Bioinformatics Approaches for Anti-cancer Drug Discovery

2019 ◽  
Vol 21 (1) ◽  
pp. 3-17 ◽  
Author(s):  
Kening Li ◽  
Yuxin Du ◽  
Lu Li ◽  
Dong-Qing Wei
Keyword(s):  
Drug Discovery ◽  
Cancer Therapy ◽  
Biological Network ◽  
Computational Prediction ◽  
Cancer Drug ◽  
Cancer Drugs ◽  
Cancer Drug Discovery ◽  
Anti Cancer

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.

scholarly journals Frontiers in Anti-cancer Drug Discovery: Challenges and Perspectives of Metformin as Anti-angiogenic Add-on Therapy in Glioblastoma

2021 ◽  
Author(s):  
Laura Guarnaccia ◽  
Stefania Elena Navone ◽  
Matteo Maria Masseroli ◽  
Melissa Balsamo ◽  
Manuela Caroli ◽  
...  
Keyword(s):  
Target Therapy ◽  
Cancer Drug ◽  
Cancer Drug Discovery ◽  
Average Survival ◽  
Anti Cancer ◽  
Tumor Neovascularization ◽  
Novel Therapeutic Strategies

Glioblastoma (GBM) is the most common primitive tumor in adult central nervous system (CNS), classified as grade IV according to WHO 2016 classification. GBM shows a poor prognosis with an average survival of approximately 15 months, representing an extreme therapeutic challenge. One of its distinctive and aggressive features is aberrant angiogenesis, which drives tumor neovascularization, representing a promising candidate for molecular target therapy. Although several pre-clinical studies and clinical trials have shown promising results, anti-angiogenic drugs have not led to a significant improvement in overall survival (OS), suggesting the necessity of identifying novel therapeutic strategies. Metformin, an anti-hyperglycemic drug of the Biguanides family, used as first line treatment in Type 2 Diabetes Mellitus (T2DM), demonstrated in vitro and in vivo antitumoral efficacy in many different tumors, including GBM. From this evidence, a process of repurposing of the drug has begun, leading to the demonstration of the inhibition of various oncopromoter mechanisms and, consequently, to the identification of the molecular pathways involved. Here, we review and discuss the potential metformin’s antitumoral effects on GBM, inspecting if it could properly act as an anti-angiogenic compound to be considered as a safely add-on therapy in the treatment and management of GBM patients.

scholarly journals Coherent Raman Scattering Microscopy in Oncology Pharmacokinetic Research

2021 ◽  
Vol 12 ◽  
Author(s):  
Junjie Zeng ◽  
Wenying Zhao ◽  
Shuhua Yue
Keyword(s):  
Raman Scattering ◽  
High Speed ◽  
Cancer Drug ◽  
Cancer Drugs ◽  
High Speed Imaging ◽  
Coherent Raman Scattering ◽  
Anti Cancer ◽  
Coherent Raman

The high attrition rates of anti-cancer drugs during clinical development remains a bottleneck problem in pharmaceutical industry. This is partially due to the lack of quantitative, selective, and rapid readouts of anti-cancer drug activity in situ with high resolution. Although fluorescence microscopy has been commonly used in oncology pharmacological research, fluorescent labels are often too large in size for small drug molecules, and thus may disturb the function or metabolism of these molecules. Such challenge can be overcome by coherent Raman scattering microscopy, which is capable of chemically selective, highly sensitive, high spatial resolution, and high-speed imaging, without the need of any labeling. Coherent Raman scattering microscopy has tremendously improved the understanding of pharmaceutical materials in the solid state, pharmacokinetics of anti-cancer drugs and nanocarriers in vitro and in vivo. This review focuses on the latest applications of coherent Raman scattering microscopy as a new emerging platform to facilitate oncology pharmacokinetic research.

scholarly journals New Anti-Cancer Strategy to Suppress Colorectal Cancer Growth Through Inhibition of ATG4B and Lysosome Function

Cancers ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1523 ◽  
Author(s):  
Yuanyuan Fu ◽  
Qianqian Gu ◽  
Li Luo ◽  
Jiecheng Xu ◽  
Yuping Luo ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cancer Therapy ◽  
Therapeutic Strategy ◽  
Screening Method ◽  
Cancer Drug ◽  
Autophagic Flux ◽  
Single Target ◽  
Anti Cancer

Autophagy inhibition has been proposed to be a potential therapeutic strategy for cancer, however, few autophagy inhibitors have been developed. Recent studies have indicated that lysosome and autophagy related 4B cysteine peptidase (ATG4B) are two promising targets in autophagy for cancer therapy. Although some inhibitors of either lysosome or ATG4B were reported, there are limitations in the use of these single target compounds. Considering multi-functional drugs have advantages, such as high efficacy and low toxicity, we first screened and validated a batch of compounds designed and synthesized in our laboratory by combining the screening method of ATG4B inhibitors and the identification method of lysosome inhibitors. ATG4B activity was effectively inhibited in vitro. Moreover, 163N inhibited autophagic flux and caused the accumulation of autolysosomes. Further studies demonstrated that 163N could not affect the autophagosome-lysosome fusion but could cause lysosome dysfunction. In addition, 163N diminished tumor cell viability and impaired the development of colorectal cancer in vivo. The current study findings indicate that the dual effect inhibitor 163N offers an attractive new anti-cancer drug and compounds having a combination of lysosome inhibition and ATG4B inhibition are a promising therapeutic strategy for colorectal cancer therapy.

scholarly journals An in vivo large-scale chemical screening platform using Drosophila for anti-cancer drug discovery

2012 ◽  
Vol 6 (2) ◽  
pp. 521-529 ◽  
Author(s):  
L. F. Willoughby ◽  
T. Schlosser ◽  
S. A. Manning ◽  
J. P. Parisot ◽  
I. P. Street ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Large Scale ◽  
Cancer Drug ◽  
Chemical Screening ◽  
Cancer Drug Discovery ◽  
Anti Cancer ◽  
Screening Platform

scholarly journals Frontiers in Anti-Cancer Drug Discovery: Challenges and Perspectives of Metformin as Anti-Angiogenic Add-On Therapy in Glioblastoma

Cancers ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 112
Author(s):  
Laura Guarnaccia ◽  
Giovanni Marfia ◽  
Matteo Maria Masseroli ◽  
Stefania Elena Navone ◽  
Melissa Balsamo ◽  
...  
Keyword(s):  
Target Therapy ◽  
Cancer Drug ◽  
Cancer Drug Discovery ◽  
Average Survival ◽  
Anti Cancer ◽  
Tumor Neovascularization ◽  
Novel Therapeutic Strategies

Glioblastoma is the most common primitive tumor in adult central nervous system (CNS), classified as grade IV according to WHO 2016 classification. Glioblastoma shows a poor prognosis with an average survival of approximately 15 months, representing an extreme therapeutic challenge. One of its distinctive and aggressive features is aberrant angiogenesis, which drives tumor neovascularization, representing a promising candidate for molecular target therapy. Although several pre-clinical studies and clinical trials have shown promising results, anti-angiogenic drugs have not led to a significant improvement in overall survival (OS), suggesting the necessity of identifying novel therapeutic strategies. Metformin, an anti-hyperglycemic drug of the Biguanides family, used as first line treatment in Type 2 Diabetes Mellitus (T2DM), has demonstrated in vitro and in vivo antitumoral efficacy in many different tumors, including glioblastoma. From this evidence, a process of repurposing of the drug has begun, leading to the demonstration of inhibition of various oncopromoter mechanisms and, consequently, to the identification of the molecular pathways involved. Here, we review and discuss metformin’s potential antitumoral effects on glioblastoma, inspecting if it could properly act as an anti-angiogenic compound to be considered as a safely add-on therapy in the treatment and management of glioblastoma patients.

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 ◽  
...  
Keyword(s):  
Natural Products ◽  
Drug Discovery ◽  
Phylogenetic Tree ◽  
Drug Approval ◽  
Cancer Drug ◽  
Cancer Drugs ◽  
Phylogenetic Distribution ◽  
Cancer Drug Discovery ◽  
Anti Cancer ◽  
Novel Drug

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.

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