Sphingolipid Metabolizing Enzymes as Novel Therapeutic Targets

2008 ◽  
pp. 487-522 ◽  
Author(s):  
Andreas Billich ◽  
Thomas Baumruker
Keyword(s):  
Therapeutic Targets ◽  
Metabolizing Enzymes ◽  
Novel Therapeutic

Identification of novel therapeutic targets in SDH-mutated cancers: tracing dysfunction

2017 ◽  
Author(s):  
Charlotte Lussey-Lepoutre ◽  
Kate E R Hollinshead ◽  
Christian Ludwig ◽  
Melanie Menara ◽  
Aurelie Morin ◽  
...  
Keyword(s):  
Therapeutic Targets ◽  
Novel Therapeutic

Novel Therapeutic Targets for Chronic Migraine

2012 ◽  
Author(s):  
Andrew Charles ◽  
Peter Goadsby
Keyword(s):  
Chronic Migraine ◽  
Therapeutic Targets ◽  
Novel Therapeutic

Oxidized Haemoglobin–Driven Endothelial Dysfunction and Immune Cell Activation: Novel Therapeutic Targets for Atherosclerosis

2013 ◽  
Vol 20 (37) ◽  
pp. 4806-4814 ◽  
Author(s):  
Brigitta Buttari ◽  
Elisabetta Profumo ◽  
Rita Businaro ◽  
Luciano Saso ◽  
Raffaele Capoano ◽  
...  
Keyword(s):  
Endothelial Dysfunction ◽  
Cell Activation ◽  
Immune Cell ◽  
Therapeutic Targets ◽  
Immune Cell Activation ◽  
Novel Therapeutic

Recent Development in Identification of Potential Novel Therapeutic Targets Against Trypanosomatids

2016 ◽  
Vol 16 (20) ◽  
pp. 2303-2315 ◽  
Author(s):  
Rajender Kumar ◽  
Parvati Sharma ◽  
Deepak Kumar Gaur ◽  
Shikha Jain
Keyword(s):  
Therapeutic Targets ◽  
Novel Therapeutic

scholarly journals Therapeutic Application of Monoclonal Antibodies in Pancreatic Cancer: Advances, Challenges and Future Opportunities

Cancers ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1781
Author(s):  
Gustavo A. Arias-Pinilla ◽  
Helmout Modjtahedi
Keyword(s):  
Pancreatic Cancer ◽  
Monoclonal Antibodies ◽  
Antibody Therapy ◽  
Therapeutic Targets ◽  
Poor Response ◽  
Therapeutic Agents ◽  
Therapeutic Interventions ◽  
Western World ◽  
Wide Range ◽  
Novel Therapeutic

Pancreatic cancer remains as one of the most aggressive cancer types. In the absence of reliable biomarkers for its early detection and more effective therapeutic interventions, pancreatic cancer is projected to become the second leading cause of cancer death in the Western world in the next decade. Therefore, it is essential to discover novel therapeutic targets and to develop more effective and pancreatic cancer-specific therapeutic agents. To date, 45 monoclonal antibodies (mAbs) have been approved for the treatment of patients with a wide range of cancers; however, none has yet been approved for pancreatic cancer. In this comprehensive review, we discuss the FDA approved anticancer mAb-based drugs, the results of preclinical studies and clinical trials with mAbs in pancreatic cancer and the factors contributing to the poor response to antibody therapy (e.g. tumour heterogeneity, desmoplastic stroma). MAb technology is an excellent tool for studying the complex biology of pancreatic cancer, to discover novel therapeutic targets and to develop various forms of antibody-based therapeutic agents and companion diagnostic tests for the selection of patients who are more likely to benefit from such therapy. These should result in the approval and routine use of antibody-based agents for the treatment of pancreatic cancer patients in the future.

scholarly journals Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer’s disease

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Shingo Tsuji ◽  
Takeshi Hase ◽  
Ayako Yachie-Kinoshita ◽  
Taiko Nishino ◽  
Samik Ghosh ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Drug Target ◽  
Target Genes ◽  
Drug Repositioning ◽  
Therapeutic Targets ◽  
Machine Learning Techniques ◽  
Computational Framework ◽  
Novel Therapeutic

Abstract Background Identifying novel therapeutic targets is crucial for the successful development of drugs. However, the cost to experimentally identify therapeutic targets is huge and only approximately 400 genes are targets for FDA-approved drugs. As a result, it is inevitable to develop powerful computational tools that can identify potential novel therapeutic targets. Fortunately, the human protein-protein interaction network (PIN) could be a useful resource to achieve this objective. Methods In this study, we developed a deep learning-based computational framework that extracts low-dimensional representations of high-dimensional PIN data. Our computational framework uses latent features and state-of-the-art machine learning techniques to infer potential drug target genes. Results We applied our computational framework to prioritize novel putative target genes for Alzheimer’s disease and successfully identified key genes that may serve as novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we could infer repositionable candidate-compounds for the disease (e.g., tamoxifen, bosutinib, and dasatinib). Conclusions Our deep learning-based computational framework could be a powerful tool to efficiently prioritize new therapeutic targets and enhance the drug repositioning strategy.

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