Three high school students used AI to identify multiple dual-effect targets and develop new solutions for the treatment of malignant glioma

Recently, Ren Ziming, a high school student from the International Department of Shanghai High School, and two foreign collaborators, who are also high school students, have found multiple dual-effect targets against aging and malignant brain tumors. Secondly, further studies can be conducted through in vivo and in vitro experiments to verify its effects on tumor growth and cancer progression. At the same time, its anti-aging potential can also be studied through animal models.

After confirming the target, you can use pharmaceutical methods or artificial intelligence methods to find compounds targeting the target or strategies for reusing old drugs.

With the help of this achievement, it is expected to develop safer and more efficient clinical treatment plans or drugs for patients with malignant glioma.

Picture | Ren Ziming (Source: Ren Ziming)

Recently, a related paper titled "Identification of dual-purpose therapeutic targets related to aging and glioblastoma multiforme using PandaOmics - an AI-enabled biological target discovery platform" Targets implicated in aging and glioblastoma multiforme using PandaOmics - an AI-enabled biological target discovery platform) was published on Aging [1].

Figure | Related papers (Source: Aging)

Andrea Olsen, a high school student at Seven Oaks Public School in the United Kingdom, Zachary Harpaz, a high school student at Pencaster School in Fort Lauderdale, the United States, and Ren Ziming are the authors of the paper.

Lu Ziming said: "Andrea, Zach and I gradually participated in this project. Andrea first proposed this project when attending the Aging Research and Drug Discovery Conference. Andrea and Zach, both high school students like me, were attending the conference for the second and first time respectively."

In the live video broadcast of that conference, Ren Ziming saw them sharing their research ideas on glioblastoma treatment projects with entrepreneurs and researchers in the field of life sciences in the auditorium of the University of Copenhagen.

Ren Ziming said: "These guests include highly cited scholars in the field of aging biology research, as well as rising stars from pharmaceutical companies, anti-aging companies, AI companies, etc. The guests in the audience are not dismayed because of their age. I despised them but put forward a lot of valuable opinions in the Q&A. This kind of science-based communication deeply attracted me. Later, we established contact and launched this cooperation."

Many people know that there is a clear connection between cancer and age. So, are there different disease driver genes between younger and older patients? With this question in mind, they established the topic of studying glioblastoma multiforme (GBM).

GBM is a common neurological malignancy whose cause is unknown. Because most patients are diagnosed between the ages of 45 and 75, age is undoubtedly one of the influencing factors.

GBM is highly malignant, grows quickly, and has a short course. As the disease worsens, patients will develop symptoms such as headache, vomiting, consciousness disturbance, and speech impairment. Most patients will die within two years after diagnosis.

Based on this, the team set the research goals in the following two directions:

On the one hand, some drug targets are pro-aging, while some drug targets are anti-aging, so they hope to find a drug target that is both effective against the disease and anti-aging, so as to achieve clinical benefits. promote;

Most current GBM treatment plans do not take patient age into consideration. They hope to discover drug targets suitable for the elderly and improve the clinical decision-making process of elderly patients.

After establishing the topic, the first step is to collect data. In the AI-driven target discovery process, both quantity and quality of data are important. Under the guidance of Insilico's intelligent scientific research team, Ren Ziming and his collaborators collected 29 different types of data from multiple public databases such as the U.S. National Center for Biotechnology Information, covering RNA sequencing/microarray, methylation and proteomics. Data etc.

While collecting data, they discussed various analysis strategies to verify the validity of the results in multiple aspects. After analysis, they identified three analysis strategies: cross-comparison of survival data, expression level differences, and genetic information related to aging.

They then used the PandaOmics artificial intelligence target identification engine to rank the targets discovered after cross-comparison and prioritize the most promising disease targets.

(Source: Data map)

Through the above process, they proposed three potential new dual-effect therapeutic targets: CNGA3, GLUD1, and SIRT1, and found that these targets can be used to treat brain glioblastoma while also anti-aging.

Then by reviewing literature information, they explored the mechanisms of these three targets. The results found that in patients with brain glioblastoma:

CNGA3 is a gene expression level that has a significant positive correlation with age. High expression of CNGA3 is associated with poor survival rate in GBM. It encodes an ion channel and plays a crucial role in the function of the nervous system;

GLUD1 also has a significantly negatively correlated gene expression level with age, and low expression of GLUD1 is associated with poor prognosis. In neural tissue, GLUD1 is also involved in learning and memory formation;

SIRT1 is one of the most studied genes in aging. Activating SIRT1 can resist aging. Small molecule activators of SIRT1 can also induce autophagy and mitophagy, which can affect GBM in vitro and in vivo. therapeutic potential.

Regarding the literature search and comparison in the study, Ren Ziming said: "We have collected a pool of potential targets. Through the search and integration of relevant literature and data, we also have a more thorough understanding of the information about these targets. , and its association with malignant glioma."

Ren Ziming said: "The entire research process made me realize the diversity of scientific research. After we studied other targets for malignant glioma drugs, we obtained some completely different conclusions, and also obtained some conclusions that were different from ours. It coincides with this, which further aroused my interest in exploring biology."

At the same time, he said that publishing a paper is not the end of this project. The next step is for him and his collaborators to verify the targets, confirm their anti-disease properties and anti-aging properties, and use Chemistry42 to generate and screen lead compounds against the nominated targets, hoping to discover treatments for glioblastoma. potential innovative treatments.

Finally, Ren Ziming added: "I think a very important part of this research is the PandaOmics platform, which provides a public and easy-to-process data list that can be used for the discovery and analysis of malignant glioma targets. Even if This platform can be used easily without much bioinformatics knowledge and experimental experience, and it is not difficult for us high school students to operate. This shows that the prospects of the biopharmaceutical industry are developing, because the AI ​​platform can make drug targets The discovery of points is more efficient and concise.”

References:

1.Olsen, A., Harpaz, Z., Ren, C., Shneyderman, A., Veviorskiy, A., Dralkina, M., ... & Zhavoronkov, A. (2023). Identification of dual -purpose therapeutic targets implicated in aging and glioblastoma multiforme using PandaOmics-an AI-enabled biological target discovery platform. Aging, 15.

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