Phytochemicals for drug discovery in Alzheimer’s disease: In silico Advances

2020 ◽  
Vol 26 ◽  
Author(s):  
Smriti Sharma ◽  
Vinayak Bhatia
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Design ◽  
In Silico ◽  
Drug Targets ◽  
Development Process ◽  
Phytochemical Analysis ◽  
Computational Drug Design ◽  
Novel Drugs ◽  
Game Changer

: The search for novel drugs that can prevent or control Alzheimer’s disease has attracted lot of attention from researchers across the globe. Phytochemicals are increasingly being used to provide scaffolds to design drugs for AD. In silico techniques, have proven to be a game-changer in this drug design and development process. In this review, the authors have focussed on current advances in the field of in silico medicine, applied to phytochemicals, to discover novel drugs to prevent or cure AD. After giving a brief context of the etiology and available drug targets for AD, authors have discussed the latest advances and techniques in computational drug design of AD from phytochemicals. Some of the prototypical studies in this area are discussed in detail. In silico phytochemical analysis is a tool of choice for researchers all across the globe and helps integrate chemical biology with drug design.

Identifying Alzheimer’s Disease-related miRNA Based on Semi-clustering

2019 ◽  
Vol 19 (4) ◽  
pp. 216-223 ◽  
Author(s):  
Tianyi Zhao ◽  
Donghua Wang ◽  
Yang Hu ◽  
Ningyi Zhang ◽  
Tianyi Zang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Targets ◽  
Molecular Mechanisms ◽  
Feature Vector ◽  
Mirna Gene ◽  
Interaction Network ◽  
Gene Interaction ◽  
Proteinprotein Interaction ◽  
Synaptic Structures

Background: More and more scholars are trying to use it as a specific biomarker for Alzheimer’s Disease (AD) and mild cognitive impairment (MCI). Multiple studies have indicated that miRNAs are associated with poor axonal growth and loss of synaptic structures, both of which are early events in AD. The overall loss of miRNA may be associated with aging, increasing the incidence of AD, and may also be involved in the disease through some specific molecular mechanisms. Objective: Identifying Alzheimer’s disease-related miRNA can help us find new drug targets, early diagnosis. Materials and Methods: We used genes as a bridge to connect AD and miRNAs. Firstly, proteinprotein interaction network is used to find more AD-related genes by known AD-related genes. Then, each miRNA’s correlation with these genes is obtained by miRNA-gene interaction. Finally, each miRNA could get a feature vector representing its correlation with AD. Unlike other studies, we do not generate negative samples randomly with using classification method to identify AD-related miRNAs. Here we use a semi-clustering method ‘one-class SVM’. AD-related miRNAs are considered as outliers and our aim is to identify the miRNAs that are similar to known AD-related miRNAs (outliers). Results and Conclusion: We identified 257 novel AD-related miRNAs and compare our method with SVM which is applied by generating negative samples. The AUC of our method is much higher than SVM and we did case studies to prove that our results are reliable.

In silico screening of pyridoxine carbamates for Anti-Alzheimer’s activities

2020 ◽  
Vol 20 ◽  
Author(s):  
Dnyaneshwar Baswar ◽  
Abha Sharma ◽  
Awanish Mishra
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
In Silico ◽  
Neurodegenerative Disorder ◽  
Biological Activities ◽  
Cholinergic Neurons ◽  
In Silico Screening ◽  
In Silico Studies ◽  
Bbb Permeability ◽  
Ld50 Value

Background: Alzheimer’s disease (AD), an irreversible complex neurodegenerative disorder, is most common type of dementia, with progressive loss of cholinergic neurons. Based on the multi- factorial etiology of Alzheimer’s disease, novel ligands strategy appears as up-coming approach for the development of newer molecules against AD. This study is envisaged to investigate anti-Alzheimer’s potential of 10 synthesized compounds. The screening of compounds (1-10) was carried out using in silico techniques. Methods: For in silico screening of physicochemical properties of compounds molinspiration property engine v.2018.03, Swiss ADME online web-server and pkCSM ADME were used. For pharmacodynamic prediction PASS software while toxicity profile of compounds were analyzed through ProTox-II online software. Simultaneously, molecular docking analysis was performed on mouse AChE enzyme (PDB ID:2JGE, obtained from RSCB PDB) using Auto Dock Tools 1.5.6. Results: Based on in silico studies, compound 9 and 10 have been found to have better drug likeness, LD50 value, and better anti-Alzheimer’s, nootropic activities. However, these compounds had poor blood brain barrier (BBB) permeability. Compound 4 and 9 were predicted with better docking score for AChE enzyme. Conclusion: The outcome of in silico studies have suggested, out of various substitutions at different positions of pyridoxine-carbamate, compound 9 have shown promising drug likeness, with better safety and efficacy profile for anti-Alzheimer’s activity. However, BBB permeability appears as one the major limitation of all these compounds. Further studies are required to confirm its biological activities.

Systematic Design and Evaluation of Drug Repurposing-Oriented Alzheimer’s Disease Ontology (Preprint)

2020 ◽  
Author(s):  
Fang Li ◽  
Muhammad "Tuan" Amith ◽  
Grace Xiong ◽  
Jingcheng Du ◽  
Yang Xiang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Targets ◽  
Drug Repurposing ◽  
Essential Elements ◽  
Genomic Research ◽  
Core Knowledge ◽  
Disease Ontology ◽  
Hybrid Strategy ◽  
Complementary Strategy

BACKGROUND Alzheimer’s Disease (AD) is a devastating neurodegenerative disease, of which the pathophysiology is insufficiently understood, and the curative drugs are long-awaited to be developed. Computational drug repurposing introduces a promising complementary strategy of drug discovery, which benefits from an accelerated development process and decreased failure rate. However, generating new hypotheses in AD drug repurposing requires multi-dimensional and multi-disciplinary data integration and connection, posing a great challenge in the era of big data. By integrating data with computable semantics, ontologies could infer unknown relationships through automated reasoning and fulfill an essential role in supporting computational drug repurposing. OBJECTIVE The study aimed to systematically design a robust Drug Repurposing-Oriented Alzheimer’s Disease Ontology (DROADO), which could model fundamental elements and their relationships involved in AD drug repurposing and integrate their up-to-date research advance comprehensively. METHODS We devised a core knowledge model of computational AD drug repurposing, based on both pre-genomic and post-genomic research paradigms. The model centered on the possible AD pathophysiology and abstracted the essential elements and their relationships. We adopted a hybrid strategy to populate the ontology (classes and properties), including importing from well-curated databases, extracting from high-quality papers and reusing the existing ontologies. We also leveraged n-ary relations and nanopublication graphs to enrich the object relations, making the knowledge stored in the ontology more powerful in supporting computational processing. The initially built ontology was evaluated by a semiotic-driven and web-based tool Ontokeeper. RESULTS The current version of DROADO was composed of 1,021 classes, 23 object properties and 3,207 axioms, depicting a fundamental network related to computational neuroscience concepts and relationships. Assessment using semiotic evaluation metrics by OntoKeeper indicated sufficient preliminary quality (semantics, usefulness and community-consensus) of the ontology. CONCLUSIONS As an in-depth knowledge base, DROADO would be promising in enabling computational algorithms to realize supervised mining from multi-source data, and ultimately, facilitating the discovery of novel AD drug targets and the realization of AD drug repurposing.

Associations Between Obesity and Alzheimer’s Disease: Multiple Bioinformatic Analyses

2021 ◽  
pp. 1-11
Author(s):  
Qi-Shuai Zhuang ◽  
Lei Meng ◽  
Zhe Wang ◽  
Liang Shen ◽  
Hong-Fang Ji
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis ◽  
Drug Targets ◽  
Protective Factor ◽  
Genetic Locus ◽  
Meta Analysis ◽  
Current Evidence ◽  
Causal Association ◽  
Ppi Network

Background: Identifying modifiable risk factors, such as obesity, to lower the prevalence of Alzheimer’s disease (AD) has gained much interest. However, whether the association is causal remains to be evaluated. Objective: The present study was designed: 1) to make a quantitative assessment of the association between obesity and AD; 2) to validate whether there was a causal association between them; and 3) to provide genetic clues for the association through a network-based analysis. Methods: Two-sample Mendelian randomization (2SMR) analysis, meta-analysis, and protein-protein interaction (PPI) network analysis, were employed. Results: Firstly, the meta-analysis based on 9 studies comprising 6,986,436 subjects indicated that midlife obesity had 33%higher AD odds than controls (OR = 1.33, 95%CI = [1.03, 1.62]), while late-life obesity were inversely associated with AD risk (OR = 0.57, 95%CI = [0.47, 0.68]). Secondly, 2SMR analysis indicated that there was no causal association between them. Thirdly, CARTPT was identified to be shared by the anti-obesity drug targets and AD susceptibility genes. Further PPI network analysis found that CARTPT interacted with CD33, a strong genetic locus linked to AD. Finally, 2SMR analysis showed that CNR1 could be a protective factor for AD. Conclusion: Multiple bioinformatic analyses indicated that midlife obesity might increase the risk of AD, while current evidence indicated that there was no causal association between them. Further, CARTPT might be an important factor linking the two disease conditions. It could help to better understand the mechanisms underlying the associations between obesity and AD.

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