Salinomycin, a Candidate Drug for the Elimination of

AbstractThe COVID-19 pandemic caused by the novel SARS-CoV-2 is more contagious than other coronaviruses and has higher rates of mortality than influenza. Identification of effective therapeutics is a crucial tool to treat those infected with SARS-CoV-2 and limit the spread of this novel disease globally. We deployed a bioinformatics workflow to identify candidate drugs for the treatment of COVID-19. Using an “omics” repository, the Library of Integrated Network-Based Cellular Signatures (LINCS), we simultaneously probed transcriptomic signatures of putative COVID-19 drugs and publicly available SARS-CoV-2 infected cell lines to identify novel therapeutics. We identified a shortlist of 20 candidate drugs: 8 are already under trial for the treatment of COVID-19, the remaining 12 have antiviral properties and 6 have antiviral efficacy against coronaviruses specifically, in vitro. All candidate drugs are either FDA approved or are under investigation. Our candidate drug findings are discordant with (i.e., reverse) SARS-CoV-2 transcriptome signatures generated in vitro, and a subset are also identified in transcriptome signatures generated from COVID-19 patient samples, like the MEK inhibitor selumetinib. Overall, our findings provide additional support for drugs that are already being explored as therapeutic agents for the treatment of COVID-19 and identify promising novel targets that are worthy of further investigation.

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A Multi-Pronged Computational Pipeline for Prioritizing Drug Target Strategies for Latent Tuberculosis

Frontiers in Chemistry ◽

10.3389/fchem.2020.593497 ◽

2020 ◽

Vol 8 ◽

Author(s):

Ushashi Banerjee ◽

Santhosh Sankar ◽

Amit Singh ◽

Nagasuma Chandra

Keyword(s):

Drug Targets ◽

Metabolic Flux ◽

Latent Tuberculosis ◽

Interaction Network ◽

New Drugs ◽

Prolonged Treatment ◽

Flux Analysis ◽

Protein Protein Interaction ◽

Candidate Drug ◽

Global Population

Tuberculosis is one of the deadliest infectious diseases worldwide and the prevalence of latent tuberculosis acts as a huge roadblock in the global effort to eradicate tuberculosis. Most of the currently available anti-tubercular drugs act against the actively replicating form of Mycobacterium tuberculosis (Mtb), and are not effective against the non-replicating dormant form present in latent tuberculosis. With about 30% of the global population harboring latent tuberculosis and the requirement for prolonged treatment duration with the available drugs in such cases, the rate of adherence and successful completion of therapy is low. This necessitates the discovery of new drugs effective against latent tuberculosis. In this work, we have employed a combination of bioinformatics and chemoinformatics approaches to identify potential targets and lead candidates against latent tuberculosis. Our pipeline adopts transcriptome-integrated metabolic flux analysis combined with an analysis of a transcriptome-integrated protein-protein interaction network to identify perturbations in dormant Mtb which leads to a shortlist of 6 potential drug targets. We perform a further selection of the candidate targets and identify potential leads for 3 targets using a range of bioinformatics methods including structural modeling, binding site association and ligand fingerprint similarities. Put together, we identify potential new strategies for targeting latent tuberculosis, new candidate drug targets as well as important lead clues for drug design.

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Fucoxanthin and Colorectal Cancer Prevention

Cancers ◽

10.3390/cancers13102379 ◽

2021 ◽

Vol 13 (10) ◽

pp. 2379

Author(s):

Masaru Terasaki ◽

Atsuhito Kubota ◽

Hiroyuki Kojima ◽

Hayato Maeda ◽

Kazuo Miyashita ◽

...

Keyword(s):

Colorectal Cancer ◽

Brown Algae ◽

Lifestyle Modification ◽

Signal Pathways ◽

Potential Candidate ◽

Anticancer Effects ◽

Cancer Models ◽

Candidate Drug ◽

Colorectal Cancer Prevention ◽

Dietary Carotenoid

Colorectal cancer (CRC), which ranks among the top 10 most prevalent cancers, can obtain a good outcome with appropriate surgery and/or chemotherapy. However, the global numbers of both new cancer cases and death from CRC are expected to increase up to 2030. Diet-induced lifestyle modification is suggested to be effective in reducing the risk of human CRC; therefore, interventional studies using diets or diet-derived compounds have been conducted to explore the prevention of CRC. Fucoxanthin (Fx), a dietary carotenoid, is predominantly contained in edible brown algae, such as Undaria pinnatifida (wakame) and Himanthalia elongata (Sea spaghetti), which are consumed particularly frequently in Asian countries but also in some Western countries. Fx is responsible for a majority of the anticancer effects exerted by the lipophilic bioactive compounds in those algae. Interventional human trials have shown that Fx and brown algae mitigate certain risk factors for CRC; however, the direct mechanisms underlying the anti-CRC properties of Fx remain elusive. Fx and its deacetylated type “fucoxanthinol” (FxOH) have been reported to exert potential anticancer effects in preclinical cancer models through the suppression of many cancer-related signal pathways and the tumor microenvironment or alteration of the gut microbiota. We herein review the most recent studies on Fx as a potential candidate drug for CRC prevention.

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Ulinastatin is a novel candidate drug for sepsis and secondary acute lung injury, evidence from an optimized CLP rat model

International Immunopharmacology ◽

10.1016/j.intimp.2013.09.004 ◽

2013 ◽

Vol 17 (3) ◽

pp. 799-807 ◽

Cited By ~ 25

Author(s):

Ning Wang ◽

Xin Liu ◽

Xinchuan Zheng ◽

Hongwei Cao ◽

Guo Wei ◽

...

Keyword(s):

Acute Lung Injury ◽

Lung Injury ◽

Rat Model ◽

Candidate Drug

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Chlorpromazine, a candidate drug for photopheresis

Journal of Photochemistry and Photobiology B Biology ◽

10.1016/1011-1344(95)07278-0 ◽

1996 ◽

Vol 34 (2-3) ◽

pp. 217-224 ◽

Cited By ~ 11

Author(s):

H.Peter van Iperen ◽

Gerard M.J.Beijersbergen van Henegouwen

Keyword(s):

Candidate Drug

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Experimental Evidence for Therapeutic Potentials of Propolis

Nutrients ◽

10.3390/nu13082528 ◽

2021 ◽

Vol 13 (8) ◽

pp. 2528

Author(s):

Priyanshu Bhargava ◽

Debajit Mahanta ◽

Ashish Kaul ◽

Yoshiyuki Ishida ◽

Keiji Terao ◽

...

Keyword(s):

New Zealand ◽

Experimental Evidence ◽

Antioxidant Properties ◽

Geographic Location ◽

Caffeic Acid Phenethyl Ester ◽

Candidate Drug ◽

Brazilian Propolis ◽

Brazilian Green Propolis

Propolis is produced by honeybees from materials collected from plants they visit. It is a resinous material having mixtures of wax and bee enzymes. Propolis is also known as bee glue and used by bees as a building material in their hives, for blocking holes and cracks, repairing the combs and strengthening their thin borders. It has been extensively used since ancient times for different purposes in traditional human healthcare practices. The quality and composition of propolis depend on its geographic location, climatic zone and local flora. The New Zealand and Brazilian green propolis are the two main kinds that have been extensively studied in recent years. Their bioactive components have been found to possess a variety of therapeutic potentials. It was found that Brazilian green propolis improves the cognitive functions of mild cognitive impairments in patients living at high altitude and protects them from neurodegenerative damage through its antioxidant properties. It possesses artepillin C (ARC) as the key component, also known to possess anticancer potential. The New Zealand propolis contains caffeic acid phenethyl ester (CAPE) as the main bioactive with multiple therapeutic potentials. Our lab performed in vitro and in vivo assays on the extracts prepared from New Zealand and Brazilian propolis and their active ingredients. We provided experimental evidence that these extracts possess anticancer, antistress and hypoxia-modulating activities. Furthermore, their conjugation with γCD proved to be more effective. In the present review, we portray the experimental evidence showing that propolis has the potential to be a candidate drug for different ailments and improve the quality of life.

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TAIJI: approaching experimental replicates-level accuracy for drug synergy prediction

Bioinformatics ◽

10.1093/bioinformatics/bty955 ◽

2018 ◽

Vol 35 (13) ◽

pp. 2338-2339 ◽

Cited By ~ 5

Author(s):

Hongyang Li ◽

Shuai Hu ◽

Nouri Neamati ◽

Yuanfang Guan

Keyword(s):

Drug Combination ◽

High Performance ◽

Synergistic Effects ◽

Field Performance ◽

Supplementary Information ◽

Drug Synergism ◽

High Throughput Drug Screening ◽

Current State ◽

Candidate Drug ◽

High Prediction

Abstract Motivation Combination therapy is widely used in cancer treatment to overcome drug resistance. High-throughput drug screening is the standard approach to study the drug combination effects, yet it becomes impractical when the number of drugs under consideration is large. Therefore, accurate and fast computational tools for predicting drug synergistic effects are needed to guide experimental design for developing candidate drug pairs. Results Here, we present TAIJI, a high-performance software for fast and accurate prediction of drug synergism. It is based on the winning algorithm in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge, which is a unique platform to unbiasedly evaluate the performance of current state-of-the-art methods, and includes 160 team-based submission methods. When tested across a broad spectrum of 85 different cancer cell lines and 1089 drug combinations, TAIJI achieved a high prediction correlation (0.53), approaching the accuracy level of experimental replicates (0.56). The runtime is at the scale of minutes to achieve this state-of-the-field performance. Availability and implementation TAIJI is freely available on GitHub (https://github.com/GuanLab/TAIJI). It is functional with built-in Perl and Python. Supplementary information Supplementary data are available at Bioinformatics online.

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