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.

scholarly journals Identification of molecular signatures and pathways to identify novel therapeutic targets in Alzheimer's disease: Insights from a systems biomedicine perspective

Genomics ◽  
2020 ◽  
Vol 112 (2) ◽  
pp. 1290-1299 ◽  
Author(s):  
Md. Rezanur Rahman ◽  
Tania Islam ◽  
Toyfiquz Zaman ◽  
Md. Shahjaman ◽  
Md. Rezaul Karim ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Therapeutic Targets ◽  
Molecular Signatures ◽  
Novel Therapeutic

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

2020 ◽  
Author(s):  
Shingo Tsuji ◽  
Takeshi Hase ◽  
Ayako Yachie ◽  
Taiko Nishino ◽  
Samik Ghosh ◽  
...  
Keyword(s):  
Target Genes ◽  
Therapeutic Targets ◽  
Computational Framework ◽  
Computational Tools ◽  
Therapeutic Drugs ◽  
Latent Space ◽  
Latent Features ◽  
Low Dimensional ◽  
Network Metrics ◽  
Novel Therapeutic

AbstractBackgroundIdentification of novel therapeutic targets is a key for successful drug development. However, the cost to experimentally identify therapeutic targets is huge and only 400 genes are targets for FDA-approved drugs. Therefore, it is inevitable to develop powerful computational tools to identify potential novel therapeutic targets. Because proteins make their functions together with their interacting partners, a protein-protein interaction network (PIN) in human could be a useful resource to build computational tools to investigate potential targets for therapeutic drugs. Network embedding methods, especially deep-learning based methods would be useful tools to extract an informative low-dimensional latent space that contains enough information required to fully represent original high-dimensional non-linear data of PINs.ResultsIn this study, we developed a deep learning based computational framework that extracts low-dimensional latent space embedded in high-dimensional data of the human PIN and uses the features in the latent space (latent features) to infer potential novel targets for therapeutic drugs. We examined the relationships between the latent features and the representative network metrics and found that the network metrics can explain a large number of the latent features, while several latent features do not correlate with all the network metrics. The results indicate that the features are likely to capture information that the representative network metrics can not capture, while the latent features also can capture information obtained from the network metrics. Our computational framework uses the latent features together with state-of-the-art machine learning techniques to infer potential drug target genes. We applied our computational framework to prioritized novel putative target genes for Alzheimer’s disease and successfully identified key genes for potential novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we inferred repositionable candidate-compounds for the disease (e.g., Tamoxifen, Bosutinib, and Dasatinib)DiscussionsOur computational framework could be powerful computational tools to efficiently prioritize new therapeutic targets and drug repositioning. It is pertinent to note here that our computational platform is easily applicable to investigate novel potential targets and repositionable compounds for any diseases, especially for rare diseases.

scholarly journals Sleep and Alzheimer’s Disease: Shared Genetic Risk Factors, Drug Targets, Molecular Mechanisms, and Causal Effects

2021 ◽  
Author(s):  
Dongze Chen ◽  
Xinpei Wang ◽  
Jinzhu Jia ◽  
Tao Huang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Association Study ◽  
Sleep Duration ◽  
Drug Target ◽  
Drug Targets ◽  
Target Genes ◽  
Lipoprotein Particle ◽  
Level Data ◽  
Particle Clearance

Abstract Background: Alzheimer’s disease (AD) was associated with sleep-related phenotypes (SRPs). Whether they share common genetic etiology remains largely unknown. We explored the shared genetics and causality between AD and SRPs by using high-definition likelihood (HDL), cross phenotype association study (CPASSOC), transcriptome wide association study (TWAS), and bidirectional Mendelian randomization (MR) in summary-level data for AD (n = 79145) and summary-level data for seven SRPs (sample size ranges from 345552 to 386577). Results: AD shared strong genetic basis with insomnia (rg = 0.20; P = 9.70×10-5), snoring (rg = 0.13; P = 2.45×10-3), and sleep duration (rg = -0.11; P = 1.18×10-3). CPASSOC identifies 31 independent loci shared between AD and SRPs, including four novel shared loci. Functional analysis and TWAS showed shared genes were enriched in liver, brain, breast, and heart tissues, and highlighted the regulatory role of immunological disorders, very-low-density lipoprotein particle clearance, triglyceride-rich lipoprotein particle clearance, chylomicron remnant clearance and positive regulation of T cell mediated cytotoxicity pathways. Protein-protein interaction analysis provided three potential drug target genes (APOE, MARK4 and HLA-DRA) that interacted with known FDA-approved drug target genes. CPASSOC and TWAS demonstrated three regions 11p11.2, 6p22.3 and 16p11.2 may account for the shared basis between AD and sleep duration or snoring. MR showed AD had causal effect on sleep duration (βIVW = -0.056, PIVW = 1.03×10-3). Conclusion: Our findings provide strong evidence of shared genetics and causation between AD and sleep, and advance our understanding the genetic overlap between them. Identifying shared drug targets and molecular pathways can be beneficial to treat AD and sleep disorders more efficiently.

scholarly journals Novel Therapeutic Approaches for Alzheimer’s Disease: An Updated Review

2021 ◽  
Vol 22 (15) ◽  
pp. 8208
Author(s):  
Tien-Wei Yu ◽  
Hsien-Yuan Lane ◽  
Chieh-Hsin Lin
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Repositioning ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Therapeutic Effects ◽  
Therapeutic Approaches ◽  
Novel Treatments ◽  
Therapeutic Development ◽  
Heavy Burden ◽  
Novel Therapeutic

Alzheimer’s disease (AD) is a progressive neurodegenerative disease and accounts for most cases of dementia. The prevalence of AD has increased in the current rapidly aging society and contributes to a heavy burden on families and society. Despite the profound impact of AD, current treatments are unable to achieve satisfactory therapeutic effects or stop the progression of the disease. Finding novel treatments for AD has become urgent. In this paper, we reviewed novel therapeutic approaches in five categories: anti-amyloid therapy, anti-tau therapy, anti-neuroinflammatory therapy, neuroprotective agents including N-methyl-D-aspartate (NMDA) receptor modulators, and brain stimulation. The trend of therapeutic development is shifting from a single pathological target to a more complex mechanism, such as the neuroinflammatory and neurodegenerative processes. While drug repositioning may accelerate pharmacological development, non-pharmacological interventions, especially repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS), also have the potential for clinical application. In the future, it is possible for physicians to choose appropriate interventions individually on the basis of precision medicine.

scholarly journals Propagation of tau pathology in Alzheimer’s disease: identification of novel therapeutic targets

2013 ◽  
Vol 5 (5) ◽  
pp. 49 ◽  
Author(s):  
Amy M Pooler ◽  
Manuela Polydoro ◽  
Susanne Wegmann ◽  
Samantha B Nicholls ◽  
Tara L Spires-Jones ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Therapeutic Targets ◽  
Tau Pathology ◽  
Disease Identification ◽  
Novel Therapeutic

Memapsin 2, a drug target for Alzheimer's disease

2003 ◽  
Vol 70 ◽  
pp. 213-220 ◽  
Author(s):  
Gerald Koelsch ◽  
Robert T. Turner ◽  
Lin Hong ◽  
Arun K. Ghosh ◽  
Jordan Tang
Keyword(s):  
Crystal Structure ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Transition State ◽  
Amyloid Precursor Protein ◽  
Therapeutic Target ◽  
Drug Target ◽  
Aspartic Protease ◽  
Precursor Protein ◽  
Memapsin 2

Mempasin 2, a ϐ-secretase, is the membrane-anchored aspartic protease that initiates the cleavage of amyloid precursor protein leading to the production of ϐ-amyloid and the onset of Alzheimer's disease. Thus memapsin 2 is a major therapeutic target for the development of inhibitor drugs for the disease. Many biochemical tools, such as the specificity and crystal structure, have been established and have led to the design of potent and relatively small transition-state inhibitors. Although developing a clinically viable mempasin 2 inhibitor remains challenging, progress to date renders hope that memapsin 2 inhibitors may ultimately be useful for therapeutic reduction of ϐ-amyloid.

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