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

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.

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Systematic Design of Drug Repurposing-Oriented Alzheimer’s Disease Ontology

2019 IEEE International Conference on Healthcare Informatics (ICHI) ◽

10.1109/ichi.2019.8904505 ◽

2019 ◽

Author(s):

Fang Li ◽

Mingqiang Wang ◽

Huy Anh Pham ◽

Yang Xiang ◽

Muhammad Tuanq Amith ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Drug Repurposing ◽

Systematic Design ◽

Disease Ontology

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

Current Pharmaceutical Design ◽

10.2174/1381612826666200928161721 ◽

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.

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

Current Gene Therapy ◽

10.2174/1566523219666190924113737 ◽

2019 ◽

Vol 19 (4) ◽

pp. 216-223 ◽

Cited By ~ 2

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.

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Old Drugs with New Tricks; Paradigm in Drug Development Pipeline for Alzheimer’s Disease

Central Nervous System Agents in Medicinal Chemistry ◽

10.2174/1871524920666201021164805 ◽

2020 ◽

Vol 20 ◽

Author(s):

Tanay Dalvi ◽

Bhaskar Dewangan ◽

Rudradip Das ◽

Jyoti Rani ◽

Suchita Dattatray Shinde ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Drug Development ◽

Molecular Targets ◽

Drug Repurposing ◽

Phase Iii ◽

Complex Nature ◽

New Drug ◽

Development Pipeline ◽

Old Drugs

: The most common reason behind dementia is Alzheimer’s disease (AD) and it is predicted to be the third lifethreatening disease apart from stroke and cancer for the geriatric population. Till now only four drugs are available in the market for symptomatic relief. The complex nature of disease pathophysiology and lack of concrete evidences of molecular targets are the major hurdles for developing new drug to treat AD. The the rate of attrition of many advanced drugs at clinical stages, makes the de novo discovery process very expensive. Alternatively, Drug Repurposing (DR) is an attractive tool to develop drugs for AD in a less tedious and economic way. Therefore, continuous efforts are being made to develop a new drug for AD by repursing old drugs through screening and data mining. For example, the survey in the drug pipeline for Phase III clinical trials (till February 2019) which has 27 candidates, and around half of the number are drugs which have already been approved for other indications. Although in the past the drug repurposing process for AD has been reviewed in the context of disease areas, molecular targets, there is no systematic review of repurposed drugs for AD from the recent drug development pipeline (2019-2020). In this manuscript, we are reviewing the clinical candidates for AD with emphasis on their development history including molecular targets and the relevance of the target for AD.

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

Journal of Alzheimer s Disease ◽

10.3233/jad-201235 ◽

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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