Cellular senescence is a hallmark of aging and age-related diseases, characterized by irreversible cell cycle arrest, resistance to apoptosis, and the secretion of pro-inflammatory factors known as the senescence-associated secretory phenotype (SASP). While senescence plays beneficial roles in development, tissue repair, and tumour suppression, its accumulation during aging is associated with chronic inflammation, tissue dysfunction, and progression of multiple diseases. Thus, the elimination of senescent cells through senolytic drugs has emerged as a promising strategy to mitigate aging-related pathologies and enhance healthspan.
To address these challenges, the SenDrug project seeks to pioneer new lines of research in the field. Unlike prior efforts seeking universally acting drugs, this project aims to identify compounds that selectively target senescence in specific cell types. To achieve this aim, we will apply a range of computational tools for drug repurposing and optimise them for our purpose. Additionally, we will expand beyond senolytic to include senomorphic drugs, which modulate the pro-inflammatory phenotype of senescent cells without inducing cell death. By directly addressing senescence heterogeneity, our approach offers a safer and more precise therapeutic strategy than broad-spectrum senolytics. To identify cell-type-specific senolytic and senomorphic drug candidates, SenDrug will employ an innovative combination of transcriptomic profiling, network analysis, and AI-driven prediction models.
First, we will conduct a comparative analysis of senescence in different cell types to explore shared and unique features and assess how drug-target interactions and molecular networks differ between them. To this end, public transcriptomic datasets will be analyzed to characterize gene expression differences between normal and senescent cells in each type. Second, network-based computational approaches will be used to identify key nodes and pathways modulating senescence in a cell-type-specific manner. Conventional network analysis will uncover regulatory hubs and pathways involved in senescence, while graph-based machine learning models will predict novel drug-target interactions for key proteins identified via network-based analyses. To further support these predictions, in silico molecular docking simulations will assess the binding affinities of selected compounds to their predicted protein targets. Finally, a selection of the most promising drug candidates will be validated by in vitro experiments to assess their potential for inducing cell-type-specific senolysis and senostasis.
The impact of SenDrug lies in its potential to considerably extend current therapeutic strategies for targeting senescence. By investigating the heterogeneity of senescence across different cell types, SenDrug can help uncover novel insights into the molecular mechanisms that define senescence. Applying complementary computational approaches, we will utilize these insights to find therapeutic interventions specific to cell types that can minimize unintended side effects and thereby enhance the chances for successful clinical translation. Ultimately, SenDrug will provide a new paradigm for senescence-targeted drug repurposing enabling safer therapeutic strategies for aging-related diseases while advancing our understanding of senescence heterogeneity.
To this end, the SenDrug project at the Centre for Innovative Biomedicine and Biotechnology is based on a close collaboration between the Bioinformatics and Data Analytics Unit, and Advanced Therapies Group.