scholarly journals Development of a Multi-Target Strategy for the Treatment of Vitiligo via Machine Learning and Network Analysis Methods

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiye Wang ◽  
Lin Luo ◽  
Qiong Ding ◽  
Zengrui Wu ◽  
Yayuan Peng ◽  
...  
Keyword(s):  
Network Analysis ◽  
Drug Targets ◽  
Characteristic Curve ◽  
Therapeutic Targets ◽  
Mapk Signaling ◽  
Therapeutic Strategies ◽  
Antioxidant Systems ◽  
Proteomic Profiling ◽  
Drug Candidates ◽  
Target Strategy

Vitiligo is a complex disorder characterized by the loss of pigment in the skin. The current therapeutic strategies are limited. The identification of novel drug targets and candidates is highly challenging for vitiligo. Here we proposed a systematic framework to discover potential therapeutic targets, and further explore the underlying mechanism of kaempferide, one of major ingredients from Vernonia anthelmintica (L.) willd, for vitiligo. By collecting transcriptome and protein-protein interactome data, the combination of random forest (RF) and greedy articulation points removal (GAPR) methods was used to discover potential therapeutic targets for vitiligo. The results showed that the RF model performed well with AUC (area under the receiver operating characteristic curve) = 0.926, and led to prioritization of 722 important transcriptomic features. Then, network analysis revealed that 44 articulation proteins in vitiligo network were considered as potential therapeutic targets by the GAPR method. Finally, through integrating the above results and proteomic profiling of kaempferide, the multi-target strategy for vitiligo was dissected, including 1) the suppression of the p38 MAPK signaling pathway by inhibiting CDK1 and PBK, and 2) the modulation of cellular redox homeostasis, especially the TXN and GSH antioxidant systems, for the purpose of melanogenesis. Meanwhile, this strategy may offer a novel perspective to discover drug candidates for vitiligo. Thus, the framework would be a useful tool to discover potential therapeutic strategies and drug candidates for complex diseases.

scholarly journals Potential Therapeutic Strategies for Alzheimer’s Disease Targeting or Beyondβ-Amyloid: Insights from Clinical Trials

2014 ◽  
Vol 2014 ◽  
pp. 1-22 ◽  
Author(s):  
Qiutian Jia ◽  
Yulin Deng ◽  
Hong Qing
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Drug Targets ◽  
Neurodegenerative Disorder ◽  
Therapeutic Strategies ◽  
Therapeutic Approaches ◽  
Drug Candidates ◽  
Effective Drugs ◽  
Novel Drug

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with two hallmarks:β-amyloid plagues and neurofibrillary tangles. It is one of the most alarming illnesses to elderly people. No effective drugs and therapies have been developed, while mechanism-based explorations of therapeutic approaches have been intensively investigated. Outcomes of clinical trials suggested several pitfalls in the choice of biomarkers, development of drug candidates, and interaction of drug-targeted molecules; however, they also aroused concerns on the potential deficiency in our understanding of pathogenesis of AD, and ultimately stimulated the advent of novel drug targets tests. The anticipated increase of AD patients in next few decades makes development of better therapy an urgent issue. Here we attempt to summarize and compare putative therapeutic strategies that have completed clinical trials or are currently being tested from various perspectives to provide insights for treatments of Alzheimer’s disease.

Deconvolution and network analysis of IDH-mutant lower grade glioma predict recurrence and indicate therapeutic targets

Epigenomics ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1323-1333 ◽  
Author(s):  
Guangqi Li ◽  
Yuanjun Jiang ◽  
Xintong Lyu ◽  
Yiru Cai ◽  
Miao Zhang ◽  
...  
Keyword(s):  
Network Analysis ◽  
Characteristic Curve ◽  
Therapeutic Targets ◽  
Recurrence Risk ◽  
The Cancer Genome Atlas ◽  
High Recurrence Rate ◽  
Lower Grade ◽  
Cell Type ◽  
Risk Area ◽  
Lower Grade Glioma

Aim: IDH-mutant lower grade glioma (LGG) has been proven to have a good prognosis. However, its high recurrence rate has become a major therapeutic difficulty. Materials & methods: We combined epigenomic deconvolution and a network analysis on The Cancer Genome Atlas IDH-mutant LGG data. Results: Cell type compositions between recurrent and primary gliomas are significantly different, and the key cell type that determines the prognosis and recurrence risk was identified. A scoring model consisting of four gene expression levels predicts the recurrence risk (area under the receiver operating characteristic curve = 0.84). Transcription factor PPAR-α explains the difference between recurrent and primary gliomas. A cell cycle-related module controls prognosis in recurrent tumors. Conclusion: Comprehensive deconvolution and network analysis predict the recurrence risk and reveal therapeutic targets for recurrent IDH-mutant LGG.

scholarly journals Molecular targets and system biology approaches for drug repurposing against SARS-CoV-2

2020 ◽  
Vol 44 (1) ◽  
Author(s):  
Rahul Kunwar Singh ◽  
Brijesh Singh Yadav ◽  
Tribhuvan Mohan Mohapatra
Keyword(s):  
Network Analysis ◽  
Drug Targets ◽  
Drug Repurposing ◽  
Main Body ◽  
Surface Receptors ◽  
Drug Candidates ◽  
Analysis Tools ◽  
Host Cell Surface ◽  
Almost All ◽  
Novel Drug

Abstract Background COVID-19, a pandemic declared by WHO, has infected about 39.5 million and killed about 1.1 million people throughout the world. There is the urgent need of more studies to identify the novel drug targets and the drug candidates against it to handle the situation. Main body To virtually screen various drugs against SARS-CoV-2, the scientists need the detail information about the various drug targets identified till date. The present review provides the information about almost all the drug targets, including structural and non-structural proteins of virus as well as host cell surface receptors, that can be used for virtual screening of drugs. Moreover, this review also focuses on the different network analysis tools that have been used for the identification of new drug targets and candidate repurposable drugs against SARS-CoV-2. Conclusion This review provides important insights of various drug targets and the network analysis tools to young bioinformaticians and will help in creating pace to the drug repurposing strategy for COVID-19 disease.

Identification of Novel Key Targets and Candidate Drugs in Oral Squamous Cell Carcinoma

2020 ◽  
Vol 15 (4) ◽  
pp. 328-337
Author(s):  
Juan Liu ◽  
Xinjie Lian ◽  
Feng Liu ◽  
Xueling Yan ◽  
Chunyan Cheng ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Network Analysis ◽  
Virtual Screening ◽  
Oral Squamous Cell Carcinoma ◽  
Small Molecules ◽  
Cell Carcinoma ◽  
Candidate Genes ◽  
Squamous Cell ◽  
Drug Targets ◽  
Therapeutic Strategies

Background: Oral Squamous Cell Carcinoma (OSCC) is the most common malignant epithelial neoplasm. It is located within the top 10 ranking incidence of cancers with a poor prognosis and low survival rates. New breakthroughs of therapeutic strategies are therefore needed to improve the survival rate of OSCC harboring patients. Objective: Since targeted therapy is considered as the most promising therapeutic strategies in cancer, it is of great significance to identify novel targets and drugs for the treatment of OSCC. Methods: A series of bioinformatics approaches were launched to identify the hub proteins and their potential agents. Microarray analysis and several online functional activity network analysis were firstly utilized to recognize drug targets in OSCC. Subsequently, molecular docking was used to screen their potential drugs from the specs chemistry database. At the same time, the assessment of ligand-based virtual screening model was also evaluated. Results: In this study, two microarray data (GSE31056, GSE23558) were firstly selected and analyzed to get consensus candidate genes including 681 candidate genes. Additionally, we selected 33 candidate genes based on whether they belong to the kinases and transcription factors and further clustered candidate hub targets based on functions and signaling pathways with significant enrichment analysis by using DAVID and STRING online databases. Then, core PPI network was then identified and we manually selected GRB2 and IGF1 as the key drug targets according to the network analysis and previous references. Lastly, virtual screening was performed to identify potential small molecules which could target these two targets, and such small molecules can serve as the promising candidate agents for future drug development. Conclusion: In summary, our study might provide novel insights for understanding of the underlying molecular events of OSCC, and our discovered candidate targets and candidate agents could be used as the promising therapeutic strategies for the treatment of OSCC.

scholarly journals Integrative network analysis of N6 methylation-related genes reveal potential therapeutic targets for spinal cord injury

2021 ◽  
Vol 18 (6) ◽  
pp. 8174-8187
Author(s):  
Shanzheng Wang ◽  
◽  
Xinhui Xie ◽  
Chao Li ◽  
Jun Jia ◽  
...  
Keyword(s):  
Gene Expression ◽  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Network Analysis ◽  
Drug Targets ◽  
Clinical Care ◽  
Therapeutic Targets ◽  
Acute Injury ◽  
Blood Leukocytes ◽  
Cord Injury

<abstract> <p>The diagnosis of the severity of spinal cord injury (SCI) and the revelation of potential therapeutic targets are crucial for urgent clinical care and improved patient outcomes. Here, we analyzed the overall gene expression data in peripheral blood leukocytes during the acute injury phase collected from Gene Expression Omnibus (GEO) and identified six m6A regulators specifically expressed in SCI compared to normal samples. LncRNA-mRNA network analysis identified AKT2/3 and PIK3R1 related to m6A methylation as potential therapeutic targets for SCI and constructed a classifier to identify patients of SCI to assist clinical diagnosis. Moreover, FTO (eraser) and RBMX (reader) were found to be significantly down-regulated in SCI and the functional gene co-expressed with them was found to be involved in the signal transduction of multiple pathways related to nerve injury. Through the construction of the drug-target gene network, eight key genes were identified as drug targets and it was emphasized that fostamatinib can be used as a potential drug for the treatment of SCI. Taken together, our study characterized the pathogenesis and identified a potential therapeutic target of SCI providing theoretical support for the development of precision medicine.</p> </abstract>

MAPK: A Key Player in the Development and Progression of Stroke

2020 ◽  
Vol 19 (4) ◽  
pp. 248-256
Author(s):  
Yangmin Zheng ◽  
Ziping Han ◽  
Haiping Zhao ◽  
Yumin Luo
Keyword(s):  
Ischemic Stroke ◽  
Signaling Pathway ◽  
Complex Disease ◽  
Therapeutic Targets ◽  
Cell Types ◽  
Mapk Signaling ◽  
Mapk Signaling Pathway ◽  
Effective Prevention ◽  
Prevention And Treatment ◽  
Different Cell Types

Conclusion: Stroke is a complex disease caused by genetic and environmental factors, and its etiological mechanism has not been fully clarified yet, which brings great challenges to its effective prevention and treatment. MAPK signaling pathway regulates gene expression of eukaryotic cells and basic cellular processes such as cell proliferation, differentiation, migration, metabolism and apoptosis, which are considered as therapeutic targets for many diseases. Up to now, mounting evidence has shown that MAPK signaling pathway is involved in the pathogenesis and development of ischemic stroke. However, the upstream kinase and downstream kinase of MAPK signaling pathway are complex and the influencing factors are numerous, the exact role of MAPK signaling pathway in the pathogenesis of ischemic stroke has not been fully elucidated. MAPK signaling molecules in different cell types in the brain respond variously after stroke injury, therefore, the present review article is committed to summarizing the pathological process of different cell types participating in stroke, discussed the mechanism of MAPK participating in stroke. We further elucidated that MAPK signaling pathway molecules can be used as therapeutic targets for stroke, thus promoting the prevention and treatment of stroke.

Covid 19 – Structural proteins and prospective drug candidates (Preprint)

2020 ◽  
Author(s):  
A.N Anoopkumar ◽  
Sharrel Rebello ◽  
Embalil Mathachan Aneesh
Keyword(s):  
Causative Agent ◽  
Protein Level ◽  
Drug Targets ◽  
Current Paper ◽  
Structural Proteins ◽  
Drug Candidates ◽  
Propagation Properties

UNSTRUCTURED Covid 19 the causative agent of the current devastating pandemic has turned out to be a notorious virus to all men-irrespective of either common to scientific calibre. Attempts to combat this deadly virus are the need of the hour and quite often the best way to defeat an opponent is to keenly study about its structural and propagation properties. The current paper describes briefly Covid 19 at the genomic, structural and protein level to the best of our knowledge. Furthermore, the prospects of possible drug targets that could aid in the control of this virus are also discussed.

scholarly journals Endoplasmic Reticulum Stress Regulators: New Drug Targets for Parkinson’s Disease

2021 ◽  
pp. 1-10
Author(s):  
Vera Kovaleva ◽  
Mart Saarma
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Endoplasmic Reticulum ◽  
Protein Folding ◽  
Er Stress ◽  
Protein Misfolding ◽  
Drug Targets ◽  
Dopamine Neurons ◽  
Drug Candidates ◽  
The Unfolded Protein Response

Parkinson’s disease (PD) pathology involves progressive degeneration and death of vulnerable dopamine neurons in the substantia nigra. Extensive axonal arborisation and distinct functions make this type of neurons particularly sensitive to homeostatic perturbations, such as protein misfolding and Ca2 + dysregulation. Endoplasmic reticulum (ER) is a cell compartment orchestrating protein synthesis and folding, as well as synthesis of lipids and maintenance of Ca2 +-homeostasis in eukaryotic cells. When misfolded proteins start to accumulate in ER lumen the unfolded protein response (UPR) is activated. UPR is an adaptive signalling machinery aimed at relieving of protein folding load in the ER. When UPR is chronic, it can either boost neurodegeneration and apoptosis or cause neuronal dysfunctions. We have recently discovered that mesencephalic astrocyte-derived neurotrophic factor (MANF) exerts its prosurvival action in dopamine neurons and in animal model of PD through the direct binding to UPR sensor inositol-requiring protein 1 alpha (IRE1α) and attenuation of UPR. In line with this, UPR targeting resulted in neuroprotection and neurorestoration in various preclinical PD animal models. Therefore, growth factors (GFs), possessing both neurorestorative activity and restoration of protein folding capacity are attractive as drug candidates for PD treatment especially their blood-brain barrier penetrating analogs and small molecule mimetics. In this review, we discuss ER stress as a therapeutic target to treat PD; we summarize the existing preclinical data on the regulation of ER stress for PD treatment. In addition, we point out the crucial aspects for successful clinical translation of UPR-regulating GFs and new prospective in GFs-based treatments of PD, focusing on ER stress regulation.

Developing novel chemical entities for the treatment of lysosomal storage disorders: an academic perspective

2015 ◽  
Vol 309 (12) ◽  
pp. F996-F999 ◽  
Author(s):  
James A. Shayman
Keyword(s):  
Drug Targets ◽  
Lysosomal Storage ◽  
Federal Drug Administration ◽  
Drug Candidates ◽  
Gaucher Disease Type 1 ◽  
Biotechnology Companies ◽  
Approved Drugs ◽  
Novel Drug ◽  
Early Drug

Historically, most Federal Drug Administration-approved drugs were the result of “in-house” efforts within large pharmaceutical companies. Over the last two decades, this paradigm has steadily shifted as the drug industry turned to startups, small biotechnology companies, and academia for the identification of novel drug targets and early drug candidates. This strategic pivot has created new opportunities for groups less traditionally associated with the creation of novel therapeutics, including small academic laboratories, for engagement in the drug discovery process. A recent example of the successful development of a drug that had its origins in academia is eliglustat tartrate, an oral agent for Gaucher disease type 1.

Review and comparative assessment of similarity-based methods for prediction of drug–protein interactions in the druggable human proteome

2018 ◽  
Vol 20 (6) ◽  
pp. 2066-2087 ◽  
Author(s):  
Chen Wang ◽  
Lukasz Kurgan
Keyword(s):  
Protein Interactions ◽  
Drug Targets ◽  
Characteristic Curve ◽  
Protein Structures ◽  
Predictive Performance ◽  
Computational Prediction ◽  
Comprehensive Analysis ◽  
Model Combining ◽  
Benchmark Database ◽  
Key Aspects

AbstractDrug–protein interactions (DPIs) underlie the desired therapeutic actions and the adverse side effects of a significant majority of drugs. Computational prediction of DPIs facilitates research in drug discovery, characterization and repurposing. Similarity-based methods that do not require knowledge of protein structures are particularly suitable for druggable genome-wide predictions of DPIs. We review 35 high-impact similarity-based predictors that were published in the past decade. We group them based on three types of similarities and their combinations that they use. We discuss and compare key aspects of these methods including source databases, internal databases and their predictive models. Using our novel benchmark database, we perform comparative empirical analysis of predictive performance of seven types of representative predictors that utilize each type of similarity individually and all possible combinations of similarities. We assess predictive quality at the database-wide DPI level and we are the first to also include evaluation over individual drugs. Our comprehensive analysis shows that predictors that use more similarity types outperform methods that employ fewer similarities, and that the model combining all three types of similarities secures area under the receiver operating characteristic curve of 0.93. We offer a comprehensive analysis of sensitivity of predictive performance to intrinsic and extrinsic characteristics of the considered predictors. We find that predictive performance is sensitive to low levels of similarities between sequences of the drug targets and several extrinsic properties of the input drug structures, drug profiles and drug targets. The benchmark database and a webserver for the seven predictors are freely available at http://biomine.cs.vcu.edu/servers/CONNECTOR/.

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