Online structure-based screening of purchasable approved drugs and natural compounds: retrospective examples of drug repositioning on cancer targets

Human primary immunodeficiency diseases (PIDs) are a large group of rare diseases and are characterized by a great genetic and phenotypic heterogeneity. A large subset of PIDs is genetically defined, which has a crucial impact for the understanding of the molecular basis of disease and the development of precision medicine. <p> Discovery and development of new therapies for rare diseases has long been de-privileged due to the length and cost of the processes involved. Interest has increased due to stimulatory regulatory and supportive reimbursement environments enabling viable business models. <p> Advancements in biomedical and computational sciences enable the development of rational, designed approaches for identification of novel indications of already approved drugs allowing faster delivery of new medicines. Drug repositioning is based either on clinical analogies of diseases or on understanding of the molecular mode of drug action and mechanisms of the disease. All of these are the basis for the development of precision medicine.

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Virtual screening of potential therapeutic inhibitors against spike, helicase and polymerase of SARS-CoV-2 (COVID-19)

Coronaviruses ◽

10.2174/2666796701999200826114306 ◽

2020 ◽

Vol 01 ◽

Author(s):

Ayesha Tazeen ◽

Farah Deeba ◽

Aftab Alam ◽

Rafat Ali ◽

Romana Ishrat ◽

...

Keyword(s):

Catalytic Domain ◽

Nigella Sativa ◽

Therapeutic Option ◽

Natural Compounds ◽

Host Cells ◽

Nucleoside Triphosphate ◽

Symptomatic Management ◽

Replication Inhibitor ◽

Inhibitory Potential ◽

Approved Drugs

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected around 13 million people and has caused more than 5.7 lakh deaths worldwide since December 2019. In the absence of FDA approved drug for its treatment, only symptomatic management is done. Methods: We attempted to uncover potential therapeutic targets of spike, helicase and RNA dependent RNA polymerase (RdRp) proteins of the SARS-CoV-2 employing computational approach. The PDB structure of spike and RdRp and predicted structure of helicase proteins were docked with 100 approved antiviral drugs, natural compounds and some other chemical compounds. Results: The anti-SARS ligands EK1 and CID_23631927, and NCGC00029283 are potential entry inhibitor as it showed affinity with immunogenic receptor binding domain (RBD) of spike protein. This RBD interacts with angiotensin converting enzyme (ACE2) receptor facilitating the entry of virion in the host cells. The FDA approved drugs including Nelfinavir, Saquinavir, Tipranavir, Setrobuvir, Indinavir and Atazanavir showed potential inhibitory activity against targeted domains and thus may act as entry or replication inhibitor or both. Furthermore, several anti-HCoV natural compounds including Amentoflavone, Rutin and Tannin are also potential entry and replication inhibitor as they showed affinity with RBD, Ploop containing nucleoside triphosphate hydrolase and catalytic domain of the respective protein. Dithymoquinone showed significant inhibitory potential against the fusion peptide of S2 domain. Importantly, Tannin, Dithymoquinone and Rutin can be extracted from Nigella sativa seeds and thus may prove to be one of the most potential anti-SARS-CoV-2 inhibitor. Conclusion: Several potential ligands were identified with already known anti-HCoVs activities. Furthermore, as our study showed that some of the ligands acted as both entry or replication inhibitor against SARS-CoV-2, it is envisaged that a combination of either inhibitors with a dual mode of action would prove to be a much desired therapeutic option against this viral infection.

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Targeting SARS-CoV-2 Polymerase with New Nucleoside Analogues

Molecules ◽

10.3390/molecules26113461 ◽

2021 ◽

Vol 26 (11) ◽

pp. 3461

Author(s):

Vasiliki Daikopoulou ◽

Panagiotis Apostolou ◽

Sofia Mourati ◽

Ioanna Vlachou ◽

Maria Gougousi ◽

...

Keyword(s):

Inhibitory Activity ◽

Drug Repositioning ◽

Nucleoside Analogues ◽

In Vitro Assays ◽

Rna Dependent Rna Polymerase ◽

Sugar Moiety ◽

The Family ◽

Approved Drugs ◽

Fda Approved Drugs

Despite the fact that COVID-19 vaccines are already available on the market, there have not been any effective FDA-approved drugs to treat this disease. There are several already known drugs that through drug repositioning have shown an inhibitory activity against SARS-CoV-2 RNA-dependent RNA polymerase. These drugs are included in the family of nucleoside analogues. In our efforts, we synthesized a group of new nucleoside analogues, which are modified at the sugar moiety that is replaced by a quinazoline entity. Different nucleobase derivatives are used in order to increase the inhibition. Five new nucleoside analogues were evaluated with in vitro assays for targeting polymerase of SARS-CoV-2.

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PS4DR: A multimodal workflow for identification and prioritization of drugs based on pathway signatures

10.21203/rs.2.12791/v4 ◽

2020 ◽

Author(s):

Mhammad Asif Emon ◽

Daniel Domingo-Fernández ◽

Charles Tapley Hoyt ◽

Martin Hofmann-Apitius

Keyword(s):

Gene Expression ◽

Genome Wide Association Study ◽

Drug Repositioning ◽

Heterogeneous Data ◽

New Drugs ◽

Gwas Data ◽

Drug Candidates ◽

Gene Expression Signatures ◽

Approved Drugs ◽

Fda Approved Drugs

Abstract Background: During the last decade, there has been a surge towards computational drug repositioning owing to constantly increasing -omics data in the biomedical research field. While numerous existing methods focus on the integration of heterogeneous data to propose candidate drugs, it is still challenging to substantiate their results with mechanistic insights of these candidate drugs. Therefore, there is a need for more innovative and efficient methods which can enable better integration of data and knowledge for drug repositioning. Results: Here, we present a customizable workflow ( PS4DR) which not only integrates high-throughput data such as genome-wide association study (GWAS) data and gene expression signatures from disease and drug perturbations but also takes pathway knowledge into consideration to predict drug candidates for repositioning. We have collected and integrated publicly available GWAS data and gene expression signatures for several diseases and hundreds of FDA-approved drugs or those under clinical trial in this study. Additionally, different pathway databases were used for mechanistic knowledge integration in the workflow. Using this systematic consolidation of data and knowledge, the workflow computes pathway signatures that assist in the prediction of new indications for approved and investigational drugs. Conclusion: We showcase PS4DR with applications demonstrating how this tool can be used for repositioning and identifying new drugs as well as proposing drugs that can simulate disease dysregulations. We were able to validate our workflow by demonstrating its capability to predict FDA-approved drugs for their known indications for several diseases. Further, PS4DR returned many potential drug candidates for repositioning that were backed up by epidemiological evidence extracted from scientific literature. Source code is freely available at https://github.com/ps4dr/ps4dr .

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Geldanamycin-Derived HSP90 Inhibitors Are Synthetic Lethal with NRF2

Molecular and Cellular Biology ◽

10.1128/mcb.00377-20 ◽

2020 ◽

Vol 40 (22) ◽

Cited By ~ 2

Author(s):

Liam Baird ◽

Takafumi Suzuki ◽

Yushi Takahashi ◽

Eiji Hishinuma ◽

Daisuke Saigusa ◽

...

Keyword(s):

Target Genes ◽

Synthetic Lethality ◽

Drug Repositioning ◽

Lethal Effect ◽

Cancer Therapies ◽

Dependent Manner ◽

Hsp90 Inhibitors ◽

Synthetic Lethal ◽

Activating Mutations ◽

Approved Drugs

ABSTRACT Activating mutations in KEAP1-NRF2 are frequently found in tumors of the lung, esophagus, and liver, where they are associated with aggressive growth, resistance to cancer therapies, and low overall survival. Despite the fact that NRF2 is a validated driver of tumorigenesis and chemotherapeutic resistance, there are currently no approved drugs which can inhibit its activity. Therefore, there is an urgent clinical need to identify NRF2-selective cancer therapies. To this end, we developed a novel synthetic lethal assay, based on fluorescently labeled isogenic wild-type and Keap1 knockout cell lines, in order to screen for compounds which selectively kill cells in an NRF2-dependent manner. Through this approach, we identified three compounds based on the geldanamycin scaffold which display synthetic lethality with NRF2. Mechanistically, we show that products of NRF2 target genes metabolize the quinone-containing geldanamycin compounds into more potent HSP90 inhibitors, which enhances their cytotoxicity while simultaneously restricting the synthetic lethal effect to cells with aberrant NRF2 activity. As all three of the geldanamycin-derived compounds have been used in clinical trials, they represent ideal candidates for drug repositioning to target the currently untreatable NRF2 activity in cancer.

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NEDD: a network embedding based method for predicting drug-disease associations

BMC Bioinformatics ◽

10.1186/s12859-020-03682-4 ◽

2020 ◽

Vol 21 (S13) ◽

Author(s):

Renyi Zhou ◽

Zhangli Lu ◽

Huimin Luo ◽

Ju Xiang ◽

Min Zeng ◽

...

Keyword(s):

Drug Repositioning ◽

Computational Method ◽

Heterogeneous Information ◽

Gold Standard Dataset ◽

Disease Similarity ◽

Disease Associations ◽

Meta Path ◽

Approved Drugs ◽

Low Dimensional ◽

Novel Associations

Abstract Background Drug discovery is known for the large amount of money and time it consumes and the high risk it takes. Drug repositioning has, therefore, become a popular approach to save time and cost by finding novel indications for approved drugs. In order to distinguish these novel indications accurately in a great many of latent associations between drugs and diseases, it is necessary to exploit abundant heterogeneous information about drugs and diseases. Results In this article, we propose a meta-path-based computational method called NEDD to predict novel associations between drugs and diseases using heterogeneous information. First, we construct a heterogeneous network as an undirected graph by integrating drug-drug similarity, disease-disease similarity, and known drug-disease associations. NEDD uses meta paths of different lengths to explicitly capture the indirect relationships, or high order proximity, within drugs and diseases, by which the low dimensional representation vectors of drugs and diseases are obtained. NEDD then uses a random forest classifier to predict novel associations between drugs and diseases. Conclusions The experiments on a gold standard dataset which contains 1933 validated drug–disease associations show that NEDD produces superior prediction results compared with the state-of-the-art approaches.

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Identification of anti-melanogenic natural compounds from Galega officinalis and further drug repositioning

Journal of Dermatological Science ◽

10.1016/j.jdermsci.2012.03.003 ◽

2012 ◽

Vol 67 (1) ◽

pp. 61-63 ◽

Cited By ~ 3

Author(s):

Ji-Seon Lee ◽

Won-Serk Kim ◽

Jin-Ju Kim ◽

Young-Won Chin ◽

Ho-Chang Jeong ◽

...

Keyword(s):

Drug Repositioning ◽

Natural Compounds ◽

Galega Officinalis

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RepCOOL: computational drug repositioning via integrating heterogeneous biological networks

Journal of Translational Medicine ◽

10.1186/s12967-020-02541-3 ◽

2020 ◽

Vol 18 (1) ◽

Author(s):

Ghazale Fahimian ◽

Javad Zahiri ◽

Seyed Shahriar Arab ◽

Reza H. Sajedi

Keyword(s):

Breast Cancer ◽

Biological Networks ◽

Drug Repositioning ◽

Machine Learning Algorithms ◽

New Drugs ◽

Stage Ii ◽

Cancer Stage ◽

New Drug ◽

Approved Drugs ◽

Fda Approved Drugs

Abstract Background It often takes more than 10 years and costs more than 1 billion dollars to develop a new drug for a particular disease and bring it to the market. Drug repositioning can significantly reduce costs and time in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed. This methodology has widely been used in order to address various medical challenges, including cancer treatment. The most common cancers are lung and breast cancers. Thus, suggesting FDA-approved drugs via drug repositioning for breast cancer would help us to circumvent the approval process and subsequently save money as well as time. Methods In this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease. Results The proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. The final drug repositioning model has been built based on a random forest classifier after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II. Conclusion Results show the potency of the proposed method in detecting true drug-disease relationships. RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab, and Tamoxifen.

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N-(1-Phenylethyl)aziridine-2-carboxylate esters in the synthesis of biologically relevant compounds

Beilstein Journal of Organic Chemistry ◽

10.3762/bjoc.15.168 ◽

2019 ◽

Vol 15 ◽

pp. 1722-1757

Author(s):

Iwona E Głowacka ◽

Aleksandra Trocha ◽

Andrzej E Wróblewski ◽

Dorota G Piotrowska

Keyword(s):

Amino Acids ◽

Natural Products ◽

Natural Compounds ◽

Review Article ◽

Chiral Auxiliary ◽

Biologically Relevant ◽

Alternative Approaches ◽

Approved Drugs ◽

Enantioselective Syntheses ◽

New Procedures

Since Garner’s aldehyde has several drawbacks, first of all is prone to racemization, alternative three-carbon chirons would be of great value in enantioselective syntheses of natural compounds and/or drugs. This review article summarizes applications of N-(1-phenylethyl)aziridine-2-carboxylates, -carbaldehydes and -methanols in syntheses of approved drugs and potential medications as well as of natural products mostly alkaloids but also sphingoids and ceramides and their 1- and 3-deoxy analogues and several hydroxy amino acids and their precursors. Designed strategies provided new procedures to several drugs and alternative approaches to natural products and proved efficiency of a 2-substituted N-(1-phenylethyl)aziridine framework as chiron bearing a chiral auxiliary.

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A Drug Repositioning Approach Reveals that Streptococcus mutans Is Susceptible to a Diverse Range of Established Antimicrobials and Nonantibiotics

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.01674-17 ◽

2017 ◽

Vol 62 (1) ◽

Cited By ~ 5

Author(s):

S. Saputo ◽

R. C. Faustoferri ◽

R. G. Quivey

Keyword(s):

Streptococcus Mutans ◽

Biofilm Formation ◽

Drug Repositioning ◽

Drug Screen ◽

Biofilm Inhibition ◽

Diverse Range ◽

Content Type ◽

Multispecies Biofilm ◽

Approved Drugs ◽

Fda Approved Drugs

ABSTRACT Streptococcus mutans is the primary causative agent of dental caries and contributes to the multispecies biofilm known as dental plaque. An adenylate kinase-based assay was optimized for S. mutans to detect cell lysis when exposed to the Selleck library (Selleck Chemical, Houston, TX) of 853 FDA-approved drugs in, to our knowledge, the first high-throughput drug screen in S. mutans. We found 126 drugs with activity against S. mutans planktonic cultures, and they were classified into six categories: antibacterials (61), antineoplastics (23), ion channel effectors (9), other antimicrobials (7), antifungals (6), and other (20). These drugs were also tested for activity against S. mutans biofilm cultures, and 24 compounds were found to inhibit biofilm formation, 6 killed preexisting biofilms, 84 exhibited biofilm inhibition and killing activity, and 12 had no activity against biofilms. The activities of 9 selected compounds that exhibited antimicrobial activity were further characterized for their activity against S. mutans planktonic and biofilm cultures. Together, our results suggest that S. mutans exhibits a susceptibility profile to a diverse array of established and novel antibacterials.

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