies have led to the identification of 16-deacetoxy-16β-ethoxyfusidic acid (58), an analog which exhibited comparable activity to FA with an in vitro MIC99 value of 0.8 µM. Preliminary SAR studies around the FA scaffold suggested that the hydrophobic side chain at C-20, like the C-11 OH group, was required for activity. The C-3 OH group, however, can be functionalized to obtain more potent compounds.

PMID: 32362386 [PubMed - as supplied by publisher]

Plausible mechanisms of Niclosamide as an antiviral agent against COVID-19.

Med Hypotheses. 2020 Apr 22;140:109765

Authors: Pindiprolu SKSS, Pindiprolu SH

Abstract
Corona virus disease 2019 (COVID-19) pandemic caused 18 440 deaths world wide as of 25 March 2020 and posing a serious threat to public health. There is a need, therefore, for effective therapeutic strategies to cure this disease. However, high attrition rates, substantial costs and slow pace are the major limitations of novel drug discovery. Drug repurposing, by employing 'old' drugs to treat 'new' diseases is an attractive approach in drug discovery. Niclosamide (NIC) is an approved anti-helminthic drug with diverse antiviral mechanisms. In this work we hypothesize, the potential antiviral mechanisms of NIC against COVID-19.

PMID: 32361588 [PubMed - as supplied by publisher]

Metformin in COVID-19: A Possible Role Beyond Diabetes.

Diabetes Res Clin Pract. 2020 Apr 29;:108183

Authors: Sharma S, Ray A, Sadasivam B

PMID: 32360697 [PubMed - as supplied by publisher]

Rational drug repurposing for cancer by inclusion of the unbiased molecular dynamics simulation in the structure-based virtual screening approach: challenges and breakthroughs.

Semin Cancer Biol. 2020 Apr 28;:

Authors: Sohraby F, Aryapour H

Abstract
Managing cancer is now one of the biggest concerns of health organizations. Many strategies have been developed in drug discovery pipelines to help rectify this problem and two of the best ones are drug repurposing and computational methods. The combination of these approaches can have immense impact on the course of drug discovery. In silico drug repurposing can significantly reduce the time, the cost and the effort of drug development. Computational methods such as structure-based drug design (SBDD) and virtual screening can predict the potentials of small molecule binders, such as drugs, for having favorable effect on a particular molecular target. However, the demand for accuracy and efficiency of SBDD requires more sophisticated and complicated approaches such as unbiased molecular dynamics (UMD) simulation that has been recently introduced. As a complementary strategy, the knowledge acquired from UMD simulations can increase the chance of finding the right candidates and the pipeline of its administration is introduced and discussed in this review. An elaboration of this pipeline is also made by detailing an example, the binding and unbinding pathways of dasatinib-c-Src kinase complex, which shows that how influential this method can be in rational drug repurposing in cancer treatment.

PMID: 32360530 [PubMed - as supplied by publisher]

Pharmacologic Therapy for COVID-19 Infection.

J Community Health. 2020 06;45(3):435-436

Authors: Nusbaum N

Abstract
The COVID-19 pandemic has focused attention on issues of epidemiology, public health, and vaccine design. I submit that attention to COVID-19 pharmacologic therapy needs similar emphasis, including identifying any existing medications that can be repurposed to treat COVID-19 patients.

PMID: 32314081 [PubMed - indexed for MEDLINE]

A SARS-CoV-2 protein interaction map reveals targets for drug repurposing.

Nature. 2020 Apr 30;:

Authors: Gordon DE, Jang GM, Bouhaddou M, Xu J, Obernier K, White KM, O'Meara MJ, Rezelj VV, Guo JZ, Swaney DL, Tummino TA, Huettenhain R, Kaake RM, Richards AL, Tutuncuoglu B, Foussard H, Batra J, Haas K, Modak M, Kim M, Haas P, Polacco BJ, Braberg H, Fabius JM, Eckhardt M, Soucheray M, Bennett MJ, Cakir M, McGregor MJ, Li Q, Meyer B, Roesch F, Vallet T, Mac Kain A, Miorin L, Moreno E, Naing ZZC, Zhou Y, Peng S, Shi Y, Zhang Z, Shen W, Kirby IT, Melnyk JE, Chorba JS, Lou K, Dai SA, Barrio-Hernandez I, Memon D, Hernandez-Armenta C, Lyu J, Mathy CJP, Perica T, Pilla KB, Ganesan SJ, Saltzberg DJ, Rakesh R, Liu X, Rosenthal SB, Calviello L, Venkataramanan S, Liboy-Lugo J, Lin Y, Huang XP, Liu Y, Wankowicz SA, Bohn M, Safari M, Ugur FS, Koh C, Savar NS, Tran QD, Shengjuler D, Fletcher SJ, O'Neal MC, Cai Y, Chang JCJ, Broadhurst DJ, Klippsten S, Sharp PP, Wenzell NA, Kuzuoglu D, Wang HY, Trenker R, Young JM, Cavero DA, Hiatt J, Roth TL, Rathore U, Subramanian A, Noack J, Hubert M, Stroud RM, Frankel AD, Rosenberg OS, Verba KA, Agard DA, Ott M, Emerman M, Jura N, von Zastrow M, Verdin E, Ashworth A, Schwartz O, d'Enfert C, Mukherjee S, Jacobson M, Malik HS, Fujimori DG, Ideker T, Craik CS, Floor SN, Fraser JS, Gross JD, Sali A, Roth BL, Ruggero D, Taunton J, Kortemme T, Beltrao P, Vignuzzi M, García-Sastre A, Shokat KM, Shoichet BK, Krogan NJ

Abstract
The novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 2.3 million people, killed over 160,000, and caused worldwide social and economic disruption1,2. There are currently no antiviral drugs with proven clinical efficacy, nor are there vaccines for its prevention, and these efforts are hampered by limited knowledge of the molecular details of SARS-CoV-2 infection. To address this, we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), identifying 332 high-confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (29 FDA-approved drugs, 12 drugs in clinical trials, and 28 preclinical compounds). Screening a subset of these in multiple viral assays identified two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the Sigma1 and Sigma2 receptors. Further studies of these host factor targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19.

PMID: 32353859 [PubMed - as supplied by publisher]

LINCS Dataset-Based Repositioning of Rosiglitazone as a Potential Anti-human Adenovirus Drug.

Antiviral Res. 2020 Apr 27;:104789

Authors: Wang X, He S, Zhou Z, Bo X, Qi D, Fu X, Wang Z, Yang J, Wang S

Abstract
Human adenoviruses (HAdVs) often cause mild respiratory infections. These infections, however, can potentially become fatal in immunosuppressive patients. Unfortunately, there has been no specific anti-HAdV drug approved for treatment of HAdV infections. In this study, a time-course transcriptome of HAdV-infected human lung epithelial cells (A549 cells) was performed and compared with perturbation datasets of 890 drug-treated A549 cells from the library of integrated network-based cellular signatures (LINCS) database to predict previously unknown therapeutic drug-HAdV relationships using a characteristic direction (CD) algorithm. We performed experiments to validate a prediction for the anti-diabetic drug rosiglitazone as a candidate drug for treatment of anti-HAdV both in vivo and in vitro. The Type I interferon (IFNs) signaling pathway was negatively regulated during the course of HAdV infection and rosiglitazone increased STAT1 phosphorylation for antiviral IFN response induction. Taken together, this study confirmed the prospect for re-exploitation of this FDA-approved drug as a potential therapeutic for HAdV infections.

PMID: 32353383 [PubMed - as supplied by publisher]

Prediction of drug adverse events using deep learning in pharmaceutical discovery.

Brief Bioinform. 2020 Apr 29;:

Authors: Lee CY, Chen YP

Abstract
Traditional machine learning methods used to detect the side effects of drugs pose significant challenges as feature engineering processes are labor-intensive, expert-dependent, time-consuming and cost-ineffective. Moreover, these methods only focus on detecting the association between drugs and their side effects or classifying drug-drug interaction. Motivated by technological advancements and the availability of big data, we provide a review on the detection and classification of side effects using deep learning approaches. It is shown that the effective integration of heterogeneous, multidimensional drug data sources, together with the innovative deployment of deep learning approaches, helps reduce or prevent the occurrence of adverse drug reactions (ADRs). Deep learning approaches can also be exploited to find replacements for drugs which have side effects or help to diversify the utilization of drugs through drug repurposing.

PMID: 32349125 [PubMed - as supplied by publisher]

Metformin ameliorates animal models of dermatitis.

Inflammopharmacology. 2020 Apr 28;:

Authors: Choi SY, Lee C, Heo MJ, Choi YM, An IS, Bae S, An S, Jung JH

Abstract
Metformin, a potent AMPK activator is the most commonly used drug for diabetes. According to recent reports, metformin lowers the risk of diabetic complications and inflammatory diseases. We found the expression levels of AMPK subunits including PRKAA1, PRKAA2, PRKAB1 and PRKAB2 are decreased in skin biopsies of dermatitis patients from multiple datasets. Interestingly, metformin treatment ameliorates dermatitis symptom in animal model of dermatitis using O-tetradecanoylphorbol-13-acetate (TPA). Especially, the levels of epidermis and dermis thickness were decreased by metformin. We found NFκB activity as well as of gene expression associated with collagen synthesis are attenuated by metformin treatment. These results suggest that metformin treatment alleviates animal model of dermatitis.

PMID: 32347398 [PubMed - as supplied by publisher]

COVID-19: an update on diagnostic and therapeutic approaches.

BMB Rep. 2020 Apr;53(4):191-205

Authors: Iyer M, Jayaramayya K, Subramaniam MD, Lee SB, Dayem AA, Cho SG, Vellingiri B

Abstract
The unexpected pandemic set off by the novel coronavirus 2019 (COVID-19) has caused severe panic among people worldwide. COVID-19 has created havoc, and scientists and physicians are urged to test the efficiency and safety of drugs used to treat this disease. In such a pandemic situation, various steps have been taken by the government to control and prevent the Severe Acute Respiratory Syndrome coronavirus 2 (SARSCoV- 2). This pandemic situation has forced scientists to rework strategies to combat infectious diseases through drugs, treatment, and control measures. COVID-19 treatment requires both limiting viral multiplication and neutralizing tissue damage induced by an inappropriate immune reaction. Currently, various diagnostic kits to test for COVID-19 are available, and repurposing therapeutics for COVID-19 has shown to be clinically effective. As the global demand for diagnostics and therapeutics continues to rise, it is essential to rapidly develop various algorithms to successfully identify and contain the virus. This review discusses the updates on specimens/samples, recent efficient diagnostics, and therapeutic approaches to control the disease and repurposed drugs mainly focusing on chloroquine/hydroxychloroquine and convalescent plasma (CP). More research is required for further understanding of the influence of diagnostics and therapeutic approaches to develop vaccines and drugs for COVID-19. [BMB Reports 2020; 53(4): 191-205].

PMID: 32336317 [PubMed - indexed for MEDLINE]

New Approaches for Cryptococcosis Treatment.

Microorganisms. 2020 Apr 23;8(4):

Authors: Spadari CC, Wirth F, Lopes LB, Ishida K

Abstract
Cryptococcosis is an important opportunistic infection and a leading cause of meningitis in patients with HIV infection. The antifungal pharmacological treatment is limited to amphotericin B, fluconazole and 5- flucytosine. In addition to the limited pharmacological options, the high toxicity, increased resistance rate and difficulty of the currently available antifungal molecules to cross the blood-brain barrier hamper the treatment. Thus, the search for new alternatives for the treatment of cryptococcal meningitis is extremely necessary. In this review, we describe the therapeutic strategies currently available, discuss new molecules with antifungal potential in different phases of clinical trials and in advanced pre-clinical phase, and examine drug nanocarriers to improve delivery to the central nervous system.

PMID: 32340403 [PubMed]

In vitro testing of combined hydroxychloroquine and azithromycin on SARS-CoV-2 shows synergistic effect.

Microb Pathog. 2020 Apr 25;:104228

Authors: Andreani J, Le Bideau M, Duflot I, Jardot P, Rolland C, Boxberger M, Wurtz N, Rolain JM, Colson P, La Scola B, Raoult D

Abstract
Human coronaviruses SARS-CoV-2 appeared at the end of 2019 and led to a pandemic with high morbidity and mortality. As there are currently no effective drugs targeting this virus, drug repurposing represents a short-term strategy to treat millions of infected patients at low costs. Hydroxychloroquine showed an antiviral effect in vitro. In vivo it showed efficacy, especially when combined with azithromycin in a preliminary clinical trial. Here we demonstrate that the combination of hydroxychloroquine and azithromycin has a synergistic effect in vitro on SARS-CoV-2 at concentrations compatible with that obtained in human lung.

PMID: 32344177 [PubMed - as supplied by publisher]

Link Prediction Only with Interaction Data and Its Application on Drug Repositioning.

IEEE Trans Nanobioscience. 2020 Apr 24;:

Authors: Liu J, Zuo Z, Wu G

Abstract
To assist drug development, many computational methods have been proposed to identify potential drug-disease treatment associations before wet experiments. Based on the assumption that similar drugs may treat similar diseases, most methods calculate the similarities of drugs and diseases by using various chemical or biological features. However, since these features may be unknown or hard to collect, such methods will not work in the face of incomplete data. Besides, due to the lack of validated negative samples in the drug-disease associations data, most methods have no choice but to simply select some unlabeled samples as negative ones, which may introduce noises and decrease the reliability of prediction. Herein, we propose a new method (TS-SVD) which only uses those known drug-protein, disease-protein and drug-disease interactions to predict the potential drug-disease associations. In a constructed drug-proteindisease heterogeneous network, assuming that drugs/diseases relating to some common proteins or diseases/drugs may be similar, we get the common neighbors count matrix of drugs/diseases, then convert it to a topological similarity matrix. After that, we get low dimensional embedding representations of drugdisease pairs by using topological features and singular value decomposition. Finally, a Random Forest classifier is trained to do the prediction. To train a more reasonable model, we select out some reliable negative samples based on the k-step neighbors relationships between drugs and diseases. Compared with some state-of-the-art methods, we use less information but achieve better or comparable performance. Meanwhile, our strategy for selecting reliable negative samples can improve the performances of these methods. Case studies have further shown the practicality of our method in discovering novel drug-disease associations.

PMID: 32340956 [PubMed - as supplied by publisher]

Some drugs for COVID-19.

Med Lett Drugs Ther. 2020 04 06;62(1595):49-50

Authors:

PMID: 32324177 [PubMed - indexed for MEDLINE]