Dual-purpose therapeutic targets for aging and glioblastoma identified with PandaOmics

“[…] AI-powered algorithms, such as PandaOmics, may accelerate subsequent gene target discovery not only for GBM but for a broader range of age-associated diseases.”

BUFFALO, NY- May 2, 2023 – A new research paper was published in Aging (listed by MEDLINE/PubMed as “Aging (Albany NY)” and “Aging-US” by Web of Science) Volume 15, Issue 8, entitled, “Identification of dual-purpose therapeutic targets implicated in aging and glioblastoma multiforme using PandaOmics – an AI-enabled biological target discovery platform.”

Glioblastoma Multiforme (GBM) is the most aggressive and most common primary malignant brain tumor. The age of GBM patients is considered as one of the disease’s negative prognostic factors and the mean age of diagnosis is 62 years. A promising approach to preventing both GBM and aging is to identify new potential therapeutic targets that are associated with both conditions as concurrent drivers.

In this new study, researchers Andrea Olsen, Zachary Harpaz, Christopher Ren, Anastasia Shneyderman, Alexander Veviorskiy, Maria Dralkina, Simon Konnov, Olga Shcheglova, Frank W. Pun, Geoffrey Ho Duen Leung, Hoi Wing Leung, Ivan V. Ozerov, Alex Aliper, Mikhail Korzinkin, and Alex Zhavoronkov from The Youth Longevity Association, Pine Crest School Science Research Department, Shanghai High School International Division, and Insilico Medicine present a multi-angled approach of identifying targets, which takes into account not only the disease-related genes but also the ones important in aging. 

“For this purpose, we developed three strategies of target identification using the results of correlation analysis augmented with survival data, differences in expression levels and previously published information of aging-related genes.”

Several studies have recently validated the robustness and applicability of AI-driven computational methods for target identification in both cancer and aging-related diseases. Therefore, the researchers leveraged the AI predictive power of the PandaOmics TargetID engine in order to rank the resulting target hypotheses and prioritize the most promising therapeutic gene targets. They propose three potentially novel dual-purpose therapeutic targets to treat aging and GBM: cyclic nucleotide gated channel subunit alpha 3 (CNGA3), glutamate dehydrogenase 1 (GLUD1) and sirtuin 1 (SIRT1).

“The next steps towards implementation of the identified therapeutic targets into the clinic would involve a generation of small molecules and their optimisation with further validation and preclinical testing to determine their safety, efficacy, and potential side effects.”

Read the full study: DOI: https://doi.org/10.18632/aging.204678 

Corresponding Author: Mikhail Korzinkin

Corresponding Email: mike@insilicomedicine.com 

Keywords: aging, target discovery, GBM, glioblastoma, PandaOmics

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About Aging-US:

Launched in 2009, Aging publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways.

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