What Makes Species Productive of Anti-Cancer Drugs? Clues from Drugs’ Species Origin, Druglikeness, Target and Pathway

2019 ◽  
Vol 19 (2) ◽  
pp. 194-203 ◽  
Author(s):  
Xiaofeng Li ◽  
Xiaoxu Li ◽  
Yinghong Li ◽  
Chunyan Yu ◽  
Weiwei Xue ◽  
...  

Background:Despite the substantial contribution of natural products to the FDA drug approval list, the discovery of anti-cancer drugs from the huge amount of species on the planet remains looking for a needle in a haystack. Objective: Drug-productive clusters in the phylogenetic tree are thus proposed to narrow the searching scope by focusing on much smaller amount of species within each cluster, which enable prioritized and rational bioprospecting for novel drug-like scaffolds. However, the way anti-cancer nature-derived drugs distribute in phylogenetic tree has not been reported, and it is oversimplified to just focus anti-cancer drug discovery on the drug-productive clusters, since the number of species in each cluster remains too large to be managed.Objective:Drug-productive clusters in the phylogenetic tree are thus proposed to narrow the searching scope by focusing on much smaller amount of species within each cluster, which enable prioritized and rational bioprospecting for novel drug-like scaffolds. However, the way anti-cancer nature-derived drugs distribute in phylogenetic tree has not been reported, and it is oversimplified to just focus anti-cancer drug discovery on the drug-productive clusters, since the number of species in each cluster remains too large to be managed.Methods:In this study, 260 anti-cancer drugs approved in the past 70 years were comprehensively analyzed by hierarchical clustering of phylogenetic distribution.Results:207 out of these 260 drugs were derived from or inspired by the natural products isolated from 58 species. Phylogenetic distribution of those drugs further revealed that nature-derived anti-cancer drugs originated mostly from drug-productive families that tend to be clustered rather than scattered on the phylogenetic tree. Moreover, based on their productivity, drug-producing species were categorized into productive (CPS), newly emerging (CNS) and lessproductive (CLS). Statistical significances in druglikeness between drugs from CPS and CLS were observed, and drugs from CNS were found to share similar drug-like properties to those from CPS.Conclusion:This finding indicated a great raise in drug approval standard, which suggested us to focus bioprospecting on the species yielding multiple drugs and keeping productive for long period of time.

2019 ◽  
Vol 21 (1) ◽  
pp. 3-17 ◽  
Author(s):  
Kening Li ◽  
Yuxin Du ◽  
Lu Li ◽  
Dong-Qing Wei

Drug discovery is important in cancer therapy and precision medicines. Traditional approaches of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening, but these methods are usually expensive and laborious. In the last decade, omics data explosion provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of drug discovery. High-throughput transcriptome data were widely used in biomarkers’ identification and drug prediction by integrating with drug-response data. Moreover, biological network theory and methodology were also successfully applied to the anti-cancer drug discovery, such as studies based on protein-protein interaction network, drug-target network and disease-gene network. In this review, we summarized and discussed the bioinformatics approaches for predicting anti-cancer drugs and drug combinations based on the multi-omic data, including transcriptomics, toxicogenomics, functional genomics and biological network. We believe that the general overview of available databases and current computational methods will be helpful for the development of novel cancer therapy strategies.


2020 ◽  
Vol 17 (9) ◽  
pp. 1102-1116
Author(s):  
Sudip Kumar Mandal ◽  
Utsab Debnath ◽  
Amresh Kumar ◽  
Sabu Thomas ◽  
Subhash Chandra Mandal ◽  
...  

Background and Introduction: Sesquiterpene lactones are a class of secondary metabolite that contains sesquiterpenoids and lactone ring as pharmacophore moiety. A large group of bioactive secondary metabolites such as phytopharmaceuticals belong to this category. From the Asteraceae family-based medicinal plants, more than 5,000 sesquiterpene lactones have been reported so far. Sesquiterpene lactone-based pharmacophore moieties hold promise for broad-spectrum biological activities against cancer, inflammation, parasitic, bacterial, fungal, viral infection and other functional disorders. Moreover, these moiety based phytocompounds have been highlighted with a new dimension in the natural drug discovery program worldwide after the 2015 Medicine Nobel Prize achieved by the Artemisinin researchers. Objective: These bitter substances often contain an α, β-unsaturated-γ-lactone as a major structural backbone, which in recent studies has been explored to be associated with anti-tumor, cytotoxic, and anti-inflammatory action. Recently, the use of sesquiterpene lactones as phytomedicine has been increased. This study will review the prospect of sesquiterpene lactones against inflammation and cancer. Methods: Hence, we emphasized on the different features of this moiety by incorporating its structural diversity on biological activities to explore structure-activity relationships (SAR) against inflammation and cancer. Results: How the dual mode of action such as anti-inflammatory and anti-cancer has been exhibitedby these phytopharmaceuticals will be forecasted in this study. Furthermore, the correlation of anti-inflammatory and anti-cancer activity executed by the sesquiterpene lactones for fruitful phytotherapy will also be revealed in the present review in the milieu of pharmacophore activity relation and pharmacodynamics study as well. Conclusion: So, these metabolites are paramount in phytopharmacological aspects. The present discussion on the future prospect of this moiety based on the reported literature could be a guide for anti-inflammatory and anti-cancer drug discovery programs for the upcoming researchers.


2016 ◽  
Vol 16 (10) ◽  
pp. 1339-1352 ◽  
Author(s):  
Alessandra C. Pinheiro ◽  
Thais C. Mendonça Nogueira ◽  
Marcus V.N. de Souza

2018 ◽  
Vol 11 (1) ◽  
pp. 015004 ◽  
Author(s):  
Wenfang Li ◽  
Xueyan Hu ◽  
Shuaitao Yang ◽  
Shuping Wang ◽  
Chenghong Zhang ◽  
...  

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