Development of Genomics-Based Gene Expression Signature Biomarkers in Oncology and Toxicology to Facilitate Drug Discovery and Translational Medicine

Wei, Tao; Li, Shuyu

doi:10.2174/157489310791268423

Cited by 3 publications

(1 citation statement)

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“…We have shown previous-ly that the transcriptional signature of a given drug response, disease state, or other physiological condition, when mapped to the signalome, can be useful for biomarker development [89–91] and drug screening [7,92,93]. Transcriptional signatures have been suggested by others as well for aiding in biomarker development [94], cancer drug screening [75] and repositioning [11], and diabetes management [95].…”

Section: Introductionmentioning

confidence: 99%

Towards natural mimetics of metformin and rapamycin

Aliper

Jellen

Cortese

et al. 2017

Aging

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Aging is now at the forefront of major challenges faced globally, creating an immediate need for safe, widescale interventions to reduce the burden of chronic disease and extend human healthspan. Metformin and rapamycin are two FDA-approved mTOR inhibitors proposed for this purpose, exhibiting significant anti-cancer and anti-aging properties beyond their current clinical applications. However, each faces issues with approval for off-label, prophylactic use due to adverse effects. Here, we initiate an effort to identify nutraceuticals—safer, naturally-occurring compounds—that mimic the anti-aging effects of metformin and rapamycin without adverse effects. We applied several bioinformatic approaches and deep learning methods to the Library of Integrated Network-based Cellular Signatures (LINCS) dataset to map the gene- and pathway-level signatures of metformin and rapamycin and screen for matches among over 800 natural compounds. We then predicted the safety of each compound with an ensemble of deep neural network classifiers. The analysis revealed many novel candidate metformin and rapamycin mimetics, including allantoin and ginsenoside (metformin), epigallocatechin gallate and isoliquiritigenin (rapamycin), and withaferin A (both). Four relatively unexplored compounds also scored well with rapamycin. This work revealed promising candidates for future experimental validation while demonstrating the applications of powerful screening methods for this and similar endeavors.

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Section: Introductionmentioning

confidence: 99%

Towards natural mimetics of metformin and rapamycin

Aliper

Jellen

Cortese

et al. 2017

Aging

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Toxicogenomics – A Drug Development Perspective

Wang¹,

Borlak²,

Tong³

2014

Genomic Biomarkers for Pharmaceutical Development

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Applications of Machine Learning in Drug Target Discovery

Gao

Chen

Zeng

et al. 2020

CDM

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: Drug target discovery is a critical step in drug development. It is the basis of modern drug development to deter-mine the target molecules related to specific diseases in advance. Predicting the drug target by computational methods saves a lot of financial and material resources than in vitro experiments, thereby a number of computational methods are designed for drug target discovery. Recently, machine learning (ML) methods have developed rapidly in biomedicine. In this paper, we present an overview of drug target discovery methods based on machine learning. Due to some machine learning meth-ods integratenetwork analysis to predict drug targets, network-based methods are also introduced in this article. Finally, the challenges and future outlook of drug target discovery are discussed.

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Development of Genomics-Based Gene Expression Signature Biomarkers in Oncology and Toxicology to Facilitate Drug Discovery and Translational Medicine

Cited by 3 publications

References 0 publications

Towards natural mimetics of metformin and rapamycin

Towards natural mimetics of metformin and rapamycin

Toxicogenomics – A Drug Development Perspective

Applications of Machine Learning in Drug Target Discovery

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