ANTENNA, a Multi-Rank, Multi-Layered Recommender System for Inferring Reliable Drug-Gene-Disease Associations: Repurposing Diazoxide as a Targeted Anti-Cancer Therapy

Wang, Annie; Lim, Hansaim; Cheng, Shu‐Yuan; Xie, Lei

doi:10.1109/tcbb.2018.2812189

Cited by 25 publications

(23 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We proposed a list of described mammalian QRs with reported Km values, the name and structure of the specific inhibitors available for these enzymes, and the IC50 value reported for cancer cells as potential targets to inhibit to promote ferroptosis in conjunction with GPX4 inhibitors (Table 3). 0.87 [226] when the substrate is lipid hydroperoxides. Other QRs, with similar or higher Km values for quinones, are also putative targets for cancer treatment combined with GPX4 inhibitors.…”

Section: The Future Of Nanoparticles (Np) For Cancer Targeting and Timentioning

confidence: 99%

Targeting Lipid Peroxidation for Cancer Treatment

et al. 2020

View full text Add to dashboard Cite

Cancer is one of the highest prevalent diseases in humans. The chances of surviving cancer and its prognosis are very dependent on the affected tissue, body location, and stage at which the disease is diagnosed. Researchers and pharmaceutical companies worldwide are pursuing many attempts to look for compounds to treat this malignancy. Most of the current strategies to fight cancer implicate the use of compounds acting on DNA damage checkpoints, non-receptor tyrosine kinases activities, regulators of the hedgehog signaling pathways, and metabolic adaptations placed in cancer. In the last decade, the finding of a lipid peroxidation increase linked to 15-lipoxygenases isoform 1 (15-LOX-1) activity stimulation has been found in specific successful treatments against cancer. This discovery contrasts with the production of other lipid oxidation signatures generated by stimulation of other lipoxygenases such as 5-LOX and 12-LOX, and cyclooxygenase (COX-2) activities, which have been suggested as cancer biomarkers and which inhibitors present anti-tumoral and antiproliferative activities. These findings support the previously proposed role of lipid hydroperoxides and their metabolites as cancer cell mediators. Depletion or promotion of lipid peroxidation is generally related to a specific production source associated with a cancer stage or tissue in which cancer originates. This review highlights the potential therapeutical use of chemical derivatives to stimulate or block specific cellular routes to generate lipid hydroperoxides to treat this disease.

show abstract

Section: The Future Of Nanoparticles (Np) For Cancer Targeting and Timentioning

confidence: 99%

Targeting Lipid Peroxidation for Cancer Treatment

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In a comprehensive benchmarking study, ANTENNA outperformed the more conventional OCCF methods. Using ANTENNA, Wang et al showed that diazoxide might inhibit the growth of triple negative breast cancer (TNBC) cells efficiently (Wang et al, 2018). Lim et al applied FASCINATE to a three-layered drug-gene-side effect network model to identify biological pathways associated with rare side effects.…”

Section: Application Of Hmln In Omics Data Integration and Analysismentioning

confidence: 99%

Heterogeneous Multi-Layered Network Model for Omics Data Integration and Analysis

et al. 2020

Self Cite

View full text Add to dashboard Cite

Advances in next-generation sequencing and high-throughput techniques have enabled the generation of vast amounts of diverse omics data. These big data provide an unprecedented opportunity in biology, but impose great challenges in data integration, data mining, and knowledge discovery due to the complexity, heterogeneity, dynamics, uncertainty, and high-dimensionality inherited in the omics data. Network has been widely used to represent relations between entities in biological system, such as protein-protein interaction, gene regulation, and brain connectivity (i.e. network construction) as well as to infer novel relations given a reconstructed network (aka link prediction). Particularly, heterogeneous multi-layered network (HMLN) has proven successful in integrating diverse biological data for the representation of the hierarchy of biological system. The HMLN provides unparalleled opportunities but imposes new computational challenges on establishing causal genotype-phenotype associations and understanding environmental impact on organisms. In this review, we focus on the recent advances in developing novel computational methods for the inference of novel biological relations from the HMLN. We first discuss the properties of biological HMLN. Then we survey four categories of state-ofthe-art methods (matrix factorization, random walk, knowledge graph, and deep learning). Thirdly, we demonstrate their applications to omics data integration and analysis. Finally, we outline strategies for future directions in the development of new HMLN models.

show abstract

“…3D-REMAP not only is scalable to tens of thousands of protein targets on a genome scale but also takes the uncertainty of observed or inferred drug-target interactions into consideration, and consolidate their consistency or conflict relations in a global context. Thus, it may predict offtargets that are missed by the protein structure-based approach and other chemoinformatics methods [26,34].…”

Section: Protein Kinases Are Predicted To Be the Off-target Of Pde3 Imentioning

confidence: 99%

Rational Discovery of Dual-Action Multi-Target Kinase Inhibitor for Precision Anti-Cancer Therapy Using Structural Systems Pharmacology

Lim

Qiu

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

Although remarkable progresses have been made in the cancer treatment, existing anti-cancer drugs are associated with increasing risk of heart failure, variable drug response, and acquired drug resistance. To address these challenges, for the first time, we develop a novel genome-scale multitarget screening platform 3D-REMAP that integrates data from structural genomics and chemical genomics as well as synthesize methods from structural bioinformatics, biophysics, and machine learning. 3D-REMAP enables us to discover marked drugs for dual-action agents that can both reduce the risk of heart failure and present anti-cancer activity. 3D-REMAP predicts that levosimendan, a drug for heart failure, inhibits serine/threonine-protein kinase RIOK1 and other kinases. Subsequent experiments confirm this prediction, and suggest that levosimendan is active against multiple cancers, notably lymphoma, through the direct inhibition of RIOK1 and RNA processing pathway. We further develop machine learning models to identify cancer cell-lines and patients that may respond to levosimendan. Our findings suggest that levosimendan can be a promising novel lead compound for the development of safe and effective multi-targeted cancer therapy, and demonstrate the potential of genome-wide multi-target screening in designing polypharmacology and drug repurposing for precision medicine. Author SummaryMulti-target drug design (a.k.a targeted polypharmacology) has emerged as a new strategy for discovering novel therapeutics that can enhance therapeutic efficacy and overcome drug resistance in tackling multi-genic diseases such as cancer. However, it is extremely challenging for conventional computational tools that are either receptor-based or ligand-based to screen compounds for selectively targeting multiple receptors. Existing multi-target drug design mainly focuses on compound screening against receptors within the same gene family but not across different gene families. Here, we develop a new computational tool 3D-REMAP that enables us to identify chemical-protein interactions across fold space on a genome scale. The genome-scale chemical-protein interaction network allows us to discover dual-action drugs that can bind to two types of targets simultaneously, one for mitigating side effect and another for enhancing the therapeutic effect. Using 3D-REMAP, we predict and subsequently experiments validate that levosimendan, a drug for heart failure, is active against multiple cancers, notably, lymphoma. This study demonstrates the potential of genome-wide multi-target screening in designing polypharmacology and drug repurposing for precision medicine.

show abstract

ANTENNA, a Multi-Rank, Multi-Layered Recommender System for Inferring Reliable Drug-Gene-Disease Associations: Repurposing Diazoxide as a Targeted Anti-Cancer Therapy

Cited by 25 publications

References 37 publications

Targeting Lipid Peroxidation for Cancer Treatment

Targeting Lipid Peroxidation for Cancer Treatment

Heterogeneous Multi-Layered Network Model for Omics Data Integration and Analysis

Rational Discovery of Dual-Action Multi-Target Kinase Inhibitor for Precision Anti-Cancer Therapy Using Structural Systems Pharmacology

Contact Info

Product

Resources

About