scholarly journals Discovery and verification of the potential targets from bioactive molecules by network pharmacology-based target prediction combined with high-throughput metabolomics

RSC Advances ◽  
2017 ◽  
Vol 7 (81) ◽  
pp. 51069-51078 ◽  
Author(s):  
Aihua Zhang ◽  
Heng Fang ◽  
Yangyang Wang ◽  
Guangli Yan ◽  
Hui Sun ◽  
...  
Keyword(s):  
Natural Products ◽  
High Throughput ◽  
Target Prediction ◽  
Bioactive Molecules ◽  
Network Pharmacology ◽  
Drug Candidates ◽  
Plasma Metabolome

Natural products are an invaluable source for drug candidates. Currently, plasma metabolome has suggested that compounds present in herbs may exert bioactivity.

scholarly journals High-Throughput Identification of Promiscuous Inhibitors from Screening Libraries with the Use of a Thiol-Containing Fluorescent Probe

2013 ◽  
Vol 18 (6) ◽  
pp. 705-713 ◽  
Author(s):  
Megan M. McCallum ◽  
Premchendar Nandhikonda ◽  
Jonathan J. Temmer ◽  
Charles Eyermann ◽  
Anton Simeonov ◽  
...  
Keyword(s):  
Small Molecules ◽  
Fluorescent Probe ◽  
High Throughput ◽  
Bioactive Molecules ◽  
Drug Candidates ◽  
Thiol Reactivity ◽  
Pharmacologically Active ◽  
Nitric Oxide Releasing ◽  
Pharmacologically Active Compounds ◽  
High Throughput Assay

Testing small molecules for their ability to modify cysteine residues of proteins in the early stages of drug discovery is expected to accelerate our ability to develop more selective drugs with lesser side effects. In addition, this approach also enables the rapid evaluation of the mode of binding of new drug candidates with respect to thiol reactivity and metabolism by glutathione. Herein, we describe the development of a fluorescence-based high-throughput assay that allows the identification of thiol-reactive compounds. A thiol-containing fluorescent probe, MSTI, was synthesized and used to evaluate small molecules from the Library of Pharmacologically Active Compounds (LOPAC) collection of bioactive molecules. LOPAC compounds that are known to react with sulfur nucleophiles were identified with this assay, for example, irreversible protease inhibitors, nitric oxide–releasing compounds, and proton-pump inhibitors. The results confirm that both electrophilic and redox reactive compounds can be quickly identified in a high-throughput manner, enabling the assessment of screening libraries with respect to thiol-reactive compounds.

Antitubercular Marine Natural Products

2018 ◽  
Vol 25 (20) ◽  
pp. 2304-2328 ◽  
Author(s):  
Lishu Wang ◽  
Jungfeng Wang ◽  
Juan Liu ◽  
Yonghong Liu
Keyword(s):  
Natural Products ◽  
Marine Natural Products ◽  
Antitubercular Activity ◽  
Marine Resources ◽  
Drug Candidates ◽  
New Drug ◽  
Chemical Constitution

Due to the importance of nature as a source of new drug candidates, the purpose of this article is to emphasize the marine natural products, which exhibit antitubercular activity, published between January 2000 and May 2016, with 138 quotations to 250 compounds obtained from marine resources. These metabolites are organized by chemical constitution and named as simple alkyl lipids derivatives, aromatics derivatives, peptides, alkaloids, terpenoids, steroids, macrolides, and polycyclic polyketides.

Preliminary Study on the Mechanism of Carvacrol Regulating Hepatocellular Carcinoma Based on Network Pharmacology

2019 ◽  
Vol 16 (11) ◽  
pp. 1286-1295
Author(s):  
Sha Li ◽  
Haixia Zhao ◽  
Lidao Bao
Keyword(s):  
Hepatocellular Carcinoma ◽  
Western Blot ◽  
Target Prediction ◽  
Network Pharmacology ◽  
Literature Mining ◽  
Active Compounds ◽  
Prediction Server ◽  
Preliminary Study ◽  
Alignment Analysis ◽  
Endogenous Interactions

Objective: To predict and analyze the target of anti-Hepatocellular Carcinoma (HCC) in the active constituents of Safflower by using network pharmacology. Methods: The active compounds of safflower were collected by TCMSP, TCM-PTD database and literature mining methods. The targets of active compounds were predicted by Swiss Target Prediction server, and the target of anti-HCC drugs was collected by DisGeNET database. The target was subjected to an alignment analysis to screen out Carvacrol, a target of safflower against HCC. The mouse HCC model was established and treated with Carvacrol. The anti-HCC target DAPK1 and PPP2R2A were verified by Western blot and co-immunoprecipitation. Results: A total of 21 safflower active ingredients were predicted. Carvacrol was identified as a possible active ingredient according to the five principles of drug-like medicine. According to Carvacrol's possible targets and possible targets of HCC, three co-targets were identified, including cancer- related are DAPK1 and PPP2R2A. After 20 weeks of Carvacrol treated, Carvacrol group significantly increased on DAPK1 levels and decreased PPP2R2A levels in the model mice by Western blot. Immunoprecipitation confirmed the endogenous interaction between DAPK1 and PPP2R2A. Conclusion: Safflower can regulate the development of HCC through its active component Carvacrol, which can affect the expression of DAPK1 and PPP2R2A proteins, and the endogenous interactions of DAPK1 and PPP2R2A proteins.

scholarly journals Partitioning Pattern of Natural Products Based on Molecular Properties Descriptors Representing Drug-Likeness

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 546
Author(s):  
Miroslava Nedyalkova ◽  
Vasil Simeonov
Keyword(s):  
Natural Products ◽  
Drug Discovery ◽  
De Novo ◽  
Target Prediction ◽  
Natural Compounds ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Unique Source ◽  
The Times ◽  
Partitioning Pattern

A cheminformatics procedure for a partitioning model based on 135 natural compounds including Flavonoids, Saponins, Alkaloids, Terpenes and Triterpenes with drug-like features based on a descriptors pool was developed. The knowledge about the applicability of natural products as a unique source for the development of new candidates towards deadly infectious disease is a contemporary challenge for drug discovery. We propose a partitioning scheme for unveiling drug-likeness candidates with properties that are important for a prompt and efficient drug discovery process. In the present study, the vantage point is about the matching of descriptors to build the partitioning model applied to natural compounds with diversity in structures and complexity of action towards the severe diseases, as the actual SARS-CoV-2 virus. In the times of the de novo design techniques, such tools based on a chemometric and symmetrical effect by the implied descriptors represent another noticeable sign for the power and level of the descriptors applicability in drug discovery in establishing activity and target prediction pipeline for unknown drugs properties.

scholarly journals Modern Synthetic Methods for the Stereoselective Construction of 1,3-Dienes

Molecules ◽  
2021 ◽  
Vol 26 (2) ◽  
pp. 249
Author(s):  
Raquel G. Soengas ◽  
Humberto Rodríguez-Solla
Keyword(s):  
Organic Chemistry ◽  
Natural Products ◽  
Functional Group ◽  
Biological Activities ◽  
Synthetic Methods ◽  
Bond Forming ◽  
Drug Candidates ◽  
The Past ◽  
Wide Range

The 1,3-butadiene motif is widely found in many natural products and drug candidates with relevant biological activities. Moreover, dienes are important targets for synthetic chemists, due to their ability to give access to a wide range of functional group transformations, including a broad range of C-C bond-forming processes. Therefore, the stereoselective preparation of dienes have attracted much attention over the past decades, and the search for new synthetic protocols continues unabated. The aim of this review is to give an overview of the diverse methodologies that have emerged in the last decade, with a focus on the synthetic processes that meet the requirements of efficiency and sustainability of modern organic chemistry.

scholarly journals Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Anna C. Aschenbrenner ◽  
◽  
Maria Mouktaroudi ◽  
Benjamin Krämer ◽  
Marie Oestreich ◽  
...  
Keyword(s):  
Immune System ◽  
Disease Severity ◽  
Whole Blood ◽  
Viral Infections ◽  
Inflammatory Diseases ◽  
Target Prediction ◽  
Data Driven ◽  
Host Responses ◽  
Drug Candidates ◽  
Drug Target Prediction

Abstract Background The SARS-CoV-2 pandemic is currently leading to increasing numbers of COVID-19 patients all over the world. Clinical presentations range from asymptomatic, mild respiratory tract infection, to severe cases with acute respiratory distress syndrome, respiratory failure, and death. Reports on a dysregulated immune system in the severe cases call for a better characterization and understanding of the changes in the immune system. Methods In order to dissect COVID-19-driven immune host responses, we performed RNA-seq of whole blood cell transcriptomes and granulocyte preparations from mild and severe COVID-19 patients and analyzed the data using a combination of conventional and data-driven co-expression analysis. Additionally, publicly available data was used to show the distinction from COVID-19 to other diseases. Reverse drug target prediction was used to identify known or novel drug candidates based on finding from data-driven findings. Results Here, we profiled whole blood transcriptomes of 39 COVID-19 patients and 10 control donors enabling a data-driven stratification based on molecular phenotype. Neutrophil activation-associated signatures were prominently enriched in severe patient groups, which was corroborated in whole blood transcriptomes from an independent second cohort of 30 as well as in granulocyte samples from a third cohort of 16 COVID-19 patients (44 samples). Comparison of COVID-19 blood transcriptomes with those of a collection of over 3100 samples derived from 12 different viral infections, inflammatory diseases, and independent control samples revealed highly specific transcriptome signatures for COVID-19. Further, stratified transcriptomes predicted patient subgroup-specific drug candidates targeting the dysregulated systemic immune response of the host. Conclusions Our study provides novel insights in the distinct molecular subgroups or phenotypes that are not simply explained by clinical parameters. We show that whole blood transcriptomes are extremely informative for COVID-19 since they capture granulocytes which are major drivers of disease severity.

scholarly journals Mechanism of Compound Houttuynia Mixture as an Anti-COVID-19 Drug Based on Network Pharmacology and Molecular Docking

2021 ◽  
Vol 16 (5) ◽  
pp. 1934578X2110167
Author(s):  
Xing-Pan Wu ◽  
Tian-Shun Wang ◽  
Zi-Xin Yuan ◽  
Yan-Fang Yang ◽  
He-Zhen Wu
Keyword(s):  
Molecular Docking ◽  
Target Prediction ◽  
Interaction Network ◽  
Scientific Basis ◽  
Network Pharmacology ◽  
Chemical Components ◽  
Active Components ◽  
Discovery Studio ◽  
The Core ◽  
Pathway Annotation

Objective To explore the anti-COVID-19 active components and mechanism of Compound Houttuynia mixture by using network pharmacology and molecular docking. Methods First, the main chemical components of Compound Houttuynia mixture were obtained by using the TCMSP database and referring to relevant chemical composition literature. The components were screened for OB ≥30% and DL ≥0.18 as the threshold values. Then Swiss Target Prediction database was used to predict the target of the active components and map the targets of COVID-19 obtained through GeneCards database to obtain the gene pool of the potential target of COVID-19 resistance of the active components of Compound Houttuynia mixture. Next, DAVID database was used for GO enrichment and KEGG pathway annotation of targets function. Cytoscape 3.8.0 software was used to construct a “components-targets-pathways” network. Then String database was used to construct a “protein-protein interaction” network. Finally, the core targets, SARS-COV-2 3 Cl, ACE2 and the core active components of Compound Houttuyna Mixture were imported into the Discovery Studio 2016 Client database for molecular docking verification. Results Eighty-two active compounds, including Xylostosidine, Arctiin, ZINC12153652 and ZINC338038, were screened from Compound Houttuyniae mixture. The key targets involved 128 targets, including MAPK1, MAPK3, MAPK8, MAPK14, TP53, TNF, and IL6. The HIF-1 signaling, VEGF signaling, TNF signaling and another 127 signaling pathways associated with COVID-19 were affected ( P < 0.05). From the results of molecular docking, the binding ability between the selected active components and the core targets was strong. Conclusion Through the combination of network pharmacology and molecular docking technology, this study revealed that the therapeutic effect of Compound Houttuynia mixture on COVID-19 was realized through multiple components, multiple targets and multiple pathways, which provided a certain scientific basis of the clinical application of Compound Houttuynia mixture.

scholarly journals High-throughput behavioral screen in C. elegans reveals Parkinson’s disease drug candidates

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Salman Sohrabi ◽  
Danielle E. Mor ◽  
Rachel Kaletsky ◽  
William Keyes ◽  
Coleen T. Murphy
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
High Throughput ◽  
Potential Candidate ◽  
Automated Scoring ◽  
C Elegans ◽  
Drug Candidates ◽  
Branched Chain ◽  
Approved Drugs

AbstractWe recently linked branched-chain amino acid transferase 1 (BCAT1) dysfunction with the movement disorder Parkinson’s disease (PD), and found that RNAi-mediated knockdown of neuronal bcat-1 in C. elegans causes abnormal spasm-like ‘curling’ behavior with age. Here we report the development of a machine learning-based workflow and its application to the discovery of potentially new therapeutics for PD. In addition to simplifying quantification and maintaining a low data overhead, our simple segment-train-quantify platform enables fully automated scoring of image stills upon training of a convolutional neural network. We have trained a highly reliable neural network for the detection and classification of worm postures in order to carry out high-throughput curling analysis without the need for user intervention or post-inspection. In a proof-of-concept screen of 50 FDA-approved drugs, enasidenib, ethosuximide, metformin, and nitisinone were identified as candidates for potential late-in-life intervention in PD. These findings point to the utility of our high-throughput platform for automated scoring of worm postures and in particular, the discovery of potential candidate treatments for PD.

scholarly journals Pyrrolidine in Drug Discovery: A Versatile Scaffold for Novel Biologically Active Compounds

2021 ◽  
Vol 379 (5) ◽  
Author(s):  
Giovanna Li Petri ◽  
Maria Valeria Raimondi ◽  
Virginia Spanò ◽  
Ralph Holl ◽  
Paola Barraja ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Binding Mode ◽  
Biologically Active ◽  
Bioactive Molecules ◽  
Biological Profile ◽  
Pyrrolidine Ring ◽  
Reaction Conditions ◽  
Drug Candidates ◽  
Steric Factors ◽  
Proline Derivatives

AbstractThe five-membered pyrrolidine ring is one of the nitrogen heterocycles used widely by medicinal chemists to obtain compounds for the treatment of human diseases. The great interest in this saturated scaffold is enhanced by (1) the possibility to efficiently explore the pharmacophore space due to sp3-hybridization, (2) the contribution to the stereochemistry of the molecule, (3) and the increased three-dimensional (3D) coverage due to the non-planarity of the ring—a phenomenon called “pseudorotation”. In this review, we report bioactive molecules with target selectivity characterized by the pyrrolidine ring and its derivatives, including pyrrolizines, pyrrolidine-2-one, pyrrolidine-2,5-diones and prolinol described in the literature from 2015 to date. After a comparison of the physicochemical parameters of pyrrolidine with the parent aromatic pyrrole and cyclopentane, we investigate the influence of steric factors on biological activity, also describing the structure–activity relationship (SAR) of the studied compounds. To aid the reader’s approach to reading the manuscript, we have planned the review on the basis of the synthetic strategies used: (1) ring construction from different cyclic or acyclic precursors, reporting the synthesis and the reaction conditions, or (2) functionalization of preformed pyrrolidine rings, e.g., proline derivatives. Since one of the most significant features of the pyrrolidine ring is the stereogenicity of carbons, we highlight how the different stereoisomers and the spatial orientation of substituents can lead to a different biological profile of drug candidates, due to the different binding mode to enantioselective proteins. We believe that this work can guide medicinal chemists to the best approach in the design of new pyrrolidine compounds with different biological profiles.

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