Hallmarks of aging-based dual-purpose disease and age-associated targets predicted using PandaOmics AI-powered discovery engine

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.

Identification of Therapeutic Targets for Amyotrophic Lateral Sclerosis Using PandaOmics – An AI-Enabled Biological Target Discovery Platform

“…During the target identification process, our AI models dynamically assessed disease targets based on a variety of measures, such as novelty, accessibility by small molecules and biologics, safety and tissue expression specificity, to collectively generate hypotheses around their potential druggability profiles. Previous studies demonstrated the effectiveness of PandaOmics to identify novel and repurposing therapeutic targets ( Insilico Medicine, 2022 ; Pun et al, 2022 ; Vera et al, 2022 ).…”

Supervised machine learning with feature selection for prioritization of targets related to time-based cellular dysfunction in aging

“…To address these challenges, increased utilization of database searches, biological assays, and machine learning techniques have been implemented to better identify targets of interest (12)(13)(14)(15). These techniques have been used to advance research in the field of neurodegeneration, with a specific focus on AD.…”

“…The careful consideration of targets identified with machine learning techniques in the context of biological processes is necessary to assess the feasibility and actionability of the identified targets. One such approach is taken by Pun et al ., (2022) by using the PandaOmics platform to investigate targets associated with aging and age-associated diseases from human cross-sectional data based on the nine hallmarks of aging (12). Although the use of human data to identify targets is highly relevant for drug discovery, obtaining rich longitudinal data to understand disease progression across an individual’s lifespan has remained extremely challenging.…”

“…Moreover, longitudinal studies of human aging as well as the development of pharmacotherapies are time consuming and costly (11). Given these challenges, a particular need arises to better prioritize targets based on their ability to modulate the aging process, without solely relying on in vitro and in vivo experiments (11,12).…”

“…The careful consideration of targets identified with machine learning techniques in the context of biological processes is necessary to assess the feasibility and actionability of the identified targets. One such approach is taken by Pun et al, (2022) by using the PandaOmics platform to investigate targets associated with aging and age-associated diseases from human cross-sectional data based on the nine hallmarks of aging (12).…”