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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
Published on: December 15, 2023
Hallmarks of aging-based dual-purpose disease and age-associated targets predicted using PandaOmics AI-powered
Frank W Pun1, Geoffrey Ho Duen Leung1, Hoi Wing Leung1
1Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China.
Abstract:
Aging biology is a promising and burgeoning research area that can yield dual-purpose pathways and protein targets that may impact multiple diseases, while retarding or possibly even reversing age-associated processes. One widely used approach to classify a multiplicity of mechanisms driving the aging process is the hallmarks of aging. In addition to the classic nine hallmarks of aging, processes such as extracellular matrix stiffness, chronic inflammation and activation of retrotransposons are also often considered, given their strong association with aging. In this study, we used a variety of target identification and prioritization techniques offered by the AI-powered PandaOmics platform, to propose a list of promising novel aging-associated targets that may be used for drug discovery. We also propose a list of more classical targets that may be used for drug repurposing within each hallmark of aging. Most of the top targets generated by this comprehensive analysis play a role in inflammation and extracellular matrix stiffness, highlighting the relevance of these processes as therapeutic targets in aging and age-related diseases. Overall, our study reveals both high confidence and novel targets associated with multiple hallmarks of aging and demonstrates application of the PandaOmics platform to target discovery across multiple disease areas.
Insights
This study identifies novel drug targets for aging by analyzing hallmarks of aging using AI. Key findings highlight inflammation and extracellular matrix stiffness as crucial therapeutic targets for age-related diseases.
Area of Science:
- Gerontology and aging research
- Computational biology and bioinformatics
- Drug discovery and development
Background:
- Aging is a complex process driven by multiple mechanisms, often categorized as hallmarks of aging.
- Additional factors like chronic inflammation and extracellular matrix stiffness are strongly associated with aging.
- Identifying therapeutic targets is crucial for addressing age-associated diseases.
Purpose of the Study:
- To leverage AI-driven target identification and prioritization for novel aging-associated targets.
- To propose targets for both new drug discovery and repurposing within aging hallmarks.
- To explore the utility of the PandaOmics platform in aging research.
Main Methods:
- Utilized AI-powered PandaOmics platform for target identification and prioritization.
- Analyzed multiple hallmarks of aging, including inflammation and extracellular matrix stiffness.
- Integrated various target identification techniques to generate a comprehensive list.
Main Results:
- Generated a list of promising novel and classical aging-associated targets.
- Top identified targets are significantly involved in inflammation and extracellular matrix stiffness.
- Demonstrated the platform's capability to uncover targets across multiple aging hallmarks.
Conclusions:
- The study successfully identified high-confidence and novel targets linked to aging hallmarks.
- Inflammation and extracellular matrix stiffness emerge as highly relevant therapeutic targets for aging.
- The PandaOmics platform is a valuable tool for target discovery in aging and age-related diseases.
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