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SA-β-Galactosidase-Based Screening Assay for the Identification of Senotherapeutic Drugs
Published on: June 28, 2019
From clock to clock: Therapeutic target discovery for aging and age-related diseases
Jianjiu Chen1, Geoffrey Ho Duen Leung1, Howell Leung1
1Insilico Medicine Hong Kong Ltd, Unit 310, 3/F, Building 8W, Hong Kong Science and Technology Park, Hong Kong, China.
Abstract:
The aging population worldwide necessitates the development of novel therapeutics that enhance the quality of life by preventing and treating age-related diseases. In this review, we first discuss the advantages of a dual-purpose target identification strategy for aging and age-related diseases, with assessment of the hallmarks of aging as an approach to identify such dual-purpose targets. Resulting from a convergence of aging research with machine learning (ML) and other artificial intelligence (AI) models, aging clocks were initially developed as aging biomarkers, but its value in identifying therapeutic targets is also increasingly recognized. Building on recently published aging clocks, we reestablish a significant proportion of known drug targets by identifying clock-associated genes, highlighting the potential of these clocks for target identification. Lastly, we discuss other applications of aging clocks in drug development such as population stratification and disease and treatment monitoring. With the growing availability of multi-omics data and rapid advancements in ML and AI, we anticipate accelerated progress in aging clock research, paving the way for innovative treatments to meet the healthcare needs of a global aging population.
Insights
Aging clocks, powered by machine learning, are emerging as powerful tools for identifying new drug targets to combat age-related diseases and improve healthspan. This approach aids in developing novel therapeutics for a growing global elderly population.
Area of Science:
- Gerontology and Bioinformatics
- Computational Biology and Drug Discovery
Background:
- The global aging population requires new therapies for age-related diseases.
- Identifying therapeutic targets that address both aging and disease is crucial.
Purpose of the Study:
- To review dual-purpose target identification strategies for aging and age-related diseases.
- To highlight the role of aging clocks in identifying therapeutic targets.
Main Methods:
- Assessment of the hallmarks of aging for target identification.
- Utilizing machine learning (ML) and artificial intelligence (AI) models for aging clock development.
- Identifying clock-associated genes to validate known drug targets.
Main Results:
- Aging clocks, initially biomarkers, show significant potential for therapeutic target identification.
- A substantial number of known drug targets were reestablished by identifying clock-associated genes.
- Aging clocks can be applied to population stratification and disease/treatment monitoring.
Conclusions:
- Aging clock research, fueled by multi-omics data and AI, is advancing rapidly.
- This progress promises innovative treatments for the healthcare needs of an aging global population.
- Dual-purpose target identification strategies are key to developing effective anti-aging therapeutics.
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