2025/07/22

Rejuvenating the brain with the help of a computational clock

The study, published in Advanced Science, constitutes a valuable resource to find compounds with therapeutic potential for neurodegenerative diseases.

What if there was a way to make aging brain cells younger again? An international research team from Spain and Luxembourg, recently set out to address this question. After developing an aging clock capable of assessing the biological age of the brain, they used it to identify possible brain-rejuvenating interventions. The computational tool they created, recently presented in the journal Advanced Science, constitutes a valuable resource to find compounds with therapeutic potential for neurodegenerative diseases.

As the world population is aging rapidly, with over two billion people projected to be above the age of 60 by 2050, age-related brain disorders are on the rise. Living longer but in poor health is not only a daunting prospect, it also places a substantial burden on healthcare systems worldwide. The idea of being able to counteract the functional decline of our brain through rejuvenating interventions sounds therefore promising. The question is how can we identify compounds that have the potential to efficiently rejuvenate brain cells and to protect the aging population from neurodegeneration? Prof. Antonio Del Sol and his teams of computational biologists, based both at CIC bioGUNE, member of BRTA, and the Luxembourg Centre for Systems Biomedicine (LCSB) from the University of Luxembourg, used their machine learning expertise to tackle the challenge.

Building a clock to measure the biological age of the brain

The researchers developed what is called an “aging clock”, a computational tool designed to measure the biological age of cells, as opposed to their chronological age. Indeed, the organs and tissues of people of the same age can evolve differently over time depending on genetic and environmental factors, leading to different biological ages. These clocks are therefore useful tools to assess aging at the molecular level and can help in understanding its causes and consequences.

The clock designed by CIC bioGUNE and LCSB researchers is specific to the brain and uses gene expression information from 365 genes to make predictions. Using a machine learning approach, it was trained on data from healthy individuals, aged from 20 to 97, and could accurately predict their age. Further tests showed that the clock is able to estimate the biological age of different cell types in the brain, especially neurons. Lastly, by looking at the predicted biological ages for healthy individuals and for patients with neurological conditions, the researchers observed that patients exhibited a higher biological age.

“Our results tell us that the biological age of the brain cells calculated by our clock reflects the decline in brain function experienced by the patients, especially between 60 and 70, and is even correlated with the degree of neurodegeneration,” explains Dr. Guillem Santamaria, first author of the study. “It supports the view of neurodegeneration as a form of accelerated aging but, more importantly, the positive association between neurodegeneration and biological age suggests that the rejuvenating interventions identified by the clock could serve as neuroprotective agents.”

Identifying compounds that have a rejuvenating potential

The aim of the researchers was to use the clock to find genetic or chemical interventions that would significantly shift back the biological age of brain cells. They explored the effect of thousands of compounds on neural progenitor cells and neurons and identified 453 unique rejuvenating interventions.

Among the identified compounds that have the potential to reverse the biological age of the two types of brain cells, several are known to extend lifespan in animal models and some are already used to treat neurological disorders, but the vast majority has not yet been studied in the context of health- or lifespan extension. “On the one hand, the fact that our computational platform identified drugs that have a known effect on brain function supports the idea that using the predicted effect of a compound on the biological age is an efficient way to evaluate its neuroprotective potential,” details Prof. Antonio Del Sol, Ikerbasque researcher and head of the Computation Biology groups at CIC bioGUNE and LCSB. “On the other, the results also highlight that our clock can help us find many new candidates that haven’t been studied before for their rejuvenating properties. It opens up a lot of new avenues.”

First validation in animal model and broadening the therapeutic horizon

As a proof of concept of their approach, the researchers then tested three of the predicted compounds in mice, in collaboration with the team of Prof. Rubén Nogueiras at the Centre for Research in Molecular Medicine and Chronic Diseases. The administration of these drugs significantly reduced anxiety and slightly increased spatial memory in older mice, addressing two well-known symptoms associated with aging. An analysis of gene expression showed that the combination of these compounds also led to a shift toward a younger phenotype. Altogether, these results show that a selection of compounds predicted to rejuvenate the brain did produce rejuvenation at the molecular level in the cortex of aged mice and had an impact on behavioural and cognitive functions.

Globally, the study, recently published in the journal Advanced Science, highlights the computational aging clock developed by the researchers as a valuable resource for identifying brain-rejuvenating interventions with therapeutic potential in neurodegenerative diseases. It provides a strong foundation for further research. “The hundreds of compounds predicted by our platform require validation across multiple biological systems to assess their efficacy and safety, offering extensive opportunities for future therapeutic development,” concludes Prof. Antonio Del Sol.

Reference: Guillem Santamaria, Cristina Iglesias, Sascha Jung, Javier Arcos Hodar, Ruben Nogueiras and Antonio del Sol. A machine-learning approach identifies rejuvenating interventions in the human brain. Advanced Science. DOI: 10.1002/advs.202503344.

About CIC bioGUNE

The Centre for Cooperative Research in Biosciences (CIC bioGUNE), member of the Basque Research & Technology Alliance (BRTA), located in the Bizkaia Technology Park, is a biomedical research organization conducting cutting-edge research at the interface between structural, chemical, molecular and cell biology, with a particular focus on generating knowledge on the molecular bases of disease, for use in the development of new diagnostic methods and advanced therapies.

About Ikerbasque

Ikerbasque - Basque Foundation for Science - is the result of an initiative of the Department of Education of the Basque Government that aims to reinforce the commitment to scientific research by attracting, recovering and consolidating excellent researchers from all over the world. Currently, it is a consolidated organization that has 290 researchers/s, who develop their work in all fields of knowledge.

About BRTA

BRTA is an alliance of 4 collaborative research centres (CIC bioGUNE, CIC nanoGUNE, CIC biomaGUNE y CIC energiGUNE) and 13 technology centres (Azterlan, Azti, Ceit, Cidetec, Gaiker, Ideko, Ikerlan, Leartiker, Lortek, Neiker, Tecnalia, Tekniker y Vicomtech) with the main objective of developing advanced technological solutions for the Basque corporate fabric.

With the support of the Basque Government, the SPRI Group and the Provincial Councils of the three territories, the alliance seeks to promote collaboration between the research centres, strengthen the conditions to generate and transfer knowledge to companies, contributing to their competitiveness and outspreading the Basque scientific-technological capacity abroad.

BRTA has a workforce of over 4,000 professionals, executes 22% of the Basque Country's R&D investment, registers an annual turnover of more than 300 million euros and generates 100 European and international patents per year.

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