scholarly journals A transcriptomics-guided drug target discovery strategy identifies novel receptor ligands for lung regeneration

2021 ◽  
Author(s):  
Xinhui Wu ◽  
Sophie Bos ◽  
Thomas M Conlon ◽  
Meshal Ansari ◽  
Vicky Verschut ◽  
...  
Keyword(s):  
Cigarette Smoke ◽  
Drug Target ◽  
Therapeutic Potential ◽  
Chronic Obstructive ◽  
Sequencing Analysis ◽  
Receptor Ligands ◽  
Drug Target Discovery ◽  
Target Discovery ◽  
Alveolar Epithelial

Currently, there is no pharmacological treatment targeting defective tissue repair in chronic disease. Here we utilized a transcriptomics-guided drug target discovery strategy using gene signatures of smoking-associated chronic obstructive pulmonary disease (COPD) and from mice chronically exposed to cigarette smoke, identifying druggable targets expressed in alveolar epithelial progenitors of which we screened the function in lung organoids. We found several drug targets with regenerative potential of which EP and IP prostanoid receptor ligands had the most significant therapeutic potential in restoring cigarette smoke-induced defects in alveolar epithelial progenitors in vitro and in vivo. Mechanistically, we discovered by using scRNA-sequencing analysis that circadian clock and cell cycle/apoptosis signaling pathways were enriched in alveolar epithelial progenitor cells in COPD patients and in a relevant model of COPD, which was prevented by PGE2 or PGI2 mimetics. Conclusively, specific targeting of EP and IP receptors offers therapeutic potential for injury to repair in COPD.

Applications of Machine Learning in Drug Target Discovery

2020 ◽  
Vol 21 (10) ◽  
pp. 790-803 ◽  
Author(s):  
Dongrui Gao ◽  
Qingyuan Chen ◽  
Yuanqi Zeng ◽  
Meng Jiang ◽  
Yongqing Zhang
Keyword(s):  
Machine Learning ◽  
Drug Development ◽  
Computational Methods ◽  
Drug Target ◽  
Drug Targets ◽  
Drug Target Discovery ◽  
Target Discovery ◽  
Modern Drug ◽  
Applications Of Machine Learning

Drug target discovery is a critical step in drug development. It is the basis of modern drug development because it determines the target molecules related to specific diseases in advance. Predicting drug targets by computational methods saves a great deal of financial and material resources compared to in vitro experiments. Therefore, several computational methods for drug target discovery have been designed. Recently, machine learning (ML) methods in biomedicine have developed rapidly. In this paper, we present an overview of drug target discovery methods based on machine learning. Considering that some machine learning methods integrate network 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.

Abstract 2139: Enabling cancer drug target discovery through genome-scale identification of synthetic lethal paralog pairs

2021 ◽  
Author(s):  
Phoebe C. Parrish ◽  
James D. Thomas ◽  
Shriya Kamlapurkar ◽  
Robert K. Bradley ◽  
Alice H. Berger
Keyword(s):  
Drug Target ◽  
Cancer Drug ◽  
Drug Target Discovery ◽  
Target Discovery ◽  
Synthetic Lethal ◽  
Genome Scale

scholarly journals Application of genomics and proteomics in drug target discovery

2014 ◽  
Vol 13 (1) ◽  
pp. 198-204 ◽  
Author(s):  
H.M. Zhang ◽  
Z.R. Nan ◽  
G.Q. Hui ◽  
X.H. Liu ◽  
Y. Sun
Keyword(s):  
Drug Target ◽  
Drug Target Discovery ◽  
Target Discovery ◽  
Genomics And Proteomics

scholarly journals Metabolomics in Drug Target Discovery

2011 ◽  
Vol 76 (0) ◽  
pp. 235-246 ◽  
Author(s):  
J. D. Rabinowitz ◽  
J. G. Purdy ◽  
L. Vastag ◽  
T. Shenk ◽  
E. Koyuncu
Keyword(s):  
Drug Target ◽  
Drug Target Discovery ◽  
Target Discovery

scholarly journals Matrine reduces cigarette smoke-induced airway neutrophilic inflammation by enhancing neutrophil apoptosis

2019 ◽  
Vol 133 (4) ◽  
pp. 551-564 ◽  
Author(s):  
Xuhua Yu ◽  
Huei Jiunn Seow ◽  
Hao Wang ◽  
Desiree Anthony ◽  
Steven Bozinovski ◽  
...  
Keyword(s):  
Cigarette Smoke ◽  
Lung Inflammation ◽  
Therapeutic Potential ◽  
Proteolytic Enzymes ◽  
Ex Vivo ◽  
Lavage Fluid ◽  
Airflow Limitation ◽  
Chronic Obstructive ◽  
Neutrophilic Inflammation ◽  
Anti Inflammatory

AbstractChronic Obstructive Pulmonary Disease (COPD) is a major incurable global health burden and will become the third largest cause of death in the world by 2030. It is well established that an exaggerated inflammatory and oxidative stress response to cigarette smoke (CS) leads to, emphysema, small airway fibrosis, mucus hypersecretion, and progressive airflow limitation. Current treatments have limited efficacy in inhibiting chronic inflammation and consequently do not reverse the pathology that initiates and drives the long-term progression of disease. In particular, there are no effective therapeutics that target neutrophilic inflammation in COPD, which is known to cause tissue damage by degranulation of a suite of proteolytic enzymes including neutrophil elastase (NE). Matrine, an alkaloid compound extracted from Sophora flavescens Ait, has well known anti-inflammatory activity. Therefore, the aim of the present study was to investigate whether matrine could inhibit CS-induced lung inflammation in mice. Matrine significantly reduced CS-induced bronchoalveolar lavage fluid (BALF) neutrophilia and NE activity in mice. The reduction in BALF neutrophils in CS-exposed mice by matrine was not due to reductions in pro-neutrophil cytokines/chemokines, but rather matrine’s ability to cause apoptosis of neutrophils, which we demonstrated ex vivo. Thus, our data suggest that matrine has anti-inflammatory actions that could be of therapeutic potential in treating CS-induced lung inflammation observed in COPD.

scholarly journals An omics perspective on drug target discovery platforms

2019 ◽  
Vol 21 (6) ◽  
pp. 1937-1953 ◽  
Author(s):  
Jussi Paananen ◽  
Vittorio Fortino
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Knowledge Bases ◽  
Scientific Data ◽  
Data Sources ◽  
Biomedical Data ◽  
Drug Target Discovery ◽  
Data Repositories ◽  
Target Discovery ◽  
Omics Technologies

Abstract The drug discovery process starts with identification of a disease-modifying target. This critical step traditionally begins with manual investigation of scientific literature and biomedical databases to gather evidence linking molecular target to disease, and to evaluate the efficacy, safety and commercial potential of the target. The high-throughput and affordability of current omics technologies, allowing quantitative measurements of many putative targets (e.g. DNA, RNA, protein, metabolite), has exponentially increased the volume of scientific data available for this arduous task. Therefore, computational platforms identifying and ranking disease-relevant targets from existing biomedical data sources, including omics databases, are needed. To date, more than 30 drug target discovery (DTD) platforms exist. They provide information-rich databases and graphical user interfaces to help scientists identify putative targets and pre-evaluate their therapeutic efficacy and potential side effects. Here we survey and compare a set of popular DTD platforms that utilize multiple data sources and omics-driven knowledge bases (either directly or indirectly) for identifying drug targets. We also provide a description of omics technologies and related data repositories which are important for DTD tasks.

Sign in / Sign up

Export Citation Format

Share Document